the multinational advantage - smu school of accountancy · for measuring valuation effects in a...
TRANSCRIPT
The Multinational Advantage
Presented by
Dr Jonathan Rogers
Associate Professor University of Chicago
Booth School of Business
201314-07
The views and opinions expressed in this working paper are those of the author(s) and not necessarily those of the School of Accountancy Singapore Management University
We thank Phil Berger Andy Bernard Marianne Bertrand Alan Bester Chris Hansen Chang-Tai Hsieh Rafael La Porta Christian Leuz Jon Lewellen Abbie Smith Doug Skinner and Jerry Zimmerman for helpful discussions and comments This paper has benefited from the comments of workshop participants at Chicago Cornell Dartmouth Florida Illinois at Chicago INSEAD LBS Michigan MIT Northwestern Rochester Syracuse University Texas UCLA and the EAA Annual Congress We thank William Steciak of KPMG for helpful discussions related to segment reporting We gratefully acknowledge the financial support of the University of Chicago Booth School of Business the Fama-Miller Center and the Neubauer Family Foundation The statistical analysis of firm-level data on US multinational firms was conducted at the International Investment Division Bureau of Economic Analysis US Department of Commerce under arrangements that maintain legal confidentiality requirements The views expressed are those of the authors and do not reflect official positions of the US Department of Commerce
The Multinational Advantage
Drew D Creal
University of Chicago Booth School of Business
Leslie A Robinson Tuck School of Business at Dartmouth
Jonathan L Rogers
University of Chicago Booth School of Business
Sarah L C Zechman University of Chicago Booth School of Business
February 2013
Chicago Booth Research Paper No 11-37
Abstract Using a proprietary dataset we evaluate whether the degree of foreign operations affects firm value by comparing actual value to imputed value for US multinational corporations (MNCs) We argue that using benchmark firms operating in the same country and industry as each MNC segment controls for differences in discount rates and expected growth rates across countries and industries This allows us to isolate the value effects of organizing a set of otherwise independent activities within a multinational network We find robust evidence that multinational networks trade at a premium relative to a benchmark portfolio of independent firms JEL Classification F23 G32 G34 M41 Keywords firm valuation multinational corporations diversification
1
1 Introduction
We investigate whether the degree of international diversification affects firm value for US
multinational corporations (MNCs) Specifically we determine whether the relative size of the
foreign operation has a positive negative or no association with the difference between the
actual observed firm value and an imputed value of the firm (called excess value)1 Our analysis
points to a statistically and economically significant positive excess value or premium to
increased foreign operations This is akin to finding that the value of the MNC is greater than the
sum of its individual parts We subject this finding to a battery of robustness tests to help
determine whether it is simply an artifact of endogeneity or measurement error problems We fail
to find evidence that this is the case The finding of a premium is robust across all specifications
examined In terms of magnitude a one percent increase in the size of the foreign operation is
associated with an increase of between 019 and 037 in excess value Collectively the
evidence suggests that on average MNCs create value by organizing a set of otherwise
independent activities within a multinational network
A large literature examines whether firms that are industrially diversified trade at a discount
or premium to non-diversified firms This literature typically finds that industrially diversified
firms trade at a discount2 A popular method to quantify this valuation effect was developed by
Berger and Ofek (1995) who compare the actual value of an industrially diversified firm with a
hypothetical firm whose value is the sum of the imputed values of its individual industrial
segments As the individual segments of the diversified firm are not traded the imputed value of
each segment is the observable median firm value of a single-segment (non-diversified) firm
1 As we study US-based multinational firms throughout the paper we refer to the US as lsquodomesticrsquo and any other country as lsquoforeignrsquo 2 Examples include Lang and Stulz (1994) Berger and Ofek (1995) Laeven and Levine (2007) Schmid and Walter (2009) Ammann Hoechle and Schmid (2012) and Hoechle Schmid Walter and Yermack (2012)
2
operating in the same industry The difference between the actual value and the imputed value is
an estimate of the premium (if positive) or discount (if negative) Evidence of a negative
association between this excess value measure and a measure of industrial diversification is
consistent with industrial diversification destroying firm value on average
While the finding of an industrial diversification discount is quite robust there is
considerable debate about the interpretation Some argue that the discount is evidence that the
costs of operating in multiple industries outweigh the benefits (eg Berger and Ofek 1995)
Others argue that the discount is driven by the types of firms that choose to diversify or the types
of businesses they invest in when diversifying (Campa and Kedia 2002 Villalonga 2004b) The
literature that uses this methodology continues to evolve improving our understanding of the
forces that drive the industrial diversification discount For example the recent study by Hoechle
et al (2012) finds that a substantial proportion of the discount can be explained by variation in
corporate governance proxies
In contrast to the literature on the industrial diversification discount comparatively few
studies examine how foreign operations affect firm value3 This contrast is surprising because
descriptive data for US firms indicates that industrial diversification has stagnated while foreign
expansion continues at a rapid pace We develop a method to quantify the value effects of
foreign operations along the lines of Berger and Ofek (1995) but that remains applicable to the
multinational setting While Berger and Ofek (1995) divide each firm into industry segments we
divide each MNC into geographic-industry segments (ie separate country-industry
components)4 We then compare the actual value of the firm to the imputed value of the firm
3 Denis Denis and Yost (2002) is a notable exception 4 In our setting we refer to a lsquosingle-segmentrsquo firm as a firm that operates in only one country and one industry Accordingly we use the term lsquoexcess valuersquo to denote the difference between the actual (observed) firm value and the hypothetical firm value computed as the sum of the imputed values of the individual country-industry segments
3
Using a method similar to Berger and Ofek (1995) we determine the imputed value for each
country-industry component by using the median single-segment firm operating exclusively in
the same country and industry (ie single-segment foreign (domestic) firms in the same industry
and country are used as benchmarks for the foreign (domestic) operations of US MNCs) In
other words our approach uses data on the observed firm values of single-segment US firms
(from Compustat) and foreign firms (from Worldscope)
This approach to measuring excess value differs from previous methods to impute the firm
value of MNCs Denis et al (2002) use single-segment domestic firms (ie US firms) in the
same industry as a benchmark for both domestic and foreign MNC operations Similarly studies
using Tobinrsquos Q to investigate valuation effects of MNCs rely only on domestic benchmarks
(eg Morck and Yeung 1991 Morck and Yeung 2001 Gande Schenzler and Senbet 2009)
As growth rates and discount rates vary by country we believe our method is more appropriate
for measuring valuation effects in a multinational context5 This method in effect compares the
value of the firm as a whole to the sum of the parts In addition our method is conceptually
consistent with theories on foreign direct investment (FDI) which note that an MNC exists when
a firm seeks to exploit its advantages and remove conflict arising in external market transactions
by combining a firm of one nationality that might otherwise exist independently under the
ownership of a firm of a different nationality (Dunning and Rugman 1985)
Our approach leverages data maintained by the Bureau of Economic Analysis (BEA) which
provides detailed accounting information about FDI allowing measurement of sales for each
5 Denis et al (2002) contemplate the use of single-segment foreign firms as a benchmark noting three constraints to implementing such a procedure (1) the country location of firmsrsquo foreign operations cannot be reliably identified using Compustat data (2) the industry membership of firmsrsquo foreign operations cannot be identified reliably using Compustat data and (3) valuation ratios may differ across countries due to differences in accounting standards As described in our study we overcome each of these constraints and find that the different benchmark is important and reverses prior findings
4
country-industry in which US-based MNCs operate These data provide a substantial advantage
over the Compustat database used by prior research While Compustat provides some
information about the countries and industries in which these firms operate it is not as detailed
about either of these dimensions (or their interaction) and heavily relies on managerial disclosure
choices which could induce measurement bias (Villalonga 2004a) In addition to the new
method of estimating the value effects of foreign operations we provide evidence that MNCs
trade at a premium which stands in contrast to the discount documented in Denis et al (2002)
This difference is primarily attributed to our use of foreign benchmarks to construct the imputed
values of foreign operations6
Our finding of a premium better reconciles with the findings in the international trade
literature By focusing on nonpublic establishment level (eg factory store or office) data the
trade literature is able to generate relatively precise proxies for total factor productivity (TFP)
and provides robust evidence that firms engaging in international trade are more productive than
those that do not (see Helpman 2006 and Syverson 2011 for reviews) Furthermore
productivity differences tend to be highly persistent even within narrowly defined industries
(Syverson 2011) Due to data limitations it is difficult to accurately measure TFP at the firm
level especially for firms which operate in multiple industries andor countries Nevertheless
Helpman Melitz and Yeaple (2004) provide some evidence that US firms engaging in FDI
have labor productivity advantages over firms that do not Reconciling the discount to
multinational operations found by Denis et al (2002) to the trade literature requires that the
persistent productivity advantages of firms engaged in international trade must be more than
offset by some other cost (eg agency costs) While this relation is possible it is difficult to
6 We repeat the analysis of Denis et al (2002) for our sample and confirm that differences in value effects of foreign operations are driven by the different methods for measuring the imputed value of foreign operations
5
conjecture why the most productive firms would have the largest agency costs In contrast the
finding of a premium easily reconciles with the trade literature
Given our result we investigate whether the premium is simply an artifact of innate
characteristics of firms that choose to invest abroad For example Helpman et al (2004) present
a simple model with heterogeneous productivity endowments The firms that receive the highest
productivity endowments are the ones capable of paying the fixed costs to establish a foreign
subsidiary To the extent productivity advantages create excess value one would expect the type
of firms that choose to invest abroad would be valued at a premium even in the absence of their
foreign investments After including firm fixed effects to control for time invariant firm
characteristics (eg innate productivity advantages) we continue to find economically and
statistically significant evidence of a premium to foreign operations
To further account for the endogenous nature of FDI we estimate a dynamic panel data
model using the generalized method of moments (GMM) estimator developed by Arellano and
Bond (1991) Arellano and Bover (1995) and Blundell and Bond (1998) This estimator allows
us to simultaneously account for the potential endogeneity of FDI industrial diversification and
firm value This method also yields a significant premium which is of similar magnitude and
significance to that estimated using fixed effects
We also perform a series of additional robustness tests First we examine whether the
measured excess value premium is driven by operations in countries with large control premiums
(Dyck and Zingales 2004) In countries with large control premiums using stock price in the
calculation of total firm value could underestimate the true value of the benchmark firms and
thereby induce an excess value premium We fail to find evidence that the excess value premium
is driven by such countries
6
Second we examine whether access to a low cost of capital is a meaningful source of the
US MNC advantage We incorporate foreign benchmarks in our method of estimating firm
excess value as it allows us to incorporate attributes of operating in those foreign countries ndash a
key attribute being the cost of capital Relative to the local benchmarks in foreign countries US
MNCs have the ability to obtain funds outside the local operating environments either by
borrowing in the US market or transferring capital internally When competing against firms in
shallow capital markets with high costs of capital MNCs may have a competitive advantage
over local foreign companies due to these alternative sources of capital However investors and
lenders are likely to expect higher rates of return from the additional risk associated with
operating in these environments making it unclear whether a multinational network will enhance
value in countries with a high cost of capital In addition foreign firms have some ability to
access international capital markets which mitigates the advantages to US firms To examine
whether the cost of capital plays a role in our finding of a premium we include a proxy for the
extent of operations in countries with higher costs of capital Our results are unaffected by the
inclusion of the proxy
Finally we control for several corporate governance proxies which Hoechle et al (2012)
find to be correlated with excess values (and industrial diversification) While magnitude of the
premium is virtually unaffected by including these proxies the significance level does decline
due to the reduced sample size
Our study contributes to the literature on multinational firms Our new method of estimating
excess values for multinational firms provides strong evidence of a statistically and economically
significant premium associated with increased international operations The premium is robust to
a variety of specifications and controls designed to evaluate alternative explanations In addition
7
this result easily reconciles with the international trade literature that establishes a link between
greater productivity and international operations though we do not find results supporting this as
the only source of the premium As segment disclosures improve and as new data sources
become available on multinational activity (eg Bureau Van Dijk) our empirical approach can
be used to investigate the extent to which the home country of the parent might influence the
extent with which foreign operations affect firm value (ie by examining non-US based
MNCs)7 Future studies could also examine the underlying forces affecting the excess value
premiums across countries
We also contribute to the understanding of international accounting standards and their
comparability Many studies seek to compare firms using different accounting standards These
comparisons generally rely on information generated by a particular accounting regime By
comparing commonly used metrics (total assets net income and sales) reported for the same
firm and year across different regimes we provide insights as to the relative comparability across
standards In particular we find that sales is reported significantly more consistently across
accounting regimes than the other metrics examined This finding should aid researchers in
reducing measurement error when comparing accounting information in multinational settings
The paper proceeds as follows Section 2 discusses the motivation for finding a premium or
discount to multinational operations Section 3 describes our data and how the excess value of a
firm is measured A discussion of our independent variables and primary findings is presented in
Section 4 with additional robustness tests provided in Section 5 Finally Section 6 concludes
7 As MNCs are ultimately bound by the tax regulatory and legal frameworks of their home country the valuation effect of foreign operations that we document for US-based MNCs may be different in a sample of non-US based MNCs with the same country-industry footprint Theorists refer to these as lsquolocational advantagesrsquo enjoyed by all multinational firms of a given nationality and obtained irrespective of skills or capabilities unique to a particular firm (Yamin 1991) There is also evidence of locational advantages in the market for corporate control (Huizinga and Voget 2009)
8
2 Premium versus discount
In a frictionless world where managers maximize firm value and markets are efficient there
should be no discount or premium to operating in foreign countries However there are a number
of forces that may cause actual firm value to deviate from the implied firm value
On one hand MNCs may have comparative advantages that generate premiums (ie positive
excess values) relative to a similar footprint of stand-alone firms Having operations in a variety
of locations that access different supplies of inputs and customers allows the firm to take
advantage of changing market conditions by shifting operations or products to maximize firm
value more so than a firm operating in a single country Similarly having access to a variety of
institutional settings such as different tax codes legal regimes and financial markets can provide
MNCs with options beyond those available to firms located in a single country
Another possible factor associated with a premium to multinational operations is the cost of
capital For operations located in countries with shallow capital markets or weaker creditor
rights operating within a conglomerate can provide the benefit of a lower cost of capital for
investments and expansion for at least two reasons First as our MNCs are domiciled in the US
their foreign segments have greater access to US capital markets relative to local competitors in
the foreign jurisdiction Second cash rich segments with few investment opportunities can
finance investments in cash poor segments with positive net present value projects (eg Myers
and Majluf 1984) Consequently diversified firms should be less liquidity constrained and better
able to shift resources to the most valuable investment opportunities Research has found that
internal financing is used more often when diversified firms have operations in countries with
more costly external financing (Desai Foley and Hines 2004) Multinational firms may also be
relatively more protected than single-country firms when negative shocks hit external capital
9
markets (Foley Desai and Forbes 2008) In a recent study Matvos and Seru (2011) find that
industrially diversified firms perform better than non-diversified firms when external capital
markets are impaired
While these arguments suggest that MNCs enjoy certain advantages when the foreign cost of
capital is high these benefits should be reduced by the additional risk associated with operating a
business in a high cost of capital environment For example Verizon is a multinational firm
operating in a number of countries One of the higher cost of capital locations in which they
operated was Venezuela While the multinational structure of Verizon may have mitigated some
of the risks associated with operating in a higher cost of capital location the firms was unable to
eliminate all of these risks In fact in 2007 Verizonrsquos operations in that country were
nationalized resulting in a net extraordinary loss of $131 million that year8 In other words
MNC investors are likely to expect higher rates of return to compensate for higher risk which
likely offsets some (if not all) of the potential excess value premium
On the other hand multinational firms may incur a discount (ie negative excess values)
relative to a similar footprint of stand-alone firms Potential reasons for a discount largely rely on
the existence of agency costs Multinational firms tend to be larger more complex and less
transparent The combination of complexity and opacity inherent in MNCs reduces a firmrsquos
reliance on external capital as the cost of such capital increases due to agency costs (Desai et al
2004) As a result of the reduced reliance on external funds MNCs face a reduced level of
monitoring As noted by Jensen (1986) managers have incentives to increase firm size beyond
that which is optimal with overinvestment or misallocation of funds to pet projects If this lack of
monitoring is in combination with available internal capital empire building becomes easier
(Hope and Thomas 2008) Furthermore the current segment reporting standards under US 8 See the 10-K of Verizon Communications Inc dated December 31 2007 (footnote 2)
10
GAAP only require highly aggregated disclosures of foreign operations and allow for
considerable managerial discretion As a result operating in multiple countries makes it easier
for management to hide poor performance ndash either their own or that of a division ndash such that low
quality managers are more likely to be retained and overcompensated and poorly performing
divisions retained longer than optimal Other reasons one might expect a discount to be applied
to multinational firms include higher coordination costs (eg coordinating across different
cultures and languages) as well as additional risks These risks include exposure to multiple
political regimes legal regimes economic regulations and currency fluctuations
Past studies investigate whether foreign operations of US firms enhance or reduce firm
value However all of these studies benchmark the value of the foreign operations of an MNC to
US domestic firms For instance Denis et al (2002) determine the implied value of a US
MNC using domestic (US) single-segment firms in the same industry as a benchmark Studies
using Tobinrsquos Q to investigate valuation effects of MNCs also rely on domestic benchmarks
(eg Morck and Yeung 1991 Morck and Yeung 2001 Gande Schenzler and Senbet 2009)
However using a US domestic firm as a benchmark for the value of foreign operations
implicitly makes two assumptions First this method assumes that the risks and expected growth
rates of foreign operations are equivalent to those of domestic operations This assumption is
inconsistent with the findings of Hail and Leuz (2009) which provides evidence that the cost of
capital varies substantially across countries Second this method assumes that there is excess
domestic capacity to allow for expansion with profitability similar to current domestic
operations We relax these assumptions by measuring the imputed value of a foreign component
of a US firm as if that component operated autonomously within that foreign country (rather
than in the US) Specifically our multiples allow the cost of capital and expected growth rates
11
to vary across not just industry but also country Furthermore our counterfactual does not
require the assumption that operations be relocated mitigating the production and sales capacity
concerns
3 Measuring excess value
31 Description of excess value
Figure 1 shows the average percent of foreign sales (based on segment disclosures under US
GAAP) across firms through time and illustrates that firms are continuing to expand
internationally9 This expansion highlights the importance of understanding whether the decision
to operate internationally is on average a value enhancing corporate strategy We contribute to
this understanding by evaluating whether the firm as a whole is worth more or less than its
imputed value (ie the firm is valued in excess of the sum of its individual components)
We measure the excess value of a firm as the logarithm of the ratio of actual firm value to
imputed value based on the method used in Berger and Ofek (1995) The imputed value is the
hypothetical value of the MNC under the assumption that its country-industry components
operate as independent entities A key innovation of our study is an alternative approach to
imputing the value of the foreign operations of an MNC Components of MNCs operating in a
given industry and country are imputed using the value of single-segment firms operating in the
same industry and country Thus we ensure the discount rates and expected growth rates are
applicable to each given industry and location in estimating the implied values of the MNC
segments
9 The discontinuity in industrial diversification (the percent of sales outside the primary industry) between 1997 and 1998 is the result of a change in segment reporting rules
12
The actual firm values are observable for all firms in our sample The hypothetical firm
values for multi-segment firms are imputed using market value to sales ratios herein referred to
as lsquomultiplesrsquo of the median single-segment firm operating in the same country-industry Thus
these single country-industry firms serve as benchmarks for an MNCrsquos operations within that
same country-industry The imputed value of the country-industry component can be viewed as
an estimate of what that component would be worth if it operated independently The sum of the
imputed values across all country-industry components represents the hypothetical value that the
multi-segment firm would be worth if it were broken apart
32 Data
Four primary data sources play a distinct role in carrying out our analysis i) Bureau of
Economic Analysis (BEA) data ii) Compustat Segment data iii) Worldscope data and iv)
Compustat Fundamentals Annual data Broadly speaking BEA data provide information about
the diversified nature of MNCs Compustat Segment data provide information about the
industrial diversification of US domiciled firms Worldscope data are used to compute
multiples for single-segment foreign firms that serve as benchmarks for the foreign operations of
MNCs (ie foreign benchmarks) and Compustat Fundamentals Annual data are used to
compute multiples for single-segment domestic firms that serve as benchmarks for the domestic
operations of multinational firms We also use the latter data source to construct our primary
control variables
13
Key to the execution of our study is our access to BEA data Federal law requires US-
domiciled MNCs to report certain financial and operating data to the BEA10 With regard to
observing the country-industry operations of MNCs use of the BEA data overcomes two
important limitations of Compustat Segment data First Compustat does not consistently identify
the specific location or scale of MNCsrsquo foreign operations11 Second Compustat does not
identify the industry activities in each country of operation This latter point is particularly
limiting for MNCs because domestic industry activity need not mirror foreign industry activity
An MNC generating an equal proportion of sales in two industries in the domestic market need
not generate industry sales in equal proportions in every foreign market12 The lack of detail and
consistency in Compustat Segment data arises because segment reporting is highly aggregated
and firms exercise substantial discretion in defining segments under Accounting Standards
Codification 280 (ASC 280) (eg Villalonga 2004a and Bens Berger and Monahan 2011)
The BEA defines an MNC as the combination of a single US entity called the US parent
and at least one foreign affiliate ndash that is these firms have a physical presence outside the US
due to FDI Since 1982 MNCs have completed a mandatory and confidential lsquoSurvey of US
Direct Investment Abroadrsquo for the domestic operation and the operations of each foreign
affiliate defined as a foreign entity in which the US parent holds at least a 10 percent equity
interest (directly or indirectly) The data are reported in US dollars on a fiscal year basis and in
10 BEA surveys are conducted pursuant to the International Investment and Trade in Services Survey Act (PL 94-472 90 Stat 2059 22 USC 3101-3108) See httpwwwbeagovsurveysdiasurvhtm and Mataloni (2003) for more detailed information on the BEA data 11 Exhibit 21 disclosures of material subsidiaries and their locations in SEC 10-K filings could potentially help overcome the inability to observe the location of the firmrsquos foreign operations from segment disclosures However at least three limitations remain (1) for industrially diversified multinational firms the researcher would be required to make an assumption about the proportion of industrial diversification in each country (2) Exhibit 21 provides no information about the scale of activity in each jurisdiction and (3) managerial discretion is used to determine a lsquomaterialrsquo subsidiary under Section 601 of SEC Regulation S-K 12 In fact the expectation that a multinational firm would exhibit different industry membership in different countries is consistent with the theory of vertical foreign direct investment where firms separate their various value chain activities and locate them in the most favorable country (see eg Hanson Mataloni and Slaughter 2005)
14
accordance with US Generally Accepted Accounting Principles (US GAAP) For each year
we observe the sales industry composition and location of not only the parent but also each
affiliate13
The procedures we use to construct our sample are similar to those used by Berger and Ofek
(1995) and Denis et al (2002) Using firm-years appearing in Compustat Fundamentals Annual
and Compustat Segment data we obtain a sample of firms domiciled in the US with total sales
exceeding $20 million We exclude firms in the financial service (SIC codes 6000 to 6999) and
utility (SIC codes 4000 to 4999) industries and eliminate firm-years when the sum of segment
sales (Compustat geographic or Compustat business) is not within 1 of total firm sales for that
year We restrict our sample period to include fiscal years beginning after December 15 1997 as
ASC 280 Segment Reporting substantially altered the definition of a reporting segment under
US GAAP This restriction ensures that Compustat Segment data reflects the use of a consistent
accounting standard Finally we require that our MNCs appear in both Compustat and BEA
data The requirement that our sample firms appear in the BEA database ensures that the firms
have observable international operations In sum these procedures result in 1166 multinational
firms incorporated in the US with 4950 observations between 1998 and 2008 that meet all of
our data requirements
33 Measuring excess value
The dependent variable in our study is excess firm value (Excess Value) defined as the
logarithm of the ratio of actual firm value to imputed firm value (Berger and Ofek 1995)
13 We obtain sales industry membership and country location for affiliates from one of two sources When affiliates exceed the BEA-determined reporting thresholds (ie if their assets sales or net income (loss) exceed $7 million in 1999 $30 million in 2000 through 2003 $10 million in 2004 and $40 million in 2005 through 2008) they must file their own detailed survey When affiliates fall below the thresholds we obtain our data from an attachment to the parent survey
15
Appendix A provides the definition of this and all other variables used in our analysis We
observe actual firm value equal to a firmrsquos market value of equity plus book value of liabilities
(ie book value of total assets minus book value of total equity) using Compustat Fundamentals
Annual data We calculate imputed firm value as the sum of the imputed values of a firmrsquos
operations in each country-industry Our method for imputing the value of the separate
components of a firm can best be described in three steps First we obtain total sales generated
by a firm for each country-industry in which it operates14 Second we obtain multiples (market
value to sales ratios) for benchmark firms operating in those same country-industries Third we
multiply a firmrsquos country-industry sales by the applicable median country-industry multiple to
obtain the imputed market value for each country-industry operation We perform each step
annually
Conceptually imputed values can be based on assets (ie Tobinrsquos Q) net income or sales
multiples We restrict ourselves to sales multiples for two reasons First using value to sales
ratios maintains consistency with the prior research on the excess value implications of
multinational operations (eg Denis et al 2002) Second as our method uses accounting data
for foreign companies to compute country-industry multiples the accounting numbers we use
need to be consistently measured across firms using different accounting standards We find that
sales data are measured most consistently
To assess the comparability of sales relative to either net income or assets across various
accounting standards we examine all firms listed on Worldscope as changing to or from US
14 Specifically we allocate total sales from Compustat to each component based on the proportion of BEA sales (after eliminating intercompany sales) occurring in each country-industry This step ensures that our total firm sales do not deviate from a firmrsquos total sales as reported in the 10-K
16
GAAP between 2005 and 201115 We obtain accounting data for the year of the change and the
prior year where electronically available in English We are able to obtain data for 66 firms
changing standards as detailed in Table 1 The distribution of years for which accounting data
are available under two different accounting standards for the same fiscal year is shown in Panel
A The years shown are the year prior to the change as these accounting numbers were provided
under the original standard and then subsequently restated in the following year under the new
standard for comparative purposes The majority of the shifts were to or from IFRS (71)
though seven other accounting standards are noted (Panel B) We examine the percent that the
non-US GAAP reported number differs from the US GAAP reported number in Panel C to
assess the comparability of these three summary accounting numbers across various accounting
standards Examining both the full sample as well as the subset that overlaps our study we find
that sales are more consistently measured than net income or assets
The sum of the imputed values across the country-industry components of an MNC is an
estimate of the value of a portfolio of unrelated businesses that mirror the related businesses of
the firm Consequently a comparison of the actual firm value with the imputed firm value is a
measure of how a multinational network affects firm value We provide descriptive statistics for
Excess Value for our sample of 4950 MNC firm-years in Table 2 Panel A This table also
includes univariate comparisons of the values between the MNC firms and the domestic single-
segment and foreign single-segment benchmark firms The Excess Value measure is significantly
higher for MNCs suggesting a premium in firm value for multinational firms relative to the
benchmark firms in total as well as the domestic and foreign subgroups (p-values for tests of
15 Due to the hand collection efforts required and the limitation on electronic filings we began our search with changes documented in 2005 which generally require filings from 2004
17
means and medians are lt 001)16 These results provide some initial evidence that operating in
multiple countries is positively associated with firm value
To estimate country-industry multiples we rely on Worldscope financial data on foreign
firms and Compustat Fundamentals Annual for domestic firms We restrict the firms to include
only those with at least 90 of sales income and assets inside the country of domicile (ie
those that do not report significant multinational activity) and that operate in a single industry
We refer to these firms as benchmark firms (either foreign or domestic depending on the country
of domicile) these firms do not appear in any of our regressions We report the number of
benchmark firms during our sample period in Table 2 Panel A The industry criterion is based on
the two-digit SIC code17 For every country that has at least five firms in the respective industry
and year we use the median ratio of market value to sales18 This ratio is the country-industry
multiple ndash an input required to compute imputed values
To determine the country-industry composition of MNCs we rely on BEA data In Table 3
we report the aggregate number of foreign affiliates and total sales by country as provided by
the BEA data for our sample of 4950 MNCs The specific countries tabulated are those that
represent at least 02 percent of total firm sales (pooled across all years) in our sample The top
five foreign countries in which MNCs generate sales are Canada United Kingdom Germany
France and Japan Table 3 also provides a comparison of the number of foreign benchmark
firms available in Worldscope in the specific countries in which our sample of MNCs generate
16 Similar to Berger and Ofek (1995) the median values differ slightly from zero for the benchmark single-segment firms due to the truncation of extreme excess values 17 BEA reporting constrains us to the two-digit SIC code level 18 When five firms are not available for a given country and industry we follow Berger and Ofek (1995) and move to a more general industry definition (eg one-digit SIC code) This procedure results in a mean (median) 630 (757) of MNC sales matched at the two-digit level
18
the majority of their sales Country coverage in Worldscope is not available for countries
representing 004 percent of total firm sales (per BEA)
4 Empirical results
41 Independent variables
Recall that our objective is to examine the overall relation between excess firm value and
multinational operations Our proxy for the extent of multinational operations is the percentage
of total firm sales made by foreign operations (Foreign Sales) By this measure the extent of
multinational activity in our sample of MNCs is non-trivial ndash Table 2 Panel B shows that
approximately 24 percent of sales are generated by foreign operations on average Our first
analysis estimates ordinary least square regressions of Excess Value on Foreign Sales and
control variables (discussed below)
We include variables in our regression to control for other potential determinants of excess
value These are the percentage of sales made by the firm outside its primary industry (Industry
Other) to control for any relation between industrial diversification and excess value as in Berger
and Ofek (1995) For this purpose we obtain industry sales using Compustat Segment data19 A
firmrsquos primary industry is the industry in which the firm generates the majority of its sales and
we determine industry sales at the business segment level We set Industry Other equal to zero
for firms that operate in a single business segment
Consistent with prior research we also include controls for firm size (Log Size) the ratio of
long term debt to total market value (Debt Firm Value) the ratio of capital expenditures to total
sales (CAPX Sales) the ratio of earnings before interest and taxes to total sales (EBIT Sales)
the ratio of research and development expenses to total sales (RampD Sales) and the ratio of 19 Our results are unaffected by using BEA data to measure Industry Other
19
advertising expenses to total sales (Ad Sales) Table 2 Panel B provides the distribution of the
control variables All independent variables are Winsorized at 1 In our OLS regressions we
use heteroskedasticity and autocorrelation consistent robust standard errors with clustering at the
firm level
42 Primary regression analysis
In this section we examine the relation between firm excess value and the amount of foreign
activity at US MNCs Table 4 contains the results of regressions of excess value on Foreign
Sales for multiple specifications In column (1) Excess Value is regressed on both the percent of
foreign sales (Foreign Sales) and the percent of sales outside the firmrsquos primary industry
(Industry Other) We add the control variables to the regression in column (2) and year
indicators in column (3) In all three specifications the coefficient on the percent of foreign sales
remains significantly positive (with a p-value lt 001) For a sense of economic magnitude in
column (3) a one standard deviation (197) increase in the percent of foreign sales (Foreign
Sales) is associated with a 41 (equal to (e0349-1) times 0197) increase in firm excess value The
effect is larger for the other two columns ndash 82 increase in firm excess value in column (1) and
46 in column (2)20
Our finding of a premium reconciles with the international trade literature which finds that
firms engaging in international trade are more productive than those that do not (see Helpman
2006 and Syverson 2011 for reviews) Due to data limitations it is difficult to accurately
measure TFP at the firm level especially for firms which operate in multiple industries andor
20 Using the alternative strategy that employs US domiciled single-segment firms as the benchmark for the imputed value of the foreign operations Denis et al (2002) find that greater foreign activity results in a discount to firm value Using the Denis et al (2002) method to calculate excess value in our sample we obtain a significantly negative regression coefficient on Foreign Sales using the models in Table 4 columns (2) and (3) (the coefficient is not significant using the model in column (1) results untabulated)
20
countries As an alternative method to evaluate whether our finding of a premium is simply the
result of more productive firms also having more multinational operations we investigate
whether the premium is simply an artifact of innate characteristics of firms that choose to invest
abroad For example Helpman et al (2004) present a simple model with heterogeneous
productivity endowments The firms that receive the highest productivity endowments are the
ones capable of paying the fixed costs to establish a foreign subsidiary
To the extent that productivity advantages create excess value one would expect the type of
firms that choose to invest more abroad would be valued at a premium even in the absence of
their foreign investments To control for time invariant firm characteristics (eg innate
productivity advantages) and hence a potential source of endogeneity we add firm-level fixed
effects in column (4) of Table 4 We continue to find that the extent of foreign operations
(Foreign Sales) is positively associated with Excess Value Across all specifications the sign
on all significant control variables is consistent with prior research (eg Berger and Ofek 1995
and Denis et al 2002)
The finding of a premium makes a significant contribution to the relatively sparse literature
on the value effects of multinational activity Our measure of imputed value expands upon the
methods used by Berger and Ofek (1995) and Denis et al (2002) but allows us to answer a
different question than Denis et al (2002) Holding constant the extent of industrial
diversification we ask whether MNCs are more valuable than a portfolio of benchmark firms of
a similar geographic footprint In contrast Denis et al (2002) ask whether holding constant the
extent of industrial diversification MNCs are more valuable than a portfolio of domestic (US)
benchmark firms We believe that asking whether an MNCrsquos foreign (domestic) operations are
21
more valuable than those of a foreign (domestic) firm is a more appropriate way to assess the
value implications of a multinational network
5 Robustness
51 Endogeneity related to dynamic relations
Prior research argues that the relation between industrial diversification and excess firm
value could be endogenous through dynamic relationships based on observing past outcomes
Managers of firms likely choose to enter or exit industries or geographic regions based upon
their previous performance For example Campa and Kedia (2002) find that firms are more
likely to enter new industries when prospects in their current lines of business are deteriorating
Similar dynamics could also drive a relation between excess value and FDI We assess the
robustness of the previous results by estimating a dynamic panel data model which allows us to
control for dynamic endogeneity in addition to firm level unobserved heterogeneity We employ
the GMM estimator for dynamic panel models developed by Arellano and Bond (1991) Arellano
and Bover (1995) and Blundell and Bond (1998) Our results from this analysis confirm our
previous findings that firms with a larger degree of foreign operations have positive excess
values
Dynamic panel data models are generalizations of the traditional fixed effects model where
lags of the dependent variable are added to the right hand side of the equation These additional
lags control for the impact a firmrsquos past performance has on its current performance through the
channels discussed above The GMM estimator for dynamic panel models developed by
Arellano and Bond (1991) starts by differencing the data at the firm level in neighboring time
periods to eliminate any firm specific time invariant unobserved heterogeneity that may be
22
present Eliminating the firm specific fixed effect by differencing the data with lags of the
dependent variable on the right hand side introduces correlation between the new error term and
the differenced explanatory variables This problem is solved by applying the standard GMM
estimator using lags of both the original explanatory variables and excess value as instruments
We note that the dynamic panel data framework allows us to treat both international and
industrial diversification (as well as the other firm-specific control variables) as endogenous
Following Hoechle et al (2012) we also allow contemporaneous values of these variables to be
treated as endogenous
Lagged values of the explanatory variables are valid instruments in a dynamic panel data
model when the model is dynamically complete (eg Wooldridge 2010) A dynamic panel data
model is dynamically complete conditional on the unobserved effect if enough lags of both the
explanatory and dependent variable are included to make the resulting error term uncorrelated
with all the right hand side variables Under this assumption any lags for the explanatory
variables beyond this can be used within the GMM estimator as instruments Like Hoechle et al
(2012) we add two years of lags of a firmrsquos excess value to the right hand side assuming that
this is enough lags to control for dynamic feedback effects We then use past values of all
variables beyond the second lag up to a total of seven years to act as instruments
The results of the dynamic panel data estimator are reported in Table 5 These results confirm
the previous finding of a positive relation between the degree of international operations and
excess value We note that the size and significance level of the coefficient on Foreign Sales
diversification are robust to changes in the number of lags used as instruments The Arellano and
Bond (1991) estimator includes testing diagnostics for correct specification of the model If the
model is correctly specified differences in the residuals from the levels equation should be
23
serially uncorrelated at lag one but correlated at lag two The p-values for tests of serial
correlation of the residuals at lags 1 and 2 are reported in Table 5 and these indicate that the
model is not rejected We also report in Table 5 the Hansen test for overidentification which is a
test of the null hypothesis that the instruments are valid The p-value for this test is 030
indicating that we fail to reject this null hypothesis Finally we report another test for exogeneity
known as the difference-in-Hansen test which tests the null hypothesis that the instruments in
the original equation are valid We fail to reject this null hypothesis as well
52 Identifying value effects of multinational operations via cross-border acquisitions
Another alternative to investigating a causal link between foreign direct investment and firm
value is an event study of foreign acquisitions21 Acquired foreign targets represent only a small
fraction of MNCsrsquo worldwide sales in the year of the acquisition ndash less than 1 percent in our
sample Additionally cross-border acquisitions are not the primary source of international
expansion for the average firm We find that foreign acquisitions account for between 18 percent
and 55 percent of the year-over-year change in foreign sales with the remainder of the growth
coming from either newly established entities (ie greenfield investments) or growth in existing
operations22 These results suggest that acquisitions are not a material source of growth for the
21 Doukas and Travlos (1988) find positive returns when the acquirer is not operating in the target firmrsquos country and insignificant returns when the acquirer is operating in the target firmrsquos country or expanding internationally for the first time Morck and Yeung (1991) find marginal evidence of positive returns for firms with intangible assets Finally Doukas (1995) finds positive returns only for firms with average Tobinrsquos q ratios greater than one More recent work also using an event study methodology provides mixed evidence on valuation effects of global diversification through acquisitions Dos Santos Errunza and Miller (2008) find insignificant returns for firms with already established foreign operations firms establishing initial foreign operations and firms acquiring targets in related industries Doukas and Kan (2006) and Moeller and Schlingemann (2005) find reductions to shareholder value while Francis Hasan and Sun (2008) find positive returns for acquirers of targets in segmented financial markets 22 New foreign affiliates to an MNC group each year report on the BEA survey whether they entered the group as an acquisition or a newly established entity (ie greenfield investment) Affiliates under the lowest reporting threshold do not report this information resulting in our inability to precisely identify the source of 33 percent of the increase in firm sales as either acquisition or greenfield
24
typical US MNC suggesting that an event study would lack power and generalizability For
these reasons we do not implement an event study
53 Control premium
A control premium or private benefits of control occurs when ldquosome value whatever the
source is not shared among all the shareholders in proportion of the shares owned but it is
enjoyed exclusively by the party in controlrdquo (Dyck and Zingales 2004 p 541) Using stock
prices of traded shares held by minority shareholders in determining the market value to sales
ratios of our benchmark firms could underestimate the true value of our benchmark firms if those
firms face large control premiums This in turn could induce an excess value premium because
the imputed value of these country-industry segments would be artificially low The existence of
such benefits is more prevalent in some countries than others and could affect the value of an
MNCrsquos operations in such domains We use the country-level control premiums from Dyck and
Zingales (2004) and using each firmrsquos country sales as a percent of total firm sales we obtain a
weighted average control premium that each MNC faces (Control Premium) For countries not
included in the Dyck and Zingales (2004) analysis we set the control premium equal to zero23 In
Table 6 we show that Foreign Sales remains positive and highly significant (p-value lt 001)
when we include the proxy for control premium As a result it does not appear that the excess
value premium is driven by MNCs operating in countries where majority shareholders are able to
extract large control premiums
23 Results are robust to replacing missing values with the mean control premium of the respective GDP quartile
25
54 Cost of capital
As discussed in Section 2 when countries have shallow capital markets a multinational
network may provide the benefit of a lower cost of capital for investments through an internal
capital market as well as through better access to the US capital market Prior research
establishes that there is substantial variation in the cost of capital across countries (Erb Harvey
and Viskanta 1996 and Hail and Leuz 2009) To determine whether the documented premium
is due to cost of capital advantages we include a control variable to capture the difference
between the MNCrsquos cost of capital and the cost of capital faced by its competitors in the global
markets in which it operates
We compute our Cost of Capital proxy in two steps First we obtain the country-year credit
rating published by Institutional Investor24 The rating ranges from 0 to 100 where higher values
imply that a country has a higher default risk Country-level credit risk is a reasonable predictor
of expected equity market returns and volatility (Erb et al 1996) and exhibits a highly
significant correlation with international accounting-based estimates of imputed cost of capital
(Hail and Leuz 2009) Second using each firmrsquos country sales as a percent of total firm sales
we obtain a weighted average credit risk rating across the countries in which each firm operates
A higher value implies an MNC operates in countries with higher cost of capital In Table 7 we
do not find a result consistent with MNCs gaining an advantage from their access to low cost
capitalndash Cost of Capital is not significantly associated with Excess Value25
24 We thank Cam Harvey for providing these data 25 Results are consistent using cost of capital as defined in Erb et al 1996
26
55 Corporate governance
In light of the recent finding that corporate governance mitigates the diversification discount
we examine whether the premium to multinational activity is sensitive to controls for corporate
governance Hoechle et al (2012) find that the magnitude of the industrial diversification
discount is decreased and in several specifications no longer significant in the presence of
governance proxies In the first column of Table 8 we include the five governance proxies that
are consistently significantly associated with excess value in Hoechle et al (2012) ndash the percent
of CEO ownership (CEO Ownership and CEO Ownership squared) the percent of institutional
ownership (Institution Ownership) whether the firm has a powerful or influential CEO
(Powerful CEO) and the governance index from Gompers Ishii and Metrick (2003)
(Governance Index)
As documented in Table 8 including these control variables reduces our sample size by
almost 50 (to 2550 observations) and tends to exclude the smaller firms from the analysis
None of the governance proxies are significant Relative to column (4) of Table 4 the coefficient
on Foreign Sales is similar in magnitude but is no longer statistically significant at
conventional levels To investigate whether the decreased significance is due to the smaller
sample or the inclusion of the governance variables we re-estimate the regression for the same
2550 observations after removing the governance controls The coefficient and t-statistic are
virtually identical suggesting that the loss of significance is due to the reduced sample and not
the governance controls We do not find evidence to suggest that variation in corporate
governance practices drives variation in excess value for US MNCs
27
6 Conclusion
Using a new method to quantify the valuation effects of foreign direct investment by US
domiciled firms we find robust evidence of a premium in excess value associated with the
degree of multinational operations Our method builds on that used in previous studies
examining firm excess value (eg Berger and Ofek 1995 and Denis et al 2002) by
incorporating the location in addition to the industry of each corporate affiliate This allows
important factors such as growth and cost of capital to vary across countries as well as industries
Examining a sample of multinational firms we find that a one-standard deviation (197)
increase in foreign operations is associated with an increase of between 41 and 82 in excess
value minus both a statistically and economically significant finding
This result complements the finding of the international trade literature that more productive
firms are more likely to operate internationally However the premium remains after including
firm fixed effects to control for persistent firm traits (eg productivity)
The finding of a premium stands up to a succession of robustness tests designed to evaluate
whether the result arises from alternative methods or explanations First we estimate a dynamic
panel data model using the generalized method of moments (GMM) estimator to simultaneously
account for the potential endogeneity of FDI industrial diversification and firm value Second
we include a control variable to proxy for whether the firm operates in countries with larger
control premiums which could result in understated multiples and mechanically induce a
premium Third we include a control variable to measure the cost of capital across the countries
in which an MNC operates to evaluate whether a relatively low cost of capital borne by
multinational firms is a meaningful source of the premium Fourth we control for several
corporate governance proxies which have been found to be correlated with excess values (and
28
industrial diversification) We fail to find evidence supporting any of these alternative
explanations ndash the premium in excess value arising at firms with greater multinational operations
remains significant
Overall our new method of estimating excess values for multinational firms provides strong
evidence of a statistically and economically significant premium associated with increased
international operations The premium is robust to a host of specifications and controls designed
to evaluate alternative explanations We leave to future research the questions of whether the
premium to US multinational firms applies more broadly to other countries of domicile and
what factors influence the premium
29
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Ammann M Hoeschel D and M Schmid 2012 Is there really no conglomerate discount
Journal of Business Finance and Accounting 39(12) 264-288 Arellano M and Bond S R 1991 Some tests of specification for panel data Monte Carlo
evidence and an application to employment equations Review of Economic Studies 58 (2) 277-298
Arellano M and Bover O 1995 Another look at instrumental variables estimation of error
components models Journal of Econometrics 68 (1) 29-61 Bens D A Berger P G and S J Monahan 2011 Discretionary disclosure in financial
reporting An examination comparing internal firm data to externally reported segment data The Accounting Review 86(2) 417-449
Berger P G and E Ofek 1995 Diversificationrsquos effect on firm value Journal of Financial
Economics 37 39-65 Blundell R and Bond S R 1998 Initial conditions and moment restrictions in dynamic panel
data models Journal of Econometrics 87(1) 115-144 Campa J M and S Kedia 2002 Explaining the diversification discount The Journal of
Finance 57 (4) 1731-1762 Denis D J Denis D K and K Yost 2002 Global diversification industrial diversification
and firm value The Journal of Finance 57 (5) 1951-1979 Desai M A Foley C F and J R Hines 2004 A multinational perspective on capital structure
choice and internal capital markets The Journal of Finance 59 (6) 2451-2487 Dos Santos MB V Errunza and D Miller 2008 Does corporate international diversification
destroy value Evidence from cross-border mergers and acquisitions Journal of Banking and Finance 32(12) 2716-2724
Doukas JA 1995 Overinvestment Tobinrsquos q and gains from foreign acquisitions Journal of
Banking and Finance 19 1285-1303 Doukas J and N G Travlos 1988 The effect of corporate multinationalism on shareholdersrsquorsquo
wealth evidence from international acquisitions Journal of Finance 43(5) 1161-1175 Doukas JA and O B Kan 2006 Does global diversification destroy firm value Journal of
International Business Studies 37(3) 352-371
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Dunning J and A Rugman 1985 The influence of Hymerrsquos dissertation on the theory of foreign direct investment The American Economic Review 75(2) 228-232
Dyck A and L Zingales 2004 Private benefits of control An international comparison The
Journal of Finance 59 (2) 537-600 Erb C Harvey C and T Viskanta 1996 Expected returns and volatility in 135 countries The
Journal of Portfolio Management 46-58 Foley C F Desai M A and K J Forbes 2008 Financial constraints and growth
Multinational and local firm responses to currency depreciations Review of Financial Studies 21 (6) 2857-2888
Francis B I Hasan and X Sun 2008 Financial market integration and the value of global
diversification Evidence for US acquirers in cross-border mergers and acquisitions Journal of Banking and Finance 321522-1540
Gande A Schenzler C and L Senbet 2009 Valuation effects of global diversification
Journal of International Business Studies 1515-1532 Gompers P Ishii J and A Metrick 2003 Corporate governance and equity prices Quarterly
Journal of Economics 118(1) 107-156 Hail L and C Leuz 2009 Cost of capital effects and changes in growth expectations around
US cross-listings Journal of Financial Economics 93 428-454 Hanson G Mataloni R and M Slaughter 2005 Vertical production networks in multinational
firms Review of Economics and Statistics 87(4) 1-15 Helpman E 2006 Trade FDI and the Organization of Firms Journal of Economic Literature
44(3) 289-630 Helpman E Melitz M J and S R Yeaple 2004 Export versus FDI with Heterogeneous Firms
American Economic Review 94(1) 300-316 Hoechle D Schmid M Walter W and D Yermack 2012 How much of the diversification
discount can be explained by poor governance Journal of Financial Economics 103 41-60 Hope O-K and W B Thomas 2008 Managerial empire building and firm disclosure Journal
of Accounting Research 46 (June) 591-626 Huizinga H P and J Voget 2009 International taxation and the direction and volume of cross-
border MampAs Journal of Finance 64 (3) 1217-1249 Jensen M 1986 Agency cost of free cash flow corporate finance and takeovers American
Economic Review Papers and Proceedings 76 323-329
31
Laeven L and R Levine 2007 Is there a diversification discount in financial conglomerates
Journal of Financial Economics 85 331-367 Lang L and R Stulz 1994 Tobinrsquos Q corporate diversification and firm performance Journal
of Political Economy 102 1248-1280 Mataloni R 2003 US multinational companies Operations in 2001 Survey of Current
Business 85-105 Matvos G and A Seru 2011 The dynamic tradeoff between corporate socialism and financial
market dislocation Estimates from internal capital markets Working paper University of Chicago Booth School of Business
Moeller SB and F P Schlingemann 2005 Global diversification and bidder gains A
comparison between cross-border and domestic acquisitions Journal of Banking and Finance 29 533-564
Morck R and B Yeung 1991 Why investors value multinationality Journal of Business 64
165-186
Morck R and B Yeung 2001 Why firms diversify Internatization vs agency behavior In Intangibles B Lev (ed) Oxford University Press pp 269-302
Myers S C and N S Majluf 1984 Corporate financing and investment decisions when firms have information that investors do not have Journal of Financial Economics 13 187-221
Schmid M and I Walter 2009 Do financial conglomerates create or destroy economic value
Journal of Financial Intermediation 18 193-216 Syverson C 2011 What determines productivity Journal of Economic Literature 49(2) 326-
365 Villalonga B 2004a Diversification discount or premium New evidence from the Business
Information Tracking Series Journal of Finance 59 479-506 Villalonga B 2004b Does diversification cause the diversification discount Financial
Management 33 5-27 Wooldridge JM 2010 Econometric Analysis of Cross Section and Panel Data (Second
Edition) Cambridge MA MIT Press Yamin M 1991 A reassessment of Hymerrsquos contribution to the theory of the transnational
corporation C Pitelis and R Sugden (eds) The Nature of the Transnational Firm London Routledge
32
Appendix A Variable Definitions
We obtain information regarding firmsrsquo operations by country and industry from the Bureau of Economic Analysis (BEA) unless otherwise noted Acronyms used below reflect Compustat mnemonics and thus indicate when we obtain data from the Compustat North America Fundamentals Annual database All independent variables are winsorized at 1
Dependent variable
Excess Value = The log of the ratio of actual firm value to imputed firm value Actual firm value is equal to market capitalization (MVE) + book value of total assets (AT) ndash book value of shareholdersrsquo equity (SEQ) Imputed firm value is calculated as the sum of the imputed values of a firmrsquos operations in each country-industry The imputed value for each country-industry is equal to sales generated in that country-industry times a multiple equal to the ratio of a benchmark firmrsquos actual value to total sales A benchmark firm is defined as a median single-segment firm operating in the same country-industry All multiples require at least five firms listed in the respective country-industry on Compustat (domestic) or Worldscope (foreign) with less than 10 of sales income and assets outside the country of domicile Similar to Berger and Ofek (1995) we eliminate extreme values where the ratio of actual to imputed firm value exceeds 400 or is less than 025
Independent variables
Foreign Sales = Sales made by the foreign operations of US MNCs as a percent of total firm sales
Industry Other = Sales outside a firmrsquos primary industry as a percent of total sales (SALE) Equal to zero for firms operating in a single business segment
Log Size = Log of total sales (SALE)
Debt Firm Value = Ratio of long-term debt (DLTT) to total market value (market capitalization (MVE) + book value of total assets (AT) ndash book value of shareholdersrsquo equity (SEQ))
CAPX Sales = Ratio of capital expenditures (CAPX) to total sales (SALE)
EBIT Sales = Ratio of earnings before interest and taxes (EBIT) to total sales (SALE)
RampD Sales = Ratio of research and development expenses (XRD) to total sales (SALE)
Ad Sales = Ratio of advertising expenses (XAD) to total sales (SALE)
Control Premium = A weighted-average (based on the sales in a given country as a percent of total firm sales) of the country-level control premium from Dyck and Zingales (2004)
Cost of Capital = A weighted-average (based on the sales in a given country as a percent of total firm sales) of the country-level credit rating from Institutional Investor (httpwwwinstitutionalinvestorcom) The country-level rating is available for 160 countries and ranges from 0 to 100 Higher values imply greater credit risk
33
CEO Ownership = Percent of shares owned by the CEO from Execucomp
Institution Ownership = Percent of shares owned by institutional investors from Thomson Reuterrsquos Institutional Holdings database
Powerful CEO = Indicator variable equal to one if the CEO is the only insider on the board of directors and also serves as the chairman and president based on data from RiskMetrics Directors database Equal to zero otherwise
Governance Index = Count measure of takeover defenses used in Gompers Ishii and Metrick (2003) from RiskMetrics Governance database As the governance data are not available for every year we follow Gompers Ishii and Metrick (2003) and assume that the firmsrsquo governance attributes are unchanged until publication of the subsequent series
34
Figure 1 Trends in multinational activity and industrial diversification from 1984-2008 This figure presents a plot of the mean percent of foreign sales and the mean percent of sales outside the firmrsquos primary industry (industrial diversification) from 1984 through 2008 Dotted lines above and below the primary lines represent 95 confidence intervals To compile the plot we include the full population of firms in Compustat
35
Table 1 Analysis of accounting data for firms reporting under multiple accounting standards This table provides a comparison of sales net income and assets for firms that changed accounting standards to or from US GAAP Comparisons are provided for the 66 firms that changed to or from US GAAP and for which we could obtain accounting data electronically and in English under both standards for the same fiscal year (eg the firm presented the accounting numbers originally under one set of standards and then restated the prior year numbers in the subsequent year under the new set of standards) Panel A shows the distribution of firms by year Panel B shows the other accounting standards to which US GAAP is compared Panel C shows the mean and median percent that the other accounting standard differs from US GAAP for the same fiscal year and accounting number Two samples are shown in Panel C The first is the full sample of firms with a change in accounting standards to or from US GAAP for which data was available The second is the subset of these firms that occurred during the sample window used in this study (ie excluding changes post-2008) The percent difference in sales is compared to the percent difference in net income and assets Tests of means are based on matched-pair t-tests Tests of medians are based on Wilcoxon matched-pair signed-rank tests Panel A Accounting standard changes by year
2003
4 6
2004
25 38
2005
8 12
2006
6 9
2007
7 11
2008
5 8
2009
5 8
2010
5 8
2011
1 2
66
Panel B Accounting standards compared to US GAAP
IFRS
47 71
Canadian GAAP
12 18
Israeli GAAP
2 3
Dutch GAAP
1 2
Indian GAAP
1 2
Japanese GAAP
1 2
Norwegian GAAP
1 2
Swiss GAAP
1 2
66
36
(Table 1 continued) Panel C Tests of differences between US GAAP and other accounting standards
Percent different from US GAAP
Comparison to sales (p-values)
N Mean Median
Mean Median
Full Sample (2001-2011)
Sales
66 236 000
Net Income
66 10653 647
005 lt001
Assets
66 1162 126
lt001 lt001
Study Sample (2001-2008)
Sales
55 257 000
Net Income
55 10303 589
011 lt001
Assets
55 1047 099
003 lt001
37
Table 2 Descriptive statistics Panel A reports the mean and medians of Excess Value for the multinational firms and single-segment benchmark firms as well as univariate tests across the subsamples Panel B reports summary statistics for the independent variables for the multinational firms The p-values of the tests of differences in means are based on t-tests The p-values of tests of differences in the medians are for a Wilcoxon rank-sum test All variables are defined in Appendix A Panel A Excess Value by sample Multinational All Benchmark Benchmark Subgroups Sample Firms US Foreign
Value Value Test of Diff (p-value) Value
Test of Diff (p-value) Value
Test of Diff (p-value)
Mean 0067 0006 lt001 -0028 lt001 0014 lt001 Median 0076 0000 lt001 -0025 lt001 0000 lt001 N 4950 33074 5998 27076 Panel B Distribution of independent variables for the multinational firms N Mean 25th 50th 75th StDev Foreign Sales 4950 0238 0074 0190 0363 0197 Industry Other 4950 0212 0000 0166 0414 0215 Log Size 4950 6955 5823 6989 8140 1687 Debt Firm Value 4950 0136 0009 0097 0212 0145 CAPX Sales 4950 0054 0020 0033 0054 0101 EBIT Sales 4950 0065 0031 0077 0121 0138 RampD Sales 4950 0049 0000 0013 0059 0083 Ad Sales 4950 0008 0000 0000 0005 0018
38
Table 3 BEA and Worldscope samples
This table reports details on affiliate sales for significant countries pooled from 1998-2008 Significant countries are defined as those in which the operations of US MNCs represent at least 02 of total firm sales (per the BEA) For significant countries the table compares their representation in terms of MNC affiliate sales in the BEA data to the distribution of firm observations in Worldscope US Foreign Affiliates (BEA) Foreign Firms (Worldscope)
Country
N (1)
Sales (millions)
(2)
Total Sales (3)
N (4)
Sales (million)
(5)
Total Sales (6)
Australia 1344 73305 056 859 255865 210 Belgium 894 52755 040 169 26136 021 Brazil 959 60245 046 1143 683167 560 Canada 2730 536767 407 1181 526750 432 China 922 75907 057 822 276541 227 France 1852 183942 139 540 105447 086 Germany 2182 275577 209 480 103888 085 Hong Kong 867 48100 036 157 34094 028 Ireland 478 36754 028 34 3467 003 Italy 1178 82116 062 111 47948 039 Japan 1494 148032 112 2281 1119223 917 Korea South 659 34500 026 3530 2752791 2255 Malaysia 386 26027 020 837 168683 138 Mexico 1405 113623 086 362 237173 194 Netherlands 1149 85105 064 47 22631 019 Singapore 1098 94917 072 139 32913 027 Spain 871 63094 048 129 37419 031 Sweden 712 32682 025 235 23090 019 Switzerland 726 60288 046 74 20242 017 Taiwan 592 30313 023 2949 787049 645 United Kingdom 3054 423934 321 1997 882423 723 United States (domicile) 4950 10460880 7923 na na na Other - Covered by WorldScope 6893 199394 151 9000 4057894 33 Other -Not Covered by WorldScope 713 5352 004 - - - Total 38108 13203609 1000 27076 12204834 100
39
Table 4 OLS regressions of firm excess values on the percent of foreign sales This table reports the results of OLS regressions of firm excess value on the percent of foreign sales (Foreign Sales) the percent of sales outside a firmrsquos primary industry (Industry Other) and control variables All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed) Dep Variable = Excess Value Coefficient t-stat Coefficient t-stat Coefficient t-stat Coefficient t-stat (1) (2) (3) (4) Foreign Sales 0349 419 0209 293 0189 266 0278 210 Industry Other -0134 -186 -0047 -072 -0053 -081 -0326 -325 Log Size 0066 634 0065 624 -0044 -096 Debt Firm Value -0196 -213 -0241 -260 -0685 -588 CAPX Sales 0901 425 0893 435 0488 227 EBIT Sales 1154 542 1170 547 0408 189 RampD Sales 2908 1242 2932 1237 1544 379 Ad Sales 1869 263 2003 280 -1696 -119 Year indicators No No Yes Yes Firm indicators No No No Yes R2 0017 0212 0226 0768 Observations 4950 4950 4950 4950
40
Table 5 Dynamic panel GMM model regressing firm excess values on the percent of foreign sales This table reports the result from a regression of the excess value on the percent of foreign sales (Foreign Sales) the percent of non-primary industry sales (Industry Other) and control variables using a dynamic panel GMM estimator All control variables are considered to be endogenous with the exception of the year indicator variables AR(1) and AR(2) are tests for first-order and second-order serial correlation in the first differenced residuals with the null hypothesis of no serial correlation The null hypothesis of the Hansen test of overidentification is that all instruments are valid The null hypothesis of the difference-in-Hansen test of exogeneity is that the instruments used for the equation in levels are exogenous All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed) Dep Variable = Excess Value Coef t-stat Excess Value (one lag) -0018 -082 Excess Value (two lags) -0048 -272 Foreign Sales 0371 270 Industry Other -0163 -171 Log Size 0010 123 Debt FirmValue -1135 -820 CAPX Sales 0590 342 EBIT Sales -0047 -300 RampD Sales 1376 531 Ad Sales -0751 -040 AR(1) test (p-value) 000 AR(2) test (p-value) 017 Hansen test of over-identification (p-value) 030 Difference-in-Hansen test of exogeneity (p-value) 093 Year indicators Yes Number of firms 1016 Number of observations 4090
41
Table 6 OLS regression of firm excess values on the percent of foreign sales controlling for a control premium This table reports the result of the OLS regression of firm excess value including firm and year fixed effects with an additional control variable to proxy for a country-level ownership control premium (Control Premium) All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed) Dep Variable = Excess Value Coef t -stat Foreign Sales 0303 224 Industry Other -0327 -325 Log Size -0045 -100 Debt FirmValue -0683 -585 CAPX Sales 0490 226 EBIT Sales 0409 189 RampD Sales 1545 379 Ad Sales -1689 -119 Control Premium -1004 -062 Year indicators Yes Firm indicators Yes R2 0768 Observations 4950
42
Table 7 OLS regression of firm excess values on the percent of foreign sales controlling for the cost of capital This table contains the results of an OLS regression of firm excess value including firm and year fixed effects with an additional control variable to proxy for a country-level cost of capital (Cost of Capital) All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed) Dep Variable = Excess Value Coef t -stat Foreign Sales 0255 178 Industry Other -0326 -324 Log Size -0043 -094 Debt FirmValue -0687 -589 CAPX Sales 0485 225 EBIT Sales 0406 188 RampD Sales 1539 379 Ad Sales -1703 -120 Cost of Capital 0004 039 Year indicators Yes Firm indicators Yes R2 0768 Observations 4950
43
Table 8 OLS regression of firm excess values on the percent of foreign sales controlling for aspects of corporate governance This table reports the results of two OLS regressions In the first column are the results of the firm excess value regression including firm and year fixed effect with five proxies for corporate governance included ndash the percent of shares owned by the CEO and the percent squared (CEO Ownership and CEO Ownership squared) the percent owned by institutional investors (Institution Ownership) whether there is a powerful CEO (Powerful CEO) and the Gompers Ishii and Metrick (2003) governance index (Governance Index) In the second column the regression includes the same sample and variables with the exception of the governance proxies which are excluded All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed)
Governance
No Governance (same sample)
Dep Variable = Excess Value Coef t-stat Coef t -stat Foreign Sales 0259 159 0251 154 Industry Other -0099 -080 -0099 -079 Log Size -0105 -185 -0102 -180 Debt FirmValue -0813 -478 -0817 -482 CAPX Sales 0440 252 0438 249 EBIT Sales 0457 247 0457 246 RampD Sales 1717 284 1716 286 Ad Sales -2210 -143 -2190 -143 CEO Ownership 0010 007
CEO Ownership squared -0029 -017 Institution Ownership 0047 067 Powerful CEO -0003 -014 Governance Index 0009 103
Year indicators Yes Yes Firm indicators Yes Yes R2 0811 0810 Observations 2550 2550
- Cover for paperpdf
- crrz_20130603_SMUpdf
-
We thank Phil Berger Andy Bernard Marianne Bertrand Alan Bester Chris Hansen Chang-Tai Hsieh Rafael La Porta Christian Leuz Jon Lewellen Abbie Smith Doug Skinner and Jerry Zimmerman for helpful discussions and comments This paper has benefited from the comments of workshop participants at Chicago Cornell Dartmouth Florida Illinois at Chicago INSEAD LBS Michigan MIT Northwestern Rochester Syracuse University Texas UCLA and the EAA Annual Congress We thank William Steciak of KPMG for helpful discussions related to segment reporting We gratefully acknowledge the financial support of the University of Chicago Booth School of Business the Fama-Miller Center and the Neubauer Family Foundation The statistical analysis of firm-level data on US multinational firms was conducted at the International Investment Division Bureau of Economic Analysis US Department of Commerce under arrangements that maintain legal confidentiality requirements The views expressed are those of the authors and do not reflect official positions of the US Department of Commerce
The Multinational Advantage
Drew D Creal
University of Chicago Booth School of Business
Leslie A Robinson Tuck School of Business at Dartmouth
Jonathan L Rogers
University of Chicago Booth School of Business
Sarah L C Zechman University of Chicago Booth School of Business
February 2013
Chicago Booth Research Paper No 11-37
Abstract Using a proprietary dataset we evaluate whether the degree of foreign operations affects firm value by comparing actual value to imputed value for US multinational corporations (MNCs) We argue that using benchmark firms operating in the same country and industry as each MNC segment controls for differences in discount rates and expected growth rates across countries and industries This allows us to isolate the value effects of organizing a set of otherwise independent activities within a multinational network We find robust evidence that multinational networks trade at a premium relative to a benchmark portfolio of independent firms JEL Classification F23 G32 G34 M41 Keywords firm valuation multinational corporations diversification
1
1 Introduction
We investigate whether the degree of international diversification affects firm value for US
multinational corporations (MNCs) Specifically we determine whether the relative size of the
foreign operation has a positive negative or no association with the difference between the
actual observed firm value and an imputed value of the firm (called excess value)1 Our analysis
points to a statistically and economically significant positive excess value or premium to
increased foreign operations This is akin to finding that the value of the MNC is greater than the
sum of its individual parts We subject this finding to a battery of robustness tests to help
determine whether it is simply an artifact of endogeneity or measurement error problems We fail
to find evidence that this is the case The finding of a premium is robust across all specifications
examined In terms of magnitude a one percent increase in the size of the foreign operation is
associated with an increase of between 019 and 037 in excess value Collectively the
evidence suggests that on average MNCs create value by organizing a set of otherwise
independent activities within a multinational network
A large literature examines whether firms that are industrially diversified trade at a discount
or premium to non-diversified firms This literature typically finds that industrially diversified
firms trade at a discount2 A popular method to quantify this valuation effect was developed by
Berger and Ofek (1995) who compare the actual value of an industrially diversified firm with a
hypothetical firm whose value is the sum of the imputed values of its individual industrial
segments As the individual segments of the diversified firm are not traded the imputed value of
each segment is the observable median firm value of a single-segment (non-diversified) firm
1 As we study US-based multinational firms throughout the paper we refer to the US as lsquodomesticrsquo and any other country as lsquoforeignrsquo 2 Examples include Lang and Stulz (1994) Berger and Ofek (1995) Laeven and Levine (2007) Schmid and Walter (2009) Ammann Hoechle and Schmid (2012) and Hoechle Schmid Walter and Yermack (2012)
2
operating in the same industry The difference between the actual value and the imputed value is
an estimate of the premium (if positive) or discount (if negative) Evidence of a negative
association between this excess value measure and a measure of industrial diversification is
consistent with industrial diversification destroying firm value on average
While the finding of an industrial diversification discount is quite robust there is
considerable debate about the interpretation Some argue that the discount is evidence that the
costs of operating in multiple industries outweigh the benefits (eg Berger and Ofek 1995)
Others argue that the discount is driven by the types of firms that choose to diversify or the types
of businesses they invest in when diversifying (Campa and Kedia 2002 Villalonga 2004b) The
literature that uses this methodology continues to evolve improving our understanding of the
forces that drive the industrial diversification discount For example the recent study by Hoechle
et al (2012) finds that a substantial proportion of the discount can be explained by variation in
corporate governance proxies
In contrast to the literature on the industrial diversification discount comparatively few
studies examine how foreign operations affect firm value3 This contrast is surprising because
descriptive data for US firms indicates that industrial diversification has stagnated while foreign
expansion continues at a rapid pace We develop a method to quantify the value effects of
foreign operations along the lines of Berger and Ofek (1995) but that remains applicable to the
multinational setting While Berger and Ofek (1995) divide each firm into industry segments we
divide each MNC into geographic-industry segments (ie separate country-industry
components)4 We then compare the actual value of the firm to the imputed value of the firm
3 Denis Denis and Yost (2002) is a notable exception 4 In our setting we refer to a lsquosingle-segmentrsquo firm as a firm that operates in only one country and one industry Accordingly we use the term lsquoexcess valuersquo to denote the difference between the actual (observed) firm value and the hypothetical firm value computed as the sum of the imputed values of the individual country-industry segments
3
Using a method similar to Berger and Ofek (1995) we determine the imputed value for each
country-industry component by using the median single-segment firm operating exclusively in
the same country and industry (ie single-segment foreign (domestic) firms in the same industry
and country are used as benchmarks for the foreign (domestic) operations of US MNCs) In
other words our approach uses data on the observed firm values of single-segment US firms
(from Compustat) and foreign firms (from Worldscope)
This approach to measuring excess value differs from previous methods to impute the firm
value of MNCs Denis et al (2002) use single-segment domestic firms (ie US firms) in the
same industry as a benchmark for both domestic and foreign MNC operations Similarly studies
using Tobinrsquos Q to investigate valuation effects of MNCs rely only on domestic benchmarks
(eg Morck and Yeung 1991 Morck and Yeung 2001 Gande Schenzler and Senbet 2009)
As growth rates and discount rates vary by country we believe our method is more appropriate
for measuring valuation effects in a multinational context5 This method in effect compares the
value of the firm as a whole to the sum of the parts In addition our method is conceptually
consistent with theories on foreign direct investment (FDI) which note that an MNC exists when
a firm seeks to exploit its advantages and remove conflict arising in external market transactions
by combining a firm of one nationality that might otherwise exist independently under the
ownership of a firm of a different nationality (Dunning and Rugman 1985)
Our approach leverages data maintained by the Bureau of Economic Analysis (BEA) which
provides detailed accounting information about FDI allowing measurement of sales for each
5 Denis et al (2002) contemplate the use of single-segment foreign firms as a benchmark noting three constraints to implementing such a procedure (1) the country location of firmsrsquo foreign operations cannot be reliably identified using Compustat data (2) the industry membership of firmsrsquo foreign operations cannot be identified reliably using Compustat data and (3) valuation ratios may differ across countries due to differences in accounting standards As described in our study we overcome each of these constraints and find that the different benchmark is important and reverses prior findings
4
country-industry in which US-based MNCs operate These data provide a substantial advantage
over the Compustat database used by prior research While Compustat provides some
information about the countries and industries in which these firms operate it is not as detailed
about either of these dimensions (or their interaction) and heavily relies on managerial disclosure
choices which could induce measurement bias (Villalonga 2004a) In addition to the new
method of estimating the value effects of foreign operations we provide evidence that MNCs
trade at a premium which stands in contrast to the discount documented in Denis et al (2002)
This difference is primarily attributed to our use of foreign benchmarks to construct the imputed
values of foreign operations6
Our finding of a premium better reconciles with the findings in the international trade
literature By focusing on nonpublic establishment level (eg factory store or office) data the
trade literature is able to generate relatively precise proxies for total factor productivity (TFP)
and provides robust evidence that firms engaging in international trade are more productive than
those that do not (see Helpman 2006 and Syverson 2011 for reviews) Furthermore
productivity differences tend to be highly persistent even within narrowly defined industries
(Syverson 2011) Due to data limitations it is difficult to accurately measure TFP at the firm
level especially for firms which operate in multiple industries andor countries Nevertheless
Helpman Melitz and Yeaple (2004) provide some evidence that US firms engaging in FDI
have labor productivity advantages over firms that do not Reconciling the discount to
multinational operations found by Denis et al (2002) to the trade literature requires that the
persistent productivity advantages of firms engaged in international trade must be more than
offset by some other cost (eg agency costs) While this relation is possible it is difficult to
6 We repeat the analysis of Denis et al (2002) for our sample and confirm that differences in value effects of foreign operations are driven by the different methods for measuring the imputed value of foreign operations
5
conjecture why the most productive firms would have the largest agency costs In contrast the
finding of a premium easily reconciles with the trade literature
Given our result we investigate whether the premium is simply an artifact of innate
characteristics of firms that choose to invest abroad For example Helpman et al (2004) present
a simple model with heterogeneous productivity endowments The firms that receive the highest
productivity endowments are the ones capable of paying the fixed costs to establish a foreign
subsidiary To the extent productivity advantages create excess value one would expect the type
of firms that choose to invest abroad would be valued at a premium even in the absence of their
foreign investments After including firm fixed effects to control for time invariant firm
characteristics (eg innate productivity advantages) we continue to find economically and
statistically significant evidence of a premium to foreign operations
To further account for the endogenous nature of FDI we estimate a dynamic panel data
model using the generalized method of moments (GMM) estimator developed by Arellano and
Bond (1991) Arellano and Bover (1995) and Blundell and Bond (1998) This estimator allows
us to simultaneously account for the potential endogeneity of FDI industrial diversification and
firm value This method also yields a significant premium which is of similar magnitude and
significance to that estimated using fixed effects
We also perform a series of additional robustness tests First we examine whether the
measured excess value premium is driven by operations in countries with large control premiums
(Dyck and Zingales 2004) In countries with large control premiums using stock price in the
calculation of total firm value could underestimate the true value of the benchmark firms and
thereby induce an excess value premium We fail to find evidence that the excess value premium
is driven by such countries
6
Second we examine whether access to a low cost of capital is a meaningful source of the
US MNC advantage We incorporate foreign benchmarks in our method of estimating firm
excess value as it allows us to incorporate attributes of operating in those foreign countries ndash a
key attribute being the cost of capital Relative to the local benchmarks in foreign countries US
MNCs have the ability to obtain funds outside the local operating environments either by
borrowing in the US market or transferring capital internally When competing against firms in
shallow capital markets with high costs of capital MNCs may have a competitive advantage
over local foreign companies due to these alternative sources of capital However investors and
lenders are likely to expect higher rates of return from the additional risk associated with
operating in these environments making it unclear whether a multinational network will enhance
value in countries with a high cost of capital In addition foreign firms have some ability to
access international capital markets which mitigates the advantages to US firms To examine
whether the cost of capital plays a role in our finding of a premium we include a proxy for the
extent of operations in countries with higher costs of capital Our results are unaffected by the
inclusion of the proxy
Finally we control for several corporate governance proxies which Hoechle et al (2012)
find to be correlated with excess values (and industrial diversification) While magnitude of the
premium is virtually unaffected by including these proxies the significance level does decline
due to the reduced sample size
Our study contributes to the literature on multinational firms Our new method of estimating
excess values for multinational firms provides strong evidence of a statistically and economically
significant premium associated with increased international operations The premium is robust to
a variety of specifications and controls designed to evaluate alternative explanations In addition
7
this result easily reconciles with the international trade literature that establishes a link between
greater productivity and international operations though we do not find results supporting this as
the only source of the premium As segment disclosures improve and as new data sources
become available on multinational activity (eg Bureau Van Dijk) our empirical approach can
be used to investigate the extent to which the home country of the parent might influence the
extent with which foreign operations affect firm value (ie by examining non-US based
MNCs)7 Future studies could also examine the underlying forces affecting the excess value
premiums across countries
We also contribute to the understanding of international accounting standards and their
comparability Many studies seek to compare firms using different accounting standards These
comparisons generally rely on information generated by a particular accounting regime By
comparing commonly used metrics (total assets net income and sales) reported for the same
firm and year across different regimes we provide insights as to the relative comparability across
standards In particular we find that sales is reported significantly more consistently across
accounting regimes than the other metrics examined This finding should aid researchers in
reducing measurement error when comparing accounting information in multinational settings
The paper proceeds as follows Section 2 discusses the motivation for finding a premium or
discount to multinational operations Section 3 describes our data and how the excess value of a
firm is measured A discussion of our independent variables and primary findings is presented in
Section 4 with additional robustness tests provided in Section 5 Finally Section 6 concludes
7 As MNCs are ultimately bound by the tax regulatory and legal frameworks of their home country the valuation effect of foreign operations that we document for US-based MNCs may be different in a sample of non-US based MNCs with the same country-industry footprint Theorists refer to these as lsquolocational advantagesrsquo enjoyed by all multinational firms of a given nationality and obtained irrespective of skills or capabilities unique to a particular firm (Yamin 1991) There is also evidence of locational advantages in the market for corporate control (Huizinga and Voget 2009)
8
2 Premium versus discount
In a frictionless world where managers maximize firm value and markets are efficient there
should be no discount or premium to operating in foreign countries However there are a number
of forces that may cause actual firm value to deviate from the implied firm value
On one hand MNCs may have comparative advantages that generate premiums (ie positive
excess values) relative to a similar footprint of stand-alone firms Having operations in a variety
of locations that access different supplies of inputs and customers allows the firm to take
advantage of changing market conditions by shifting operations or products to maximize firm
value more so than a firm operating in a single country Similarly having access to a variety of
institutional settings such as different tax codes legal regimes and financial markets can provide
MNCs with options beyond those available to firms located in a single country
Another possible factor associated with a premium to multinational operations is the cost of
capital For operations located in countries with shallow capital markets or weaker creditor
rights operating within a conglomerate can provide the benefit of a lower cost of capital for
investments and expansion for at least two reasons First as our MNCs are domiciled in the US
their foreign segments have greater access to US capital markets relative to local competitors in
the foreign jurisdiction Second cash rich segments with few investment opportunities can
finance investments in cash poor segments with positive net present value projects (eg Myers
and Majluf 1984) Consequently diversified firms should be less liquidity constrained and better
able to shift resources to the most valuable investment opportunities Research has found that
internal financing is used more often when diversified firms have operations in countries with
more costly external financing (Desai Foley and Hines 2004) Multinational firms may also be
relatively more protected than single-country firms when negative shocks hit external capital
9
markets (Foley Desai and Forbes 2008) In a recent study Matvos and Seru (2011) find that
industrially diversified firms perform better than non-diversified firms when external capital
markets are impaired
While these arguments suggest that MNCs enjoy certain advantages when the foreign cost of
capital is high these benefits should be reduced by the additional risk associated with operating a
business in a high cost of capital environment For example Verizon is a multinational firm
operating in a number of countries One of the higher cost of capital locations in which they
operated was Venezuela While the multinational structure of Verizon may have mitigated some
of the risks associated with operating in a higher cost of capital location the firms was unable to
eliminate all of these risks In fact in 2007 Verizonrsquos operations in that country were
nationalized resulting in a net extraordinary loss of $131 million that year8 In other words
MNC investors are likely to expect higher rates of return to compensate for higher risk which
likely offsets some (if not all) of the potential excess value premium
On the other hand multinational firms may incur a discount (ie negative excess values)
relative to a similar footprint of stand-alone firms Potential reasons for a discount largely rely on
the existence of agency costs Multinational firms tend to be larger more complex and less
transparent The combination of complexity and opacity inherent in MNCs reduces a firmrsquos
reliance on external capital as the cost of such capital increases due to agency costs (Desai et al
2004) As a result of the reduced reliance on external funds MNCs face a reduced level of
monitoring As noted by Jensen (1986) managers have incentives to increase firm size beyond
that which is optimal with overinvestment or misallocation of funds to pet projects If this lack of
monitoring is in combination with available internal capital empire building becomes easier
(Hope and Thomas 2008) Furthermore the current segment reporting standards under US 8 See the 10-K of Verizon Communications Inc dated December 31 2007 (footnote 2)
10
GAAP only require highly aggregated disclosures of foreign operations and allow for
considerable managerial discretion As a result operating in multiple countries makes it easier
for management to hide poor performance ndash either their own or that of a division ndash such that low
quality managers are more likely to be retained and overcompensated and poorly performing
divisions retained longer than optimal Other reasons one might expect a discount to be applied
to multinational firms include higher coordination costs (eg coordinating across different
cultures and languages) as well as additional risks These risks include exposure to multiple
political regimes legal regimes economic regulations and currency fluctuations
Past studies investigate whether foreign operations of US firms enhance or reduce firm
value However all of these studies benchmark the value of the foreign operations of an MNC to
US domestic firms For instance Denis et al (2002) determine the implied value of a US
MNC using domestic (US) single-segment firms in the same industry as a benchmark Studies
using Tobinrsquos Q to investigate valuation effects of MNCs also rely on domestic benchmarks
(eg Morck and Yeung 1991 Morck and Yeung 2001 Gande Schenzler and Senbet 2009)
However using a US domestic firm as a benchmark for the value of foreign operations
implicitly makes two assumptions First this method assumes that the risks and expected growth
rates of foreign operations are equivalent to those of domestic operations This assumption is
inconsistent with the findings of Hail and Leuz (2009) which provides evidence that the cost of
capital varies substantially across countries Second this method assumes that there is excess
domestic capacity to allow for expansion with profitability similar to current domestic
operations We relax these assumptions by measuring the imputed value of a foreign component
of a US firm as if that component operated autonomously within that foreign country (rather
than in the US) Specifically our multiples allow the cost of capital and expected growth rates
11
to vary across not just industry but also country Furthermore our counterfactual does not
require the assumption that operations be relocated mitigating the production and sales capacity
concerns
3 Measuring excess value
31 Description of excess value
Figure 1 shows the average percent of foreign sales (based on segment disclosures under US
GAAP) across firms through time and illustrates that firms are continuing to expand
internationally9 This expansion highlights the importance of understanding whether the decision
to operate internationally is on average a value enhancing corporate strategy We contribute to
this understanding by evaluating whether the firm as a whole is worth more or less than its
imputed value (ie the firm is valued in excess of the sum of its individual components)
We measure the excess value of a firm as the logarithm of the ratio of actual firm value to
imputed value based on the method used in Berger and Ofek (1995) The imputed value is the
hypothetical value of the MNC under the assumption that its country-industry components
operate as independent entities A key innovation of our study is an alternative approach to
imputing the value of the foreign operations of an MNC Components of MNCs operating in a
given industry and country are imputed using the value of single-segment firms operating in the
same industry and country Thus we ensure the discount rates and expected growth rates are
applicable to each given industry and location in estimating the implied values of the MNC
segments
9 The discontinuity in industrial diversification (the percent of sales outside the primary industry) between 1997 and 1998 is the result of a change in segment reporting rules
12
The actual firm values are observable for all firms in our sample The hypothetical firm
values for multi-segment firms are imputed using market value to sales ratios herein referred to
as lsquomultiplesrsquo of the median single-segment firm operating in the same country-industry Thus
these single country-industry firms serve as benchmarks for an MNCrsquos operations within that
same country-industry The imputed value of the country-industry component can be viewed as
an estimate of what that component would be worth if it operated independently The sum of the
imputed values across all country-industry components represents the hypothetical value that the
multi-segment firm would be worth if it were broken apart
32 Data
Four primary data sources play a distinct role in carrying out our analysis i) Bureau of
Economic Analysis (BEA) data ii) Compustat Segment data iii) Worldscope data and iv)
Compustat Fundamentals Annual data Broadly speaking BEA data provide information about
the diversified nature of MNCs Compustat Segment data provide information about the
industrial diversification of US domiciled firms Worldscope data are used to compute
multiples for single-segment foreign firms that serve as benchmarks for the foreign operations of
MNCs (ie foreign benchmarks) and Compustat Fundamentals Annual data are used to
compute multiples for single-segment domestic firms that serve as benchmarks for the domestic
operations of multinational firms We also use the latter data source to construct our primary
control variables
13
Key to the execution of our study is our access to BEA data Federal law requires US-
domiciled MNCs to report certain financial and operating data to the BEA10 With regard to
observing the country-industry operations of MNCs use of the BEA data overcomes two
important limitations of Compustat Segment data First Compustat does not consistently identify
the specific location or scale of MNCsrsquo foreign operations11 Second Compustat does not
identify the industry activities in each country of operation This latter point is particularly
limiting for MNCs because domestic industry activity need not mirror foreign industry activity
An MNC generating an equal proportion of sales in two industries in the domestic market need
not generate industry sales in equal proportions in every foreign market12 The lack of detail and
consistency in Compustat Segment data arises because segment reporting is highly aggregated
and firms exercise substantial discretion in defining segments under Accounting Standards
Codification 280 (ASC 280) (eg Villalonga 2004a and Bens Berger and Monahan 2011)
The BEA defines an MNC as the combination of a single US entity called the US parent
and at least one foreign affiliate ndash that is these firms have a physical presence outside the US
due to FDI Since 1982 MNCs have completed a mandatory and confidential lsquoSurvey of US
Direct Investment Abroadrsquo for the domestic operation and the operations of each foreign
affiliate defined as a foreign entity in which the US parent holds at least a 10 percent equity
interest (directly or indirectly) The data are reported in US dollars on a fiscal year basis and in
10 BEA surveys are conducted pursuant to the International Investment and Trade in Services Survey Act (PL 94-472 90 Stat 2059 22 USC 3101-3108) See httpwwwbeagovsurveysdiasurvhtm and Mataloni (2003) for more detailed information on the BEA data 11 Exhibit 21 disclosures of material subsidiaries and their locations in SEC 10-K filings could potentially help overcome the inability to observe the location of the firmrsquos foreign operations from segment disclosures However at least three limitations remain (1) for industrially diversified multinational firms the researcher would be required to make an assumption about the proportion of industrial diversification in each country (2) Exhibit 21 provides no information about the scale of activity in each jurisdiction and (3) managerial discretion is used to determine a lsquomaterialrsquo subsidiary under Section 601 of SEC Regulation S-K 12 In fact the expectation that a multinational firm would exhibit different industry membership in different countries is consistent with the theory of vertical foreign direct investment where firms separate their various value chain activities and locate them in the most favorable country (see eg Hanson Mataloni and Slaughter 2005)
14
accordance with US Generally Accepted Accounting Principles (US GAAP) For each year
we observe the sales industry composition and location of not only the parent but also each
affiliate13
The procedures we use to construct our sample are similar to those used by Berger and Ofek
(1995) and Denis et al (2002) Using firm-years appearing in Compustat Fundamentals Annual
and Compustat Segment data we obtain a sample of firms domiciled in the US with total sales
exceeding $20 million We exclude firms in the financial service (SIC codes 6000 to 6999) and
utility (SIC codes 4000 to 4999) industries and eliminate firm-years when the sum of segment
sales (Compustat geographic or Compustat business) is not within 1 of total firm sales for that
year We restrict our sample period to include fiscal years beginning after December 15 1997 as
ASC 280 Segment Reporting substantially altered the definition of a reporting segment under
US GAAP This restriction ensures that Compustat Segment data reflects the use of a consistent
accounting standard Finally we require that our MNCs appear in both Compustat and BEA
data The requirement that our sample firms appear in the BEA database ensures that the firms
have observable international operations In sum these procedures result in 1166 multinational
firms incorporated in the US with 4950 observations between 1998 and 2008 that meet all of
our data requirements
33 Measuring excess value
The dependent variable in our study is excess firm value (Excess Value) defined as the
logarithm of the ratio of actual firm value to imputed firm value (Berger and Ofek 1995)
13 We obtain sales industry membership and country location for affiliates from one of two sources When affiliates exceed the BEA-determined reporting thresholds (ie if their assets sales or net income (loss) exceed $7 million in 1999 $30 million in 2000 through 2003 $10 million in 2004 and $40 million in 2005 through 2008) they must file their own detailed survey When affiliates fall below the thresholds we obtain our data from an attachment to the parent survey
15
Appendix A provides the definition of this and all other variables used in our analysis We
observe actual firm value equal to a firmrsquos market value of equity plus book value of liabilities
(ie book value of total assets minus book value of total equity) using Compustat Fundamentals
Annual data We calculate imputed firm value as the sum of the imputed values of a firmrsquos
operations in each country-industry Our method for imputing the value of the separate
components of a firm can best be described in three steps First we obtain total sales generated
by a firm for each country-industry in which it operates14 Second we obtain multiples (market
value to sales ratios) for benchmark firms operating in those same country-industries Third we
multiply a firmrsquos country-industry sales by the applicable median country-industry multiple to
obtain the imputed market value for each country-industry operation We perform each step
annually
Conceptually imputed values can be based on assets (ie Tobinrsquos Q) net income or sales
multiples We restrict ourselves to sales multiples for two reasons First using value to sales
ratios maintains consistency with the prior research on the excess value implications of
multinational operations (eg Denis et al 2002) Second as our method uses accounting data
for foreign companies to compute country-industry multiples the accounting numbers we use
need to be consistently measured across firms using different accounting standards We find that
sales data are measured most consistently
To assess the comparability of sales relative to either net income or assets across various
accounting standards we examine all firms listed on Worldscope as changing to or from US
14 Specifically we allocate total sales from Compustat to each component based on the proportion of BEA sales (after eliminating intercompany sales) occurring in each country-industry This step ensures that our total firm sales do not deviate from a firmrsquos total sales as reported in the 10-K
16
GAAP between 2005 and 201115 We obtain accounting data for the year of the change and the
prior year where electronically available in English We are able to obtain data for 66 firms
changing standards as detailed in Table 1 The distribution of years for which accounting data
are available under two different accounting standards for the same fiscal year is shown in Panel
A The years shown are the year prior to the change as these accounting numbers were provided
under the original standard and then subsequently restated in the following year under the new
standard for comparative purposes The majority of the shifts were to or from IFRS (71)
though seven other accounting standards are noted (Panel B) We examine the percent that the
non-US GAAP reported number differs from the US GAAP reported number in Panel C to
assess the comparability of these three summary accounting numbers across various accounting
standards Examining both the full sample as well as the subset that overlaps our study we find
that sales are more consistently measured than net income or assets
The sum of the imputed values across the country-industry components of an MNC is an
estimate of the value of a portfolio of unrelated businesses that mirror the related businesses of
the firm Consequently a comparison of the actual firm value with the imputed firm value is a
measure of how a multinational network affects firm value We provide descriptive statistics for
Excess Value for our sample of 4950 MNC firm-years in Table 2 Panel A This table also
includes univariate comparisons of the values between the MNC firms and the domestic single-
segment and foreign single-segment benchmark firms The Excess Value measure is significantly
higher for MNCs suggesting a premium in firm value for multinational firms relative to the
benchmark firms in total as well as the domestic and foreign subgroups (p-values for tests of
15 Due to the hand collection efforts required and the limitation on electronic filings we began our search with changes documented in 2005 which generally require filings from 2004
17
means and medians are lt 001)16 These results provide some initial evidence that operating in
multiple countries is positively associated with firm value
To estimate country-industry multiples we rely on Worldscope financial data on foreign
firms and Compustat Fundamentals Annual for domestic firms We restrict the firms to include
only those with at least 90 of sales income and assets inside the country of domicile (ie
those that do not report significant multinational activity) and that operate in a single industry
We refer to these firms as benchmark firms (either foreign or domestic depending on the country
of domicile) these firms do not appear in any of our regressions We report the number of
benchmark firms during our sample period in Table 2 Panel A The industry criterion is based on
the two-digit SIC code17 For every country that has at least five firms in the respective industry
and year we use the median ratio of market value to sales18 This ratio is the country-industry
multiple ndash an input required to compute imputed values
To determine the country-industry composition of MNCs we rely on BEA data In Table 3
we report the aggregate number of foreign affiliates and total sales by country as provided by
the BEA data for our sample of 4950 MNCs The specific countries tabulated are those that
represent at least 02 percent of total firm sales (pooled across all years) in our sample The top
five foreign countries in which MNCs generate sales are Canada United Kingdom Germany
France and Japan Table 3 also provides a comparison of the number of foreign benchmark
firms available in Worldscope in the specific countries in which our sample of MNCs generate
16 Similar to Berger and Ofek (1995) the median values differ slightly from zero for the benchmark single-segment firms due to the truncation of extreme excess values 17 BEA reporting constrains us to the two-digit SIC code level 18 When five firms are not available for a given country and industry we follow Berger and Ofek (1995) and move to a more general industry definition (eg one-digit SIC code) This procedure results in a mean (median) 630 (757) of MNC sales matched at the two-digit level
18
the majority of their sales Country coverage in Worldscope is not available for countries
representing 004 percent of total firm sales (per BEA)
4 Empirical results
41 Independent variables
Recall that our objective is to examine the overall relation between excess firm value and
multinational operations Our proxy for the extent of multinational operations is the percentage
of total firm sales made by foreign operations (Foreign Sales) By this measure the extent of
multinational activity in our sample of MNCs is non-trivial ndash Table 2 Panel B shows that
approximately 24 percent of sales are generated by foreign operations on average Our first
analysis estimates ordinary least square regressions of Excess Value on Foreign Sales and
control variables (discussed below)
We include variables in our regression to control for other potential determinants of excess
value These are the percentage of sales made by the firm outside its primary industry (Industry
Other) to control for any relation between industrial diversification and excess value as in Berger
and Ofek (1995) For this purpose we obtain industry sales using Compustat Segment data19 A
firmrsquos primary industry is the industry in which the firm generates the majority of its sales and
we determine industry sales at the business segment level We set Industry Other equal to zero
for firms that operate in a single business segment
Consistent with prior research we also include controls for firm size (Log Size) the ratio of
long term debt to total market value (Debt Firm Value) the ratio of capital expenditures to total
sales (CAPX Sales) the ratio of earnings before interest and taxes to total sales (EBIT Sales)
the ratio of research and development expenses to total sales (RampD Sales) and the ratio of 19 Our results are unaffected by using BEA data to measure Industry Other
19
advertising expenses to total sales (Ad Sales) Table 2 Panel B provides the distribution of the
control variables All independent variables are Winsorized at 1 In our OLS regressions we
use heteroskedasticity and autocorrelation consistent robust standard errors with clustering at the
firm level
42 Primary regression analysis
In this section we examine the relation between firm excess value and the amount of foreign
activity at US MNCs Table 4 contains the results of regressions of excess value on Foreign
Sales for multiple specifications In column (1) Excess Value is regressed on both the percent of
foreign sales (Foreign Sales) and the percent of sales outside the firmrsquos primary industry
(Industry Other) We add the control variables to the regression in column (2) and year
indicators in column (3) In all three specifications the coefficient on the percent of foreign sales
remains significantly positive (with a p-value lt 001) For a sense of economic magnitude in
column (3) a one standard deviation (197) increase in the percent of foreign sales (Foreign
Sales) is associated with a 41 (equal to (e0349-1) times 0197) increase in firm excess value The
effect is larger for the other two columns ndash 82 increase in firm excess value in column (1) and
46 in column (2)20
Our finding of a premium reconciles with the international trade literature which finds that
firms engaging in international trade are more productive than those that do not (see Helpman
2006 and Syverson 2011 for reviews) Due to data limitations it is difficult to accurately
measure TFP at the firm level especially for firms which operate in multiple industries andor
20 Using the alternative strategy that employs US domiciled single-segment firms as the benchmark for the imputed value of the foreign operations Denis et al (2002) find that greater foreign activity results in a discount to firm value Using the Denis et al (2002) method to calculate excess value in our sample we obtain a significantly negative regression coefficient on Foreign Sales using the models in Table 4 columns (2) and (3) (the coefficient is not significant using the model in column (1) results untabulated)
20
countries As an alternative method to evaluate whether our finding of a premium is simply the
result of more productive firms also having more multinational operations we investigate
whether the premium is simply an artifact of innate characteristics of firms that choose to invest
abroad For example Helpman et al (2004) present a simple model with heterogeneous
productivity endowments The firms that receive the highest productivity endowments are the
ones capable of paying the fixed costs to establish a foreign subsidiary
To the extent that productivity advantages create excess value one would expect the type of
firms that choose to invest more abroad would be valued at a premium even in the absence of
their foreign investments To control for time invariant firm characteristics (eg innate
productivity advantages) and hence a potential source of endogeneity we add firm-level fixed
effects in column (4) of Table 4 We continue to find that the extent of foreign operations
(Foreign Sales) is positively associated with Excess Value Across all specifications the sign
on all significant control variables is consistent with prior research (eg Berger and Ofek 1995
and Denis et al 2002)
The finding of a premium makes a significant contribution to the relatively sparse literature
on the value effects of multinational activity Our measure of imputed value expands upon the
methods used by Berger and Ofek (1995) and Denis et al (2002) but allows us to answer a
different question than Denis et al (2002) Holding constant the extent of industrial
diversification we ask whether MNCs are more valuable than a portfolio of benchmark firms of
a similar geographic footprint In contrast Denis et al (2002) ask whether holding constant the
extent of industrial diversification MNCs are more valuable than a portfolio of domestic (US)
benchmark firms We believe that asking whether an MNCrsquos foreign (domestic) operations are
21
more valuable than those of a foreign (domestic) firm is a more appropriate way to assess the
value implications of a multinational network
5 Robustness
51 Endogeneity related to dynamic relations
Prior research argues that the relation between industrial diversification and excess firm
value could be endogenous through dynamic relationships based on observing past outcomes
Managers of firms likely choose to enter or exit industries or geographic regions based upon
their previous performance For example Campa and Kedia (2002) find that firms are more
likely to enter new industries when prospects in their current lines of business are deteriorating
Similar dynamics could also drive a relation between excess value and FDI We assess the
robustness of the previous results by estimating a dynamic panel data model which allows us to
control for dynamic endogeneity in addition to firm level unobserved heterogeneity We employ
the GMM estimator for dynamic panel models developed by Arellano and Bond (1991) Arellano
and Bover (1995) and Blundell and Bond (1998) Our results from this analysis confirm our
previous findings that firms with a larger degree of foreign operations have positive excess
values
Dynamic panel data models are generalizations of the traditional fixed effects model where
lags of the dependent variable are added to the right hand side of the equation These additional
lags control for the impact a firmrsquos past performance has on its current performance through the
channels discussed above The GMM estimator for dynamic panel models developed by
Arellano and Bond (1991) starts by differencing the data at the firm level in neighboring time
periods to eliminate any firm specific time invariant unobserved heterogeneity that may be
22
present Eliminating the firm specific fixed effect by differencing the data with lags of the
dependent variable on the right hand side introduces correlation between the new error term and
the differenced explanatory variables This problem is solved by applying the standard GMM
estimator using lags of both the original explanatory variables and excess value as instruments
We note that the dynamic panel data framework allows us to treat both international and
industrial diversification (as well as the other firm-specific control variables) as endogenous
Following Hoechle et al (2012) we also allow contemporaneous values of these variables to be
treated as endogenous
Lagged values of the explanatory variables are valid instruments in a dynamic panel data
model when the model is dynamically complete (eg Wooldridge 2010) A dynamic panel data
model is dynamically complete conditional on the unobserved effect if enough lags of both the
explanatory and dependent variable are included to make the resulting error term uncorrelated
with all the right hand side variables Under this assumption any lags for the explanatory
variables beyond this can be used within the GMM estimator as instruments Like Hoechle et al
(2012) we add two years of lags of a firmrsquos excess value to the right hand side assuming that
this is enough lags to control for dynamic feedback effects We then use past values of all
variables beyond the second lag up to a total of seven years to act as instruments
The results of the dynamic panel data estimator are reported in Table 5 These results confirm
the previous finding of a positive relation between the degree of international operations and
excess value We note that the size and significance level of the coefficient on Foreign Sales
diversification are robust to changes in the number of lags used as instruments The Arellano and
Bond (1991) estimator includes testing diagnostics for correct specification of the model If the
model is correctly specified differences in the residuals from the levels equation should be
23
serially uncorrelated at lag one but correlated at lag two The p-values for tests of serial
correlation of the residuals at lags 1 and 2 are reported in Table 5 and these indicate that the
model is not rejected We also report in Table 5 the Hansen test for overidentification which is a
test of the null hypothesis that the instruments are valid The p-value for this test is 030
indicating that we fail to reject this null hypothesis Finally we report another test for exogeneity
known as the difference-in-Hansen test which tests the null hypothesis that the instruments in
the original equation are valid We fail to reject this null hypothesis as well
52 Identifying value effects of multinational operations via cross-border acquisitions
Another alternative to investigating a causal link between foreign direct investment and firm
value is an event study of foreign acquisitions21 Acquired foreign targets represent only a small
fraction of MNCsrsquo worldwide sales in the year of the acquisition ndash less than 1 percent in our
sample Additionally cross-border acquisitions are not the primary source of international
expansion for the average firm We find that foreign acquisitions account for between 18 percent
and 55 percent of the year-over-year change in foreign sales with the remainder of the growth
coming from either newly established entities (ie greenfield investments) or growth in existing
operations22 These results suggest that acquisitions are not a material source of growth for the
21 Doukas and Travlos (1988) find positive returns when the acquirer is not operating in the target firmrsquos country and insignificant returns when the acquirer is operating in the target firmrsquos country or expanding internationally for the first time Morck and Yeung (1991) find marginal evidence of positive returns for firms with intangible assets Finally Doukas (1995) finds positive returns only for firms with average Tobinrsquos q ratios greater than one More recent work also using an event study methodology provides mixed evidence on valuation effects of global diversification through acquisitions Dos Santos Errunza and Miller (2008) find insignificant returns for firms with already established foreign operations firms establishing initial foreign operations and firms acquiring targets in related industries Doukas and Kan (2006) and Moeller and Schlingemann (2005) find reductions to shareholder value while Francis Hasan and Sun (2008) find positive returns for acquirers of targets in segmented financial markets 22 New foreign affiliates to an MNC group each year report on the BEA survey whether they entered the group as an acquisition or a newly established entity (ie greenfield investment) Affiliates under the lowest reporting threshold do not report this information resulting in our inability to precisely identify the source of 33 percent of the increase in firm sales as either acquisition or greenfield
24
typical US MNC suggesting that an event study would lack power and generalizability For
these reasons we do not implement an event study
53 Control premium
A control premium or private benefits of control occurs when ldquosome value whatever the
source is not shared among all the shareholders in proportion of the shares owned but it is
enjoyed exclusively by the party in controlrdquo (Dyck and Zingales 2004 p 541) Using stock
prices of traded shares held by minority shareholders in determining the market value to sales
ratios of our benchmark firms could underestimate the true value of our benchmark firms if those
firms face large control premiums This in turn could induce an excess value premium because
the imputed value of these country-industry segments would be artificially low The existence of
such benefits is more prevalent in some countries than others and could affect the value of an
MNCrsquos operations in such domains We use the country-level control premiums from Dyck and
Zingales (2004) and using each firmrsquos country sales as a percent of total firm sales we obtain a
weighted average control premium that each MNC faces (Control Premium) For countries not
included in the Dyck and Zingales (2004) analysis we set the control premium equal to zero23 In
Table 6 we show that Foreign Sales remains positive and highly significant (p-value lt 001)
when we include the proxy for control premium As a result it does not appear that the excess
value premium is driven by MNCs operating in countries where majority shareholders are able to
extract large control premiums
23 Results are robust to replacing missing values with the mean control premium of the respective GDP quartile
25
54 Cost of capital
As discussed in Section 2 when countries have shallow capital markets a multinational
network may provide the benefit of a lower cost of capital for investments through an internal
capital market as well as through better access to the US capital market Prior research
establishes that there is substantial variation in the cost of capital across countries (Erb Harvey
and Viskanta 1996 and Hail and Leuz 2009) To determine whether the documented premium
is due to cost of capital advantages we include a control variable to capture the difference
between the MNCrsquos cost of capital and the cost of capital faced by its competitors in the global
markets in which it operates
We compute our Cost of Capital proxy in two steps First we obtain the country-year credit
rating published by Institutional Investor24 The rating ranges from 0 to 100 where higher values
imply that a country has a higher default risk Country-level credit risk is a reasonable predictor
of expected equity market returns and volatility (Erb et al 1996) and exhibits a highly
significant correlation with international accounting-based estimates of imputed cost of capital
(Hail and Leuz 2009) Second using each firmrsquos country sales as a percent of total firm sales
we obtain a weighted average credit risk rating across the countries in which each firm operates
A higher value implies an MNC operates in countries with higher cost of capital In Table 7 we
do not find a result consistent with MNCs gaining an advantage from their access to low cost
capitalndash Cost of Capital is not significantly associated with Excess Value25
24 We thank Cam Harvey for providing these data 25 Results are consistent using cost of capital as defined in Erb et al 1996
26
55 Corporate governance
In light of the recent finding that corporate governance mitigates the diversification discount
we examine whether the premium to multinational activity is sensitive to controls for corporate
governance Hoechle et al (2012) find that the magnitude of the industrial diversification
discount is decreased and in several specifications no longer significant in the presence of
governance proxies In the first column of Table 8 we include the five governance proxies that
are consistently significantly associated with excess value in Hoechle et al (2012) ndash the percent
of CEO ownership (CEO Ownership and CEO Ownership squared) the percent of institutional
ownership (Institution Ownership) whether the firm has a powerful or influential CEO
(Powerful CEO) and the governance index from Gompers Ishii and Metrick (2003)
(Governance Index)
As documented in Table 8 including these control variables reduces our sample size by
almost 50 (to 2550 observations) and tends to exclude the smaller firms from the analysis
None of the governance proxies are significant Relative to column (4) of Table 4 the coefficient
on Foreign Sales is similar in magnitude but is no longer statistically significant at
conventional levels To investigate whether the decreased significance is due to the smaller
sample or the inclusion of the governance variables we re-estimate the regression for the same
2550 observations after removing the governance controls The coefficient and t-statistic are
virtually identical suggesting that the loss of significance is due to the reduced sample and not
the governance controls We do not find evidence to suggest that variation in corporate
governance practices drives variation in excess value for US MNCs
27
6 Conclusion
Using a new method to quantify the valuation effects of foreign direct investment by US
domiciled firms we find robust evidence of a premium in excess value associated with the
degree of multinational operations Our method builds on that used in previous studies
examining firm excess value (eg Berger and Ofek 1995 and Denis et al 2002) by
incorporating the location in addition to the industry of each corporate affiliate This allows
important factors such as growth and cost of capital to vary across countries as well as industries
Examining a sample of multinational firms we find that a one-standard deviation (197)
increase in foreign operations is associated with an increase of between 41 and 82 in excess
value minus both a statistically and economically significant finding
This result complements the finding of the international trade literature that more productive
firms are more likely to operate internationally However the premium remains after including
firm fixed effects to control for persistent firm traits (eg productivity)
The finding of a premium stands up to a succession of robustness tests designed to evaluate
whether the result arises from alternative methods or explanations First we estimate a dynamic
panel data model using the generalized method of moments (GMM) estimator to simultaneously
account for the potential endogeneity of FDI industrial diversification and firm value Second
we include a control variable to proxy for whether the firm operates in countries with larger
control premiums which could result in understated multiples and mechanically induce a
premium Third we include a control variable to measure the cost of capital across the countries
in which an MNC operates to evaluate whether a relatively low cost of capital borne by
multinational firms is a meaningful source of the premium Fourth we control for several
corporate governance proxies which have been found to be correlated with excess values (and
28
industrial diversification) We fail to find evidence supporting any of these alternative
explanations ndash the premium in excess value arising at firms with greater multinational operations
remains significant
Overall our new method of estimating excess values for multinational firms provides strong
evidence of a statistically and economically significant premium associated with increased
international operations The premium is robust to a host of specifications and controls designed
to evaluate alternative explanations We leave to future research the questions of whether the
premium to US multinational firms applies more broadly to other countries of domicile and
what factors influence the premium
29
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Ammann M Hoeschel D and M Schmid 2012 Is there really no conglomerate discount
Journal of Business Finance and Accounting 39(12) 264-288 Arellano M and Bond S R 1991 Some tests of specification for panel data Monte Carlo
evidence and an application to employment equations Review of Economic Studies 58 (2) 277-298
Arellano M and Bover O 1995 Another look at instrumental variables estimation of error
components models Journal of Econometrics 68 (1) 29-61 Bens D A Berger P G and S J Monahan 2011 Discretionary disclosure in financial
reporting An examination comparing internal firm data to externally reported segment data The Accounting Review 86(2) 417-449
Berger P G and E Ofek 1995 Diversificationrsquos effect on firm value Journal of Financial
Economics 37 39-65 Blundell R and Bond S R 1998 Initial conditions and moment restrictions in dynamic panel
data models Journal of Econometrics 87(1) 115-144 Campa J M and S Kedia 2002 Explaining the diversification discount The Journal of
Finance 57 (4) 1731-1762 Denis D J Denis D K and K Yost 2002 Global diversification industrial diversification
and firm value The Journal of Finance 57 (5) 1951-1979 Desai M A Foley C F and J R Hines 2004 A multinational perspective on capital structure
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Doukas JA 1995 Overinvestment Tobinrsquos q and gains from foreign acquisitions Journal of
Banking and Finance 19 1285-1303 Doukas J and N G Travlos 1988 The effect of corporate multinationalism on shareholdersrsquorsquo
wealth evidence from international acquisitions Journal of Finance 43(5) 1161-1175 Doukas JA and O B Kan 2006 Does global diversification destroy firm value Journal of
International Business Studies 37(3) 352-371
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Dunning J and A Rugman 1985 The influence of Hymerrsquos dissertation on the theory of foreign direct investment The American Economic Review 75(2) 228-232
Dyck A and L Zingales 2004 Private benefits of control An international comparison The
Journal of Finance 59 (2) 537-600 Erb C Harvey C and T Viskanta 1996 Expected returns and volatility in 135 countries The
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Multinational and local firm responses to currency depreciations Review of Financial Studies 21 (6) 2857-2888
Francis B I Hasan and X Sun 2008 Financial market integration and the value of global
diversification Evidence for US acquirers in cross-border mergers and acquisitions Journal of Banking and Finance 321522-1540
Gande A Schenzler C and L Senbet 2009 Valuation effects of global diversification
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Journal of Economics 118(1) 107-156 Hail L and C Leuz 2009 Cost of capital effects and changes in growth expectations around
US cross-listings Journal of Financial Economics 93 428-454 Hanson G Mataloni R and M Slaughter 2005 Vertical production networks in multinational
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44(3) 289-630 Helpman E Melitz M J and S R Yeaple 2004 Export versus FDI with Heterogeneous Firms
American Economic Review 94(1) 300-316 Hoechle D Schmid M Walter W and D Yermack 2012 How much of the diversification
discount can be explained by poor governance Journal of Financial Economics 103 41-60 Hope O-K and W B Thomas 2008 Managerial empire building and firm disclosure Journal
of Accounting Research 46 (June) 591-626 Huizinga H P and J Voget 2009 International taxation and the direction and volume of cross-
border MampAs Journal of Finance 64 (3) 1217-1249 Jensen M 1986 Agency cost of free cash flow corporate finance and takeovers American
Economic Review Papers and Proceedings 76 323-329
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Laeven L and R Levine 2007 Is there a diversification discount in financial conglomerates
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Moeller SB and F P Schlingemann 2005 Global diversification and bidder gains A
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Morck R and B Yeung 2001 Why firms diversify Internatization vs agency behavior In Intangibles B Lev (ed) Oxford University Press pp 269-302
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365 Villalonga B 2004a Diversification discount or premium New evidence from the Business
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32
Appendix A Variable Definitions
We obtain information regarding firmsrsquo operations by country and industry from the Bureau of Economic Analysis (BEA) unless otherwise noted Acronyms used below reflect Compustat mnemonics and thus indicate when we obtain data from the Compustat North America Fundamentals Annual database All independent variables are winsorized at 1
Dependent variable
Excess Value = The log of the ratio of actual firm value to imputed firm value Actual firm value is equal to market capitalization (MVE) + book value of total assets (AT) ndash book value of shareholdersrsquo equity (SEQ) Imputed firm value is calculated as the sum of the imputed values of a firmrsquos operations in each country-industry The imputed value for each country-industry is equal to sales generated in that country-industry times a multiple equal to the ratio of a benchmark firmrsquos actual value to total sales A benchmark firm is defined as a median single-segment firm operating in the same country-industry All multiples require at least five firms listed in the respective country-industry on Compustat (domestic) or Worldscope (foreign) with less than 10 of sales income and assets outside the country of domicile Similar to Berger and Ofek (1995) we eliminate extreme values where the ratio of actual to imputed firm value exceeds 400 or is less than 025
Independent variables
Foreign Sales = Sales made by the foreign operations of US MNCs as a percent of total firm sales
Industry Other = Sales outside a firmrsquos primary industry as a percent of total sales (SALE) Equal to zero for firms operating in a single business segment
Log Size = Log of total sales (SALE)
Debt Firm Value = Ratio of long-term debt (DLTT) to total market value (market capitalization (MVE) + book value of total assets (AT) ndash book value of shareholdersrsquo equity (SEQ))
CAPX Sales = Ratio of capital expenditures (CAPX) to total sales (SALE)
EBIT Sales = Ratio of earnings before interest and taxes (EBIT) to total sales (SALE)
RampD Sales = Ratio of research and development expenses (XRD) to total sales (SALE)
Ad Sales = Ratio of advertising expenses (XAD) to total sales (SALE)
Control Premium = A weighted-average (based on the sales in a given country as a percent of total firm sales) of the country-level control premium from Dyck and Zingales (2004)
Cost of Capital = A weighted-average (based on the sales in a given country as a percent of total firm sales) of the country-level credit rating from Institutional Investor (httpwwwinstitutionalinvestorcom) The country-level rating is available for 160 countries and ranges from 0 to 100 Higher values imply greater credit risk
33
CEO Ownership = Percent of shares owned by the CEO from Execucomp
Institution Ownership = Percent of shares owned by institutional investors from Thomson Reuterrsquos Institutional Holdings database
Powerful CEO = Indicator variable equal to one if the CEO is the only insider on the board of directors and also serves as the chairman and president based on data from RiskMetrics Directors database Equal to zero otherwise
Governance Index = Count measure of takeover defenses used in Gompers Ishii and Metrick (2003) from RiskMetrics Governance database As the governance data are not available for every year we follow Gompers Ishii and Metrick (2003) and assume that the firmsrsquo governance attributes are unchanged until publication of the subsequent series
34
Figure 1 Trends in multinational activity and industrial diversification from 1984-2008 This figure presents a plot of the mean percent of foreign sales and the mean percent of sales outside the firmrsquos primary industry (industrial diversification) from 1984 through 2008 Dotted lines above and below the primary lines represent 95 confidence intervals To compile the plot we include the full population of firms in Compustat
35
Table 1 Analysis of accounting data for firms reporting under multiple accounting standards This table provides a comparison of sales net income and assets for firms that changed accounting standards to or from US GAAP Comparisons are provided for the 66 firms that changed to or from US GAAP and for which we could obtain accounting data electronically and in English under both standards for the same fiscal year (eg the firm presented the accounting numbers originally under one set of standards and then restated the prior year numbers in the subsequent year under the new set of standards) Panel A shows the distribution of firms by year Panel B shows the other accounting standards to which US GAAP is compared Panel C shows the mean and median percent that the other accounting standard differs from US GAAP for the same fiscal year and accounting number Two samples are shown in Panel C The first is the full sample of firms with a change in accounting standards to or from US GAAP for which data was available The second is the subset of these firms that occurred during the sample window used in this study (ie excluding changes post-2008) The percent difference in sales is compared to the percent difference in net income and assets Tests of means are based on matched-pair t-tests Tests of medians are based on Wilcoxon matched-pair signed-rank tests Panel A Accounting standard changes by year
2003
4 6
2004
25 38
2005
8 12
2006
6 9
2007
7 11
2008
5 8
2009
5 8
2010
5 8
2011
1 2
66
Panel B Accounting standards compared to US GAAP
IFRS
47 71
Canadian GAAP
12 18
Israeli GAAP
2 3
Dutch GAAP
1 2
Indian GAAP
1 2
Japanese GAAP
1 2
Norwegian GAAP
1 2
Swiss GAAP
1 2
66
36
(Table 1 continued) Panel C Tests of differences between US GAAP and other accounting standards
Percent different from US GAAP
Comparison to sales (p-values)
N Mean Median
Mean Median
Full Sample (2001-2011)
Sales
66 236 000
Net Income
66 10653 647
005 lt001
Assets
66 1162 126
lt001 lt001
Study Sample (2001-2008)
Sales
55 257 000
Net Income
55 10303 589
011 lt001
Assets
55 1047 099
003 lt001
37
Table 2 Descriptive statistics Panel A reports the mean and medians of Excess Value for the multinational firms and single-segment benchmark firms as well as univariate tests across the subsamples Panel B reports summary statistics for the independent variables for the multinational firms The p-values of the tests of differences in means are based on t-tests The p-values of tests of differences in the medians are for a Wilcoxon rank-sum test All variables are defined in Appendix A Panel A Excess Value by sample Multinational All Benchmark Benchmark Subgroups Sample Firms US Foreign
Value Value Test of Diff (p-value) Value
Test of Diff (p-value) Value
Test of Diff (p-value)
Mean 0067 0006 lt001 -0028 lt001 0014 lt001 Median 0076 0000 lt001 -0025 lt001 0000 lt001 N 4950 33074 5998 27076 Panel B Distribution of independent variables for the multinational firms N Mean 25th 50th 75th StDev Foreign Sales 4950 0238 0074 0190 0363 0197 Industry Other 4950 0212 0000 0166 0414 0215 Log Size 4950 6955 5823 6989 8140 1687 Debt Firm Value 4950 0136 0009 0097 0212 0145 CAPX Sales 4950 0054 0020 0033 0054 0101 EBIT Sales 4950 0065 0031 0077 0121 0138 RampD Sales 4950 0049 0000 0013 0059 0083 Ad Sales 4950 0008 0000 0000 0005 0018
38
Table 3 BEA and Worldscope samples
This table reports details on affiliate sales for significant countries pooled from 1998-2008 Significant countries are defined as those in which the operations of US MNCs represent at least 02 of total firm sales (per the BEA) For significant countries the table compares their representation in terms of MNC affiliate sales in the BEA data to the distribution of firm observations in Worldscope US Foreign Affiliates (BEA) Foreign Firms (Worldscope)
Country
N (1)
Sales (millions)
(2)
Total Sales (3)
N (4)
Sales (million)
(5)
Total Sales (6)
Australia 1344 73305 056 859 255865 210 Belgium 894 52755 040 169 26136 021 Brazil 959 60245 046 1143 683167 560 Canada 2730 536767 407 1181 526750 432 China 922 75907 057 822 276541 227 France 1852 183942 139 540 105447 086 Germany 2182 275577 209 480 103888 085 Hong Kong 867 48100 036 157 34094 028 Ireland 478 36754 028 34 3467 003 Italy 1178 82116 062 111 47948 039 Japan 1494 148032 112 2281 1119223 917 Korea South 659 34500 026 3530 2752791 2255 Malaysia 386 26027 020 837 168683 138 Mexico 1405 113623 086 362 237173 194 Netherlands 1149 85105 064 47 22631 019 Singapore 1098 94917 072 139 32913 027 Spain 871 63094 048 129 37419 031 Sweden 712 32682 025 235 23090 019 Switzerland 726 60288 046 74 20242 017 Taiwan 592 30313 023 2949 787049 645 United Kingdom 3054 423934 321 1997 882423 723 United States (domicile) 4950 10460880 7923 na na na Other - Covered by WorldScope 6893 199394 151 9000 4057894 33 Other -Not Covered by WorldScope 713 5352 004 - - - Total 38108 13203609 1000 27076 12204834 100
39
Table 4 OLS regressions of firm excess values on the percent of foreign sales This table reports the results of OLS regressions of firm excess value on the percent of foreign sales (Foreign Sales) the percent of sales outside a firmrsquos primary industry (Industry Other) and control variables All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed) Dep Variable = Excess Value Coefficient t-stat Coefficient t-stat Coefficient t-stat Coefficient t-stat (1) (2) (3) (4) Foreign Sales 0349 419 0209 293 0189 266 0278 210 Industry Other -0134 -186 -0047 -072 -0053 -081 -0326 -325 Log Size 0066 634 0065 624 -0044 -096 Debt Firm Value -0196 -213 -0241 -260 -0685 -588 CAPX Sales 0901 425 0893 435 0488 227 EBIT Sales 1154 542 1170 547 0408 189 RampD Sales 2908 1242 2932 1237 1544 379 Ad Sales 1869 263 2003 280 -1696 -119 Year indicators No No Yes Yes Firm indicators No No No Yes R2 0017 0212 0226 0768 Observations 4950 4950 4950 4950
40
Table 5 Dynamic panel GMM model regressing firm excess values on the percent of foreign sales This table reports the result from a regression of the excess value on the percent of foreign sales (Foreign Sales) the percent of non-primary industry sales (Industry Other) and control variables using a dynamic panel GMM estimator All control variables are considered to be endogenous with the exception of the year indicator variables AR(1) and AR(2) are tests for first-order and second-order serial correlation in the first differenced residuals with the null hypothesis of no serial correlation The null hypothesis of the Hansen test of overidentification is that all instruments are valid The null hypothesis of the difference-in-Hansen test of exogeneity is that the instruments used for the equation in levels are exogenous All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed) Dep Variable = Excess Value Coef t-stat Excess Value (one lag) -0018 -082 Excess Value (two lags) -0048 -272 Foreign Sales 0371 270 Industry Other -0163 -171 Log Size 0010 123 Debt FirmValue -1135 -820 CAPX Sales 0590 342 EBIT Sales -0047 -300 RampD Sales 1376 531 Ad Sales -0751 -040 AR(1) test (p-value) 000 AR(2) test (p-value) 017 Hansen test of over-identification (p-value) 030 Difference-in-Hansen test of exogeneity (p-value) 093 Year indicators Yes Number of firms 1016 Number of observations 4090
41
Table 6 OLS regression of firm excess values on the percent of foreign sales controlling for a control premium This table reports the result of the OLS regression of firm excess value including firm and year fixed effects with an additional control variable to proxy for a country-level ownership control premium (Control Premium) All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed) Dep Variable = Excess Value Coef t -stat Foreign Sales 0303 224 Industry Other -0327 -325 Log Size -0045 -100 Debt FirmValue -0683 -585 CAPX Sales 0490 226 EBIT Sales 0409 189 RampD Sales 1545 379 Ad Sales -1689 -119 Control Premium -1004 -062 Year indicators Yes Firm indicators Yes R2 0768 Observations 4950
42
Table 7 OLS regression of firm excess values on the percent of foreign sales controlling for the cost of capital This table contains the results of an OLS regression of firm excess value including firm and year fixed effects with an additional control variable to proxy for a country-level cost of capital (Cost of Capital) All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed) Dep Variable = Excess Value Coef t -stat Foreign Sales 0255 178 Industry Other -0326 -324 Log Size -0043 -094 Debt FirmValue -0687 -589 CAPX Sales 0485 225 EBIT Sales 0406 188 RampD Sales 1539 379 Ad Sales -1703 -120 Cost of Capital 0004 039 Year indicators Yes Firm indicators Yes R2 0768 Observations 4950
43
Table 8 OLS regression of firm excess values on the percent of foreign sales controlling for aspects of corporate governance This table reports the results of two OLS regressions In the first column are the results of the firm excess value regression including firm and year fixed effect with five proxies for corporate governance included ndash the percent of shares owned by the CEO and the percent squared (CEO Ownership and CEO Ownership squared) the percent owned by institutional investors (Institution Ownership) whether there is a powerful CEO (Powerful CEO) and the Gompers Ishii and Metrick (2003) governance index (Governance Index) In the second column the regression includes the same sample and variables with the exception of the governance proxies which are excluded All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed)
Governance
No Governance (same sample)
Dep Variable = Excess Value Coef t-stat Coef t -stat Foreign Sales 0259 159 0251 154 Industry Other -0099 -080 -0099 -079 Log Size -0105 -185 -0102 -180 Debt FirmValue -0813 -478 -0817 -482 CAPX Sales 0440 252 0438 249 EBIT Sales 0457 247 0457 246 RampD Sales 1717 284 1716 286 Ad Sales -2210 -143 -2190 -143 CEO Ownership 0010 007
CEO Ownership squared -0029 -017 Institution Ownership 0047 067 Powerful CEO -0003 -014 Governance Index 0009 103
Year indicators Yes Yes Firm indicators Yes Yes R2 0811 0810 Observations 2550 2550
- Cover for paperpdf
- crrz_20130603_SMUpdf
-
1
1 Introduction
We investigate whether the degree of international diversification affects firm value for US
multinational corporations (MNCs) Specifically we determine whether the relative size of the
foreign operation has a positive negative or no association with the difference between the
actual observed firm value and an imputed value of the firm (called excess value)1 Our analysis
points to a statistically and economically significant positive excess value or premium to
increased foreign operations This is akin to finding that the value of the MNC is greater than the
sum of its individual parts We subject this finding to a battery of robustness tests to help
determine whether it is simply an artifact of endogeneity or measurement error problems We fail
to find evidence that this is the case The finding of a premium is robust across all specifications
examined In terms of magnitude a one percent increase in the size of the foreign operation is
associated with an increase of between 019 and 037 in excess value Collectively the
evidence suggests that on average MNCs create value by organizing a set of otherwise
independent activities within a multinational network
A large literature examines whether firms that are industrially diversified trade at a discount
or premium to non-diversified firms This literature typically finds that industrially diversified
firms trade at a discount2 A popular method to quantify this valuation effect was developed by
Berger and Ofek (1995) who compare the actual value of an industrially diversified firm with a
hypothetical firm whose value is the sum of the imputed values of its individual industrial
segments As the individual segments of the diversified firm are not traded the imputed value of
each segment is the observable median firm value of a single-segment (non-diversified) firm
1 As we study US-based multinational firms throughout the paper we refer to the US as lsquodomesticrsquo and any other country as lsquoforeignrsquo 2 Examples include Lang and Stulz (1994) Berger and Ofek (1995) Laeven and Levine (2007) Schmid and Walter (2009) Ammann Hoechle and Schmid (2012) and Hoechle Schmid Walter and Yermack (2012)
2
operating in the same industry The difference between the actual value and the imputed value is
an estimate of the premium (if positive) or discount (if negative) Evidence of a negative
association between this excess value measure and a measure of industrial diversification is
consistent with industrial diversification destroying firm value on average
While the finding of an industrial diversification discount is quite robust there is
considerable debate about the interpretation Some argue that the discount is evidence that the
costs of operating in multiple industries outweigh the benefits (eg Berger and Ofek 1995)
Others argue that the discount is driven by the types of firms that choose to diversify or the types
of businesses they invest in when diversifying (Campa and Kedia 2002 Villalonga 2004b) The
literature that uses this methodology continues to evolve improving our understanding of the
forces that drive the industrial diversification discount For example the recent study by Hoechle
et al (2012) finds that a substantial proportion of the discount can be explained by variation in
corporate governance proxies
In contrast to the literature on the industrial diversification discount comparatively few
studies examine how foreign operations affect firm value3 This contrast is surprising because
descriptive data for US firms indicates that industrial diversification has stagnated while foreign
expansion continues at a rapid pace We develop a method to quantify the value effects of
foreign operations along the lines of Berger and Ofek (1995) but that remains applicable to the
multinational setting While Berger and Ofek (1995) divide each firm into industry segments we
divide each MNC into geographic-industry segments (ie separate country-industry
components)4 We then compare the actual value of the firm to the imputed value of the firm
3 Denis Denis and Yost (2002) is a notable exception 4 In our setting we refer to a lsquosingle-segmentrsquo firm as a firm that operates in only one country and one industry Accordingly we use the term lsquoexcess valuersquo to denote the difference between the actual (observed) firm value and the hypothetical firm value computed as the sum of the imputed values of the individual country-industry segments
3
Using a method similar to Berger and Ofek (1995) we determine the imputed value for each
country-industry component by using the median single-segment firm operating exclusively in
the same country and industry (ie single-segment foreign (domestic) firms in the same industry
and country are used as benchmarks for the foreign (domestic) operations of US MNCs) In
other words our approach uses data on the observed firm values of single-segment US firms
(from Compustat) and foreign firms (from Worldscope)
This approach to measuring excess value differs from previous methods to impute the firm
value of MNCs Denis et al (2002) use single-segment domestic firms (ie US firms) in the
same industry as a benchmark for both domestic and foreign MNC operations Similarly studies
using Tobinrsquos Q to investigate valuation effects of MNCs rely only on domestic benchmarks
(eg Morck and Yeung 1991 Morck and Yeung 2001 Gande Schenzler and Senbet 2009)
As growth rates and discount rates vary by country we believe our method is more appropriate
for measuring valuation effects in a multinational context5 This method in effect compares the
value of the firm as a whole to the sum of the parts In addition our method is conceptually
consistent with theories on foreign direct investment (FDI) which note that an MNC exists when
a firm seeks to exploit its advantages and remove conflict arising in external market transactions
by combining a firm of one nationality that might otherwise exist independently under the
ownership of a firm of a different nationality (Dunning and Rugman 1985)
Our approach leverages data maintained by the Bureau of Economic Analysis (BEA) which
provides detailed accounting information about FDI allowing measurement of sales for each
5 Denis et al (2002) contemplate the use of single-segment foreign firms as a benchmark noting three constraints to implementing such a procedure (1) the country location of firmsrsquo foreign operations cannot be reliably identified using Compustat data (2) the industry membership of firmsrsquo foreign operations cannot be identified reliably using Compustat data and (3) valuation ratios may differ across countries due to differences in accounting standards As described in our study we overcome each of these constraints and find that the different benchmark is important and reverses prior findings
4
country-industry in which US-based MNCs operate These data provide a substantial advantage
over the Compustat database used by prior research While Compustat provides some
information about the countries and industries in which these firms operate it is not as detailed
about either of these dimensions (or their interaction) and heavily relies on managerial disclosure
choices which could induce measurement bias (Villalonga 2004a) In addition to the new
method of estimating the value effects of foreign operations we provide evidence that MNCs
trade at a premium which stands in contrast to the discount documented in Denis et al (2002)
This difference is primarily attributed to our use of foreign benchmarks to construct the imputed
values of foreign operations6
Our finding of a premium better reconciles with the findings in the international trade
literature By focusing on nonpublic establishment level (eg factory store or office) data the
trade literature is able to generate relatively precise proxies for total factor productivity (TFP)
and provides robust evidence that firms engaging in international trade are more productive than
those that do not (see Helpman 2006 and Syverson 2011 for reviews) Furthermore
productivity differences tend to be highly persistent even within narrowly defined industries
(Syverson 2011) Due to data limitations it is difficult to accurately measure TFP at the firm
level especially for firms which operate in multiple industries andor countries Nevertheless
Helpman Melitz and Yeaple (2004) provide some evidence that US firms engaging in FDI
have labor productivity advantages over firms that do not Reconciling the discount to
multinational operations found by Denis et al (2002) to the trade literature requires that the
persistent productivity advantages of firms engaged in international trade must be more than
offset by some other cost (eg agency costs) While this relation is possible it is difficult to
6 We repeat the analysis of Denis et al (2002) for our sample and confirm that differences in value effects of foreign operations are driven by the different methods for measuring the imputed value of foreign operations
5
conjecture why the most productive firms would have the largest agency costs In contrast the
finding of a premium easily reconciles with the trade literature
Given our result we investigate whether the premium is simply an artifact of innate
characteristics of firms that choose to invest abroad For example Helpman et al (2004) present
a simple model with heterogeneous productivity endowments The firms that receive the highest
productivity endowments are the ones capable of paying the fixed costs to establish a foreign
subsidiary To the extent productivity advantages create excess value one would expect the type
of firms that choose to invest abroad would be valued at a premium even in the absence of their
foreign investments After including firm fixed effects to control for time invariant firm
characteristics (eg innate productivity advantages) we continue to find economically and
statistically significant evidence of a premium to foreign operations
To further account for the endogenous nature of FDI we estimate a dynamic panel data
model using the generalized method of moments (GMM) estimator developed by Arellano and
Bond (1991) Arellano and Bover (1995) and Blundell and Bond (1998) This estimator allows
us to simultaneously account for the potential endogeneity of FDI industrial diversification and
firm value This method also yields a significant premium which is of similar magnitude and
significance to that estimated using fixed effects
We also perform a series of additional robustness tests First we examine whether the
measured excess value premium is driven by operations in countries with large control premiums
(Dyck and Zingales 2004) In countries with large control premiums using stock price in the
calculation of total firm value could underestimate the true value of the benchmark firms and
thereby induce an excess value premium We fail to find evidence that the excess value premium
is driven by such countries
6
Second we examine whether access to a low cost of capital is a meaningful source of the
US MNC advantage We incorporate foreign benchmarks in our method of estimating firm
excess value as it allows us to incorporate attributes of operating in those foreign countries ndash a
key attribute being the cost of capital Relative to the local benchmarks in foreign countries US
MNCs have the ability to obtain funds outside the local operating environments either by
borrowing in the US market or transferring capital internally When competing against firms in
shallow capital markets with high costs of capital MNCs may have a competitive advantage
over local foreign companies due to these alternative sources of capital However investors and
lenders are likely to expect higher rates of return from the additional risk associated with
operating in these environments making it unclear whether a multinational network will enhance
value in countries with a high cost of capital In addition foreign firms have some ability to
access international capital markets which mitigates the advantages to US firms To examine
whether the cost of capital plays a role in our finding of a premium we include a proxy for the
extent of operations in countries with higher costs of capital Our results are unaffected by the
inclusion of the proxy
Finally we control for several corporate governance proxies which Hoechle et al (2012)
find to be correlated with excess values (and industrial diversification) While magnitude of the
premium is virtually unaffected by including these proxies the significance level does decline
due to the reduced sample size
Our study contributes to the literature on multinational firms Our new method of estimating
excess values for multinational firms provides strong evidence of a statistically and economically
significant premium associated with increased international operations The premium is robust to
a variety of specifications and controls designed to evaluate alternative explanations In addition
7
this result easily reconciles with the international trade literature that establishes a link between
greater productivity and international operations though we do not find results supporting this as
the only source of the premium As segment disclosures improve and as new data sources
become available on multinational activity (eg Bureau Van Dijk) our empirical approach can
be used to investigate the extent to which the home country of the parent might influence the
extent with which foreign operations affect firm value (ie by examining non-US based
MNCs)7 Future studies could also examine the underlying forces affecting the excess value
premiums across countries
We also contribute to the understanding of international accounting standards and their
comparability Many studies seek to compare firms using different accounting standards These
comparisons generally rely on information generated by a particular accounting regime By
comparing commonly used metrics (total assets net income and sales) reported for the same
firm and year across different regimes we provide insights as to the relative comparability across
standards In particular we find that sales is reported significantly more consistently across
accounting regimes than the other metrics examined This finding should aid researchers in
reducing measurement error when comparing accounting information in multinational settings
The paper proceeds as follows Section 2 discusses the motivation for finding a premium or
discount to multinational operations Section 3 describes our data and how the excess value of a
firm is measured A discussion of our independent variables and primary findings is presented in
Section 4 with additional robustness tests provided in Section 5 Finally Section 6 concludes
7 As MNCs are ultimately bound by the tax regulatory and legal frameworks of their home country the valuation effect of foreign operations that we document for US-based MNCs may be different in a sample of non-US based MNCs with the same country-industry footprint Theorists refer to these as lsquolocational advantagesrsquo enjoyed by all multinational firms of a given nationality and obtained irrespective of skills or capabilities unique to a particular firm (Yamin 1991) There is also evidence of locational advantages in the market for corporate control (Huizinga and Voget 2009)
8
2 Premium versus discount
In a frictionless world where managers maximize firm value and markets are efficient there
should be no discount or premium to operating in foreign countries However there are a number
of forces that may cause actual firm value to deviate from the implied firm value
On one hand MNCs may have comparative advantages that generate premiums (ie positive
excess values) relative to a similar footprint of stand-alone firms Having operations in a variety
of locations that access different supplies of inputs and customers allows the firm to take
advantage of changing market conditions by shifting operations or products to maximize firm
value more so than a firm operating in a single country Similarly having access to a variety of
institutional settings such as different tax codes legal regimes and financial markets can provide
MNCs with options beyond those available to firms located in a single country
Another possible factor associated with a premium to multinational operations is the cost of
capital For operations located in countries with shallow capital markets or weaker creditor
rights operating within a conglomerate can provide the benefit of a lower cost of capital for
investments and expansion for at least two reasons First as our MNCs are domiciled in the US
their foreign segments have greater access to US capital markets relative to local competitors in
the foreign jurisdiction Second cash rich segments with few investment opportunities can
finance investments in cash poor segments with positive net present value projects (eg Myers
and Majluf 1984) Consequently diversified firms should be less liquidity constrained and better
able to shift resources to the most valuable investment opportunities Research has found that
internal financing is used more often when diversified firms have operations in countries with
more costly external financing (Desai Foley and Hines 2004) Multinational firms may also be
relatively more protected than single-country firms when negative shocks hit external capital
9
markets (Foley Desai and Forbes 2008) In a recent study Matvos and Seru (2011) find that
industrially diversified firms perform better than non-diversified firms when external capital
markets are impaired
While these arguments suggest that MNCs enjoy certain advantages when the foreign cost of
capital is high these benefits should be reduced by the additional risk associated with operating a
business in a high cost of capital environment For example Verizon is a multinational firm
operating in a number of countries One of the higher cost of capital locations in which they
operated was Venezuela While the multinational structure of Verizon may have mitigated some
of the risks associated with operating in a higher cost of capital location the firms was unable to
eliminate all of these risks In fact in 2007 Verizonrsquos operations in that country were
nationalized resulting in a net extraordinary loss of $131 million that year8 In other words
MNC investors are likely to expect higher rates of return to compensate for higher risk which
likely offsets some (if not all) of the potential excess value premium
On the other hand multinational firms may incur a discount (ie negative excess values)
relative to a similar footprint of stand-alone firms Potential reasons for a discount largely rely on
the existence of agency costs Multinational firms tend to be larger more complex and less
transparent The combination of complexity and opacity inherent in MNCs reduces a firmrsquos
reliance on external capital as the cost of such capital increases due to agency costs (Desai et al
2004) As a result of the reduced reliance on external funds MNCs face a reduced level of
monitoring As noted by Jensen (1986) managers have incentives to increase firm size beyond
that which is optimal with overinvestment or misallocation of funds to pet projects If this lack of
monitoring is in combination with available internal capital empire building becomes easier
(Hope and Thomas 2008) Furthermore the current segment reporting standards under US 8 See the 10-K of Verizon Communications Inc dated December 31 2007 (footnote 2)
10
GAAP only require highly aggregated disclosures of foreign operations and allow for
considerable managerial discretion As a result operating in multiple countries makes it easier
for management to hide poor performance ndash either their own or that of a division ndash such that low
quality managers are more likely to be retained and overcompensated and poorly performing
divisions retained longer than optimal Other reasons one might expect a discount to be applied
to multinational firms include higher coordination costs (eg coordinating across different
cultures and languages) as well as additional risks These risks include exposure to multiple
political regimes legal regimes economic regulations and currency fluctuations
Past studies investigate whether foreign operations of US firms enhance or reduce firm
value However all of these studies benchmark the value of the foreign operations of an MNC to
US domestic firms For instance Denis et al (2002) determine the implied value of a US
MNC using domestic (US) single-segment firms in the same industry as a benchmark Studies
using Tobinrsquos Q to investigate valuation effects of MNCs also rely on domestic benchmarks
(eg Morck and Yeung 1991 Morck and Yeung 2001 Gande Schenzler and Senbet 2009)
However using a US domestic firm as a benchmark for the value of foreign operations
implicitly makes two assumptions First this method assumes that the risks and expected growth
rates of foreign operations are equivalent to those of domestic operations This assumption is
inconsistent with the findings of Hail and Leuz (2009) which provides evidence that the cost of
capital varies substantially across countries Second this method assumes that there is excess
domestic capacity to allow for expansion with profitability similar to current domestic
operations We relax these assumptions by measuring the imputed value of a foreign component
of a US firm as if that component operated autonomously within that foreign country (rather
than in the US) Specifically our multiples allow the cost of capital and expected growth rates
11
to vary across not just industry but also country Furthermore our counterfactual does not
require the assumption that operations be relocated mitigating the production and sales capacity
concerns
3 Measuring excess value
31 Description of excess value
Figure 1 shows the average percent of foreign sales (based on segment disclosures under US
GAAP) across firms through time and illustrates that firms are continuing to expand
internationally9 This expansion highlights the importance of understanding whether the decision
to operate internationally is on average a value enhancing corporate strategy We contribute to
this understanding by evaluating whether the firm as a whole is worth more or less than its
imputed value (ie the firm is valued in excess of the sum of its individual components)
We measure the excess value of a firm as the logarithm of the ratio of actual firm value to
imputed value based on the method used in Berger and Ofek (1995) The imputed value is the
hypothetical value of the MNC under the assumption that its country-industry components
operate as independent entities A key innovation of our study is an alternative approach to
imputing the value of the foreign operations of an MNC Components of MNCs operating in a
given industry and country are imputed using the value of single-segment firms operating in the
same industry and country Thus we ensure the discount rates and expected growth rates are
applicable to each given industry and location in estimating the implied values of the MNC
segments
9 The discontinuity in industrial diversification (the percent of sales outside the primary industry) between 1997 and 1998 is the result of a change in segment reporting rules
12
The actual firm values are observable for all firms in our sample The hypothetical firm
values for multi-segment firms are imputed using market value to sales ratios herein referred to
as lsquomultiplesrsquo of the median single-segment firm operating in the same country-industry Thus
these single country-industry firms serve as benchmarks for an MNCrsquos operations within that
same country-industry The imputed value of the country-industry component can be viewed as
an estimate of what that component would be worth if it operated independently The sum of the
imputed values across all country-industry components represents the hypothetical value that the
multi-segment firm would be worth if it were broken apart
32 Data
Four primary data sources play a distinct role in carrying out our analysis i) Bureau of
Economic Analysis (BEA) data ii) Compustat Segment data iii) Worldscope data and iv)
Compustat Fundamentals Annual data Broadly speaking BEA data provide information about
the diversified nature of MNCs Compustat Segment data provide information about the
industrial diversification of US domiciled firms Worldscope data are used to compute
multiples for single-segment foreign firms that serve as benchmarks for the foreign operations of
MNCs (ie foreign benchmarks) and Compustat Fundamentals Annual data are used to
compute multiples for single-segment domestic firms that serve as benchmarks for the domestic
operations of multinational firms We also use the latter data source to construct our primary
control variables
13
Key to the execution of our study is our access to BEA data Federal law requires US-
domiciled MNCs to report certain financial and operating data to the BEA10 With regard to
observing the country-industry operations of MNCs use of the BEA data overcomes two
important limitations of Compustat Segment data First Compustat does not consistently identify
the specific location or scale of MNCsrsquo foreign operations11 Second Compustat does not
identify the industry activities in each country of operation This latter point is particularly
limiting for MNCs because domestic industry activity need not mirror foreign industry activity
An MNC generating an equal proportion of sales in two industries in the domestic market need
not generate industry sales in equal proportions in every foreign market12 The lack of detail and
consistency in Compustat Segment data arises because segment reporting is highly aggregated
and firms exercise substantial discretion in defining segments under Accounting Standards
Codification 280 (ASC 280) (eg Villalonga 2004a and Bens Berger and Monahan 2011)
The BEA defines an MNC as the combination of a single US entity called the US parent
and at least one foreign affiliate ndash that is these firms have a physical presence outside the US
due to FDI Since 1982 MNCs have completed a mandatory and confidential lsquoSurvey of US
Direct Investment Abroadrsquo for the domestic operation and the operations of each foreign
affiliate defined as a foreign entity in which the US parent holds at least a 10 percent equity
interest (directly or indirectly) The data are reported in US dollars on a fiscal year basis and in
10 BEA surveys are conducted pursuant to the International Investment and Trade in Services Survey Act (PL 94-472 90 Stat 2059 22 USC 3101-3108) See httpwwwbeagovsurveysdiasurvhtm and Mataloni (2003) for more detailed information on the BEA data 11 Exhibit 21 disclosures of material subsidiaries and their locations in SEC 10-K filings could potentially help overcome the inability to observe the location of the firmrsquos foreign operations from segment disclosures However at least three limitations remain (1) for industrially diversified multinational firms the researcher would be required to make an assumption about the proportion of industrial diversification in each country (2) Exhibit 21 provides no information about the scale of activity in each jurisdiction and (3) managerial discretion is used to determine a lsquomaterialrsquo subsidiary under Section 601 of SEC Regulation S-K 12 In fact the expectation that a multinational firm would exhibit different industry membership in different countries is consistent with the theory of vertical foreign direct investment where firms separate their various value chain activities and locate them in the most favorable country (see eg Hanson Mataloni and Slaughter 2005)
14
accordance with US Generally Accepted Accounting Principles (US GAAP) For each year
we observe the sales industry composition and location of not only the parent but also each
affiliate13
The procedures we use to construct our sample are similar to those used by Berger and Ofek
(1995) and Denis et al (2002) Using firm-years appearing in Compustat Fundamentals Annual
and Compustat Segment data we obtain a sample of firms domiciled in the US with total sales
exceeding $20 million We exclude firms in the financial service (SIC codes 6000 to 6999) and
utility (SIC codes 4000 to 4999) industries and eliminate firm-years when the sum of segment
sales (Compustat geographic or Compustat business) is not within 1 of total firm sales for that
year We restrict our sample period to include fiscal years beginning after December 15 1997 as
ASC 280 Segment Reporting substantially altered the definition of a reporting segment under
US GAAP This restriction ensures that Compustat Segment data reflects the use of a consistent
accounting standard Finally we require that our MNCs appear in both Compustat and BEA
data The requirement that our sample firms appear in the BEA database ensures that the firms
have observable international operations In sum these procedures result in 1166 multinational
firms incorporated in the US with 4950 observations between 1998 and 2008 that meet all of
our data requirements
33 Measuring excess value
The dependent variable in our study is excess firm value (Excess Value) defined as the
logarithm of the ratio of actual firm value to imputed firm value (Berger and Ofek 1995)
13 We obtain sales industry membership and country location for affiliates from one of two sources When affiliates exceed the BEA-determined reporting thresholds (ie if their assets sales or net income (loss) exceed $7 million in 1999 $30 million in 2000 through 2003 $10 million in 2004 and $40 million in 2005 through 2008) they must file their own detailed survey When affiliates fall below the thresholds we obtain our data from an attachment to the parent survey
15
Appendix A provides the definition of this and all other variables used in our analysis We
observe actual firm value equal to a firmrsquos market value of equity plus book value of liabilities
(ie book value of total assets minus book value of total equity) using Compustat Fundamentals
Annual data We calculate imputed firm value as the sum of the imputed values of a firmrsquos
operations in each country-industry Our method for imputing the value of the separate
components of a firm can best be described in three steps First we obtain total sales generated
by a firm for each country-industry in which it operates14 Second we obtain multiples (market
value to sales ratios) for benchmark firms operating in those same country-industries Third we
multiply a firmrsquos country-industry sales by the applicable median country-industry multiple to
obtain the imputed market value for each country-industry operation We perform each step
annually
Conceptually imputed values can be based on assets (ie Tobinrsquos Q) net income or sales
multiples We restrict ourselves to sales multiples for two reasons First using value to sales
ratios maintains consistency with the prior research on the excess value implications of
multinational operations (eg Denis et al 2002) Second as our method uses accounting data
for foreign companies to compute country-industry multiples the accounting numbers we use
need to be consistently measured across firms using different accounting standards We find that
sales data are measured most consistently
To assess the comparability of sales relative to either net income or assets across various
accounting standards we examine all firms listed on Worldscope as changing to or from US
14 Specifically we allocate total sales from Compustat to each component based on the proportion of BEA sales (after eliminating intercompany sales) occurring in each country-industry This step ensures that our total firm sales do not deviate from a firmrsquos total sales as reported in the 10-K
16
GAAP between 2005 and 201115 We obtain accounting data for the year of the change and the
prior year where electronically available in English We are able to obtain data for 66 firms
changing standards as detailed in Table 1 The distribution of years for which accounting data
are available under two different accounting standards for the same fiscal year is shown in Panel
A The years shown are the year prior to the change as these accounting numbers were provided
under the original standard and then subsequently restated in the following year under the new
standard for comparative purposes The majority of the shifts were to or from IFRS (71)
though seven other accounting standards are noted (Panel B) We examine the percent that the
non-US GAAP reported number differs from the US GAAP reported number in Panel C to
assess the comparability of these three summary accounting numbers across various accounting
standards Examining both the full sample as well as the subset that overlaps our study we find
that sales are more consistently measured than net income or assets
The sum of the imputed values across the country-industry components of an MNC is an
estimate of the value of a portfolio of unrelated businesses that mirror the related businesses of
the firm Consequently a comparison of the actual firm value with the imputed firm value is a
measure of how a multinational network affects firm value We provide descriptive statistics for
Excess Value for our sample of 4950 MNC firm-years in Table 2 Panel A This table also
includes univariate comparisons of the values between the MNC firms and the domestic single-
segment and foreign single-segment benchmark firms The Excess Value measure is significantly
higher for MNCs suggesting a premium in firm value for multinational firms relative to the
benchmark firms in total as well as the domestic and foreign subgroups (p-values for tests of
15 Due to the hand collection efforts required and the limitation on electronic filings we began our search with changes documented in 2005 which generally require filings from 2004
17
means and medians are lt 001)16 These results provide some initial evidence that operating in
multiple countries is positively associated with firm value
To estimate country-industry multiples we rely on Worldscope financial data on foreign
firms and Compustat Fundamentals Annual for domestic firms We restrict the firms to include
only those with at least 90 of sales income and assets inside the country of domicile (ie
those that do not report significant multinational activity) and that operate in a single industry
We refer to these firms as benchmark firms (either foreign or domestic depending on the country
of domicile) these firms do not appear in any of our regressions We report the number of
benchmark firms during our sample period in Table 2 Panel A The industry criterion is based on
the two-digit SIC code17 For every country that has at least five firms in the respective industry
and year we use the median ratio of market value to sales18 This ratio is the country-industry
multiple ndash an input required to compute imputed values
To determine the country-industry composition of MNCs we rely on BEA data In Table 3
we report the aggregate number of foreign affiliates and total sales by country as provided by
the BEA data for our sample of 4950 MNCs The specific countries tabulated are those that
represent at least 02 percent of total firm sales (pooled across all years) in our sample The top
five foreign countries in which MNCs generate sales are Canada United Kingdom Germany
France and Japan Table 3 also provides a comparison of the number of foreign benchmark
firms available in Worldscope in the specific countries in which our sample of MNCs generate
16 Similar to Berger and Ofek (1995) the median values differ slightly from zero for the benchmark single-segment firms due to the truncation of extreme excess values 17 BEA reporting constrains us to the two-digit SIC code level 18 When five firms are not available for a given country and industry we follow Berger and Ofek (1995) and move to a more general industry definition (eg one-digit SIC code) This procedure results in a mean (median) 630 (757) of MNC sales matched at the two-digit level
18
the majority of their sales Country coverage in Worldscope is not available for countries
representing 004 percent of total firm sales (per BEA)
4 Empirical results
41 Independent variables
Recall that our objective is to examine the overall relation between excess firm value and
multinational operations Our proxy for the extent of multinational operations is the percentage
of total firm sales made by foreign operations (Foreign Sales) By this measure the extent of
multinational activity in our sample of MNCs is non-trivial ndash Table 2 Panel B shows that
approximately 24 percent of sales are generated by foreign operations on average Our first
analysis estimates ordinary least square regressions of Excess Value on Foreign Sales and
control variables (discussed below)
We include variables in our regression to control for other potential determinants of excess
value These are the percentage of sales made by the firm outside its primary industry (Industry
Other) to control for any relation between industrial diversification and excess value as in Berger
and Ofek (1995) For this purpose we obtain industry sales using Compustat Segment data19 A
firmrsquos primary industry is the industry in which the firm generates the majority of its sales and
we determine industry sales at the business segment level We set Industry Other equal to zero
for firms that operate in a single business segment
Consistent with prior research we also include controls for firm size (Log Size) the ratio of
long term debt to total market value (Debt Firm Value) the ratio of capital expenditures to total
sales (CAPX Sales) the ratio of earnings before interest and taxes to total sales (EBIT Sales)
the ratio of research and development expenses to total sales (RampD Sales) and the ratio of 19 Our results are unaffected by using BEA data to measure Industry Other
19
advertising expenses to total sales (Ad Sales) Table 2 Panel B provides the distribution of the
control variables All independent variables are Winsorized at 1 In our OLS regressions we
use heteroskedasticity and autocorrelation consistent robust standard errors with clustering at the
firm level
42 Primary regression analysis
In this section we examine the relation between firm excess value and the amount of foreign
activity at US MNCs Table 4 contains the results of regressions of excess value on Foreign
Sales for multiple specifications In column (1) Excess Value is regressed on both the percent of
foreign sales (Foreign Sales) and the percent of sales outside the firmrsquos primary industry
(Industry Other) We add the control variables to the regression in column (2) and year
indicators in column (3) In all three specifications the coefficient on the percent of foreign sales
remains significantly positive (with a p-value lt 001) For a sense of economic magnitude in
column (3) a one standard deviation (197) increase in the percent of foreign sales (Foreign
Sales) is associated with a 41 (equal to (e0349-1) times 0197) increase in firm excess value The
effect is larger for the other two columns ndash 82 increase in firm excess value in column (1) and
46 in column (2)20
Our finding of a premium reconciles with the international trade literature which finds that
firms engaging in international trade are more productive than those that do not (see Helpman
2006 and Syverson 2011 for reviews) Due to data limitations it is difficult to accurately
measure TFP at the firm level especially for firms which operate in multiple industries andor
20 Using the alternative strategy that employs US domiciled single-segment firms as the benchmark for the imputed value of the foreign operations Denis et al (2002) find that greater foreign activity results in a discount to firm value Using the Denis et al (2002) method to calculate excess value in our sample we obtain a significantly negative regression coefficient on Foreign Sales using the models in Table 4 columns (2) and (3) (the coefficient is not significant using the model in column (1) results untabulated)
20
countries As an alternative method to evaluate whether our finding of a premium is simply the
result of more productive firms also having more multinational operations we investigate
whether the premium is simply an artifact of innate characteristics of firms that choose to invest
abroad For example Helpman et al (2004) present a simple model with heterogeneous
productivity endowments The firms that receive the highest productivity endowments are the
ones capable of paying the fixed costs to establish a foreign subsidiary
To the extent that productivity advantages create excess value one would expect the type of
firms that choose to invest more abroad would be valued at a premium even in the absence of
their foreign investments To control for time invariant firm characteristics (eg innate
productivity advantages) and hence a potential source of endogeneity we add firm-level fixed
effects in column (4) of Table 4 We continue to find that the extent of foreign operations
(Foreign Sales) is positively associated with Excess Value Across all specifications the sign
on all significant control variables is consistent with prior research (eg Berger and Ofek 1995
and Denis et al 2002)
The finding of a premium makes a significant contribution to the relatively sparse literature
on the value effects of multinational activity Our measure of imputed value expands upon the
methods used by Berger and Ofek (1995) and Denis et al (2002) but allows us to answer a
different question than Denis et al (2002) Holding constant the extent of industrial
diversification we ask whether MNCs are more valuable than a portfolio of benchmark firms of
a similar geographic footprint In contrast Denis et al (2002) ask whether holding constant the
extent of industrial diversification MNCs are more valuable than a portfolio of domestic (US)
benchmark firms We believe that asking whether an MNCrsquos foreign (domestic) operations are
21
more valuable than those of a foreign (domestic) firm is a more appropriate way to assess the
value implications of a multinational network
5 Robustness
51 Endogeneity related to dynamic relations
Prior research argues that the relation between industrial diversification and excess firm
value could be endogenous through dynamic relationships based on observing past outcomes
Managers of firms likely choose to enter or exit industries or geographic regions based upon
their previous performance For example Campa and Kedia (2002) find that firms are more
likely to enter new industries when prospects in their current lines of business are deteriorating
Similar dynamics could also drive a relation between excess value and FDI We assess the
robustness of the previous results by estimating a dynamic panel data model which allows us to
control for dynamic endogeneity in addition to firm level unobserved heterogeneity We employ
the GMM estimator for dynamic panel models developed by Arellano and Bond (1991) Arellano
and Bover (1995) and Blundell and Bond (1998) Our results from this analysis confirm our
previous findings that firms with a larger degree of foreign operations have positive excess
values
Dynamic panel data models are generalizations of the traditional fixed effects model where
lags of the dependent variable are added to the right hand side of the equation These additional
lags control for the impact a firmrsquos past performance has on its current performance through the
channels discussed above The GMM estimator for dynamic panel models developed by
Arellano and Bond (1991) starts by differencing the data at the firm level in neighboring time
periods to eliminate any firm specific time invariant unobserved heterogeneity that may be
22
present Eliminating the firm specific fixed effect by differencing the data with lags of the
dependent variable on the right hand side introduces correlation between the new error term and
the differenced explanatory variables This problem is solved by applying the standard GMM
estimator using lags of both the original explanatory variables and excess value as instruments
We note that the dynamic panel data framework allows us to treat both international and
industrial diversification (as well as the other firm-specific control variables) as endogenous
Following Hoechle et al (2012) we also allow contemporaneous values of these variables to be
treated as endogenous
Lagged values of the explanatory variables are valid instruments in a dynamic panel data
model when the model is dynamically complete (eg Wooldridge 2010) A dynamic panel data
model is dynamically complete conditional on the unobserved effect if enough lags of both the
explanatory and dependent variable are included to make the resulting error term uncorrelated
with all the right hand side variables Under this assumption any lags for the explanatory
variables beyond this can be used within the GMM estimator as instruments Like Hoechle et al
(2012) we add two years of lags of a firmrsquos excess value to the right hand side assuming that
this is enough lags to control for dynamic feedback effects We then use past values of all
variables beyond the second lag up to a total of seven years to act as instruments
The results of the dynamic panel data estimator are reported in Table 5 These results confirm
the previous finding of a positive relation between the degree of international operations and
excess value We note that the size and significance level of the coefficient on Foreign Sales
diversification are robust to changes in the number of lags used as instruments The Arellano and
Bond (1991) estimator includes testing diagnostics for correct specification of the model If the
model is correctly specified differences in the residuals from the levels equation should be
23
serially uncorrelated at lag one but correlated at lag two The p-values for tests of serial
correlation of the residuals at lags 1 and 2 are reported in Table 5 and these indicate that the
model is not rejected We also report in Table 5 the Hansen test for overidentification which is a
test of the null hypothesis that the instruments are valid The p-value for this test is 030
indicating that we fail to reject this null hypothesis Finally we report another test for exogeneity
known as the difference-in-Hansen test which tests the null hypothesis that the instruments in
the original equation are valid We fail to reject this null hypothesis as well
52 Identifying value effects of multinational operations via cross-border acquisitions
Another alternative to investigating a causal link between foreign direct investment and firm
value is an event study of foreign acquisitions21 Acquired foreign targets represent only a small
fraction of MNCsrsquo worldwide sales in the year of the acquisition ndash less than 1 percent in our
sample Additionally cross-border acquisitions are not the primary source of international
expansion for the average firm We find that foreign acquisitions account for between 18 percent
and 55 percent of the year-over-year change in foreign sales with the remainder of the growth
coming from either newly established entities (ie greenfield investments) or growth in existing
operations22 These results suggest that acquisitions are not a material source of growth for the
21 Doukas and Travlos (1988) find positive returns when the acquirer is not operating in the target firmrsquos country and insignificant returns when the acquirer is operating in the target firmrsquos country or expanding internationally for the first time Morck and Yeung (1991) find marginal evidence of positive returns for firms with intangible assets Finally Doukas (1995) finds positive returns only for firms with average Tobinrsquos q ratios greater than one More recent work also using an event study methodology provides mixed evidence on valuation effects of global diversification through acquisitions Dos Santos Errunza and Miller (2008) find insignificant returns for firms with already established foreign operations firms establishing initial foreign operations and firms acquiring targets in related industries Doukas and Kan (2006) and Moeller and Schlingemann (2005) find reductions to shareholder value while Francis Hasan and Sun (2008) find positive returns for acquirers of targets in segmented financial markets 22 New foreign affiliates to an MNC group each year report on the BEA survey whether they entered the group as an acquisition or a newly established entity (ie greenfield investment) Affiliates under the lowest reporting threshold do not report this information resulting in our inability to precisely identify the source of 33 percent of the increase in firm sales as either acquisition or greenfield
24
typical US MNC suggesting that an event study would lack power and generalizability For
these reasons we do not implement an event study
53 Control premium
A control premium or private benefits of control occurs when ldquosome value whatever the
source is not shared among all the shareholders in proportion of the shares owned but it is
enjoyed exclusively by the party in controlrdquo (Dyck and Zingales 2004 p 541) Using stock
prices of traded shares held by minority shareholders in determining the market value to sales
ratios of our benchmark firms could underestimate the true value of our benchmark firms if those
firms face large control premiums This in turn could induce an excess value premium because
the imputed value of these country-industry segments would be artificially low The existence of
such benefits is more prevalent in some countries than others and could affect the value of an
MNCrsquos operations in such domains We use the country-level control premiums from Dyck and
Zingales (2004) and using each firmrsquos country sales as a percent of total firm sales we obtain a
weighted average control premium that each MNC faces (Control Premium) For countries not
included in the Dyck and Zingales (2004) analysis we set the control premium equal to zero23 In
Table 6 we show that Foreign Sales remains positive and highly significant (p-value lt 001)
when we include the proxy for control premium As a result it does not appear that the excess
value premium is driven by MNCs operating in countries where majority shareholders are able to
extract large control premiums
23 Results are robust to replacing missing values with the mean control premium of the respective GDP quartile
25
54 Cost of capital
As discussed in Section 2 when countries have shallow capital markets a multinational
network may provide the benefit of a lower cost of capital for investments through an internal
capital market as well as through better access to the US capital market Prior research
establishes that there is substantial variation in the cost of capital across countries (Erb Harvey
and Viskanta 1996 and Hail and Leuz 2009) To determine whether the documented premium
is due to cost of capital advantages we include a control variable to capture the difference
between the MNCrsquos cost of capital and the cost of capital faced by its competitors in the global
markets in which it operates
We compute our Cost of Capital proxy in two steps First we obtain the country-year credit
rating published by Institutional Investor24 The rating ranges from 0 to 100 where higher values
imply that a country has a higher default risk Country-level credit risk is a reasonable predictor
of expected equity market returns and volatility (Erb et al 1996) and exhibits a highly
significant correlation with international accounting-based estimates of imputed cost of capital
(Hail and Leuz 2009) Second using each firmrsquos country sales as a percent of total firm sales
we obtain a weighted average credit risk rating across the countries in which each firm operates
A higher value implies an MNC operates in countries with higher cost of capital In Table 7 we
do not find a result consistent with MNCs gaining an advantage from their access to low cost
capitalndash Cost of Capital is not significantly associated with Excess Value25
24 We thank Cam Harvey for providing these data 25 Results are consistent using cost of capital as defined in Erb et al 1996
26
55 Corporate governance
In light of the recent finding that corporate governance mitigates the diversification discount
we examine whether the premium to multinational activity is sensitive to controls for corporate
governance Hoechle et al (2012) find that the magnitude of the industrial diversification
discount is decreased and in several specifications no longer significant in the presence of
governance proxies In the first column of Table 8 we include the five governance proxies that
are consistently significantly associated with excess value in Hoechle et al (2012) ndash the percent
of CEO ownership (CEO Ownership and CEO Ownership squared) the percent of institutional
ownership (Institution Ownership) whether the firm has a powerful or influential CEO
(Powerful CEO) and the governance index from Gompers Ishii and Metrick (2003)
(Governance Index)
As documented in Table 8 including these control variables reduces our sample size by
almost 50 (to 2550 observations) and tends to exclude the smaller firms from the analysis
None of the governance proxies are significant Relative to column (4) of Table 4 the coefficient
on Foreign Sales is similar in magnitude but is no longer statistically significant at
conventional levels To investigate whether the decreased significance is due to the smaller
sample or the inclusion of the governance variables we re-estimate the regression for the same
2550 observations after removing the governance controls The coefficient and t-statistic are
virtually identical suggesting that the loss of significance is due to the reduced sample and not
the governance controls We do not find evidence to suggest that variation in corporate
governance practices drives variation in excess value for US MNCs
27
6 Conclusion
Using a new method to quantify the valuation effects of foreign direct investment by US
domiciled firms we find robust evidence of a premium in excess value associated with the
degree of multinational operations Our method builds on that used in previous studies
examining firm excess value (eg Berger and Ofek 1995 and Denis et al 2002) by
incorporating the location in addition to the industry of each corporate affiliate This allows
important factors such as growth and cost of capital to vary across countries as well as industries
Examining a sample of multinational firms we find that a one-standard deviation (197)
increase in foreign operations is associated with an increase of between 41 and 82 in excess
value minus both a statistically and economically significant finding
This result complements the finding of the international trade literature that more productive
firms are more likely to operate internationally However the premium remains after including
firm fixed effects to control for persistent firm traits (eg productivity)
The finding of a premium stands up to a succession of robustness tests designed to evaluate
whether the result arises from alternative methods or explanations First we estimate a dynamic
panel data model using the generalized method of moments (GMM) estimator to simultaneously
account for the potential endogeneity of FDI industrial diversification and firm value Second
we include a control variable to proxy for whether the firm operates in countries with larger
control premiums which could result in understated multiples and mechanically induce a
premium Third we include a control variable to measure the cost of capital across the countries
in which an MNC operates to evaluate whether a relatively low cost of capital borne by
multinational firms is a meaningful source of the premium Fourth we control for several
corporate governance proxies which have been found to be correlated with excess values (and
28
industrial diversification) We fail to find evidence supporting any of these alternative
explanations ndash the premium in excess value arising at firms with greater multinational operations
remains significant
Overall our new method of estimating excess values for multinational firms provides strong
evidence of a statistically and economically significant premium associated with increased
international operations The premium is robust to a host of specifications and controls designed
to evaluate alternative explanations We leave to future research the questions of whether the
premium to US multinational firms applies more broadly to other countries of domicile and
what factors influence the premium
29
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Management 33 5-27 Wooldridge JM 2010 Econometric Analysis of Cross Section and Panel Data (Second
Edition) Cambridge MA MIT Press Yamin M 1991 A reassessment of Hymerrsquos contribution to the theory of the transnational
corporation C Pitelis and R Sugden (eds) The Nature of the Transnational Firm London Routledge
32
Appendix A Variable Definitions
We obtain information regarding firmsrsquo operations by country and industry from the Bureau of Economic Analysis (BEA) unless otherwise noted Acronyms used below reflect Compustat mnemonics and thus indicate when we obtain data from the Compustat North America Fundamentals Annual database All independent variables are winsorized at 1
Dependent variable
Excess Value = The log of the ratio of actual firm value to imputed firm value Actual firm value is equal to market capitalization (MVE) + book value of total assets (AT) ndash book value of shareholdersrsquo equity (SEQ) Imputed firm value is calculated as the sum of the imputed values of a firmrsquos operations in each country-industry The imputed value for each country-industry is equal to sales generated in that country-industry times a multiple equal to the ratio of a benchmark firmrsquos actual value to total sales A benchmark firm is defined as a median single-segment firm operating in the same country-industry All multiples require at least five firms listed in the respective country-industry on Compustat (domestic) or Worldscope (foreign) with less than 10 of sales income and assets outside the country of domicile Similar to Berger and Ofek (1995) we eliminate extreme values where the ratio of actual to imputed firm value exceeds 400 or is less than 025
Independent variables
Foreign Sales = Sales made by the foreign operations of US MNCs as a percent of total firm sales
Industry Other = Sales outside a firmrsquos primary industry as a percent of total sales (SALE) Equal to zero for firms operating in a single business segment
Log Size = Log of total sales (SALE)
Debt Firm Value = Ratio of long-term debt (DLTT) to total market value (market capitalization (MVE) + book value of total assets (AT) ndash book value of shareholdersrsquo equity (SEQ))
CAPX Sales = Ratio of capital expenditures (CAPX) to total sales (SALE)
EBIT Sales = Ratio of earnings before interest and taxes (EBIT) to total sales (SALE)
RampD Sales = Ratio of research and development expenses (XRD) to total sales (SALE)
Ad Sales = Ratio of advertising expenses (XAD) to total sales (SALE)
Control Premium = A weighted-average (based on the sales in a given country as a percent of total firm sales) of the country-level control premium from Dyck and Zingales (2004)
Cost of Capital = A weighted-average (based on the sales in a given country as a percent of total firm sales) of the country-level credit rating from Institutional Investor (httpwwwinstitutionalinvestorcom) The country-level rating is available for 160 countries and ranges from 0 to 100 Higher values imply greater credit risk
33
CEO Ownership = Percent of shares owned by the CEO from Execucomp
Institution Ownership = Percent of shares owned by institutional investors from Thomson Reuterrsquos Institutional Holdings database
Powerful CEO = Indicator variable equal to one if the CEO is the only insider on the board of directors and also serves as the chairman and president based on data from RiskMetrics Directors database Equal to zero otherwise
Governance Index = Count measure of takeover defenses used in Gompers Ishii and Metrick (2003) from RiskMetrics Governance database As the governance data are not available for every year we follow Gompers Ishii and Metrick (2003) and assume that the firmsrsquo governance attributes are unchanged until publication of the subsequent series
34
Figure 1 Trends in multinational activity and industrial diversification from 1984-2008 This figure presents a plot of the mean percent of foreign sales and the mean percent of sales outside the firmrsquos primary industry (industrial diversification) from 1984 through 2008 Dotted lines above and below the primary lines represent 95 confidence intervals To compile the plot we include the full population of firms in Compustat
35
Table 1 Analysis of accounting data for firms reporting under multiple accounting standards This table provides a comparison of sales net income and assets for firms that changed accounting standards to or from US GAAP Comparisons are provided for the 66 firms that changed to or from US GAAP and for which we could obtain accounting data electronically and in English under both standards for the same fiscal year (eg the firm presented the accounting numbers originally under one set of standards and then restated the prior year numbers in the subsequent year under the new set of standards) Panel A shows the distribution of firms by year Panel B shows the other accounting standards to which US GAAP is compared Panel C shows the mean and median percent that the other accounting standard differs from US GAAP for the same fiscal year and accounting number Two samples are shown in Panel C The first is the full sample of firms with a change in accounting standards to or from US GAAP for which data was available The second is the subset of these firms that occurred during the sample window used in this study (ie excluding changes post-2008) The percent difference in sales is compared to the percent difference in net income and assets Tests of means are based on matched-pair t-tests Tests of medians are based on Wilcoxon matched-pair signed-rank tests Panel A Accounting standard changes by year
2003
4 6
2004
25 38
2005
8 12
2006
6 9
2007
7 11
2008
5 8
2009
5 8
2010
5 8
2011
1 2
66
Panel B Accounting standards compared to US GAAP
IFRS
47 71
Canadian GAAP
12 18
Israeli GAAP
2 3
Dutch GAAP
1 2
Indian GAAP
1 2
Japanese GAAP
1 2
Norwegian GAAP
1 2
Swiss GAAP
1 2
66
36
(Table 1 continued) Panel C Tests of differences between US GAAP and other accounting standards
Percent different from US GAAP
Comparison to sales (p-values)
N Mean Median
Mean Median
Full Sample (2001-2011)
Sales
66 236 000
Net Income
66 10653 647
005 lt001
Assets
66 1162 126
lt001 lt001
Study Sample (2001-2008)
Sales
55 257 000
Net Income
55 10303 589
011 lt001
Assets
55 1047 099
003 lt001
37
Table 2 Descriptive statistics Panel A reports the mean and medians of Excess Value for the multinational firms and single-segment benchmark firms as well as univariate tests across the subsamples Panel B reports summary statistics for the independent variables for the multinational firms The p-values of the tests of differences in means are based on t-tests The p-values of tests of differences in the medians are for a Wilcoxon rank-sum test All variables are defined in Appendix A Panel A Excess Value by sample Multinational All Benchmark Benchmark Subgroups Sample Firms US Foreign
Value Value Test of Diff (p-value) Value
Test of Diff (p-value) Value
Test of Diff (p-value)
Mean 0067 0006 lt001 -0028 lt001 0014 lt001 Median 0076 0000 lt001 -0025 lt001 0000 lt001 N 4950 33074 5998 27076 Panel B Distribution of independent variables for the multinational firms N Mean 25th 50th 75th StDev Foreign Sales 4950 0238 0074 0190 0363 0197 Industry Other 4950 0212 0000 0166 0414 0215 Log Size 4950 6955 5823 6989 8140 1687 Debt Firm Value 4950 0136 0009 0097 0212 0145 CAPX Sales 4950 0054 0020 0033 0054 0101 EBIT Sales 4950 0065 0031 0077 0121 0138 RampD Sales 4950 0049 0000 0013 0059 0083 Ad Sales 4950 0008 0000 0000 0005 0018
38
Table 3 BEA and Worldscope samples
This table reports details on affiliate sales for significant countries pooled from 1998-2008 Significant countries are defined as those in which the operations of US MNCs represent at least 02 of total firm sales (per the BEA) For significant countries the table compares their representation in terms of MNC affiliate sales in the BEA data to the distribution of firm observations in Worldscope US Foreign Affiliates (BEA) Foreign Firms (Worldscope)
Country
N (1)
Sales (millions)
(2)
Total Sales (3)
N (4)
Sales (million)
(5)
Total Sales (6)
Australia 1344 73305 056 859 255865 210 Belgium 894 52755 040 169 26136 021 Brazil 959 60245 046 1143 683167 560 Canada 2730 536767 407 1181 526750 432 China 922 75907 057 822 276541 227 France 1852 183942 139 540 105447 086 Germany 2182 275577 209 480 103888 085 Hong Kong 867 48100 036 157 34094 028 Ireland 478 36754 028 34 3467 003 Italy 1178 82116 062 111 47948 039 Japan 1494 148032 112 2281 1119223 917 Korea South 659 34500 026 3530 2752791 2255 Malaysia 386 26027 020 837 168683 138 Mexico 1405 113623 086 362 237173 194 Netherlands 1149 85105 064 47 22631 019 Singapore 1098 94917 072 139 32913 027 Spain 871 63094 048 129 37419 031 Sweden 712 32682 025 235 23090 019 Switzerland 726 60288 046 74 20242 017 Taiwan 592 30313 023 2949 787049 645 United Kingdom 3054 423934 321 1997 882423 723 United States (domicile) 4950 10460880 7923 na na na Other - Covered by WorldScope 6893 199394 151 9000 4057894 33 Other -Not Covered by WorldScope 713 5352 004 - - - Total 38108 13203609 1000 27076 12204834 100
39
Table 4 OLS regressions of firm excess values on the percent of foreign sales This table reports the results of OLS regressions of firm excess value on the percent of foreign sales (Foreign Sales) the percent of sales outside a firmrsquos primary industry (Industry Other) and control variables All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed) Dep Variable = Excess Value Coefficient t-stat Coefficient t-stat Coefficient t-stat Coefficient t-stat (1) (2) (3) (4) Foreign Sales 0349 419 0209 293 0189 266 0278 210 Industry Other -0134 -186 -0047 -072 -0053 -081 -0326 -325 Log Size 0066 634 0065 624 -0044 -096 Debt Firm Value -0196 -213 -0241 -260 -0685 -588 CAPX Sales 0901 425 0893 435 0488 227 EBIT Sales 1154 542 1170 547 0408 189 RampD Sales 2908 1242 2932 1237 1544 379 Ad Sales 1869 263 2003 280 -1696 -119 Year indicators No No Yes Yes Firm indicators No No No Yes R2 0017 0212 0226 0768 Observations 4950 4950 4950 4950
40
Table 5 Dynamic panel GMM model regressing firm excess values on the percent of foreign sales This table reports the result from a regression of the excess value on the percent of foreign sales (Foreign Sales) the percent of non-primary industry sales (Industry Other) and control variables using a dynamic panel GMM estimator All control variables are considered to be endogenous with the exception of the year indicator variables AR(1) and AR(2) are tests for first-order and second-order serial correlation in the first differenced residuals with the null hypothesis of no serial correlation The null hypothesis of the Hansen test of overidentification is that all instruments are valid The null hypothesis of the difference-in-Hansen test of exogeneity is that the instruments used for the equation in levels are exogenous All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed) Dep Variable = Excess Value Coef t-stat Excess Value (one lag) -0018 -082 Excess Value (two lags) -0048 -272 Foreign Sales 0371 270 Industry Other -0163 -171 Log Size 0010 123 Debt FirmValue -1135 -820 CAPX Sales 0590 342 EBIT Sales -0047 -300 RampD Sales 1376 531 Ad Sales -0751 -040 AR(1) test (p-value) 000 AR(2) test (p-value) 017 Hansen test of over-identification (p-value) 030 Difference-in-Hansen test of exogeneity (p-value) 093 Year indicators Yes Number of firms 1016 Number of observations 4090
41
Table 6 OLS regression of firm excess values on the percent of foreign sales controlling for a control premium This table reports the result of the OLS regression of firm excess value including firm and year fixed effects with an additional control variable to proxy for a country-level ownership control premium (Control Premium) All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed) Dep Variable = Excess Value Coef t -stat Foreign Sales 0303 224 Industry Other -0327 -325 Log Size -0045 -100 Debt FirmValue -0683 -585 CAPX Sales 0490 226 EBIT Sales 0409 189 RampD Sales 1545 379 Ad Sales -1689 -119 Control Premium -1004 -062 Year indicators Yes Firm indicators Yes R2 0768 Observations 4950
42
Table 7 OLS regression of firm excess values on the percent of foreign sales controlling for the cost of capital This table contains the results of an OLS regression of firm excess value including firm and year fixed effects with an additional control variable to proxy for a country-level cost of capital (Cost of Capital) All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed) Dep Variable = Excess Value Coef t -stat Foreign Sales 0255 178 Industry Other -0326 -324 Log Size -0043 -094 Debt FirmValue -0687 -589 CAPX Sales 0485 225 EBIT Sales 0406 188 RampD Sales 1539 379 Ad Sales -1703 -120 Cost of Capital 0004 039 Year indicators Yes Firm indicators Yes R2 0768 Observations 4950
43
Table 8 OLS regression of firm excess values on the percent of foreign sales controlling for aspects of corporate governance This table reports the results of two OLS regressions In the first column are the results of the firm excess value regression including firm and year fixed effect with five proxies for corporate governance included ndash the percent of shares owned by the CEO and the percent squared (CEO Ownership and CEO Ownership squared) the percent owned by institutional investors (Institution Ownership) whether there is a powerful CEO (Powerful CEO) and the Gompers Ishii and Metrick (2003) governance index (Governance Index) In the second column the regression includes the same sample and variables with the exception of the governance proxies which are excluded All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed)
Governance
No Governance (same sample)
Dep Variable = Excess Value Coef t-stat Coef t -stat Foreign Sales 0259 159 0251 154 Industry Other -0099 -080 -0099 -079 Log Size -0105 -185 -0102 -180 Debt FirmValue -0813 -478 -0817 -482 CAPX Sales 0440 252 0438 249 EBIT Sales 0457 247 0457 246 RampD Sales 1717 284 1716 286 Ad Sales -2210 -143 -2190 -143 CEO Ownership 0010 007
CEO Ownership squared -0029 -017 Institution Ownership 0047 067 Powerful CEO -0003 -014 Governance Index 0009 103
Year indicators Yes Yes Firm indicators Yes Yes R2 0811 0810 Observations 2550 2550
- Cover for paperpdf
- crrz_20130603_SMUpdf
-
2
operating in the same industry The difference between the actual value and the imputed value is
an estimate of the premium (if positive) or discount (if negative) Evidence of a negative
association between this excess value measure and a measure of industrial diversification is
consistent with industrial diversification destroying firm value on average
While the finding of an industrial diversification discount is quite robust there is
considerable debate about the interpretation Some argue that the discount is evidence that the
costs of operating in multiple industries outweigh the benefits (eg Berger and Ofek 1995)
Others argue that the discount is driven by the types of firms that choose to diversify or the types
of businesses they invest in when diversifying (Campa and Kedia 2002 Villalonga 2004b) The
literature that uses this methodology continues to evolve improving our understanding of the
forces that drive the industrial diversification discount For example the recent study by Hoechle
et al (2012) finds that a substantial proportion of the discount can be explained by variation in
corporate governance proxies
In contrast to the literature on the industrial diversification discount comparatively few
studies examine how foreign operations affect firm value3 This contrast is surprising because
descriptive data for US firms indicates that industrial diversification has stagnated while foreign
expansion continues at a rapid pace We develop a method to quantify the value effects of
foreign operations along the lines of Berger and Ofek (1995) but that remains applicable to the
multinational setting While Berger and Ofek (1995) divide each firm into industry segments we
divide each MNC into geographic-industry segments (ie separate country-industry
components)4 We then compare the actual value of the firm to the imputed value of the firm
3 Denis Denis and Yost (2002) is a notable exception 4 In our setting we refer to a lsquosingle-segmentrsquo firm as a firm that operates in only one country and one industry Accordingly we use the term lsquoexcess valuersquo to denote the difference between the actual (observed) firm value and the hypothetical firm value computed as the sum of the imputed values of the individual country-industry segments
3
Using a method similar to Berger and Ofek (1995) we determine the imputed value for each
country-industry component by using the median single-segment firm operating exclusively in
the same country and industry (ie single-segment foreign (domestic) firms in the same industry
and country are used as benchmarks for the foreign (domestic) operations of US MNCs) In
other words our approach uses data on the observed firm values of single-segment US firms
(from Compustat) and foreign firms (from Worldscope)
This approach to measuring excess value differs from previous methods to impute the firm
value of MNCs Denis et al (2002) use single-segment domestic firms (ie US firms) in the
same industry as a benchmark for both domestic and foreign MNC operations Similarly studies
using Tobinrsquos Q to investigate valuation effects of MNCs rely only on domestic benchmarks
(eg Morck and Yeung 1991 Morck and Yeung 2001 Gande Schenzler and Senbet 2009)
As growth rates and discount rates vary by country we believe our method is more appropriate
for measuring valuation effects in a multinational context5 This method in effect compares the
value of the firm as a whole to the sum of the parts In addition our method is conceptually
consistent with theories on foreign direct investment (FDI) which note that an MNC exists when
a firm seeks to exploit its advantages and remove conflict arising in external market transactions
by combining a firm of one nationality that might otherwise exist independently under the
ownership of a firm of a different nationality (Dunning and Rugman 1985)
Our approach leverages data maintained by the Bureau of Economic Analysis (BEA) which
provides detailed accounting information about FDI allowing measurement of sales for each
5 Denis et al (2002) contemplate the use of single-segment foreign firms as a benchmark noting three constraints to implementing such a procedure (1) the country location of firmsrsquo foreign operations cannot be reliably identified using Compustat data (2) the industry membership of firmsrsquo foreign operations cannot be identified reliably using Compustat data and (3) valuation ratios may differ across countries due to differences in accounting standards As described in our study we overcome each of these constraints and find that the different benchmark is important and reverses prior findings
4
country-industry in which US-based MNCs operate These data provide a substantial advantage
over the Compustat database used by prior research While Compustat provides some
information about the countries and industries in which these firms operate it is not as detailed
about either of these dimensions (or their interaction) and heavily relies on managerial disclosure
choices which could induce measurement bias (Villalonga 2004a) In addition to the new
method of estimating the value effects of foreign operations we provide evidence that MNCs
trade at a premium which stands in contrast to the discount documented in Denis et al (2002)
This difference is primarily attributed to our use of foreign benchmarks to construct the imputed
values of foreign operations6
Our finding of a premium better reconciles with the findings in the international trade
literature By focusing on nonpublic establishment level (eg factory store or office) data the
trade literature is able to generate relatively precise proxies for total factor productivity (TFP)
and provides robust evidence that firms engaging in international trade are more productive than
those that do not (see Helpman 2006 and Syverson 2011 for reviews) Furthermore
productivity differences tend to be highly persistent even within narrowly defined industries
(Syverson 2011) Due to data limitations it is difficult to accurately measure TFP at the firm
level especially for firms which operate in multiple industries andor countries Nevertheless
Helpman Melitz and Yeaple (2004) provide some evidence that US firms engaging in FDI
have labor productivity advantages over firms that do not Reconciling the discount to
multinational operations found by Denis et al (2002) to the trade literature requires that the
persistent productivity advantages of firms engaged in international trade must be more than
offset by some other cost (eg agency costs) While this relation is possible it is difficult to
6 We repeat the analysis of Denis et al (2002) for our sample and confirm that differences in value effects of foreign operations are driven by the different methods for measuring the imputed value of foreign operations
5
conjecture why the most productive firms would have the largest agency costs In contrast the
finding of a premium easily reconciles with the trade literature
Given our result we investigate whether the premium is simply an artifact of innate
characteristics of firms that choose to invest abroad For example Helpman et al (2004) present
a simple model with heterogeneous productivity endowments The firms that receive the highest
productivity endowments are the ones capable of paying the fixed costs to establish a foreign
subsidiary To the extent productivity advantages create excess value one would expect the type
of firms that choose to invest abroad would be valued at a premium even in the absence of their
foreign investments After including firm fixed effects to control for time invariant firm
characteristics (eg innate productivity advantages) we continue to find economically and
statistically significant evidence of a premium to foreign operations
To further account for the endogenous nature of FDI we estimate a dynamic panel data
model using the generalized method of moments (GMM) estimator developed by Arellano and
Bond (1991) Arellano and Bover (1995) and Blundell and Bond (1998) This estimator allows
us to simultaneously account for the potential endogeneity of FDI industrial diversification and
firm value This method also yields a significant premium which is of similar magnitude and
significance to that estimated using fixed effects
We also perform a series of additional robustness tests First we examine whether the
measured excess value premium is driven by operations in countries with large control premiums
(Dyck and Zingales 2004) In countries with large control premiums using stock price in the
calculation of total firm value could underestimate the true value of the benchmark firms and
thereby induce an excess value premium We fail to find evidence that the excess value premium
is driven by such countries
6
Second we examine whether access to a low cost of capital is a meaningful source of the
US MNC advantage We incorporate foreign benchmarks in our method of estimating firm
excess value as it allows us to incorporate attributes of operating in those foreign countries ndash a
key attribute being the cost of capital Relative to the local benchmarks in foreign countries US
MNCs have the ability to obtain funds outside the local operating environments either by
borrowing in the US market or transferring capital internally When competing against firms in
shallow capital markets with high costs of capital MNCs may have a competitive advantage
over local foreign companies due to these alternative sources of capital However investors and
lenders are likely to expect higher rates of return from the additional risk associated with
operating in these environments making it unclear whether a multinational network will enhance
value in countries with a high cost of capital In addition foreign firms have some ability to
access international capital markets which mitigates the advantages to US firms To examine
whether the cost of capital plays a role in our finding of a premium we include a proxy for the
extent of operations in countries with higher costs of capital Our results are unaffected by the
inclusion of the proxy
Finally we control for several corporate governance proxies which Hoechle et al (2012)
find to be correlated with excess values (and industrial diversification) While magnitude of the
premium is virtually unaffected by including these proxies the significance level does decline
due to the reduced sample size
Our study contributes to the literature on multinational firms Our new method of estimating
excess values for multinational firms provides strong evidence of a statistically and economically
significant premium associated with increased international operations The premium is robust to
a variety of specifications and controls designed to evaluate alternative explanations In addition
7
this result easily reconciles with the international trade literature that establishes a link between
greater productivity and international operations though we do not find results supporting this as
the only source of the premium As segment disclosures improve and as new data sources
become available on multinational activity (eg Bureau Van Dijk) our empirical approach can
be used to investigate the extent to which the home country of the parent might influence the
extent with which foreign operations affect firm value (ie by examining non-US based
MNCs)7 Future studies could also examine the underlying forces affecting the excess value
premiums across countries
We also contribute to the understanding of international accounting standards and their
comparability Many studies seek to compare firms using different accounting standards These
comparisons generally rely on information generated by a particular accounting regime By
comparing commonly used metrics (total assets net income and sales) reported for the same
firm and year across different regimes we provide insights as to the relative comparability across
standards In particular we find that sales is reported significantly more consistently across
accounting regimes than the other metrics examined This finding should aid researchers in
reducing measurement error when comparing accounting information in multinational settings
The paper proceeds as follows Section 2 discusses the motivation for finding a premium or
discount to multinational operations Section 3 describes our data and how the excess value of a
firm is measured A discussion of our independent variables and primary findings is presented in
Section 4 with additional robustness tests provided in Section 5 Finally Section 6 concludes
7 As MNCs are ultimately bound by the tax regulatory and legal frameworks of their home country the valuation effect of foreign operations that we document for US-based MNCs may be different in a sample of non-US based MNCs with the same country-industry footprint Theorists refer to these as lsquolocational advantagesrsquo enjoyed by all multinational firms of a given nationality and obtained irrespective of skills or capabilities unique to a particular firm (Yamin 1991) There is also evidence of locational advantages in the market for corporate control (Huizinga and Voget 2009)
8
2 Premium versus discount
In a frictionless world where managers maximize firm value and markets are efficient there
should be no discount or premium to operating in foreign countries However there are a number
of forces that may cause actual firm value to deviate from the implied firm value
On one hand MNCs may have comparative advantages that generate premiums (ie positive
excess values) relative to a similar footprint of stand-alone firms Having operations in a variety
of locations that access different supplies of inputs and customers allows the firm to take
advantage of changing market conditions by shifting operations or products to maximize firm
value more so than a firm operating in a single country Similarly having access to a variety of
institutional settings such as different tax codes legal regimes and financial markets can provide
MNCs with options beyond those available to firms located in a single country
Another possible factor associated with a premium to multinational operations is the cost of
capital For operations located in countries with shallow capital markets or weaker creditor
rights operating within a conglomerate can provide the benefit of a lower cost of capital for
investments and expansion for at least two reasons First as our MNCs are domiciled in the US
their foreign segments have greater access to US capital markets relative to local competitors in
the foreign jurisdiction Second cash rich segments with few investment opportunities can
finance investments in cash poor segments with positive net present value projects (eg Myers
and Majluf 1984) Consequently diversified firms should be less liquidity constrained and better
able to shift resources to the most valuable investment opportunities Research has found that
internal financing is used more often when diversified firms have operations in countries with
more costly external financing (Desai Foley and Hines 2004) Multinational firms may also be
relatively more protected than single-country firms when negative shocks hit external capital
9
markets (Foley Desai and Forbes 2008) In a recent study Matvos and Seru (2011) find that
industrially diversified firms perform better than non-diversified firms when external capital
markets are impaired
While these arguments suggest that MNCs enjoy certain advantages when the foreign cost of
capital is high these benefits should be reduced by the additional risk associated with operating a
business in a high cost of capital environment For example Verizon is a multinational firm
operating in a number of countries One of the higher cost of capital locations in which they
operated was Venezuela While the multinational structure of Verizon may have mitigated some
of the risks associated with operating in a higher cost of capital location the firms was unable to
eliminate all of these risks In fact in 2007 Verizonrsquos operations in that country were
nationalized resulting in a net extraordinary loss of $131 million that year8 In other words
MNC investors are likely to expect higher rates of return to compensate for higher risk which
likely offsets some (if not all) of the potential excess value premium
On the other hand multinational firms may incur a discount (ie negative excess values)
relative to a similar footprint of stand-alone firms Potential reasons for a discount largely rely on
the existence of agency costs Multinational firms tend to be larger more complex and less
transparent The combination of complexity and opacity inherent in MNCs reduces a firmrsquos
reliance on external capital as the cost of such capital increases due to agency costs (Desai et al
2004) As a result of the reduced reliance on external funds MNCs face a reduced level of
monitoring As noted by Jensen (1986) managers have incentives to increase firm size beyond
that which is optimal with overinvestment or misallocation of funds to pet projects If this lack of
monitoring is in combination with available internal capital empire building becomes easier
(Hope and Thomas 2008) Furthermore the current segment reporting standards under US 8 See the 10-K of Verizon Communications Inc dated December 31 2007 (footnote 2)
10
GAAP only require highly aggregated disclosures of foreign operations and allow for
considerable managerial discretion As a result operating in multiple countries makes it easier
for management to hide poor performance ndash either their own or that of a division ndash such that low
quality managers are more likely to be retained and overcompensated and poorly performing
divisions retained longer than optimal Other reasons one might expect a discount to be applied
to multinational firms include higher coordination costs (eg coordinating across different
cultures and languages) as well as additional risks These risks include exposure to multiple
political regimes legal regimes economic regulations and currency fluctuations
Past studies investigate whether foreign operations of US firms enhance or reduce firm
value However all of these studies benchmark the value of the foreign operations of an MNC to
US domestic firms For instance Denis et al (2002) determine the implied value of a US
MNC using domestic (US) single-segment firms in the same industry as a benchmark Studies
using Tobinrsquos Q to investigate valuation effects of MNCs also rely on domestic benchmarks
(eg Morck and Yeung 1991 Morck and Yeung 2001 Gande Schenzler and Senbet 2009)
However using a US domestic firm as a benchmark for the value of foreign operations
implicitly makes two assumptions First this method assumes that the risks and expected growth
rates of foreign operations are equivalent to those of domestic operations This assumption is
inconsistent with the findings of Hail and Leuz (2009) which provides evidence that the cost of
capital varies substantially across countries Second this method assumes that there is excess
domestic capacity to allow for expansion with profitability similar to current domestic
operations We relax these assumptions by measuring the imputed value of a foreign component
of a US firm as if that component operated autonomously within that foreign country (rather
than in the US) Specifically our multiples allow the cost of capital and expected growth rates
11
to vary across not just industry but also country Furthermore our counterfactual does not
require the assumption that operations be relocated mitigating the production and sales capacity
concerns
3 Measuring excess value
31 Description of excess value
Figure 1 shows the average percent of foreign sales (based on segment disclosures under US
GAAP) across firms through time and illustrates that firms are continuing to expand
internationally9 This expansion highlights the importance of understanding whether the decision
to operate internationally is on average a value enhancing corporate strategy We contribute to
this understanding by evaluating whether the firm as a whole is worth more or less than its
imputed value (ie the firm is valued in excess of the sum of its individual components)
We measure the excess value of a firm as the logarithm of the ratio of actual firm value to
imputed value based on the method used in Berger and Ofek (1995) The imputed value is the
hypothetical value of the MNC under the assumption that its country-industry components
operate as independent entities A key innovation of our study is an alternative approach to
imputing the value of the foreign operations of an MNC Components of MNCs operating in a
given industry and country are imputed using the value of single-segment firms operating in the
same industry and country Thus we ensure the discount rates and expected growth rates are
applicable to each given industry and location in estimating the implied values of the MNC
segments
9 The discontinuity in industrial diversification (the percent of sales outside the primary industry) between 1997 and 1998 is the result of a change in segment reporting rules
12
The actual firm values are observable for all firms in our sample The hypothetical firm
values for multi-segment firms are imputed using market value to sales ratios herein referred to
as lsquomultiplesrsquo of the median single-segment firm operating in the same country-industry Thus
these single country-industry firms serve as benchmarks for an MNCrsquos operations within that
same country-industry The imputed value of the country-industry component can be viewed as
an estimate of what that component would be worth if it operated independently The sum of the
imputed values across all country-industry components represents the hypothetical value that the
multi-segment firm would be worth if it were broken apart
32 Data
Four primary data sources play a distinct role in carrying out our analysis i) Bureau of
Economic Analysis (BEA) data ii) Compustat Segment data iii) Worldscope data and iv)
Compustat Fundamentals Annual data Broadly speaking BEA data provide information about
the diversified nature of MNCs Compustat Segment data provide information about the
industrial diversification of US domiciled firms Worldscope data are used to compute
multiples for single-segment foreign firms that serve as benchmarks for the foreign operations of
MNCs (ie foreign benchmarks) and Compustat Fundamentals Annual data are used to
compute multiples for single-segment domestic firms that serve as benchmarks for the domestic
operations of multinational firms We also use the latter data source to construct our primary
control variables
13
Key to the execution of our study is our access to BEA data Federal law requires US-
domiciled MNCs to report certain financial and operating data to the BEA10 With regard to
observing the country-industry operations of MNCs use of the BEA data overcomes two
important limitations of Compustat Segment data First Compustat does not consistently identify
the specific location or scale of MNCsrsquo foreign operations11 Second Compustat does not
identify the industry activities in each country of operation This latter point is particularly
limiting for MNCs because domestic industry activity need not mirror foreign industry activity
An MNC generating an equal proportion of sales in two industries in the domestic market need
not generate industry sales in equal proportions in every foreign market12 The lack of detail and
consistency in Compustat Segment data arises because segment reporting is highly aggregated
and firms exercise substantial discretion in defining segments under Accounting Standards
Codification 280 (ASC 280) (eg Villalonga 2004a and Bens Berger and Monahan 2011)
The BEA defines an MNC as the combination of a single US entity called the US parent
and at least one foreign affiliate ndash that is these firms have a physical presence outside the US
due to FDI Since 1982 MNCs have completed a mandatory and confidential lsquoSurvey of US
Direct Investment Abroadrsquo for the domestic operation and the operations of each foreign
affiliate defined as a foreign entity in which the US parent holds at least a 10 percent equity
interest (directly or indirectly) The data are reported in US dollars on a fiscal year basis and in
10 BEA surveys are conducted pursuant to the International Investment and Trade in Services Survey Act (PL 94-472 90 Stat 2059 22 USC 3101-3108) See httpwwwbeagovsurveysdiasurvhtm and Mataloni (2003) for more detailed information on the BEA data 11 Exhibit 21 disclosures of material subsidiaries and their locations in SEC 10-K filings could potentially help overcome the inability to observe the location of the firmrsquos foreign operations from segment disclosures However at least three limitations remain (1) for industrially diversified multinational firms the researcher would be required to make an assumption about the proportion of industrial diversification in each country (2) Exhibit 21 provides no information about the scale of activity in each jurisdiction and (3) managerial discretion is used to determine a lsquomaterialrsquo subsidiary under Section 601 of SEC Regulation S-K 12 In fact the expectation that a multinational firm would exhibit different industry membership in different countries is consistent with the theory of vertical foreign direct investment where firms separate their various value chain activities and locate them in the most favorable country (see eg Hanson Mataloni and Slaughter 2005)
14
accordance with US Generally Accepted Accounting Principles (US GAAP) For each year
we observe the sales industry composition and location of not only the parent but also each
affiliate13
The procedures we use to construct our sample are similar to those used by Berger and Ofek
(1995) and Denis et al (2002) Using firm-years appearing in Compustat Fundamentals Annual
and Compustat Segment data we obtain a sample of firms domiciled in the US with total sales
exceeding $20 million We exclude firms in the financial service (SIC codes 6000 to 6999) and
utility (SIC codes 4000 to 4999) industries and eliminate firm-years when the sum of segment
sales (Compustat geographic or Compustat business) is not within 1 of total firm sales for that
year We restrict our sample period to include fiscal years beginning after December 15 1997 as
ASC 280 Segment Reporting substantially altered the definition of a reporting segment under
US GAAP This restriction ensures that Compustat Segment data reflects the use of a consistent
accounting standard Finally we require that our MNCs appear in both Compustat and BEA
data The requirement that our sample firms appear in the BEA database ensures that the firms
have observable international operations In sum these procedures result in 1166 multinational
firms incorporated in the US with 4950 observations between 1998 and 2008 that meet all of
our data requirements
33 Measuring excess value
The dependent variable in our study is excess firm value (Excess Value) defined as the
logarithm of the ratio of actual firm value to imputed firm value (Berger and Ofek 1995)
13 We obtain sales industry membership and country location for affiliates from one of two sources When affiliates exceed the BEA-determined reporting thresholds (ie if their assets sales or net income (loss) exceed $7 million in 1999 $30 million in 2000 through 2003 $10 million in 2004 and $40 million in 2005 through 2008) they must file their own detailed survey When affiliates fall below the thresholds we obtain our data from an attachment to the parent survey
15
Appendix A provides the definition of this and all other variables used in our analysis We
observe actual firm value equal to a firmrsquos market value of equity plus book value of liabilities
(ie book value of total assets minus book value of total equity) using Compustat Fundamentals
Annual data We calculate imputed firm value as the sum of the imputed values of a firmrsquos
operations in each country-industry Our method for imputing the value of the separate
components of a firm can best be described in three steps First we obtain total sales generated
by a firm for each country-industry in which it operates14 Second we obtain multiples (market
value to sales ratios) for benchmark firms operating in those same country-industries Third we
multiply a firmrsquos country-industry sales by the applicable median country-industry multiple to
obtain the imputed market value for each country-industry operation We perform each step
annually
Conceptually imputed values can be based on assets (ie Tobinrsquos Q) net income or sales
multiples We restrict ourselves to sales multiples for two reasons First using value to sales
ratios maintains consistency with the prior research on the excess value implications of
multinational operations (eg Denis et al 2002) Second as our method uses accounting data
for foreign companies to compute country-industry multiples the accounting numbers we use
need to be consistently measured across firms using different accounting standards We find that
sales data are measured most consistently
To assess the comparability of sales relative to either net income or assets across various
accounting standards we examine all firms listed on Worldscope as changing to or from US
14 Specifically we allocate total sales from Compustat to each component based on the proportion of BEA sales (after eliminating intercompany sales) occurring in each country-industry This step ensures that our total firm sales do not deviate from a firmrsquos total sales as reported in the 10-K
16
GAAP between 2005 and 201115 We obtain accounting data for the year of the change and the
prior year where electronically available in English We are able to obtain data for 66 firms
changing standards as detailed in Table 1 The distribution of years for which accounting data
are available under two different accounting standards for the same fiscal year is shown in Panel
A The years shown are the year prior to the change as these accounting numbers were provided
under the original standard and then subsequently restated in the following year under the new
standard for comparative purposes The majority of the shifts were to or from IFRS (71)
though seven other accounting standards are noted (Panel B) We examine the percent that the
non-US GAAP reported number differs from the US GAAP reported number in Panel C to
assess the comparability of these three summary accounting numbers across various accounting
standards Examining both the full sample as well as the subset that overlaps our study we find
that sales are more consistently measured than net income or assets
The sum of the imputed values across the country-industry components of an MNC is an
estimate of the value of a portfolio of unrelated businesses that mirror the related businesses of
the firm Consequently a comparison of the actual firm value with the imputed firm value is a
measure of how a multinational network affects firm value We provide descriptive statistics for
Excess Value for our sample of 4950 MNC firm-years in Table 2 Panel A This table also
includes univariate comparisons of the values between the MNC firms and the domestic single-
segment and foreign single-segment benchmark firms The Excess Value measure is significantly
higher for MNCs suggesting a premium in firm value for multinational firms relative to the
benchmark firms in total as well as the domestic and foreign subgroups (p-values for tests of
15 Due to the hand collection efforts required and the limitation on electronic filings we began our search with changes documented in 2005 which generally require filings from 2004
17
means and medians are lt 001)16 These results provide some initial evidence that operating in
multiple countries is positively associated with firm value
To estimate country-industry multiples we rely on Worldscope financial data on foreign
firms and Compustat Fundamentals Annual for domestic firms We restrict the firms to include
only those with at least 90 of sales income and assets inside the country of domicile (ie
those that do not report significant multinational activity) and that operate in a single industry
We refer to these firms as benchmark firms (either foreign or domestic depending on the country
of domicile) these firms do not appear in any of our regressions We report the number of
benchmark firms during our sample period in Table 2 Panel A The industry criterion is based on
the two-digit SIC code17 For every country that has at least five firms in the respective industry
and year we use the median ratio of market value to sales18 This ratio is the country-industry
multiple ndash an input required to compute imputed values
To determine the country-industry composition of MNCs we rely on BEA data In Table 3
we report the aggregate number of foreign affiliates and total sales by country as provided by
the BEA data for our sample of 4950 MNCs The specific countries tabulated are those that
represent at least 02 percent of total firm sales (pooled across all years) in our sample The top
five foreign countries in which MNCs generate sales are Canada United Kingdom Germany
France and Japan Table 3 also provides a comparison of the number of foreign benchmark
firms available in Worldscope in the specific countries in which our sample of MNCs generate
16 Similar to Berger and Ofek (1995) the median values differ slightly from zero for the benchmark single-segment firms due to the truncation of extreme excess values 17 BEA reporting constrains us to the two-digit SIC code level 18 When five firms are not available for a given country and industry we follow Berger and Ofek (1995) and move to a more general industry definition (eg one-digit SIC code) This procedure results in a mean (median) 630 (757) of MNC sales matched at the two-digit level
18
the majority of their sales Country coverage in Worldscope is not available for countries
representing 004 percent of total firm sales (per BEA)
4 Empirical results
41 Independent variables
Recall that our objective is to examine the overall relation between excess firm value and
multinational operations Our proxy for the extent of multinational operations is the percentage
of total firm sales made by foreign operations (Foreign Sales) By this measure the extent of
multinational activity in our sample of MNCs is non-trivial ndash Table 2 Panel B shows that
approximately 24 percent of sales are generated by foreign operations on average Our first
analysis estimates ordinary least square regressions of Excess Value on Foreign Sales and
control variables (discussed below)
We include variables in our regression to control for other potential determinants of excess
value These are the percentage of sales made by the firm outside its primary industry (Industry
Other) to control for any relation between industrial diversification and excess value as in Berger
and Ofek (1995) For this purpose we obtain industry sales using Compustat Segment data19 A
firmrsquos primary industry is the industry in which the firm generates the majority of its sales and
we determine industry sales at the business segment level We set Industry Other equal to zero
for firms that operate in a single business segment
Consistent with prior research we also include controls for firm size (Log Size) the ratio of
long term debt to total market value (Debt Firm Value) the ratio of capital expenditures to total
sales (CAPX Sales) the ratio of earnings before interest and taxes to total sales (EBIT Sales)
the ratio of research and development expenses to total sales (RampD Sales) and the ratio of 19 Our results are unaffected by using BEA data to measure Industry Other
19
advertising expenses to total sales (Ad Sales) Table 2 Panel B provides the distribution of the
control variables All independent variables are Winsorized at 1 In our OLS regressions we
use heteroskedasticity and autocorrelation consistent robust standard errors with clustering at the
firm level
42 Primary regression analysis
In this section we examine the relation between firm excess value and the amount of foreign
activity at US MNCs Table 4 contains the results of regressions of excess value on Foreign
Sales for multiple specifications In column (1) Excess Value is regressed on both the percent of
foreign sales (Foreign Sales) and the percent of sales outside the firmrsquos primary industry
(Industry Other) We add the control variables to the regression in column (2) and year
indicators in column (3) In all three specifications the coefficient on the percent of foreign sales
remains significantly positive (with a p-value lt 001) For a sense of economic magnitude in
column (3) a one standard deviation (197) increase in the percent of foreign sales (Foreign
Sales) is associated with a 41 (equal to (e0349-1) times 0197) increase in firm excess value The
effect is larger for the other two columns ndash 82 increase in firm excess value in column (1) and
46 in column (2)20
Our finding of a premium reconciles with the international trade literature which finds that
firms engaging in international trade are more productive than those that do not (see Helpman
2006 and Syverson 2011 for reviews) Due to data limitations it is difficult to accurately
measure TFP at the firm level especially for firms which operate in multiple industries andor
20 Using the alternative strategy that employs US domiciled single-segment firms as the benchmark for the imputed value of the foreign operations Denis et al (2002) find that greater foreign activity results in a discount to firm value Using the Denis et al (2002) method to calculate excess value in our sample we obtain a significantly negative regression coefficient on Foreign Sales using the models in Table 4 columns (2) and (3) (the coefficient is not significant using the model in column (1) results untabulated)
20
countries As an alternative method to evaluate whether our finding of a premium is simply the
result of more productive firms also having more multinational operations we investigate
whether the premium is simply an artifact of innate characteristics of firms that choose to invest
abroad For example Helpman et al (2004) present a simple model with heterogeneous
productivity endowments The firms that receive the highest productivity endowments are the
ones capable of paying the fixed costs to establish a foreign subsidiary
To the extent that productivity advantages create excess value one would expect the type of
firms that choose to invest more abroad would be valued at a premium even in the absence of
their foreign investments To control for time invariant firm characteristics (eg innate
productivity advantages) and hence a potential source of endogeneity we add firm-level fixed
effects in column (4) of Table 4 We continue to find that the extent of foreign operations
(Foreign Sales) is positively associated with Excess Value Across all specifications the sign
on all significant control variables is consistent with prior research (eg Berger and Ofek 1995
and Denis et al 2002)
The finding of a premium makes a significant contribution to the relatively sparse literature
on the value effects of multinational activity Our measure of imputed value expands upon the
methods used by Berger and Ofek (1995) and Denis et al (2002) but allows us to answer a
different question than Denis et al (2002) Holding constant the extent of industrial
diversification we ask whether MNCs are more valuable than a portfolio of benchmark firms of
a similar geographic footprint In contrast Denis et al (2002) ask whether holding constant the
extent of industrial diversification MNCs are more valuable than a portfolio of domestic (US)
benchmark firms We believe that asking whether an MNCrsquos foreign (domestic) operations are
21
more valuable than those of a foreign (domestic) firm is a more appropriate way to assess the
value implications of a multinational network
5 Robustness
51 Endogeneity related to dynamic relations
Prior research argues that the relation between industrial diversification and excess firm
value could be endogenous through dynamic relationships based on observing past outcomes
Managers of firms likely choose to enter or exit industries or geographic regions based upon
their previous performance For example Campa and Kedia (2002) find that firms are more
likely to enter new industries when prospects in their current lines of business are deteriorating
Similar dynamics could also drive a relation between excess value and FDI We assess the
robustness of the previous results by estimating a dynamic panel data model which allows us to
control for dynamic endogeneity in addition to firm level unobserved heterogeneity We employ
the GMM estimator for dynamic panel models developed by Arellano and Bond (1991) Arellano
and Bover (1995) and Blundell and Bond (1998) Our results from this analysis confirm our
previous findings that firms with a larger degree of foreign operations have positive excess
values
Dynamic panel data models are generalizations of the traditional fixed effects model where
lags of the dependent variable are added to the right hand side of the equation These additional
lags control for the impact a firmrsquos past performance has on its current performance through the
channels discussed above The GMM estimator for dynamic panel models developed by
Arellano and Bond (1991) starts by differencing the data at the firm level in neighboring time
periods to eliminate any firm specific time invariant unobserved heterogeneity that may be
22
present Eliminating the firm specific fixed effect by differencing the data with lags of the
dependent variable on the right hand side introduces correlation between the new error term and
the differenced explanatory variables This problem is solved by applying the standard GMM
estimator using lags of both the original explanatory variables and excess value as instruments
We note that the dynamic panel data framework allows us to treat both international and
industrial diversification (as well as the other firm-specific control variables) as endogenous
Following Hoechle et al (2012) we also allow contemporaneous values of these variables to be
treated as endogenous
Lagged values of the explanatory variables are valid instruments in a dynamic panel data
model when the model is dynamically complete (eg Wooldridge 2010) A dynamic panel data
model is dynamically complete conditional on the unobserved effect if enough lags of both the
explanatory and dependent variable are included to make the resulting error term uncorrelated
with all the right hand side variables Under this assumption any lags for the explanatory
variables beyond this can be used within the GMM estimator as instruments Like Hoechle et al
(2012) we add two years of lags of a firmrsquos excess value to the right hand side assuming that
this is enough lags to control for dynamic feedback effects We then use past values of all
variables beyond the second lag up to a total of seven years to act as instruments
The results of the dynamic panel data estimator are reported in Table 5 These results confirm
the previous finding of a positive relation between the degree of international operations and
excess value We note that the size and significance level of the coefficient on Foreign Sales
diversification are robust to changes in the number of lags used as instruments The Arellano and
Bond (1991) estimator includes testing diagnostics for correct specification of the model If the
model is correctly specified differences in the residuals from the levels equation should be
23
serially uncorrelated at lag one but correlated at lag two The p-values for tests of serial
correlation of the residuals at lags 1 and 2 are reported in Table 5 and these indicate that the
model is not rejected We also report in Table 5 the Hansen test for overidentification which is a
test of the null hypothesis that the instruments are valid The p-value for this test is 030
indicating that we fail to reject this null hypothesis Finally we report another test for exogeneity
known as the difference-in-Hansen test which tests the null hypothesis that the instruments in
the original equation are valid We fail to reject this null hypothesis as well
52 Identifying value effects of multinational operations via cross-border acquisitions
Another alternative to investigating a causal link between foreign direct investment and firm
value is an event study of foreign acquisitions21 Acquired foreign targets represent only a small
fraction of MNCsrsquo worldwide sales in the year of the acquisition ndash less than 1 percent in our
sample Additionally cross-border acquisitions are not the primary source of international
expansion for the average firm We find that foreign acquisitions account for between 18 percent
and 55 percent of the year-over-year change in foreign sales with the remainder of the growth
coming from either newly established entities (ie greenfield investments) or growth in existing
operations22 These results suggest that acquisitions are not a material source of growth for the
21 Doukas and Travlos (1988) find positive returns when the acquirer is not operating in the target firmrsquos country and insignificant returns when the acquirer is operating in the target firmrsquos country or expanding internationally for the first time Morck and Yeung (1991) find marginal evidence of positive returns for firms with intangible assets Finally Doukas (1995) finds positive returns only for firms with average Tobinrsquos q ratios greater than one More recent work also using an event study methodology provides mixed evidence on valuation effects of global diversification through acquisitions Dos Santos Errunza and Miller (2008) find insignificant returns for firms with already established foreign operations firms establishing initial foreign operations and firms acquiring targets in related industries Doukas and Kan (2006) and Moeller and Schlingemann (2005) find reductions to shareholder value while Francis Hasan and Sun (2008) find positive returns for acquirers of targets in segmented financial markets 22 New foreign affiliates to an MNC group each year report on the BEA survey whether they entered the group as an acquisition or a newly established entity (ie greenfield investment) Affiliates under the lowest reporting threshold do not report this information resulting in our inability to precisely identify the source of 33 percent of the increase in firm sales as either acquisition or greenfield
24
typical US MNC suggesting that an event study would lack power and generalizability For
these reasons we do not implement an event study
53 Control premium
A control premium or private benefits of control occurs when ldquosome value whatever the
source is not shared among all the shareholders in proportion of the shares owned but it is
enjoyed exclusively by the party in controlrdquo (Dyck and Zingales 2004 p 541) Using stock
prices of traded shares held by minority shareholders in determining the market value to sales
ratios of our benchmark firms could underestimate the true value of our benchmark firms if those
firms face large control premiums This in turn could induce an excess value premium because
the imputed value of these country-industry segments would be artificially low The existence of
such benefits is more prevalent in some countries than others and could affect the value of an
MNCrsquos operations in such domains We use the country-level control premiums from Dyck and
Zingales (2004) and using each firmrsquos country sales as a percent of total firm sales we obtain a
weighted average control premium that each MNC faces (Control Premium) For countries not
included in the Dyck and Zingales (2004) analysis we set the control premium equal to zero23 In
Table 6 we show that Foreign Sales remains positive and highly significant (p-value lt 001)
when we include the proxy for control premium As a result it does not appear that the excess
value premium is driven by MNCs operating in countries where majority shareholders are able to
extract large control premiums
23 Results are robust to replacing missing values with the mean control premium of the respective GDP quartile
25
54 Cost of capital
As discussed in Section 2 when countries have shallow capital markets a multinational
network may provide the benefit of a lower cost of capital for investments through an internal
capital market as well as through better access to the US capital market Prior research
establishes that there is substantial variation in the cost of capital across countries (Erb Harvey
and Viskanta 1996 and Hail and Leuz 2009) To determine whether the documented premium
is due to cost of capital advantages we include a control variable to capture the difference
between the MNCrsquos cost of capital and the cost of capital faced by its competitors in the global
markets in which it operates
We compute our Cost of Capital proxy in two steps First we obtain the country-year credit
rating published by Institutional Investor24 The rating ranges from 0 to 100 where higher values
imply that a country has a higher default risk Country-level credit risk is a reasonable predictor
of expected equity market returns and volatility (Erb et al 1996) and exhibits a highly
significant correlation with international accounting-based estimates of imputed cost of capital
(Hail and Leuz 2009) Second using each firmrsquos country sales as a percent of total firm sales
we obtain a weighted average credit risk rating across the countries in which each firm operates
A higher value implies an MNC operates in countries with higher cost of capital In Table 7 we
do not find a result consistent with MNCs gaining an advantage from their access to low cost
capitalndash Cost of Capital is not significantly associated with Excess Value25
24 We thank Cam Harvey for providing these data 25 Results are consistent using cost of capital as defined in Erb et al 1996
26
55 Corporate governance
In light of the recent finding that corporate governance mitigates the diversification discount
we examine whether the premium to multinational activity is sensitive to controls for corporate
governance Hoechle et al (2012) find that the magnitude of the industrial diversification
discount is decreased and in several specifications no longer significant in the presence of
governance proxies In the first column of Table 8 we include the five governance proxies that
are consistently significantly associated with excess value in Hoechle et al (2012) ndash the percent
of CEO ownership (CEO Ownership and CEO Ownership squared) the percent of institutional
ownership (Institution Ownership) whether the firm has a powerful or influential CEO
(Powerful CEO) and the governance index from Gompers Ishii and Metrick (2003)
(Governance Index)
As documented in Table 8 including these control variables reduces our sample size by
almost 50 (to 2550 observations) and tends to exclude the smaller firms from the analysis
None of the governance proxies are significant Relative to column (4) of Table 4 the coefficient
on Foreign Sales is similar in magnitude but is no longer statistically significant at
conventional levels To investigate whether the decreased significance is due to the smaller
sample or the inclusion of the governance variables we re-estimate the regression for the same
2550 observations after removing the governance controls The coefficient and t-statistic are
virtually identical suggesting that the loss of significance is due to the reduced sample and not
the governance controls We do not find evidence to suggest that variation in corporate
governance practices drives variation in excess value for US MNCs
27
6 Conclusion
Using a new method to quantify the valuation effects of foreign direct investment by US
domiciled firms we find robust evidence of a premium in excess value associated with the
degree of multinational operations Our method builds on that used in previous studies
examining firm excess value (eg Berger and Ofek 1995 and Denis et al 2002) by
incorporating the location in addition to the industry of each corporate affiliate This allows
important factors such as growth and cost of capital to vary across countries as well as industries
Examining a sample of multinational firms we find that a one-standard deviation (197)
increase in foreign operations is associated with an increase of between 41 and 82 in excess
value minus both a statistically and economically significant finding
This result complements the finding of the international trade literature that more productive
firms are more likely to operate internationally However the premium remains after including
firm fixed effects to control for persistent firm traits (eg productivity)
The finding of a premium stands up to a succession of robustness tests designed to evaluate
whether the result arises from alternative methods or explanations First we estimate a dynamic
panel data model using the generalized method of moments (GMM) estimator to simultaneously
account for the potential endogeneity of FDI industrial diversification and firm value Second
we include a control variable to proxy for whether the firm operates in countries with larger
control premiums which could result in understated multiples and mechanically induce a
premium Third we include a control variable to measure the cost of capital across the countries
in which an MNC operates to evaluate whether a relatively low cost of capital borne by
multinational firms is a meaningful source of the premium Fourth we control for several
corporate governance proxies which have been found to be correlated with excess values (and
28
industrial diversification) We fail to find evidence supporting any of these alternative
explanations ndash the premium in excess value arising at firms with greater multinational operations
remains significant
Overall our new method of estimating excess values for multinational firms provides strong
evidence of a statistically and economically significant premium associated with increased
international operations The premium is robust to a host of specifications and controls designed
to evaluate alternative explanations We leave to future research the questions of whether the
premium to US multinational firms applies more broadly to other countries of domicile and
what factors influence the premium
29
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of Accounting Research 46 (June) 591-626 Huizinga H P and J Voget 2009 International taxation and the direction and volume of cross-
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Economic Review Papers and Proceedings 76 323-329
31
Laeven L and R Levine 2007 Is there a diversification discount in financial conglomerates
Journal of Financial Economics 85 331-367 Lang L and R Stulz 1994 Tobinrsquos Q corporate diversification and firm performance Journal
of Political Economy 102 1248-1280 Mataloni R 2003 US multinational companies Operations in 2001 Survey of Current
Business 85-105 Matvos G and A Seru 2011 The dynamic tradeoff between corporate socialism and financial
market dislocation Estimates from internal capital markets Working paper University of Chicago Booth School of Business
Moeller SB and F P Schlingemann 2005 Global diversification and bidder gains A
comparison between cross-border and domestic acquisitions Journal of Banking and Finance 29 533-564
Morck R and B Yeung 1991 Why investors value multinationality Journal of Business 64
165-186
Morck R and B Yeung 2001 Why firms diversify Internatization vs agency behavior In Intangibles B Lev (ed) Oxford University Press pp 269-302
Myers S C and N S Majluf 1984 Corporate financing and investment decisions when firms have information that investors do not have Journal of Financial Economics 13 187-221
Schmid M and I Walter 2009 Do financial conglomerates create or destroy economic value
Journal of Financial Intermediation 18 193-216 Syverson C 2011 What determines productivity Journal of Economic Literature 49(2) 326-
365 Villalonga B 2004a Diversification discount or premium New evidence from the Business
Information Tracking Series Journal of Finance 59 479-506 Villalonga B 2004b Does diversification cause the diversification discount Financial
Management 33 5-27 Wooldridge JM 2010 Econometric Analysis of Cross Section and Panel Data (Second
Edition) Cambridge MA MIT Press Yamin M 1991 A reassessment of Hymerrsquos contribution to the theory of the transnational
corporation C Pitelis and R Sugden (eds) The Nature of the Transnational Firm London Routledge
32
Appendix A Variable Definitions
We obtain information regarding firmsrsquo operations by country and industry from the Bureau of Economic Analysis (BEA) unless otherwise noted Acronyms used below reflect Compustat mnemonics and thus indicate when we obtain data from the Compustat North America Fundamentals Annual database All independent variables are winsorized at 1
Dependent variable
Excess Value = The log of the ratio of actual firm value to imputed firm value Actual firm value is equal to market capitalization (MVE) + book value of total assets (AT) ndash book value of shareholdersrsquo equity (SEQ) Imputed firm value is calculated as the sum of the imputed values of a firmrsquos operations in each country-industry The imputed value for each country-industry is equal to sales generated in that country-industry times a multiple equal to the ratio of a benchmark firmrsquos actual value to total sales A benchmark firm is defined as a median single-segment firm operating in the same country-industry All multiples require at least five firms listed in the respective country-industry on Compustat (domestic) or Worldscope (foreign) with less than 10 of sales income and assets outside the country of domicile Similar to Berger and Ofek (1995) we eliminate extreme values where the ratio of actual to imputed firm value exceeds 400 or is less than 025
Independent variables
Foreign Sales = Sales made by the foreign operations of US MNCs as a percent of total firm sales
Industry Other = Sales outside a firmrsquos primary industry as a percent of total sales (SALE) Equal to zero for firms operating in a single business segment
Log Size = Log of total sales (SALE)
Debt Firm Value = Ratio of long-term debt (DLTT) to total market value (market capitalization (MVE) + book value of total assets (AT) ndash book value of shareholdersrsquo equity (SEQ))
CAPX Sales = Ratio of capital expenditures (CAPX) to total sales (SALE)
EBIT Sales = Ratio of earnings before interest and taxes (EBIT) to total sales (SALE)
RampD Sales = Ratio of research and development expenses (XRD) to total sales (SALE)
Ad Sales = Ratio of advertising expenses (XAD) to total sales (SALE)
Control Premium = A weighted-average (based on the sales in a given country as a percent of total firm sales) of the country-level control premium from Dyck and Zingales (2004)
Cost of Capital = A weighted-average (based on the sales in a given country as a percent of total firm sales) of the country-level credit rating from Institutional Investor (httpwwwinstitutionalinvestorcom) The country-level rating is available for 160 countries and ranges from 0 to 100 Higher values imply greater credit risk
33
CEO Ownership = Percent of shares owned by the CEO from Execucomp
Institution Ownership = Percent of shares owned by institutional investors from Thomson Reuterrsquos Institutional Holdings database
Powerful CEO = Indicator variable equal to one if the CEO is the only insider on the board of directors and also serves as the chairman and president based on data from RiskMetrics Directors database Equal to zero otherwise
Governance Index = Count measure of takeover defenses used in Gompers Ishii and Metrick (2003) from RiskMetrics Governance database As the governance data are not available for every year we follow Gompers Ishii and Metrick (2003) and assume that the firmsrsquo governance attributes are unchanged until publication of the subsequent series
34
Figure 1 Trends in multinational activity and industrial diversification from 1984-2008 This figure presents a plot of the mean percent of foreign sales and the mean percent of sales outside the firmrsquos primary industry (industrial diversification) from 1984 through 2008 Dotted lines above and below the primary lines represent 95 confidence intervals To compile the plot we include the full population of firms in Compustat
35
Table 1 Analysis of accounting data for firms reporting under multiple accounting standards This table provides a comparison of sales net income and assets for firms that changed accounting standards to or from US GAAP Comparisons are provided for the 66 firms that changed to or from US GAAP and for which we could obtain accounting data electronically and in English under both standards for the same fiscal year (eg the firm presented the accounting numbers originally under one set of standards and then restated the prior year numbers in the subsequent year under the new set of standards) Panel A shows the distribution of firms by year Panel B shows the other accounting standards to which US GAAP is compared Panel C shows the mean and median percent that the other accounting standard differs from US GAAP for the same fiscal year and accounting number Two samples are shown in Panel C The first is the full sample of firms with a change in accounting standards to or from US GAAP for which data was available The second is the subset of these firms that occurred during the sample window used in this study (ie excluding changes post-2008) The percent difference in sales is compared to the percent difference in net income and assets Tests of means are based on matched-pair t-tests Tests of medians are based on Wilcoxon matched-pair signed-rank tests Panel A Accounting standard changes by year
2003
4 6
2004
25 38
2005
8 12
2006
6 9
2007
7 11
2008
5 8
2009
5 8
2010
5 8
2011
1 2
66
Panel B Accounting standards compared to US GAAP
IFRS
47 71
Canadian GAAP
12 18
Israeli GAAP
2 3
Dutch GAAP
1 2
Indian GAAP
1 2
Japanese GAAP
1 2
Norwegian GAAP
1 2
Swiss GAAP
1 2
66
36
(Table 1 continued) Panel C Tests of differences between US GAAP and other accounting standards
Percent different from US GAAP
Comparison to sales (p-values)
N Mean Median
Mean Median
Full Sample (2001-2011)
Sales
66 236 000
Net Income
66 10653 647
005 lt001
Assets
66 1162 126
lt001 lt001
Study Sample (2001-2008)
Sales
55 257 000
Net Income
55 10303 589
011 lt001
Assets
55 1047 099
003 lt001
37
Table 2 Descriptive statistics Panel A reports the mean and medians of Excess Value for the multinational firms and single-segment benchmark firms as well as univariate tests across the subsamples Panel B reports summary statistics for the independent variables for the multinational firms The p-values of the tests of differences in means are based on t-tests The p-values of tests of differences in the medians are for a Wilcoxon rank-sum test All variables are defined in Appendix A Panel A Excess Value by sample Multinational All Benchmark Benchmark Subgroups Sample Firms US Foreign
Value Value Test of Diff (p-value) Value
Test of Diff (p-value) Value
Test of Diff (p-value)
Mean 0067 0006 lt001 -0028 lt001 0014 lt001 Median 0076 0000 lt001 -0025 lt001 0000 lt001 N 4950 33074 5998 27076 Panel B Distribution of independent variables for the multinational firms N Mean 25th 50th 75th StDev Foreign Sales 4950 0238 0074 0190 0363 0197 Industry Other 4950 0212 0000 0166 0414 0215 Log Size 4950 6955 5823 6989 8140 1687 Debt Firm Value 4950 0136 0009 0097 0212 0145 CAPX Sales 4950 0054 0020 0033 0054 0101 EBIT Sales 4950 0065 0031 0077 0121 0138 RampD Sales 4950 0049 0000 0013 0059 0083 Ad Sales 4950 0008 0000 0000 0005 0018
38
Table 3 BEA and Worldscope samples
This table reports details on affiliate sales for significant countries pooled from 1998-2008 Significant countries are defined as those in which the operations of US MNCs represent at least 02 of total firm sales (per the BEA) For significant countries the table compares their representation in terms of MNC affiliate sales in the BEA data to the distribution of firm observations in Worldscope US Foreign Affiliates (BEA) Foreign Firms (Worldscope)
Country
N (1)
Sales (millions)
(2)
Total Sales (3)
N (4)
Sales (million)
(5)
Total Sales (6)
Australia 1344 73305 056 859 255865 210 Belgium 894 52755 040 169 26136 021 Brazil 959 60245 046 1143 683167 560 Canada 2730 536767 407 1181 526750 432 China 922 75907 057 822 276541 227 France 1852 183942 139 540 105447 086 Germany 2182 275577 209 480 103888 085 Hong Kong 867 48100 036 157 34094 028 Ireland 478 36754 028 34 3467 003 Italy 1178 82116 062 111 47948 039 Japan 1494 148032 112 2281 1119223 917 Korea South 659 34500 026 3530 2752791 2255 Malaysia 386 26027 020 837 168683 138 Mexico 1405 113623 086 362 237173 194 Netherlands 1149 85105 064 47 22631 019 Singapore 1098 94917 072 139 32913 027 Spain 871 63094 048 129 37419 031 Sweden 712 32682 025 235 23090 019 Switzerland 726 60288 046 74 20242 017 Taiwan 592 30313 023 2949 787049 645 United Kingdom 3054 423934 321 1997 882423 723 United States (domicile) 4950 10460880 7923 na na na Other - Covered by WorldScope 6893 199394 151 9000 4057894 33 Other -Not Covered by WorldScope 713 5352 004 - - - Total 38108 13203609 1000 27076 12204834 100
39
Table 4 OLS regressions of firm excess values on the percent of foreign sales This table reports the results of OLS regressions of firm excess value on the percent of foreign sales (Foreign Sales) the percent of sales outside a firmrsquos primary industry (Industry Other) and control variables All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed) Dep Variable = Excess Value Coefficient t-stat Coefficient t-stat Coefficient t-stat Coefficient t-stat (1) (2) (3) (4) Foreign Sales 0349 419 0209 293 0189 266 0278 210 Industry Other -0134 -186 -0047 -072 -0053 -081 -0326 -325 Log Size 0066 634 0065 624 -0044 -096 Debt Firm Value -0196 -213 -0241 -260 -0685 -588 CAPX Sales 0901 425 0893 435 0488 227 EBIT Sales 1154 542 1170 547 0408 189 RampD Sales 2908 1242 2932 1237 1544 379 Ad Sales 1869 263 2003 280 -1696 -119 Year indicators No No Yes Yes Firm indicators No No No Yes R2 0017 0212 0226 0768 Observations 4950 4950 4950 4950
40
Table 5 Dynamic panel GMM model regressing firm excess values on the percent of foreign sales This table reports the result from a regression of the excess value on the percent of foreign sales (Foreign Sales) the percent of non-primary industry sales (Industry Other) and control variables using a dynamic panel GMM estimator All control variables are considered to be endogenous with the exception of the year indicator variables AR(1) and AR(2) are tests for first-order and second-order serial correlation in the first differenced residuals with the null hypothesis of no serial correlation The null hypothesis of the Hansen test of overidentification is that all instruments are valid The null hypothesis of the difference-in-Hansen test of exogeneity is that the instruments used for the equation in levels are exogenous All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed) Dep Variable = Excess Value Coef t-stat Excess Value (one lag) -0018 -082 Excess Value (two lags) -0048 -272 Foreign Sales 0371 270 Industry Other -0163 -171 Log Size 0010 123 Debt FirmValue -1135 -820 CAPX Sales 0590 342 EBIT Sales -0047 -300 RampD Sales 1376 531 Ad Sales -0751 -040 AR(1) test (p-value) 000 AR(2) test (p-value) 017 Hansen test of over-identification (p-value) 030 Difference-in-Hansen test of exogeneity (p-value) 093 Year indicators Yes Number of firms 1016 Number of observations 4090
41
Table 6 OLS regression of firm excess values on the percent of foreign sales controlling for a control premium This table reports the result of the OLS regression of firm excess value including firm and year fixed effects with an additional control variable to proxy for a country-level ownership control premium (Control Premium) All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed) Dep Variable = Excess Value Coef t -stat Foreign Sales 0303 224 Industry Other -0327 -325 Log Size -0045 -100 Debt FirmValue -0683 -585 CAPX Sales 0490 226 EBIT Sales 0409 189 RampD Sales 1545 379 Ad Sales -1689 -119 Control Premium -1004 -062 Year indicators Yes Firm indicators Yes R2 0768 Observations 4950
42
Table 7 OLS regression of firm excess values on the percent of foreign sales controlling for the cost of capital This table contains the results of an OLS regression of firm excess value including firm and year fixed effects with an additional control variable to proxy for a country-level cost of capital (Cost of Capital) All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed) Dep Variable = Excess Value Coef t -stat Foreign Sales 0255 178 Industry Other -0326 -324 Log Size -0043 -094 Debt FirmValue -0687 -589 CAPX Sales 0485 225 EBIT Sales 0406 188 RampD Sales 1539 379 Ad Sales -1703 -120 Cost of Capital 0004 039 Year indicators Yes Firm indicators Yes R2 0768 Observations 4950
43
Table 8 OLS regression of firm excess values on the percent of foreign sales controlling for aspects of corporate governance This table reports the results of two OLS regressions In the first column are the results of the firm excess value regression including firm and year fixed effect with five proxies for corporate governance included ndash the percent of shares owned by the CEO and the percent squared (CEO Ownership and CEO Ownership squared) the percent owned by institutional investors (Institution Ownership) whether there is a powerful CEO (Powerful CEO) and the Gompers Ishii and Metrick (2003) governance index (Governance Index) In the second column the regression includes the same sample and variables with the exception of the governance proxies which are excluded All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed)
Governance
No Governance (same sample)
Dep Variable = Excess Value Coef t-stat Coef t -stat Foreign Sales 0259 159 0251 154 Industry Other -0099 -080 -0099 -079 Log Size -0105 -185 -0102 -180 Debt FirmValue -0813 -478 -0817 -482 CAPX Sales 0440 252 0438 249 EBIT Sales 0457 247 0457 246 RampD Sales 1717 284 1716 286 Ad Sales -2210 -143 -2190 -143 CEO Ownership 0010 007
CEO Ownership squared -0029 -017 Institution Ownership 0047 067 Powerful CEO -0003 -014 Governance Index 0009 103
Year indicators Yes Yes Firm indicators Yes Yes R2 0811 0810 Observations 2550 2550
- Cover for paperpdf
- crrz_20130603_SMUpdf
-
3
Using a method similar to Berger and Ofek (1995) we determine the imputed value for each
country-industry component by using the median single-segment firm operating exclusively in
the same country and industry (ie single-segment foreign (domestic) firms in the same industry
and country are used as benchmarks for the foreign (domestic) operations of US MNCs) In
other words our approach uses data on the observed firm values of single-segment US firms
(from Compustat) and foreign firms (from Worldscope)
This approach to measuring excess value differs from previous methods to impute the firm
value of MNCs Denis et al (2002) use single-segment domestic firms (ie US firms) in the
same industry as a benchmark for both domestic and foreign MNC operations Similarly studies
using Tobinrsquos Q to investigate valuation effects of MNCs rely only on domestic benchmarks
(eg Morck and Yeung 1991 Morck and Yeung 2001 Gande Schenzler and Senbet 2009)
As growth rates and discount rates vary by country we believe our method is more appropriate
for measuring valuation effects in a multinational context5 This method in effect compares the
value of the firm as a whole to the sum of the parts In addition our method is conceptually
consistent with theories on foreign direct investment (FDI) which note that an MNC exists when
a firm seeks to exploit its advantages and remove conflict arising in external market transactions
by combining a firm of one nationality that might otherwise exist independently under the
ownership of a firm of a different nationality (Dunning and Rugman 1985)
Our approach leverages data maintained by the Bureau of Economic Analysis (BEA) which
provides detailed accounting information about FDI allowing measurement of sales for each
5 Denis et al (2002) contemplate the use of single-segment foreign firms as a benchmark noting three constraints to implementing such a procedure (1) the country location of firmsrsquo foreign operations cannot be reliably identified using Compustat data (2) the industry membership of firmsrsquo foreign operations cannot be identified reliably using Compustat data and (3) valuation ratios may differ across countries due to differences in accounting standards As described in our study we overcome each of these constraints and find that the different benchmark is important and reverses prior findings
4
country-industry in which US-based MNCs operate These data provide a substantial advantage
over the Compustat database used by prior research While Compustat provides some
information about the countries and industries in which these firms operate it is not as detailed
about either of these dimensions (or their interaction) and heavily relies on managerial disclosure
choices which could induce measurement bias (Villalonga 2004a) In addition to the new
method of estimating the value effects of foreign operations we provide evidence that MNCs
trade at a premium which stands in contrast to the discount documented in Denis et al (2002)
This difference is primarily attributed to our use of foreign benchmarks to construct the imputed
values of foreign operations6
Our finding of a premium better reconciles with the findings in the international trade
literature By focusing on nonpublic establishment level (eg factory store or office) data the
trade literature is able to generate relatively precise proxies for total factor productivity (TFP)
and provides robust evidence that firms engaging in international trade are more productive than
those that do not (see Helpman 2006 and Syverson 2011 for reviews) Furthermore
productivity differences tend to be highly persistent even within narrowly defined industries
(Syverson 2011) Due to data limitations it is difficult to accurately measure TFP at the firm
level especially for firms which operate in multiple industries andor countries Nevertheless
Helpman Melitz and Yeaple (2004) provide some evidence that US firms engaging in FDI
have labor productivity advantages over firms that do not Reconciling the discount to
multinational operations found by Denis et al (2002) to the trade literature requires that the
persistent productivity advantages of firms engaged in international trade must be more than
offset by some other cost (eg agency costs) While this relation is possible it is difficult to
6 We repeat the analysis of Denis et al (2002) for our sample and confirm that differences in value effects of foreign operations are driven by the different methods for measuring the imputed value of foreign operations
5
conjecture why the most productive firms would have the largest agency costs In contrast the
finding of a premium easily reconciles with the trade literature
Given our result we investigate whether the premium is simply an artifact of innate
characteristics of firms that choose to invest abroad For example Helpman et al (2004) present
a simple model with heterogeneous productivity endowments The firms that receive the highest
productivity endowments are the ones capable of paying the fixed costs to establish a foreign
subsidiary To the extent productivity advantages create excess value one would expect the type
of firms that choose to invest abroad would be valued at a premium even in the absence of their
foreign investments After including firm fixed effects to control for time invariant firm
characteristics (eg innate productivity advantages) we continue to find economically and
statistically significant evidence of a premium to foreign operations
To further account for the endogenous nature of FDI we estimate a dynamic panel data
model using the generalized method of moments (GMM) estimator developed by Arellano and
Bond (1991) Arellano and Bover (1995) and Blundell and Bond (1998) This estimator allows
us to simultaneously account for the potential endogeneity of FDI industrial diversification and
firm value This method also yields a significant premium which is of similar magnitude and
significance to that estimated using fixed effects
We also perform a series of additional robustness tests First we examine whether the
measured excess value premium is driven by operations in countries with large control premiums
(Dyck and Zingales 2004) In countries with large control premiums using stock price in the
calculation of total firm value could underestimate the true value of the benchmark firms and
thereby induce an excess value premium We fail to find evidence that the excess value premium
is driven by such countries
6
Second we examine whether access to a low cost of capital is a meaningful source of the
US MNC advantage We incorporate foreign benchmarks in our method of estimating firm
excess value as it allows us to incorporate attributes of operating in those foreign countries ndash a
key attribute being the cost of capital Relative to the local benchmarks in foreign countries US
MNCs have the ability to obtain funds outside the local operating environments either by
borrowing in the US market or transferring capital internally When competing against firms in
shallow capital markets with high costs of capital MNCs may have a competitive advantage
over local foreign companies due to these alternative sources of capital However investors and
lenders are likely to expect higher rates of return from the additional risk associated with
operating in these environments making it unclear whether a multinational network will enhance
value in countries with a high cost of capital In addition foreign firms have some ability to
access international capital markets which mitigates the advantages to US firms To examine
whether the cost of capital plays a role in our finding of a premium we include a proxy for the
extent of operations in countries with higher costs of capital Our results are unaffected by the
inclusion of the proxy
Finally we control for several corporate governance proxies which Hoechle et al (2012)
find to be correlated with excess values (and industrial diversification) While magnitude of the
premium is virtually unaffected by including these proxies the significance level does decline
due to the reduced sample size
Our study contributes to the literature on multinational firms Our new method of estimating
excess values for multinational firms provides strong evidence of a statistically and economically
significant premium associated with increased international operations The premium is robust to
a variety of specifications and controls designed to evaluate alternative explanations In addition
7
this result easily reconciles with the international trade literature that establishes a link between
greater productivity and international operations though we do not find results supporting this as
the only source of the premium As segment disclosures improve and as new data sources
become available on multinational activity (eg Bureau Van Dijk) our empirical approach can
be used to investigate the extent to which the home country of the parent might influence the
extent with which foreign operations affect firm value (ie by examining non-US based
MNCs)7 Future studies could also examine the underlying forces affecting the excess value
premiums across countries
We also contribute to the understanding of international accounting standards and their
comparability Many studies seek to compare firms using different accounting standards These
comparisons generally rely on information generated by a particular accounting regime By
comparing commonly used metrics (total assets net income and sales) reported for the same
firm and year across different regimes we provide insights as to the relative comparability across
standards In particular we find that sales is reported significantly more consistently across
accounting regimes than the other metrics examined This finding should aid researchers in
reducing measurement error when comparing accounting information in multinational settings
The paper proceeds as follows Section 2 discusses the motivation for finding a premium or
discount to multinational operations Section 3 describes our data and how the excess value of a
firm is measured A discussion of our independent variables and primary findings is presented in
Section 4 with additional robustness tests provided in Section 5 Finally Section 6 concludes
7 As MNCs are ultimately bound by the tax regulatory and legal frameworks of their home country the valuation effect of foreign operations that we document for US-based MNCs may be different in a sample of non-US based MNCs with the same country-industry footprint Theorists refer to these as lsquolocational advantagesrsquo enjoyed by all multinational firms of a given nationality and obtained irrespective of skills or capabilities unique to a particular firm (Yamin 1991) There is also evidence of locational advantages in the market for corporate control (Huizinga and Voget 2009)
8
2 Premium versus discount
In a frictionless world where managers maximize firm value and markets are efficient there
should be no discount or premium to operating in foreign countries However there are a number
of forces that may cause actual firm value to deviate from the implied firm value
On one hand MNCs may have comparative advantages that generate premiums (ie positive
excess values) relative to a similar footprint of stand-alone firms Having operations in a variety
of locations that access different supplies of inputs and customers allows the firm to take
advantage of changing market conditions by shifting operations or products to maximize firm
value more so than a firm operating in a single country Similarly having access to a variety of
institutional settings such as different tax codes legal regimes and financial markets can provide
MNCs with options beyond those available to firms located in a single country
Another possible factor associated with a premium to multinational operations is the cost of
capital For operations located in countries with shallow capital markets or weaker creditor
rights operating within a conglomerate can provide the benefit of a lower cost of capital for
investments and expansion for at least two reasons First as our MNCs are domiciled in the US
their foreign segments have greater access to US capital markets relative to local competitors in
the foreign jurisdiction Second cash rich segments with few investment opportunities can
finance investments in cash poor segments with positive net present value projects (eg Myers
and Majluf 1984) Consequently diversified firms should be less liquidity constrained and better
able to shift resources to the most valuable investment opportunities Research has found that
internal financing is used more often when diversified firms have operations in countries with
more costly external financing (Desai Foley and Hines 2004) Multinational firms may also be
relatively more protected than single-country firms when negative shocks hit external capital
9
markets (Foley Desai and Forbes 2008) In a recent study Matvos and Seru (2011) find that
industrially diversified firms perform better than non-diversified firms when external capital
markets are impaired
While these arguments suggest that MNCs enjoy certain advantages when the foreign cost of
capital is high these benefits should be reduced by the additional risk associated with operating a
business in a high cost of capital environment For example Verizon is a multinational firm
operating in a number of countries One of the higher cost of capital locations in which they
operated was Venezuela While the multinational structure of Verizon may have mitigated some
of the risks associated with operating in a higher cost of capital location the firms was unable to
eliminate all of these risks In fact in 2007 Verizonrsquos operations in that country were
nationalized resulting in a net extraordinary loss of $131 million that year8 In other words
MNC investors are likely to expect higher rates of return to compensate for higher risk which
likely offsets some (if not all) of the potential excess value premium
On the other hand multinational firms may incur a discount (ie negative excess values)
relative to a similar footprint of stand-alone firms Potential reasons for a discount largely rely on
the existence of agency costs Multinational firms tend to be larger more complex and less
transparent The combination of complexity and opacity inherent in MNCs reduces a firmrsquos
reliance on external capital as the cost of such capital increases due to agency costs (Desai et al
2004) As a result of the reduced reliance on external funds MNCs face a reduced level of
monitoring As noted by Jensen (1986) managers have incentives to increase firm size beyond
that which is optimal with overinvestment or misallocation of funds to pet projects If this lack of
monitoring is in combination with available internal capital empire building becomes easier
(Hope and Thomas 2008) Furthermore the current segment reporting standards under US 8 See the 10-K of Verizon Communications Inc dated December 31 2007 (footnote 2)
10
GAAP only require highly aggregated disclosures of foreign operations and allow for
considerable managerial discretion As a result operating in multiple countries makes it easier
for management to hide poor performance ndash either their own or that of a division ndash such that low
quality managers are more likely to be retained and overcompensated and poorly performing
divisions retained longer than optimal Other reasons one might expect a discount to be applied
to multinational firms include higher coordination costs (eg coordinating across different
cultures and languages) as well as additional risks These risks include exposure to multiple
political regimes legal regimes economic regulations and currency fluctuations
Past studies investigate whether foreign operations of US firms enhance or reduce firm
value However all of these studies benchmark the value of the foreign operations of an MNC to
US domestic firms For instance Denis et al (2002) determine the implied value of a US
MNC using domestic (US) single-segment firms in the same industry as a benchmark Studies
using Tobinrsquos Q to investigate valuation effects of MNCs also rely on domestic benchmarks
(eg Morck and Yeung 1991 Morck and Yeung 2001 Gande Schenzler and Senbet 2009)
However using a US domestic firm as a benchmark for the value of foreign operations
implicitly makes two assumptions First this method assumes that the risks and expected growth
rates of foreign operations are equivalent to those of domestic operations This assumption is
inconsistent with the findings of Hail and Leuz (2009) which provides evidence that the cost of
capital varies substantially across countries Second this method assumes that there is excess
domestic capacity to allow for expansion with profitability similar to current domestic
operations We relax these assumptions by measuring the imputed value of a foreign component
of a US firm as if that component operated autonomously within that foreign country (rather
than in the US) Specifically our multiples allow the cost of capital and expected growth rates
11
to vary across not just industry but also country Furthermore our counterfactual does not
require the assumption that operations be relocated mitigating the production and sales capacity
concerns
3 Measuring excess value
31 Description of excess value
Figure 1 shows the average percent of foreign sales (based on segment disclosures under US
GAAP) across firms through time and illustrates that firms are continuing to expand
internationally9 This expansion highlights the importance of understanding whether the decision
to operate internationally is on average a value enhancing corporate strategy We contribute to
this understanding by evaluating whether the firm as a whole is worth more or less than its
imputed value (ie the firm is valued in excess of the sum of its individual components)
We measure the excess value of a firm as the logarithm of the ratio of actual firm value to
imputed value based on the method used in Berger and Ofek (1995) The imputed value is the
hypothetical value of the MNC under the assumption that its country-industry components
operate as independent entities A key innovation of our study is an alternative approach to
imputing the value of the foreign operations of an MNC Components of MNCs operating in a
given industry and country are imputed using the value of single-segment firms operating in the
same industry and country Thus we ensure the discount rates and expected growth rates are
applicable to each given industry and location in estimating the implied values of the MNC
segments
9 The discontinuity in industrial diversification (the percent of sales outside the primary industry) between 1997 and 1998 is the result of a change in segment reporting rules
12
The actual firm values are observable for all firms in our sample The hypothetical firm
values for multi-segment firms are imputed using market value to sales ratios herein referred to
as lsquomultiplesrsquo of the median single-segment firm operating in the same country-industry Thus
these single country-industry firms serve as benchmarks for an MNCrsquos operations within that
same country-industry The imputed value of the country-industry component can be viewed as
an estimate of what that component would be worth if it operated independently The sum of the
imputed values across all country-industry components represents the hypothetical value that the
multi-segment firm would be worth if it were broken apart
32 Data
Four primary data sources play a distinct role in carrying out our analysis i) Bureau of
Economic Analysis (BEA) data ii) Compustat Segment data iii) Worldscope data and iv)
Compustat Fundamentals Annual data Broadly speaking BEA data provide information about
the diversified nature of MNCs Compustat Segment data provide information about the
industrial diversification of US domiciled firms Worldscope data are used to compute
multiples for single-segment foreign firms that serve as benchmarks for the foreign operations of
MNCs (ie foreign benchmarks) and Compustat Fundamentals Annual data are used to
compute multiples for single-segment domestic firms that serve as benchmarks for the domestic
operations of multinational firms We also use the latter data source to construct our primary
control variables
13
Key to the execution of our study is our access to BEA data Federal law requires US-
domiciled MNCs to report certain financial and operating data to the BEA10 With regard to
observing the country-industry operations of MNCs use of the BEA data overcomes two
important limitations of Compustat Segment data First Compustat does not consistently identify
the specific location or scale of MNCsrsquo foreign operations11 Second Compustat does not
identify the industry activities in each country of operation This latter point is particularly
limiting for MNCs because domestic industry activity need not mirror foreign industry activity
An MNC generating an equal proportion of sales in two industries in the domestic market need
not generate industry sales in equal proportions in every foreign market12 The lack of detail and
consistency in Compustat Segment data arises because segment reporting is highly aggregated
and firms exercise substantial discretion in defining segments under Accounting Standards
Codification 280 (ASC 280) (eg Villalonga 2004a and Bens Berger and Monahan 2011)
The BEA defines an MNC as the combination of a single US entity called the US parent
and at least one foreign affiliate ndash that is these firms have a physical presence outside the US
due to FDI Since 1982 MNCs have completed a mandatory and confidential lsquoSurvey of US
Direct Investment Abroadrsquo for the domestic operation and the operations of each foreign
affiliate defined as a foreign entity in which the US parent holds at least a 10 percent equity
interest (directly or indirectly) The data are reported in US dollars on a fiscal year basis and in
10 BEA surveys are conducted pursuant to the International Investment and Trade in Services Survey Act (PL 94-472 90 Stat 2059 22 USC 3101-3108) See httpwwwbeagovsurveysdiasurvhtm and Mataloni (2003) for more detailed information on the BEA data 11 Exhibit 21 disclosures of material subsidiaries and their locations in SEC 10-K filings could potentially help overcome the inability to observe the location of the firmrsquos foreign operations from segment disclosures However at least three limitations remain (1) for industrially diversified multinational firms the researcher would be required to make an assumption about the proportion of industrial diversification in each country (2) Exhibit 21 provides no information about the scale of activity in each jurisdiction and (3) managerial discretion is used to determine a lsquomaterialrsquo subsidiary under Section 601 of SEC Regulation S-K 12 In fact the expectation that a multinational firm would exhibit different industry membership in different countries is consistent with the theory of vertical foreign direct investment where firms separate their various value chain activities and locate them in the most favorable country (see eg Hanson Mataloni and Slaughter 2005)
14
accordance with US Generally Accepted Accounting Principles (US GAAP) For each year
we observe the sales industry composition and location of not only the parent but also each
affiliate13
The procedures we use to construct our sample are similar to those used by Berger and Ofek
(1995) and Denis et al (2002) Using firm-years appearing in Compustat Fundamentals Annual
and Compustat Segment data we obtain a sample of firms domiciled in the US with total sales
exceeding $20 million We exclude firms in the financial service (SIC codes 6000 to 6999) and
utility (SIC codes 4000 to 4999) industries and eliminate firm-years when the sum of segment
sales (Compustat geographic or Compustat business) is not within 1 of total firm sales for that
year We restrict our sample period to include fiscal years beginning after December 15 1997 as
ASC 280 Segment Reporting substantially altered the definition of a reporting segment under
US GAAP This restriction ensures that Compustat Segment data reflects the use of a consistent
accounting standard Finally we require that our MNCs appear in both Compustat and BEA
data The requirement that our sample firms appear in the BEA database ensures that the firms
have observable international operations In sum these procedures result in 1166 multinational
firms incorporated in the US with 4950 observations between 1998 and 2008 that meet all of
our data requirements
33 Measuring excess value
The dependent variable in our study is excess firm value (Excess Value) defined as the
logarithm of the ratio of actual firm value to imputed firm value (Berger and Ofek 1995)
13 We obtain sales industry membership and country location for affiliates from one of two sources When affiliates exceed the BEA-determined reporting thresholds (ie if their assets sales or net income (loss) exceed $7 million in 1999 $30 million in 2000 through 2003 $10 million in 2004 and $40 million in 2005 through 2008) they must file their own detailed survey When affiliates fall below the thresholds we obtain our data from an attachment to the parent survey
15
Appendix A provides the definition of this and all other variables used in our analysis We
observe actual firm value equal to a firmrsquos market value of equity plus book value of liabilities
(ie book value of total assets minus book value of total equity) using Compustat Fundamentals
Annual data We calculate imputed firm value as the sum of the imputed values of a firmrsquos
operations in each country-industry Our method for imputing the value of the separate
components of a firm can best be described in three steps First we obtain total sales generated
by a firm for each country-industry in which it operates14 Second we obtain multiples (market
value to sales ratios) for benchmark firms operating in those same country-industries Third we
multiply a firmrsquos country-industry sales by the applicable median country-industry multiple to
obtain the imputed market value for each country-industry operation We perform each step
annually
Conceptually imputed values can be based on assets (ie Tobinrsquos Q) net income or sales
multiples We restrict ourselves to sales multiples for two reasons First using value to sales
ratios maintains consistency with the prior research on the excess value implications of
multinational operations (eg Denis et al 2002) Second as our method uses accounting data
for foreign companies to compute country-industry multiples the accounting numbers we use
need to be consistently measured across firms using different accounting standards We find that
sales data are measured most consistently
To assess the comparability of sales relative to either net income or assets across various
accounting standards we examine all firms listed on Worldscope as changing to or from US
14 Specifically we allocate total sales from Compustat to each component based on the proportion of BEA sales (after eliminating intercompany sales) occurring in each country-industry This step ensures that our total firm sales do not deviate from a firmrsquos total sales as reported in the 10-K
16
GAAP between 2005 and 201115 We obtain accounting data for the year of the change and the
prior year where electronically available in English We are able to obtain data for 66 firms
changing standards as detailed in Table 1 The distribution of years for which accounting data
are available under two different accounting standards for the same fiscal year is shown in Panel
A The years shown are the year prior to the change as these accounting numbers were provided
under the original standard and then subsequently restated in the following year under the new
standard for comparative purposes The majority of the shifts were to or from IFRS (71)
though seven other accounting standards are noted (Panel B) We examine the percent that the
non-US GAAP reported number differs from the US GAAP reported number in Panel C to
assess the comparability of these three summary accounting numbers across various accounting
standards Examining both the full sample as well as the subset that overlaps our study we find
that sales are more consistently measured than net income or assets
The sum of the imputed values across the country-industry components of an MNC is an
estimate of the value of a portfolio of unrelated businesses that mirror the related businesses of
the firm Consequently a comparison of the actual firm value with the imputed firm value is a
measure of how a multinational network affects firm value We provide descriptive statistics for
Excess Value for our sample of 4950 MNC firm-years in Table 2 Panel A This table also
includes univariate comparisons of the values between the MNC firms and the domestic single-
segment and foreign single-segment benchmark firms The Excess Value measure is significantly
higher for MNCs suggesting a premium in firm value for multinational firms relative to the
benchmark firms in total as well as the domestic and foreign subgroups (p-values for tests of
15 Due to the hand collection efforts required and the limitation on electronic filings we began our search with changes documented in 2005 which generally require filings from 2004
17
means and medians are lt 001)16 These results provide some initial evidence that operating in
multiple countries is positively associated with firm value
To estimate country-industry multiples we rely on Worldscope financial data on foreign
firms and Compustat Fundamentals Annual for domestic firms We restrict the firms to include
only those with at least 90 of sales income and assets inside the country of domicile (ie
those that do not report significant multinational activity) and that operate in a single industry
We refer to these firms as benchmark firms (either foreign or domestic depending on the country
of domicile) these firms do not appear in any of our regressions We report the number of
benchmark firms during our sample period in Table 2 Panel A The industry criterion is based on
the two-digit SIC code17 For every country that has at least five firms in the respective industry
and year we use the median ratio of market value to sales18 This ratio is the country-industry
multiple ndash an input required to compute imputed values
To determine the country-industry composition of MNCs we rely on BEA data In Table 3
we report the aggregate number of foreign affiliates and total sales by country as provided by
the BEA data for our sample of 4950 MNCs The specific countries tabulated are those that
represent at least 02 percent of total firm sales (pooled across all years) in our sample The top
five foreign countries in which MNCs generate sales are Canada United Kingdom Germany
France and Japan Table 3 also provides a comparison of the number of foreign benchmark
firms available in Worldscope in the specific countries in which our sample of MNCs generate
16 Similar to Berger and Ofek (1995) the median values differ slightly from zero for the benchmark single-segment firms due to the truncation of extreme excess values 17 BEA reporting constrains us to the two-digit SIC code level 18 When five firms are not available for a given country and industry we follow Berger and Ofek (1995) and move to a more general industry definition (eg one-digit SIC code) This procedure results in a mean (median) 630 (757) of MNC sales matched at the two-digit level
18
the majority of their sales Country coverage in Worldscope is not available for countries
representing 004 percent of total firm sales (per BEA)
4 Empirical results
41 Independent variables
Recall that our objective is to examine the overall relation between excess firm value and
multinational operations Our proxy for the extent of multinational operations is the percentage
of total firm sales made by foreign operations (Foreign Sales) By this measure the extent of
multinational activity in our sample of MNCs is non-trivial ndash Table 2 Panel B shows that
approximately 24 percent of sales are generated by foreign operations on average Our first
analysis estimates ordinary least square regressions of Excess Value on Foreign Sales and
control variables (discussed below)
We include variables in our regression to control for other potential determinants of excess
value These are the percentage of sales made by the firm outside its primary industry (Industry
Other) to control for any relation between industrial diversification and excess value as in Berger
and Ofek (1995) For this purpose we obtain industry sales using Compustat Segment data19 A
firmrsquos primary industry is the industry in which the firm generates the majority of its sales and
we determine industry sales at the business segment level We set Industry Other equal to zero
for firms that operate in a single business segment
Consistent with prior research we also include controls for firm size (Log Size) the ratio of
long term debt to total market value (Debt Firm Value) the ratio of capital expenditures to total
sales (CAPX Sales) the ratio of earnings before interest and taxes to total sales (EBIT Sales)
the ratio of research and development expenses to total sales (RampD Sales) and the ratio of 19 Our results are unaffected by using BEA data to measure Industry Other
19
advertising expenses to total sales (Ad Sales) Table 2 Panel B provides the distribution of the
control variables All independent variables are Winsorized at 1 In our OLS regressions we
use heteroskedasticity and autocorrelation consistent robust standard errors with clustering at the
firm level
42 Primary regression analysis
In this section we examine the relation between firm excess value and the amount of foreign
activity at US MNCs Table 4 contains the results of regressions of excess value on Foreign
Sales for multiple specifications In column (1) Excess Value is regressed on both the percent of
foreign sales (Foreign Sales) and the percent of sales outside the firmrsquos primary industry
(Industry Other) We add the control variables to the regression in column (2) and year
indicators in column (3) In all three specifications the coefficient on the percent of foreign sales
remains significantly positive (with a p-value lt 001) For a sense of economic magnitude in
column (3) a one standard deviation (197) increase in the percent of foreign sales (Foreign
Sales) is associated with a 41 (equal to (e0349-1) times 0197) increase in firm excess value The
effect is larger for the other two columns ndash 82 increase in firm excess value in column (1) and
46 in column (2)20
Our finding of a premium reconciles with the international trade literature which finds that
firms engaging in international trade are more productive than those that do not (see Helpman
2006 and Syverson 2011 for reviews) Due to data limitations it is difficult to accurately
measure TFP at the firm level especially for firms which operate in multiple industries andor
20 Using the alternative strategy that employs US domiciled single-segment firms as the benchmark for the imputed value of the foreign operations Denis et al (2002) find that greater foreign activity results in a discount to firm value Using the Denis et al (2002) method to calculate excess value in our sample we obtain a significantly negative regression coefficient on Foreign Sales using the models in Table 4 columns (2) and (3) (the coefficient is not significant using the model in column (1) results untabulated)
20
countries As an alternative method to evaluate whether our finding of a premium is simply the
result of more productive firms also having more multinational operations we investigate
whether the premium is simply an artifact of innate characteristics of firms that choose to invest
abroad For example Helpman et al (2004) present a simple model with heterogeneous
productivity endowments The firms that receive the highest productivity endowments are the
ones capable of paying the fixed costs to establish a foreign subsidiary
To the extent that productivity advantages create excess value one would expect the type of
firms that choose to invest more abroad would be valued at a premium even in the absence of
their foreign investments To control for time invariant firm characteristics (eg innate
productivity advantages) and hence a potential source of endogeneity we add firm-level fixed
effects in column (4) of Table 4 We continue to find that the extent of foreign operations
(Foreign Sales) is positively associated with Excess Value Across all specifications the sign
on all significant control variables is consistent with prior research (eg Berger and Ofek 1995
and Denis et al 2002)
The finding of a premium makes a significant contribution to the relatively sparse literature
on the value effects of multinational activity Our measure of imputed value expands upon the
methods used by Berger and Ofek (1995) and Denis et al (2002) but allows us to answer a
different question than Denis et al (2002) Holding constant the extent of industrial
diversification we ask whether MNCs are more valuable than a portfolio of benchmark firms of
a similar geographic footprint In contrast Denis et al (2002) ask whether holding constant the
extent of industrial diversification MNCs are more valuable than a portfolio of domestic (US)
benchmark firms We believe that asking whether an MNCrsquos foreign (domestic) operations are
21
more valuable than those of a foreign (domestic) firm is a more appropriate way to assess the
value implications of a multinational network
5 Robustness
51 Endogeneity related to dynamic relations
Prior research argues that the relation between industrial diversification and excess firm
value could be endogenous through dynamic relationships based on observing past outcomes
Managers of firms likely choose to enter or exit industries or geographic regions based upon
their previous performance For example Campa and Kedia (2002) find that firms are more
likely to enter new industries when prospects in their current lines of business are deteriorating
Similar dynamics could also drive a relation between excess value and FDI We assess the
robustness of the previous results by estimating a dynamic panel data model which allows us to
control for dynamic endogeneity in addition to firm level unobserved heterogeneity We employ
the GMM estimator for dynamic panel models developed by Arellano and Bond (1991) Arellano
and Bover (1995) and Blundell and Bond (1998) Our results from this analysis confirm our
previous findings that firms with a larger degree of foreign operations have positive excess
values
Dynamic panel data models are generalizations of the traditional fixed effects model where
lags of the dependent variable are added to the right hand side of the equation These additional
lags control for the impact a firmrsquos past performance has on its current performance through the
channels discussed above The GMM estimator for dynamic panel models developed by
Arellano and Bond (1991) starts by differencing the data at the firm level in neighboring time
periods to eliminate any firm specific time invariant unobserved heterogeneity that may be
22
present Eliminating the firm specific fixed effect by differencing the data with lags of the
dependent variable on the right hand side introduces correlation between the new error term and
the differenced explanatory variables This problem is solved by applying the standard GMM
estimator using lags of both the original explanatory variables and excess value as instruments
We note that the dynamic panel data framework allows us to treat both international and
industrial diversification (as well as the other firm-specific control variables) as endogenous
Following Hoechle et al (2012) we also allow contemporaneous values of these variables to be
treated as endogenous
Lagged values of the explanatory variables are valid instruments in a dynamic panel data
model when the model is dynamically complete (eg Wooldridge 2010) A dynamic panel data
model is dynamically complete conditional on the unobserved effect if enough lags of both the
explanatory and dependent variable are included to make the resulting error term uncorrelated
with all the right hand side variables Under this assumption any lags for the explanatory
variables beyond this can be used within the GMM estimator as instruments Like Hoechle et al
(2012) we add two years of lags of a firmrsquos excess value to the right hand side assuming that
this is enough lags to control for dynamic feedback effects We then use past values of all
variables beyond the second lag up to a total of seven years to act as instruments
The results of the dynamic panel data estimator are reported in Table 5 These results confirm
the previous finding of a positive relation between the degree of international operations and
excess value We note that the size and significance level of the coefficient on Foreign Sales
diversification are robust to changes in the number of lags used as instruments The Arellano and
Bond (1991) estimator includes testing diagnostics for correct specification of the model If the
model is correctly specified differences in the residuals from the levels equation should be
23
serially uncorrelated at lag one but correlated at lag two The p-values for tests of serial
correlation of the residuals at lags 1 and 2 are reported in Table 5 and these indicate that the
model is not rejected We also report in Table 5 the Hansen test for overidentification which is a
test of the null hypothesis that the instruments are valid The p-value for this test is 030
indicating that we fail to reject this null hypothesis Finally we report another test for exogeneity
known as the difference-in-Hansen test which tests the null hypothesis that the instruments in
the original equation are valid We fail to reject this null hypothesis as well
52 Identifying value effects of multinational operations via cross-border acquisitions
Another alternative to investigating a causal link between foreign direct investment and firm
value is an event study of foreign acquisitions21 Acquired foreign targets represent only a small
fraction of MNCsrsquo worldwide sales in the year of the acquisition ndash less than 1 percent in our
sample Additionally cross-border acquisitions are not the primary source of international
expansion for the average firm We find that foreign acquisitions account for between 18 percent
and 55 percent of the year-over-year change in foreign sales with the remainder of the growth
coming from either newly established entities (ie greenfield investments) or growth in existing
operations22 These results suggest that acquisitions are not a material source of growth for the
21 Doukas and Travlos (1988) find positive returns when the acquirer is not operating in the target firmrsquos country and insignificant returns when the acquirer is operating in the target firmrsquos country or expanding internationally for the first time Morck and Yeung (1991) find marginal evidence of positive returns for firms with intangible assets Finally Doukas (1995) finds positive returns only for firms with average Tobinrsquos q ratios greater than one More recent work also using an event study methodology provides mixed evidence on valuation effects of global diversification through acquisitions Dos Santos Errunza and Miller (2008) find insignificant returns for firms with already established foreign operations firms establishing initial foreign operations and firms acquiring targets in related industries Doukas and Kan (2006) and Moeller and Schlingemann (2005) find reductions to shareholder value while Francis Hasan and Sun (2008) find positive returns for acquirers of targets in segmented financial markets 22 New foreign affiliates to an MNC group each year report on the BEA survey whether they entered the group as an acquisition or a newly established entity (ie greenfield investment) Affiliates under the lowest reporting threshold do not report this information resulting in our inability to precisely identify the source of 33 percent of the increase in firm sales as either acquisition or greenfield
24
typical US MNC suggesting that an event study would lack power and generalizability For
these reasons we do not implement an event study
53 Control premium
A control premium or private benefits of control occurs when ldquosome value whatever the
source is not shared among all the shareholders in proportion of the shares owned but it is
enjoyed exclusively by the party in controlrdquo (Dyck and Zingales 2004 p 541) Using stock
prices of traded shares held by minority shareholders in determining the market value to sales
ratios of our benchmark firms could underestimate the true value of our benchmark firms if those
firms face large control premiums This in turn could induce an excess value premium because
the imputed value of these country-industry segments would be artificially low The existence of
such benefits is more prevalent in some countries than others and could affect the value of an
MNCrsquos operations in such domains We use the country-level control premiums from Dyck and
Zingales (2004) and using each firmrsquos country sales as a percent of total firm sales we obtain a
weighted average control premium that each MNC faces (Control Premium) For countries not
included in the Dyck and Zingales (2004) analysis we set the control premium equal to zero23 In
Table 6 we show that Foreign Sales remains positive and highly significant (p-value lt 001)
when we include the proxy for control premium As a result it does not appear that the excess
value premium is driven by MNCs operating in countries where majority shareholders are able to
extract large control premiums
23 Results are robust to replacing missing values with the mean control premium of the respective GDP quartile
25
54 Cost of capital
As discussed in Section 2 when countries have shallow capital markets a multinational
network may provide the benefit of a lower cost of capital for investments through an internal
capital market as well as through better access to the US capital market Prior research
establishes that there is substantial variation in the cost of capital across countries (Erb Harvey
and Viskanta 1996 and Hail and Leuz 2009) To determine whether the documented premium
is due to cost of capital advantages we include a control variable to capture the difference
between the MNCrsquos cost of capital and the cost of capital faced by its competitors in the global
markets in which it operates
We compute our Cost of Capital proxy in two steps First we obtain the country-year credit
rating published by Institutional Investor24 The rating ranges from 0 to 100 where higher values
imply that a country has a higher default risk Country-level credit risk is a reasonable predictor
of expected equity market returns and volatility (Erb et al 1996) and exhibits a highly
significant correlation with international accounting-based estimates of imputed cost of capital
(Hail and Leuz 2009) Second using each firmrsquos country sales as a percent of total firm sales
we obtain a weighted average credit risk rating across the countries in which each firm operates
A higher value implies an MNC operates in countries with higher cost of capital In Table 7 we
do not find a result consistent with MNCs gaining an advantage from their access to low cost
capitalndash Cost of Capital is not significantly associated with Excess Value25
24 We thank Cam Harvey for providing these data 25 Results are consistent using cost of capital as defined in Erb et al 1996
26
55 Corporate governance
In light of the recent finding that corporate governance mitigates the diversification discount
we examine whether the premium to multinational activity is sensitive to controls for corporate
governance Hoechle et al (2012) find that the magnitude of the industrial diversification
discount is decreased and in several specifications no longer significant in the presence of
governance proxies In the first column of Table 8 we include the five governance proxies that
are consistently significantly associated with excess value in Hoechle et al (2012) ndash the percent
of CEO ownership (CEO Ownership and CEO Ownership squared) the percent of institutional
ownership (Institution Ownership) whether the firm has a powerful or influential CEO
(Powerful CEO) and the governance index from Gompers Ishii and Metrick (2003)
(Governance Index)
As documented in Table 8 including these control variables reduces our sample size by
almost 50 (to 2550 observations) and tends to exclude the smaller firms from the analysis
None of the governance proxies are significant Relative to column (4) of Table 4 the coefficient
on Foreign Sales is similar in magnitude but is no longer statistically significant at
conventional levels To investigate whether the decreased significance is due to the smaller
sample or the inclusion of the governance variables we re-estimate the regression for the same
2550 observations after removing the governance controls The coefficient and t-statistic are
virtually identical suggesting that the loss of significance is due to the reduced sample and not
the governance controls We do not find evidence to suggest that variation in corporate
governance practices drives variation in excess value for US MNCs
27
6 Conclusion
Using a new method to quantify the valuation effects of foreign direct investment by US
domiciled firms we find robust evidence of a premium in excess value associated with the
degree of multinational operations Our method builds on that used in previous studies
examining firm excess value (eg Berger and Ofek 1995 and Denis et al 2002) by
incorporating the location in addition to the industry of each corporate affiliate This allows
important factors such as growth and cost of capital to vary across countries as well as industries
Examining a sample of multinational firms we find that a one-standard deviation (197)
increase in foreign operations is associated with an increase of between 41 and 82 in excess
value minus both a statistically and economically significant finding
This result complements the finding of the international trade literature that more productive
firms are more likely to operate internationally However the premium remains after including
firm fixed effects to control for persistent firm traits (eg productivity)
The finding of a premium stands up to a succession of robustness tests designed to evaluate
whether the result arises from alternative methods or explanations First we estimate a dynamic
panel data model using the generalized method of moments (GMM) estimator to simultaneously
account for the potential endogeneity of FDI industrial diversification and firm value Second
we include a control variable to proxy for whether the firm operates in countries with larger
control premiums which could result in understated multiples and mechanically induce a
premium Third we include a control variable to measure the cost of capital across the countries
in which an MNC operates to evaluate whether a relatively low cost of capital borne by
multinational firms is a meaningful source of the premium Fourth we control for several
corporate governance proxies which have been found to be correlated with excess values (and
28
industrial diversification) We fail to find evidence supporting any of these alternative
explanations ndash the premium in excess value arising at firms with greater multinational operations
remains significant
Overall our new method of estimating excess values for multinational firms provides strong
evidence of a statistically and economically significant premium associated with increased
international operations The premium is robust to a host of specifications and controls designed
to evaluate alternative explanations We leave to future research the questions of whether the
premium to US multinational firms applies more broadly to other countries of domicile and
what factors influence the premium
29
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Myers S C and N S Majluf 1984 Corporate financing and investment decisions when firms have information that investors do not have Journal of Financial Economics 13 187-221
Schmid M and I Walter 2009 Do financial conglomerates create or destroy economic value
Journal of Financial Intermediation 18 193-216 Syverson C 2011 What determines productivity Journal of Economic Literature 49(2) 326-
365 Villalonga B 2004a Diversification discount or premium New evidence from the Business
Information Tracking Series Journal of Finance 59 479-506 Villalonga B 2004b Does diversification cause the diversification discount Financial
Management 33 5-27 Wooldridge JM 2010 Econometric Analysis of Cross Section and Panel Data (Second
Edition) Cambridge MA MIT Press Yamin M 1991 A reassessment of Hymerrsquos contribution to the theory of the transnational
corporation C Pitelis and R Sugden (eds) The Nature of the Transnational Firm London Routledge
32
Appendix A Variable Definitions
We obtain information regarding firmsrsquo operations by country and industry from the Bureau of Economic Analysis (BEA) unless otherwise noted Acronyms used below reflect Compustat mnemonics and thus indicate when we obtain data from the Compustat North America Fundamentals Annual database All independent variables are winsorized at 1
Dependent variable
Excess Value = The log of the ratio of actual firm value to imputed firm value Actual firm value is equal to market capitalization (MVE) + book value of total assets (AT) ndash book value of shareholdersrsquo equity (SEQ) Imputed firm value is calculated as the sum of the imputed values of a firmrsquos operations in each country-industry The imputed value for each country-industry is equal to sales generated in that country-industry times a multiple equal to the ratio of a benchmark firmrsquos actual value to total sales A benchmark firm is defined as a median single-segment firm operating in the same country-industry All multiples require at least five firms listed in the respective country-industry on Compustat (domestic) or Worldscope (foreign) with less than 10 of sales income and assets outside the country of domicile Similar to Berger and Ofek (1995) we eliminate extreme values where the ratio of actual to imputed firm value exceeds 400 or is less than 025
Independent variables
Foreign Sales = Sales made by the foreign operations of US MNCs as a percent of total firm sales
Industry Other = Sales outside a firmrsquos primary industry as a percent of total sales (SALE) Equal to zero for firms operating in a single business segment
Log Size = Log of total sales (SALE)
Debt Firm Value = Ratio of long-term debt (DLTT) to total market value (market capitalization (MVE) + book value of total assets (AT) ndash book value of shareholdersrsquo equity (SEQ))
CAPX Sales = Ratio of capital expenditures (CAPX) to total sales (SALE)
EBIT Sales = Ratio of earnings before interest and taxes (EBIT) to total sales (SALE)
RampD Sales = Ratio of research and development expenses (XRD) to total sales (SALE)
Ad Sales = Ratio of advertising expenses (XAD) to total sales (SALE)
Control Premium = A weighted-average (based on the sales in a given country as a percent of total firm sales) of the country-level control premium from Dyck and Zingales (2004)
Cost of Capital = A weighted-average (based on the sales in a given country as a percent of total firm sales) of the country-level credit rating from Institutional Investor (httpwwwinstitutionalinvestorcom) The country-level rating is available for 160 countries and ranges from 0 to 100 Higher values imply greater credit risk
33
CEO Ownership = Percent of shares owned by the CEO from Execucomp
Institution Ownership = Percent of shares owned by institutional investors from Thomson Reuterrsquos Institutional Holdings database
Powerful CEO = Indicator variable equal to one if the CEO is the only insider on the board of directors and also serves as the chairman and president based on data from RiskMetrics Directors database Equal to zero otherwise
Governance Index = Count measure of takeover defenses used in Gompers Ishii and Metrick (2003) from RiskMetrics Governance database As the governance data are not available for every year we follow Gompers Ishii and Metrick (2003) and assume that the firmsrsquo governance attributes are unchanged until publication of the subsequent series
34
Figure 1 Trends in multinational activity and industrial diversification from 1984-2008 This figure presents a plot of the mean percent of foreign sales and the mean percent of sales outside the firmrsquos primary industry (industrial diversification) from 1984 through 2008 Dotted lines above and below the primary lines represent 95 confidence intervals To compile the plot we include the full population of firms in Compustat
35
Table 1 Analysis of accounting data for firms reporting under multiple accounting standards This table provides a comparison of sales net income and assets for firms that changed accounting standards to or from US GAAP Comparisons are provided for the 66 firms that changed to or from US GAAP and for which we could obtain accounting data electronically and in English under both standards for the same fiscal year (eg the firm presented the accounting numbers originally under one set of standards and then restated the prior year numbers in the subsequent year under the new set of standards) Panel A shows the distribution of firms by year Panel B shows the other accounting standards to which US GAAP is compared Panel C shows the mean and median percent that the other accounting standard differs from US GAAP for the same fiscal year and accounting number Two samples are shown in Panel C The first is the full sample of firms with a change in accounting standards to or from US GAAP for which data was available The second is the subset of these firms that occurred during the sample window used in this study (ie excluding changes post-2008) The percent difference in sales is compared to the percent difference in net income and assets Tests of means are based on matched-pair t-tests Tests of medians are based on Wilcoxon matched-pair signed-rank tests Panel A Accounting standard changes by year
2003
4 6
2004
25 38
2005
8 12
2006
6 9
2007
7 11
2008
5 8
2009
5 8
2010
5 8
2011
1 2
66
Panel B Accounting standards compared to US GAAP
IFRS
47 71
Canadian GAAP
12 18
Israeli GAAP
2 3
Dutch GAAP
1 2
Indian GAAP
1 2
Japanese GAAP
1 2
Norwegian GAAP
1 2
Swiss GAAP
1 2
66
36
(Table 1 continued) Panel C Tests of differences between US GAAP and other accounting standards
Percent different from US GAAP
Comparison to sales (p-values)
N Mean Median
Mean Median
Full Sample (2001-2011)
Sales
66 236 000
Net Income
66 10653 647
005 lt001
Assets
66 1162 126
lt001 lt001
Study Sample (2001-2008)
Sales
55 257 000
Net Income
55 10303 589
011 lt001
Assets
55 1047 099
003 lt001
37
Table 2 Descriptive statistics Panel A reports the mean and medians of Excess Value for the multinational firms and single-segment benchmark firms as well as univariate tests across the subsamples Panel B reports summary statistics for the independent variables for the multinational firms The p-values of the tests of differences in means are based on t-tests The p-values of tests of differences in the medians are for a Wilcoxon rank-sum test All variables are defined in Appendix A Panel A Excess Value by sample Multinational All Benchmark Benchmark Subgroups Sample Firms US Foreign
Value Value Test of Diff (p-value) Value
Test of Diff (p-value) Value
Test of Diff (p-value)
Mean 0067 0006 lt001 -0028 lt001 0014 lt001 Median 0076 0000 lt001 -0025 lt001 0000 lt001 N 4950 33074 5998 27076 Panel B Distribution of independent variables for the multinational firms N Mean 25th 50th 75th StDev Foreign Sales 4950 0238 0074 0190 0363 0197 Industry Other 4950 0212 0000 0166 0414 0215 Log Size 4950 6955 5823 6989 8140 1687 Debt Firm Value 4950 0136 0009 0097 0212 0145 CAPX Sales 4950 0054 0020 0033 0054 0101 EBIT Sales 4950 0065 0031 0077 0121 0138 RampD Sales 4950 0049 0000 0013 0059 0083 Ad Sales 4950 0008 0000 0000 0005 0018
38
Table 3 BEA and Worldscope samples
This table reports details on affiliate sales for significant countries pooled from 1998-2008 Significant countries are defined as those in which the operations of US MNCs represent at least 02 of total firm sales (per the BEA) For significant countries the table compares their representation in terms of MNC affiliate sales in the BEA data to the distribution of firm observations in Worldscope US Foreign Affiliates (BEA) Foreign Firms (Worldscope)
Country
N (1)
Sales (millions)
(2)
Total Sales (3)
N (4)
Sales (million)
(5)
Total Sales (6)
Australia 1344 73305 056 859 255865 210 Belgium 894 52755 040 169 26136 021 Brazil 959 60245 046 1143 683167 560 Canada 2730 536767 407 1181 526750 432 China 922 75907 057 822 276541 227 France 1852 183942 139 540 105447 086 Germany 2182 275577 209 480 103888 085 Hong Kong 867 48100 036 157 34094 028 Ireland 478 36754 028 34 3467 003 Italy 1178 82116 062 111 47948 039 Japan 1494 148032 112 2281 1119223 917 Korea South 659 34500 026 3530 2752791 2255 Malaysia 386 26027 020 837 168683 138 Mexico 1405 113623 086 362 237173 194 Netherlands 1149 85105 064 47 22631 019 Singapore 1098 94917 072 139 32913 027 Spain 871 63094 048 129 37419 031 Sweden 712 32682 025 235 23090 019 Switzerland 726 60288 046 74 20242 017 Taiwan 592 30313 023 2949 787049 645 United Kingdom 3054 423934 321 1997 882423 723 United States (domicile) 4950 10460880 7923 na na na Other - Covered by WorldScope 6893 199394 151 9000 4057894 33 Other -Not Covered by WorldScope 713 5352 004 - - - Total 38108 13203609 1000 27076 12204834 100
39
Table 4 OLS regressions of firm excess values on the percent of foreign sales This table reports the results of OLS regressions of firm excess value on the percent of foreign sales (Foreign Sales) the percent of sales outside a firmrsquos primary industry (Industry Other) and control variables All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed) Dep Variable = Excess Value Coefficient t-stat Coefficient t-stat Coefficient t-stat Coefficient t-stat (1) (2) (3) (4) Foreign Sales 0349 419 0209 293 0189 266 0278 210 Industry Other -0134 -186 -0047 -072 -0053 -081 -0326 -325 Log Size 0066 634 0065 624 -0044 -096 Debt Firm Value -0196 -213 -0241 -260 -0685 -588 CAPX Sales 0901 425 0893 435 0488 227 EBIT Sales 1154 542 1170 547 0408 189 RampD Sales 2908 1242 2932 1237 1544 379 Ad Sales 1869 263 2003 280 -1696 -119 Year indicators No No Yes Yes Firm indicators No No No Yes R2 0017 0212 0226 0768 Observations 4950 4950 4950 4950
40
Table 5 Dynamic panel GMM model regressing firm excess values on the percent of foreign sales This table reports the result from a regression of the excess value on the percent of foreign sales (Foreign Sales) the percent of non-primary industry sales (Industry Other) and control variables using a dynamic panel GMM estimator All control variables are considered to be endogenous with the exception of the year indicator variables AR(1) and AR(2) are tests for first-order and second-order serial correlation in the first differenced residuals with the null hypothesis of no serial correlation The null hypothesis of the Hansen test of overidentification is that all instruments are valid The null hypothesis of the difference-in-Hansen test of exogeneity is that the instruments used for the equation in levels are exogenous All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed) Dep Variable = Excess Value Coef t-stat Excess Value (one lag) -0018 -082 Excess Value (two lags) -0048 -272 Foreign Sales 0371 270 Industry Other -0163 -171 Log Size 0010 123 Debt FirmValue -1135 -820 CAPX Sales 0590 342 EBIT Sales -0047 -300 RampD Sales 1376 531 Ad Sales -0751 -040 AR(1) test (p-value) 000 AR(2) test (p-value) 017 Hansen test of over-identification (p-value) 030 Difference-in-Hansen test of exogeneity (p-value) 093 Year indicators Yes Number of firms 1016 Number of observations 4090
41
Table 6 OLS regression of firm excess values on the percent of foreign sales controlling for a control premium This table reports the result of the OLS regression of firm excess value including firm and year fixed effects with an additional control variable to proxy for a country-level ownership control premium (Control Premium) All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed) Dep Variable = Excess Value Coef t -stat Foreign Sales 0303 224 Industry Other -0327 -325 Log Size -0045 -100 Debt FirmValue -0683 -585 CAPX Sales 0490 226 EBIT Sales 0409 189 RampD Sales 1545 379 Ad Sales -1689 -119 Control Premium -1004 -062 Year indicators Yes Firm indicators Yes R2 0768 Observations 4950
42
Table 7 OLS regression of firm excess values on the percent of foreign sales controlling for the cost of capital This table contains the results of an OLS regression of firm excess value including firm and year fixed effects with an additional control variable to proxy for a country-level cost of capital (Cost of Capital) All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed) Dep Variable = Excess Value Coef t -stat Foreign Sales 0255 178 Industry Other -0326 -324 Log Size -0043 -094 Debt FirmValue -0687 -589 CAPX Sales 0485 225 EBIT Sales 0406 188 RampD Sales 1539 379 Ad Sales -1703 -120 Cost of Capital 0004 039 Year indicators Yes Firm indicators Yes R2 0768 Observations 4950
43
Table 8 OLS regression of firm excess values on the percent of foreign sales controlling for aspects of corporate governance This table reports the results of two OLS regressions In the first column are the results of the firm excess value regression including firm and year fixed effect with five proxies for corporate governance included ndash the percent of shares owned by the CEO and the percent squared (CEO Ownership and CEO Ownership squared) the percent owned by institutional investors (Institution Ownership) whether there is a powerful CEO (Powerful CEO) and the Gompers Ishii and Metrick (2003) governance index (Governance Index) In the second column the regression includes the same sample and variables with the exception of the governance proxies which are excluded All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed)
Governance
No Governance (same sample)
Dep Variable = Excess Value Coef t-stat Coef t -stat Foreign Sales 0259 159 0251 154 Industry Other -0099 -080 -0099 -079 Log Size -0105 -185 -0102 -180 Debt FirmValue -0813 -478 -0817 -482 CAPX Sales 0440 252 0438 249 EBIT Sales 0457 247 0457 246 RampD Sales 1717 284 1716 286 Ad Sales -2210 -143 -2190 -143 CEO Ownership 0010 007
CEO Ownership squared -0029 -017 Institution Ownership 0047 067 Powerful CEO -0003 -014 Governance Index 0009 103
Year indicators Yes Yes Firm indicators Yes Yes R2 0811 0810 Observations 2550 2550
- Cover for paperpdf
- crrz_20130603_SMUpdf
-
4
country-industry in which US-based MNCs operate These data provide a substantial advantage
over the Compustat database used by prior research While Compustat provides some
information about the countries and industries in which these firms operate it is not as detailed
about either of these dimensions (or their interaction) and heavily relies on managerial disclosure
choices which could induce measurement bias (Villalonga 2004a) In addition to the new
method of estimating the value effects of foreign operations we provide evidence that MNCs
trade at a premium which stands in contrast to the discount documented in Denis et al (2002)
This difference is primarily attributed to our use of foreign benchmarks to construct the imputed
values of foreign operations6
Our finding of a premium better reconciles with the findings in the international trade
literature By focusing on nonpublic establishment level (eg factory store or office) data the
trade literature is able to generate relatively precise proxies for total factor productivity (TFP)
and provides robust evidence that firms engaging in international trade are more productive than
those that do not (see Helpman 2006 and Syverson 2011 for reviews) Furthermore
productivity differences tend to be highly persistent even within narrowly defined industries
(Syverson 2011) Due to data limitations it is difficult to accurately measure TFP at the firm
level especially for firms which operate in multiple industries andor countries Nevertheless
Helpman Melitz and Yeaple (2004) provide some evidence that US firms engaging in FDI
have labor productivity advantages over firms that do not Reconciling the discount to
multinational operations found by Denis et al (2002) to the trade literature requires that the
persistent productivity advantages of firms engaged in international trade must be more than
offset by some other cost (eg agency costs) While this relation is possible it is difficult to
6 We repeat the analysis of Denis et al (2002) for our sample and confirm that differences in value effects of foreign operations are driven by the different methods for measuring the imputed value of foreign operations
5
conjecture why the most productive firms would have the largest agency costs In contrast the
finding of a premium easily reconciles with the trade literature
Given our result we investigate whether the premium is simply an artifact of innate
characteristics of firms that choose to invest abroad For example Helpman et al (2004) present
a simple model with heterogeneous productivity endowments The firms that receive the highest
productivity endowments are the ones capable of paying the fixed costs to establish a foreign
subsidiary To the extent productivity advantages create excess value one would expect the type
of firms that choose to invest abroad would be valued at a premium even in the absence of their
foreign investments After including firm fixed effects to control for time invariant firm
characteristics (eg innate productivity advantages) we continue to find economically and
statistically significant evidence of a premium to foreign operations
To further account for the endogenous nature of FDI we estimate a dynamic panel data
model using the generalized method of moments (GMM) estimator developed by Arellano and
Bond (1991) Arellano and Bover (1995) and Blundell and Bond (1998) This estimator allows
us to simultaneously account for the potential endogeneity of FDI industrial diversification and
firm value This method also yields a significant premium which is of similar magnitude and
significance to that estimated using fixed effects
We also perform a series of additional robustness tests First we examine whether the
measured excess value premium is driven by operations in countries with large control premiums
(Dyck and Zingales 2004) In countries with large control premiums using stock price in the
calculation of total firm value could underestimate the true value of the benchmark firms and
thereby induce an excess value premium We fail to find evidence that the excess value premium
is driven by such countries
6
Second we examine whether access to a low cost of capital is a meaningful source of the
US MNC advantage We incorporate foreign benchmarks in our method of estimating firm
excess value as it allows us to incorporate attributes of operating in those foreign countries ndash a
key attribute being the cost of capital Relative to the local benchmarks in foreign countries US
MNCs have the ability to obtain funds outside the local operating environments either by
borrowing in the US market or transferring capital internally When competing against firms in
shallow capital markets with high costs of capital MNCs may have a competitive advantage
over local foreign companies due to these alternative sources of capital However investors and
lenders are likely to expect higher rates of return from the additional risk associated with
operating in these environments making it unclear whether a multinational network will enhance
value in countries with a high cost of capital In addition foreign firms have some ability to
access international capital markets which mitigates the advantages to US firms To examine
whether the cost of capital plays a role in our finding of a premium we include a proxy for the
extent of operations in countries with higher costs of capital Our results are unaffected by the
inclusion of the proxy
Finally we control for several corporate governance proxies which Hoechle et al (2012)
find to be correlated with excess values (and industrial diversification) While magnitude of the
premium is virtually unaffected by including these proxies the significance level does decline
due to the reduced sample size
Our study contributes to the literature on multinational firms Our new method of estimating
excess values for multinational firms provides strong evidence of a statistically and economically
significant premium associated with increased international operations The premium is robust to
a variety of specifications and controls designed to evaluate alternative explanations In addition
7
this result easily reconciles with the international trade literature that establishes a link between
greater productivity and international operations though we do not find results supporting this as
the only source of the premium As segment disclosures improve and as new data sources
become available on multinational activity (eg Bureau Van Dijk) our empirical approach can
be used to investigate the extent to which the home country of the parent might influence the
extent with which foreign operations affect firm value (ie by examining non-US based
MNCs)7 Future studies could also examine the underlying forces affecting the excess value
premiums across countries
We also contribute to the understanding of international accounting standards and their
comparability Many studies seek to compare firms using different accounting standards These
comparisons generally rely on information generated by a particular accounting regime By
comparing commonly used metrics (total assets net income and sales) reported for the same
firm and year across different regimes we provide insights as to the relative comparability across
standards In particular we find that sales is reported significantly more consistently across
accounting regimes than the other metrics examined This finding should aid researchers in
reducing measurement error when comparing accounting information in multinational settings
The paper proceeds as follows Section 2 discusses the motivation for finding a premium or
discount to multinational operations Section 3 describes our data and how the excess value of a
firm is measured A discussion of our independent variables and primary findings is presented in
Section 4 with additional robustness tests provided in Section 5 Finally Section 6 concludes
7 As MNCs are ultimately bound by the tax regulatory and legal frameworks of their home country the valuation effect of foreign operations that we document for US-based MNCs may be different in a sample of non-US based MNCs with the same country-industry footprint Theorists refer to these as lsquolocational advantagesrsquo enjoyed by all multinational firms of a given nationality and obtained irrespective of skills or capabilities unique to a particular firm (Yamin 1991) There is also evidence of locational advantages in the market for corporate control (Huizinga and Voget 2009)
8
2 Premium versus discount
In a frictionless world where managers maximize firm value and markets are efficient there
should be no discount or premium to operating in foreign countries However there are a number
of forces that may cause actual firm value to deviate from the implied firm value
On one hand MNCs may have comparative advantages that generate premiums (ie positive
excess values) relative to a similar footprint of stand-alone firms Having operations in a variety
of locations that access different supplies of inputs and customers allows the firm to take
advantage of changing market conditions by shifting operations or products to maximize firm
value more so than a firm operating in a single country Similarly having access to a variety of
institutional settings such as different tax codes legal regimes and financial markets can provide
MNCs with options beyond those available to firms located in a single country
Another possible factor associated with a premium to multinational operations is the cost of
capital For operations located in countries with shallow capital markets or weaker creditor
rights operating within a conglomerate can provide the benefit of a lower cost of capital for
investments and expansion for at least two reasons First as our MNCs are domiciled in the US
their foreign segments have greater access to US capital markets relative to local competitors in
the foreign jurisdiction Second cash rich segments with few investment opportunities can
finance investments in cash poor segments with positive net present value projects (eg Myers
and Majluf 1984) Consequently diversified firms should be less liquidity constrained and better
able to shift resources to the most valuable investment opportunities Research has found that
internal financing is used more often when diversified firms have operations in countries with
more costly external financing (Desai Foley and Hines 2004) Multinational firms may also be
relatively more protected than single-country firms when negative shocks hit external capital
9
markets (Foley Desai and Forbes 2008) In a recent study Matvos and Seru (2011) find that
industrially diversified firms perform better than non-diversified firms when external capital
markets are impaired
While these arguments suggest that MNCs enjoy certain advantages when the foreign cost of
capital is high these benefits should be reduced by the additional risk associated with operating a
business in a high cost of capital environment For example Verizon is a multinational firm
operating in a number of countries One of the higher cost of capital locations in which they
operated was Venezuela While the multinational structure of Verizon may have mitigated some
of the risks associated with operating in a higher cost of capital location the firms was unable to
eliminate all of these risks In fact in 2007 Verizonrsquos operations in that country were
nationalized resulting in a net extraordinary loss of $131 million that year8 In other words
MNC investors are likely to expect higher rates of return to compensate for higher risk which
likely offsets some (if not all) of the potential excess value premium
On the other hand multinational firms may incur a discount (ie negative excess values)
relative to a similar footprint of stand-alone firms Potential reasons for a discount largely rely on
the existence of agency costs Multinational firms tend to be larger more complex and less
transparent The combination of complexity and opacity inherent in MNCs reduces a firmrsquos
reliance on external capital as the cost of such capital increases due to agency costs (Desai et al
2004) As a result of the reduced reliance on external funds MNCs face a reduced level of
monitoring As noted by Jensen (1986) managers have incentives to increase firm size beyond
that which is optimal with overinvestment or misallocation of funds to pet projects If this lack of
monitoring is in combination with available internal capital empire building becomes easier
(Hope and Thomas 2008) Furthermore the current segment reporting standards under US 8 See the 10-K of Verizon Communications Inc dated December 31 2007 (footnote 2)
10
GAAP only require highly aggregated disclosures of foreign operations and allow for
considerable managerial discretion As a result operating in multiple countries makes it easier
for management to hide poor performance ndash either their own or that of a division ndash such that low
quality managers are more likely to be retained and overcompensated and poorly performing
divisions retained longer than optimal Other reasons one might expect a discount to be applied
to multinational firms include higher coordination costs (eg coordinating across different
cultures and languages) as well as additional risks These risks include exposure to multiple
political regimes legal regimes economic regulations and currency fluctuations
Past studies investigate whether foreign operations of US firms enhance or reduce firm
value However all of these studies benchmark the value of the foreign operations of an MNC to
US domestic firms For instance Denis et al (2002) determine the implied value of a US
MNC using domestic (US) single-segment firms in the same industry as a benchmark Studies
using Tobinrsquos Q to investigate valuation effects of MNCs also rely on domestic benchmarks
(eg Morck and Yeung 1991 Morck and Yeung 2001 Gande Schenzler and Senbet 2009)
However using a US domestic firm as a benchmark for the value of foreign operations
implicitly makes two assumptions First this method assumes that the risks and expected growth
rates of foreign operations are equivalent to those of domestic operations This assumption is
inconsistent with the findings of Hail and Leuz (2009) which provides evidence that the cost of
capital varies substantially across countries Second this method assumes that there is excess
domestic capacity to allow for expansion with profitability similar to current domestic
operations We relax these assumptions by measuring the imputed value of a foreign component
of a US firm as if that component operated autonomously within that foreign country (rather
than in the US) Specifically our multiples allow the cost of capital and expected growth rates
11
to vary across not just industry but also country Furthermore our counterfactual does not
require the assumption that operations be relocated mitigating the production and sales capacity
concerns
3 Measuring excess value
31 Description of excess value
Figure 1 shows the average percent of foreign sales (based on segment disclosures under US
GAAP) across firms through time and illustrates that firms are continuing to expand
internationally9 This expansion highlights the importance of understanding whether the decision
to operate internationally is on average a value enhancing corporate strategy We contribute to
this understanding by evaluating whether the firm as a whole is worth more or less than its
imputed value (ie the firm is valued in excess of the sum of its individual components)
We measure the excess value of a firm as the logarithm of the ratio of actual firm value to
imputed value based on the method used in Berger and Ofek (1995) The imputed value is the
hypothetical value of the MNC under the assumption that its country-industry components
operate as independent entities A key innovation of our study is an alternative approach to
imputing the value of the foreign operations of an MNC Components of MNCs operating in a
given industry and country are imputed using the value of single-segment firms operating in the
same industry and country Thus we ensure the discount rates and expected growth rates are
applicable to each given industry and location in estimating the implied values of the MNC
segments
9 The discontinuity in industrial diversification (the percent of sales outside the primary industry) between 1997 and 1998 is the result of a change in segment reporting rules
12
The actual firm values are observable for all firms in our sample The hypothetical firm
values for multi-segment firms are imputed using market value to sales ratios herein referred to
as lsquomultiplesrsquo of the median single-segment firm operating in the same country-industry Thus
these single country-industry firms serve as benchmarks for an MNCrsquos operations within that
same country-industry The imputed value of the country-industry component can be viewed as
an estimate of what that component would be worth if it operated independently The sum of the
imputed values across all country-industry components represents the hypothetical value that the
multi-segment firm would be worth if it were broken apart
32 Data
Four primary data sources play a distinct role in carrying out our analysis i) Bureau of
Economic Analysis (BEA) data ii) Compustat Segment data iii) Worldscope data and iv)
Compustat Fundamentals Annual data Broadly speaking BEA data provide information about
the diversified nature of MNCs Compustat Segment data provide information about the
industrial diversification of US domiciled firms Worldscope data are used to compute
multiples for single-segment foreign firms that serve as benchmarks for the foreign operations of
MNCs (ie foreign benchmarks) and Compustat Fundamentals Annual data are used to
compute multiples for single-segment domestic firms that serve as benchmarks for the domestic
operations of multinational firms We also use the latter data source to construct our primary
control variables
13
Key to the execution of our study is our access to BEA data Federal law requires US-
domiciled MNCs to report certain financial and operating data to the BEA10 With regard to
observing the country-industry operations of MNCs use of the BEA data overcomes two
important limitations of Compustat Segment data First Compustat does not consistently identify
the specific location or scale of MNCsrsquo foreign operations11 Second Compustat does not
identify the industry activities in each country of operation This latter point is particularly
limiting for MNCs because domestic industry activity need not mirror foreign industry activity
An MNC generating an equal proportion of sales in two industries in the domestic market need
not generate industry sales in equal proportions in every foreign market12 The lack of detail and
consistency in Compustat Segment data arises because segment reporting is highly aggregated
and firms exercise substantial discretion in defining segments under Accounting Standards
Codification 280 (ASC 280) (eg Villalonga 2004a and Bens Berger and Monahan 2011)
The BEA defines an MNC as the combination of a single US entity called the US parent
and at least one foreign affiliate ndash that is these firms have a physical presence outside the US
due to FDI Since 1982 MNCs have completed a mandatory and confidential lsquoSurvey of US
Direct Investment Abroadrsquo for the domestic operation and the operations of each foreign
affiliate defined as a foreign entity in which the US parent holds at least a 10 percent equity
interest (directly or indirectly) The data are reported in US dollars on a fiscal year basis and in
10 BEA surveys are conducted pursuant to the International Investment and Trade in Services Survey Act (PL 94-472 90 Stat 2059 22 USC 3101-3108) See httpwwwbeagovsurveysdiasurvhtm and Mataloni (2003) for more detailed information on the BEA data 11 Exhibit 21 disclosures of material subsidiaries and their locations in SEC 10-K filings could potentially help overcome the inability to observe the location of the firmrsquos foreign operations from segment disclosures However at least three limitations remain (1) for industrially diversified multinational firms the researcher would be required to make an assumption about the proportion of industrial diversification in each country (2) Exhibit 21 provides no information about the scale of activity in each jurisdiction and (3) managerial discretion is used to determine a lsquomaterialrsquo subsidiary under Section 601 of SEC Regulation S-K 12 In fact the expectation that a multinational firm would exhibit different industry membership in different countries is consistent with the theory of vertical foreign direct investment where firms separate their various value chain activities and locate them in the most favorable country (see eg Hanson Mataloni and Slaughter 2005)
14
accordance with US Generally Accepted Accounting Principles (US GAAP) For each year
we observe the sales industry composition and location of not only the parent but also each
affiliate13
The procedures we use to construct our sample are similar to those used by Berger and Ofek
(1995) and Denis et al (2002) Using firm-years appearing in Compustat Fundamentals Annual
and Compustat Segment data we obtain a sample of firms domiciled in the US with total sales
exceeding $20 million We exclude firms in the financial service (SIC codes 6000 to 6999) and
utility (SIC codes 4000 to 4999) industries and eliminate firm-years when the sum of segment
sales (Compustat geographic or Compustat business) is not within 1 of total firm sales for that
year We restrict our sample period to include fiscal years beginning after December 15 1997 as
ASC 280 Segment Reporting substantially altered the definition of a reporting segment under
US GAAP This restriction ensures that Compustat Segment data reflects the use of a consistent
accounting standard Finally we require that our MNCs appear in both Compustat and BEA
data The requirement that our sample firms appear in the BEA database ensures that the firms
have observable international operations In sum these procedures result in 1166 multinational
firms incorporated in the US with 4950 observations between 1998 and 2008 that meet all of
our data requirements
33 Measuring excess value
The dependent variable in our study is excess firm value (Excess Value) defined as the
logarithm of the ratio of actual firm value to imputed firm value (Berger and Ofek 1995)
13 We obtain sales industry membership and country location for affiliates from one of two sources When affiliates exceed the BEA-determined reporting thresholds (ie if their assets sales or net income (loss) exceed $7 million in 1999 $30 million in 2000 through 2003 $10 million in 2004 and $40 million in 2005 through 2008) they must file their own detailed survey When affiliates fall below the thresholds we obtain our data from an attachment to the parent survey
15
Appendix A provides the definition of this and all other variables used in our analysis We
observe actual firm value equal to a firmrsquos market value of equity plus book value of liabilities
(ie book value of total assets minus book value of total equity) using Compustat Fundamentals
Annual data We calculate imputed firm value as the sum of the imputed values of a firmrsquos
operations in each country-industry Our method for imputing the value of the separate
components of a firm can best be described in three steps First we obtain total sales generated
by a firm for each country-industry in which it operates14 Second we obtain multiples (market
value to sales ratios) for benchmark firms operating in those same country-industries Third we
multiply a firmrsquos country-industry sales by the applicable median country-industry multiple to
obtain the imputed market value for each country-industry operation We perform each step
annually
Conceptually imputed values can be based on assets (ie Tobinrsquos Q) net income or sales
multiples We restrict ourselves to sales multiples for two reasons First using value to sales
ratios maintains consistency with the prior research on the excess value implications of
multinational operations (eg Denis et al 2002) Second as our method uses accounting data
for foreign companies to compute country-industry multiples the accounting numbers we use
need to be consistently measured across firms using different accounting standards We find that
sales data are measured most consistently
To assess the comparability of sales relative to either net income or assets across various
accounting standards we examine all firms listed on Worldscope as changing to or from US
14 Specifically we allocate total sales from Compustat to each component based on the proportion of BEA sales (after eliminating intercompany sales) occurring in each country-industry This step ensures that our total firm sales do not deviate from a firmrsquos total sales as reported in the 10-K
16
GAAP between 2005 and 201115 We obtain accounting data for the year of the change and the
prior year where electronically available in English We are able to obtain data for 66 firms
changing standards as detailed in Table 1 The distribution of years for which accounting data
are available under two different accounting standards for the same fiscal year is shown in Panel
A The years shown are the year prior to the change as these accounting numbers were provided
under the original standard and then subsequently restated in the following year under the new
standard for comparative purposes The majority of the shifts were to or from IFRS (71)
though seven other accounting standards are noted (Panel B) We examine the percent that the
non-US GAAP reported number differs from the US GAAP reported number in Panel C to
assess the comparability of these three summary accounting numbers across various accounting
standards Examining both the full sample as well as the subset that overlaps our study we find
that sales are more consistently measured than net income or assets
The sum of the imputed values across the country-industry components of an MNC is an
estimate of the value of a portfolio of unrelated businesses that mirror the related businesses of
the firm Consequently a comparison of the actual firm value with the imputed firm value is a
measure of how a multinational network affects firm value We provide descriptive statistics for
Excess Value for our sample of 4950 MNC firm-years in Table 2 Panel A This table also
includes univariate comparisons of the values between the MNC firms and the domestic single-
segment and foreign single-segment benchmark firms The Excess Value measure is significantly
higher for MNCs suggesting a premium in firm value for multinational firms relative to the
benchmark firms in total as well as the domestic and foreign subgroups (p-values for tests of
15 Due to the hand collection efforts required and the limitation on electronic filings we began our search with changes documented in 2005 which generally require filings from 2004
17
means and medians are lt 001)16 These results provide some initial evidence that operating in
multiple countries is positively associated with firm value
To estimate country-industry multiples we rely on Worldscope financial data on foreign
firms and Compustat Fundamentals Annual for domestic firms We restrict the firms to include
only those with at least 90 of sales income and assets inside the country of domicile (ie
those that do not report significant multinational activity) and that operate in a single industry
We refer to these firms as benchmark firms (either foreign or domestic depending on the country
of domicile) these firms do not appear in any of our regressions We report the number of
benchmark firms during our sample period in Table 2 Panel A The industry criterion is based on
the two-digit SIC code17 For every country that has at least five firms in the respective industry
and year we use the median ratio of market value to sales18 This ratio is the country-industry
multiple ndash an input required to compute imputed values
To determine the country-industry composition of MNCs we rely on BEA data In Table 3
we report the aggregate number of foreign affiliates and total sales by country as provided by
the BEA data for our sample of 4950 MNCs The specific countries tabulated are those that
represent at least 02 percent of total firm sales (pooled across all years) in our sample The top
five foreign countries in which MNCs generate sales are Canada United Kingdom Germany
France and Japan Table 3 also provides a comparison of the number of foreign benchmark
firms available in Worldscope in the specific countries in which our sample of MNCs generate
16 Similar to Berger and Ofek (1995) the median values differ slightly from zero for the benchmark single-segment firms due to the truncation of extreme excess values 17 BEA reporting constrains us to the two-digit SIC code level 18 When five firms are not available for a given country and industry we follow Berger and Ofek (1995) and move to a more general industry definition (eg one-digit SIC code) This procedure results in a mean (median) 630 (757) of MNC sales matched at the two-digit level
18
the majority of their sales Country coverage in Worldscope is not available for countries
representing 004 percent of total firm sales (per BEA)
4 Empirical results
41 Independent variables
Recall that our objective is to examine the overall relation between excess firm value and
multinational operations Our proxy for the extent of multinational operations is the percentage
of total firm sales made by foreign operations (Foreign Sales) By this measure the extent of
multinational activity in our sample of MNCs is non-trivial ndash Table 2 Panel B shows that
approximately 24 percent of sales are generated by foreign operations on average Our first
analysis estimates ordinary least square regressions of Excess Value on Foreign Sales and
control variables (discussed below)
We include variables in our regression to control for other potential determinants of excess
value These are the percentage of sales made by the firm outside its primary industry (Industry
Other) to control for any relation between industrial diversification and excess value as in Berger
and Ofek (1995) For this purpose we obtain industry sales using Compustat Segment data19 A
firmrsquos primary industry is the industry in which the firm generates the majority of its sales and
we determine industry sales at the business segment level We set Industry Other equal to zero
for firms that operate in a single business segment
Consistent with prior research we also include controls for firm size (Log Size) the ratio of
long term debt to total market value (Debt Firm Value) the ratio of capital expenditures to total
sales (CAPX Sales) the ratio of earnings before interest and taxes to total sales (EBIT Sales)
the ratio of research and development expenses to total sales (RampD Sales) and the ratio of 19 Our results are unaffected by using BEA data to measure Industry Other
19
advertising expenses to total sales (Ad Sales) Table 2 Panel B provides the distribution of the
control variables All independent variables are Winsorized at 1 In our OLS regressions we
use heteroskedasticity and autocorrelation consistent robust standard errors with clustering at the
firm level
42 Primary regression analysis
In this section we examine the relation between firm excess value and the amount of foreign
activity at US MNCs Table 4 contains the results of regressions of excess value on Foreign
Sales for multiple specifications In column (1) Excess Value is regressed on both the percent of
foreign sales (Foreign Sales) and the percent of sales outside the firmrsquos primary industry
(Industry Other) We add the control variables to the regression in column (2) and year
indicators in column (3) In all three specifications the coefficient on the percent of foreign sales
remains significantly positive (with a p-value lt 001) For a sense of economic magnitude in
column (3) a one standard deviation (197) increase in the percent of foreign sales (Foreign
Sales) is associated with a 41 (equal to (e0349-1) times 0197) increase in firm excess value The
effect is larger for the other two columns ndash 82 increase in firm excess value in column (1) and
46 in column (2)20
Our finding of a premium reconciles with the international trade literature which finds that
firms engaging in international trade are more productive than those that do not (see Helpman
2006 and Syverson 2011 for reviews) Due to data limitations it is difficult to accurately
measure TFP at the firm level especially for firms which operate in multiple industries andor
20 Using the alternative strategy that employs US domiciled single-segment firms as the benchmark for the imputed value of the foreign operations Denis et al (2002) find that greater foreign activity results in a discount to firm value Using the Denis et al (2002) method to calculate excess value in our sample we obtain a significantly negative regression coefficient on Foreign Sales using the models in Table 4 columns (2) and (3) (the coefficient is not significant using the model in column (1) results untabulated)
20
countries As an alternative method to evaluate whether our finding of a premium is simply the
result of more productive firms also having more multinational operations we investigate
whether the premium is simply an artifact of innate characteristics of firms that choose to invest
abroad For example Helpman et al (2004) present a simple model with heterogeneous
productivity endowments The firms that receive the highest productivity endowments are the
ones capable of paying the fixed costs to establish a foreign subsidiary
To the extent that productivity advantages create excess value one would expect the type of
firms that choose to invest more abroad would be valued at a premium even in the absence of
their foreign investments To control for time invariant firm characteristics (eg innate
productivity advantages) and hence a potential source of endogeneity we add firm-level fixed
effects in column (4) of Table 4 We continue to find that the extent of foreign operations
(Foreign Sales) is positively associated with Excess Value Across all specifications the sign
on all significant control variables is consistent with prior research (eg Berger and Ofek 1995
and Denis et al 2002)
The finding of a premium makes a significant contribution to the relatively sparse literature
on the value effects of multinational activity Our measure of imputed value expands upon the
methods used by Berger and Ofek (1995) and Denis et al (2002) but allows us to answer a
different question than Denis et al (2002) Holding constant the extent of industrial
diversification we ask whether MNCs are more valuable than a portfolio of benchmark firms of
a similar geographic footprint In contrast Denis et al (2002) ask whether holding constant the
extent of industrial diversification MNCs are more valuable than a portfolio of domestic (US)
benchmark firms We believe that asking whether an MNCrsquos foreign (domestic) operations are
21
more valuable than those of a foreign (domestic) firm is a more appropriate way to assess the
value implications of a multinational network
5 Robustness
51 Endogeneity related to dynamic relations
Prior research argues that the relation between industrial diversification and excess firm
value could be endogenous through dynamic relationships based on observing past outcomes
Managers of firms likely choose to enter or exit industries or geographic regions based upon
their previous performance For example Campa and Kedia (2002) find that firms are more
likely to enter new industries when prospects in their current lines of business are deteriorating
Similar dynamics could also drive a relation between excess value and FDI We assess the
robustness of the previous results by estimating a dynamic panel data model which allows us to
control for dynamic endogeneity in addition to firm level unobserved heterogeneity We employ
the GMM estimator for dynamic panel models developed by Arellano and Bond (1991) Arellano
and Bover (1995) and Blundell and Bond (1998) Our results from this analysis confirm our
previous findings that firms with a larger degree of foreign operations have positive excess
values
Dynamic panel data models are generalizations of the traditional fixed effects model where
lags of the dependent variable are added to the right hand side of the equation These additional
lags control for the impact a firmrsquos past performance has on its current performance through the
channels discussed above The GMM estimator for dynamic panel models developed by
Arellano and Bond (1991) starts by differencing the data at the firm level in neighboring time
periods to eliminate any firm specific time invariant unobserved heterogeneity that may be
22
present Eliminating the firm specific fixed effect by differencing the data with lags of the
dependent variable on the right hand side introduces correlation between the new error term and
the differenced explanatory variables This problem is solved by applying the standard GMM
estimator using lags of both the original explanatory variables and excess value as instruments
We note that the dynamic panel data framework allows us to treat both international and
industrial diversification (as well as the other firm-specific control variables) as endogenous
Following Hoechle et al (2012) we also allow contemporaneous values of these variables to be
treated as endogenous
Lagged values of the explanatory variables are valid instruments in a dynamic panel data
model when the model is dynamically complete (eg Wooldridge 2010) A dynamic panel data
model is dynamically complete conditional on the unobserved effect if enough lags of both the
explanatory and dependent variable are included to make the resulting error term uncorrelated
with all the right hand side variables Under this assumption any lags for the explanatory
variables beyond this can be used within the GMM estimator as instruments Like Hoechle et al
(2012) we add two years of lags of a firmrsquos excess value to the right hand side assuming that
this is enough lags to control for dynamic feedback effects We then use past values of all
variables beyond the second lag up to a total of seven years to act as instruments
The results of the dynamic panel data estimator are reported in Table 5 These results confirm
the previous finding of a positive relation between the degree of international operations and
excess value We note that the size and significance level of the coefficient on Foreign Sales
diversification are robust to changes in the number of lags used as instruments The Arellano and
Bond (1991) estimator includes testing diagnostics for correct specification of the model If the
model is correctly specified differences in the residuals from the levels equation should be
23
serially uncorrelated at lag one but correlated at lag two The p-values for tests of serial
correlation of the residuals at lags 1 and 2 are reported in Table 5 and these indicate that the
model is not rejected We also report in Table 5 the Hansen test for overidentification which is a
test of the null hypothesis that the instruments are valid The p-value for this test is 030
indicating that we fail to reject this null hypothesis Finally we report another test for exogeneity
known as the difference-in-Hansen test which tests the null hypothesis that the instruments in
the original equation are valid We fail to reject this null hypothesis as well
52 Identifying value effects of multinational operations via cross-border acquisitions
Another alternative to investigating a causal link between foreign direct investment and firm
value is an event study of foreign acquisitions21 Acquired foreign targets represent only a small
fraction of MNCsrsquo worldwide sales in the year of the acquisition ndash less than 1 percent in our
sample Additionally cross-border acquisitions are not the primary source of international
expansion for the average firm We find that foreign acquisitions account for between 18 percent
and 55 percent of the year-over-year change in foreign sales with the remainder of the growth
coming from either newly established entities (ie greenfield investments) or growth in existing
operations22 These results suggest that acquisitions are not a material source of growth for the
21 Doukas and Travlos (1988) find positive returns when the acquirer is not operating in the target firmrsquos country and insignificant returns when the acquirer is operating in the target firmrsquos country or expanding internationally for the first time Morck and Yeung (1991) find marginal evidence of positive returns for firms with intangible assets Finally Doukas (1995) finds positive returns only for firms with average Tobinrsquos q ratios greater than one More recent work also using an event study methodology provides mixed evidence on valuation effects of global diversification through acquisitions Dos Santos Errunza and Miller (2008) find insignificant returns for firms with already established foreign operations firms establishing initial foreign operations and firms acquiring targets in related industries Doukas and Kan (2006) and Moeller and Schlingemann (2005) find reductions to shareholder value while Francis Hasan and Sun (2008) find positive returns for acquirers of targets in segmented financial markets 22 New foreign affiliates to an MNC group each year report on the BEA survey whether they entered the group as an acquisition or a newly established entity (ie greenfield investment) Affiliates under the lowest reporting threshold do not report this information resulting in our inability to precisely identify the source of 33 percent of the increase in firm sales as either acquisition or greenfield
24
typical US MNC suggesting that an event study would lack power and generalizability For
these reasons we do not implement an event study
53 Control premium
A control premium or private benefits of control occurs when ldquosome value whatever the
source is not shared among all the shareholders in proportion of the shares owned but it is
enjoyed exclusively by the party in controlrdquo (Dyck and Zingales 2004 p 541) Using stock
prices of traded shares held by minority shareholders in determining the market value to sales
ratios of our benchmark firms could underestimate the true value of our benchmark firms if those
firms face large control premiums This in turn could induce an excess value premium because
the imputed value of these country-industry segments would be artificially low The existence of
such benefits is more prevalent in some countries than others and could affect the value of an
MNCrsquos operations in such domains We use the country-level control premiums from Dyck and
Zingales (2004) and using each firmrsquos country sales as a percent of total firm sales we obtain a
weighted average control premium that each MNC faces (Control Premium) For countries not
included in the Dyck and Zingales (2004) analysis we set the control premium equal to zero23 In
Table 6 we show that Foreign Sales remains positive and highly significant (p-value lt 001)
when we include the proxy for control premium As a result it does not appear that the excess
value premium is driven by MNCs operating in countries where majority shareholders are able to
extract large control premiums
23 Results are robust to replacing missing values with the mean control premium of the respective GDP quartile
25
54 Cost of capital
As discussed in Section 2 when countries have shallow capital markets a multinational
network may provide the benefit of a lower cost of capital for investments through an internal
capital market as well as through better access to the US capital market Prior research
establishes that there is substantial variation in the cost of capital across countries (Erb Harvey
and Viskanta 1996 and Hail and Leuz 2009) To determine whether the documented premium
is due to cost of capital advantages we include a control variable to capture the difference
between the MNCrsquos cost of capital and the cost of capital faced by its competitors in the global
markets in which it operates
We compute our Cost of Capital proxy in two steps First we obtain the country-year credit
rating published by Institutional Investor24 The rating ranges from 0 to 100 where higher values
imply that a country has a higher default risk Country-level credit risk is a reasonable predictor
of expected equity market returns and volatility (Erb et al 1996) and exhibits a highly
significant correlation with international accounting-based estimates of imputed cost of capital
(Hail and Leuz 2009) Second using each firmrsquos country sales as a percent of total firm sales
we obtain a weighted average credit risk rating across the countries in which each firm operates
A higher value implies an MNC operates in countries with higher cost of capital In Table 7 we
do not find a result consistent with MNCs gaining an advantage from their access to low cost
capitalndash Cost of Capital is not significantly associated with Excess Value25
24 We thank Cam Harvey for providing these data 25 Results are consistent using cost of capital as defined in Erb et al 1996
26
55 Corporate governance
In light of the recent finding that corporate governance mitigates the diversification discount
we examine whether the premium to multinational activity is sensitive to controls for corporate
governance Hoechle et al (2012) find that the magnitude of the industrial diversification
discount is decreased and in several specifications no longer significant in the presence of
governance proxies In the first column of Table 8 we include the five governance proxies that
are consistently significantly associated with excess value in Hoechle et al (2012) ndash the percent
of CEO ownership (CEO Ownership and CEO Ownership squared) the percent of institutional
ownership (Institution Ownership) whether the firm has a powerful or influential CEO
(Powerful CEO) and the governance index from Gompers Ishii and Metrick (2003)
(Governance Index)
As documented in Table 8 including these control variables reduces our sample size by
almost 50 (to 2550 observations) and tends to exclude the smaller firms from the analysis
None of the governance proxies are significant Relative to column (4) of Table 4 the coefficient
on Foreign Sales is similar in magnitude but is no longer statistically significant at
conventional levels To investigate whether the decreased significance is due to the smaller
sample or the inclusion of the governance variables we re-estimate the regression for the same
2550 observations after removing the governance controls The coefficient and t-statistic are
virtually identical suggesting that the loss of significance is due to the reduced sample and not
the governance controls We do not find evidence to suggest that variation in corporate
governance practices drives variation in excess value for US MNCs
27
6 Conclusion
Using a new method to quantify the valuation effects of foreign direct investment by US
domiciled firms we find robust evidence of a premium in excess value associated with the
degree of multinational operations Our method builds on that used in previous studies
examining firm excess value (eg Berger and Ofek 1995 and Denis et al 2002) by
incorporating the location in addition to the industry of each corporate affiliate This allows
important factors such as growth and cost of capital to vary across countries as well as industries
Examining a sample of multinational firms we find that a one-standard deviation (197)
increase in foreign operations is associated with an increase of between 41 and 82 in excess
value minus both a statistically and economically significant finding
This result complements the finding of the international trade literature that more productive
firms are more likely to operate internationally However the premium remains after including
firm fixed effects to control for persistent firm traits (eg productivity)
The finding of a premium stands up to a succession of robustness tests designed to evaluate
whether the result arises from alternative methods or explanations First we estimate a dynamic
panel data model using the generalized method of moments (GMM) estimator to simultaneously
account for the potential endogeneity of FDI industrial diversification and firm value Second
we include a control variable to proxy for whether the firm operates in countries with larger
control premiums which could result in understated multiples and mechanically induce a
premium Third we include a control variable to measure the cost of capital across the countries
in which an MNC operates to evaluate whether a relatively low cost of capital borne by
multinational firms is a meaningful source of the premium Fourth we control for several
corporate governance proxies which have been found to be correlated with excess values (and
28
industrial diversification) We fail to find evidence supporting any of these alternative
explanations ndash the premium in excess value arising at firms with greater multinational operations
remains significant
Overall our new method of estimating excess values for multinational firms provides strong
evidence of a statistically and economically significant premium associated with increased
international operations The premium is robust to a host of specifications and controls designed
to evaluate alternative explanations We leave to future research the questions of whether the
premium to US multinational firms applies more broadly to other countries of domicile and
what factors influence the premium
29
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Ammann M Hoeschel D and M Schmid 2012 Is there really no conglomerate discount
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Arellano M and Bover O 1995 Another look at instrumental variables estimation of error
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Berger P G and E Ofek 1995 Diversificationrsquos effect on firm value Journal of Financial
Economics 37 39-65 Blundell R and Bond S R 1998 Initial conditions and moment restrictions in dynamic panel
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Doukas JA 1995 Overinvestment Tobinrsquos q and gains from foreign acquisitions Journal of
Banking and Finance 19 1285-1303 Doukas J and N G Travlos 1988 The effect of corporate multinationalism on shareholdersrsquorsquo
wealth evidence from international acquisitions Journal of Finance 43(5) 1161-1175 Doukas JA and O B Kan 2006 Does global diversification destroy firm value Journal of
International Business Studies 37(3) 352-371
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Journal of Finance 59 (2) 537-600 Erb C Harvey C and T Viskanta 1996 Expected returns and volatility in 135 countries The
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Multinational and local firm responses to currency depreciations Review of Financial Studies 21 (6) 2857-2888
Francis B I Hasan and X Sun 2008 Financial market integration and the value of global
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Journal of Economics 118(1) 107-156 Hail L and C Leuz 2009 Cost of capital effects and changes in growth expectations around
US cross-listings Journal of Financial Economics 93 428-454 Hanson G Mataloni R and M Slaughter 2005 Vertical production networks in multinational
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44(3) 289-630 Helpman E Melitz M J and S R Yeaple 2004 Export versus FDI with Heterogeneous Firms
American Economic Review 94(1) 300-316 Hoechle D Schmid M Walter W and D Yermack 2012 How much of the diversification
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of Accounting Research 46 (June) 591-626 Huizinga H P and J Voget 2009 International taxation and the direction and volume of cross-
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Economic Review Papers and Proceedings 76 323-329
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Laeven L and R Levine 2007 Is there a diversification discount in financial conglomerates
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Moeller SB and F P Schlingemann 2005 Global diversification and bidder gains A
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365 Villalonga B 2004a Diversification discount or premium New evidence from the Business
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corporation C Pitelis and R Sugden (eds) The Nature of the Transnational Firm London Routledge
32
Appendix A Variable Definitions
We obtain information regarding firmsrsquo operations by country and industry from the Bureau of Economic Analysis (BEA) unless otherwise noted Acronyms used below reflect Compustat mnemonics and thus indicate when we obtain data from the Compustat North America Fundamentals Annual database All independent variables are winsorized at 1
Dependent variable
Excess Value = The log of the ratio of actual firm value to imputed firm value Actual firm value is equal to market capitalization (MVE) + book value of total assets (AT) ndash book value of shareholdersrsquo equity (SEQ) Imputed firm value is calculated as the sum of the imputed values of a firmrsquos operations in each country-industry The imputed value for each country-industry is equal to sales generated in that country-industry times a multiple equal to the ratio of a benchmark firmrsquos actual value to total sales A benchmark firm is defined as a median single-segment firm operating in the same country-industry All multiples require at least five firms listed in the respective country-industry on Compustat (domestic) or Worldscope (foreign) with less than 10 of sales income and assets outside the country of domicile Similar to Berger and Ofek (1995) we eliminate extreme values where the ratio of actual to imputed firm value exceeds 400 or is less than 025
Independent variables
Foreign Sales = Sales made by the foreign operations of US MNCs as a percent of total firm sales
Industry Other = Sales outside a firmrsquos primary industry as a percent of total sales (SALE) Equal to zero for firms operating in a single business segment
Log Size = Log of total sales (SALE)
Debt Firm Value = Ratio of long-term debt (DLTT) to total market value (market capitalization (MVE) + book value of total assets (AT) ndash book value of shareholdersrsquo equity (SEQ))
CAPX Sales = Ratio of capital expenditures (CAPX) to total sales (SALE)
EBIT Sales = Ratio of earnings before interest and taxes (EBIT) to total sales (SALE)
RampD Sales = Ratio of research and development expenses (XRD) to total sales (SALE)
Ad Sales = Ratio of advertising expenses (XAD) to total sales (SALE)
Control Premium = A weighted-average (based on the sales in a given country as a percent of total firm sales) of the country-level control premium from Dyck and Zingales (2004)
Cost of Capital = A weighted-average (based on the sales in a given country as a percent of total firm sales) of the country-level credit rating from Institutional Investor (httpwwwinstitutionalinvestorcom) The country-level rating is available for 160 countries and ranges from 0 to 100 Higher values imply greater credit risk
33
CEO Ownership = Percent of shares owned by the CEO from Execucomp
Institution Ownership = Percent of shares owned by institutional investors from Thomson Reuterrsquos Institutional Holdings database
Powerful CEO = Indicator variable equal to one if the CEO is the only insider on the board of directors and also serves as the chairman and president based on data from RiskMetrics Directors database Equal to zero otherwise
Governance Index = Count measure of takeover defenses used in Gompers Ishii and Metrick (2003) from RiskMetrics Governance database As the governance data are not available for every year we follow Gompers Ishii and Metrick (2003) and assume that the firmsrsquo governance attributes are unchanged until publication of the subsequent series
34
Figure 1 Trends in multinational activity and industrial diversification from 1984-2008 This figure presents a plot of the mean percent of foreign sales and the mean percent of sales outside the firmrsquos primary industry (industrial diversification) from 1984 through 2008 Dotted lines above and below the primary lines represent 95 confidence intervals To compile the plot we include the full population of firms in Compustat
35
Table 1 Analysis of accounting data for firms reporting under multiple accounting standards This table provides a comparison of sales net income and assets for firms that changed accounting standards to or from US GAAP Comparisons are provided for the 66 firms that changed to or from US GAAP and for which we could obtain accounting data electronically and in English under both standards for the same fiscal year (eg the firm presented the accounting numbers originally under one set of standards and then restated the prior year numbers in the subsequent year under the new set of standards) Panel A shows the distribution of firms by year Panel B shows the other accounting standards to which US GAAP is compared Panel C shows the mean and median percent that the other accounting standard differs from US GAAP for the same fiscal year and accounting number Two samples are shown in Panel C The first is the full sample of firms with a change in accounting standards to or from US GAAP for which data was available The second is the subset of these firms that occurred during the sample window used in this study (ie excluding changes post-2008) The percent difference in sales is compared to the percent difference in net income and assets Tests of means are based on matched-pair t-tests Tests of medians are based on Wilcoxon matched-pair signed-rank tests Panel A Accounting standard changes by year
2003
4 6
2004
25 38
2005
8 12
2006
6 9
2007
7 11
2008
5 8
2009
5 8
2010
5 8
2011
1 2
66
Panel B Accounting standards compared to US GAAP
IFRS
47 71
Canadian GAAP
12 18
Israeli GAAP
2 3
Dutch GAAP
1 2
Indian GAAP
1 2
Japanese GAAP
1 2
Norwegian GAAP
1 2
Swiss GAAP
1 2
66
36
(Table 1 continued) Panel C Tests of differences between US GAAP and other accounting standards
Percent different from US GAAP
Comparison to sales (p-values)
N Mean Median
Mean Median
Full Sample (2001-2011)
Sales
66 236 000
Net Income
66 10653 647
005 lt001
Assets
66 1162 126
lt001 lt001
Study Sample (2001-2008)
Sales
55 257 000
Net Income
55 10303 589
011 lt001
Assets
55 1047 099
003 lt001
37
Table 2 Descriptive statistics Panel A reports the mean and medians of Excess Value for the multinational firms and single-segment benchmark firms as well as univariate tests across the subsamples Panel B reports summary statistics for the independent variables for the multinational firms The p-values of the tests of differences in means are based on t-tests The p-values of tests of differences in the medians are for a Wilcoxon rank-sum test All variables are defined in Appendix A Panel A Excess Value by sample Multinational All Benchmark Benchmark Subgroups Sample Firms US Foreign
Value Value Test of Diff (p-value) Value
Test of Diff (p-value) Value
Test of Diff (p-value)
Mean 0067 0006 lt001 -0028 lt001 0014 lt001 Median 0076 0000 lt001 -0025 lt001 0000 lt001 N 4950 33074 5998 27076 Panel B Distribution of independent variables for the multinational firms N Mean 25th 50th 75th StDev Foreign Sales 4950 0238 0074 0190 0363 0197 Industry Other 4950 0212 0000 0166 0414 0215 Log Size 4950 6955 5823 6989 8140 1687 Debt Firm Value 4950 0136 0009 0097 0212 0145 CAPX Sales 4950 0054 0020 0033 0054 0101 EBIT Sales 4950 0065 0031 0077 0121 0138 RampD Sales 4950 0049 0000 0013 0059 0083 Ad Sales 4950 0008 0000 0000 0005 0018
38
Table 3 BEA and Worldscope samples
This table reports details on affiliate sales for significant countries pooled from 1998-2008 Significant countries are defined as those in which the operations of US MNCs represent at least 02 of total firm sales (per the BEA) For significant countries the table compares their representation in terms of MNC affiliate sales in the BEA data to the distribution of firm observations in Worldscope US Foreign Affiliates (BEA) Foreign Firms (Worldscope)
Country
N (1)
Sales (millions)
(2)
Total Sales (3)
N (4)
Sales (million)
(5)
Total Sales (6)
Australia 1344 73305 056 859 255865 210 Belgium 894 52755 040 169 26136 021 Brazil 959 60245 046 1143 683167 560 Canada 2730 536767 407 1181 526750 432 China 922 75907 057 822 276541 227 France 1852 183942 139 540 105447 086 Germany 2182 275577 209 480 103888 085 Hong Kong 867 48100 036 157 34094 028 Ireland 478 36754 028 34 3467 003 Italy 1178 82116 062 111 47948 039 Japan 1494 148032 112 2281 1119223 917 Korea South 659 34500 026 3530 2752791 2255 Malaysia 386 26027 020 837 168683 138 Mexico 1405 113623 086 362 237173 194 Netherlands 1149 85105 064 47 22631 019 Singapore 1098 94917 072 139 32913 027 Spain 871 63094 048 129 37419 031 Sweden 712 32682 025 235 23090 019 Switzerland 726 60288 046 74 20242 017 Taiwan 592 30313 023 2949 787049 645 United Kingdom 3054 423934 321 1997 882423 723 United States (domicile) 4950 10460880 7923 na na na Other - Covered by WorldScope 6893 199394 151 9000 4057894 33 Other -Not Covered by WorldScope 713 5352 004 - - - Total 38108 13203609 1000 27076 12204834 100
39
Table 4 OLS regressions of firm excess values on the percent of foreign sales This table reports the results of OLS regressions of firm excess value on the percent of foreign sales (Foreign Sales) the percent of sales outside a firmrsquos primary industry (Industry Other) and control variables All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed) Dep Variable = Excess Value Coefficient t-stat Coefficient t-stat Coefficient t-stat Coefficient t-stat (1) (2) (3) (4) Foreign Sales 0349 419 0209 293 0189 266 0278 210 Industry Other -0134 -186 -0047 -072 -0053 -081 -0326 -325 Log Size 0066 634 0065 624 -0044 -096 Debt Firm Value -0196 -213 -0241 -260 -0685 -588 CAPX Sales 0901 425 0893 435 0488 227 EBIT Sales 1154 542 1170 547 0408 189 RampD Sales 2908 1242 2932 1237 1544 379 Ad Sales 1869 263 2003 280 -1696 -119 Year indicators No No Yes Yes Firm indicators No No No Yes R2 0017 0212 0226 0768 Observations 4950 4950 4950 4950
40
Table 5 Dynamic panel GMM model regressing firm excess values on the percent of foreign sales This table reports the result from a regression of the excess value on the percent of foreign sales (Foreign Sales) the percent of non-primary industry sales (Industry Other) and control variables using a dynamic panel GMM estimator All control variables are considered to be endogenous with the exception of the year indicator variables AR(1) and AR(2) are tests for first-order and second-order serial correlation in the first differenced residuals with the null hypothesis of no serial correlation The null hypothesis of the Hansen test of overidentification is that all instruments are valid The null hypothesis of the difference-in-Hansen test of exogeneity is that the instruments used for the equation in levels are exogenous All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed) Dep Variable = Excess Value Coef t-stat Excess Value (one lag) -0018 -082 Excess Value (two lags) -0048 -272 Foreign Sales 0371 270 Industry Other -0163 -171 Log Size 0010 123 Debt FirmValue -1135 -820 CAPX Sales 0590 342 EBIT Sales -0047 -300 RampD Sales 1376 531 Ad Sales -0751 -040 AR(1) test (p-value) 000 AR(2) test (p-value) 017 Hansen test of over-identification (p-value) 030 Difference-in-Hansen test of exogeneity (p-value) 093 Year indicators Yes Number of firms 1016 Number of observations 4090
41
Table 6 OLS regression of firm excess values on the percent of foreign sales controlling for a control premium This table reports the result of the OLS regression of firm excess value including firm and year fixed effects with an additional control variable to proxy for a country-level ownership control premium (Control Premium) All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed) Dep Variable = Excess Value Coef t -stat Foreign Sales 0303 224 Industry Other -0327 -325 Log Size -0045 -100 Debt FirmValue -0683 -585 CAPX Sales 0490 226 EBIT Sales 0409 189 RampD Sales 1545 379 Ad Sales -1689 -119 Control Premium -1004 -062 Year indicators Yes Firm indicators Yes R2 0768 Observations 4950
42
Table 7 OLS regression of firm excess values on the percent of foreign sales controlling for the cost of capital This table contains the results of an OLS regression of firm excess value including firm and year fixed effects with an additional control variable to proxy for a country-level cost of capital (Cost of Capital) All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed) Dep Variable = Excess Value Coef t -stat Foreign Sales 0255 178 Industry Other -0326 -324 Log Size -0043 -094 Debt FirmValue -0687 -589 CAPX Sales 0485 225 EBIT Sales 0406 188 RampD Sales 1539 379 Ad Sales -1703 -120 Cost of Capital 0004 039 Year indicators Yes Firm indicators Yes R2 0768 Observations 4950
43
Table 8 OLS regression of firm excess values on the percent of foreign sales controlling for aspects of corporate governance This table reports the results of two OLS regressions In the first column are the results of the firm excess value regression including firm and year fixed effect with five proxies for corporate governance included ndash the percent of shares owned by the CEO and the percent squared (CEO Ownership and CEO Ownership squared) the percent owned by institutional investors (Institution Ownership) whether there is a powerful CEO (Powerful CEO) and the Gompers Ishii and Metrick (2003) governance index (Governance Index) In the second column the regression includes the same sample and variables with the exception of the governance proxies which are excluded All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed)
Governance
No Governance (same sample)
Dep Variable = Excess Value Coef t-stat Coef t -stat Foreign Sales 0259 159 0251 154 Industry Other -0099 -080 -0099 -079 Log Size -0105 -185 -0102 -180 Debt FirmValue -0813 -478 -0817 -482 CAPX Sales 0440 252 0438 249 EBIT Sales 0457 247 0457 246 RampD Sales 1717 284 1716 286 Ad Sales -2210 -143 -2190 -143 CEO Ownership 0010 007
CEO Ownership squared -0029 -017 Institution Ownership 0047 067 Powerful CEO -0003 -014 Governance Index 0009 103
Year indicators Yes Yes Firm indicators Yes Yes R2 0811 0810 Observations 2550 2550
- Cover for paperpdf
- crrz_20130603_SMUpdf
-
5
conjecture why the most productive firms would have the largest agency costs In contrast the
finding of a premium easily reconciles with the trade literature
Given our result we investigate whether the premium is simply an artifact of innate
characteristics of firms that choose to invest abroad For example Helpman et al (2004) present
a simple model with heterogeneous productivity endowments The firms that receive the highest
productivity endowments are the ones capable of paying the fixed costs to establish a foreign
subsidiary To the extent productivity advantages create excess value one would expect the type
of firms that choose to invest abroad would be valued at a premium even in the absence of their
foreign investments After including firm fixed effects to control for time invariant firm
characteristics (eg innate productivity advantages) we continue to find economically and
statistically significant evidence of a premium to foreign operations
To further account for the endogenous nature of FDI we estimate a dynamic panel data
model using the generalized method of moments (GMM) estimator developed by Arellano and
Bond (1991) Arellano and Bover (1995) and Blundell and Bond (1998) This estimator allows
us to simultaneously account for the potential endogeneity of FDI industrial diversification and
firm value This method also yields a significant premium which is of similar magnitude and
significance to that estimated using fixed effects
We also perform a series of additional robustness tests First we examine whether the
measured excess value premium is driven by operations in countries with large control premiums
(Dyck and Zingales 2004) In countries with large control premiums using stock price in the
calculation of total firm value could underestimate the true value of the benchmark firms and
thereby induce an excess value premium We fail to find evidence that the excess value premium
is driven by such countries
6
Second we examine whether access to a low cost of capital is a meaningful source of the
US MNC advantage We incorporate foreign benchmarks in our method of estimating firm
excess value as it allows us to incorporate attributes of operating in those foreign countries ndash a
key attribute being the cost of capital Relative to the local benchmarks in foreign countries US
MNCs have the ability to obtain funds outside the local operating environments either by
borrowing in the US market or transferring capital internally When competing against firms in
shallow capital markets with high costs of capital MNCs may have a competitive advantage
over local foreign companies due to these alternative sources of capital However investors and
lenders are likely to expect higher rates of return from the additional risk associated with
operating in these environments making it unclear whether a multinational network will enhance
value in countries with a high cost of capital In addition foreign firms have some ability to
access international capital markets which mitigates the advantages to US firms To examine
whether the cost of capital plays a role in our finding of a premium we include a proxy for the
extent of operations in countries with higher costs of capital Our results are unaffected by the
inclusion of the proxy
Finally we control for several corporate governance proxies which Hoechle et al (2012)
find to be correlated with excess values (and industrial diversification) While magnitude of the
premium is virtually unaffected by including these proxies the significance level does decline
due to the reduced sample size
Our study contributes to the literature on multinational firms Our new method of estimating
excess values for multinational firms provides strong evidence of a statistically and economically
significant premium associated with increased international operations The premium is robust to
a variety of specifications and controls designed to evaluate alternative explanations In addition
7
this result easily reconciles with the international trade literature that establishes a link between
greater productivity and international operations though we do not find results supporting this as
the only source of the premium As segment disclosures improve and as new data sources
become available on multinational activity (eg Bureau Van Dijk) our empirical approach can
be used to investigate the extent to which the home country of the parent might influence the
extent with which foreign operations affect firm value (ie by examining non-US based
MNCs)7 Future studies could also examine the underlying forces affecting the excess value
premiums across countries
We also contribute to the understanding of international accounting standards and their
comparability Many studies seek to compare firms using different accounting standards These
comparisons generally rely on information generated by a particular accounting regime By
comparing commonly used metrics (total assets net income and sales) reported for the same
firm and year across different regimes we provide insights as to the relative comparability across
standards In particular we find that sales is reported significantly more consistently across
accounting regimes than the other metrics examined This finding should aid researchers in
reducing measurement error when comparing accounting information in multinational settings
The paper proceeds as follows Section 2 discusses the motivation for finding a premium or
discount to multinational operations Section 3 describes our data and how the excess value of a
firm is measured A discussion of our independent variables and primary findings is presented in
Section 4 with additional robustness tests provided in Section 5 Finally Section 6 concludes
7 As MNCs are ultimately bound by the tax regulatory and legal frameworks of their home country the valuation effect of foreign operations that we document for US-based MNCs may be different in a sample of non-US based MNCs with the same country-industry footprint Theorists refer to these as lsquolocational advantagesrsquo enjoyed by all multinational firms of a given nationality and obtained irrespective of skills or capabilities unique to a particular firm (Yamin 1991) There is also evidence of locational advantages in the market for corporate control (Huizinga and Voget 2009)
8
2 Premium versus discount
In a frictionless world where managers maximize firm value and markets are efficient there
should be no discount or premium to operating in foreign countries However there are a number
of forces that may cause actual firm value to deviate from the implied firm value
On one hand MNCs may have comparative advantages that generate premiums (ie positive
excess values) relative to a similar footprint of stand-alone firms Having operations in a variety
of locations that access different supplies of inputs and customers allows the firm to take
advantage of changing market conditions by shifting operations or products to maximize firm
value more so than a firm operating in a single country Similarly having access to a variety of
institutional settings such as different tax codes legal regimes and financial markets can provide
MNCs with options beyond those available to firms located in a single country
Another possible factor associated with a premium to multinational operations is the cost of
capital For operations located in countries with shallow capital markets or weaker creditor
rights operating within a conglomerate can provide the benefit of a lower cost of capital for
investments and expansion for at least two reasons First as our MNCs are domiciled in the US
their foreign segments have greater access to US capital markets relative to local competitors in
the foreign jurisdiction Second cash rich segments with few investment opportunities can
finance investments in cash poor segments with positive net present value projects (eg Myers
and Majluf 1984) Consequently diversified firms should be less liquidity constrained and better
able to shift resources to the most valuable investment opportunities Research has found that
internal financing is used more often when diversified firms have operations in countries with
more costly external financing (Desai Foley and Hines 2004) Multinational firms may also be
relatively more protected than single-country firms when negative shocks hit external capital
9
markets (Foley Desai and Forbes 2008) In a recent study Matvos and Seru (2011) find that
industrially diversified firms perform better than non-diversified firms when external capital
markets are impaired
While these arguments suggest that MNCs enjoy certain advantages when the foreign cost of
capital is high these benefits should be reduced by the additional risk associated with operating a
business in a high cost of capital environment For example Verizon is a multinational firm
operating in a number of countries One of the higher cost of capital locations in which they
operated was Venezuela While the multinational structure of Verizon may have mitigated some
of the risks associated with operating in a higher cost of capital location the firms was unable to
eliminate all of these risks In fact in 2007 Verizonrsquos operations in that country were
nationalized resulting in a net extraordinary loss of $131 million that year8 In other words
MNC investors are likely to expect higher rates of return to compensate for higher risk which
likely offsets some (if not all) of the potential excess value premium
On the other hand multinational firms may incur a discount (ie negative excess values)
relative to a similar footprint of stand-alone firms Potential reasons for a discount largely rely on
the existence of agency costs Multinational firms tend to be larger more complex and less
transparent The combination of complexity and opacity inherent in MNCs reduces a firmrsquos
reliance on external capital as the cost of such capital increases due to agency costs (Desai et al
2004) As a result of the reduced reliance on external funds MNCs face a reduced level of
monitoring As noted by Jensen (1986) managers have incentives to increase firm size beyond
that which is optimal with overinvestment or misallocation of funds to pet projects If this lack of
monitoring is in combination with available internal capital empire building becomes easier
(Hope and Thomas 2008) Furthermore the current segment reporting standards under US 8 See the 10-K of Verizon Communications Inc dated December 31 2007 (footnote 2)
10
GAAP only require highly aggregated disclosures of foreign operations and allow for
considerable managerial discretion As a result operating in multiple countries makes it easier
for management to hide poor performance ndash either their own or that of a division ndash such that low
quality managers are more likely to be retained and overcompensated and poorly performing
divisions retained longer than optimal Other reasons one might expect a discount to be applied
to multinational firms include higher coordination costs (eg coordinating across different
cultures and languages) as well as additional risks These risks include exposure to multiple
political regimes legal regimes economic regulations and currency fluctuations
Past studies investigate whether foreign operations of US firms enhance or reduce firm
value However all of these studies benchmark the value of the foreign operations of an MNC to
US domestic firms For instance Denis et al (2002) determine the implied value of a US
MNC using domestic (US) single-segment firms in the same industry as a benchmark Studies
using Tobinrsquos Q to investigate valuation effects of MNCs also rely on domestic benchmarks
(eg Morck and Yeung 1991 Morck and Yeung 2001 Gande Schenzler and Senbet 2009)
However using a US domestic firm as a benchmark for the value of foreign operations
implicitly makes two assumptions First this method assumes that the risks and expected growth
rates of foreign operations are equivalent to those of domestic operations This assumption is
inconsistent with the findings of Hail and Leuz (2009) which provides evidence that the cost of
capital varies substantially across countries Second this method assumes that there is excess
domestic capacity to allow for expansion with profitability similar to current domestic
operations We relax these assumptions by measuring the imputed value of a foreign component
of a US firm as if that component operated autonomously within that foreign country (rather
than in the US) Specifically our multiples allow the cost of capital and expected growth rates
11
to vary across not just industry but also country Furthermore our counterfactual does not
require the assumption that operations be relocated mitigating the production and sales capacity
concerns
3 Measuring excess value
31 Description of excess value
Figure 1 shows the average percent of foreign sales (based on segment disclosures under US
GAAP) across firms through time and illustrates that firms are continuing to expand
internationally9 This expansion highlights the importance of understanding whether the decision
to operate internationally is on average a value enhancing corporate strategy We contribute to
this understanding by evaluating whether the firm as a whole is worth more or less than its
imputed value (ie the firm is valued in excess of the sum of its individual components)
We measure the excess value of a firm as the logarithm of the ratio of actual firm value to
imputed value based on the method used in Berger and Ofek (1995) The imputed value is the
hypothetical value of the MNC under the assumption that its country-industry components
operate as independent entities A key innovation of our study is an alternative approach to
imputing the value of the foreign operations of an MNC Components of MNCs operating in a
given industry and country are imputed using the value of single-segment firms operating in the
same industry and country Thus we ensure the discount rates and expected growth rates are
applicable to each given industry and location in estimating the implied values of the MNC
segments
9 The discontinuity in industrial diversification (the percent of sales outside the primary industry) between 1997 and 1998 is the result of a change in segment reporting rules
12
The actual firm values are observable for all firms in our sample The hypothetical firm
values for multi-segment firms are imputed using market value to sales ratios herein referred to
as lsquomultiplesrsquo of the median single-segment firm operating in the same country-industry Thus
these single country-industry firms serve as benchmarks for an MNCrsquos operations within that
same country-industry The imputed value of the country-industry component can be viewed as
an estimate of what that component would be worth if it operated independently The sum of the
imputed values across all country-industry components represents the hypothetical value that the
multi-segment firm would be worth if it were broken apart
32 Data
Four primary data sources play a distinct role in carrying out our analysis i) Bureau of
Economic Analysis (BEA) data ii) Compustat Segment data iii) Worldscope data and iv)
Compustat Fundamentals Annual data Broadly speaking BEA data provide information about
the diversified nature of MNCs Compustat Segment data provide information about the
industrial diversification of US domiciled firms Worldscope data are used to compute
multiples for single-segment foreign firms that serve as benchmarks for the foreign operations of
MNCs (ie foreign benchmarks) and Compustat Fundamentals Annual data are used to
compute multiples for single-segment domestic firms that serve as benchmarks for the domestic
operations of multinational firms We also use the latter data source to construct our primary
control variables
13
Key to the execution of our study is our access to BEA data Federal law requires US-
domiciled MNCs to report certain financial and operating data to the BEA10 With regard to
observing the country-industry operations of MNCs use of the BEA data overcomes two
important limitations of Compustat Segment data First Compustat does not consistently identify
the specific location or scale of MNCsrsquo foreign operations11 Second Compustat does not
identify the industry activities in each country of operation This latter point is particularly
limiting for MNCs because domestic industry activity need not mirror foreign industry activity
An MNC generating an equal proportion of sales in two industries in the domestic market need
not generate industry sales in equal proportions in every foreign market12 The lack of detail and
consistency in Compustat Segment data arises because segment reporting is highly aggregated
and firms exercise substantial discretion in defining segments under Accounting Standards
Codification 280 (ASC 280) (eg Villalonga 2004a and Bens Berger and Monahan 2011)
The BEA defines an MNC as the combination of a single US entity called the US parent
and at least one foreign affiliate ndash that is these firms have a physical presence outside the US
due to FDI Since 1982 MNCs have completed a mandatory and confidential lsquoSurvey of US
Direct Investment Abroadrsquo for the domestic operation and the operations of each foreign
affiliate defined as a foreign entity in which the US parent holds at least a 10 percent equity
interest (directly or indirectly) The data are reported in US dollars on a fiscal year basis and in
10 BEA surveys are conducted pursuant to the International Investment and Trade in Services Survey Act (PL 94-472 90 Stat 2059 22 USC 3101-3108) See httpwwwbeagovsurveysdiasurvhtm and Mataloni (2003) for more detailed information on the BEA data 11 Exhibit 21 disclosures of material subsidiaries and their locations in SEC 10-K filings could potentially help overcome the inability to observe the location of the firmrsquos foreign operations from segment disclosures However at least three limitations remain (1) for industrially diversified multinational firms the researcher would be required to make an assumption about the proportion of industrial diversification in each country (2) Exhibit 21 provides no information about the scale of activity in each jurisdiction and (3) managerial discretion is used to determine a lsquomaterialrsquo subsidiary under Section 601 of SEC Regulation S-K 12 In fact the expectation that a multinational firm would exhibit different industry membership in different countries is consistent with the theory of vertical foreign direct investment where firms separate their various value chain activities and locate them in the most favorable country (see eg Hanson Mataloni and Slaughter 2005)
14
accordance with US Generally Accepted Accounting Principles (US GAAP) For each year
we observe the sales industry composition and location of not only the parent but also each
affiliate13
The procedures we use to construct our sample are similar to those used by Berger and Ofek
(1995) and Denis et al (2002) Using firm-years appearing in Compustat Fundamentals Annual
and Compustat Segment data we obtain a sample of firms domiciled in the US with total sales
exceeding $20 million We exclude firms in the financial service (SIC codes 6000 to 6999) and
utility (SIC codes 4000 to 4999) industries and eliminate firm-years when the sum of segment
sales (Compustat geographic or Compustat business) is not within 1 of total firm sales for that
year We restrict our sample period to include fiscal years beginning after December 15 1997 as
ASC 280 Segment Reporting substantially altered the definition of a reporting segment under
US GAAP This restriction ensures that Compustat Segment data reflects the use of a consistent
accounting standard Finally we require that our MNCs appear in both Compustat and BEA
data The requirement that our sample firms appear in the BEA database ensures that the firms
have observable international operations In sum these procedures result in 1166 multinational
firms incorporated in the US with 4950 observations between 1998 and 2008 that meet all of
our data requirements
33 Measuring excess value
The dependent variable in our study is excess firm value (Excess Value) defined as the
logarithm of the ratio of actual firm value to imputed firm value (Berger and Ofek 1995)
13 We obtain sales industry membership and country location for affiliates from one of two sources When affiliates exceed the BEA-determined reporting thresholds (ie if their assets sales or net income (loss) exceed $7 million in 1999 $30 million in 2000 through 2003 $10 million in 2004 and $40 million in 2005 through 2008) they must file their own detailed survey When affiliates fall below the thresholds we obtain our data from an attachment to the parent survey
15
Appendix A provides the definition of this and all other variables used in our analysis We
observe actual firm value equal to a firmrsquos market value of equity plus book value of liabilities
(ie book value of total assets minus book value of total equity) using Compustat Fundamentals
Annual data We calculate imputed firm value as the sum of the imputed values of a firmrsquos
operations in each country-industry Our method for imputing the value of the separate
components of a firm can best be described in three steps First we obtain total sales generated
by a firm for each country-industry in which it operates14 Second we obtain multiples (market
value to sales ratios) for benchmark firms operating in those same country-industries Third we
multiply a firmrsquos country-industry sales by the applicable median country-industry multiple to
obtain the imputed market value for each country-industry operation We perform each step
annually
Conceptually imputed values can be based on assets (ie Tobinrsquos Q) net income or sales
multiples We restrict ourselves to sales multiples for two reasons First using value to sales
ratios maintains consistency with the prior research on the excess value implications of
multinational operations (eg Denis et al 2002) Second as our method uses accounting data
for foreign companies to compute country-industry multiples the accounting numbers we use
need to be consistently measured across firms using different accounting standards We find that
sales data are measured most consistently
To assess the comparability of sales relative to either net income or assets across various
accounting standards we examine all firms listed on Worldscope as changing to or from US
14 Specifically we allocate total sales from Compustat to each component based on the proportion of BEA sales (after eliminating intercompany sales) occurring in each country-industry This step ensures that our total firm sales do not deviate from a firmrsquos total sales as reported in the 10-K
16
GAAP between 2005 and 201115 We obtain accounting data for the year of the change and the
prior year where electronically available in English We are able to obtain data for 66 firms
changing standards as detailed in Table 1 The distribution of years for which accounting data
are available under two different accounting standards for the same fiscal year is shown in Panel
A The years shown are the year prior to the change as these accounting numbers were provided
under the original standard and then subsequently restated in the following year under the new
standard for comparative purposes The majority of the shifts were to or from IFRS (71)
though seven other accounting standards are noted (Panel B) We examine the percent that the
non-US GAAP reported number differs from the US GAAP reported number in Panel C to
assess the comparability of these three summary accounting numbers across various accounting
standards Examining both the full sample as well as the subset that overlaps our study we find
that sales are more consistently measured than net income or assets
The sum of the imputed values across the country-industry components of an MNC is an
estimate of the value of a portfolio of unrelated businesses that mirror the related businesses of
the firm Consequently a comparison of the actual firm value with the imputed firm value is a
measure of how a multinational network affects firm value We provide descriptive statistics for
Excess Value for our sample of 4950 MNC firm-years in Table 2 Panel A This table also
includes univariate comparisons of the values between the MNC firms and the domestic single-
segment and foreign single-segment benchmark firms The Excess Value measure is significantly
higher for MNCs suggesting a premium in firm value for multinational firms relative to the
benchmark firms in total as well as the domestic and foreign subgroups (p-values for tests of
15 Due to the hand collection efforts required and the limitation on electronic filings we began our search with changes documented in 2005 which generally require filings from 2004
17
means and medians are lt 001)16 These results provide some initial evidence that operating in
multiple countries is positively associated with firm value
To estimate country-industry multiples we rely on Worldscope financial data on foreign
firms and Compustat Fundamentals Annual for domestic firms We restrict the firms to include
only those with at least 90 of sales income and assets inside the country of domicile (ie
those that do not report significant multinational activity) and that operate in a single industry
We refer to these firms as benchmark firms (either foreign or domestic depending on the country
of domicile) these firms do not appear in any of our regressions We report the number of
benchmark firms during our sample period in Table 2 Panel A The industry criterion is based on
the two-digit SIC code17 For every country that has at least five firms in the respective industry
and year we use the median ratio of market value to sales18 This ratio is the country-industry
multiple ndash an input required to compute imputed values
To determine the country-industry composition of MNCs we rely on BEA data In Table 3
we report the aggregate number of foreign affiliates and total sales by country as provided by
the BEA data for our sample of 4950 MNCs The specific countries tabulated are those that
represent at least 02 percent of total firm sales (pooled across all years) in our sample The top
five foreign countries in which MNCs generate sales are Canada United Kingdom Germany
France and Japan Table 3 also provides a comparison of the number of foreign benchmark
firms available in Worldscope in the specific countries in which our sample of MNCs generate
16 Similar to Berger and Ofek (1995) the median values differ slightly from zero for the benchmark single-segment firms due to the truncation of extreme excess values 17 BEA reporting constrains us to the two-digit SIC code level 18 When five firms are not available for a given country and industry we follow Berger and Ofek (1995) and move to a more general industry definition (eg one-digit SIC code) This procedure results in a mean (median) 630 (757) of MNC sales matched at the two-digit level
18
the majority of their sales Country coverage in Worldscope is not available for countries
representing 004 percent of total firm sales (per BEA)
4 Empirical results
41 Independent variables
Recall that our objective is to examine the overall relation between excess firm value and
multinational operations Our proxy for the extent of multinational operations is the percentage
of total firm sales made by foreign operations (Foreign Sales) By this measure the extent of
multinational activity in our sample of MNCs is non-trivial ndash Table 2 Panel B shows that
approximately 24 percent of sales are generated by foreign operations on average Our first
analysis estimates ordinary least square regressions of Excess Value on Foreign Sales and
control variables (discussed below)
We include variables in our regression to control for other potential determinants of excess
value These are the percentage of sales made by the firm outside its primary industry (Industry
Other) to control for any relation between industrial diversification and excess value as in Berger
and Ofek (1995) For this purpose we obtain industry sales using Compustat Segment data19 A
firmrsquos primary industry is the industry in which the firm generates the majority of its sales and
we determine industry sales at the business segment level We set Industry Other equal to zero
for firms that operate in a single business segment
Consistent with prior research we also include controls for firm size (Log Size) the ratio of
long term debt to total market value (Debt Firm Value) the ratio of capital expenditures to total
sales (CAPX Sales) the ratio of earnings before interest and taxes to total sales (EBIT Sales)
the ratio of research and development expenses to total sales (RampD Sales) and the ratio of 19 Our results are unaffected by using BEA data to measure Industry Other
19
advertising expenses to total sales (Ad Sales) Table 2 Panel B provides the distribution of the
control variables All independent variables are Winsorized at 1 In our OLS regressions we
use heteroskedasticity and autocorrelation consistent robust standard errors with clustering at the
firm level
42 Primary regression analysis
In this section we examine the relation between firm excess value and the amount of foreign
activity at US MNCs Table 4 contains the results of regressions of excess value on Foreign
Sales for multiple specifications In column (1) Excess Value is regressed on both the percent of
foreign sales (Foreign Sales) and the percent of sales outside the firmrsquos primary industry
(Industry Other) We add the control variables to the regression in column (2) and year
indicators in column (3) In all three specifications the coefficient on the percent of foreign sales
remains significantly positive (with a p-value lt 001) For a sense of economic magnitude in
column (3) a one standard deviation (197) increase in the percent of foreign sales (Foreign
Sales) is associated with a 41 (equal to (e0349-1) times 0197) increase in firm excess value The
effect is larger for the other two columns ndash 82 increase in firm excess value in column (1) and
46 in column (2)20
Our finding of a premium reconciles with the international trade literature which finds that
firms engaging in international trade are more productive than those that do not (see Helpman
2006 and Syverson 2011 for reviews) Due to data limitations it is difficult to accurately
measure TFP at the firm level especially for firms which operate in multiple industries andor
20 Using the alternative strategy that employs US domiciled single-segment firms as the benchmark for the imputed value of the foreign operations Denis et al (2002) find that greater foreign activity results in a discount to firm value Using the Denis et al (2002) method to calculate excess value in our sample we obtain a significantly negative regression coefficient on Foreign Sales using the models in Table 4 columns (2) and (3) (the coefficient is not significant using the model in column (1) results untabulated)
20
countries As an alternative method to evaluate whether our finding of a premium is simply the
result of more productive firms also having more multinational operations we investigate
whether the premium is simply an artifact of innate characteristics of firms that choose to invest
abroad For example Helpman et al (2004) present a simple model with heterogeneous
productivity endowments The firms that receive the highest productivity endowments are the
ones capable of paying the fixed costs to establish a foreign subsidiary
To the extent that productivity advantages create excess value one would expect the type of
firms that choose to invest more abroad would be valued at a premium even in the absence of
their foreign investments To control for time invariant firm characteristics (eg innate
productivity advantages) and hence a potential source of endogeneity we add firm-level fixed
effects in column (4) of Table 4 We continue to find that the extent of foreign operations
(Foreign Sales) is positively associated with Excess Value Across all specifications the sign
on all significant control variables is consistent with prior research (eg Berger and Ofek 1995
and Denis et al 2002)
The finding of a premium makes a significant contribution to the relatively sparse literature
on the value effects of multinational activity Our measure of imputed value expands upon the
methods used by Berger and Ofek (1995) and Denis et al (2002) but allows us to answer a
different question than Denis et al (2002) Holding constant the extent of industrial
diversification we ask whether MNCs are more valuable than a portfolio of benchmark firms of
a similar geographic footprint In contrast Denis et al (2002) ask whether holding constant the
extent of industrial diversification MNCs are more valuable than a portfolio of domestic (US)
benchmark firms We believe that asking whether an MNCrsquos foreign (domestic) operations are
21
more valuable than those of a foreign (domestic) firm is a more appropriate way to assess the
value implications of a multinational network
5 Robustness
51 Endogeneity related to dynamic relations
Prior research argues that the relation between industrial diversification and excess firm
value could be endogenous through dynamic relationships based on observing past outcomes
Managers of firms likely choose to enter or exit industries or geographic regions based upon
their previous performance For example Campa and Kedia (2002) find that firms are more
likely to enter new industries when prospects in their current lines of business are deteriorating
Similar dynamics could also drive a relation between excess value and FDI We assess the
robustness of the previous results by estimating a dynamic panel data model which allows us to
control for dynamic endogeneity in addition to firm level unobserved heterogeneity We employ
the GMM estimator for dynamic panel models developed by Arellano and Bond (1991) Arellano
and Bover (1995) and Blundell and Bond (1998) Our results from this analysis confirm our
previous findings that firms with a larger degree of foreign operations have positive excess
values
Dynamic panel data models are generalizations of the traditional fixed effects model where
lags of the dependent variable are added to the right hand side of the equation These additional
lags control for the impact a firmrsquos past performance has on its current performance through the
channels discussed above The GMM estimator for dynamic panel models developed by
Arellano and Bond (1991) starts by differencing the data at the firm level in neighboring time
periods to eliminate any firm specific time invariant unobserved heterogeneity that may be
22
present Eliminating the firm specific fixed effect by differencing the data with lags of the
dependent variable on the right hand side introduces correlation between the new error term and
the differenced explanatory variables This problem is solved by applying the standard GMM
estimator using lags of both the original explanatory variables and excess value as instruments
We note that the dynamic panel data framework allows us to treat both international and
industrial diversification (as well as the other firm-specific control variables) as endogenous
Following Hoechle et al (2012) we also allow contemporaneous values of these variables to be
treated as endogenous
Lagged values of the explanatory variables are valid instruments in a dynamic panel data
model when the model is dynamically complete (eg Wooldridge 2010) A dynamic panel data
model is dynamically complete conditional on the unobserved effect if enough lags of both the
explanatory and dependent variable are included to make the resulting error term uncorrelated
with all the right hand side variables Under this assumption any lags for the explanatory
variables beyond this can be used within the GMM estimator as instruments Like Hoechle et al
(2012) we add two years of lags of a firmrsquos excess value to the right hand side assuming that
this is enough lags to control for dynamic feedback effects We then use past values of all
variables beyond the second lag up to a total of seven years to act as instruments
The results of the dynamic panel data estimator are reported in Table 5 These results confirm
the previous finding of a positive relation between the degree of international operations and
excess value We note that the size and significance level of the coefficient on Foreign Sales
diversification are robust to changes in the number of lags used as instruments The Arellano and
Bond (1991) estimator includes testing diagnostics for correct specification of the model If the
model is correctly specified differences in the residuals from the levels equation should be
23
serially uncorrelated at lag one but correlated at lag two The p-values for tests of serial
correlation of the residuals at lags 1 and 2 are reported in Table 5 and these indicate that the
model is not rejected We also report in Table 5 the Hansen test for overidentification which is a
test of the null hypothesis that the instruments are valid The p-value for this test is 030
indicating that we fail to reject this null hypothesis Finally we report another test for exogeneity
known as the difference-in-Hansen test which tests the null hypothesis that the instruments in
the original equation are valid We fail to reject this null hypothesis as well
52 Identifying value effects of multinational operations via cross-border acquisitions
Another alternative to investigating a causal link between foreign direct investment and firm
value is an event study of foreign acquisitions21 Acquired foreign targets represent only a small
fraction of MNCsrsquo worldwide sales in the year of the acquisition ndash less than 1 percent in our
sample Additionally cross-border acquisitions are not the primary source of international
expansion for the average firm We find that foreign acquisitions account for between 18 percent
and 55 percent of the year-over-year change in foreign sales with the remainder of the growth
coming from either newly established entities (ie greenfield investments) or growth in existing
operations22 These results suggest that acquisitions are not a material source of growth for the
21 Doukas and Travlos (1988) find positive returns when the acquirer is not operating in the target firmrsquos country and insignificant returns when the acquirer is operating in the target firmrsquos country or expanding internationally for the first time Morck and Yeung (1991) find marginal evidence of positive returns for firms with intangible assets Finally Doukas (1995) finds positive returns only for firms with average Tobinrsquos q ratios greater than one More recent work also using an event study methodology provides mixed evidence on valuation effects of global diversification through acquisitions Dos Santos Errunza and Miller (2008) find insignificant returns for firms with already established foreign operations firms establishing initial foreign operations and firms acquiring targets in related industries Doukas and Kan (2006) and Moeller and Schlingemann (2005) find reductions to shareholder value while Francis Hasan and Sun (2008) find positive returns for acquirers of targets in segmented financial markets 22 New foreign affiliates to an MNC group each year report on the BEA survey whether they entered the group as an acquisition or a newly established entity (ie greenfield investment) Affiliates under the lowest reporting threshold do not report this information resulting in our inability to precisely identify the source of 33 percent of the increase in firm sales as either acquisition or greenfield
24
typical US MNC suggesting that an event study would lack power and generalizability For
these reasons we do not implement an event study
53 Control premium
A control premium or private benefits of control occurs when ldquosome value whatever the
source is not shared among all the shareholders in proportion of the shares owned but it is
enjoyed exclusively by the party in controlrdquo (Dyck and Zingales 2004 p 541) Using stock
prices of traded shares held by minority shareholders in determining the market value to sales
ratios of our benchmark firms could underestimate the true value of our benchmark firms if those
firms face large control premiums This in turn could induce an excess value premium because
the imputed value of these country-industry segments would be artificially low The existence of
such benefits is more prevalent in some countries than others and could affect the value of an
MNCrsquos operations in such domains We use the country-level control premiums from Dyck and
Zingales (2004) and using each firmrsquos country sales as a percent of total firm sales we obtain a
weighted average control premium that each MNC faces (Control Premium) For countries not
included in the Dyck and Zingales (2004) analysis we set the control premium equal to zero23 In
Table 6 we show that Foreign Sales remains positive and highly significant (p-value lt 001)
when we include the proxy for control premium As a result it does not appear that the excess
value premium is driven by MNCs operating in countries where majority shareholders are able to
extract large control premiums
23 Results are robust to replacing missing values with the mean control premium of the respective GDP quartile
25
54 Cost of capital
As discussed in Section 2 when countries have shallow capital markets a multinational
network may provide the benefit of a lower cost of capital for investments through an internal
capital market as well as through better access to the US capital market Prior research
establishes that there is substantial variation in the cost of capital across countries (Erb Harvey
and Viskanta 1996 and Hail and Leuz 2009) To determine whether the documented premium
is due to cost of capital advantages we include a control variable to capture the difference
between the MNCrsquos cost of capital and the cost of capital faced by its competitors in the global
markets in which it operates
We compute our Cost of Capital proxy in two steps First we obtain the country-year credit
rating published by Institutional Investor24 The rating ranges from 0 to 100 where higher values
imply that a country has a higher default risk Country-level credit risk is a reasonable predictor
of expected equity market returns and volatility (Erb et al 1996) and exhibits a highly
significant correlation with international accounting-based estimates of imputed cost of capital
(Hail and Leuz 2009) Second using each firmrsquos country sales as a percent of total firm sales
we obtain a weighted average credit risk rating across the countries in which each firm operates
A higher value implies an MNC operates in countries with higher cost of capital In Table 7 we
do not find a result consistent with MNCs gaining an advantage from their access to low cost
capitalndash Cost of Capital is not significantly associated with Excess Value25
24 We thank Cam Harvey for providing these data 25 Results are consistent using cost of capital as defined in Erb et al 1996
26
55 Corporate governance
In light of the recent finding that corporate governance mitigates the diversification discount
we examine whether the premium to multinational activity is sensitive to controls for corporate
governance Hoechle et al (2012) find that the magnitude of the industrial diversification
discount is decreased and in several specifications no longer significant in the presence of
governance proxies In the first column of Table 8 we include the five governance proxies that
are consistently significantly associated with excess value in Hoechle et al (2012) ndash the percent
of CEO ownership (CEO Ownership and CEO Ownership squared) the percent of institutional
ownership (Institution Ownership) whether the firm has a powerful or influential CEO
(Powerful CEO) and the governance index from Gompers Ishii and Metrick (2003)
(Governance Index)
As documented in Table 8 including these control variables reduces our sample size by
almost 50 (to 2550 observations) and tends to exclude the smaller firms from the analysis
None of the governance proxies are significant Relative to column (4) of Table 4 the coefficient
on Foreign Sales is similar in magnitude but is no longer statistically significant at
conventional levels To investigate whether the decreased significance is due to the smaller
sample or the inclusion of the governance variables we re-estimate the regression for the same
2550 observations after removing the governance controls The coefficient and t-statistic are
virtually identical suggesting that the loss of significance is due to the reduced sample and not
the governance controls We do not find evidence to suggest that variation in corporate
governance practices drives variation in excess value for US MNCs
27
6 Conclusion
Using a new method to quantify the valuation effects of foreign direct investment by US
domiciled firms we find robust evidence of a premium in excess value associated with the
degree of multinational operations Our method builds on that used in previous studies
examining firm excess value (eg Berger and Ofek 1995 and Denis et al 2002) by
incorporating the location in addition to the industry of each corporate affiliate This allows
important factors such as growth and cost of capital to vary across countries as well as industries
Examining a sample of multinational firms we find that a one-standard deviation (197)
increase in foreign operations is associated with an increase of between 41 and 82 in excess
value minus both a statistically and economically significant finding
This result complements the finding of the international trade literature that more productive
firms are more likely to operate internationally However the premium remains after including
firm fixed effects to control for persistent firm traits (eg productivity)
The finding of a premium stands up to a succession of robustness tests designed to evaluate
whether the result arises from alternative methods or explanations First we estimate a dynamic
panel data model using the generalized method of moments (GMM) estimator to simultaneously
account for the potential endogeneity of FDI industrial diversification and firm value Second
we include a control variable to proxy for whether the firm operates in countries with larger
control premiums which could result in understated multiples and mechanically induce a
premium Third we include a control variable to measure the cost of capital across the countries
in which an MNC operates to evaluate whether a relatively low cost of capital borne by
multinational firms is a meaningful source of the premium Fourth we control for several
corporate governance proxies which have been found to be correlated with excess values (and
28
industrial diversification) We fail to find evidence supporting any of these alternative
explanations ndash the premium in excess value arising at firms with greater multinational operations
remains significant
Overall our new method of estimating excess values for multinational firms provides strong
evidence of a statistically and economically significant premium associated with increased
international operations The premium is robust to a host of specifications and controls designed
to evaluate alternative explanations We leave to future research the questions of whether the
premium to US multinational firms applies more broadly to other countries of domicile and
what factors influence the premium
29
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Ammann M Hoeschel D and M Schmid 2012 Is there really no conglomerate discount
Journal of Business Finance and Accounting 39(12) 264-288 Arellano M and Bond S R 1991 Some tests of specification for panel data Monte Carlo
evidence and an application to employment equations Review of Economic Studies 58 (2) 277-298
Arellano M and Bover O 1995 Another look at instrumental variables estimation of error
components models Journal of Econometrics 68 (1) 29-61 Bens D A Berger P G and S J Monahan 2011 Discretionary disclosure in financial
reporting An examination comparing internal firm data to externally reported segment data The Accounting Review 86(2) 417-449
Berger P G and E Ofek 1995 Diversificationrsquos effect on firm value Journal of Financial
Economics 37 39-65 Blundell R and Bond S R 1998 Initial conditions and moment restrictions in dynamic panel
data models Journal of Econometrics 87(1) 115-144 Campa J M and S Kedia 2002 Explaining the diversification discount The Journal of
Finance 57 (4) 1731-1762 Denis D J Denis D K and K Yost 2002 Global diversification industrial diversification
and firm value The Journal of Finance 57 (5) 1951-1979 Desai M A Foley C F and J R Hines 2004 A multinational perspective on capital structure
choice and internal capital markets The Journal of Finance 59 (6) 2451-2487 Dos Santos MB V Errunza and D Miller 2008 Does corporate international diversification
destroy value Evidence from cross-border mergers and acquisitions Journal of Banking and Finance 32(12) 2716-2724
Doukas JA 1995 Overinvestment Tobinrsquos q and gains from foreign acquisitions Journal of
Banking and Finance 19 1285-1303 Doukas J and N G Travlos 1988 The effect of corporate multinationalism on shareholdersrsquorsquo
wealth evidence from international acquisitions Journal of Finance 43(5) 1161-1175 Doukas JA and O B Kan 2006 Does global diversification destroy firm value Journal of
International Business Studies 37(3) 352-371
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Dunning J and A Rugman 1985 The influence of Hymerrsquos dissertation on the theory of foreign direct investment The American Economic Review 75(2) 228-232
Dyck A and L Zingales 2004 Private benefits of control An international comparison The
Journal of Finance 59 (2) 537-600 Erb C Harvey C and T Viskanta 1996 Expected returns and volatility in 135 countries The
Journal of Portfolio Management 46-58 Foley C F Desai M A and K J Forbes 2008 Financial constraints and growth
Multinational and local firm responses to currency depreciations Review of Financial Studies 21 (6) 2857-2888
Francis B I Hasan and X Sun 2008 Financial market integration and the value of global
diversification Evidence for US acquirers in cross-border mergers and acquisitions Journal of Banking and Finance 321522-1540
Gande A Schenzler C and L Senbet 2009 Valuation effects of global diversification
Journal of International Business Studies 1515-1532 Gompers P Ishii J and A Metrick 2003 Corporate governance and equity prices Quarterly
Journal of Economics 118(1) 107-156 Hail L and C Leuz 2009 Cost of capital effects and changes in growth expectations around
US cross-listings Journal of Financial Economics 93 428-454 Hanson G Mataloni R and M Slaughter 2005 Vertical production networks in multinational
firms Review of Economics and Statistics 87(4) 1-15 Helpman E 2006 Trade FDI and the Organization of Firms Journal of Economic Literature
44(3) 289-630 Helpman E Melitz M J and S R Yeaple 2004 Export versus FDI with Heterogeneous Firms
American Economic Review 94(1) 300-316 Hoechle D Schmid M Walter W and D Yermack 2012 How much of the diversification
discount can be explained by poor governance Journal of Financial Economics 103 41-60 Hope O-K and W B Thomas 2008 Managerial empire building and firm disclosure Journal
of Accounting Research 46 (June) 591-626 Huizinga H P and J Voget 2009 International taxation and the direction and volume of cross-
border MampAs Journal of Finance 64 (3) 1217-1249 Jensen M 1986 Agency cost of free cash flow corporate finance and takeovers American
Economic Review Papers and Proceedings 76 323-329
31
Laeven L and R Levine 2007 Is there a diversification discount in financial conglomerates
Journal of Financial Economics 85 331-367 Lang L and R Stulz 1994 Tobinrsquos Q corporate diversification and firm performance Journal
of Political Economy 102 1248-1280 Mataloni R 2003 US multinational companies Operations in 2001 Survey of Current
Business 85-105 Matvos G and A Seru 2011 The dynamic tradeoff between corporate socialism and financial
market dislocation Estimates from internal capital markets Working paper University of Chicago Booth School of Business
Moeller SB and F P Schlingemann 2005 Global diversification and bidder gains A
comparison between cross-border and domestic acquisitions Journal of Banking and Finance 29 533-564
Morck R and B Yeung 1991 Why investors value multinationality Journal of Business 64
165-186
Morck R and B Yeung 2001 Why firms diversify Internatization vs agency behavior In Intangibles B Lev (ed) Oxford University Press pp 269-302
Myers S C and N S Majluf 1984 Corporate financing and investment decisions when firms have information that investors do not have Journal of Financial Economics 13 187-221
Schmid M and I Walter 2009 Do financial conglomerates create or destroy economic value
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365 Villalonga B 2004a Diversification discount or premium New evidence from the Business
Information Tracking Series Journal of Finance 59 479-506 Villalonga B 2004b Does diversification cause the diversification discount Financial
Management 33 5-27 Wooldridge JM 2010 Econometric Analysis of Cross Section and Panel Data (Second
Edition) Cambridge MA MIT Press Yamin M 1991 A reassessment of Hymerrsquos contribution to the theory of the transnational
corporation C Pitelis and R Sugden (eds) The Nature of the Transnational Firm London Routledge
32
Appendix A Variable Definitions
We obtain information regarding firmsrsquo operations by country and industry from the Bureau of Economic Analysis (BEA) unless otherwise noted Acronyms used below reflect Compustat mnemonics and thus indicate when we obtain data from the Compustat North America Fundamentals Annual database All independent variables are winsorized at 1
Dependent variable
Excess Value = The log of the ratio of actual firm value to imputed firm value Actual firm value is equal to market capitalization (MVE) + book value of total assets (AT) ndash book value of shareholdersrsquo equity (SEQ) Imputed firm value is calculated as the sum of the imputed values of a firmrsquos operations in each country-industry The imputed value for each country-industry is equal to sales generated in that country-industry times a multiple equal to the ratio of a benchmark firmrsquos actual value to total sales A benchmark firm is defined as a median single-segment firm operating in the same country-industry All multiples require at least five firms listed in the respective country-industry on Compustat (domestic) or Worldscope (foreign) with less than 10 of sales income and assets outside the country of domicile Similar to Berger and Ofek (1995) we eliminate extreme values where the ratio of actual to imputed firm value exceeds 400 or is less than 025
Independent variables
Foreign Sales = Sales made by the foreign operations of US MNCs as a percent of total firm sales
Industry Other = Sales outside a firmrsquos primary industry as a percent of total sales (SALE) Equal to zero for firms operating in a single business segment
Log Size = Log of total sales (SALE)
Debt Firm Value = Ratio of long-term debt (DLTT) to total market value (market capitalization (MVE) + book value of total assets (AT) ndash book value of shareholdersrsquo equity (SEQ))
CAPX Sales = Ratio of capital expenditures (CAPX) to total sales (SALE)
EBIT Sales = Ratio of earnings before interest and taxes (EBIT) to total sales (SALE)
RampD Sales = Ratio of research and development expenses (XRD) to total sales (SALE)
Ad Sales = Ratio of advertising expenses (XAD) to total sales (SALE)
Control Premium = A weighted-average (based on the sales in a given country as a percent of total firm sales) of the country-level control premium from Dyck and Zingales (2004)
Cost of Capital = A weighted-average (based on the sales in a given country as a percent of total firm sales) of the country-level credit rating from Institutional Investor (httpwwwinstitutionalinvestorcom) The country-level rating is available for 160 countries and ranges from 0 to 100 Higher values imply greater credit risk
33
CEO Ownership = Percent of shares owned by the CEO from Execucomp
Institution Ownership = Percent of shares owned by institutional investors from Thomson Reuterrsquos Institutional Holdings database
Powerful CEO = Indicator variable equal to one if the CEO is the only insider on the board of directors and also serves as the chairman and president based on data from RiskMetrics Directors database Equal to zero otherwise
Governance Index = Count measure of takeover defenses used in Gompers Ishii and Metrick (2003) from RiskMetrics Governance database As the governance data are not available for every year we follow Gompers Ishii and Metrick (2003) and assume that the firmsrsquo governance attributes are unchanged until publication of the subsequent series
34
Figure 1 Trends in multinational activity and industrial diversification from 1984-2008 This figure presents a plot of the mean percent of foreign sales and the mean percent of sales outside the firmrsquos primary industry (industrial diversification) from 1984 through 2008 Dotted lines above and below the primary lines represent 95 confidence intervals To compile the plot we include the full population of firms in Compustat
35
Table 1 Analysis of accounting data for firms reporting under multiple accounting standards This table provides a comparison of sales net income and assets for firms that changed accounting standards to or from US GAAP Comparisons are provided for the 66 firms that changed to or from US GAAP and for which we could obtain accounting data electronically and in English under both standards for the same fiscal year (eg the firm presented the accounting numbers originally under one set of standards and then restated the prior year numbers in the subsequent year under the new set of standards) Panel A shows the distribution of firms by year Panel B shows the other accounting standards to which US GAAP is compared Panel C shows the mean and median percent that the other accounting standard differs from US GAAP for the same fiscal year and accounting number Two samples are shown in Panel C The first is the full sample of firms with a change in accounting standards to or from US GAAP for which data was available The second is the subset of these firms that occurred during the sample window used in this study (ie excluding changes post-2008) The percent difference in sales is compared to the percent difference in net income and assets Tests of means are based on matched-pair t-tests Tests of medians are based on Wilcoxon matched-pair signed-rank tests Panel A Accounting standard changes by year
2003
4 6
2004
25 38
2005
8 12
2006
6 9
2007
7 11
2008
5 8
2009
5 8
2010
5 8
2011
1 2
66
Panel B Accounting standards compared to US GAAP
IFRS
47 71
Canadian GAAP
12 18
Israeli GAAP
2 3
Dutch GAAP
1 2
Indian GAAP
1 2
Japanese GAAP
1 2
Norwegian GAAP
1 2
Swiss GAAP
1 2
66
36
(Table 1 continued) Panel C Tests of differences between US GAAP and other accounting standards
Percent different from US GAAP
Comparison to sales (p-values)
N Mean Median
Mean Median
Full Sample (2001-2011)
Sales
66 236 000
Net Income
66 10653 647
005 lt001
Assets
66 1162 126
lt001 lt001
Study Sample (2001-2008)
Sales
55 257 000
Net Income
55 10303 589
011 lt001
Assets
55 1047 099
003 lt001
37
Table 2 Descriptive statistics Panel A reports the mean and medians of Excess Value for the multinational firms and single-segment benchmark firms as well as univariate tests across the subsamples Panel B reports summary statistics for the independent variables for the multinational firms The p-values of the tests of differences in means are based on t-tests The p-values of tests of differences in the medians are for a Wilcoxon rank-sum test All variables are defined in Appendix A Panel A Excess Value by sample Multinational All Benchmark Benchmark Subgroups Sample Firms US Foreign
Value Value Test of Diff (p-value) Value
Test of Diff (p-value) Value
Test of Diff (p-value)
Mean 0067 0006 lt001 -0028 lt001 0014 lt001 Median 0076 0000 lt001 -0025 lt001 0000 lt001 N 4950 33074 5998 27076 Panel B Distribution of independent variables for the multinational firms N Mean 25th 50th 75th StDev Foreign Sales 4950 0238 0074 0190 0363 0197 Industry Other 4950 0212 0000 0166 0414 0215 Log Size 4950 6955 5823 6989 8140 1687 Debt Firm Value 4950 0136 0009 0097 0212 0145 CAPX Sales 4950 0054 0020 0033 0054 0101 EBIT Sales 4950 0065 0031 0077 0121 0138 RampD Sales 4950 0049 0000 0013 0059 0083 Ad Sales 4950 0008 0000 0000 0005 0018
38
Table 3 BEA and Worldscope samples
This table reports details on affiliate sales for significant countries pooled from 1998-2008 Significant countries are defined as those in which the operations of US MNCs represent at least 02 of total firm sales (per the BEA) For significant countries the table compares their representation in terms of MNC affiliate sales in the BEA data to the distribution of firm observations in Worldscope US Foreign Affiliates (BEA) Foreign Firms (Worldscope)
Country
N (1)
Sales (millions)
(2)
Total Sales (3)
N (4)
Sales (million)
(5)
Total Sales (6)
Australia 1344 73305 056 859 255865 210 Belgium 894 52755 040 169 26136 021 Brazil 959 60245 046 1143 683167 560 Canada 2730 536767 407 1181 526750 432 China 922 75907 057 822 276541 227 France 1852 183942 139 540 105447 086 Germany 2182 275577 209 480 103888 085 Hong Kong 867 48100 036 157 34094 028 Ireland 478 36754 028 34 3467 003 Italy 1178 82116 062 111 47948 039 Japan 1494 148032 112 2281 1119223 917 Korea South 659 34500 026 3530 2752791 2255 Malaysia 386 26027 020 837 168683 138 Mexico 1405 113623 086 362 237173 194 Netherlands 1149 85105 064 47 22631 019 Singapore 1098 94917 072 139 32913 027 Spain 871 63094 048 129 37419 031 Sweden 712 32682 025 235 23090 019 Switzerland 726 60288 046 74 20242 017 Taiwan 592 30313 023 2949 787049 645 United Kingdom 3054 423934 321 1997 882423 723 United States (domicile) 4950 10460880 7923 na na na Other - Covered by WorldScope 6893 199394 151 9000 4057894 33 Other -Not Covered by WorldScope 713 5352 004 - - - Total 38108 13203609 1000 27076 12204834 100
39
Table 4 OLS regressions of firm excess values on the percent of foreign sales This table reports the results of OLS regressions of firm excess value on the percent of foreign sales (Foreign Sales) the percent of sales outside a firmrsquos primary industry (Industry Other) and control variables All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed) Dep Variable = Excess Value Coefficient t-stat Coefficient t-stat Coefficient t-stat Coefficient t-stat (1) (2) (3) (4) Foreign Sales 0349 419 0209 293 0189 266 0278 210 Industry Other -0134 -186 -0047 -072 -0053 -081 -0326 -325 Log Size 0066 634 0065 624 -0044 -096 Debt Firm Value -0196 -213 -0241 -260 -0685 -588 CAPX Sales 0901 425 0893 435 0488 227 EBIT Sales 1154 542 1170 547 0408 189 RampD Sales 2908 1242 2932 1237 1544 379 Ad Sales 1869 263 2003 280 -1696 -119 Year indicators No No Yes Yes Firm indicators No No No Yes R2 0017 0212 0226 0768 Observations 4950 4950 4950 4950
40
Table 5 Dynamic panel GMM model regressing firm excess values on the percent of foreign sales This table reports the result from a regression of the excess value on the percent of foreign sales (Foreign Sales) the percent of non-primary industry sales (Industry Other) and control variables using a dynamic panel GMM estimator All control variables are considered to be endogenous with the exception of the year indicator variables AR(1) and AR(2) are tests for first-order and second-order serial correlation in the first differenced residuals with the null hypothesis of no serial correlation The null hypothesis of the Hansen test of overidentification is that all instruments are valid The null hypothesis of the difference-in-Hansen test of exogeneity is that the instruments used for the equation in levels are exogenous All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed) Dep Variable = Excess Value Coef t-stat Excess Value (one lag) -0018 -082 Excess Value (two lags) -0048 -272 Foreign Sales 0371 270 Industry Other -0163 -171 Log Size 0010 123 Debt FirmValue -1135 -820 CAPX Sales 0590 342 EBIT Sales -0047 -300 RampD Sales 1376 531 Ad Sales -0751 -040 AR(1) test (p-value) 000 AR(2) test (p-value) 017 Hansen test of over-identification (p-value) 030 Difference-in-Hansen test of exogeneity (p-value) 093 Year indicators Yes Number of firms 1016 Number of observations 4090
41
Table 6 OLS regression of firm excess values on the percent of foreign sales controlling for a control premium This table reports the result of the OLS regression of firm excess value including firm and year fixed effects with an additional control variable to proxy for a country-level ownership control premium (Control Premium) All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed) Dep Variable = Excess Value Coef t -stat Foreign Sales 0303 224 Industry Other -0327 -325 Log Size -0045 -100 Debt FirmValue -0683 -585 CAPX Sales 0490 226 EBIT Sales 0409 189 RampD Sales 1545 379 Ad Sales -1689 -119 Control Premium -1004 -062 Year indicators Yes Firm indicators Yes R2 0768 Observations 4950
42
Table 7 OLS regression of firm excess values on the percent of foreign sales controlling for the cost of capital This table contains the results of an OLS regression of firm excess value including firm and year fixed effects with an additional control variable to proxy for a country-level cost of capital (Cost of Capital) All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed) Dep Variable = Excess Value Coef t -stat Foreign Sales 0255 178 Industry Other -0326 -324 Log Size -0043 -094 Debt FirmValue -0687 -589 CAPX Sales 0485 225 EBIT Sales 0406 188 RampD Sales 1539 379 Ad Sales -1703 -120 Cost of Capital 0004 039 Year indicators Yes Firm indicators Yes R2 0768 Observations 4950
43
Table 8 OLS regression of firm excess values on the percent of foreign sales controlling for aspects of corporate governance This table reports the results of two OLS regressions In the first column are the results of the firm excess value regression including firm and year fixed effect with five proxies for corporate governance included ndash the percent of shares owned by the CEO and the percent squared (CEO Ownership and CEO Ownership squared) the percent owned by institutional investors (Institution Ownership) whether there is a powerful CEO (Powerful CEO) and the Gompers Ishii and Metrick (2003) governance index (Governance Index) In the second column the regression includes the same sample and variables with the exception of the governance proxies which are excluded All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed)
Governance
No Governance (same sample)
Dep Variable = Excess Value Coef t-stat Coef t -stat Foreign Sales 0259 159 0251 154 Industry Other -0099 -080 -0099 -079 Log Size -0105 -185 -0102 -180 Debt FirmValue -0813 -478 -0817 -482 CAPX Sales 0440 252 0438 249 EBIT Sales 0457 247 0457 246 RampD Sales 1717 284 1716 286 Ad Sales -2210 -143 -2190 -143 CEO Ownership 0010 007
CEO Ownership squared -0029 -017 Institution Ownership 0047 067 Powerful CEO -0003 -014 Governance Index 0009 103
Year indicators Yes Yes Firm indicators Yes Yes R2 0811 0810 Observations 2550 2550
- Cover for paperpdf
- crrz_20130603_SMUpdf
-
6
Second we examine whether access to a low cost of capital is a meaningful source of the
US MNC advantage We incorporate foreign benchmarks in our method of estimating firm
excess value as it allows us to incorporate attributes of operating in those foreign countries ndash a
key attribute being the cost of capital Relative to the local benchmarks in foreign countries US
MNCs have the ability to obtain funds outside the local operating environments either by
borrowing in the US market or transferring capital internally When competing against firms in
shallow capital markets with high costs of capital MNCs may have a competitive advantage
over local foreign companies due to these alternative sources of capital However investors and
lenders are likely to expect higher rates of return from the additional risk associated with
operating in these environments making it unclear whether a multinational network will enhance
value in countries with a high cost of capital In addition foreign firms have some ability to
access international capital markets which mitigates the advantages to US firms To examine
whether the cost of capital plays a role in our finding of a premium we include a proxy for the
extent of operations in countries with higher costs of capital Our results are unaffected by the
inclusion of the proxy
Finally we control for several corporate governance proxies which Hoechle et al (2012)
find to be correlated with excess values (and industrial diversification) While magnitude of the
premium is virtually unaffected by including these proxies the significance level does decline
due to the reduced sample size
Our study contributes to the literature on multinational firms Our new method of estimating
excess values for multinational firms provides strong evidence of a statistically and economically
significant premium associated with increased international operations The premium is robust to
a variety of specifications and controls designed to evaluate alternative explanations In addition
7
this result easily reconciles with the international trade literature that establishes a link between
greater productivity and international operations though we do not find results supporting this as
the only source of the premium As segment disclosures improve and as new data sources
become available on multinational activity (eg Bureau Van Dijk) our empirical approach can
be used to investigate the extent to which the home country of the parent might influence the
extent with which foreign operations affect firm value (ie by examining non-US based
MNCs)7 Future studies could also examine the underlying forces affecting the excess value
premiums across countries
We also contribute to the understanding of international accounting standards and their
comparability Many studies seek to compare firms using different accounting standards These
comparisons generally rely on information generated by a particular accounting regime By
comparing commonly used metrics (total assets net income and sales) reported for the same
firm and year across different regimes we provide insights as to the relative comparability across
standards In particular we find that sales is reported significantly more consistently across
accounting regimes than the other metrics examined This finding should aid researchers in
reducing measurement error when comparing accounting information in multinational settings
The paper proceeds as follows Section 2 discusses the motivation for finding a premium or
discount to multinational operations Section 3 describes our data and how the excess value of a
firm is measured A discussion of our independent variables and primary findings is presented in
Section 4 with additional robustness tests provided in Section 5 Finally Section 6 concludes
7 As MNCs are ultimately bound by the tax regulatory and legal frameworks of their home country the valuation effect of foreign operations that we document for US-based MNCs may be different in a sample of non-US based MNCs with the same country-industry footprint Theorists refer to these as lsquolocational advantagesrsquo enjoyed by all multinational firms of a given nationality and obtained irrespective of skills or capabilities unique to a particular firm (Yamin 1991) There is also evidence of locational advantages in the market for corporate control (Huizinga and Voget 2009)
8
2 Premium versus discount
In a frictionless world where managers maximize firm value and markets are efficient there
should be no discount or premium to operating in foreign countries However there are a number
of forces that may cause actual firm value to deviate from the implied firm value
On one hand MNCs may have comparative advantages that generate premiums (ie positive
excess values) relative to a similar footprint of stand-alone firms Having operations in a variety
of locations that access different supplies of inputs and customers allows the firm to take
advantage of changing market conditions by shifting operations or products to maximize firm
value more so than a firm operating in a single country Similarly having access to a variety of
institutional settings such as different tax codes legal regimes and financial markets can provide
MNCs with options beyond those available to firms located in a single country
Another possible factor associated with a premium to multinational operations is the cost of
capital For operations located in countries with shallow capital markets or weaker creditor
rights operating within a conglomerate can provide the benefit of a lower cost of capital for
investments and expansion for at least two reasons First as our MNCs are domiciled in the US
their foreign segments have greater access to US capital markets relative to local competitors in
the foreign jurisdiction Second cash rich segments with few investment opportunities can
finance investments in cash poor segments with positive net present value projects (eg Myers
and Majluf 1984) Consequently diversified firms should be less liquidity constrained and better
able to shift resources to the most valuable investment opportunities Research has found that
internal financing is used more often when diversified firms have operations in countries with
more costly external financing (Desai Foley and Hines 2004) Multinational firms may also be
relatively more protected than single-country firms when negative shocks hit external capital
9
markets (Foley Desai and Forbes 2008) In a recent study Matvos and Seru (2011) find that
industrially diversified firms perform better than non-diversified firms when external capital
markets are impaired
While these arguments suggest that MNCs enjoy certain advantages when the foreign cost of
capital is high these benefits should be reduced by the additional risk associated with operating a
business in a high cost of capital environment For example Verizon is a multinational firm
operating in a number of countries One of the higher cost of capital locations in which they
operated was Venezuela While the multinational structure of Verizon may have mitigated some
of the risks associated with operating in a higher cost of capital location the firms was unable to
eliminate all of these risks In fact in 2007 Verizonrsquos operations in that country were
nationalized resulting in a net extraordinary loss of $131 million that year8 In other words
MNC investors are likely to expect higher rates of return to compensate for higher risk which
likely offsets some (if not all) of the potential excess value premium
On the other hand multinational firms may incur a discount (ie negative excess values)
relative to a similar footprint of stand-alone firms Potential reasons for a discount largely rely on
the existence of agency costs Multinational firms tend to be larger more complex and less
transparent The combination of complexity and opacity inherent in MNCs reduces a firmrsquos
reliance on external capital as the cost of such capital increases due to agency costs (Desai et al
2004) As a result of the reduced reliance on external funds MNCs face a reduced level of
monitoring As noted by Jensen (1986) managers have incentives to increase firm size beyond
that which is optimal with overinvestment or misallocation of funds to pet projects If this lack of
monitoring is in combination with available internal capital empire building becomes easier
(Hope and Thomas 2008) Furthermore the current segment reporting standards under US 8 See the 10-K of Verizon Communications Inc dated December 31 2007 (footnote 2)
10
GAAP only require highly aggregated disclosures of foreign operations and allow for
considerable managerial discretion As a result operating in multiple countries makes it easier
for management to hide poor performance ndash either their own or that of a division ndash such that low
quality managers are more likely to be retained and overcompensated and poorly performing
divisions retained longer than optimal Other reasons one might expect a discount to be applied
to multinational firms include higher coordination costs (eg coordinating across different
cultures and languages) as well as additional risks These risks include exposure to multiple
political regimes legal regimes economic regulations and currency fluctuations
Past studies investigate whether foreign operations of US firms enhance or reduce firm
value However all of these studies benchmark the value of the foreign operations of an MNC to
US domestic firms For instance Denis et al (2002) determine the implied value of a US
MNC using domestic (US) single-segment firms in the same industry as a benchmark Studies
using Tobinrsquos Q to investigate valuation effects of MNCs also rely on domestic benchmarks
(eg Morck and Yeung 1991 Morck and Yeung 2001 Gande Schenzler and Senbet 2009)
However using a US domestic firm as a benchmark for the value of foreign operations
implicitly makes two assumptions First this method assumes that the risks and expected growth
rates of foreign operations are equivalent to those of domestic operations This assumption is
inconsistent with the findings of Hail and Leuz (2009) which provides evidence that the cost of
capital varies substantially across countries Second this method assumes that there is excess
domestic capacity to allow for expansion with profitability similar to current domestic
operations We relax these assumptions by measuring the imputed value of a foreign component
of a US firm as if that component operated autonomously within that foreign country (rather
than in the US) Specifically our multiples allow the cost of capital and expected growth rates
11
to vary across not just industry but also country Furthermore our counterfactual does not
require the assumption that operations be relocated mitigating the production and sales capacity
concerns
3 Measuring excess value
31 Description of excess value
Figure 1 shows the average percent of foreign sales (based on segment disclosures under US
GAAP) across firms through time and illustrates that firms are continuing to expand
internationally9 This expansion highlights the importance of understanding whether the decision
to operate internationally is on average a value enhancing corporate strategy We contribute to
this understanding by evaluating whether the firm as a whole is worth more or less than its
imputed value (ie the firm is valued in excess of the sum of its individual components)
We measure the excess value of a firm as the logarithm of the ratio of actual firm value to
imputed value based on the method used in Berger and Ofek (1995) The imputed value is the
hypothetical value of the MNC under the assumption that its country-industry components
operate as independent entities A key innovation of our study is an alternative approach to
imputing the value of the foreign operations of an MNC Components of MNCs operating in a
given industry and country are imputed using the value of single-segment firms operating in the
same industry and country Thus we ensure the discount rates and expected growth rates are
applicable to each given industry and location in estimating the implied values of the MNC
segments
9 The discontinuity in industrial diversification (the percent of sales outside the primary industry) between 1997 and 1998 is the result of a change in segment reporting rules
12
The actual firm values are observable for all firms in our sample The hypothetical firm
values for multi-segment firms are imputed using market value to sales ratios herein referred to
as lsquomultiplesrsquo of the median single-segment firm operating in the same country-industry Thus
these single country-industry firms serve as benchmarks for an MNCrsquos operations within that
same country-industry The imputed value of the country-industry component can be viewed as
an estimate of what that component would be worth if it operated independently The sum of the
imputed values across all country-industry components represents the hypothetical value that the
multi-segment firm would be worth if it were broken apart
32 Data
Four primary data sources play a distinct role in carrying out our analysis i) Bureau of
Economic Analysis (BEA) data ii) Compustat Segment data iii) Worldscope data and iv)
Compustat Fundamentals Annual data Broadly speaking BEA data provide information about
the diversified nature of MNCs Compustat Segment data provide information about the
industrial diversification of US domiciled firms Worldscope data are used to compute
multiples for single-segment foreign firms that serve as benchmarks for the foreign operations of
MNCs (ie foreign benchmarks) and Compustat Fundamentals Annual data are used to
compute multiples for single-segment domestic firms that serve as benchmarks for the domestic
operations of multinational firms We also use the latter data source to construct our primary
control variables
13
Key to the execution of our study is our access to BEA data Federal law requires US-
domiciled MNCs to report certain financial and operating data to the BEA10 With regard to
observing the country-industry operations of MNCs use of the BEA data overcomes two
important limitations of Compustat Segment data First Compustat does not consistently identify
the specific location or scale of MNCsrsquo foreign operations11 Second Compustat does not
identify the industry activities in each country of operation This latter point is particularly
limiting for MNCs because domestic industry activity need not mirror foreign industry activity
An MNC generating an equal proportion of sales in two industries in the domestic market need
not generate industry sales in equal proportions in every foreign market12 The lack of detail and
consistency in Compustat Segment data arises because segment reporting is highly aggregated
and firms exercise substantial discretion in defining segments under Accounting Standards
Codification 280 (ASC 280) (eg Villalonga 2004a and Bens Berger and Monahan 2011)
The BEA defines an MNC as the combination of a single US entity called the US parent
and at least one foreign affiliate ndash that is these firms have a physical presence outside the US
due to FDI Since 1982 MNCs have completed a mandatory and confidential lsquoSurvey of US
Direct Investment Abroadrsquo for the domestic operation and the operations of each foreign
affiliate defined as a foreign entity in which the US parent holds at least a 10 percent equity
interest (directly or indirectly) The data are reported in US dollars on a fiscal year basis and in
10 BEA surveys are conducted pursuant to the International Investment and Trade in Services Survey Act (PL 94-472 90 Stat 2059 22 USC 3101-3108) See httpwwwbeagovsurveysdiasurvhtm and Mataloni (2003) for more detailed information on the BEA data 11 Exhibit 21 disclosures of material subsidiaries and their locations in SEC 10-K filings could potentially help overcome the inability to observe the location of the firmrsquos foreign operations from segment disclosures However at least three limitations remain (1) for industrially diversified multinational firms the researcher would be required to make an assumption about the proportion of industrial diversification in each country (2) Exhibit 21 provides no information about the scale of activity in each jurisdiction and (3) managerial discretion is used to determine a lsquomaterialrsquo subsidiary under Section 601 of SEC Regulation S-K 12 In fact the expectation that a multinational firm would exhibit different industry membership in different countries is consistent with the theory of vertical foreign direct investment where firms separate their various value chain activities and locate them in the most favorable country (see eg Hanson Mataloni and Slaughter 2005)
14
accordance with US Generally Accepted Accounting Principles (US GAAP) For each year
we observe the sales industry composition and location of not only the parent but also each
affiliate13
The procedures we use to construct our sample are similar to those used by Berger and Ofek
(1995) and Denis et al (2002) Using firm-years appearing in Compustat Fundamentals Annual
and Compustat Segment data we obtain a sample of firms domiciled in the US with total sales
exceeding $20 million We exclude firms in the financial service (SIC codes 6000 to 6999) and
utility (SIC codes 4000 to 4999) industries and eliminate firm-years when the sum of segment
sales (Compustat geographic or Compustat business) is not within 1 of total firm sales for that
year We restrict our sample period to include fiscal years beginning after December 15 1997 as
ASC 280 Segment Reporting substantially altered the definition of a reporting segment under
US GAAP This restriction ensures that Compustat Segment data reflects the use of a consistent
accounting standard Finally we require that our MNCs appear in both Compustat and BEA
data The requirement that our sample firms appear in the BEA database ensures that the firms
have observable international operations In sum these procedures result in 1166 multinational
firms incorporated in the US with 4950 observations between 1998 and 2008 that meet all of
our data requirements
33 Measuring excess value
The dependent variable in our study is excess firm value (Excess Value) defined as the
logarithm of the ratio of actual firm value to imputed firm value (Berger and Ofek 1995)
13 We obtain sales industry membership and country location for affiliates from one of two sources When affiliates exceed the BEA-determined reporting thresholds (ie if their assets sales or net income (loss) exceed $7 million in 1999 $30 million in 2000 through 2003 $10 million in 2004 and $40 million in 2005 through 2008) they must file their own detailed survey When affiliates fall below the thresholds we obtain our data from an attachment to the parent survey
15
Appendix A provides the definition of this and all other variables used in our analysis We
observe actual firm value equal to a firmrsquos market value of equity plus book value of liabilities
(ie book value of total assets minus book value of total equity) using Compustat Fundamentals
Annual data We calculate imputed firm value as the sum of the imputed values of a firmrsquos
operations in each country-industry Our method for imputing the value of the separate
components of a firm can best be described in three steps First we obtain total sales generated
by a firm for each country-industry in which it operates14 Second we obtain multiples (market
value to sales ratios) for benchmark firms operating in those same country-industries Third we
multiply a firmrsquos country-industry sales by the applicable median country-industry multiple to
obtain the imputed market value for each country-industry operation We perform each step
annually
Conceptually imputed values can be based on assets (ie Tobinrsquos Q) net income or sales
multiples We restrict ourselves to sales multiples for two reasons First using value to sales
ratios maintains consistency with the prior research on the excess value implications of
multinational operations (eg Denis et al 2002) Second as our method uses accounting data
for foreign companies to compute country-industry multiples the accounting numbers we use
need to be consistently measured across firms using different accounting standards We find that
sales data are measured most consistently
To assess the comparability of sales relative to either net income or assets across various
accounting standards we examine all firms listed on Worldscope as changing to or from US
14 Specifically we allocate total sales from Compustat to each component based on the proportion of BEA sales (after eliminating intercompany sales) occurring in each country-industry This step ensures that our total firm sales do not deviate from a firmrsquos total sales as reported in the 10-K
16
GAAP between 2005 and 201115 We obtain accounting data for the year of the change and the
prior year where electronically available in English We are able to obtain data for 66 firms
changing standards as detailed in Table 1 The distribution of years for which accounting data
are available under two different accounting standards for the same fiscal year is shown in Panel
A The years shown are the year prior to the change as these accounting numbers were provided
under the original standard and then subsequently restated in the following year under the new
standard for comparative purposes The majority of the shifts were to or from IFRS (71)
though seven other accounting standards are noted (Panel B) We examine the percent that the
non-US GAAP reported number differs from the US GAAP reported number in Panel C to
assess the comparability of these three summary accounting numbers across various accounting
standards Examining both the full sample as well as the subset that overlaps our study we find
that sales are more consistently measured than net income or assets
The sum of the imputed values across the country-industry components of an MNC is an
estimate of the value of a portfolio of unrelated businesses that mirror the related businesses of
the firm Consequently a comparison of the actual firm value with the imputed firm value is a
measure of how a multinational network affects firm value We provide descriptive statistics for
Excess Value for our sample of 4950 MNC firm-years in Table 2 Panel A This table also
includes univariate comparisons of the values between the MNC firms and the domestic single-
segment and foreign single-segment benchmark firms The Excess Value measure is significantly
higher for MNCs suggesting a premium in firm value for multinational firms relative to the
benchmark firms in total as well as the domestic and foreign subgroups (p-values for tests of
15 Due to the hand collection efforts required and the limitation on electronic filings we began our search with changes documented in 2005 which generally require filings from 2004
17
means and medians are lt 001)16 These results provide some initial evidence that operating in
multiple countries is positively associated with firm value
To estimate country-industry multiples we rely on Worldscope financial data on foreign
firms and Compustat Fundamentals Annual for domestic firms We restrict the firms to include
only those with at least 90 of sales income and assets inside the country of domicile (ie
those that do not report significant multinational activity) and that operate in a single industry
We refer to these firms as benchmark firms (either foreign or domestic depending on the country
of domicile) these firms do not appear in any of our regressions We report the number of
benchmark firms during our sample period in Table 2 Panel A The industry criterion is based on
the two-digit SIC code17 For every country that has at least five firms in the respective industry
and year we use the median ratio of market value to sales18 This ratio is the country-industry
multiple ndash an input required to compute imputed values
To determine the country-industry composition of MNCs we rely on BEA data In Table 3
we report the aggregate number of foreign affiliates and total sales by country as provided by
the BEA data for our sample of 4950 MNCs The specific countries tabulated are those that
represent at least 02 percent of total firm sales (pooled across all years) in our sample The top
five foreign countries in which MNCs generate sales are Canada United Kingdom Germany
France and Japan Table 3 also provides a comparison of the number of foreign benchmark
firms available in Worldscope in the specific countries in which our sample of MNCs generate
16 Similar to Berger and Ofek (1995) the median values differ slightly from zero for the benchmark single-segment firms due to the truncation of extreme excess values 17 BEA reporting constrains us to the two-digit SIC code level 18 When five firms are not available for a given country and industry we follow Berger and Ofek (1995) and move to a more general industry definition (eg one-digit SIC code) This procedure results in a mean (median) 630 (757) of MNC sales matched at the two-digit level
18
the majority of their sales Country coverage in Worldscope is not available for countries
representing 004 percent of total firm sales (per BEA)
4 Empirical results
41 Independent variables
Recall that our objective is to examine the overall relation between excess firm value and
multinational operations Our proxy for the extent of multinational operations is the percentage
of total firm sales made by foreign operations (Foreign Sales) By this measure the extent of
multinational activity in our sample of MNCs is non-trivial ndash Table 2 Panel B shows that
approximately 24 percent of sales are generated by foreign operations on average Our first
analysis estimates ordinary least square regressions of Excess Value on Foreign Sales and
control variables (discussed below)
We include variables in our regression to control for other potential determinants of excess
value These are the percentage of sales made by the firm outside its primary industry (Industry
Other) to control for any relation between industrial diversification and excess value as in Berger
and Ofek (1995) For this purpose we obtain industry sales using Compustat Segment data19 A
firmrsquos primary industry is the industry in which the firm generates the majority of its sales and
we determine industry sales at the business segment level We set Industry Other equal to zero
for firms that operate in a single business segment
Consistent with prior research we also include controls for firm size (Log Size) the ratio of
long term debt to total market value (Debt Firm Value) the ratio of capital expenditures to total
sales (CAPX Sales) the ratio of earnings before interest and taxes to total sales (EBIT Sales)
the ratio of research and development expenses to total sales (RampD Sales) and the ratio of 19 Our results are unaffected by using BEA data to measure Industry Other
19
advertising expenses to total sales (Ad Sales) Table 2 Panel B provides the distribution of the
control variables All independent variables are Winsorized at 1 In our OLS regressions we
use heteroskedasticity and autocorrelation consistent robust standard errors with clustering at the
firm level
42 Primary regression analysis
In this section we examine the relation between firm excess value and the amount of foreign
activity at US MNCs Table 4 contains the results of regressions of excess value on Foreign
Sales for multiple specifications In column (1) Excess Value is regressed on both the percent of
foreign sales (Foreign Sales) and the percent of sales outside the firmrsquos primary industry
(Industry Other) We add the control variables to the regression in column (2) and year
indicators in column (3) In all three specifications the coefficient on the percent of foreign sales
remains significantly positive (with a p-value lt 001) For a sense of economic magnitude in
column (3) a one standard deviation (197) increase in the percent of foreign sales (Foreign
Sales) is associated with a 41 (equal to (e0349-1) times 0197) increase in firm excess value The
effect is larger for the other two columns ndash 82 increase in firm excess value in column (1) and
46 in column (2)20
Our finding of a premium reconciles with the international trade literature which finds that
firms engaging in international trade are more productive than those that do not (see Helpman
2006 and Syverson 2011 for reviews) Due to data limitations it is difficult to accurately
measure TFP at the firm level especially for firms which operate in multiple industries andor
20 Using the alternative strategy that employs US domiciled single-segment firms as the benchmark for the imputed value of the foreign operations Denis et al (2002) find that greater foreign activity results in a discount to firm value Using the Denis et al (2002) method to calculate excess value in our sample we obtain a significantly negative regression coefficient on Foreign Sales using the models in Table 4 columns (2) and (3) (the coefficient is not significant using the model in column (1) results untabulated)
20
countries As an alternative method to evaluate whether our finding of a premium is simply the
result of more productive firms also having more multinational operations we investigate
whether the premium is simply an artifact of innate characteristics of firms that choose to invest
abroad For example Helpman et al (2004) present a simple model with heterogeneous
productivity endowments The firms that receive the highest productivity endowments are the
ones capable of paying the fixed costs to establish a foreign subsidiary
To the extent that productivity advantages create excess value one would expect the type of
firms that choose to invest more abroad would be valued at a premium even in the absence of
their foreign investments To control for time invariant firm characteristics (eg innate
productivity advantages) and hence a potential source of endogeneity we add firm-level fixed
effects in column (4) of Table 4 We continue to find that the extent of foreign operations
(Foreign Sales) is positively associated with Excess Value Across all specifications the sign
on all significant control variables is consistent with prior research (eg Berger and Ofek 1995
and Denis et al 2002)
The finding of a premium makes a significant contribution to the relatively sparse literature
on the value effects of multinational activity Our measure of imputed value expands upon the
methods used by Berger and Ofek (1995) and Denis et al (2002) but allows us to answer a
different question than Denis et al (2002) Holding constant the extent of industrial
diversification we ask whether MNCs are more valuable than a portfolio of benchmark firms of
a similar geographic footprint In contrast Denis et al (2002) ask whether holding constant the
extent of industrial diversification MNCs are more valuable than a portfolio of domestic (US)
benchmark firms We believe that asking whether an MNCrsquos foreign (domestic) operations are
21
more valuable than those of a foreign (domestic) firm is a more appropriate way to assess the
value implications of a multinational network
5 Robustness
51 Endogeneity related to dynamic relations
Prior research argues that the relation between industrial diversification and excess firm
value could be endogenous through dynamic relationships based on observing past outcomes
Managers of firms likely choose to enter or exit industries or geographic regions based upon
their previous performance For example Campa and Kedia (2002) find that firms are more
likely to enter new industries when prospects in their current lines of business are deteriorating
Similar dynamics could also drive a relation between excess value and FDI We assess the
robustness of the previous results by estimating a dynamic panel data model which allows us to
control for dynamic endogeneity in addition to firm level unobserved heterogeneity We employ
the GMM estimator for dynamic panel models developed by Arellano and Bond (1991) Arellano
and Bover (1995) and Blundell and Bond (1998) Our results from this analysis confirm our
previous findings that firms with a larger degree of foreign operations have positive excess
values
Dynamic panel data models are generalizations of the traditional fixed effects model where
lags of the dependent variable are added to the right hand side of the equation These additional
lags control for the impact a firmrsquos past performance has on its current performance through the
channels discussed above The GMM estimator for dynamic panel models developed by
Arellano and Bond (1991) starts by differencing the data at the firm level in neighboring time
periods to eliminate any firm specific time invariant unobserved heterogeneity that may be
22
present Eliminating the firm specific fixed effect by differencing the data with lags of the
dependent variable on the right hand side introduces correlation between the new error term and
the differenced explanatory variables This problem is solved by applying the standard GMM
estimator using lags of both the original explanatory variables and excess value as instruments
We note that the dynamic panel data framework allows us to treat both international and
industrial diversification (as well as the other firm-specific control variables) as endogenous
Following Hoechle et al (2012) we also allow contemporaneous values of these variables to be
treated as endogenous
Lagged values of the explanatory variables are valid instruments in a dynamic panel data
model when the model is dynamically complete (eg Wooldridge 2010) A dynamic panel data
model is dynamically complete conditional on the unobserved effect if enough lags of both the
explanatory and dependent variable are included to make the resulting error term uncorrelated
with all the right hand side variables Under this assumption any lags for the explanatory
variables beyond this can be used within the GMM estimator as instruments Like Hoechle et al
(2012) we add two years of lags of a firmrsquos excess value to the right hand side assuming that
this is enough lags to control for dynamic feedback effects We then use past values of all
variables beyond the second lag up to a total of seven years to act as instruments
The results of the dynamic panel data estimator are reported in Table 5 These results confirm
the previous finding of a positive relation between the degree of international operations and
excess value We note that the size and significance level of the coefficient on Foreign Sales
diversification are robust to changes in the number of lags used as instruments The Arellano and
Bond (1991) estimator includes testing diagnostics for correct specification of the model If the
model is correctly specified differences in the residuals from the levels equation should be
23
serially uncorrelated at lag one but correlated at lag two The p-values for tests of serial
correlation of the residuals at lags 1 and 2 are reported in Table 5 and these indicate that the
model is not rejected We also report in Table 5 the Hansen test for overidentification which is a
test of the null hypothesis that the instruments are valid The p-value for this test is 030
indicating that we fail to reject this null hypothesis Finally we report another test for exogeneity
known as the difference-in-Hansen test which tests the null hypothesis that the instruments in
the original equation are valid We fail to reject this null hypothesis as well
52 Identifying value effects of multinational operations via cross-border acquisitions
Another alternative to investigating a causal link between foreign direct investment and firm
value is an event study of foreign acquisitions21 Acquired foreign targets represent only a small
fraction of MNCsrsquo worldwide sales in the year of the acquisition ndash less than 1 percent in our
sample Additionally cross-border acquisitions are not the primary source of international
expansion for the average firm We find that foreign acquisitions account for between 18 percent
and 55 percent of the year-over-year change in foreign sales with the remainder of the growth
coming from either newly established entities (ie greenfield investments) or growth in existing
operations22 These results suggest that acquisitions are not a material source of growth for the
21 Doukas and Travlos (1988) find positive returns when the acquirer is not operating in the target firmrsquos country and insignificant returns when the acquirer is operating in the target firmrsquos country or expanding internationally for the first time Morck and Yeung (1991) find marginal evidence of positive returns for firms with intangible assets Finally Doukas (1995) finds positive returns only for firms with average Tobinrsquos q ratios greater than one More recent work also using an event study methodology provides mixed evidence on valuation effects of global diversification through acquisitions Dos Santos Errunza and Miller (2008) find insignificant returns for firms with already established foreign operations firms establishing initial foreign operations and firms acquiring targets in related industries Doukas and Kan (2006) and Moeller and Schlingemann (2005) find reductions to shareholder value while Francis Hasan and Sun (2008) find positive returns for acquirers of targets in segmented financial markets 22 New foreign affiliates to an MNC group each year report on the BEA survey whether they entered the group as an acquisition or a newly established entity (ie greenfield investment) Affiliates under the lowest reporting threshold do not report this information resulting in our inability to precisely identify the source of 33 percent of the increase in firm sales as either acquisition or greenfield
24
typical US MNC suggesting that an event study would lack power and generalizability For
these reasons we do not implement an event study
53 Control premium
A control premium or private benefits of control occurs when ldquosome value whatever the
source is not shared among all the shareholders in proportion of the shares owned but it is
enjoyed exclusively by the party in controlrdquo (Dyck and Zingales 2004 p 541) Using stock
prices of traded shares held by minority shareholders in determining the market value to sales
ratios of our benchmark firms could underestimate the true value of our benchmark firms if those
firms face large control premiums This in turn could induce an excess value premium because
the imputed value of these country-industry segments would be artificially low The existence of
such benefits is more prevalent in some countries than others and could affect the value of an
MNCrsquos operations in such domains We use the country-level control premiums from Dyck and
Zingales (2004) and using each firmrsquos country sales as a percent of total firm sales we obtain a
weighted average control premium that each MNC faces (Control Premium) For countries not
included in the Dyck and Zingales (2004) analysis we set the control premium equal to zero23 In
Table 6 we show that Foreign Sales remains positive and highly significant (p-value lt 001)
when we include the proxy for control premium As a result it does not appear that the excess
value premium is driven by MNCs operating in countries where majority shareholders are able to
extract large control premiums
23 Results are robust to replacing missing values with the mean control premium of the respective GDP quartile
25
54 Cost of capital
As discussed in Section 2 when countries have shallow capital markets a multinational
network may provide the benefit of a lower cost of capital for investments through an internal
capital market as well as through better access to the US capital market Prior research
establishes that there is substantial variation in the cost of capital across countries (Erb Harvey
and Viskanta 1996 and Hail and Leuz 2009) To determine whether the documented premium
is due to cost of capital advantages we include a control variable to capture the difference
between the MNCrsquos cost of capital and the cost of capital faced by its competitors in the global
markets in which it operates
We compute our Cost of Capital proxy in two steps First we obtain the country-year credit
rating published by Institutional Investor24 The rating ranges from 0 to 100 where higher values
imply that a country has a higher default risk Country-level credit risk is a reasonable predictor
of expected equity market returns and volatility (Erb et al 1996) and exhibits a highly
significant correlation with international accounting-based estimates of imputed cost of capital
(Hail and Leuz 2009) Second using each firmrsquos country sales as a percent of total firm sales
we obtain a weighted average credit risk rating across the countries in which each firm operates
A higher value implies an MNC operates in countries with higher cost of capital In Table 7 we
do not find a result consistent with MNCs gaining an advantage from their access to low cost
capitalndash Cost of Capital is not significantly associated with Excess Value25
24 We thank Cam Harvey for providing these data 25 Results are consistent using cost of capital as defined in Erb et al 1996
26
55 Corporate governance
In light of the recent finding that corporate governance mitigates the diversification discount
we examine whether the premium to multinational activity is sensitive to controls for corporate
governance Hoechle et al (2012) find that the magnitude of the industrial diversification
discount is decreased and in several specifications no longer significant in the presence of
governance proxies In the first column of Table 8 we include the five governance proxies that
are consistently significantly associated with excess value in Hoechle et al (2012) ndash the percent
of CEO ownership (CEO Ownership and CEO Ownership squared) the percent of institutional
ownership (Institution Ownership) whether the firm has a powerful or influential CEO
(Powerful CEO) and the governance index from Gompers Ishii and Metrick (2003)
(Governance Index)
As documented in Table 8 including these control variables reduces our sample size by
almost 50 (to 2550 observations) and tends to exclude the smaller firms from the analysis
None of the governance proxies are significant Relative to column (4) of Table 4 the coefficient
on Foreign Sales is similar in magnitude but is no longer statistically significant at
conventional levels To investigate whether the decreased significance is due to the smaller
sample or the inclusion of the governance variables we re-estimate the regression for the same
2550 observations after removing the governance controls The coefficient and t-statistic are
virtually identical suggesting that the loss of significance is due to the reduced sample and not
the governance controls We do not find evidence to suggest that variation in corporate
governance practices drives variation in excess value for US MNCs
27
6 Conclusion
Using a new method to quantify the valuation effects of foreign direct investment by US
domiciled firms we find robust evidence of a premium in excess value associated with the
degree of multinational operations Our method builds on that used in previous studies
examining firm excess value (eg Berger and Ofek 1995 and Denis et al 2002) by
incorporating the location in addition to the industry of each corporate affiliate This allows
important factors such as growth and cost of capital to vary across countries as well as industries
Examining a sample of multinational firms we find that a one-standard deviation (197)
increase in foreign operations is associated with an increase of between 41 and 82 in excess
value minus both a statistically and economically significant finding
This result complements the finding of the international trade literature that more productive
firms are more likely to operate internationally However the premium remains after including
firm fixed effects to control for persistent firm traits (eg productivity)
The finding of a premium stands up to a succession of robustness tests designed to evaluate
whether the result arises from alternative methods or explanations First we estimate a dynamic
panel data model using the generalized method of moments (GMM) estimator to simultaneously
account for the potential endogeneity of FDI industrial diversification and firm value Second
we include a control variable to proxy for whether the firm operates in countries with larger
control premiums which could result in understated multiples and mechanically induce a
premium Third we include a control variable to measure the cost of capital across the countries
in which an MNC operates to evaluate whether a relatively low cost of capital borne by
multinational firms is a meaningful source of the premium Fourth we control for several
corporate governance proxies which have been found to be correlated with excess values (and
28
industrial diversification) We fail to find evidence supporting any of these alternative
explanations ndash the premium in excess value arising at firms with greater multinational operations
remains significant
Overall our new method of estimating excess values for multinational firms provides strong
evidence of a statistically and economically significant premium associated with increased
international operations The premium is robust to a host of specifications and controls designed
to evaluate alternative explanations We leave to future research the questions of whether the
premium to US multinational firms applies more broadly to other countries of domicile and
what factors influence the premium
29
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Ammann M Hoeschel D and M Schmid 2012 Is there really no conglomerate discount
Journal of Business Finance and Accounting 39(12) 264-288 Arellano M and Bond S R 1991 Some tests of specification for panel data Monte Carlo
evidence and an application to employment equations Review of Economic Studies 58 (2) 277-298
Arellano M and Bover O 1995 Another look at instrumental variables estimation of error
components models Journal of Econometrics 68 (1) 29-61 Bens D A Berger P G and S J Monahan 2011 Discretionary disclosure in financial
reporting An examination comparing internal firm data to externally reported segment data The Accounting Review 86(2) 417-449
Berger P G and E Ofek 1995 Diversificationrsquos effect on firm value Journal of Financial
Economics 37 39-65 Blundell R and Bond S R 1998 Initial conditions and moment restrictions in dynamic panel
data models Journal of Econometrics 87(1) 115-144 Campa J M and S Kedia 2002 Explaining the diversification discount The Journal of
Finance 57 (4) 1731-1762 Denis D J Denis D K and K Yost 2002 Global diversification industrial diversification
and firm value The Journal of Finance 57 (5) 1951-1979 Desai M A Foley C F and J R Hines 2004 A multinational perspective on capital structure
choice and internal capital markets The Journal of Finance 59 (6) 2451-2487 Dos Santos MB V Errunza and D Miller 2008 Does corporate international diversification
destroy value Evidence from cross-border mergers and acquisitions Journal of Banking and Finance 32(12) 2716-2724
Doukas JA 1995 Overinvestment Tobinrsquos q and gains from foreign acquisitions Journal of
Banking and Finance 19 1285-1303 Doukas J and N G Travlos 1988 The effect of corporate multinationalism on shareholdersrsquorsquo
wealth evidence from international acquisitions Journal of Finance 43(5) 1161-1175 Doukas JA and O B Kan 2006 Does global diversification destroy firm value Journal of
International Business Studies 37(3) 352-371
30
Dunning J and A Rugman 1985 The influence of Hymerrsquos dissertation on the theory of foreign direct investment The American Economic Review 75(2) 228-232
Dyck A and L Zingales 2004 Private benefits of control An international comparison The
Journal of Finance 59 (2) 537-600 Erb C Harvey C and T Viskanta 1996 Expected returns and volatility in 135 countries The
Journal of Portfolio Management 46-58 Foley C F Desai M A and K J Forbes 2008 Financial constraints and growth
Multinational and local firm responses to currency depreciations Review of Financial Studies 21 (6) 2857-2888
Francis B I Hasan and X Sun 2008 Financial market integration and the value of global
diversification Evidence for US acquirers in cross-border mergers and acquisitions Journal of Banking and Finance 321522-1540
Gande A Schenzler C and L Senbet 2009 Valuation effects of global diversification
Journal of International Business Studies 1515-1532 Gompers P Ishii J and A Metrick 2003 Corporate governance and equity prices Quarterly
Journal of Economics 118(1) 107-156 Hail L and C Leuz 2009 Cost of capital effects and changes in growth expectations around
US cross-listings Journal of Financial Economics 93 428-454 Hanson G Mataloni R and M Slaughter 2005 Vertical production networks in multinational
firms Review of Economics and Statistics 87(4) 1-15 Helpman E 2006 Trade FDI and the Organization of Firms Journal of Economic Literature
44(3) 289-630 Helpman E Melitz M J and S R Yeaple 2004 Export versus FDI with Heterogeneous Firms
American Economic Review 94(1) 300-316 Hoechle D Schmid M Walter W and D Yermack 2012 How much of the diversification
discount can be explained by poor governance Journal of Financial Economics 103 41-60 Hope O-K and W B Thomas 2008 Managerial empire building and firm disclosure Journal
of Accounting Research 46 (June) 591-626 Huizinga H P and J Voget 2009 International taxation and the direction and volume of cross-
border MampAs Journal of Finance 64 (3) 1217-1249 Jensen M 1986 Agency cost of free cash flow corporate finance and takeovers American
Economic Review Papers and Proceedings 76 323-329
31
Laeven L and R Levine 2007 Is there a diversification discount in financial conglomerates
Journal of Financial Economics 85 331-367 Lang L and R Stulz 1994 Tobinrsquos Q corporate diversification and firm performance Journal
of Political Economy 102 1248-1280 Mataloni R 2003 US multinational companies Operations in 2001 Survey of Current
Business 85-105 Matvos G and A Seru 2011 The dynamic tradeoff between corporate socialism and financial
market dislocation Estimates from internal capital markets Working paper University of Chicago Booth School of Business
Moeller SB and F P Schlingemann 2005 Global diversification and bidder gains A
comparison between cross-border and domestic acquisitions Journal of Banking and Finance 29 533-564
Morck R and B Yeung 1991 Why investors value multinationality Journal of Business 64
165-186
Morck R and B Yeung 2001 Why firms diversify Internatization vs agency behavior In Intangibles B Lev (ed) Oxford University Press pp 269-302
Myers S C and N S Majluf 1984 Corporate financing and investment decisions when firms have information that investors do not have Journal of Financial Economics 13 187-221
Schmid M and I Walter 2009 Do financial conglomerates create or destroy economic value
Journal of Financial Intermediation 18 193-216 Syverson C 2011 What determines productivity Journal of Economic Literature 49(2) 326-
365 Villalonga B 2004a Diversification discount or premium New evidence from the Business
Information Tracking Series Journal of Finance 59 479-506 Villalonga B 2004b Does diversification cause the diversification discount Financial
Management 33 5-27 Wooldridge JM 2010 Econometric Analysis of Cross Section and Panel Data (Second
Edition) Cambridge MA MIT Press Yamin M 1991 A reassessment of Hymerrsquos contribution to the theory of the transnational
corporation C Pitelis and R Sugden (eds) The Nature of the Transnational Firm London Routledge
32
Appendix A Variable Definitions
We obtain information regarding firmsrsquo operations by country and industry from the Bureau of Economic Analysis (BEA) unless otherwise noted Acronyms used below reflect Compustat mnemonics and thus indicate when we obtain data from the Compustat North America Fundamentals Annual database All independent variables are winsorized at 1
Dependent variable
Excess Value = The log of the ratio of actual firm value to imputed firm value Actual firm value is equal to market capitalization (MVE) + book value of total assets (AT) ndash book value of shareholdersrsquo equity (SEQ) Imputed firm value is calculated as the sum of the imputed values of a firmrsquos operations in each country-industry The imputed value for each country-industry is equal to sales generated in that country-industry times a multiple equal to the ratio of a benchmark firmrsquos actual value to total sales A benchmark firm is defined as a median single-segment firm operating in the same country-industry All multiples require at least five firms listed in the respective country-industry on Compustat (domestic) or Worldscope (foreign) with less than 10 of sales income and assets outside the country of domicile Similar to Berger and Ofek (1995) we eliminate extreme values where the ratio of actual to imputed firm value exceeds 400 or is less than 025
Independent variables
Foreign Sales = Sales made by the foreign operations of US MNCs as a percent of total firm sales
Industry Other = Sales outside a firmrsquos primary industry as a percent of total sales (SALE) Equal to zero for firms operating in a single business segment
Log Size = Log of total sales (SALE)
Debt Firm Value = Ratio of long-term debt (DLTT) to total market value (market capitalization (MVE) + book value of total assets (AT) ndash book value of shareholdersrsquo equity (SEQ))
CAPX Sales = Ratio of capital expenditures (CAPX) to total sales (SALE)
EBIT Sales = Ratio of earnings before interest and taxes (EBIT) to total sales (SALE)
RampD Sales = Ratio of research and development expenses (XRD) to total sales (SALE)
Ad Sales = Ratio of advertising expenses (XAD) to total sales (SALE)
Control Premium = A weighted-average (based on the sales in a given country as a percent of total firm sales) of the country-level control premium from Dyck and Zingales (2004)
Cost of Capital = A weighted-average (based on the sales in a given country as a percent of total firm sales) of the country-level credit rating from Institutional Investor (httpwwwinstitutionalinvestorcom) The country-level rating is available for 160 countries and ranges from 0 to 100 Higher values imply greater credit risk
33
CEO Ownership = Percent of shares owned by the CEO from Execucomp
Institution Ownership = Percent of shares owned by institutional investors from Thomson Reuterrsquos Institutional Holdings database
Powerful CEO = Indicator variable equal to one if the CEO is the only insider on the board of directors and also serves as the chairman and president based on data from RiskMetrics Directors database Equal to zero otherwise
Governance Index = Count measure of takeover defenses used in Gompers Ishii and Metrick (2003) from RiskMetrics Governance database As the governance data are not available for every year we follow Gompers Ishii and Metrick (2003) and assume that the firmsrsquo governance attributes are unchanged until publication of the subsequent series
34
Figure 1 Trends in multinational activity and industrial diversification from 1984-2008 This figure presents a plot of the mean percent of foreign sales and the mean percent of sales outside the firmrsquos primary industry (industrial diversification) from 1984 through 2008 Dotted lines above and below the primary lines represent 95 confidence intervals To compile the plot we include the full population of firms in Compustat
35
Table 1 Analysis of accounting data for firms reporting under multiple accounting standards This table provides a comparison of sales net income and assets for firms that changed accounting standards to or from US GAAP Comparisons are provided for the 66 firms that changed to or from US GAAP and for which we could obtain accounting data electronically and in English under both standards for the same fiscal year (eg the firm presented the accounting numbers originally under one set of standards and then restated the prior year numbers in the subsequent year under the new set of standards) Panel A shows the distribution of firms by year Panel B shows the other accounting standards to which US GAAP is compared Panel C shows the mean and median percent that the other accounting standard differs from US GAAP for the same fiscal year and accounting number Two samples are shown in Panel C The first is the full sample of firms with a change in accounting standards to or from US GAAP for which data was available The second is the subset of these firms that occurred during the sample window used in this study (ie excluding changes post-2008) The percent difference in sales is compared to the percent difference in net income and assets Tests of means are based on matched-pair t-tests Tests of medians are based on Wilcoxon matched-pair signed-rank tests Panel A Accounting standard changes by year
2003
4 6
2004
25 38
2005
8 12
2006
6 9
2007
7 11
2008
5 8
2009
5 8
2010
5 8
2011
1 2
66
Panel B Accounting standards compared to US GAAP
IFRS
47 71
Canadian GAAP
12 18
Israeli GAAP
2 3
Dutch GAAP
1 2
Indian GAAP
1 2
Japanese GAAP
1 2
Norwegian GAAP
1 2
Swiss GAAP
1 2
66
36
(Table 1 continued) Panel C Tests of differences between US GAAP and other accounting standards
Percent different from US GAAP
Comparison to sales (p-values)
N Mean Median
Mean Median
Full Sample (2001-2011)
Sales
66 236 000
Net Income
66 10653 647
005 lt001
Assets
66 1162 126
lt001 lt001
Study Sample (2001-2008)
Sales
55 257 000
Net Income
55 10303 589
011 lt001
Assets
55 1047 099
003 lt001
37
Table 2 Descriptive statistics Panel A reports the mean and medians of Excess Value for the multinational firms and single-segment benchmark firms as well as univariate tests across the subsamples Panel B reports summary statistics for the independent variables for the multinational firms The p-values of the tests of differences in means are based on t-tests The p-values of tests of differences in the medians are for a Wilcoxon rank-sum test All variables are defined in Appendix A Panel A Excess Value by sample Multinational All Benchmark Benchmark Subgroups Sample Firms US Foreign
Value Value Test of Diff (p-value) Value
Test of Diff (p-value) Value
Test of Diff (p-value)
Mean 0067 0006 lt001 -0028 lt001 0014 lt001 Median 0076 0000 lt001 -0025 lt001 0000 lt001 N 4950 33074 5998 27076 Panel B Distribution of independent variables for the multinational firms N Mean 25th 50th 75th StDev Foreign Sales 4950 0238 0074 0190 0363 0197 Industry Other 4950 0212 0000 0166 0414 0215 Log Size 4950 6955 5823 6989 8140 1687 Debt Firm Value 4950 0136 0009 0097 0212 0145 CAPX Sales 4950 0054 0020 0033 0054 0101 EBIT Sales 4950 0065 0031 0077 0121 0138 RampD Sales 4950 0049 0000 0013 0059 0083 Ad Sales 4950 0008 0000 0000 0005 0018
38
Table 3 BEA and Worldscope samples
This table reports details on affiliate sales for significant countries pooled from 1998-2008 Significant countries are defined as those in which the operations of US MNCs represent at least 02 of total firm sales (per the BEA) For significant countries the table compares their representation in terms of MNC affiliate sales in the BEA data to the distribution of firm observations in Worldscope US Foreign Affiliates (BEA) Foreign Firms (Worldscope)
Country
N (1)
Sales (millions)
(2)
Total Sales (3)
N (4)
Sales (million)
(5)
Total Sales (6)
Australia 1344 73305 056 859 255865 210 Belgium 894 52755 040 169 26136 021 Brazil 959 60245 046 1143 683167 560 Canada 2730 536767 407 1181 526750 432 China 922 75907 057 822 276541 227 France 1852 183942 139 540 105447 086 Germany 2182 275577 209 480 103888 085 Hong Kong 867 48100 036 157 34094 028 Ireland 478 36754 028 34 3467 003 Italy 1178 82116 062 111 47948 039 Japan 1494 148032 112 2281 1119223 917 Korea South 659 34500 026 3530 2752791 2255 Malaysia 386 26027 020 837 168683 138 Mexico 1405 113623 086 362 237173 194 Netherlands 1149 85105 064 47 22631 019 Singapore 1098 94917 072 139 32913 027 Spain 871 63094 048 129 37419 031 Sweden 712 32682 025 235 23090 019 Switzerland 726 60288 046 74 20242 017 Taiwan 592 30313 023 2949 787049 645 United Kingdom 3054 423934 321 1997 882423 723 United States (domicile) 4950 10460880 7923 na na na Other - Covered by WorldScope 6893 199394 151 9000 4057894 33 Other -Not Covered by WorldScope 713 5352 004 - - - Total 38108 13203609 1000 27076 12204834 100
39
Table 4 OLS regressions of firm excess values on the percent of foreign sales This table reports the results of OLS regressions of firm excess value on the percent of foreign sales (Foreign Sales) the percent of sales outside a firmrsquos primary industry (Industry Other) and control variables All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed) Dep Variable = Excess Value Coefficient t-stat Coefficient t-stat Coefficient t-stat Coefficient t-stat (1) (2) (3) (4) Foreign Sales 0349 419 0209 293 0189 266 0278 210 Industry Other -0134 -186 -0047 -072 -0053 -081 -0326 -325 Log Size 0066 634 0065 624 -0044 -096 Debt Firm Value -0196 -213 -0241 -260 -0685 -588 CAPX Sales 0901 425 0893 435 0488 227 EBIT Sales 1154 542 1170 547 0408 189 RampD Sales 2908 1242 2932 1237 1544 379 Ad Sales 1869 263 2003 280 -1696 -119 Year indicators No No Yes Yes Firm indicators No No No Yes R2 0017 0212 0226 0768 Observations 4950 4950 4950 4950
40
Table 5 Dynamic panel GMM model regressing firm excess values on the percent of foreign sales This table reports the result from a regression of the excess value on the percent of foreign sales (Foreign Sales) the percent of non-primary industry sales (Industry Other) and control variables using a dynamic panel GMM estimator All control variables are considered to be endogenous with the exception of the year indicator variables AR(1) and AR(2) are tests for first-order and second-order serial correlation in the first differenced residuals with the null hypothesis of no serial correlation The null hypothesis of the Hansen test of overidentification is that all instruments are valid The null hypothesis of the difference-in-Hansen test of exogeneity is that the instruments used for the equation in levels are exogenous All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed) Dep Variable = Excess Value Coef t-stat Excess Value (one lag) -0018 -082 Excess Value (two lags) -0048 -272 Foreign Sales 0371 270 Industry Other -0163 -171 Log Size 0010 123 Debt FirmValue -1135 -820 CAPX Sales 0590 342 EBIT Sales -0047 -300 RampD Sales 1376 531 Ad Sales -0751 -040 AR(1) test (p-value) 000 AR(2) test (p-value) 017 Hansen test of over-identification (p-value) 030 Difference-in-Hansen test of exogeneity (p-value) 093 Year indicators Yes Number of firms 1016 Number of observations 4090
41
Table 6 OLS regression of firm excess values on the percent of foreign sales controlling for a control premium This table reports the result of the OLS regression of firm excess value including firm and year fixed effects with an additional control variable to proxy for a country-level ownership control premium (Control Premium) All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed) Dep Variable = Excess Value Coef t -stat Foreign Sales 0303 224 Industry Other -0327 -325 Log Size -0045 -100 Debt FirmValue -0683 -585 CAPX Sales 0490 226 EBIT Sales 0409 189 RampD Sales 1545 379 Ad Sales -1689 -119 Control Premium -1004 -062 Year indicators Yes Firm indicators Yes R2 0768 Observations 4950
42
Table 7 OLS regression of firm excess values on the percent of foreign sales controlling for the cost of capital This table contains the results of an OLS regression of firm excess value including firm and year fixed effects with an additional control variable to proxy for a country-level cost of capital (Cost of Capital) All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed) Dep Variable = Excess Value Coef t -stat Foreign Sales 0255 178 Industry Other -0326 -324 Log Size -0043 -094 Debt FirmValue -0687 -589 CAPX Sales 0485 225 EBIT Sales 0406 188 RampD Sales 1539 379 Ad Sales -1703 -120 Cost of Capital 0004 039 Year indicators Yes Firm indicators Yes R2 0768 Observations 4950
43
Table 8 OLS regression of firm excess values on the percent of foreign sales controlling for aspects of corporate governance This table reports the results of two OLS regressions In the first column are the results of the firm excess value regression including firm and year fixed effect with five proxies for corporate governance included ndash the percent of shares owned by the CEO and the percent squared (CEO Ownership and CEO Ownership squared) the percent owned by institutional investors (Institution Ownership) whether there is a powerful CEO (Powerful CEO) and the Gompers Ishii and Metrick (2003) governance index (Governance Index) In the second column the regression includes the same sample and variables with the exception of the governance proxies which are excluded All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed)
Governance
No Governance (same sample)
Dep Variable = Excess Value Coef t-stat Coef t -stat Foreign Sales 0259 159 0251 154 Industry Other -0099 -080 -0099 -079 Log Size -0105 -185 -0102 -180 Debt FirmValue -0813 -478 -0817 -482 CAPX Sales 0440 252 0438 249 EBIT Sales 0457 247 0457 246 RampD Sales 1717 284 1716 286 Ad Sales -2210 -143 -2190 -143 CEO Ownership 0010 007
CEO Ownership squared -0029 -017 Institution Ownership 0047 067 Powerful CEO -0003 -014 Governance Index 0009 103
Year indicators Yes Yes Firm indicators Yes Yes R2 0811 0810 Observations 2550 2550
- Cover for paperpdf
- crrz_20130603_SMUpdf
-
7
this result easily reconciles with the international trade literature that establishes a link between
greater productivity and international operations though we do not find results supporting this as
the only source of the premium As segment disclosures improve and as new data sources
become available on multinational activity (eg Bureau Van Dijk) our empirical approach can
be used to investigate the extent to which the home country of the parent might influence the
extent with which foreign operations affect firm value (ie by examining non-US based
MNCs)7 Future studies could also examine the underlying forces affecting the excess value
premiums across countries
We also contribute to the understanding of international accounting standards and their
comparability Many studies seek to compare firms using different accounting standards These
comparisons generally rely on information generated by a particular accounting regime By
comparing commonly used metrics (total assets net income and sales) reported for the same
firm and year across different regimes we provide insights as to the relative comparability across
standards In particular we find that sales is reported significantly more consistently across
accounting regimes than the other metrics examined This finding should aid researchers in
reducing measurement error when comparing accounting information in multinational settings
The paper proceeds as follows Section 2 discusses the motivation for finding a premium or
discount to multinational operations Section 3 describes our data and how the excess value of a
firm is measured A discussion of our independent variables and primary findings is presented in
Section 4 with additional robustness tests provided in Section 5 Finally Section 6 concludes
7 As MNCs are ultimately bound by the tax regulatory and legal frameworks of their home country the valuation effect of foreign operations that we document for US-based MNCs may be different in a sample of non-US based MNCs with the same country-industry footprint Theorists refer to these as lsquolocational advantagesrsquo enjoyed by all multinational firms of a given nationality and obtained irrespective of skills or capabilities unique to a particular firm (Yamin 1991) There is also evidence of locational advantages in the market for corporate control (Huizinga and Voget 2009)
8
2 Premium versus discount
In a frictionless world where managers maximize firm value and markets are efficient there
should be no discount or premium to operating in foreign countries However there are a number
of forces that may cause actual firm value to deviate from the implied firm value
On one hand MNCs may have comparative advantages that generate premiums (ie positive
excess values) relative to a similar footprint of stand-alone firms Having operations in a variety
of locations that access different supplies of inputs and customers allows the firm to take
advantage of changing market conditions by shifting operations or products to maximize firm
value more so than a firm operating in a single country Similarly having access to a variety of
institutional settings such as different tax codes legal regimes and financial markets can provide
MNCs with options beyond those available to firms located in a single country
Another possible factor associated with a premium to multinational operations is the cost of
capital For operations located in countries with shallow capital markets or weaker creditor
rights operating within a conglomerate can provide the benefit of a lower cost of capital for
investments and expansion for at least two reasons First as our MNCs are domiciled in the US
their foreign segments have greater access to US capital markets relative to local competitors in
the foreign jurisdiction Second cash rich segments with few investment opportunities can
finance investments in cash poor segments with positive net present value projects (eg Myers
and Majluf 1984) Consequently diversified firms should be less liquidity constrained and better
able to shift resources to the most valuable investment opportunities Research has found that
internal financing is used more often when diversified firms have operations in countries with
more costly external financing (Desai Foley and Hines 2004) Multinational firms may also be
relatively more protected than single-country firms when negative shocks hit external capital
9
markets (Foley Desai and Forbes 2008) In a recent study Matvos and Seru (2011) find that
industrially diversified firms perform better than non-diversified firms when external capital
markets are impaired
While these arguments suggest that MNCs enjoy certain advantages when the foreign cost of
capital is high these benefits should be reduced by the additional risk associated with operating a
business in a high cost of capital environment For example Verizon is a multinational firm
operating in a number of countries One of the higher cost of capital locations in which they
operated was Venezuela While the multinational structure of Verizon may have mitigated some
of the risks associated with operating in a higher cost of capital location the firms was unable to
eliminate all of these risks In fact in 2007 Verizonrsquos operations in that country were
nationalized resulting in a net extraordinary loss of $131 million that year8 In other words
MNC investors are likely to expect higher rates of return to compensate for higher risk which
likely offsets some (if not all) of the potential excess value premium
On the other hand multinational firms may incur a discount (ie negative excess values)
relative to a similar footprint of stand-alone firms Potential reasons for a discount largely rely on
the existence of agency costs Multinational firms tend to be larger more complex and less
transparent The combination of complexity and opacity inherent in MNCs reduces a firmrsquos
reliance on external capital as the cost of such capital increases due to agency costs (Desai et al
2004) As a result of the reduced reliance on external funds MNCs face a reduced level of
monitoring As noted by Jensen (1986) managers have incentives to increase firm size beyond
that which is optimal with overinvestment or misallocation of funds to pet projects If this lack of
monitoring is in combination with available internal capital empire building becomes easier
(Hope and Thomas 2008) Furthermore the current segment reporting standards under US 8 See the 10-K of Verizon Communications Inc dated December 31 2007 (footnote 2)
10
GAAP only require highly aggregated disclosures of foreign operations and allow for
considerable managerial discretion As a result operating in multiple countries makes it easier
for management to hide poor performance ndash either their own or that of a division ndash such that low
quality managers are more likely to be retained and overcompensated and poorly performing
divisions retained longer than optimal Other reasons one might expect a discount to be applied
to multinational firms include higher coordination costs (eg coordinating across different
cultures and languages) as well as additional risks These risks include exposure to multiple
political regimes legal regimes economic regulations and currency fluctuations
Past studies investigate whether foreign operations of US firms enhance or reduce firm
value However all of these studies benchmark the value of the foreign operations of an MNC to
US domestic firms For instance Denis et al (2002) determine the implied value of a US
MNC using domestic (US) single-segment firms in the same industry as a benchmark Studies
using Tobinrsquos Q to investigate valuation effects of MNCs also rely on domestic benchmarks
(eg Morck and Yeung 1991 Morck and Yeung 2001 Gande Schenzler and Senbet 2009)
However using a US domestic firm as a benchmark for the value of foreign operations
implicitly makes two assumptions First this method assumes that the risks and expected growth
rates of foreign operations are equivalent to those of domestic operations This assumption is
inconsistent with the findings of Hail and Leuz (2009) which provides evidence that the cost of
capital varies substantially across countries Second this method assumes that there is excess
domestic capacity to allow for expansion with profitability similar to current domestic
operations We relax these assumptions by measuring the imputed value of a foreign component
of a US firm as if that component operated autonomously within that foreign country (rather
than in the US) Specifically our multiples allow the cost of capital and expected growth rates
11
to vary across not just industry but also country Furthermore our counterfactual does not
require the assumption that operations be relocated mitigating the production and sales capacity
concerns
3 Measuring excess value
31 Description of excess value
Figure 1 shows the average percent of foreign sales (based on segment disclosures under US
GAAP) across firms through time and illustrates that firms are continuing to expand
internationally9 This expansion highlights the importance of understanding whether the decision
to operate internationally is on average a value enhancing corporate strategy We contribute to
this understanding by evaluating whether the firm as a whole is worth more or less than its
imputed value (ie the firm is valued in excess of the sum of its individual components)
We measure the excess value of a firm as the logarithm of the ratio of actual firm value to
imputed value based on the method used in Berger and Ofek (1995) The imputed value is the
hypothetical value of the MNC under the assumption that its country-industry components
operate as independent entities A key innovation of our study is an alternative approach to
imputing the value of the foreign operations of an MNC Components of MNCs operating in a
given industry and country are imputed using the value of single-segment firms operating in the
same industry and country Thus we ensure the discount rates and expected growth rates are
applicable to each given industry and location in estimating the implied values of the MNC
segments
9 The discontinuity in industrial diversification (the percent of sales outside the primary industry) between 1997 and 1998 is the result of a change in segment reporting rules
12
The actual firm values are observable for all firms in our sample The hypothetical firm
values for multi-segment firms are imputed using market value to sales ratios herein referred to
as lsquomultiplesrsquo of the median single-segment firm operating in the same country-industry Thus
these single country-industry firms serve as benchmarks for an MNCrsquos operations within that
same country-industry The imputed value of the country-industry component can be viewed as
an estimate of what that component would be worth if it operated independently The sum of the
imputed values across all country-industry components represents the hypothetical value that the
multi-segment firm would be worth if it were broken apart
32 Data
Four primary data sources play a distinct role in carrying out our analysis i) Bureau of
Economic Analysis (BEA) data ii) Compustat Segment data iii) Worldscope data and iv)
Compustat Fundamentals Annual data Broadly speaking BEA data provide information about
the diversified nature of MNCs Compustat Segment data provide information about the
industrial diversification of US domiciled firms Worldscope data are used to compute
multiples for single-segment foreign firms that serve as benchmarks for the foreign operations of
MNCs (ie foreign benchmarks) and Compustat Fundamentals Annual data are used to
compute multiples for single-segment domestic firms that serve as benchmarks for the domestic
operations of multinational firms We also use the latter data source to construct our primary
control variables
13
Key to the execution of our study is our access to BEA data Federal law requires US-
domiciled MNCs to report certain financial and operating data to the BEA10 With regard to
observing the country-industry operations of MNCs use of the BEA data overcomes two
important limitations of Compustat Segment data First Compustat does not consistently identify
the specific location or scale of MNCsrsquo foreign operations11 Second Compustat does not
identify the industry activities in each country of operation This latter point is particularly
limiting for MNCs because domestic industry activity need not mirror foreign industry activity
An MNC generating an equal proportion of sales in two industries in the domestic market need
not generate industry sales in equal proportions in every foreign market12 The lack of detail and
consistency in Compustat Segment data arises because segment reporting is highly aggregated
and firms exercise substantial discretion in defining segments under Accounting Standards
Codification 280 (ASC 280) (eg Villalonga 2004a and Bens Berger and Monahan 2011)
The BEA defines an MNC as the combination of a single US entity called the US parent
and at least one foreign affiliate ndash that is these firms have a physical presence outside the US
due to FDI Since 1982 MNCs have completed a mandatory and confidential lsquoSurvey of US
Direct Investment Abroadrsquo for the domestic operation and the operations of each foreign
affiliate defined as a foreign entity in which the US parent holds at least a 10 percent equity
interest (directly or indirectly) The data are reported in US dollars on a fiscal year basis and in
10 BEA surveys are conducted pursuant to the International Investment and Trade in Services Survey Act (PL 94-472 90 Stat 2059 22 USC 3101-3108) See httpwwwbeagovsurveysdiasurvhtm and Mataloni (2003) for more detailed information on the BEA data 11 Exhibit 21 disclosures of material subsidiaries and their locations in SEC 10-K filings could potentially help overcome the inability to observe the location of the firmrsquos foreign operations from segment disclosures However at least three limitations remain (1) for industrially diversified multinational firms the researcher would be required to make an assumption about the proportion of industrial diversification in each country (2) Exhibit 21 provides no information about the scale of activity in each jurisdiction and (3) managerial discretion is used to determine a lsquomaterialrsquo subsidiary under Section 601 of SEC Regulation S-K 12 In fact the expectation that a multinational firm would exhibit different industry membership in different countries is consistent with the theory of vertical foreign direct investment where firms separate their various value chain activities and locate them in the most favorable country (see eg Hanson Mataloni and Slaughter 2005)
14
accordance with US Generally Accepted Accounting Principles (US GAAP) For each year
we observe the sales industry composition and location of not only the parent but also each
affiliate13
The procedures we use to construct our sample are similar to those used by Berger and Ofek
(1995) and Denis et al (2002) Using firm-years appearing in Compustat Fundamentals Annual
and Compustat Segment data we obtain a sample of firms domiciled in the US with total sales
exceeding $20 million We exclude firms in the financial service (SIC codes 6000 to 6999) and
utility (SIC codes 4000 to 4999) industries and eliminate firm-years when the sum of segment
sales (Compustat geographic or Compustat business) is not within 1 of total firm sales for that
year We restrict our sample period to include fiscal years beginning after December 15 1997 as
ASC 280 Segment Reporting substantially altered the definition of a reporting segment under
US GAAP This restriction ensures that Compustat Segment data reflects the use of a consistent
accounting standard Finally we require that our MNCs appear in both Compustat and BEA
data The requirement that our sample firms appear in the BEA database ensures that the firms
have observable international operations In sum these procedures result in 1166 multinational
firms incorporated in the US with 4950 observations between 1998 and 2008 that meet all of
our data requirements
33 Measuring excess value
The dependent variable in our study is excess firm value (Excess Value) defined as the
logarithm of the ratio of actual firm value to imputed firm value (Berger and Ofek 1995)
13 We obtain sales industry membership and country location for affiliates from one of two sources When affiliates exceed the BEA-determined reporting thresholds (ie if their assets sales or net income (loss) exceed $7 million in 1999 $30 million in 2000 through 2003 $10 million in 2004 and $40 million in 2005 through 2008) they must file their own detailed survey When affiliates fall below the thresholds we obtain our data from an attachment to the parent survey
15
Appendix A provides the definition of this and all other variables used in our analysis We
observe actual firm value equal to a firmrsquos market value of equity plus book value of liabilities
(ie book value of total assets minus book value of total equity) using Compustat Fundamentals
Annual data We calculate imputed firm value as the sum of the imputed values of a firmrsquos
operations in each country-industry Our method for imputing the value of the separate
components of a firm can best be described in three steps First we obtain total sales generated
by a firm for each country-industry in which it operates14 Second we obtain multiples (market
value to sales ratios) for benchmark firms operating in those same country-industries Third we
multiply a firmrsquos country-industry sales by the applicable median country-industry multiple to
obtain the imputed market value for each country-industry operation We perform each step
annually
Conceptually imputed values can be based on assets (ie Tobinrsquos Q) net income or sales
multiples We restrict ourselves to sales multiples for two reasons First using value to sales
ratios maintains consistency with the prior research on the excess value implications of
multinational operations (eg Denis et al 2002) Second as our method uses accounting data
for foreign companies to compute country-industry multiples the accounting numbers we use
need to be consistently measured across firms using different accounting standards We find that
sales data are measured most consistently
To assess the comparability of sales relative to either net income or assets across various
accounting standards we examine all firms listed on Worldscope as changing to or from US
14 Specifically we allocate total sales from Compustat to each component based on the proportion of BEA sales (after eliminating intercompany sales) occurring in each country-industry This step ensures that our total firm sales do not deviate from a firmrsquos total sales as reported in the 10-K
16
GAAP between 2005 and 201115 We obtain accounting data for the year of the change and the
prior year where electronically available in English We are able to obtain data for 66 firms
changing standards as detailed in Table 1 The distribution of years for which accounting data
are available under two different accounting standards for the same fiscal year is shown in Panel
A The years shown are the year prior to the change as these accounting numbers were provided
under the original standard and then subsequently restated in the following year under the new
standard for comparative purposes The majority of the shifts were to or from IFRS (71)
though seven other accounting standards are noted (Panel B) We examine the percent that the
non-US GAAP reported number differs from the US GAAP reported number in Panel C to
assess the comparability of these three summary accounting numbers across various accounting
standards Examining both the full sample as well as the subset that overlaps our study we find
that sales are more consistently measured than net income or assets
The sum of the imputed values across the country-industry components of an MNC is an
estimate of the value of a portfolio of unrelated businesses that mirror the related businesses of
the firm Consequently a comparison of the actual firm value with the imputed firm value is a
measure of how a multinational network affects firm value We provide descriptive statistics for
Excess Value for our sample of 4950 MNC firm-years in Table 2 Panel A This table also
includes univariate comparisons of the values between the MNC firms and the domestic single-
segment and foreign single-segment benchmark firms The Excess Value measure is significantly
higher for MNCs suggesting a premium in firm value for multinational firms relative to the
benchmark firms in total as well as the domestic and foreign subgroups (p-values for tests of
15 Due to the hand collection efforts required and the limitation on electronic filings we began our search with changes documented in 2005 which generally require filings from 2004
17
means and medians are lt 001)16 These results provide some initial evidence that operating in
multiple countries is positively associated with firm value
To estimate country-industry multiples we rely on Worldscope financial data on foreign
firms and Compustat Fundamentals Annual for domestic firms We restrict the firms to include
only those with at least 90 of sales income and assets inside the country of domicile (ie
those that do not report significant multinational activity) and that operate in a single industry
We refer to these firms as benchmark firms (either foreign or domestic depending on the country
of domicile) these firms do not appear in any of our regressions We report the number of
benchmark firms during our sample period in Table 2 Panel A The industry criterion is based on
the two-digit SIC code17 For every country that has at least five firms in the respective industry
and year we use the median ratio of market value to sales18 This ratio is the country-industry
multiple ndash an input required to compute imputed values
To determine the country-industry composition of MNCs we rely on BEA data In Table 3
we report the aggregate number of foreign affiliates and total sales by country as provided by
the BEA data for our sample of 4950 MNCs The specific countries tabulated are those that
represent at least 02 percent of total firm sales (pooled across all years) in our sample The top
five foreign countries in which MNCs generate sales are Canada United Kingdom Germany
France and Japan Table 3 also provides a comparison of the number of foreign benchmark
firms available in Worldscope in the specific countries in which our sample of MNCs generate
16 Similar to Berger and Ofek (1995) the median values differ slightly from zero for the benchmark single-segment firms due to the truncation of extreme excess values 17 BEA reporting constrains us to the two-digit SIC code level 18 When five firms are not available for a given country and industry we follow Berger and Ofek (1995) and move to a more general industry definition (eg one-digit SIC code) This procedure results in a mean (median) 630 (757) of MNC sales matched at the two-digit level
18
the majority of their sales Country coverage in Worldscope is not available for countries
representing 004 percent of total firm sales (per BEA)
4 Empirical results
41 Independent variables
Recall that our objective is to examine the overall relation between excess firm value and
multinational operations Our proxy for the extent of multinational operations is the percentage
of total firm sales made by foreign operations (Foreign Sales) By this measure the extent of
multinational activity in our sample of MNCs is non-trivial ndash Table 2 Panel B shows that
approximately 24 percent of sales are generated by foreign operations on average Our first
analysis estimates ordinary least square regressions of Excess Value on Foreign Sales and
control variables (discussed below)
We include variables in our regression to control for other potential determinants of excess
value These are the percentage of sales made by the firm outside its primary industry (Industry
Other) to control for any relation between industrial diversification and excess value as in Berger
and Ofek (1995) For this purpose we obtain industry sales using Compustat Segment data19 A
firmrsquos primary industry is the industry in which the firm generates the majority of its sales and
we determine industry sales at the business segment level We set Industry Other equal to zero
for firms that operate in a single business segment
Consistent with prior research we also include controls for firm size (Log Size) the ratio of
long term debt to total market value (Debt Firm Value) the ratio of capital expenditures to total
sales (CAPX Sales) the ratio of earnings before interest and taxes to total sales (EBIT Sales)
the ratio of research and development expenses to total sales (RampD Sales) and the ratio of 19 Our results are unaffected by using BEA data to measure Industry Other
19
advertising expenses to total sales (Ad Sales) Table 2 Panel B provides the distribution of the
control variables All independent variables are Winsorized at 1 In our OLS regressions we
use heteroskedasticity and autocorrelation consistent robust standard errors with clustering at the
firm level
42 Primary regression analysis
In this section we examine the relation between firm excess value and the amount of foreign
activity at US MNCs Table 4 contains the results of regressions of excess value on Foreign
Sales for multiple specifications In column (1) Excess Value is regressed on both the percent of
foreign sales (Foreign Sales) and the percent of sales outside the firmrsquos primary industry
(Industry Other) We add the control variables to the regression in column (2) and year
indicators in column (3) In all three specifications the coefficient on the percent of foreign sales
remains significantly positive (with a p-value lt 001) For a sense of economic magnitude in
column (3) a one standard deviation (197) increase in the percent of foreign sales (Foreign
Sales) is associated with a 41 (equal to (e0349-1) times 0197) increase in firm excess value The
effect is larger for the other two columns ndash 82 increase in firm excess value in column (1) and
46 in column (2)20
Our finding of a premium reconciles with the international trade literature which finds that
firms engaging in international trade are more productive than those that do not (see Helpman
2006 and Syverson 2011 for reviews) Due to data limitations it is difficult to accurately
measure TFP at the firm level especially for firms which operate in multiple industries andor
20 Using the alternative strategy that employs US domiciled single-segment firms as the benchmark for the imputed value of the foreign operations Denis et al (2002) find that greater foreign activity results in a discount to firm value Using the Denis et al (2002) method to calculate excess value in our sample we obtain a significantly negative regression coefficient on Foreign Sales using the models in Table 4 columns (2) and (3) (the coefficient is not significant using the model in column (1) results untabulated)
20
countries As an alternative method to evaluate whether our finding of a premium is simply the
result of more productive firms also having more multinational operations we investigate
whether the premium is simply an artifact of innate characteristics of firms that choose to invest
abroad For example Helpman et al (2004) present a simple model with heterogeneous
productivity endowments The firms that receive the highest productivity endowments are the
ones capable of paying the fixed costs to establish a foreign subsidiary
To the extent that productivity advantages create excess value one would expect the type of
firms that choose to invest more abroad would be valued at a premium even in the absence of
their foreign investments To control for time invariant firm characteristics (eg innate
productivity advantages) and hence a potential source of endogeneity we add firm-level fixed
effects in column (4) of Table 4 We continue to find that the extent of foreign operations
(Foreign Sales) is positively associated with Excess Value Across all specifications the sign
on all significant control variables is consistent with prior research (eg Berger and Ofek 1995
and Denis et al 2002)
The finding of a premium makes a significant contribution to the relatively sparse literature
on the value effects of multinational activity Our measure of imputed value expands upon the
methods used by Berger and Ofek (1995) and Denis et al (2002) but allows us to answer a
different question than Denis et al (2002) Holding constant the extent of industrial
diversification we ask whether MNCs are more valuable than a portfolio of benchmark firms of
a similar geographic footprint In contrast Denis et al (2002) ask whether holding constant the
extent of industrial diversification MNCs are more valuable than a portfolio of domestic (US)
benchmark firms We believe that asking whether an MNCrsquos foreign (domestic) operations are
21
more valuable than those of a foreign (domestic) firm is a more appropriate way to assess the
value implications of a multinational network
5 Robustness
51 Endogeneity related to dynamic relations
Prior research argues that the relation between industrial diversification and excess firm
value could be endogenous through dynamic relationships based on observing past outcomes
Managers of firms likely choose to enter or exit industries or geographic regions based upon
their previous performance For example Campa and Kedia (2002) find that firms are more
likely to enter new industries when prospects in their current lines of business are deteriorating
Similar dynamics could also drive a relation between excess value and FDI We assess the
robustness of the previous results by estimating a dynamic panel data model which allows us to
control for dynamic endogeneity in addition to firm level unobserved heterogeneity We employ
the GMM estimator for dynamic panel models developed by Arellano and Bond (1991) Arellano
and Bover (1995) and Blundell and Bond (1998) Our results from this analysis confirm our
previous findings that firms with a larger degree of foreign operations have positive excess
values
Dynamic panel data models are generalizations of the traditional fixed effects model where
lags of the dependent variable are added to the right hand side of the equation These additional
lags control for the impact a firmrsquos past performance has on its current performance through the
channels discussed above The GMM estimator for dynamic panel models developed by
Arellano and Bond (1991) starts by differencing the data at the firm level in neighboring time
periods to eliminate any firm specific time invariant unobserved heterogeneity that may be
22
present Eliminating the firm specific fixed effect by differencing the data with lags of the
dependent variable on the right hand side introduces correlation between the new error term and
the differenced explanatory variables This problem is solved by applying the standard GMM
estimator using lags of both the original explanatory variables and excess value as instruments
We note that the dynamic panel data framework allows us to treat both international and
industrial diversification (as well as the other firm-specific control variables) as endogenous
Following Hoechle et al (2012) we also allow contemporaneous values of these variables to be
treated as endogenous
Lagged values of the explanatory variables are valid instruments in a dynamic panel data
model when the model is dynamically complete (eg Wooldridge 2010) A dynamic panel data
model is dynamically complete conditional on the unobserved effect if enough lags of both the
explanatory and dependent variable are included to make the resulting error term uncorrelated
with all the right hand side variables Under this assumption any lags for the explanatory
variables beyond this can be used within the GMM estimator as instruments Like Hoechle et al
(2012) we add two years of lags of a firmrsquos excess value to the right hand side assuming that
this is enough lags to control for dynamic feedback effects We then use past values of all
variables beyond the second lag up to a total of seven years to act as instruments
The results of the dynamic panel data estimator are reported in Table 5 These results confirm
the previous finding of a positive relation between the degree of international operations and
excess value We note that the size and significance level of the coefficient on Foreign Sales
diversification are robust to changes in the number of lags used as instruments The Arellano and
Bond (1991) estimator includes testing diagnostics for correct specification of the model If the
model is correctly specified differences in the residuals from the levels equation should be
23
serially uncorrelated at lag one but correlated at lag two The p-values for tests of serial
correlation of the residuals at lags 1 and 2 are reported in Table 5 and these indicate that the
model is not rejected We also report in Table 5 the Hansen test for overidentification which is a
test of the null hypothesis that the instruments are valid The p-value for this test is 030
indicating that we fail to reject this null hypothesis Finally we report another test for exogeneity
known as the difference-in-Hansen test which tests the null hypothesis that the instruments in
the original equation are valid We fail to reject this null hypothesis as well
52 Identifying value effects of multinational operations via cross-border acquisitions
Another alternative to investigating a causal link between foreign direct investment and firm
value is an event study of foreign acquisitions21 Acquired foreign targets represent only a small
fraction of MNCsrsquo worldwide sales in the year of the acquisition ndash less than 1 percent in our
sample Additionally cross-border acquisitions are not the primary source of international
expansion for the average firm We find that foreign acquisitions account for between 18 percent
and 55 percent of the year-over-year change in foreign sales with the remainder of the growth
coming from either newly established entities (ie greenfield investments) or growth in existing
operations22 These results suggest that acquisitions are not a material source of growth for the
21 Doukas and Travlos (1988) find positive returns when the acquirer is not operating in the target firmrsquos country and insignificant returns when the acquirer is operating in the target firmrsquos country or expanding internationally for the first time Morck and Yeung (1991) find marginal evidence of positive returns for firms with intangible assets Finally Doukas (1995) finds positive returns only for firms with average Tobinrsquos q ratios greater than one More recent work also using an event study methodology provides mixed evidence on valuation effects of global diversification through acquisitions Dos Santos Errunza and Miller (2008) find insignificant returns for firms with already established foreign operations firms establishing initial foreign operations and firms acquiring targets in related industries Doukas and Kan (2006) and Moeller and Schlingemann (2005) find reductions to shareholder value while Francis Hasan and Sun (2008) find positive returns for acquirers of targets in segmented financial markets 22 New foreign affiliates to an MNC group each year report on the BEA survey whether they entered the group as an acquisition or a newly established entity (ie greenfield investment) Affiliates under the lowest reporting threshold do not report this information resulting in our inability to precisely identify the source of 33 percent of the increase in firm sales as either acquisition or greenfield
24
typical US MNC suggesting that an event study would lack power and generalizability For
these reasons we do not implement an event study
53 Control premium
A control premium or private benefits of control occurs when ldquosome value whatever the
source is not shared among all the shareholders in proportion of the shares owned but it is
enjoyed exclusively by the party in controlrdquo (Dyck and Zingales 2004 p 541) Using stock
prices of traded shares held by minority shareholders in determining the market value to sales
ratios of our benchmark firms could underestimate the true value of our benchmark firms if those
firms face large control premiums This in turn could induce an excess value premium because
the imputed value of these country-industry segments would be artificially low The existence of
such benefits is more prevalent in some countries than others and could affect the value of an
MNCrsquos operations in such domains We use the country-level control premiums from Dyck and
Zingales (2004) and using each firmrsquos country sales as a percent of total firm sales we obtain a
weighted average control premium that each MNC faces (Control Premium) For countries not
included in the Dyck and Zingales (2004) analysis we set the control premium equal to zero23 In
Table 6 we show that Foreign Sales remains positive and highly significant (p-value lt 001)
when we include the proxy for control premium As a result it does not appear that the excess
value premium is driven by MNCs operating in countries where majority shareholders are able to
extract large control premiums
23 Results are robust to replacing missing values with the mean control premium of the respective GDP quartile
25
54 Cost of capital
As discussed in Section 2 when countries have shallow capital markets a multinational
network may provide the benefit of a lower cost of capital for investments through an internal
capital market as well as through better access to the US capital market Prior research
establishes that there is substantial variation in the cost of capital across countries (Erb Harvey
and Viskanta 1996 and Hail and Leuz 2009) To determine whether the documented premium
is due to cost of capital advantages we include a control variable to capture the difference
between the MNCrsquos cost of capital and the cost of capital faced by its competitors in the global
markets in which it operates
We compute our Cost of Capital proxy in two steps First we obtain the country-year credit
rating published by Institutional Investor24 The rating ranges from 0 to 100 where higher values
imply that a country has a higher default risk Country-level credit risk is a reasonable predictor
of expected equity market returns and volatility (Erb et al 1996) and exhibits a highly
significant correlation with international accounting-based estimates of imputed cost of capital
(Hail and Leuz 2009) Second using each firmrsquos country sales as a percent of total firm sales
we obtain a weighted average credit risk rating across the countries in which each firm operates
A higher value implies an MNC operates in countries with higher cost of capital In Table 7 we
do not find a result consistent with MNCs gaining an advantage from their access to low cost
capitalndash Cost of Capital is not significantly associated with Excess Value25
24 We thank Cam Harvey for providing these data 25 Results are consistent using cost of capital as defined in Erb et al 1996
26
55 Corporate governance
In light of the recent finding that corporate governance mitigates the diversification discount
we examine whether the premium to multinational activity is sensitive to controls for corporate
governance Hoechle et al (2012) find that the magnitude of the industrial diversification
discount is decreased and in several specifications no longer significant in the presence of
governance proxies In the first column of Table 8 we include the five governance proxies that
are consistently significantly associated with excess value in Hoechle et al (2012) ndash the percent
of CEO ownership (CEO Ownership and CEO Ownership squared) the percent of institutional
ownership (Institution Ownership) whether the firm has a powerful or influential CEO
(Powerful CEO) and the governance index from Gompers Ishii and Metrick (2003)
(Governance Index)
As documented in Table 8 including these control variables reduces our sample size by
almost 50 (to 2550 observations) and tends to exclude the smaller firms from the analysis
None of the governance proxies are significant Relative to column (4) of Table 4 the coefficient
on Foreign Sales is similar in magnitude but is no longer statistically significant at
conventional levels To investigate whether the decreased significance is due to the smaller
sample or the inclusion of the governance variables we re-estimate the regression for the same
2550 observations after removing the governance controls The coefficient and t-statistic are
virtually identical suggesting that the loss of significance is due to the reduced sample and not
the governance controls We do not find evidence to suggest that variation in corporate
governance practices drives variation in excess value for US MNCs
27
6 Conclusion
Using a new method to quantify the valuation effects of foreign direct investment by US
domiciled firms we find robust evidence of a premium in excess value associated with the
degree of multinational operations Our method builds on that used in previous studies
examining firm excess value (eg Berger and Ofek 1995 and Denis et al 2002) by
incorporating the location in addition to the industry of each corporate affiliate This allows
important factors such as growth and cost of capital to vary across countries as well as industries
Examining a sample of multinational firms we find that a one-standard deviation (197)
increase in foreign operations is associated with an increase of between 41 and 82 in excess
value minus both a statistically and economically significant finding
This result complements the finding of the international trade literature that more productive
firms are more likely to operate internationally However the premium remains after including
firm fixed effects to control for persistent firm traits (eg productivity)
The finding of a premium stands up to a succession of robustness tests designed to evaluate
whether the result arises from alternative methods or explanations First we estimate a dynamic
panel data model using the generalized method of moments (GMM) estimator to simultaneously
account for the potential endogeneity of FDI industrial diversification and firm value Second
we include a control variable to proxy for whether the firm operates in countries with larger
control premiums which could result in understated multiples and mechanically induce a
premium Third we include a control variable to measure the cost of capital across the countries
in which an MNC operates to evaluate whether a relatively low cost of capital borne by
multinational firms is a meaningful source of the premium Fourth we control for several
corporate governance proxies which have been found to be correlated with excess values (and
28
industrial diversification) We fail to find evidence supporting any of these alternative
explanations ndash the premium in excess value arising at firms with greater multinational operations
remains significant
Overall our new method of estimating excess values for multinational firms provides strong
evidence of a statistically and economically significant premium associated with increased
international operations The premium is robust to a host of specifications and controls designed
to evaluate alternative explanations We leave to future research the questions of whether the
premium to US multinational firms applies more broadly to other countries of domicile and
what factors influence the premium
29
References
Ammann M Hoeschel D and M Schmid 2012 Is there really no conglomerate discount
Journal of Business Finance and Accounting 39(12) 264-288 Arellano M and Bond S R 1991 Some tests of specification for panel data Monte Carlo
evidence and an application to employment equations Review of Economic Studies 58 (2) 277-298
Arellano M and Bover O 1995 Another look at instrumental variables estimation of error
components models Journal of Econometrics 68 (1) 29-61 Bens D A Berger P G and S J Monahan 2011 Discretionary disclosure in financial
reporting An examination comparing internal firm data to externally reported segment data The Accounting Review 86(2) 417-449
Berger P G and E Ofek 1995 Diversificationrsquos effect on firm value Journal of Financial
Economics 37 39-65 Blundell R and Bond S R 1998 Initial conditions and moment restrictions in dynamic panel
data models Journal of Econometrics 87(1) 115-144 Campa J M and S Kedia 2002 Explaining the diversification discount The Journal of
Finance 57 (4) 1731-1762 Denis D J Denis D K and K Yost 2002 Global diversification industrial diversification
and firm value The Journal of Finance 57 (5) 1951-1979 Desai M A Foley C F and J R Hines 2004 A multinational perspective on capital structure
choice and internal capital markets The Journal of Finance 59 (6) 2451-2487 Dos Santos MB V Errunza and D Miller 2008 Does corporate international diversification
destroy value Evidence from cross-border mergers and acquisitions Journal of Banking and Finance 32(12) 2716-2724
Doukas JA 1995 Overinvestment Tobinrsquos q and gains from foreign acquisitions Journal of
Banking and Finance 19 1285-1303 Doukas J and N G Travlos 1988 The effect of corporate multinationalism on shareholdersrsquorsquo
wealth evidence from international acquisitions Journal of Finance 43(5) 1161-1175 Doukas JA and O B Kan 2006 Does global diversification destroy firm value Journal of
International Business Studies 37(3) 352-371
30
Dunning J and A Rugman 1985 The influence of Hymerrsquos dissertation on the theory of foreign direct investment The American Economic Review 75(2) 228-232
Dyck A and L Zingales 2004 Private benefits of control An international comparison The
Journal of Finance 59 (2) 537-600 Erb C Harvey C and T Viskanta 1996 Expected returns and volatility in 135 countries The
Journal of Portfolio Management 46-58 Foley C F Desai M A and K J Forbes 2008 Financial constraints and growth
Multinational and local firm responses to currency depreciations Review of Financial Studies 21 (6) 2857-2888
Francis B I Hasan and X Sun 2008 Financial market integration and the value of global
diversification Evidence for US acquirers in cross-border mergers and acquisitions Journal of Banking and Finance 321522-1540
Gande A Schenzler C and L Senbet 2009 Valuation effects of global diversification
Journal of International Business Studies 1515-1532 Gompers P Ishii J and A Metrick 2003 Corporate governance and equity prices Quarterly
Journal of Economics 118(1) 107-156 Hail L and C Leuz 2009 Cost of capital effects and changes in growth expectations around
US cross-listings Journal of Financial Economics 93 428-454 Hanson G Mataloni R and M Slaughter 2005 Vertical production networks in multinational
firms Review of Economics and Statistics 87(4) 1-15 Helpman E 2006 Trade FDI and the Organization of Firms Journal of Economic Literature
44(3) 289-630 Helpman E Melitz M J and S R Yeaple 2004 Export versus FDI with Heterogeneous Firms
American Economic Review 94(1) 300-316 Hoechle D Schmid M Walter W and D Yermack 2012 How much of the diversification
discount can be explained by poor governance Journal of Financial Economics 103 41-60 Hope O-K and W B Thomas 2008 Managerial empire building and firm disclosure Journal
of Accounting Research 46 (June) 591-626 Huizinga H P and J Voget 2009 International taxation and the direction and volume of cross-
border MampAs Journal of Finance 64 (3) 1217-1249 Jensen M 1986 Agency cost of free cash flow corporate finance and takeovers American
Economic Review Papers and Proceedings 76 323-329
31
Laeven L and R Levine 2007 Is there a diversification discount in financial conglomerates
Journal of Financial Economics 85 331-367 Lang L and R Stulz 1994 Tobinrsquos Q corporate diversification and firm performance Journal
of Political Economy 102 1248-1280 Mataloni R 2003 US multinational companies Operations in 2001 Survey of Current
Business 85-105 Matvos G and A Seru 2011 The dynamic tradeoff between corporate socialism and financial
market dislocation Estimates from internal capital markets Working paper University of Chicago Booth School of Business
Moeller SB and F P Schlingemann 2005 Global diversification and bidder gains A
comparison between cross-border and domestic acquisitions Journal of Banking and Finance 29 533-564
Morck R and B Yeung 1991 Why investors value multinationality Journal of Business 64
165-186
Morck R and B Yeung 2001 Why firms diversify Internatization vs agency behavior In Intangibles B Lev (ed) Oxford University Press pp 269-302
Myers S C and N S Majluf 1984 Corporate financing and investment decisions when firms have information that investors do not have Journal of Financial Economics 13 187-221
Schmid M and I Walter 2009 Do financial conglomerates create or destroy economic value
Journal of Financial Intermediation 18 193-216 Syverson C 2011 What determines productivity Journal of Economic Literature 49(2) 326-
365 Villalonga B 2004a Diversification discount or premium New evidence from the Business
Information Tracking Series Journal of Finance 59 479-506 Villalonga B 2004b Does diversification cause the diversification discount Financial
Management 33 5-27 Wooldridge JM 2010 Econometric Analysis of Cross Section and Panel Data (Second
Edition) Cambridge MA MIT Press Yamin M 1991 A reassessment of Hymerrsquos contribution to the theory of the transnational
corporation C Pitelis and R Sugden (eds) The Nature of the Transnational Firm London Routledge
32
Appendix A Variable Definitions
We obtain information regarding firmsrsquo operations by country and industry from the Bureau of Economic Analysis (BEA) unless otherwise noted Acronyms used below reflect Compustat mnemonics and thus indicate when we obtain data from the Compustat North America Fundamentals Annual database All independent variables are winsorized at 1
Dependent variable
Excess Value = The log of the ratio of actual firm value to imputed firm value Actual firm value is equal to market capitalization (MVE) + book value of total assets (AT) ndash book value of shareholdersrsquo equity (SEQ) Imputed firm value is calculated as the sum of the imputed values of a firmrsquos operations in each country-industry The imputed value for each country-industry is equal to sales generated in that country-industry times a multiple equal to the ratio of a benchmark firmrsquos actual value to total sales A benchmark firm is defined as a median single-segment firm operating in the same country-industry All multiples require at least five firms listed in the respective country-industry on Compustat (domestic) or Worldscope (foreign) with less than 10 of sales income and assets outside the country of domicile Similar to Berger and Ofek (1995) we eliminate extreme values where the ratio of actual to imputed firm value exceeds 400 or is less than 025
Independent variables
Foreign Sales = Sales made by the foreign operations of US MNCs as a percent of total firm sales
Industry Other = Sales outside a firmrsquos primary industry as a percent of total sales (SALE) Equal to zero for firms operating in a single business segment
Log Size = Log of total sales (SALE)
Debt Firm Value = Ratio of long-term debt (DLTT) to total market value (market capitalization (MVE) + book value of total assets (AT) ndash book value of shareholdersrsquo equity (SEQ))
CAPX Sales = Ratio of capital expenditures (CAPX) to total sales (SALE)
EBIT Sales = Ratio of earnings before interest and taxes (EBIT) to total sales (SALE)
RampD Sales = Ratio of research and development expenses (XRD) to total sales (SALE)
Ad Sales = Ratio of advertising expenses (XAD) to total sales (SALE)
Control Premium = A weighted-average (based on the sales in a given country as a percent of total firm sales) of the country-level control premium from Dyck and Zingales (2004)
Cost of Capital = A weighted-average (based on the sales in a given country as a percent of total firm sales) of the country-level credit rating from Institutional Investor (httpwwwinstitutionalinvestorcom) The country-level rating is available for 160 countries and ranges from 0 to 100 Higher values imply greater credit risk
33
CEO Ownership = Percent of shares owned by the CEO from Execucomp
Institution Ownership = Percent of shares owned by institutional investors from Thomson Reuterrsquos Institutional Holdings database
Powerful CEO = Indicator variable equal to one if the CEO is the only insider on the board of directors and also serves as the chairman and president based on data from RiskMetrics Directors database Equal to zero otherwise
Governance Index = Count measure of takeover defenses used in Gompers Ishii and Metrick (2003) from RiskMetrics Governance database As the governance data are not available for every year we follow Gompers Ishii and Metrick (2003) and assume that the firmsrsquo governance attributes are unchanged until publication of the subsequent series
34
Figure 1 Trends in multinational activity and industrial diversification from 1984-2008 This figure presents a plot of the mean percent of foreign sales and the mean percent of sales outside the firmrsquos primary industry (industrial diversification) from 1984 through 2008 Dotted lines above and below the primary lines represent 95 confidence intervals To compile the plot we include the full population of firms in Compustat
35
Table 1 Analysis of accounting data for firms reporting under multiple accounting standards This table provides a comparison of sales net income and assets for firms that changed accounting standards to or from US GAAP Comparisons are provided for the 66 firms that changed to or from US GAAP and for which we could obtain accounting data electronically and in English under both standards for the same fiscal year (eg the firm presented the accounting numbers originally under one set of standards and then restated the prior year numbers in the subsequent year under the new set of standards) Panel A shows the distribution of firms by year Panel B shows the other accounting standards to which US GAAP is compared Panel C shows the mean and median percent that the other accounting standard differs from US GAAP for the same fiscal year and accounting number Two samples are shown in Panel C The first is the full sample of firms with a change in accounting standards to or from US GAAP for which data was available The second is the subset of these firms that occurred during the sample window used in this study (ie excluding changes post-2008) The percent difference in sales is compared to the percent difference in net income and assets Tests of means are based on matched-pair t-tests Tests of medians are based on Wilcoxon matched-pair signed-rank tests Panel A Accounting standard changes by year
2003
4 6
2004
25 38
2005
8 12
2006
6 9
2007
7 11
2008
5 8
2009
5 8
2010
5 8
2011
1 2
66
Panel B Accounting standards compared to US GAAP
IFRS
47 71
Canadian GAAP
12 18
Israeli GAAP
2 3
Dutch GAAP
1 2
Indian GAAP
1 2
Japanese GAAP
1 2
Norwegian GAAP
1 2
Swiss GAAP
1 2
66
36
(Table 1 continued) Panel C Tests of differences between US GAAP and other accounting standards
Percent different from US GAAP
Comparison to sales (p-values)
N Mean Median
Mean Median
Full Sample (2001-2011)
Sales
66 236 000
Net Income
66 10653 647
005 lt001
Assets
66 1162 126
lt001 lt001
Study Sample (2001-2008)
Sales
55 257 000
Net Income
55 10303 589
011 lt001
Assets
55 1047 099
003 lt001
37
Table 2 Descriptive statistics Panel A reports the mean and medians of Excess Value for the multinational firms and single-segment benchmark firms as well as univariate tests across the subsamples Panel B reports summary statistics for the independent variables for the multinational firms The p-values of the tests of differences in means are based on t-tests The p-values of tests of differences in the medians are for a Wilcoxon rank-sum test All variables are defined in Appendix A Panel A Excess Value by sample Multinational All Benchmark Benchmark Subgroups Sample Firms US Foreign
Value Value Test of Diff (p-value) Value
Test of Diff (p-value) Value
Test of Diff (p-value)
Mean 0067 0006 lt001 -0028 lt001 0014 lt001 Median 0076 0000 lt001 -0025 lt001 0000 lt001 N 4950 33074 5998 27076 Panel B Distribution of independent variables for the multinational firms N Mean 25th 50th 75th StDev Foreign Sales 4950 0238 0074 0190 0363 0197 Industry Other 4950 0212 0000 0166 0414 0215 Log Size 4950 6955 5823 6989 8140 1687 Debt Firm Value 4950 0136 0009 0097 0212 0145 CAPX Sales 4950 0054 0020 0033 0054 0101 EBIT Sales 4950 0065 0031 0077 0121 0138 RampD Sales 4950 0049 0000 0013 0059 0083 Ad Sales 4950 0008 0000 0000 0005 0018
38
Table 3 BEA and Worldscope samples
This table reports details on affiliate sales for significant countries pooled from 1998-2008 Significant countries are defined as those in which the operations of US MNCs represent at least 02 of total firm sales (per the BEA) For significant countries the table compares their representation in terms of MNC affiliate sales in the BEA data to the distribution of firm observations in Worldscope US Foreign Affiliates (BEA) Foreign Firms (Worldscope)
Country
N (1)
Sales (millions)
(2)
Total Sales (3)
N (4)
Sales (million)
(5)
Total Sales (6)
Australia 1344 73305 056 859 255865 210 Belgium 894 52755 040 169 26136 021 Brazil 959 60245 046 1143 683167 560 Canada 2730 536767 407 1181 526750 432 China 922 75907 057 822 276541 227 France 1852 183942 139 540 105447 086 Germany 2182 275577 209 480 103888 085 Hong Kong 867 48100 036 157 34094 028 Ireland 478 36754 028 34 3467 003 Italy 1178 82116 062 111 47948 039 Japan 1494 148032 112 2281 1119223 917 Korea South 659 34500 026 3530 2752791 2255 Malaysia 386 26027 020 837 168683 138 Mexico 1405 113623 086 362 237173 194 Netherlands 1149 85105 064 47 22631 019 Singapore 1098 94917 072 139 32913 027 Spain 871 63094 048 129 37419 031 Sweden 712 32682 025 235 23090 019 Switzerland 726 60288 046 74 20242 017 Taiwan 592 30313 023 2949 787049 645 United Kingdom 3054 423934 321 1997 882423 723 United States (domicile) 4950 10460880 7923 na na na Other - Covered by WorldScope 6893 199394 151 9000 4057894 33 Other -Not Covered by WorldScope 713 5352 004 - - - Total 38108 13203609 1000 27076 12204834 100
39
Table 4 OLS regressions of firm excess values on the percent of foreign sales This table reports the results of OLS regressions of firm excess value on the percent of foreign sales (Foreign Sales) the percent of sales outside a firmrsquos primary industry (Industry Other) and control variables All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed) Dep Variable = Excess Value Coefficient t-stat Coefficient t-stat Coefficient t-stat Coefficient t-stat (1) (2) (3) (4) Foreign Sales 0349 419 0209 293 0189 266 0278 210 Industry Other -0134 -186 -0047 -072 -0053 -081 -0326 -325 Log Size 0066 634 0065 624 -0044 -096 Debt Firm Value -0196 -213 -0241 -260 -0685 -588 CAPX Sales 0901 425 0893 435 0488 227 EBIT Sales 1154 542 1170 547 0408 189 RampD Sales 2908 1242 2932 1237 1544 379 Ad Sales 1869 263 2003 280 -1696 -119 Year indicators No No Yes Yes Firm indicators No No No Yes R2 0017 0212 0226 0768 Observations 4950 4950 4950 4950
40
Table 5 Dynamic panel GMM model regressing firm excess values on the percent of foreign sales This table reports the result from a regression of the excess value on the percent of foreign sales (Foreign Sales) the percent of non-primary industry sales (Industry Other) and control variables using a dynamic panel GMM estimator All control variables are considered to be endogenous with the exception of the year indicator variables AR(1) and AR(2) are tests for first-order and second-order serial correlation in the first differenced residuals with the null hypothesis of no serial correlation The null hypothesis of the Hansen test of overidentification is that all instruments are valid The null hypothesis of the difference-in-Hansen test of exogeneity is that the instruments used for the equation in levels are exogenous All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed) Dep Variable = Excess Value Coef t-stat Excess Value (one lag) -0018 -082 Excess Value (two lags) -0048 -272 Foreign Sales 0371 270 Industry Other -0163 -171 Log Size 0010 123 Debt FirmValue -1135 -820 CAPX Sales 0590 342 EBIT Sales -0047 -300 RampD Sales 1376 531 Ad Sales -0751 -040 AR(1) test (p-value) 000 AR(2) test (p-value) 017 Hansen test of over-identification (p-value) 030 Difference-in-Hansen test of exogeneity (p-value) 093 Year indicators Yes Number of firms 1016 Number of observations 4090
41
Table 6 OLS regression of firm excess values on the percent of foreign sales controlling for a control premium This table reports the result of the OLS regression of firm excess value including firm and year fixed effects with an additional control variable to proxy for a country-level ownership control premium (Control Premium) All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed) Dep Variable = Excess Value Coef t -stat Foreign Sales 0303 224 Industry Other -0327 -325 Log Size -0045 -100 Debt FirmValue -0683 -585 CAPX Sales 0490 226 EBIT Sales 0409 189 RampD Sales 1545 379 Ad Sales -1689 -119 Control Premium -1004 -062 Year indicators Yes Firm indicators Yes R2 0768 Observations 4950
42
Table 7 OLS regression of firm excess values on the percent of foreign sales controlling for the cost of capital This table contains the results of an OLS regression of firm excess value including firm and year fixed effects with an additional control variable to proxy for a country-level cost of capital (Cost of Capital) All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed) Dep Variable = Excess Value Coef t -stat Foreign Sales 0255 178 Industry Other -0326 -324 Log Size -0043 -094 Debt FirmValue -0687 -589 CAPX Sales 0485 225 EBIT Sales 0406 188 RampD Sales 1539 379 Ad Sales -1703 -120 Cost of Capital 0004 039 Year indicators Yes Firm indicators Yes R2 0768 Observations 4950
43
Table 8 OLS regression of firm excess values on the percent of foreign sales controlling for aspects of corporate governance This table reports the results of two OLS regressions In the first column are the results of the firm excess value regression including firm and year fixed effect with five proxies for corporate governance included ndash the percent of shares owned by the CEO and the percent squared (CEO Ownership and CEO Ownership squared) the percent owned by institutional investors (Institution Ownership) whether there is a powerful CEO (Powerful CEO) and the Gompers Ishii and Metrick (2003) governance index (Governance Index) In the second column the regression includes the same sample and variables with the exception of the governance proxies which are excluded All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed)
Governance
No Governance (same sample)
Dep Variable = Excess Value Coef t-stat Coef t -stat Foreign Sales 0259 159 0251 154 Industry Other -0099 -080 -0099 -079 Log Size -0105 -185 -0102 -180 Debt FirmValue -0813 -478 -0817 -482 CAPX Sales 0440 252 0438 249 EBIT Sales 0457 247 0457 246 RampD Sales 1717 284 1716 286 Ad Sales -2210 -143 -2190 -143 CEO Ownership 0010 007
CEO Ownership squared -0029 -017 Institution Ownership 0047 067 Powerful CEO -0003 -014 Governance Index 0009 103
Year indicators Yes Yes Firm indicators Yes Yes R2 0811 0810 Observations 2550 2550
- Cover for paperpdf
- crrz_20130603_SMUpdf
-
8
2 Premium versus discount
In a frictionless world where managers maximize firm value and markets are efficient there
should be no discount or premium to operating in foreign countries However there are a number
of forces that may cause actual firm value to deviate from the implied firm value
On one hand MNCs may have comparative advantages that generate premiums (ie positive
excess values) relative to a similar footprint of stand-alone firms Having operations in a variety
of locations that access different supplies of inputs and customers allows the firm to take
advantage of changing market conditions by shifting operations or products to maximize firm
value more so than a firm operating in a single country Similarly having access to a variety of
institutional settings such as different tax codes legal regimes and financial markets can provide
MNCs with options beyond those available to firms located in a single country
Another possible factor associated with a premium to multinational operations is the cost of
capital For operations located in countries with shallow capital markets or weaker creditor
rights operating within a conglomerate can provide the benefit of a lower cost of capital for
investments and expansion for at least two reasons First as our MNCs are domiciled in the US
their foreign segments have greater access to US capital markets relative to local competitors in
the foreign jurisdiction Second cash rich segments with few investment opportunities can
finance investments in cash poor segments with positive net present value projects (eg Myers
and Majluf 1984) Consequently diversified firms should be less liquidity constrained and better
able to shift resources to the most valuable investment opportunities Research has found that
internal financing is used more often when diversified firms have operations in countries with
more costly external financing (Desai Foley and Hines 2004) Multinational firms may also be
relatively more protected than single-country firms when negative shocks hit external capital
9
markets (Foley Desai and Forbes 2008) In a recent study Matvos and Seru (2011) find that
industrially diversified firms perform better than non-diversified firms when external capital
markets are impaired
While these arguments suggest that MNCs enjoy certain advantages when the foreign cost of
capital is high these benefits should be reduced by the additional risk associated with operating a
business in a high cost of capital environment For example Verizon is a multinational firm
operating in a number of countries One of the higher cost of capital locations in which they
operated was Venezuela While the multinational structure of Verizon may have mitigated some
of the risks associated with operating in a higher cost of capital location the firms was unable to
eliminate all of these risks In fact in 2007 Verizonrsquos operations in that country were
nationalized resulting in a net extraordinary loss of $131 million that year8 In other words
MNC investors are likely to expect higher rates of return to compensate for higher risk which
likely offsets some (if not all) of the potential excess value premium
On the other hand multinational firms may incur a discount (ie negative excess values)
relative to a similar footprint of stand-alone firms Potential reasons for a discount largely rely on
the existence of agency costs Multinational firms tend to be larger more complex and less
transparent The combination of complexity and opacity inherent in MNCs reduces a firmrsquos
reliance on external capital as the cost of such capital increases due to agency costs (Desai et al
2004) As a result of the reduced reliance on external funds MNCs face a reduced level of
monitoring As noted by Jensen (1986) managers have incentives to increase firm size beyond
that which is optimal with overinvestment or misallocation of funds to pet projects If this lack of
monitoring is in combination with available internal capital empire building becomes easier
(Hope and Thomas 2008) Furthermore the current segment reporting standards under US 8 See the 10-K of Verizon Communications Inc dated December 31 2007 (footnote 2)
10
GAAP only require highly aggregated disclosures of foreign operations and allow for
considerable managerial discretion As a result operating in multiple countries makes it easier
for management to hide poor performance ndash either their own or that of a division ndash such that low
quality managers are more likely to be retained and overcompensated and poorly performing
divisions retained longer than optimal Other reasons one might expect a discount to be applied
to multinational firms include higher coordination costs (eg coordinating across different
cultures and languages) as well as additional risks These risks include exposure to multiple
political regimes legal regimes economic regulations and currency fluctuations
Past studies investigate whether foreign operations of US firms enhance or reduce firm
value However all of these studies benchmark the value of the foreign operations of an MNC to
US domestic firms For instance Denis et al (2002) determine the implied value of a US
MNC using domestic (US) single-segment firms in the same industry as a benchmark Studies
using Tobinrsquos Q to investigate valuation effects of MNCs also rely on domestic benchmarks
(eg Morck and Yeung 1991 Morck and Yeung 2001 Gande Schenzler and Senbet 2009)
However using a US domestic firm as a benchmark for the value of foreign operations
implicitly makes two assumptions First this method assumes that the risks and expected growth
rates of foreign operations are equivalent to those of domestic operations This assumption is
inconsistent with the findings of Hail and Leuz (2009) which provides evidence that the cost of
capital varies substantially across countries Second this method assumes that there is excess
domestic capacity to allow for expansion with profitability similar to current domestic
operations We relax these assumptions by measuring the imputed value of a foreign component
of a US firm as if that component operated autonomously within that foreign country (rather
than in the US) Specifically our multiples allow the cost of capital and expected growth rates
11
to vary across not just industry but also country Furthermore our counterfactual does not
require the assumption that operations be relocated mitigating the production and sales capacity
concerns
3 Measuring excess value
31 Description of excess value
Figure 1 shows the average percent of foreign sales (based on segment disclosures under US
GAAP) across firms through time and illustrates that firms are continuing to expand
internationally9 This expansion highlights the importance of understanding whether the decision
to operate internationally is on average a value enhancing corporate strategy We contribute to
this understanding by evaluating whether the firm as a whole is worth more or less than its
imputed value (ie the firm is valued in excess of the sum of its individual components)
We measure the excess value of a firm as the logarithm of the ratio of actual firm value to
imputed value based on the method used in Berger and Ofek (1995) The imputed value is the
hypothetical value of the MNC under the assumption that its country-industry components
operate as independent entities A key innovation of our study is an alternative approach to
imputing the value of the foreign operations of an MNC Components of MNCs operating in a
given industry and country are imputed using the value of single-segment firms operating in the
same industry and country Thus we ensure the discount rates and expected growth rates are
applicable to each given industry and location in estimating the implied values of the MNC
segments
9 The discontinuity in industrial diversification (the percent of sales outside the primary industry) between 1997 and 1998 is the result of a change in segment reporting rules
12
The actual firm values are observable for all firms in our sample The hypothetical firm
values for multi-segment firms are imputed using market value to sales ratios herein referred to
as lsquomultiplesrsquo of the median single-segment firm operating in the same country-industry Thus
these single country-industry firms serve as benchmarks for an MNCrsquos operations within that
same country-industry The imputed value of the country-industry component can be viewed as
an estimate of what that component would be worth if it operated independently The sum of the
imputed values across all country-industry components represents the hypothetical value that the
multi-segment firm would be worth if it were broken apart
32 Data
Four primary data sources play a distinct role in carrying out our analysis i) Bureau of
Economic Analysis (BEA) data ii) Compustat Segment data iii) Worldscope data and iv)
Compustat Fundamentals Annual data Broadly speaking BEA data provide information about
the diversified nature of MNCs Compustat Segment data provide information about the
industrial diversification of US domiciled firms Worldscope data are used to compute
multiples for single-segment foreign firms that serve as benchmarks for the foreign operations of
MNCs (ie foreign benchmarks) and Compustat Fundamentals Annual data are used to
compute multiples for single-segment domestic firms that serve as benchmarks for the domestic
operations of multinational firms We also use the latter data source to construct our primary
control variables
13
Key to the execution of our study is our access to BEA data Federal law requires US-
domiciled MNCs to report certain financial and operating data to the BEA10 With regard to
observing the country-industry operations of MNCs use of the BEA data overcomes two
important limitations of Compustat Segment data First Compustat does not consistently identify
the specific location or scale of MNCsrsquo foreign operations11 Second Compustat does not
identify the industry activities in each country of operation This latter point is particularly
limiting for MNCs because domestic industry activity need not mirror foreign industry activity
An MNC generating an equal proportion of sales in two industries in the domestic market need
not generate industry sales in equal proportions in every foreign market12 The lack of detail and
consistency in Compustat Segment data arises because segment reporting is highly aggregated
and firms exercise substantial discretion in defining segments under Accounting Standards
Codification 280 (ASC 280) (eg Villalonga 2004a and Bens Berger and Monahan 2011)
The BEA defines an MNC as the combination of a single US entity called the US parent
and at least one foreign affiliate ndash that is these firms have a physical presence outside the US
due to FDI Since 1982 MNCs have completed a mandatory and confidential lsquoSurvey of US
Direct Investment Abroadrsquo for the domestic operation and the operations of each foreign
affiliate defined as a foreign entity in which the US parent holds at least a 10 percent equity
interest (directly or indirectly) The data are reported in US dollars on a fiscal year basis and in
10 BEA surveys are conducted pursuant to the International Investment and Trade in Services Survey Act (PL 94-472 90 Stat 2059 22 USC 3101-3108) See httpwwwbeagovsurveysdiasurvhtm and Mataloni (2003) for more detailed information on the BEA data 11 Exhibit 21 disclosures of material subsidiaries and their locations in SEC 10-K filings could potentially help overcome the inability to observe the location of the firmrsquos foreign operations from segment disclosures However at least three limitations remain (1) for industrially diversified multinational firms the researcher would be required to make an assumption about the proportion of industrial diversification in each country (2) Exhibit 21 provides no information about the scale of activity in each jurisdiction and (3) managerial discretion is used to determine a lsquomaterialrsquo subsidiary under Section 601 of SEC Regulation S-K 12 In fact the expectation that a multinational firm would exhibit different industry membership in different countries is consistent with the theory of vertical foreign direct investment where firms separate their various value chain activities and locate them in the most favorable country (see eg Hanson Mataloni and Slaughter 2005)
14
accordance with US Generally Accepted Accounting Principles (US GAAP) For each year
we observe the sales industry composition and location of not only the parent but also each
affiliate13
The procedures we use to construct our sample are similar to those used by Berger and Ofek
(1995) and Denis et al (2002) Using firm-years appearing in Compustat Fundamentals Annual
and Compustat Segment data we obtain a sample of firms domiciled in the US with total sales
exceeding $20 million We exclude firms in the financial service (SIC codes 6000 to 6999) and
utility (SIC codes 4000 to 4999) industries and eliminate firm-years when the sum of segment
sales (Compustat geographic or Compustat business) is not within 1 of total firm sales for that
year We restrict our sample period to include fiscal years beginning after December 15 1997 as
ASC 280 Segment Reporting substantially altered the definition of a reporting segment under
US GAAP This restriction ensures that Compustat Segment data reflects the use of a consistent
accounting standard Finally we require that our MNCs appear in both Compustat and BEA
data The requirement that our sample firms appear in the BEA database ensures that the firms
have observable international operations In sum these procedures result in 1166 multinational
firms incorporated in the US with 4950 observations between 1998 and 2008 that meet all of
our data requirements
33 Measuring excess value
The dependent variable in our study is excess firm value (Excess Value) defined as the
logarithm of the ratio of actual firm value to imputed firm value (Berger and Ofek 1995)
13 We obtain sales industry membership and country location for affiliates from one of two sources When affiliates exceed the BEA-determined reporting thresholds (ie if their assets sales or net income (loss) exceed $7 million in 1999 $30 million in 2000 through 2003 $10 million in 2004 and $40 million in 2005 through 2008) they must file their own detailed survey When affiliates fall below the thresholds we obtain our data from an attachment to the parent survey
15
Appendix A provides the definition of this and all other variables used in our analysis We
observe actual firm value equal to a firmrsquos market value of equity plus book value of liabilities
(ie book value of total assets minus book value of total equity) using Compustat Fundamentals
Annual data We calculate imputed firm value as the sum of the imputed values of a firmrsquos
operations in each country-industry Our method for imputing the value of the separate
components of a firm can best be described in three steps First we obtain total sales generated
by a firm for each country-industry in which it operates14 Second we obtain multiples (market
value to sales ratios) for benchmark firms operating in those same country-industries Third we
multiply a firmrsquos country-industry sales by the applicable median country-industry multiple to
obtain the imputed market value for each country-industry operation We perform each step
annually
Conceptually imputed values can be based on assets (ie Tobinrsquos Q) net income or sales
multiples We restrict ourselves to sales multiples for two reasons First using value to sales
ratios maintains consistency with the prior research on the excess value implications of
multinational operations (eg Denis et al 2002) Second as our method uses accounting data
for foreign companies to compute country-industry multiples the accounting numbers we use
need to be consistently measured across firms using different accounting standards We find that
sales data are measured most consistently
To assess the comparability of sales relative to either net income or assets across various
accounting standards we examine all firms listed on Worldscope as changing to or from US
14 Specifically we allocate total sales from Compustat to each component based on the proportion of BEA sales (after eliminating intercompany sales) occurring in each country-industry This step ensures that our total firm sales do not deviate from a firmrsquos total sales as reported in the 10-K
16
GAAP between 2005 and 201115 We obtain accounting data for the year of the change and the
prior year where electronically available in English We are able to obtain data for 66 firms
changing standards as detailed in Table 1 The distribution of years for which accounting data
are available under two different accounting standards for the same fiscal year is shown in Panel
A The years shown are the year prior to the change as these accounting numbers were provided
under the original standard and then subsequently restated in the following year under the new
standard for comparative purposes The majority of the shifts were to or from IFRS (71)
though seven other accounting standards are noted (Panel B) We examine the percent that the
non-US GAAP reported number differs from the US GAAP reported number in Panel C to
assess the comparability of these three summary accounting numbers across various accounting
standards Examining both the full sample as well as the subset that overlaps our study we find
that sales are more consistently measured than net income or assets
The sum of the imputed values across the country-industry components of an MNC is an
estimate of the value of a portfolio of unrelated businesses that mirror the related businesses of
the firm Consequently a comparison of the actual firm value with the imputed firm value is a
measure of how a multinational network affects firm value We provide descriptive statistics for
Excess Value for our sample of 4950 MNC firm-years in Table 2 Panel A This table also
includes univariate comparisons of the values between the MNC firms and the domestic single-
segment and foreign single-segment benchmark firms The Excess Value measure is significantly
higher for MNCs suggesting a premium in firm value for multinational firms relative to the
benchmark firms in total as well as the domestic and foreign subgroups (p-values for tests of
15 Due to the hand collection efforts required and the limitation on electronic filings we began our search with changes documented in 2005 which generally require filings from 2004
17
means and medians are lt 001)16 These results provide some initial evidence that operating in
multiple countries is positively associated with firm value
To estimate country-industry multiples we rely on Worldscope financial data on foreign
firms and Compustat Fundamentals Annual for domestic firms We restrict the firms to include
only those with at least 90 of sales income and assets inside the country of domicile (ie
those that do not report significant multinational activity) and that operate in a single industry
We refer to these firms as benchmark firms (either foreign or domestic depending on the country
of domicile) these firms do not appear in any of our regressions We report the number of
benchmark firms during our sample period in Table 2 Panel A The industry criterion is based on
the two-digit SIC code17 For every country that has at least five firms in the respective industry
and year we use the median ratio of market value to sales18 This ratio is the country-industry
multiple ndash an input required to compute imputed values
To determine the country-industry composition of MNCs we rely on BEA data In Table 3
we report the aggregate number of foreign affiliates and total sales by country as provided by
the BEA data for our sample of 4950 MNCs The specific countries tabulated are those that
represent at least 02 percent of total firm sales (pooled across all years) in our sample The top
five foreign countries in which MNCs generate sales are Canada United Kingdom Germany
France and Japan Table 3 also provides a comparison of the number of foreign benchmark
firms available in Worldscope in the specific countries in which our sample of MNCs generate
16 Similar to Berger and Ofek (1995) the median values differ slightly from zero for the benchmark single-segment firms due to the truncation of extreme excess values 17 BEA reporting constrains us to the two-digit SIC code level 18 When five firms are not available for a given country and industry we follow Berger and Ofek (1995) and move to a more general industry definition (eg one-digit SIC code) This procedure results in a mean (median) 630 (757) of MNC sales matched at the two-digit level
18
the majority of their sales Country coverage in Worldscope is not available for countries
representing 004 percent of total firm sales (per BEA)
4 Empirical results
41 Independent variables
Recall that our objective is to examine the overall relation between excess firm value and
multinational operations Our proxy for the extent of multinational operations is the percentage
of total firm sales made by foreign operations (Foreign Sales) By this measure the extent of
multinational activity in our sample of MNCs is non-trivial ndash Table 2 Panel B shows that
approximately 24 percent of sales are generated by foreign operations on average Our first
analysis estimates ordinary least square regressions of Excess Value on Foreign Sales and
control variables (discussed below)
We include variables in our regression to control for other potential determinants of excess
value These are the percentage of sales made by the firm outside its primary industry (Industry
Other) to control for any relation between industrial diversification and excess value as in Berger
and Ofek (1995) For this purpose we obtain industry sales using Compustat Segment data19 A
firmrsquos primary industry is the industry in which the firm generates the majority of its sales and
we determine industry sales at the business segment level We set Industry Other equal to zero
for firms that operate in a single business segment
Consistent with prior research we also include controls for firm size (Log Size) the ratio of
long term debt to total market value (Debt Firm Value) the ratio of capital expenditures to total
sales (CAPX Sales) the ratio of earnings before interest and taxes to total sales (EBIT Sales)
the ratio of research and development expenses to total sales (RampD Sales) and the ratio of 19 Our results are unaffected by using BEA data to measure Industry Other
19
advertising expenses to total sales (Ad Sales) Table 2 Panel B provides the distribution of the
control variables All independent variables are Winsorized at 1 In our OLS regressions we
use heteroskedasticity and autocorrelation consistent robust standard errors with clustering at the
firm level
42 Primary regression analysis
In this section we examine the relation between firm excess value and the amount of foreign
activity at US MNCs Table 4 contains the results of regressions of excess value on Foreign
Sales for multiple specifications In column (1) Excess Value is regressed on both the percent of
foreign sales (Foreign Sales) and the percent of sales outside the firmrsquos primary industry
(Industry Other) We add the control variables to the regression in column (2) and year
indicators in column (3) In all three specifications the coefficient on the percent of foreign sales
remains significantly positive (with a p-value lt 001) For a sense of economic magnitude in
column (3) a one standard deviation (197) increase in the percent of foreign sales (Foreign
Sales) is associated with a 41 (equal to (e0349-1) times 0197) increase in firm excess value The
effect is larger for the other two columns ndash 82 increase in firm excess value in column (1) and
46 in column (2)20
Our finding of a premium reconciles with the international trade literature which finds that
firms engaging in international trade are more productive than those that do not (see Helpman
2006 and Syverson 2011 for reviews) Due to data limitations it is difficult to accurately
measure TFP at the firm level especially for firms which operate in multiple industries andor
20 Using the alternative strategy that employs US domiciled single-segment firms as the benchmark for the imputed value of the foreign operations Denis et al (2002) find that greater foreign activity results in a discount to firm value Using the Denis et al (2002) method to calculate excess value in our sample we obtain a significantly negative regression coefficient on Foreign Sales using the models in Table 4 columns (2) and (3) (the coefficient is not significant using the model in column (1) results untabulated)
20
countries As an alternative method to evaluate whether our finding of a premium is simply the
result of more productive firms also having more multinational operations we investigate
whether the premium is simply an artifact of innate characteristics of firms that choose to invest
abroad For example Helpman et al (2004) present a simple model with heterogeneous
productivity endowments The firms that receive the highest productivity endowments are the
ones capable of paying the fixed costs to establish a foreign subsidiary
To the extent that productivity advantages create excess value one would expect the type of
firms that choose to invest more abroad would be valued at a premium even in the absence of
their foreign investments To control for time invariant firm characteristics (eg innate
productivity advantages) and hence a potential source of endogeneity we add firm-level fixed
effects in column (4) of Table 4 We continue to find that the extent of foreign operations
(Foreign Sales) is positively associated with Excess Value Across all specifications the sign
on all significant control variables is consistent with prior research (eg Berger and Ofek 1995
and Denis et al 2002)
The finding of a premium makes a significant contribution to the relatively sparse literature
on the value effects of multinational activity Our measure of imputed value expands upon the
methods used by Berger and Ofek (1995) and Denis et al (2002) but allows us to answer a
different question than Denis et al (2002) Holding constant the extent of industrial
diversification we ask whether MNCs are more valuable than a portfolio of benchmark firms of
a similar geographic footprint In contrast Denis et al (2002) ask whether holding constant the
extent of industrial diversification MNCs are more valuable than a portfolio of domestic (US)
benchmark firms We believe that asking whether an MNCrsquos foreign (domestic) operations are
21
more valuable than those of a foreign (domestic) firm is a more appropriate way to assess the
value implications of a multinational network
5 Robustness
51 Endogeneity related to dynamic relations
Prior research argues that the relation between industrial diversification and excess firm
value could be endogenous through dynamic relationships based on observing past outcomes
Managers of firms likely choose to enter or exit industries or geographic regions based upon
their previous performance For example Campa and Kedia (2002) find that firms are more
likely to enter new industries when prospects in their current lines of business are deteriorating
Similar dynamics could also drive a relation between excess value and FDI We assess the
robustness of the previous results by estimating a dynamic panel data model which allows us to
control for dynamic endogeneity in addition to firm level unobserved heterogeneity We employ
the GMM estimator for dynamic panel models developed by Arellano and Bond (1991) Arellano
and Bover (1995) and Blundell and Bond (1998) Our results from this analysis confirm our
previous findings that firms with a larger degree of foreign operations have positive excess
values
Dynamic panel data models are generalizations of the traditional fixed effects model where
lags of the dependent variable are added to the right hand side of the equation These additional
lags control for the impact a firmrsquos past performance has on its current performance through the
channels discussed above The GMM estimator for dynamic panel models developed by
Arellano and Bond (1991) starts by differencing the data at the firm level in neighboring time
periods to eliminate any firm specific time invariant unobserved heterogeneity that may be
22
present Eliminating the firm specific fixed effect by differencing the data with lags of the
dependent variable on the right hand side introduces correlation between the new error term and
the differenced explanatory variables This problem is solved by applying the standard GMM
estimator using lags of both the original explanatory variables and excess value as instruments
We note that the dynamic panel data framework allows us to treat both international and
industrial diversification (as well as the other firm-specific control variables) as endogenous
Following Hoechle et al (2012) we also allow contemporaneous values of these variables to be
treated as endogenous
Lagged values of the explanatory variables are valid instruments in a dynamic panel data
model when the model is dynamically complete (eg Wooldridge 2010) A dynamic panel data
model is dynamically complete conditional on the unobserved effect if enough lags of both the
explanatory and dependent variable are included to make the resulting error term uncorrelated
with all the right hand side variables Under this assumption any lags for the explanatory
variables beyond this can be used within the GMM estimator as instruments Like Hoechle et al
(2012) we add two years of lags of a firmrsquos excess value to the right hand side assuming that
this is enough lags to control for dynamic feedback effects We then use past values of all
variables beyond the second lag up to a total of seven years to act as instruments
The results of the dynamic panel data estimator are reported in Table 5 These results confirm
the previous finding of a positive relation between the degree of international operations and
excess value We note that the size and significance level of the coefficient on Foreign Sales
diversification are robust to changes in the number of lags used as instruments The Arellano and
Bond (1991) estimator includes testing diagnostics for correct specification of the model If the
model is correctly specified differences in the residuals from the levels equation should be
23
serially uncorrelated at lag one but correlated at lag two The p-values for tests of serial
correlation of the residuals at lags 1 and 2 are reported in Table 5 and these indicate that the
model is not rejected We also report in Table 5 the Hansen test for overidentification which is a
test of the null hypothesis that the instruments are valid The p-value for this test is 030
indicating that we fail to reject this null hypothesis Finally we report another test for exogeneity
known as the difference-in-Hansen test which tests the null hypothesis that the instruments in
the original equation are valid We fail to reject this null hypothesis as well
52 Identifying value effects of multinational operations via cross-border acquisitions
Another alternative to investigating a causal link between foreign direct investment and firm
value is an event study of foreign acquisitions21 Acquired foreign targets represent only a small
fraction of MNCsrsquo worldwide sales in the year of the acquisition ndash less than 1 percent in our
sample Additionally cross-border acquisitions are not the primary source of international
expansion for the average firm We find that foreign acquisitions account for between 18 percent
and 55 percent of the year-over-year change in foreign sales with the remainder of the growth
coming from either newly established entities (ie greenfield investments) or growth in existing
operations22 These results suggest that acquisitions are not a material source of growth for the
21 Doukas and Travlos (1988) find positive returns when the acquirer is not operating in the target firmrsquos country and insignificant returns when the acquirer is operating in the target firmrsquos country or expanding internationally for the first time Morck and Yeung (1991) find marginal evidence of positive returns for firms with intangible assets Finally Doukas (1995) finds positive returns only for firms with average Tobinrsquos q ratios greater than one More recent work also using an event study methodology provides mixed evidence on valuation effects of global diversification through acquisitions Dos Santos Errunza and Miller (2008) find insignificant returns for firms with already established foreign operations firms establishing initial foreign operations and firms acquiring targets in related industries Doukas and Kan (2006) and Moeller and Schlingemann (2005) find reductions to shareholder value while Francis Hasan and Sun (2008) find positive returns for acquirers of targets in segmented financial markets 22 New foreign affiliates to an MNC group each year report on the BEA survey whether they entered the group as an acquisition or a newly established entity (ie greenfield investment) Affiliates under the lowest reporting threshold do not report this information resulting in our inability to precisely identify the source of 33 percent of the increase in firm sales as either acquisition or greenfield
24
typical US MNC suggesting that an event study would lack power and generalizability For
these reasons we do not implement an event study
53 Control premium
A control premium or private benefits of control occurs when ldquosome value whatever the
source is not shared among all the shareholders in proportion of the shares owned but it is
enjoyed exclusively by the party in controlrdquo (Dyck and Zingales 2004 p 541) Using stock
prices of traded shares held by minority shareholders in determining the market value to sales
ratios of our benchmark firms could underestimate the true value of our benchmark firms if those
firms face large control premiums This in turn could induce an excess value premium because
the imputed value of these country-industry segments would be artificially low The existence of
such benefits is more prevalent in some countries than others and could affect the value of an
MNCrsquos operations in such domains We use the country-level control premiums from Dyck and
Zingales (2004) and using each firmrsquos country sales as a percent of total firm sales we obtain a
weighted average control premium that each MNC faces (Control Premium) For countries not
included in the Dyck and Zingales (2004) analysis we set the control premium equal to zero23 In
Table 6 we show that Foreign Sales remains positive and highly significant (p-value lt 001)
when we include the proxy for control premium As a result it does not appear that the excess
value premium is driven by MNCs operating in countries where majority shareholders are able to
extract large control premiums
23 Results are robust to replacing missing values with the mean control premium of the respective GDP quartile
25
54 Cost of capital
As discussed in Section 2 when countries have shallow capital markets a multinational
network may provide the benefit of a lower cost of capital for investments through an internal
capital market as well as through better access to the US capital market Prior research
establishes that there is substantial variation in the cost of capital across countries (Erb Harvey
and Viskanta 1996 and Hail and Leuz 2009) To determine whether the documented premium
is due to cost of capital advantages we include a control variable to capture the difference
between the MNCrsquos cost of capital and the cost of capital faced by its competitors in the global
markets in which it operates
We compute our Cost of Capital proxy in two steps First we obtain the country-year credit
rating published by Institutional Investor24 The rating ranges from 0 to 100 where higher values
imply that a country has a higher default risk Country-level credit risk is a reasonable predictor
of expected equity market returns and volatility (Erb et al 1996) and exhibits a highly
significant correlation with international accounting-based estimates of imputed cost of capital
(Hail and Leuz 2009) Second using each firmrsquos country sales as a percent of total firm sales
we obtain a weighted average credit risk rating across the countries in which each firm operates
A higher value implies an MNC operates in countries with higher cost of capital In Table 7 we
do not find a result consistent with MNCs gaining an advantage from their access to low cost
capitalndash Cost of Capital is not significantly associated with Excess Value25
24 We thank Cam Harvey for providing these data 25 Results are consistent using cost of capital as defined in Erb et al 1996
26
55 Corporate governance
In light of the recent finding that corporate governance mitigates the diversification discount
we examine whether the premium to multinational activity is sensitive to controls for corporate
governance Hoechle et al (2012) find that the magnitude of the industrial diversification
discount is decreased and in several specifications no longer significant in the presence of
governance proxies In the first column of Table 8 we include the five governance proxies that
are consistently significantly associated with excess value in Hoechle et al (2012) ndash the percent
of CEO ownership (CEO Ownership and CEO Ownership squared) the percent of institutional
ownership (Institution Ownership) whether the firm has a powerful or influential CEO
(Powerful CEO) and the governance index from Gompers Ishii and Metrick (2003)
(Governance Index)
As documented in Table 8 including these control variables reduces our sample size by
almost 50 (to 2550 observations) and tends to exclude the smaller firms from the analysis
None of the governance proxies are significant Relative to column (4) of Table 4 the coefficient
on Foreign Sales is similar in magnitude but is no longer statistically significant at
conventional levels To investigate whether the decreased significance is due to the smaller
sample or the inclusion of the governance variables we re-estimate the regression for the same
2550 observations after removing the governance controls The coefficient and t-statistic are
virtually identical suggesting that the loss of significance is due to the reduced sample and not
the governance controls We do not find evidence to suggest that variation in corporate
governance practices drives variation in excess value for US MNCs
27
6 Conclusion
Using a new method to quantify the valuation effects of foreign direct investment by US
domiciled firms we find robust evidence of a premium in excess value associated with the
degree of multinational operations Our method builds on that used in previous studies
examining firm excess value (eg Berger and Ofek 1995 and Denis et al 2002) by
incorporating the location in addition to the industry of each corporate affiliate This allows
important factors such as growth and cost of capital to vary across countries as well as industries
Examining a sample of multinational firms we find that a one-standard deviation (197)
increase in foreign operations is associated with an increase of between 41 and 82 in excess
value minus both a statistically and economically significant finding
This result complements the finding of the international trade literature that more productive
firms are more likely to operate internationally However the premium remains after including
firm fixed effects to control for persistent firm traits (eg productivity)
The finding of a premium stands up to a succession of robustness tests designed to evaluate
whether the result arises from alternative methods or explanations First we estimate a dynamic
panel data model using the generalized method of moments (GMM) estimator to simultaneously
account for the potential endogeneity of FDI industrial diversification and firm value Second
we include a control variable to proxy for whether the firm operates in countries with larger
control premiums which could result in understated multiples and mechanically induce a
premium Third we include a control variable to measure the cost of capital across the countries
in which an MNC operates to evaluate whether a relatively low cost of capital borne by
multinational firms is a meaningful source of the premium Fourth we control for several
corporate governance proxies which have been found to be correlated with excess values (and
28
industrial diversification) We fail to find evidence supporting any of these alternative
explanations ndash the premium in excess value arising at firms with greater multinational operations
remains significant
Overall our new method of estimating excess values for multinational firms provides strong
evidence of a statistically and economically significant premium associated with increased
international operations The premium is robust to a host of specifications and controls designed
to evaluate alternative explanations We leave to future research the questions of whether the
premium to US multinational firms applies more broadly to other countries of domicile and
what factors influence the premium
29
References
Ammann M Hoeschel D and M Schmid 2012 Is there really no conglomerate discount
Journal of Business Finance and Accounting 39(12) 264-288 Arellano M and Bond S R 1991 Some tests of specification for panel data Monte Carlo
evidence and an application to employment equations Review of Economic Studies 58 (2) 277-298
Arellano M and Bover O 1995 Another look at instrumental variables estimation of error
components models Journal of Econometrics 68 (1) 29-61 Bens D A Berger P G and S J Monahan 2011 Discretionary disclosure in financial
reporting An examination comparing internal firm data to externally reported segment data The Accounting Review 86(2) 417-449
Berger P G and E Ofek 1995 Diversificationrsquos effect on firm value Journal of Financial
Economics 37 39-65 Blundell R and Bond S R 1998 Initial conditions and moment restrictions in dynamic panel
data models Journal of Econometrics 87(1) 115-144 Campa J M and S Kedia 2002 Explaining the diversification discount The Journal of
Finance 57 (4) 1731-1762 Denis D J Denis D K and K Yost 2002 Global diversification industrial diversification
and firm value The Journal of Finance 57 (5) 1951-1979 Desai M A Foley C F and J R Hines 2004 A multinational perspective on capital structure
choice and internal capital markets The Journal of Finance 59 (6) 2451-2487 Dos Santos MB V Errunza and D Miller 2008 Does corporate international diversification
destroy value Evidence from cross-border mergers and acquisitions Journal of Banking and Finance 32(12) 2716-2724
Doukas JA 1995 Overinvestment Tobinrsquos q and gains from foreign acquisitions Journal of
Banking and Finance 19 1285-1303 Doukas J and N G Travlos 1988 The effect of corporate multinationalism on shareholdersrsquorsquo
wealth evidence from international acquisitions Journal of Finance 43(5) 1161-1175 Doukas JA and O B Kan 2006 Does global diversification destroy firm value Journal of
International Business Studies 37(3) 352-371
30
Dunning J and A Rugman 1985 The influence of Hymerrsquos dissertation on the theory of foreign direct investment The American Economic Review 75(2) 228-232
Dyck A and L Zingales 2004 Private benefits of control An international comparison The
Journal of Finance 59 (2) 537-600 Erb C Harvey C and T Viskanta 1996 Expected returns and volatility in 135 countries The
Journal of Portfolio Management 46-58 Foley C F Desai M A and K J Forbes 2008 Financial constraints and growth
Multinational and local firm responses to currency depreciations Review of Financial Studies 21 (6) 2857-2888
Francis B I Hasan and X Sun 2008 Financial market integration and the value of global
diversification Evidence for US acquirers in cross-border mergers and acquisitions Journal of Banking and Finance 321522-1540
Gande A Schenzler C and L Senbet 2009 Valuation effects of global diversification
Journal of International Business Studies 1515-1532 Gompers P Ishii J and A Metrick 2003 Corporate governance and equity prices Quarterly
Journal of Economics 118(1) 107-156 Hail L and C Leuz 2009 Cost of capital effects and changes in growth expectations around
US cross-listings Journal of Financial Economics 93 428-454 Hanson G Mataloni R and M Slaughter 2005 Vertical production networks in multinational
firms Review of Economics and Statistics 87(4) 1-15 Helpman E 2006 Trade FDI and the Organization of Firms Journal of Economic Literature
44(3) 289-630 Helpman E Melitz M J and S R Yeaple 2004 Export versus FDI with Heterogeneous Firms
American Economic Review 94(1) 300-316 Hoechle D Schmid M Walter W and D Yermack 2012 How much of the diversification
discount can be explained by poor governance Journal of Financial Economics 103 41-60 Hope O-K and W B Thomas 2008 Managerial empire building and firm disclosure Journal
of Accounting Research 46 (June) 591-626 Huizinga H P and J Voget 2009 International taxation and the direction and volume of cross-
border MampAs Journal of Finance 64 (3) 1217-1249 Jensen M 1986 Agency cost of free cash flow corporate finance and takeovers American
Economic Review Papers and Proceedings 76 323-329
31
Laeven L and R Levine 2007 Is there a diversification discount in financial conglomerates
Journal of Financial Economics 85 331-367 Lang L and R Stulz 1994 Tobinrsquos Q corporate diversification and firm performance Journal
of Political Economy 102 1248-1280 Mataloni R 2003 US multinational companies Operations in 2001 Survey of Current
Business 85-105 Matvos G and A Seru 2011 The dynamic tradeoff between corporate socialism and financial
market dislocation Estimates from internal capital markets Working paper University of Chicago Booth School of Business
Moeller SB and F P Schlingemann 2005 Global diversification and bidder gains A
comparison between cross-border and domestic acquisitions Journal of Banking and Finance 29 533-564
Morck R and B Yeung 1991 Why investors value multinationality Journal of Business 64
165-186
Morck R and B Yeung 2001 Why firms diversify Internatization vs agency behavior In Intangibles B Lev (ed) Oxford University Press pp 269-302
Myers S C and N S Majluf 1984 Corporate financing and investment decisions when firms have information that investors do not have Journal of Financial Economics 13 187-221
Schmid M and I Walter 2009 Do financial conglomerates create or destroy economic value
Journal of Financial Intermediation 18 193-216 Syverson C 2011 What determines productivity Journal of Economic Literature 49(2) 326-
365 Villalonga B 2004a Diversification discount or premium New evidence from the Business
Information Tracking Series Journal of Finance 59 479-506 Villalonga B 2004b Does diversification cause the diversification discount Financial
Management 33 5-27 Wooldridge JM 2010 Econometric Analysis of Cross Section and Panel Data (Second
Edition) Cambridge MA MIT Press Yamin M 1991 A reassessment of Hymerrsquos contribution to the theory of the transnational
corporation C Pitelis and R Sugden (eds) The Nature of the Transnational Firm London Routledge
32
Appendix A Variable Definitions
We obtain information regarding firmsrsquo operations by country and industry from the Bureau of Economic Analysis (BEA) unless otherwise noted Acronyms used below reflect Compustat mnemonics and thus indicate when we obtain data from the Compustat North America Fundamentals Annual database All independent variables are winsorized at 1
Dependent variable
Excess Value = The log of the ratio of actual firm value to imputed firm value Actual firm value is equal to market capitalization (MVE) + book value of total assets (AT) ndash book value of shareholdersrsquo equity (SEQ) Imputed firm value is calculated as the sum of the imputed values of a firmrsquos operations in each country-industry The imputed value for each country-industry is equal to sales generated in that country-industry times a multiple equal to the ratio of a benchmark firmrsquos actual value to total sales A benchmark firm is defined as a median single-segment firm operating in the same country-industry All multiples require at least five firms listed in the respective country-industry on Compustat (domestic) or Worldscope (foreign) with less than 10 of sales income and assets outside the country of domicile Similar to Berger and Ofek (1995) we eliminate extreme values where the ratio of actual to imputed firm value exceeds 400 or is less than 025
Independent variables
Foreign Sales = Sales made by the foreign operations of US MNCs as a percent of total firm sales
Industry Other = Sales outside a firmrsquos primary industry as a percent of total sales (SALE) Equal to zero for firms operating in a single business segment
Log Size = Log of total sales (SALE)
Debt Firm Value = Ratio of long-term debt (DLTT) to total market value (market capitalization (MVE) + book value of total assets (AT) ndash book value of shareholdersrsquo equity (SEQ))
CAPX Sales = Ratio of capital expenditures (CAPX) to total sales (SALE)
EBIT Sales = Ratio of earnings before interest and taxes (EBIT) to total sales (SALE)
RampD Sales = Ratio of research and development expenses (XRD) to total sales (SALE)
Ad Sales = Ratio of advertising expenses (XAD) to total sales (SALE)
Control Premium = A weighted-average (based on the sales in a given country as a percent of total firm sales) of the country-level control premium from Dyck and Zingales (2004)
Cost of Capital = A weighted-average (based on the sales in a given country as a percent of total firm sales) of the country-level credit rating from Institutional Investor (httpwwwinstitutionalinvestorcom) The country-level rating is available for 160 countries and ranges from 0 to 100 Higher values imply greater credit risk
33
CEO Ownership = Percent of shares owned by the CEO from Execucomp
Institution Ownership = Percent of shares owned by institutional investors from Thomson Reuterrsquos Institutional Holdings database
Powerful CEO = Indicator variable equal to one if the CEO is the only insider on the board of directors and also serves as the chairman and president based on data from RiskMetrics Directors database Equal to zero otherwise
Governance Index = Count measure of takeover defenses used in Gompers Ishii and Metrick (2003) from RiskMetrics Governance database As the governance data are not available for every year we follow Gompers Ishii and Metrick (2003) and assume that the firmsrsquo governance attributes are unchanged until publication of the subsequent series
34
Figure 1 Trends in multinational activity and industrial diversification from 1984-2008 This figure presents a plot of the mean percent of foreign sales and the mean percent of sales outside the firmrsquos primary industry (industrial diversification) from 1984 through 2008 Dotted lines above and below the primary lines represent 95 confidence intervals To compile the plot we include the full population of firms in Compustat
35
Table 1 Analysis of accounting data for firms reporting under multiple accounting standards This table provides a comparison of sales net income and assets for firms that changed accounting standards to or from US GAAP Comparisons are provided for the 66 firms that changed to or from US GAAP and for which we could obtain accounting data electronically and in English under both standards for the same fiscal year (eg the firm presented the accounting numbers originally under one set of standards and then restated the prior year numbers in the subsequent year under the new set of standards) Panel A shows the distribution of firms by year Panel B shows the other accounting standards to which US GAAP is compared Panel C shows the mean and median percent that the other accounting standard differs from US GAAP for the same fiscal year and accounting number Two samples are shown in Panel C The first is the full sample of firms with a change in accounting standards to or from US GAAP for which data was available The second is the subset of these firms that occurred during the sample window used in this study (ie excluding changes post-2008) The percent difference in sales is compared to the percent difference in net income and assets Tests of means are based on matched-pair t-tests Tests of medians are based on Wilcoxon matched-pair signed-rank tests Panel A Accounting standard changes by year
2003
4 6
2004
25 38
2005
8 12
2006
6 9
2007
7 11
2008
5 8
2009
5 8
2010
5 8
2011
1 2
66
Panel B Accounting standards compared to US GAAP
IFRS
47 71
Canadian GAAP
12 18
Israeli GAAP
2 3
Dutch GAAP
1 2
Indian GAAP
1 2
Japanese GAAP
1 2
Norwegian GAAP
1 2
Swiss GAAP
1 2
66
36
(Table 1 continued) Panel C Tests of differences between US GAAP and other accounting standards
Percent different from US GAAP
Comparison to sales (p-values)
N Mean Median
Mean Median
Full Sample (2001-2011)
Sales
66 236 000
Net Income
66 10653 647
005 lt001
Assets
66 1162 126
lt001 lt001
Study Sample (2001-2008)
Sales
55 257 000
Net Income
55 10303 589
011 lt001
Assets
55 1047 099
003 lt001
37
Table 2 Descriptive statistics Panel A reports the mean and medians of Excess Value for the multinational firms and single-segment benchmark firms as well as univariate tests across the subsamples Panel B reports summary statistics for the independent variables for the multinational firms The p-values of the tests of differences in means are based on t-tests The p-values of tests of differences in the medians are for a Wilcoxon rank-sum test All variables are defined in Appendix A Panel A Excess Value by sample Multinational All Benchmark Benchmark Subgroups Sample Firms US Foreign
Value Value Test of Diff (p-value) Value
Test of Diff (p-value) Value
Test of Diff (p-value)
Mean 0067 0006 lt001 -0028 lt001 0014 lt001 Median 0076 0000 lt001 -0025 lt001 0000 lt001 N 4950 33074 5998 27076 Panel B Distribution of independent variables for the multinational firms N Mean 25th 50th 75th StDev Foreign Sales 4950 0238 0074 0190 0363 0197 Industry Other 4950 0212 0000 0166 0414 0215 Log Size 4950 6955 5823 6989 8140 1687 Debt Firm Value 4950 0136 0009 0097 0212 0145 CAPX Sales 4950 0054 0020 0033 0054 0101 EBIT Sales 4950 0065 0031 0077 0121 0138 RampD Sales 4950 0049 0000 0013 0059 0083 Ad Sales 4950 0008 0000 0000 0005 0018
38
Table 3 BEA and Worldscope samples
This table reports details on affiliate sales for significant countries pooled from 1998-2008 Significant countries are defined as those in which the operations of US MNCs represent at least 02 of total firm sales (per the BEA) For significant countries the table compares their representation in terms of MNC affiliate sales in the BEA data to the distribution of firm observations in Worldscope US Foreign Affiliates (BEA) Foreign Firms (Worldscope)
Country
N (1)
Sales (millions)
(2)
Total Sales (3)
N (4)
Sales (million)
(5)
Total Sales (6)
Australia 1344 73305 056 859 255865 210 Belgium 894 52755 040 169 26136 021 Brazil 959 60245 046 1143 683167 560 Canada 2730 536767 407 1181 526750 432 China 922 75907 057 822 276541 227 France 1852 183942 139 540 105447 086 Germany 2182 275577 209 480 103888 085 Hong Kong 867 48100 036 157 34094 028 Ireland 478 36754 028 34 3467 003 Italy 1178 82116 062 111 47948 039 Japan 1494 148032 112 2281 1119223 917 Korea South 659 34500 026 3530 2752791 2255 Malaysia 386 26027 020 837 168683 138 Mexico 1405 113623 086 362 237173 194 Netherlands 1149 85105 064 47 22631 019 Singapore 1098 94917 072 139 32913 027 Spain 871 63094 048 129 37419 031 Sweden 712 32682 025 235 23090 019 Switzerland 726 60288 046 74 20242 017 Taiwan 592 30313 023 2949 787049 645 United Kingdom 3054 423934 321 1997 882423 723 United States (domicile) 4950 10460880 7923 na na na Other - Covered by WorldScope 6893 199394 151 9000 4057894 33 Other -Not Covered by WorldScope 713 5352 004 - - - Total 38108 13203609 1000 27076 12204834 100
39
Table 4 OLS regressions of firm excess values on the percent of foreign sales This table reports the results of OLS regressions of firm excess value on the percent of foreign sales (Foreign Sales) the percent of sales outside a firmrsquos primary industry (Industry Other) and control variables All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed) Dep Variable = Excess Value Coefficient t-stat Coefficient t-stat Coefficient t-stat Coefficient t-stat (1) (2) (3) (4) Foreign Sales 0349 419 0209 293 0189 266 0278 210 Industry Other -0134 -186 -0047 -072 -0053 -081 -0326 -325 Log Size 0066 634 0065 624 -0044 -096 Debt Firm Value -0196 -213 -0241 -260 -0685 -588 CAPX Sales 0901 425 0893 435 0488 227 EBIT Sales 1154 542 1170 547 0408 189 RampD Sales 2908 1242 2932 1237 1544 379 Ad Sales 1869 263 2003 280 -1696 -119 Year indicators No No Yes Yes Firm indicators No No No Yes R2 0017 0212 0226 0768 Observations 4950 4950 4950 4950
40
Table 5 Dynamic panel GMM model regressing firm excess values on the percent of foreign sales This table reports the result from a regression of the excess value on the percent of foreign sales (Foreign Sales) the percent of non-primary industry sales (Industry Other) and control variables using a dynamic panel GMM estimator All control variables are considered to be endogenous with the exception of the year indicator variables AR(1) and AR(2) are tests for first-order and second-order serial correlation in the first differenced residuals with the null hypothesis of no serial correlation The null hypothesis of the Hansen test of overidentification is that all instruments are valid The null hypothesis of the difference-in-Hansen test of exogeneity is that the instruments used for the equation in levels are exogenous All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed) Dep Variable = Excess Value Coef t-stat Excess Value (one lag) -0018 -082 Excess Value (two lags) -0048 -272 Foreign Sales 0371 270 Industry Other -0163 -171 Log Size 0010 123 Debt FirmValue -1135 -820 CAPX Sales 0590 342 EBIT Sales -0047 -300 RampD Sales 1376 531 Ad Sales -0751 -040 AR(1) test (p-value) 000 AR(2) test (p-value) 017 Hansen test of over-identification (p-value) 030 Difference-in-Hansen test of exogeneity (p-value) 093 Year indicators Yes Number of firms 1016 Number of observations 4090
41
Table 6 OLS regression of firm excess values on the percent of foreign sales controlling for a control premium This table reports the result of the OLS regression of firm excess value including firm and year fixed effects with an additional control variable to proxy for a country-level ownership control premium (Control Premium) All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed) Dep Variable = Excess Value Coef t -stat Foreign Sales 0303 224 Industry Other -0327 -325 Log Size -0045 -100 Debt FirmValue -0683 -585 CAPX Sales 0490 226 EBIT Sales 0409 189 RampD Sales 1545 379 Ad Sales -1689 -119 Control Premium -1004 -062 Year indicators Yes Firm indicators Yes R2 0768 Observations 4950
42
Table 7 OLS regression of firm excess values on the percent of foreign sales controlling for the cost of capital This table contains the results of an OLS regression of firm excess value including firm and year fixed effects with an additional control variable to proxy for a country-level cost of capital (Cost of Capital) All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed) Dep Variable = Excess Value Coef t -stat Foreign Sales 0255 178 Industry Other -0326 -324 Log Size -0043 -094 Debt FirmValue -0687 -589 CAPX Sales 0485 225 EBIT Sales 0406 188 RampD Sales 1539 379 Ad Sales -1703 -120 Cost of Capital 0004 039 Year indicators Yes Firm indicators Yes R2 0768 Observations 4950
43
Table 8 OLS regression of firm excess values on the percent of foreign sales controlling for aspects of corporate governance This table reports the results of two OLS regressions In the first column are the results of the firm excess value regression including firm and year fixed effect with five proxies for corporate governance included ndash the percent of shares owned by the CEO and the percent squared (CEO Ownership and CEO Ownership squared) the percent owned by institutional investors (Institution Ownership) whether there is a powerful CEO (Powerful CEO) and the Gompers Ishii and Metrick (2003) governance index (Governance Index) In the second column the regression includes the same sample and variables with the exception of the governance proxies which are excluded All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed)
Governance
No Governance (same sample)
Dep Variable = Excess Value Coef t-stat Coef t -stat Foreign Sales 0259 159 0251 154 Industry Other -0099 -080 -0099 -079 Log Size -0105 -185 -0102 -180 Debt FirmValue -0813 -478 -0817 -482 CAPX Sales 0440 252 0438 249 EBIT Sales 0457 247 0457 246 RampD Sales 1717 284 1716 286 Ad Sales -2210 -143 -2190 -143 CEO Ownership 0010 007
CEO Ownership squared -0029 -017 Institution Ownership 0047 067 Powerful CEO -0003 -014 Governance Index 0009 103
Year indicators Yes Yes Firm indicators Yes Yes R2 0811 0810 Observations 2550 2550
- Cover for paperpdf
- crrz_20130603_SMUpdf
-
9
markets (Foley Desai and Forbes 2008) In a recent study Matvos and Seru (2011) find that
industrially diversified firms perform better than non-diversified firms when external capital
markets are impaired
While these arguments suggest that MNCs enjoy certain advantages when the foreign cost of
capital is high these benefits should be reduced by the additional risk associated with operating a
business in a high cost of capital environment For example Verizon is a multinational firm
operating in a number of countries One of the higher cost of capital locations in which they
operated was Venezuela While the multinational structure of Verizon may have mitigated some
of the risks associated with operating in a higher cost of capital location the firms was unable to
eliminate all of these risks In fact in 2007 Verizonrsquos operations in that country were
nationalized resulting in a net extraordinary loss of $131 million that year8 In other words
MNC investors are likely to expect higher rates of return to compensate for higher risk which
likely offsets some (if not all) of the potential excess value premium
On the other hand multinational firms may incur a discount (ie negative excess values)
relative to a similar footprint of stand-alone firms Potential reasons for a discount largely rely on
the existence of agency costs Multinational firms tend to be larger more complex and less
transparent The combination of complexity and opacity inherent in MNCs reduces a firmrsquos
reliance on external capital as the cost of such capital increases due to agency costs (Desai et al
2004) As a result of the reduced reliance on external funds MNCs face a reduced level of
monitoring As noted by Jensen (1986) managers have incentives to increase firm size beyond
that which is optimal with overinvestment or misallocation of funds to pet projects If this lack of
monitoring is in combination with available internal capital empire building becomes easier
(Hope and Thomas 2008) Furthermore the current segment reporting standards under US 8 See the 10-K of Verizon Communications Inc dated December 31 2007 (footnote 2)
10
GAAP only require highly aggregated disclosures of foreign operations and allow for
considerable managerial discretion As a result operating in multiple countries makes it easier
for management to hide poor performance ndash either their own or that of a division ndash such that low
quality managers are more likely to be retained and overcompensated and poorly performing
divisions retained longer than optimal Other reasons one might expect a discount to be applied
to multinational firms include higher coordination costs (eg coordinating across different
cultures and languages) as well as additional risks These risks include exposure to multiple
political regimes legal regimes economic regulations and currency fluctuations
Past studies investigate whether foreign operations of US firms enhance or reduce firm
value However all of these studies benchmark the value of the foreign operations of an MNC to
US domestic firms For instance Denis et al (2002) determine the implied value of a US
MNC using domestic (US) single-segment firms in the same industry as a benchmark Studies
using Tobinrsquos Q to investigate valuation effects of MNCs also rely on domestic benchmarks
(eg Morck and Yeung 1991 Morck and Yeung 2001 Gande Schenzler and Senbet 2009)
However using a US domestic firm as a benchmark for the value of foreign operations
implicitly makes two assumptions First this method assumes that the risks and expected growth
rates of foreign operations are equivalent to those of domestic operations This assumption is
inconsistent with the findings of Hail and Leuz (2009) which provides evidence that the cost of
capital varies substantially across countries Second this method assumes that there is excess
domestic capacity to allow for expansion with profitability similar to current domestic
operations We relax these assumptions by measuring the imputed value of a foreign component
of a US firm as if that component operated autonomously within that foreign country (rather
than in the US) Specifically our multiples allow the cost of capital and expected growth rates
11
to vary across not just industry but also country Furthermore our counterfactual does not
require the assumption that operations be relocated mitigating the production and sales capacity
concerns
3 Measuring excess value
31 Description of excess value
Figure 1 shows the average percent of foreign sales (based on segment disclosures under US
GAAP) across firms through time and illustrates that firms are continuing to expand
internationally9 This expansion highlights the importance of understanding whether the decision
to operate internationally is on average a value enhancing corporate strategy We contribute to
this understanding by evaluating whether the firm as a whole is worth more or less than its
imputed value (ie the firm is valued in excess of the sum of its individual components)
We measure the excess value of a firm as the logarithm of the ratio of actual firm value to
imputed value based on the method used in Berger and Ofek (1995) The imputed value is the
hypothetical value of the MNC under the assumption that its country-industry components
operate as independent entities A key innovation of our study is an alternative approach to
imputing the value of the foreign operations of an MNC Components of MNCs operating in a
given industry and country are imputed using the value of single-segment firms operating in the
same industry and country Thus we ensure the discount rates and expected growth rates are
applicable to each given industry and location in estimating the implied values of the MNC
segments
9 The discontinuity in industrial diversification (the percent of sales outside the primary industry) between 1997 and 1998 is the result of a change in segment reporting rules
12
The actual firm values are observable for all firms in our sample The hypothetical firm
values for multi-segment firms are imputed using market value to sales ratios herein referred to
as lsquomultiplesrsquo of the median single-segment firm operating in the same country-industry Thus
these single country-industry firms serve as benchmarks for an MNCrsquos operations within that
same country-industry The imputed value of the country-industry component can be viewed as
an estimate of what that component would be worth if it operated independently The sum of the
imputed values across all country-industry components represents the hypothetical value that the
multi-segment firm would be worth if it were broken apart
32 Data
Four primary data sources play a distinct role in carrying out our analysis i) Bureau of
Economic Analysis (BEA) data ii) Compustat Segment data iii) Worldscope data and iv)
Compustat Fundamentals Annual data Broadly speaking BEA data provide information about
the diversified nature of MNCs Compustat Segment data provide information about the
industrial diversification of US domiciled firms Worldscope data are used to compute
multiples for single-segment foreign firms that serve as benchmarks for the foreign operations of
MNCs (ie foreign benchmarks) and Compustat Fundamentals Annual data are used to
compute multiples for single-segment domestic firms that serve as benchmarks for the domestic
operations of multinational firms We also use the latter data source to construct our primary
control variables
13
Key to the execution of our study is our access to BEA data Federal law requires US-
domiciled MNCs to report certain financial and operating data to the BEA10 With regard to
observing the country-industry operations of MNCs use of the BEA data overcomes two
important limitations of Compustat Segment data First Compustat does not consistently identify
the specific location or scale of MNCsrsquo foreign operations11 Second Compustat does not
identify the industry activities in each country of operation This latter point is particularly
limiting for MNCs because domestic industry activity need not mirror foreign industry activity
An MNC generating an equal proportion of sales in two industries in the domestic market need
not generate industry sales in equal proportions in every foreign market12 The lack of detail and
consistency in Compustat Segment data arises because segment reporting is highly aggregated
and firms exercise substantial discretion in defining segments under Accounting Standards
Codification 280 (ASC 280) (eg Villalonga 2004a and Bens Berger and Monahan 2011)
The BEA defines an MNC as the combination of a single US entity called the US parent
and at least one foreign affiliate ndash that is these firms have a physical presence outside the US
due to FDI Since 1982 MNCs have completed a mandatory and confidential lsquoSurvey of US
Direct Investment Abroadrsquo for the domestic operation and the operations of each foreign
affiliate defined as a foreign entity in which the US parent holds at least a 10 percent equity
interest (directly or indirectly) The data are reported in US dollars on a fiscal year basis and in
10 BEA surveys are conducted pursuant to the International Investment and Trade in Services Survey Act (PL 94-472 90 Stat 2059 22 USC 3101-3108) See httpwwwbeagovsurveysdiasurvhtm and Mataloni (2003) for more detailed information on the BEA data 11 Exhibit 21 disclosures of material subsidiaries and their locations in SEC 10-K filings could potentially help overcome the inability to observe the location of the firmrsquos foreign operations from segment disclosures However at least three limitations remain (1) for industrially diversified multinational firms the researcher would be required to make an assumption about the proportion of industrial diversification in each country (2) Exhibit 21 provides no information about the scale of activity in each jurisdiction and (3) managerial discretion is used to determine a lsquomaterialrsquo subsidiary under Section 601 of SEC Regulation S-K 12 In fact the expectation that a multinational firm would exhibit different industry membership in different countries is consistent with the theory of vertical foreign direct investment where firms separate their various value chain activities and locate them in the most favorable country (see eg Hanson Mataloni and Slaughter 2005)
14
accordance with US Generally Accepted Accounting Principles (US GAAP) For each year
we observe the sales industry composition and location of not only the parent but also each
affiliate13
The procedures we use to construct our sample are similar to those used by Berger and Ofek
(1995) and Denis et al (2002) Using firm-years appearing in Compustat Fundamentals Annual
and Compustat Segment data we obtain a sample of firms domiciled in the US with total sales
exceeding $20 million We exclude firms in the financial service (SIC codes 6000 to 6999) and
utility (SIC codes 4000 to 4999) industries and eliminate firm-years when the sum of segment
sales (Compustat geographic or Compustat business) is not within 1 of total firm sales for that
year We restrict our sample period to include fiscal years beginning after December 15 1997 as
ASC 280 Segment Reporting substantially altered the definition of a reporting segment under
US GAAP This restriction ensures that Compustat Segment data reflects the use of a consistent
accounting standard Finally we require that our MNCs appear in both Compustat and BEA
data The requirement that our sample firms appear in the BEA database ensures that the firms
have observable international operations In sum these procedures result in 1166 multinational
firms incorporated in the US with 4950 observations between 1998 and 2008 that meet all of
our data requirements
33 Measuring excess value
The dependent variable in our study is excess firm value (Excess Value) defined as the
logarithm of the ratio of actual firm value to imputed firm value (Berger and Ofek 1995)
13 We obtain sales industry membership and country location for affiliates from one of two sources When affiliates exceed the BEA-determined reporting thresholds (ie if their assets sales or net income (loss) exceed $7 million in 1999 $30 million in 2000 through 2003 $10 million in 2004 and $40 million in 2005 through 2008) they must file their own detailed survey When affiliates fall below the thresholds we obtain our data from an attachment to the parent survey
15
Appendix A provides the definition of this and all other variables used in our analysis We
observe actual firm value equal to a firmrsquos market value of equity plus book value of liabilities
(ie book value of total assets minus book value of total equity) using Compustat Fundamentals
Annual data We calculate imputed firm value as the sum of the imputed values of a firmrsquos
operations in each country-industry Our method for imputing the value of the separate
components of a firm can best be described in three steps First we obtain total sales generated
by a firm for each country-industry in which it operates14 Second we obtain multiples (market
value to sales ratios) for benchmark firms operating in those same country-industries Third we
multiply a firmrsquos country-industry sales by the applicable median country-industry multiple to
obtain the imputed market value for each country-industry operation We perform each step
annually
Conceptually imputed values can be based on assets (ie Tobinrsquos Q) net income or sales
multiples We restrict ourselves to sales multiples for two reasons First using value to sales
ratios maintains consistency with the prior research on the excess value implications of
multinational operations (eg Denis et al 2002) Second as our method uses accounting data
for foreign companies to compute country-industry multiples the accounting numbers we use
need to be consistently measured across firms using different accounting standards We find that
sales data are measured most consistently
To assess the comparability of sales relative to either net income or assets across various
accounting standards we examine all firms listed on Worldscope as changing to or from US
14 Specifically we allocate total sales from Compustat to each component based on the proportion of BEA sales (after eliminating intercompany sales) occurring in each country-industry This step ensures that our total firm sales do not deviate from a firmrsquos total sales as reported in the 10-K
16
GAAP between 2005 and 201115 We obtain accounting data for the year of the change and the
prior year where electronically available in English We are able to obtain data for 66 firms
changing standards as detailed in Table 1 The distribution of years for which accounting data
are available under two different accounting standards for the same fiscal year is shown in Panel
A The years shown are the year prior to the change as these accounting numbers were provided
under the original standard and then subsequently restated in the following year under the new
standard for comparative purposes The majority of the shifts were to or from IFRS (71)
though seven other accounting standards are noted (Panel B) We examine the percent that the
non-US GAAP reported number differs from the US GAAP reported number in Panel C to
assess the comparability of these three summary accounting numbers across various accounting
standards Examining both the full sample as well as the subset that overlaps our study we find
that sales are more consistently measured than net income or assets
The sum of the imputed values across the country-industry components of an MNC is an
estimate of the value of a portfolio of unrelated businesses that mirror the related businesses of
the firm Consequently a comparison of the actual firm value with the imputed firm value is a
measure of how a multinational network affects firm value We provide descriptive statistics for
Excess Value for our sample of 4950 MNC firm-years in Table 2 Panel A This table also
includes univariate comparisons of the values between the MNC firms and the domestic single-
segment and foreign single-segment benchmark firms The Excess Value measure is significantly
higher for MNCs suggesting a premium in firm value for multinational firms relative to the
benchmark firms in total as well as the domestic and foreign subgroups (p-values for tests of
15 Due to the hand collection efforts required and the limitation on electronic filings we began our search with changes documented in 2005 which generally require filings from 2004
17
means and medians are lt 001)16 These results provide some initial evidence that operating in
multiple countries is positively associated with firm value
To estimate country-industry multiples we rely on Worldscope financial data on foreign
firms and Compustat Fundamentals Annual for domestic firms We restrict the firms to include
only those with at least 90 of sales income and assets inside the country of domicile (ie
those that do not report significant multinational activity) and that operate in a single industry
We refer to these firms as benchmark firms (either foreign or domestic depending on the country
of domicile) these firms do not appear in any of our regressions We report the number of
benchmark firms during our sample period in Table 2 Panel A The industry criterion is based on
the two-digit SIC code17 For every country that has at least five firms in the respective industry
and year we use the median ratio of market value to sales18 This ratio is the country-industry
multiple ndash an input required to compute imputed values
To determine the country-industry composition of MNCs we rely on BEA data In Table 3
we report the aggregate number of foreign affiliates and total sales by country as provided by
the BEA data for our sample of 4950 MNCs The specific countries tabulated are those that
represent at least 02 percent of total firm sales (pooled across all years) in our sample The top
five foreign countries in which MNCs generate sales are Canada United Kingdom Germany
France and Japan Table 3 also provides a comparison of the number of foreign benchmark
firms available in Worldscope in the specific countries in which our sample of MNCs generate
16 Similar to Berger and Ofek (1995) the median values differ slightly from zero for the benchmark single-segment firms due to the truncation of extreme excess values 17 BEA reporting constrains us to the two-digit SIC code level 18 When five firms are not available for a given country and industry we follow Berger and Ofek (1995) and move to a more general industry definition (eg one-digit SIC code) This procedure results in a mean (median) 630 (757) of MNC sales matched at the two-digit level
18
the majority of their sales Country coverage in Worldscope is not available for countries
representing 004 percent of total firm sales (per BEA)
4 Empirical results
41 Independent variables
Recall that our objective is to examine the overall relation between excess firm value and
multinational operations Our proxy for the extent of multinational operations is the percentage
of total firm sales made by foreign operations (Foreign Sales) By this measure the extent of
multinational activity in our sample of MNCs is non-trivial ndash Table 2 Panel B shows that
approximately 24 percent of sales are generated by foreign operations on average Our first
analysis estimates ordinary least square regressions of Excess Value on Foreign Sales and
control variables (discussed below)
We include variables in our regression to control for other potential determinants of excess
value These are the percentage of sales made by the firm outside its primary industry (Industry
Other) to control for any relation between industrial diversification and excess value as in Berger
and Ofek (1995) For this purpose we obtain industry sales using Compustat Segment data19 A
firmrsquos primary industry is the industry in which the firm generates the majority of its sales and
we determine industry sales at the business segment level We set Industry Other equal to zero
for firms that operate in a single business segment
Consistent with prior research we also include controls for firm size (Log Size) the ratio of
long term debt to total market value (Debt Firm Value) the ratio of capital expenditures to total
sales (CAPX Sales) the ratio of earnings before interest and taxes to total sales (EBIT Sales)
the ratio of research and development expenses to total sales (RampD Sales) and the ratio of 19 Our results are unaffected by using BEA data to measure Industry Other
19
advertising expenses to total sales (Ad Sales) Table 2 Panel B provides the distribution of the
control variables All independent variables are Winsorized at 1 In our OLS regressions we
use heteroskedasticity and autocorrelation consistent robust standard errors with clustering at the
firm level
42 Primary regression analysis
In this section we examine the relation between firm excess value and the amount of foreign
activity at US MNCs Table 4 contains the results of regressions of excess value on Foreign
Sales for multiple specifications In column (1) Excess Value is regressed on both the percent of
foreign sales (Foreign Sales) and the percent of sales outside the firmrsquos primary industry
(Industry Other) We add the control variables to the regression in column (2) and year
indicators in column (3) In all three specifications the coefficient on the percent of foreign sales
remains significantly positive (with a p-value lt 001) For a sense of economic magnitude in
column (3) a one standard deviation (197) increase in the percent of foreign sales (Foreign
Sales) is associated with a 41 (equal to (e0349-1) times 0197) increase in firm excess value The
effect is larger for the other two columns ndash 82 increase in firm excess value in column (1) and
46 in column (2)20
Our finding of a premium reconciles with the international trade literature which finds that
firms engaging in international trade are more productive than those that do not (see Helpman
2006 and Syverson 2011 for reviews) Due to data limitations it is difficult to accurately
measure TFP at the firm level especially for firms which operate in multiple industries andor
20 Using the alternative strategy that employs US domiciled single-segment firms as the benchmark for the imputed value of the foreign operations Denis et al (2002) find that greater foreign activity results in a discount to firm value Using the Denis et al (2002) method to calculate excess value in our sample we obtain a significantly negative regression coefficient on Foreign Sales using the models in Table 4 columns (2) and (3) (the coefficient is not significant using the model in column (1) results untabulated)
20
countries As an alternative method to evaluate whether our finding of a premium is simply the
result of more productive firms also having more multinational operations we investigate
whether the premium is simply an artifact of innate characteristics of firms that choose to invest
abroad For example Helpman et al (2004) present a simple model with heterogeneous
productivity endowments The firms that receive the highest productivity endowments are the
ones capable of paying the fixed costs to establish a foreign subsidiary
To the extent that productivity advantages create excess value one would expect the type of
firms that choose to invest more abroad would be valued at a premium even in the absence of
their foreign investments To control for time invariant firm characteristics (eg innate
productivity advantages) and hence a potential source of endogeneity we add firm-level fixed
effects in column (4) of Table 4 We continue to find that the extent of foreign operations
(Foreign Sales) is positively associated with Excess Value Across all specifications the sign
on all significant control variables is consistent with prior research (eg Berger and Ofek 1995
and Denis et al 2002)
The finding of a premium makes a significant contribution to the relatively sparse literature
on the value effects of multinational activity Our measure of imputed value expands upon the
methods used by Berger and Ofek (1995) and Denis et al (2002) but allows us to answer a
different question than Denis et al (2002) Holding constant the extent of industrial
diversification we ask whether MNCs are more valuable than a portfolio of benchmark firms of
a similar geographic footprint In contrast Denis et al (2002) ask whether holding constant the
extent of industrial diversification MNCs are more valuable than a portfolio of domestic (US)
benchmark firms We believe that asking whether an MNCrsquos foreign (domestic) operations are
21
more valuable than those of a foreign (domestic) firm is a more appropriate way to assess the
value implications of a multinational network
5 Robustness
51 Endogeneity related to dynamic relations
Prior research argues that the relation between industrial diversification and excess firm
value could be endogenous through dynamic relationships based on observing past outcomes
Managers of firms likely choose to enter or exit industries or geographic regions based upon
their previous performance For example Campa and Kedia (2002) find that firms are more
likely to enter new industries when prospects in their current lines of business are deteriorating
Similar dynamics could also drive a relation between excess value and FDI We assess the
robustness of the previous results by estimating a dynamic panel data model which allows us to
control for dynamic endogeneity in addition to firm level unobserved heterogeneity We employ
the GMM estimator for dynamic panel models developed by Arellano and Bond (1991) Arellano
and Bover (1995) and Blundell and Bond (1998) Our results from this analysis confirm our
previous findings that firms with a larger degree of foreign operations have positive excess
values
Dynamic panel data models are generalizations of the traditional fixed effects model where
lags of the dependent variable are added to the right hand side of the equation These additional
lags control for the impact a firmrsquos past performance has on its current performance through the
channels discussed above The GMM estimator for dynamic panel models developed by
Arellano and Bond (1991) starts by differencing the data at the firm level in neighboring time
periods to eliminate any firm specific time invariant unobserved heterogeneity that may be
22
present Eliminating the firm specific fixed effect by differencing the data with lags of the
dependent variable on the right hand side introduces correlation between the new error term and
the differenced explanatory variables This problem is solved by applying the standard GMM
estimator using lags of both the original explanatory variables and excess value as instruments
We note that the dynamic panel data framework allows us to treat both international and
industrial diversification (as well as the other firm-specific control variables) as endogenous
Following Hoechle et al (2012) we also allow contemporaneous values of these variables to be
treated as endogenous
Lagged values of the explanatory variables are valid instruments in a dynamic panel data
model when the model is dynamically complete (eg Wooldridge 2010) A dynamic panel data
model is dynamically complete conditional on the unobserved effect if enough lags of both the
explanatory and dependent variable are included to make the resulting error term uncorrelated
with all the right hand side variables Under this assumption any lags for the explanatory
variables beyond this can be used within the GMM estimator as instruments Like Hoechle et al
(2012) we add two years of lags of a firmrsquos excess value to the right hand side assuming that
this is enough lags to control for dynamic feedback effects We then use past values of all
variables beyond the second lag up to a total of seven years to act as instruments
The results of the dynamic panel data estimator are reported in Table 5 These results confirm
the previous finding of a positive relation between the degree of international operations and
excess value We note that the size and significance level of the coefficient on Foreign Sales
diversification are robust to changes in the number of lags used as instruments The Arellano and
Bond (1991) estimator includes testing diagnostics for correct specification of the model If the
model is correctly specified differences in the residuals from the levels equation should be
23
serially uncorrelated at lag one but correlated at lag two The p-values for tests of serial
correlation of the residuals at lags 1 and 2 are reported in Table 5 and these indicate that the
model is not rejected We also report in Table 5 the Hansen test for overidentification which is a
test of the null hypothesis that the instruments are valid The p-value for this test is 030
indicating that we fail to reject this null hypothesis Finally we report another test for exogeneity
known as the difference-in-Hansen test which tests the null hypothesis that the instruments in
the original equation are valid We fail to reject this null hypothesis as well
52 Identifying value effects of multinational operations via cross-border acquisitions
Another alternative to investigating a causal link between foreign direct investment and firm
value is an event study of foreign acquisitions21 Acquired foreign targets represent only a small
fraction of MNCsrsquo worldwide sales in the year of the acquisition ndash less than 1 percent in our
sample Additionally cross-border acquisitions are not the primary source of international
expansion for the average firm We find that foreign acquisitions account for between 18 percent
and 55 percent of the year-over-year change in foreign sales with the remainder of the growth
coming from either newly established entities (ie greenfield investments) or growth in existing
operations22 These results suggest that acquisitions are not a material source of growth for the
21 Doukas and Travlos (1988) find positive returns when the acquirer is not operating in the target firmrsquos country and insignificant returns when the acquirer is operating in the target firmrsquos country or expanding internationally for the first time Morck and Yeung (1991) find marginal evidence of positive returns for firms with intangible assets Finally Doukas (1995) finds positive returns only for firms with average Tobinrsquos q ratios greater than one More recent work also using an event study methodology provides mixed evidence on valuation effects of global diversification through acquisitions Dos Santos Errunza and Miller (2008) find insignificant returns for firms with already established foreign operations firms establishing initial foreign operations and firms acquiring targets in related industries Doukas and Kan (2006) and Moeller and Schlingemann (2005) find reductions to shareholder value while Francis Hasan and Sun (2008) find positive returns for acquirers of targets in segmented financial markets 22 New foreign affiliates to an MNC group each year report on the BEA survey whether they entered the group as an acquisition or a newly established entity (ie greenfield investment) Affiliates under the lowest reporting threshold do not report this information resulting in our inability to precisely identify the source of 33 percent of the increase in firm sales as either acquisition or greenfield
24
typical US MNC suggesting that an event study would lack power and generalizability For
these reasons we do not implement an event study
53 Control premium
A control premium or private benefits of control occurs when ldquosome value whatever the
source is not shared among all the shareholders in proportion of the shares owned but it is
enjoyed exclusively by the party in controlrdquo (Dyck and Zingales 2004 p 541) Using stock
prices of traded shares held by minority shareholders in determining the market value to sales
ratios of our benchmark firms could underestimate the true value of our benchmark firms if those
firms face large control premiums This in turn could induce an excess value premium because
the imputed value of these country-industry segments would be artificially low The existence of
such benefits is more prevalent in some countries than others and could affect the value of an
MNCrsquos operations in such domains We use the country-level control premiums from Dyck and
Zingales (2004) and using each firmrsquos country sales as a percent of total firm sales we obtain a
weighted average control premium that each MNC faces (Control Premium) For countries not
included in the Dyck and Zingales (2004) analysis we set the control premium equal to zero23 In
Table 6 we show that Foreign Sales remains positive and highly significant (p-value lt 001)
when we include the proxy for control premium As a result it does not appear that the excess
value premium is driven by MNCs operating in countries where majority shareholders are able to
extract large control premiums
23 Results are robust to replacing missing values with the mean control premium of the respective GDP quartile
25
54 Cost of capital
As discussed in Section 2 when countries have shallow capital markets a multinational
network may provide the benefit of a lower cost of capital for investments through an internal
capital market as well as through better access to the US capital market Prior research
establishes that there is substantial variation in the cost of capital across countries (Erb Harvey
and Viskanta 1996 and Hail and Leuz 2009) To determine whether the documented premium
is due to cost of capital advantages we include a control variable to capture the difference
between the MNCrsquos cost of capital and the cost of capital faced by its competitors in the global
markets in which it operates
We compute our Cost of Capital proxy in two steps First we obtain the country-year credit
rating published by Institutional Investor24 The rating ranges from 0 to 100 where higher values
imply that a country has a higher default risk Country-level credit risk is a reasonable predictor
of expected equity market returns and volatility (Erb et al 1996) and exhibits a highly
significant correlation with international accounting-based estimates of imputed cost of capital
(Hail and Leuz 2009) Second using each firmrsquos country sales as a percent of total firm sales
we obtain a weighted average credit risk rating across the countries in which each firm operates
A higher value implies an MNC operates in countries with higher cost of capital In Table 7 we
do not find a result consistent with MNCs gaining an advantage from their access to low cost
capitalndash Cost of Capital is not significantly associated with Excess Value25
24 We thank Cam Harvey for providing these data 25 Results are consistent using cost of capital as defined in Erb et al 1996
26
55 Corporate governance
In light of the recent finding that corporate governance mitigates the diversification discount
we examine whether the premium to multinational activity is sensitive to controls for corporate
governance Hoechle et al (2012) find that the magnitude of the industrial diversification
discount is decreased and in several specifications no longer significant in the presence of
governance proxies In the first column of Table 8 we include the five governance proxies that
are consistently significantly associated with excess value in Hoechle et al (2012) ndash the percent
of CEO ownership (CEO Ownership and CEO Ownership squared) the percent of institutional
ownership (Institution Ownership) whether the firm has a powerful or influential CEO
(Powerful CEO) and the governance index from Gompers Ishii and Metrick (2003)
(Governance Index)
As documented in Table 8 including these control variables reduces our sample size by
almost 50 (to 2550 observations) and tends to exclude the smaller firms from the analysis
None of the governance proxies are significant Relative to column (4) of Table 4 the coefficient
on Foreign Sales is similar in magnitude but is no longer statistically significant at
conventional levels To investigate whether the decreased significance is due to the smaller
sample or the inclusion of the governance variables we re-estimate the regression for the same
2550 observations after removing the governance controls The coefficient and t-statistic are
virtually identical suggesting that the loss of significance is due to the reduced sample and not
the governance controls We do not find evidence to suggest that variation in corporate
governance practices drives variation in excess value for US MNCs
27
6 Conclusion
Using a new method to quantify the valuation effects of foreign direct investment by US
domiciled firms we find robust evidence of a premium in excess value associated with the
degree of multinational operations Our method builds on that used in previous studies
examining firm excess value (eg Berger and Ofek 1995 and Denis et al 2002) by
incorporating the location in addition to the industry of each corporate affiliate This allows
important factors such as growth and cost of capital to vary across countries as well as industries
Examining a sample of multinational firms we find that a one-standard deviation (197)
increase in foreign operations is associated with an increase of between 41 and 82 in excess
value minus both a statistically and economically significant finding
This result complements the finding of the international trade literature that more productive
firms are more likely to operate internationally However the premium remains after including
firm fixed effects to control for persistent firm traits (eg productivity)
The finding of a premium stands up to a succession of robustness tests designed to evaluate
whether the result arises from alternative methods or explanations First we estimate a dynamic
panel data model using the generalized method of moments (GMM) estimator to simultaneously
account for the potential endogeneity of FDI industrial diversification and firm value Second
we include a control variable to proxy for whether the firm operates in countries with larger
control premiums which could result in understated multiples and mechanically induce a
premium Third we include a control variable to measure the cost of capital across the countries
in which an MNC operates to evaluate whether a relatively low cost of capital borne by
multinational firms is a meaningful source of the premium Fourth we control for several
corporate governance proxies which have been found to be correlated with excess values (and
28
industrial diversification) We fail to find evidence supporting any of these alternative
explanations ndash the premium in excess value arising at firms with greater multinational operations
remains significant
Overall our new method of estimating excess values for multinational firms provides strong
evidence of a statistically and economically significant premium associated with increased
international operations The premium is robust to a host of specifications and controls designed
to evaluate alternative explanations We leave to future research the questions of whether the
premium to US multinational firms applies more broadly to other countries of domicile and
what factors influence the premium
29
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Ammann M Hoeschel D and M Schmid 2012 Is there really no conglomerate discount
Journal of Business Finance and Accounting 39(12) 264-288 Arellano M and Bond S R 1991 Some tests of specification for panel data Monte Carlo
evidence and an application to employment equations Review of Economic Studies 58 (2) 277-298
Arellano M and Bover O 1995 Another look at instrumental variables estimation of error
components models Journal of Econometrics 68 (1) 29-61 Bens D A Berger P G and S J Monahan 2011 Discretionary disclosure in financial
reporting An examination comparing internal firm data to externally reported segment data The Accounting Review 86(2) 417-449
Berger P G and E Ofek 1995 Diversificationrsquos effect on firm value Journal of Financial
Economics 37 39-65 Blundell R and Bond S R 1998 Initial conditions and moment restrictions in dynamic panel
data models Journal of Econometrics 87(1) 115-144 Campa J M and S Kedia 2002 Explaining the diversification discount The Journal of
Finance 57 (4) 1731-1762 Denis D J Denis D K and K Yost 2002 Global diversification industrial diversification
and firm value The Journal of Finance 57 (5) 1951-1979 Desai M A Foley C F and J R Hines 2004 A multinational perspective on capital structure
choice and internal capital markets The Journal of Finance 59 (6) 2451-2487 Dos Santos MB V Errunza and D Miller 2008 Does corporate international diversification
destroy value Evidence from cross-border mergers and acquisitions Journal of Banking and Finance 32(12) 2716-2724
Doukas JA 1995 Overinvestment Tobinrsquos q and gains from foreign acquisitions Journal of
Banking and Finance 19 1285-1303 Doukas J and N G Travlos 1988 The effect of corporate multinationalism on shareholdersrsquorsquo
wealth evidence from international acquisitions Journal of Finance 43(5) 1161-1175 Doukas JA and O B Kan 2006 Does global diversification destroy firm value Journal of
International Business Studies 37(3) 352-371
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Dunning J and A Rugman 1985 The influence of Hymerrsquos dissertation on the theory of foreign direct investment The American Economic Review 75(2) 228-232
Dyck A and L Zingales 2004 Private benefits of control An international comparison The
Journal of Finance 59 (2) 537-600 Erb C Harvey C and T Viskanta 1996 Expected returns and volatility in 135 countries The
Journal of Portfolio Management 46-58 Foley C F Desai M A and K J Forbes 2008 Financial constraints and growth
Multinational and local firm responses to currency depreciations Review of Financial Studies 21 (6) 2857-2888
Francis B I Hasan and X Sun 2008 Financial market integration and the value of global
diversification Evidence for US acquirers in cross-border mergers and acquisitions Journal of Banking and Finance 321522-1540
Gande A Schenzler C and L Senbet 2009 Valuation effects of global diversification
Journal of International Business Studies 1515-1532 Gompers P Ishii J and A Metrick 2003 Corporate governance and equity prices Quarterly
Journal of Economics 118(1) 107-156 Hail L and C Leuz 2009 Cost of capital effects and changes in growth expectations around
US cross-listings Journal of Financial Economics 93 428-454 Hanson G Mataloni R and M Slaughter 2005 Vertical production networks in multinational
firms Review of Economics and Statistics 87(4) 1-15 Helpman E 2006 Trade FDI and the Organization of Firms Journal of Economic Literature
44(3) 289-630 Helpman E Melitz M J and S R Yeaple 2004 Export versus FDI with Heterogeneous Firms
American Economic Review 94(1) 300-316 Hoechle D Schmid M Walter W and D Yermack 2012 How much of the diversification
discount can be explained by poor governance Journal of Financial Economics 103 41-60 Hope O-K and W B Thomas 2008 Managerial empire building and firm disclosure Journal
of Accounting Research 46 (June) 591-626 Huizinga H P and J Voget 2009 International taxation and the direction and volume of cross-
border MampAs Journal of Finance 64 (3) 1217-1249 Jensen M 1986 Agency cost of free cash flow corporate finance and takeovers American
Economic Review Papers and Proceedings 76 323-329
31
Laeven L and R Levine 2007 Is there a diversification discount in financial conglomerates
Journal of Financial Economics 85 331-367 Lang L and R Stulz 1994 Tobinrsquos Q corporate diversification and firm performance Journal
of Political Economy 102 1248-1280 Mataloni R 2003 US multinational companies Operations in 2001 Survey of Current
Business 85-105 Matvos G and A Seru 2011 The dynamic tradeoff between corporate socialism and financial
market dislocation Estimates from internal capital markets Working paper University of Chicago Booth School of Business
Moeller SB and F P Schlingemann 2005 Global diversification and bidder gains A
comparison between cross-border and domestic acquisitions Journal of Banking and Finance 29 533-564
Morck R and B Yeung 1991 Why investors value multinationality Journal of Business 64
165-186
Morck R and B Yeung 2001 Why firms diversify Internatization vs agency behavior In Intangibles B Lev (ed) Oxford University Press pp 269-302
Myers S C and N S Majluf 1984 Corporate financing and investment decisions when firms have information that investors do not have Journal of Financial Economics 13 187-221
Schmid M and I Walter 2009 Do financial conglomerates create or destroy economic value
Journal of Financial Intermediation 18 193-216 Syverson C 2011 What determines productivity Journal of Economic Literature 49(2) 326-
365 Villalonga B 2004a Diversification discount or premium New evidence from the Business
Information Tracking Series Journal of Finance 59 479-506 Villalonga B 2004b Does diversification cause the diversification discount Financial
Management 33 5-27 Wooldridge JM 2010 Econometric Analysis of Cross Section and Panel Data (Second
Edition) Cambridge MA MIT Press Yamin M 1991 A reassessment of Hymerrsquos contribution to the theory of the transnational
corporation C Pitelis and R Sugden (eds) The Nature of the Transnational Firm London Routledge
32
Appendix A Variable Definitions
We obtain information regarding firmsrsquo operations by country and industry from the Bureau of Economic Analysis (BEA) unless otherwise noted Acronyms used below reflect Compustat mnemonics and thus indicate when we obtain data from the Compustat North America Fundamentals Annual database All independent variables are winsorized at 1
Dependent variable
Excess Value = The log of the ratio of actual firm value to imputed firm value Actual firm value is equal to market capitalization (MVE) + book value of total assets (AT) ndash book value of shareholdersrsquo equity (SEQ) Imputed firm value is calculated as the sum of the imputed values of a firmrsquos operations in each country-industry The imputed value for each country-industry is equal to sales generated in that country-industry times a multiple equal to the ratio of a benchmark firmrsquos actual value to total sales A benchmark firm is defined as a median single-segment firm operating in the same country-industry All multiples require at least five firms listed in the respective country-industry on Compustat (domestic) or Worldscope (foreign) with less than 10 of sales income and assets outside the country of domicile Similar to Berger and Ofek (1995) we eliminate extreme values where the ratio of actual to imputed firm value exceeds 400 or is less than 025
Independent variables
Foreign Sales = Sales made by the foreign operations of US MNCs as a percent of total firm sales
Industry Other = Sales outside a firmrsquos primary industry as a percent of total sales (SALE) Equal to zero for firms operating in a single business segment
Log Size = Log of total sales (SALE)
Debt Firm Value = Ratio of long-term debt (DLTT) to total market value (market capitalization (MVE) + book value of total assets (AT) ndash book value of shareholdersrsquo equity (SEQ))
CAPX Sales = Ratio of capital expenditures (CAPX) to total sales (SALE)
EBIT Sales = Ratio of earnings before interest and taxes (EBIT) to total sales (SALE)
RampD Sales = Ratio of research and development expenses (XRD) to total sales (SALE)
Ad Sales = Ratio of advertising expenses (XAD) to total sales (SALE)
Control Premium = A weighted-average (based on the sales in a given country as a percent of total firm sales) of the country-level control premium from Dyck and Zingales (2004)
Cost of Capital = A weighted-average (based on the sales in a given country as a percent of total firm sales) of the country-level credit rating from Institutional Investor (httpwwwinstitutionalinvestorcom) The country-level rating is available for 160 countries and ranges from 0 to 100 Higher values imply greater credit risk
33
CEO Ownership = Percent of shares owned by the CEO from Execucomp
Institution Ownership = Percent of shares owned by institutional investors from Thomson Reuterrsquos Institutional Holdings database
Powerful CEO = Indicator variable equal to one if the CEO is the only insider on the board of directors and also serves as the chairman and president based on data from RiskMetrics Directors database Equal to zero otherwise
Governance Index = Count measure of takeover defenses used in Gompers Ishii and Metrick (2003) from RiskMetrics Governance database As the governance data are not available for every year we follow Gompers Ishii and Metrick (2003) and assume that the firmsrsquo governance attributes are unchanged until publication of the subsequent series
34
Figure 1 Trends in multinational activity and industrial diversification from 1984-2008 This figure presents a plot of the mean percent of foreign sales and the mean percent of sales outside the firmrsquos primary industry (industrial diversification) from 1984 through 2008 Dotted lines above and below the primary lines represent 95 confidence intervals To compile the plot we include the full population of firms in Compustat
35
Table 1 Analysis of accounting data for firms reporting under multiple accounting standards This table provides a comparison of sales net income and assets for firms that changed accounting standards to or from US GAAP Comparisons are provided for the 66 firms that changed to or from US GAAP and for which we could obtain accounting data electronically and in English under both standards for the same fiscal year (eg the firm presented the accounting numbers originally under one set of standards and then restated the prior year numbers in the subsequent year under the new set of standards) Panel A shows the distribution of firms by year Panel B shows the other accounting standards to which US GAAP is compared Panel C shows the mean and median percent that the other accounting standard differs from US GAAP for the same fiscal year and accounting number Two samples are shown in Panel C The first is the full sample of firms with a change in accounting standards to or from US GAAP for which data was available The second is the subset of these firms that occurred during the sample window used in this study (ie excluding changes post-2008) The percent difference in sales is compared to the percent difference in net income and assets Tests of means are based on matched-pair t-tests Tests of medians are based on Wilcoxon matched-pair signed-rank tests Panel A Accounting standard changes by year
2003
4 6
2004
25 38
2005
8 12
2006
6 9
2007
7 11
2008
5 8
2009
5 8
2010
5 8
2011
1 2
66
Panel B Accounting standards compared to US GAAP
IFRS
47 71
Canadian GAAP
12 18
Israeli GAAP
2 3
Dutch GAAP
1 2
Indian GAAP
1 2
Japanese GAAP
1 2
Norwegian GAAP
1 2
Swiss GAAP
1 2
66
36
(Table 1 continued) Panel C Tests of differences between US GAAP and other accounting standards
Percent different from US GAAP
Comparison to sales (p-values)
N Mean Median
Mean Median
Full Sample (2001-2011)
Sales
66 236 000
Net Income
66 10653 647
005 lt001
Assets
66 1162 126
lt001 lt001
Study Sample (2001-2008)
Sales
55 257 000
Net Income
55 10303 589
011 lt001
Assets
55 1047 099
003 lt001
37
Table 2 Descriptive statistics Panel A reports the mean and medians of Excess Value for the multinational firms and single-segment benchmark firms as well as univariate tests across the subsamples Panel B reports summary statistics for the independent variables for the multinational firms The p-values of the tests of differences in means are based on t-tests The p-values of tests of differences in the medians are for a Wilcoxon rank-sum test All variables are defined in Appendix A Panel A Excess Value by sample Multinational All Benchmark Benchmark Subgroups Sample Firms US Foreign
Value Value Test of Diff (p-value) Value
Test of Diff (p-value) Value
Test of Diff (p-value)
Mean 0067 0006 lt001 -0028 lt001 0014 lt001 Median 0076 0000 lt001 -0025 lt001 0000 lt001 N 4950 33074 5998 27076 Panel B Distribution of independent variables for the multinational firms N Mean 25th 50th 75th StDev Foreign Sales 4950 0238 0074 0190 0363 0197 Industry Other 4950 0212 0000 0166 0414 0215 Log Size 4950 6955 5823 6989 8140 1687 Debt Firm Value 4950 0136 0009 0097 0212 0145 CAPX Sales 4950 0054 0020 0033 0054 0101 EBIT Sales 4950 0065 0031 0077 0121 0138 RampD Sales 4950 0049 0000 0013 0059 0083 Ad Sales 4950 0008 0000 0000 0005 0018
38
Table 3 BEA and Worldscope samples
This table reports details on affiliate sales for significant countries pooled from 1998-2008 Significant countries are defined as those in which the operations of US MNCs represent at least 02 of total firm sales (per the BEA) For significant countries the table compares their representation in terms of MNC affiliate sales in the BEA data to the distribution of firm observations in Worldscope US Foreign Affiliates (BEA) Foreign Firms (Worldscope)
Country
N (1)
Sales (millions)
(2)
Total Sales (3)
N (4)
Sales (million)
(5)
Total Sales (6)
Australia 1344 73305 056 859 255865 210 Belgium 894 52755 040 169 26136 021 Brazil 959 60245 046 1143 683167 560 Canada 2730 536767 407 1181 526750 432 China 922 75907 057 822 276541 227 France 1852 183942 139 540 105447 086 Germany 2182 275577 209 480 103888 085 Hong Kong 867 48100 036 157 34094 028 Ireland 478 36754 028 34 3467 003 Italy 1178 82116 062 111 47948 039 Japan 1494 148032 112 2281 1119223 917 Korea South 659 34500 026 3530 2752791 2255 Malaysia 386 26027 020 837 168683 138 Mexico 1405 113623 086 362 237173 194 Netherlands 1149 85105 064 47 22631 019 Singapore 1098 94917 072 139 32913 027 Spain 871 63094 048 129 37419 031 Sweden 712 32682 025 235 23090 019 Switzerland 726 60288 046 74 20242 017 Taiwan 592 30313 023 2949 787049 645 United Kingdom 3054 423934 321 1997 882423 723 United States (domicile) 4950 10460880 7923 na na na Other - Covered by WorldScope 6893 199394 151 9000 4057894 33 Other -Not Covered by WorldScope 713 5352 004 - - - Total 38108 13203609 1000 27076 12204834 100
39
Table 4 OLS regressions of firm excess values on the percent of foreign sales This table reports the results of OLS regressions of firm excess value on the percent of foreign sales (Foreign Sales) the percent of sales outside a firmrsquos primary industry (Industry Other) and control variables All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed) Dep Variable = Excess Value Coefficient t-stat Coefficient t-stat Coefficient t-stat Coefficient t-stat (1) (2) (3) (4) Foreign Sales 0349 419 0209 293 0189 266 0278 210 Industry Other -0134 -186 -0047 -072 -0053 -081 -0326 -325 Log Size 0066 634 0065 624 -0044 -096 Debt Firm Value -0196 -213 -0241 -260 -0685 -588 CAPX Sales 0901 425 0893 435 0488 227 EBIT Sales 1154 542 1170 547 0408 189 RampD Sales 2908 1242 2932 1237 1544 379 Ad Sales 1869 263 2003 280 -1696 -119 Year indicators No No Yes Yes Firm indicators No No No Yes R2 0017 0212 0226 0768 Observations 4950 4950 4950 4950
40
Table 5 Dynamic panel GMM model regressing firm excess values on the percent of foreign sales This table reports the result from a regression of the excess value on the percent of foreign sales (Foreign Sales) the percent of non-primary industry sales (Industry Other) and control variables using a dynamic panel GMM estimator All control variables are considered to be endogenous with the exception of the year indicator variables AR(1) and AR(2) are tests for first-order and second-order serial correlation in the first differenced residuals with the null hypothesis of no serial correlation The null hypothesis of the Hansen test of overidentification is that all instruments are valid The null hypothesis of the difference-in-Hansen test of exogeneity is that the instruments used for the equation in levels are exogenous All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed) Dep Variable = Excess Value Coef t-stat Excess Value (one lag) -0018 -082 Excess Value (two lags) -0048 -272 Foreign Sales 0371 270 Industry Other -0163 -171 Log Size 0010 123 Debt FirmValue -1135 -820 CAPX Sales 0590 342 EBIT Sales -0047 -300 RampD Sales 1376 531 Ad Sales -0751 -040 AR(1) test (p-value) 000 AR(2) test (p-value) 017 Hansen test of over-identification (p-value) 030 Difference-in-Hansen test of exogeneity (p-value) 093 Year indicators Yes Number of firms 1016 Number of observations 4090
41
Table 6 OLS regression of firm excess values on the percent of foreign sales controlling for a control premium This table reports the result of the OLS regression of firm excess value including firm and year fixed effects with an additional control variable to proxy for a country-level ownership control premium (Control Premium) All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed) Dep Variable = Excess Value Coef t -stat Foreign Sales 0303 224 Industry Other -0327 -325 Log Size -0045 -100 Debt FirmValue -0683 -585 CAPX Sales 0490 226 EBIT Sales 0409 189 RampD Sales 1545 379 Ad Sales -1689 -119 Control Premium -1004 -062 Year indicators Yes Firm indicators Yes R2 0768 Observations 4950
42
Table 7 OLS regression of firm excess values on the percent of foreign sales controlling for the cost of capital This table contains the results of an OLS regression of firm excess value including firm and year fixed effects with an additional control variable to proxy for a country-level cost of capital (Cost of Capital) All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed) Dep Variable = Excess Value Coef t -stat Foreign Sales 0255 178 Industry Other -0326 -324 Log Size -0043 -094 Debt FirmValue -0687 -589 CAPX Sales 0485 225 EBIT Sales 0406 188 RampD Sales 1539 379 Ad Sales -1703 -120 Cost of Capital 0004 039 Year indicators Yes Firm indicators Yes R2 0768 Observations 4950
43
Table 8 OLS regression of firm excess values on the percent of foreign sales controlling for aspects of corporate governance This table reports the results of two OLS regressions In the first column are the results of the firm excess value regression including firm and year fixed effect with five proxies for corporate governance included ndash the percent of shares owned by the CEO and the percent squared (CEO Ownership and CEO Ownership squared) the percent owned by institutional investors (Institution Ownership) whether there is a powerful CEO (Powerful CEO) and the Gompers Ishii and Metrick (2003) governance index (Governance Index) In the second column the regression includes the same sample and variables with the exception of the governance proxies which are excluded All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed)
Governance
No Governance (same sample)
Dep Variable = Excess Value Coef t-stat Coef t -stat Foreign Sales 0259 159 0251 154 Industry Other -0099 -080 -0099 -079 Log Size -0105 -185 -0102 -180 Debt FirmValue -0813 -478 -0817 -482 CAPX Sales 0440 252 0438 249 EBIT Sales 0457 247 0457 246 RampD Sales 1717 284 1716 286 Ad Sales -2210 -143 -2190 -143 CEO Ownership 0010 007
CEO Ownership squared -0029 -017 Institution Ownership 0047 067 Powerful CEO -0003 -014 Governance Index 0009 103
Year indicators Yes Yes Firm indicators Yes Yes R2 0811 0810 Observations 2550 2550
- Cover for paperpdf
- crrz_20130603_SMUpdf
-
10
GAAP only require highly aggregated disclosures of foreign operations and allow for
considerable managerial discretion As a result operating in multiple countries makes it easier
for management to hide poor performance ndash either their own or that of a division ndash such that low
quality managers are more likely to be retained and overcompensated and poorly performing
divisions retained longer than optimal Other reasons one might expect a discount to be applied
to multinational firms include higher coordination costs (eg coordinating across different
cultures and languages) as well as additional risks These risks include exposure to multiple
political regimes legal regimes economic regulations and currency fluctuations
Past studies investigate whether foreign operations of US firms enhance or reduce firm
value However all of these studies benchmark the value of the foreign operations of an MNC to
US domestic firms For instance Denis et al (2002) determine the implied value of a US
MNC using domestic (US) single-segment firms in the same industry as a benchmark Studies
using Tobinrsquos Q to investigate valuation effects of MNCs also rely on domestic benchmarks
(eg Morck and Yeung 1991 Morck and Yeung 2001 Gande Schenzler and Senbet 2009)
However using a US domestic firm as a benchmark for the value of foreign operations
implicitly makes two assumptions First this method assumes that the risks and expected growth
rates of foreign operations are equivalent to those of domestic operations This assumption is
inconsistent with the findings of Hail and Leuz (2009) which provides evidence that the cost of
capital varies substantially across countries Second this method assumes that there is excess
domestic capacity to allow for expansion with profitability similar to current domestic
operations We relax these assumptions by measuring the imputed value of a foreign component
of a US firm as if that component operated autonomously within that foreign country (rather
than in the US) Specifically our multiples allow the cost of capital and expected growth rates
11
to vary across not just industry but also country Furthermore our counterfactual does not
require the assumption that operations be relocated mitigating the production and sales capacity
concerns
3 Measuring excess value
31 Description of excess value
Figure 1 shows the average percent of foreign sales (based on segment disclosures under US
GAAP) across firms through time and illustrates that firms are continuing to expand
internationally9 This expansion highlights the importance of understanding whether the decision
to operate internationally is on average a value enhancing corporate strategy We contribute to
this understanding by evaluating whether the firm as a whole is worth more or less than its
imputed value (ie the firm is valued in excess of the sum of its individual components)
We measure the excess value of a firm as the logarithm of the ratio of actual firm value to
imputed value based on the method used in Berger and Ofek (1995) The imputed value is the
hypothetical value of the MNC under the assumption that its country-industry components
operate as independent entities A key innovation of our study is an alternative approach to
imputing the value of the foreign operations of an MNC Components of MNCs operating in a
given industry and country are imputed using the value of single-segment firms operating in the
same industry and country Thus we ensure the discount rates and expected growth rates are
applicable to each given industry and location in estimating the implied values of the MNC
segments
9 The discontinuity in industrial diversification (the percent of sales outside the primary industry) between 1997 and 1998 is the result of a change in segment reporting rules
12
The actual firm values are observable for all firms in our sample The hypothetical firm
values for multi-segment firms are imputed using market value to sales ratios herein referred to
as lsquomultiplesrsquo of the median single-segment firm operating in the same country-industry Thus
these single country-industry firms serve as benchmarks for an MNCrsquos operations within that
same country-industry The imputed value of the country-industry component can be viewed as
an estimate of what that component would be worth if it operated independently The sum of the
imputed values across all country-industry components represents the hypothetical value that the
multi-segment firm would be worth if it were broken apart
32 Data
Four primary data sources play a distinct role in carrying out our analysis i) Bureau of
Economic Analysis (BEA) data ii) Compustat Segment data iii) Worldscope data and iv)
Compustat Fundamentals Annual data Broadly speaking BEA data provide information about
the diversified nature of MNCs Compustat Segment data provide information about the
industrial diversification of US domiciled firms Worldscope data are used to compute
multiples for single-segment foreign firms that serve as benchmarks for the foreign operations of
MNCs (ie foreign benchmarks) and Compustat Fundamentals Annual data are used to
compute multiples for single-segment domestic firms that serve as benchmarks for the domestic
operations of multinational firms We also use the latter data source to construct our primary
control variables
13
Key to the execution of our study is our access to BEA data Federal law requires US-
domiciled MNCs to report certain financial and operating data to the BEA10 With regard to
observing the country-industry operations of MNCs use of the BEA data overcomes two
important limitations of Compustat Segment data First Compustat does not consistently identify
the specific location or scale of MNCsrsquo foreign operations11 Second Compustat does not
identify the industry activities in each country of operation This latter point is particularly
limiting for MNCs because domestic industry activity need not mirror foreign industry activity
An MNC generating an equal proportion of sales in two industries in the domestic market need
not generate industry sales in equal proportions in every foreign market12 The lack of detail and
consistency in Compustat Segment data arises because segment reporting is highly aggregated
and firms exercise substantial discretion in defining segments under Accounting Standards
Codification 280 (ASC 280) (eg Villalonga 2004a and Bens Berger and Monahan 2011)
The BEA defines an MNC as the combination of a single US entity called the US parent
and at least one foreign affiliate ndash that is these firms have a physical presence outside the US
due to FDI Since 1982 MNCs have completed a mandatory and confidential lsquoSurvey of US
Direct Investment Abroadrsquo for the domestic operation and the operations of each foreign
affiliate defined as a foreign entity in which the US parent holds at least a 10 percent equity
interest (directly or indirectly) The data are reported in US dollars on a fiscal year basis and in
10 BEA surveys are conducted pursuant to the International Investment and Trade in Services Survey Act (PL 94-472 90 Stat 2059 22 USC 3101-3108) See httpwwwbeagovsurveysdiasurvhtm and Mataloni (2003) for more detailed information on the BEA data 11 Exhibit 21 disclosures of material subsidiaries and their locations in SEC 10-K filings could potentially help overcome the inability to observe the location of the firmrsquos foreign operations from segment disclosures However at least three limitations remain (1) for industrially diversified multinational firms the researcher would be required to make an assumption about the proportion of industrial diversification in each country (2) Exhibit 21 provides no information about the scale of activity in each jurisdiction and (3) managerial discretion is used to determine a lsquomaterialrsquo subsidiary under Section 601 of SEC Regulation S-K 12 In fact the expectation that a multinational firm would exhibit different industry membership in different countries is consistent with the theory of vertical foreign direct investment where firms separate their various value chain activities and locate them in the most favorable country (see eg Hanson Mataloni and Slaughter 2005)
14
accordance with US Generally Accepted Accounting Principles (US GAAP) For each year
we observe the sales industry composition and location of not only the parent but also each
affiliate13
The procedures we use to construct our sample are similar to those used by Berger and Ofek
(1995) and Denis et al (2002) Using firm-years appearing in Compustat Fundamentals Annual
and Compustat Segment data we obtain a sample of firms domiciled in the US with total sales
exceeding $20 million We exclude firms in the financial service (SIC codes 6000 to 6999) and
utility (SIC codes 4000 to 4999) industries and eliminate firm-years when the sum of segment
sales (Compustat geographic or Compustat business) is not within 1 of total firm sales for that
year We restrict our sample period to include fiscal years beginning after December 15 1997 as
ASC 280 Segment Reporting substantially altered the definition of a reporting segment under
US GAAP This restriction ensures that Compustat Segment data reflects the use of a consistent
accounting standard Finally we require that our MNCs appear in both Compustat and BEA
data The requirement that our sample firms appear in the BEA database ensures that the firms
have observable international operations In sum these procedures result in 1166 multinational
firms incorporated in the US with 4950 observations between 1998 and 2008 that meet all of
our data requirements
33 Measuring excess value
The dependent variable in our study is excess firm value (Excess Value) defined as the
logarithm of the ratio of actual firm value to imputed firm value (Berger and Ofek 1995)
13 We obtain sales industry membership and country location for affiliates from one of two sources When affiliates exceed the BEA-determined reporting thresholds (ie if their assets sales or net income (loss) exceed $7 million in 1999 $30 million in 2000 through 2003 $10 million in 2004 and $40 million in 2005 through 2008) they must file their own detailed survey When affiliates fall below the thresholds we obtain our data from an attachment to the parent survey
15
Appendix A provides the definition of this and all other variables used in our analysis We
observe actual firm value equal to a firmrsquos market value of equity plus book value of liabilities
(ie book value of total assets minus book value of total equity) using Compustat Fundamentals
Annual data We calculate imputed firm value as the sum of the imputed values of a firmrsquos
operations in each country-industry Our method for imputing the value of the separate
components of a firm can best be described in three steps First we obtain total sales generated
by a firm for each country-industry in which it operates14 Second we obtain multiples (market
value to sales ratios) for benchmark firms operating in those same country-industries Third we
multiply a firmrsquos country-industry sales by the applicable median country-industry multiple to
obtain the imputed market value for each country-industry operation We perform each step
annually
Conceptually imputed values can be based on assets (ie Tobinrsquos Q) net income or sales
multiples We restrict ourselves to sales multiples for two reasons First using value to sales
ratios maintains consistency with the prior research on the excess value implications of
multinational operations (eg Denis et al 2002) Second as our method uses accounting data
for foreign companies to compute country-industry multiples the accounting numbers we use
need to be consistently measured across firms using different accounting standards We find that
sales data are measured most consistently
To assess the comparability of sales relative to either net income or assets across various
accounting standards we examine all firms listed on Worldscope as changing to or from US
14 Specifically we allocate total sales from Compustat to each component based on the proportion of BEA sales (after eliminating intercompany sales) occurring in each country-industry This step ensures that our total firm sales do not deviate from a firmrsquos total sales as reported in the 10-K
16
GAAP between 2005 and 201115 We obtain accounting data for the year of the change and the
prior year where electronically available in English We are able to obtain data for 66 firms
changing standards as detailed in Table 1 The distribution of years for which accounting data
are available under two different accounting standards for the same fiscal year is shown in Panel
A The years shown are the year prior to the change as these accounting numbers were provided
under the original standard and then subsequently restated in the following year under the new
standard for comparative purposes The majority of the shifts were to or from IFRS (71)
though seven other accounting standards are noted (Panel B) We examine the percent that the
non-US GAAP reported number differs from the US GAAP reported number in Panel C to
assess the comparability of these three summary accounting numbers across various accounting
standards Examining both the full sample as well as the subset that overlaps our study we find
that sales are more consistently measured than net income or assets
The sum of the imputed values across the country-industry components of an MNC is an
estimate of the value of a portfolio of unrelated businesses that mirror the related businesses of
the firm Consequently a comparison of the actual firm value with the imputed firm value is a
measure of how a multinational network affects firm value We provide descriptive statistics for
Excess Value for our sample of 4950 MNC firm-years in Table 2 Panel A This table also
includes univariate comparisons of the values between the MNC firms and the domestic single-
segment and foreign single-segment benchmark firms The Excess Value measure is significantly
higher for MNCs suggesting a premium in firm value for multinational firms relative to the
benchmark firms in total as well as the domestic and foreign subgroups (p-values for tests of
15 Due to the hand collection efforts required and the limitation on electronic filings we began our search with changes documented in 2005 which generally require filings from 2004
17
means and medians are lt 001)16 These results provide some initial evidence that operating in
multiple countries is positively associated with firm value
To estimate country-industry multiples we rely on Worldscope financial data on foreign
firms and Compustat Fundamentals Annual for domestic firms We restrict the firms to include
only those with at least 90 of sales income and assets inside the country of domicile (ie
those that do not report significant multinational activity) and that operate in a single industry
We refer to these firms as benchmark firms (either foreign or domestic depending on the country
of domicile) these firms do not appear in any of our regressions We report the number of
benchmark firms during our sample period in Table 2 Panel A The industry criterion is based on
the two-digit SIC code17 For every country that has at least five firms in the respective industry
and year we use the median ratio of market value to sales18 This ratio is the country-industry
multiple ndash an input required to compute imputed values
To determine the country-industry composition of MNCs we rely on BEA data In Table 3
we report the aggregate number of foreign affiliates and total sales by country as provided by
the BEA data for our sample of 4950 MNCs The specific countries tabulated are those that
represent at least 02 percent of total firm sales (pooled across all years) in our sample The top
five foreign countries in which MNCs generate sales are Canada United Kingdom Germany
France and Japan Table 3 also provides a comparison of the number of foreign benchmark
firms available in Worldscope in the specific countries in which our sample of MNCs generate
16 Similar to Berger and Ofek (1995) the median values differ slightly from zero for the benchmark single-segment firms due to the truncation of extreme excess values 17 BEA reporting constrains us to the two-digit SIC code level 18 When five firms are not available for a given country and industry we follow Berger and Ofek (1995) and move to a more general industry definition (eg one-digit SIC code) This procedure results in a mean (median) 630 (757) of MNC sales matched at the two-digit level
18
the majority of their sales Country coverage in Worldscope is not available for countries
representing 004 percent of total firm sales (per BEA)
4 Empirical results
41 Independent variables
Recall that our objective is to examine the overall relation between excess firm value and
multinational operations Our proxy for the extent of multinational operations is the percentage
of total firm sales made by foreign operations (Foreign Sales) By this measure the extent of
multinational activity in our sample of MNCs is non-trivial ndash Table 2 Panel B shows that
approximately 24 percent of sales are generated by foreign operations on average Our first
analysis estimates ordinary least square regressions of Excess Value on Foreign Sales and
control variables (discussed below)
We include variables in our regression to control for other potential determinants of excess
value These are the percentage of sales made by the firm outside its primary industry (Industry
Other) to control for any relation between industrial diversification and excess value as in Berger
and Ofek (1995) For this purpose we obtain industry sales using Compustat Segment data19 A
firmrsquos primary industry is the industry in which the firm generates the majority of its sales and
we determine industry sales at the business segment level We set Industry Other equal to zero
for firms that operate in a single business segment
Consistent with prior research we also include controls for firm size (Log Size) the ratio of
long term debt to total market value (Debt Firm Value) the ratio of capital expenditures to total
sales (CAPX Sales) the ratio of earnings before interest and taxes to total sales (EBIT Sales)
the ratio of research and development expenses to total sales (RampD Sales) and the ratio of 19 Our results are unaffected by using BEA data to measure Industry Other
19
advertising expenses to total sales (Ad Sales) Table 2 Panel B provides the distribution of the
control variables All independent variables are Winsorized at 1 In our OLS regressions we
use heteroskedasticity and autocorrelation consistent robust standard errors with clustering at the
firm level
42 Primary regression analysis
In this section we examine the relation between firm excess value and the amount of foreign
activity at US MNCs Table 4 contains the results of regressions of excess value on Foreign
Sales for multiple specifications In column (1) Excess Value is regressed on both the percent of
foreign sales (Foreign Sales) and the percent of sales outside the firmrsquos primary industry
(Industry Other) We add the control variables to the regression in column (2) and year
indicators in column (3) In all three specifications the coefficient on the percent of foreign sales
remains significantly positive (with a p-value lt 001) For a sense of economic magnitude in
column (3) a one standard deviation (197) increase in the percent of foreign sales (Foreign
Sales) is associated with a 41 (equal to (e0349-1) times 0197) increase in firm excess value The
effect is larger for the other two columns ndash 82 increase in firm excess value in column (1) and
46 in column (2)20
Our finding of a premium reconciles with the international trade literature which finds that
firms engaging in international trade are more productive than those that do not (see Helpman
2006 and Syverson 2011 for reviews) Due to data limitations it is difficult to accurately
measure TFP at the firm level especially for firms which operate in multiple industries andor
20 Using the alternative strategy that employs US domiciled single-segment firms as the benchmark for the imputed value of the foreign operations Denis et al (2002) find that greater foreign activity results in a discount to firm value Using the Denis et al (2002) method to calculate excess value in our sample we obtain a significantly negative regression coefficient on Foreign Sales using the models in Table 4 columns (2) and (3) (the coefficient is not significant using the model in column (1) results untabulated)
20
countries As an alternative method to evaluate whether our finding of a premium is simply the
result of more productive firms also having more multinational operations we investigate
whether the premium is simply an artifact of innate characteristics of firms that choose to invest
abroad For example Helpman et al (2004) present a simple model with heterogeneous
productivity endowments The firms that receive the highest productivity endowments are the
ones capable of paying the fixed costs to establish a foreign subsidiary
To the extent that productivity advantages create excess value one would expect the type of
firms that choose to invest more abroad would be valued at a premium even in the absence of
their foreign investments To control for time invariant firm characteristics (eg innate
productivity advantages) and hence a potential source of endogeneity we add firm-level fixed
effects in column (4) of Table 4 We continue to find that the extent of foreign operations
(Foreign Sales) is positively associated with Excess Value Across all specifications the sign
on all significant control variables is consistent with prior research (eg Berger and Ofek 1995
and Denis et al 2002)
The finding of a premium makes a significant contribution to the relatively sparse literature
on the value effects of multinational activity Our measure of imputed value expands upon the
methods used by Berger and Ofek (1995) and Denis et al (2002) but allows us to answer a
different question than Denis et al (2002) Holding constant the extent of industrial
diversification we ask whether MNCs are more valuable than a portfolio of benchmark firms of
a similar geographic footprint In contrast Denis et al (2002) ask whether holding constant the
extent of industrial diversification MNCs are more valuable than a portfolio of domestic (US)
benchmark firms We believe that asking whether an MNCrsquos foreign (domestic) operations are
21
more valuable than those of a foreign (domestic) firm is a more appropriate way to assess the
value implications of a multinational network
5 Robustness
51 Endogeneity related to dynamic relations
Prior research argues that the relation between industrial diversification and excess firm
value could be endogenous through dynamic relationships based on observing past outcomes
Managers of firms likely choose to enter or exit industries or geographic regions based upon
their previous performance For example Campa and Kedia (2002) find that firms are more
likely to enter new industries when prospects in their current lines of business are deteriorating
Similar dynamics could also drive a relation between excess value and FDI We assess the
robustness of the previous results by estimating a dynamic panel data model which allows us to
control for dynamic endogeneity in addition to firm level unobserved heterogeneity We employ
the GMM estimator for dynamic panel models developed by Arellano and Bond (1991) Arellano
and Bover (1995) and Blundell and Bond (1998) Our results from this analysis confirm our
previous findings that firms with a larger degree of foreign operations have positive excess
values
Dynamic panel data models are generalizations of the traditional fixed effects model where
lags of the dependent variable are added to the right hand side of the equation These additional
lags control for the impact a firmrsquos past performance has on its current performance through the
channels discussed above The GMM estimator for dynamic panel models developed by
Arellano and Bond (1991) starts by differencing the data at the firm level in neighboring time
periods to eliminate any firm specific time invariant unobserved heterogeneity that may be
22
present Eliminating the firm specific fixed effect by differencing the data with lags of the
dependent variable on the right hand side introduces correlation between the new error term and
the differenced explanatory variables This problem is solved by applying the standard GMM
estimator using lags of both the original explanatory variables and excess value as instruments
We note that the dynamic panel data framework allows us to treat both international and
industrial diversification (as well as the other firm-specific control variables) as endogenous
Following Hoechle et al (2012) we also allow contemporaneous values of these variables to be
treated as endogenous
Lagged values of the explanatory variables are valid instruments in a dynamic panel data
model when the model is dynamically complete (eg Wooldridge 2010) A dynamic panel data
model is dynamically complete conditional on the unobserved effect if enough lags of both the
explanatory and dependent variable are included to make the resulting error term uncorrelated
with all the right hand side variables Under this assumption any lags for the explanatory
variables beyond this can be used within the GMM estimator as instruments Like Hoechle et al
(2012) we add two years of lags of a firmrsquos excess value to the right hand side assuming that
this is enough lags to control for dynamic feedback effects We then use past values of all
variables beyond the second lag up to a total of seven years to act as instruments
The results of the dynamic panel data estimator are reported in Table 5 These results confirm
the previous finding of a positive relation between the degree of international operations and
excess value We note that the size and significance level of the coefficient on Foreign Sales
diversification are robust to changes in the number of lags used as instruments The Arellano and
Bond (1991) estimator includes testing diagnostics for correct specification of the model If the
model is correctly specified differences in the residuals from the levels equation should be
23
serially uncorrelated at lag one but correlated at lag two The p-values for tests of serial
correlation of the residuals at lags 1 and 2 are reported in Table 5 and these indicate that the
model is not rejected We also report in Table 5 the Hansen test for overidentification which is a
test of the null hypothesis that the instruments are valid The p-value for this test is 030
indicating that we fail to reject this null hypothesis Finally we report another test for exogeneity
known as the difference-in-Hansen test which tests the null hypothesis that the instruments in
the original equation are valid We fail to reject this null hypothesis as well
52 Identifying value effects of multinational operations via cross-border acquisitions
Another alternative to investigating a causal link between foreign direct investment and firm
value is an event study of foreign acquisitions21 Acquired foreign targets represent only a small
fraction of MNCsrsquo worldwide sales in the year of the acquisition ndash less than 1 percent in our
sample Additionally cross-border acquisitions are not the primary source of international
expansion for the average firm We find that foreign acquisitions account for between 18 percent
and 55 percent of the year-over-year change in foreign sales with the remainder of the growth
coming from either newly established entities (ie greenfield investments) or growth in existing
operations22 These results suggest that acquisitions are not a material source of growth for the
21 Doukas and Travlos (1988) find positive returns when the acquirer is not operating in the target firmrsquos country and insignificant returns when the acquirer is operating in the target firmrsquos country or expanding internationally for the first time Morck and Yeung (1991) find marginal evidence of positive returns for firms with intangible assets Finally Doukas (1995) finds positive returns only for firms with average Tobinrsquos q ratios greater than one More recent work also using an event study methodology provides mixed evidence on valuation effects of global diversification through acquisitions Dos Santos Errunza and Miller (2008) find insignificant returns for firms with already established foreign operations firms establishing initial foreign operations and firms acquiring targets in related industries Doukas and Kan (2006) and Moeller and Schlingemann (2005) find reductions to shareholder value while Francis Hasan and Sun (2008) find positive returns for acquirers of targets in segmented financial markets 22 New foreign affiliates to an MNC group each year report on the BEA survey whether they entered the group as an acquisition or a newly established entity (ie greenfield investment) Affiliates under the lowest reporting threshold do not report this information resulting in our inability to precisely identify the source of 33 percent of the increase in firm sales as either acquisition or greenfield
24
typical US MNC suggesting that an event study would lack power and generalizability For
these reasons we do not implement an event study
53 Control premium
A control premium or private benefits of control occurs when ldquosome value whatever the
source is not shared among all the shareholders in proportion of the shares owned but it is
enjoyed exclusively by the party in controlrdquo (Dyck and Zingales 2004 p 541) Using stock
prices of traded shares held by minority shareholders in determining the market value to sales
ratios of our benchmark firms could underestimate the true value of our benchmark firms if those
firms face large control premiums This in turn could induce an excess value premium because
the imputed value of these country-industry segments would be artificially low The existence of
such benefits is more prevalent in some countries than others and could affect the value of an
MNCrsquos operations in such domains We use the country-level control premiums from Dyck and
Zingales (2004) and using each firmrsquos country sales as a percent of total firm sales we obtain a
weighted average control premium that each MNC faces (Control Premium) For countries not
included in the Dyck and Zingales (2004) analysis we set the control premium equal to zero23 In
Table 6 we show that Foreign Sales remains positive and highly significant (p-value lt 001)
when we include the proxy for control premium As a result it does not appear that the excess
value premium is driven by MNCs operating in countries where majority shareholders are able to
extract large control premiums
23 Results are robust to replacing missing values with the mean control premium of the respective GDP quartile
25
54 Cost of capital
As discussed in Section 2 when countries have shallow capital markets a multinational
network may provide the benefit of a lower cost of capital for investments through an internal
capital market as well as through better access to the US capital market Prior research
establishes that there is substantial variation in the cost of capital across countries (Erb Harvey
and Viskanta 1996 and Hail and Leuz 2009) To determine whether the documented premium
is due to cost of capital advantages we include a control variable to capture the difference
between the MNCrsquos cost of capital and the cost of capital faced by its competitors in the global
markets in which it operates
We compute our Cost of Capital proxy in two steps First we obtain the country-year credit
rating published by Institutional Investor24 The rating ranges from 0 to 100 where higher values
imply that a country has a higher default risk Country-level credit risk is a reasonable predictor
of expected equity market returns and volatility (Erb et al 1996) and exhibits a highly
significant correlation with international accounting-based estimates of imputed cost of capital
(Hail and Leuz 2009) Second using each firmrsquos country sales as a percent of total firm sales
we obtain a weighted average credit risk rating across the countries in which each firm operates
A higher value implies an MNC operates in countries with higher cost of capital In Table 7 we
do not find a result consistent with MNCs gaining an advantage from their access to low cost
capitalndash Cost of Capital is not significantly associated with Excess Value25
24 We thank Cam Harvey for providing these data 25 Results are consistent using cost of capital as defined in Erb et al 1996
26
55 Corporate governance
In light of the recent finding that corporate governance mitigates the diversification discount
we examine whether the premium to multinational activity is sensitive to controls for corporate
governance Hoechle et al (2012) find that the magnitude of the industrial diversification
discount is decreased and in several specifications no longer significant in the presence of
governance proxies In the first column of Table 8 we include the five governance proxies that
are consistently significantly associated with excess value in Hoechle et al (2012) ndash the percent
of CEO ownership (CEO Ownership and CEO Ownership squared) the percent of institutional
ownership (Institution Ownership) whether the firm has a powerful or influential CEO
(Powerful CEO) and the governance index from Gompers Ishii and Metrick (2003)
(Governance Index)
As documented in Table 8 including these control variables reduces our sample size by
almost 50 (to 2550 observations) and tends to exclude the smaller firms from the analysis
None of the governance proxies are significant Relative to column (4) of Table 4 the coefficient
on Foreign Sales is similar in magnitude but is no longer statistically significant at
conventional levels To investigate whether the decreased significance is due to the smaller
sample or the inclusion of the governance variables we re-estimate the regression for the same
2550 observations after removing the governance controls The coefficient and t-statistic are
virtually identical suggesting that the loss of significance is due to the reduced sample and not
the governance controls We do not find evidence to suggest that variation in corporate
governance practices drives variation in excess value for US MNCs
27
6 Conclusion
Using a new method to quantify the valuation effects of foreign direct investment by US
domiciled firms we find robust evidence of a premium in excess value associated with the
degree of multinational operations Our method builds on that used in previous studies
examining firm excess value (eg Berger and Ofek 1995 and Denis et al 2002) by
incorporating the location in addition to the industry of each corporate affiliate This allows
important factors such as growth and cost of capital to vary across countries as well as industries
Examining a sample of multinational firms we find that a one-standard deviation (197)
increase in foreign operations is associated with an increase of between 41 and 82 in excess
value minus both a statistically and economically significant finding
This result complements the finding of the international trade literature that more productive
firms are more likely to operate internationally However the premium remains after including
firm fixed effects to control for persistent firm traits (eg productivity)
The finding of a premium stands up to a succession of robustness tests designed to evaluate
whether the result arises from alternative methods or explanations First we estimate a dynamic
panel data model using the generalized method of moments (GMM) estimator to simultaneously
account for the potential endogeneity of FDI industrial diversification and firm value Second
we include a control variable to proxy for whether the firm operates in countries with larger
control premiums which could result in understated multiples and mechanically induce a
premium Third we include a control variable to measure the cost of capital across the countries
in which an MNC operates to evaluate whether a relatively low cost of capital borne by
multinational firms is a meaningful source of the premium Fourth we control for several
corporate governance proxies which have been found to be correlated with excess values (and
28
industrial diversification) We fail to find evidence supporting any of these alternative
explanations ndash the premium in excess value arising at firms with greater multinational operations
remains significant
Overall our new method of estimating excess values for multinational firms provides strong
evidence of a statistically and economically significant premium associated with increased
international operations The premium is robust to a host of specifications and controls designed
to evaluate alternative explanations We leave to future research the questions of whether the
premium to US multinational firms applies more broadly to other countries of domicile and
what factors influence the premium
29
References
Ammann M Hoeschel D and M Schmid 2012 Is there really no conglomerate discount
Journal of Business Finance and Accounting 39(12) 264-288 Arellano M and Bond S R 1991 Some tests of specification for panel data Monte Carlo
evidence and an application to employment equations Review of Economic Studies 58 (2) 277-298
Arellano M and Bover O 1995 Another look at instrumental variables estimation of error
components models Journal of Econometrics 68 (1) 29-61 Bens D A Berger P G and S J Monahan 2011 Discretionary disclosure in financial
reporting An examination comparing internal firm data to externally reported segment data The Accounting Review 86(2) 417-449
Berger P G and E Ofek 1995 Diversificationrsquos effect on firm value Journal of Financial
Economics 37 39-65 Blundell R and Bond S R 1998 Initial conditions and moment restrictions in dynamic panel
data models Journal of Econometrics 87(1) 115-144 Campa J M and S Kedia 2002 Explaining the diversification discount The Journal of
Finance 57 (4) 1731-1762 Denis D J Denis D K and K Yost 2002 Global diversification industrial diversification
and firm value The Journal of Finance 57 (5) 1951-1979 Desai M A Foley C F and J R Hines 2004 A multinational perspective on capital structure
choice and internal capital markets The Journal of Finance 59 (6) 2451-2487 Dos Santos MB V Errunza and D Miller 2008 Does corporate international diversification
destroy value Evidence from cross-border mergers and acquisitions Journal of Banking and Finance 32(12) 2716-2724
Doukas JA 1995 Overinvestment Tobinrsquos q and gains from foreign acquisitions Journal of
Banking and Finance 19 1285-1303 Doukas J and N G Travlos 1988 The effect of corporate multinationalism on shareholdersrsquorsquo
wealth evidence from international acquisitions Journal of Finance 43(5) 1161-1175 Doukas JA and O B Kan 2006 Does global diversification destroy firm value Journal of
International Business Studies 37(3) 352-371
30
Dunning J and A Rugman 1985 The influence of Hymerrsquos dissertation on the theory of foreign direct investment The American Economic Review 75(2) 228-232
Dyck A and L Zingales 2004 Private benefits of control An international comparison The
Journal of Finance 59 (2) 537-600 Erb C Harvey C and T Viskanta 1996 Expected returns and volatility in 135 countries The
Journal of Portfolio Management 46-58 Foley C F Desai M A and K J Forbes 2008 Financial constraints and growth
Multinational and local firm responses to currency depreciations Review of Financial Studies 21 (6) 2857-2888
Francis B I Hasan and X Sun 2008 Financial market integration and the value of global
diversification Evidence for US acquirers in cross-border mergers and acquisitions Journal of Banking and Finance 321522-1540
Gande A Schenzler C and L Senbet 2009 Valuation effects of global diversification
Journal of International Business Studies 1515-1532 Gompers P Ishii J and A Metrick 2003 Corporate governance and equity prices Quarterly
Journal of Economics 118(1) 107-156 Hail L and C Leuz 2009 Cost of capital effects and changes in growth expectations around
US cross-listings Journal of Financial Economics 93 428-454 Hanson G Mataloni R and M Slaughter 2005 Vertical production networks in multinational
firms Review of Economics and Statistics 87(4) 1-15 Helpman E 2006 Trade FDI and the Organization of Firms Journal of Economic Literature
44(3) 289-630 Helpman E Melitz M J and S R Yeaple 2004 Export versus FDI with Heterogeneous Firms
American Economic Review 94(1) 300-316 Hoechle D Schmid M Walter W and D Yermack 2012 How much of the diversification
discount can be explained by poor governance Journal of Financial Economics 103 41-60 Hope O-K and W B Thomas 2008 Managerial empire building and firm disclosure Journal
of Accounting Research 46 (June) 591-626 Huizinga H P and J Voget 2009 International taxation and the direction and volume of cross-
border MampAs Journal of Finance 64 (3) 1217-1249 Jensen M 1986 Agency cost of free cash flow corporate finance and takeovers American
Economic Review Papers and Proceedings 76 323-329
31
Laeven L and R Levine 2007 Is there a diversification discount in financial conglomerates
Journal of Financial Economics 85 331-367 Lang L and R Stulz 1994 Tobinrsquos Q corporate diversification and firm performance Journal
of Political Economy 102 1248-1280 Mataloni R 2003 US multinational companies Operations in 2001 Survey of Current
Business 85-105 Matvos G and A Seru 2011 The dynamic tradeoff between corporate socialism and financial
market dislocation Estimates from internal capital markets Working paper University of Chicago Booth School of Business
Moeller SB and F P Schlingemann 2005 Global diversification and bidder gains A
comparison between cross-border and domestic acquisitions Journal of Banking and Finance 29 533-564
Morck R and B Yeung 1991 Why investors value multinationality Journal of Business 64
165-186
Morck R and B Yeung 2001 Why firms diversify Internatization vs agency behavior In Intangibles B Lev (ed) Oxford University Press pp 269-302
Myers S C and N S Majluf 1984 Corporate financing and investment decisions when firms have information that investors do not have Journal of Financial Economics 13 187-221
Schmid M and I Walter 2009 Do financial conglomerates create or destroy economic value
Journal of Financial Intermediation 18 193-216 Syverson C 2011 What determines productivity Journal of Economic Literature 49(2) 326-
365 Villalonga B 2004a Diversification discount or premium New evidence from the Business
Information Tracking Series Journal of Finance 59 479-506 Villalonga B 2004b Does diversification cause the diversification discount Financial
Management 33 5-27 Wooldridge JM 2010 Econometric Analysis of Cross Section and Panel Data (Second
Edition) Cambridge MA MIT Press Yamin M 1991 A reassessment of Hymerrsquos contribution to the theory of the transnational
corporation C Pitelis and R Sugden (eds) The Nature of the Transnational Firm London Routledge
32
Appendix A Variable Definitions
We obtain information regarding firmsrsquo operations by country and industry from the Bureau of Economic Analysis (BEA) unless otherwise noted Acronyms used below reflect Compustat mnemonics and thus indicate when we obtain data from the Compustat North America Fundamentals Annual database All independent variables are winsorized at 1
Dependent variable
Excess Value = The log of the ratio of actual firm value to imputed firm value Actual firm value is equal to market capitalization (MVE) + book value of total assets (AT) ndash book value of shareholdersrsquo equity (SEQ) Imputed firm value is calculated as the sum of the imputed values of a firmrsquos operations in each country-industry The imputed value for each country-industry is equal to sales generated in that country-industry times a multiple equal to the ratio of a benchmark firmrsquos actual value to total sales A benchmark firm is defined as a median single-segment firm operating in the same country-industry All multiples require at least five firms listed in the respective country-industry on Compustat (domestic) or Worldscope (foreign) with less than 10 of sales income and assets outside the country of domicile Similar to Berger and Ofek (1995) we eliminate extreme values where the ratio of actual to imputed firm value exceeds 400 or is less than 025
Independent variables
Foreign Sales = Sales made by the foreign operations of US MNCs as a percent of total firm sales
Industry Other = Sales outside a firmrsquos primary industry as a percent of total sales (SALE) Equal to zero for firms operating in a single business segment
Log Size = Log of total sales (SALE)
Debt Firm Value = Ratio of long-term debt (DLTT) to total market value (market capitalization (MVE) + book value of total assets (AT) ndash book value of shareholdersrsquo equity (SEQ))
CAPX Sales = Ratio of capital expenditures (CAPX) to total sales (SALE)
EBIT Sales = Ratio of earnings before interest and taxes (EBIT) to total sales (SALE)
RampD Sales = Ratio of research and development expenses (XRD) to total sales (SALE)
Ad Sales = Ratio of advertising expenses (XAD) to total sales (SALE)
Control Premium = A weighted-average (based on the sales in a given country as a percent of total firm sales) of the country-level control premium from Dyck and Zingales (2004)
Cost of Capital = A weighted-average (based on the sales in a given country as a percent of total firm sales) of the country-level credit rating from Institutional Investor (httpwwwinstitutionalinvestorcom) The country-level rating is available for 160 countries and ranges from 0 to 100 Higher values imply greater credit risk
33
CEO Ownership = Percent of shares owned by the CEO from Execucomp
Institution Ownership = Percent of shares owned by institutional investors from Thomson Reuterrsquos Institutional Holdings database
Powerful CEO = Indicator variable equal to one if the CEO is the only insider on the board of directors and also serves as the chairman and president based on data from RiskMetrics Directors database Equal to zero otherwise
Governance Index = Count measure of takeover defenses used in Gompers Ishii and Metrick (2003) from RiskMetrics Governance database As the governance data are not available for every year we follow Gompers Ishii and Metrick (2003) and assume that the firmsrsquo governance attributes are unchanged until publication of the subsequent series
34
Figure 1 Trends in multinational activity and industrial diversification from 1984-2008 This figure presents a plot of the mean percent of foreign sales and the mean percent of sales outside the firmrsquos primary industry (industrial diversification) from 1984 through 2008 Dotted lines above and below the primary lines represent 95 confidence intervals To compile the plot we include the full population of firms in Compustat
35
Table 1 Analysis of accounting data for firms reporting under multiple accounting standards This table provides a comparison of sales net income and assets for firms that changed accounting standards to or from US GAAP Comparisons are provided for the 66 firms that changed to or from US GAAP and for which we could obtain accounting data electronically and in English under both standards for the same fiscal year (eg the firm presented the accounting numbers originally under one set of standards and then restated the prior year numbers in the subsequent year under the new set of standards) Panel A shows the distribution of firms by year Panel B shows the other accounting standards to which US GAAP is compared Panel C shows the mean and median percent that the other accounting standard differs from US GAAP for the same fiscal year and accounting number Two samples are shown in Panel C The first is the full sample of firms with a change in accounting standards to or from US GAAP for which data was available The second is the subset of these firms that occurred during the sample window used in this study (ie excluding changes post-2008) The percent difference in sales is compared to the percent difference in net income and assets Tests of means are based on matched-pair t-tests Tests of medians are based on Wilcoxon matched-pair signed-rank tests Panel A Accounting standard changes by year
2003
4 6
2004
25 38
2005
8 12
2006
6 9
2007
7 11
2008
5 8
2009
5 8
2010
5 8
2011
1 2
66
Panel B Accounting standards compared to US GAAP
IFRS
47 71
Canadian GAAP
12 18
Israeli GAAP
2 3
Dutch GAAP
1 2
Indian GAAP
1 2
Japanese GAAP
1 2
Norwegian GAAP
1 2
Swiss GAAP
1 2
66
36
(Table 1 continued) Panel C Tests of differences between US GAAP and other accounting standards
Percent different from US GAAP
Comparison to sales (p-values)
N Mean Median
Mean Median
Full Sample (2001-2011)
Sales
66 236 000
Net Income
66 10653 647
005 lt001
Assets
66 1162 126
lt001 lt001
Study Sample (2001-2008)
Sales
55 257 000
Net Income
55 10303 589
011 lt001
Assets
55 1047 099
003 lt001
37
Table 2 Descriptive statistics Panel A reports the mean and medians of Excess Value for the multinational firms and single-segment benchmark firms as well as univariate tests across the subsamples Panel B reports summary statistics for the independent variables for the multinational firms The p-values of the tests of differences in means are based on t-tests The p-values of tests of differences in the medians are for a Wilcoxon rank-sum test All variables are defined in Appendix A Panel A Excess Value by sample Multinational All Benchmark Benchmark Subgroups Sample Firms US Foreign
Value Value Test of Diff (p-value) Value
Test of Diff (p-value) Value
Test of Diff (p-value)
Mean 0067 0006 lt001 -0028 lt001 0014 lt001 Median 0076 0000 lt001 -0025 lt001 0000 lt001 N 4950 33074 5998 27076 Panel B Distribution of independent variables for the multinational firms N Mean 25th 50th 75th StDev Foreign Sales 4950 0238 0074 0190 0363 0197 Industry Other 4950 0212 0000 0166 0414 0215 Log Size 4950 6955 5823 6989 8140 1687 Debt Firm Value 4950 0136 0009 0097 0212 0145 CAPX Sales 4950 0054 0020 0033 0054 0101 EBIT Sales 4950 0065 0031 0077 0121 0138 RampD Sales 4950 0049 0000 0013 0059 0083 Ad Sales 4950 0008 0000 0000 0005 0018
38
Table 3 BEA and Worldscope samples
This table reports details on affiliate sales for significant countries pooled from 1998-2008 Significant countries are defined as those in which the operations of US MNCs represent at least 02 of total firm sales (per the BEA) For significant countries the table compares their representation in terms of MNC affiliate sales in the BEA data to the distribution of firm observations in Worldscope US Foreign Affiliates (BEA) Foreign Firms (Worldscope)
Country
N (1)
Sales (millions)
(2)
Total Sales (3)
N (4)
Sales (million)
(5)
Total Sales (6)
Australia 1344 73305 056 859 255865 210 Belgium 894 52755 040 169 26136 021 Brazil 959 60245 046 1143 683167 560 Canada 2730 536767 407 1181 526750 432 China 922 75907 057 822 276541 227 France 1852 183942 139 540 105447 086 Germany 2182 275577 209 480 103888 085 Hong Kong 867 48100 036 157 34094 028 Ireland 478 36754 028 34 3467 003 Italy 1178 82116 062 111 47948 039 Japan 1494 148032 112 2281 1119223 917 Korea South 659 34500 026 3530 2752791 2255 Malaysia 386 26027 020 837 168683 138 Mexico 1405 113623 086 362 237173 194 Netherlands 1149 85105 064 47 22631 019 Singapore 1098 94917 072 139 32913 027 Spain 871 63094 048 129 37419 031 Sweden 712 32682 025 235 23090 019 Switzerland 726 60288 046 74 20242 017 Taiwan 592 30313 023 2949 787049 645 United Kingdom 3054 423934 321 1997 882423 723 United States (domicile) 4950 10460880 7923 na na na Other - Covered by WorldScope 6893 199394 151 9000 4057894 33 Other -Not Covered by WorldScope 713 5352 004 - - - Total 38108 13203609 1000 27076 12204834 100
39
Table 4 OLS regressions of firm excess values on the percent of foreign sales This table reports the results of OLS regressions of firm excess value on the percent of foreign sales (Foreign Sales) the percent of sales outside a firmrsquos primary industry (Industry Other) and control variables All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed) Dep Variable = Excess Value Coefficient t-stat Coefficient t-stat Coefficient t-stat Coefficient t-stat (1) (2) (3) (4) Foreign Sales 0349 419 0209 293 0189 266 0278 210 Industry Other -0134 -186 -0047 -072 -0053 -081 -0326 -325 Log Size 0066 634 0065 624 -0044 -096 Debt Firm Value -0196 -213 -0241 -260 -0685 -588 CAPX Sales 0901 425 0893 435 0488 227 EBIT Sales 1154 542 1170 547 0408 189 RampD Sales 2908 1242 2932 1237 1544 379 Ad Sales 1869 263 2003 280 -1696 -119 Year indicators No No Yes Yes Firm indicators No No No Yes R2 0017 0212 0226 0768 Observations 4950 4950 4950 4950
40
Table 5 Dynamic panel GMM model regressing firm excess values on the percent of foreign sales This table reports the result from a regression of the excess value on the percent of foreign sales (Foreign Sales) the percent of non-primary industry sales (Industry Other) and control variables using a dynamic panel GMM estimator All control variables are considered to be endogenous with the exception of the year indicator variables AR(1) and AR(2) are tests for first-order and second-order serial correlation in the first differenced residuals with the null hypothesis of no serial correlation The null hypothesis of the Hansen test of overidentification is that all instruments are valid The null hypothesis of the difference-in-Hansen test of exogeneity is that the instruments used for the equation in levels are exogenous All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed) Dep Variable = Excess Value Coef t-stat Excess Value (one lag) -0018 -082 Excess Value (two lags) -0048 -272 Foreign Sales 0371 270 Industry Other -0163 -171 Log Size 0010 123 Debt FirmValue -1135 -820 CAPX Sales 0590 342 EBIT Sales -0047 -300 RampD Sales 1376 531 Ad Sales -0751 -040 AR(1) test (p-value) 000 AR(2) test (p-value) 017 Hansen test of over-identification (p-value) 030 Difference-in-Hansen test of exogeneity (p-value) 093 Year indicators Yes Number of firms 1016 Number of observations 4090
41
Table 6 OLS regression of firm excess values on the percent of foreign sales controlling for a control premium This table reports the result of the OLS regression of firm excess value including firm and year fixed effects with an additional control variable to proxy for a country-level ownership control premium (Control Premium) All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed) Dep Variable = Excess Value Coef t -stat Foreign Sales 0303 224 Industry Other -0327 -325 Log Size -0045 -100 Debt FirmValue -0683 -585 CAPX Sales 0490 226 EBIT Sales 0409 189 RampD Sales 1545 379 Ad Sales -1689 -119 Control Premium -1004 -062 Year indicators Yes Firm indicators Yes R2 0768 Observations 4950
42
Table 7 OLS regression of firm excess values on the percent of foreign sales controlling for the cost of capital This table contains the results of an OLS regression of firm excess value including firm and year fixed effects with an additional control variable to proxy for a country-level cost of capital (Cost of Capital) All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed) Dep Variable = Excess Value Coef t -stat Foreign Sales 0255 178 Industry Other -0326 -324 Log Size -0043 -094 Debt FirmValue -0687 -589 CAPX Sales 0485 225 EBIT Sales 0406 188 RampD Sales 1539 379 Ad Sales -1703 -120 Cost of Capital 0004 039 Year indicators Yes Firm indicators Yes R2 0768 Observations 4950
43
Table 8 OLS regression of firm excess values on the percent of foreign sales controlling for aspects of corporate governance This table reports the results of two OLS regressions In the first column are the results of the firm excess value regression including firm and year fixed effect with five proxies for corporate governance included ndash the percent of shares owned by the CEO and the percent squared (CEO Ownership and CEO Ownership squared) the percent owned by institutional investors (Institution Ownership) whether there is a powerful CEO (Powerful CEO) and the Gompers Ishii and Metrick (2003) governance index (Governance Index) In the second column the regression includes the same sample and variables with the exception of the governance proxies which are excluded All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed)
Governance
No Governance (same sample)
Dep Variable = Excess Value Coef t-stat Coef t -stat Foreign Sales 0259 159 0251 154 Industry Other -0099 -080 -0099 -079 Log Size -0105 -185 -0102 -180 Debt FirmValue -0813 -478 -0817 -482 CAPX Sales 0440 252 0438 249 EBIT Sales 0457 247 0457 246 RampD Sales 1717 284 1716 286 Ad Sales -2210 -143 -2190 -143 CEO Ownership 0010 007
CEO Ownership squared -0029 -017 Institution Ownership 0047 067 Powerful CEO -0003 -014 Governance Index 0009 103
Year indicators Yes Yes Firm indicators Yes Yes R2 0811 0810 Observations 2550 2550
- Cover for paperpdf
- crrz_20130603_SMUpdf
-
11
to vary across not just industry but also country Furthermore our counterfactual does not
require the assumption that operations be relocated mitigating the production and sales capacity
concerns
3 Measuring excess value
31 Description of excess value
Figure 1 shows the average percent of foreign sales (based on segment disclosures under US
GAAP) across firms through time and illustrates that firms are continuing to expand
internationally9 This expansion highlights the importance of understanding whether the decision
to operate internationally is on average a value enhancing corporate strategy We contribute to
this understanding by evaluating whether the firm as a whole is worth more or less than its
imputed value (ie the firm is valued in excess of the sum of its individual components)
We measure the excess value of a firm as the logarithm of the ratio of actual firm value to
imputed value based on the method used in Berger and Ofek (1995) The imputed value is the
hypothetical value of the MNC under the assumption that its country-industry components
operate as independent entities A key innovation of our study is an alternative approach to
imputing the value of the foreign operations of an MNC Components of MNCs operating in a
given industry and country are imputed using the value of single-segment firms operating in the
same industry and country Thus we ensure the discount rates and expected growth rates are
applicable to each given industry and location in estimating the implied values of the MNC
segments
9 The discontinuity in industrial diversification (the percent of sales outside the primary industry) between 1997 and 1998 is the result of a change in segment reporting rules
12
The actual firm values are observable for all firms in our sample The hypothetical firm
values for multi-segment firms are imputed using market value to sales ratios herein referred to
as lsquomultiplesrsquo of the median single-segment firm operating in the same country-industry Thus
these single country-industry firms serve as benchmarks for an MNCrsquos operations within that
same country-industry The imputed value of the country-industry component can be viewed as
an estimate of what that component would be worth if it operated independently The sum of the
imputed values across all country-industry components represents the hypothetical value that the
multi-segment firm would be worth if it were broken apart
32 Data
Four primary data sources play a distinct role in carrying out our analysis i) Bureau of
Economic Analysis (BEA) data ii) Compustat Segment data iii) Worldscope data and iv)
Compustat Fundamentals Annual data Broadly speaking BEA data provide information about
the diversified nature of MNCs Compustat Segment data provide information about the
industrial diversification of US domiciled firms Worldscope data are used to compute
multiples for single-segment foreign firms that serve as benchmarks for the foreign operations of
MNCs (ie foreign benchmarks) and Compustat Fundamentals Annual data are used to
compute multiples for single-segment domestic firms that serve as benchmarks for the domestic
operations of multinational firms We also use the latter data source to construct our primary
control variables
13
Key to the execution of our study is our access to BEA data Federal law requires US-
domiciled MNCs to report certain financial and operating data to the BEA10 With regard to
observing the country-industry operations of MNCs use of the BEA data overcomes two
important limitations of Compustat Segment data First Compustat does not consistently identify
the specific location or scale of MNCsrsquo foreign operations11 Second Compustat does not
identify the industry activities in each country of operation This latter point is particularly
limiting for MNCs because domestic industry activity need not mirror foreign industry activity
An MNC generating an equal proportion of sales in two industries in the domestic market need
not generate industry sales in equal proportions in every foreign market12 The lack of detail and
consistency in Compustat Segment data arises because segment reporting is highly aggregated
and firms exercise substantial discretion in defining segments under Accounting Standards
Codification 280 (ASC 280) (eg Villalonga 2004a and Bens Berger and Monahan 2011)
The BEA defines an MNC as the combination of a single US entity called the US parent
and at least one foreign affiliate ndash that is these firms have a physical presence outside the US
due to FDI Since 1982 MNCs have completed a mandatory and confidential lsquoSurvey of US
Direct Investment Abroadrsquo for the domestic operation and the operations of each foreign
affiliate defined as a foreign entity in which the US parent holds at least a 10 percent equity
interest (directly or indirectly) The data are reported in US dollars on a fiscal year basis and in
10 BEA surveys are conducted pursuant to the International Investment and Trade in Services Survey Act (PL 94-472 90 Stat 2059 22 USC 3101-3108) See httpwwwbeagovsurveysdiasurvhtm and Mataloni (2003) for more detailed information on the BEA data 11 Exhibit 21 disclosures of material subsidiaries and their locations in SEC 10-K filings could potentially help overcome the inability to observe the location of the firmrsquos foreign operations from segment disclosures However at least three limitations remain (1) for industrially diversified multinational firms the researcher would be required to make an assumption about the proportion of industrial diversification in each country (2) Exhibit 21 provides no information about the scale of activity in each jurisdiction and (3) managerial discretion is used to determine a lsquomaterialrsquo subsidiary under Section 601 of SEC Regulation S-K 12 In fact the expectation that a multinational firm would exhibit different industry membership in different countries is consistent with the theory of vertical foreign direct investment where firms separate their various value chain activities and locate them in the most favorable country (see eg Hanson Mataloni and Slaughter 2005)
14
accordance with US Generally Accepted Accounting Principles (US GAAP) For each year
we observe the sales industry composition and location of not only the parent but also each
affiliate13
The procedures we use to construct our sample are similar to those used by Berger and Ofek
(1995) and Denis et al (2002) Using firm-years appearing in Compustat Fundamentals Annual
and Compustat Segment data we obtain a sample of firms domiciled in the US with total sales
exceeding $20 million We exclude firms in the financial service (SIC codes 6000 to 6999) and
utility (SIC codes 4000 to 4999) industries and eliminate firm-years when the sum of segment
sales (Compustat geographic or Compustat business) is not within 1 of total firm sales for that
year We restrict our sample period to include fiscal years beginning after December 15 1997 as
ASC 280 Segment Reporting substantially altered the definition of a reporting segment under
US GAAP This restriction ensures that Compustat Segment data reflects the use of a consistent
accounting standard Finally we require that our MNCs appear in both Compustat and BEA
data The requirement that our sample firms appear in the BEA database ensures that the firms
have observable international operations In sum these procedures result in 1166 multinational
firms incorporated in the US with 4950 observations between 1998 and 2008 that meet all of
our data requirements
33 Measuring excess value
The dependent variable in our study is excess firm value (Excess Value) defined as the
logarithm of the ratio of actual firm value to imputed firm value (Berger and Ofek 1995)
13 We obtain sales industry membership and country location for affiliates from one of two sources When affiliates exceed the BEA-determined reporting thresholds (ie if their assets sales or net income (loss) exceed $7 million in 1999 $30 million in 2000 through 2003 $10 million in 2004 and $40 million in 2005 through 2008) they must file their own detailed survey When affiliates fall below the thresholds we obtain our data from an attachment to the parent survey
15
Appendix A provides the definition of this and all other variables used in our analysis We
observe actual firm value equal to a firmrsquos market value of equity plus book value of liabilities
(ie book value of total assets minus book value of total equity) using Compustat Fundamentals
Annual data We calculate imputed firm value as the sum of the imputed values of a firmrsquos
operations in each country-industry Our method for imputing the value of the separate
components of a firm can best be described in three steps First we obtain total sales generated
by a firm for each country-industry in which it operates14 Second we obtain multiples (market
value to sales ratios) for benchmark firms operating in those same country-industries Third we
multiply a firmrsquos country-industry sales by the applicable median country-industry multiple to
obtain the imputed market value for each country-industry operation We perform each step
annually
Conceptually imputed values can be based on assets (ie Tobinrsquos Q) net income or sales
multiples We restrict ourselves to sales multiples for two reasons First using value to sales
ratios maintains consistency with the prior research on the excess value implications of
multinational operations (eg Denis et al 2002) Second as our method uses accounting data
for foreign companies to compute country-industry multiples the accounting numbers we use
need to be consistently measured across firms using different accounting standards We find that
sales data are measured most consistently
To assess the comparability of sales relative to either net income or assets across various
accounting standards we examine all firms listed on Worldscope as changing to or from US
14 Specifically we allocate total sales from Compustat to each component based on the proportion of BEA sales (after eliminating intercompany sales) occurring in each country-industry This step ensures that our total firm sales do not deviate from a firmrsquos total sales as reported in the 10-K
16
GAAP between 2005 and 201115 We obtain accounting data for the year of the change and the
prior year where electronically available in English We are able to obtain data for 66 firms
changing standards as detailed in Table 1 The distribution of years for which accounting data
are available under two different accounting standards for the same fiscal year is shown in Panel
A The years shown are the year prior to the change as these accounting numbers were provided
under the original standard and then subsequently restated in the following year under the new
standard for comparative purposes The majority of the shifts were to or from IFRS (71)
though seven other accounting standards are noted (Panel B) We examine the percent that the
non-US GAAP reported number differs from the US GAAP reported number in Panel C to
assess the comparability of these three summary accounting numbers across various accounting
standards Examining both the full sample as well as the subset that overlaps our study we find
that sales are more consistently measured than net income or assets
The sum of the imputed values across the country-industry components of an MNC is an
estimate of the value of a portfolio of unrelated businesses that mirror the related businesses of
the firm Consequently a comparison of the actual firm value with the imputed firm value is a
measure of how a multinational network affects firm value We provide descriptive statistics for
Excess Value for our sample of 4950 MNC firm-years in Table 2 Panel A This table also
includes univariate comparisons of the values between the MNC firms and the domestic single-
segment and foreign single-segment benchmark firms The Excess Value measure is significantly
higher for MNCs suggesting a premium in firm value for multinational firms relative to the
benchmark firms in total as well as the domestic and foreign subgroups (p-values for tests of
15 Due to the hand collection efforts required and the limitation on electronic filings we began our search with changes documented in 2005 which generally require filings from 2004
17
means and medians are lt 001)16 These results provide some initial evidence that operating in
multiple countries is positively associated with firm value
To estimate country-industry multiples we rely on Worldscope financial data on foreign
firms and Compustat Fundamentals Annual for domestic firms We restrict the firms to include
only those with at least 90 of sales income and assets inside the country of domicile (ie
those that do not report significant multinational activity) and that operate in a single industry
We refer to these firms as benchmark firms (either foreign or domestic depending on the country
of domicile) these firms do not appear in any of our regressions We report the number of
benchmark firms during our sample period in Table 2 Panel A The industry criterion is based on
the two-digit SIC code17 For every country that has at least five firms in the respective industry
and year we use the median ratio of market value to sales18 This ratio is the country-industry
multiple ndash an input required to compute imputed values
To determine the country-industry composition of MNCs we rely on BEA data In Table 3
we report the aggregate number of foreign affiliates and total sales by country as provided by
the BEA data for our sample of 4950 MNCs The specific countries tabulated are those that
represent at least 02 percent of total firm sales (pooled across all years) in our sample The top
five foreign countries in which MNCs generate sales are Canada United Kingdom Germany
France and Japan Table 3 also provides a comparison of the number of foreign benchmark
firms available in Worldscope in the specific countries in which our sample of MNCs generate
16 Similar to Berger and Ofek (1995) the median values differ slightly from zero for the benchmark single-segment firms due to the truncation of extreme excess values 17 BEA reporting constrains us to the two-digit SIC code level 18 When five firms are not available for a given country and industry we follow Berger and Ofek (1995) and move to a more general industry definition (eg one-digit SIC code) This procedure results in a mean (median) 630 (757) of MNC sales matched at the two-digit level
18
the majority of their sales Country coverage in Worldscope is not available for countries
representing 004 percent of total firm sales (per BEA)
4 Empirical results
41 Independent variables
Recall that our objective is to examine the overall relation between excess firm value and
multinational operations Our proxy for the extent of multinational operations is the percentage
of total firm sales made by foreign operations (Foreign Sales) By this measure the extent of
multinational activity in our sample of MNCs is non-trivial ndash Table 2 Panel B shows that
approximately 24 percent of sales are generated by foreign operations on average Our first
analysis estimates ordinary least square regressions of Excess Value on Foreign Sales and
control variables (discussed below)
We include variables in our regression to control for other potential determinants of excess
value These are the percentage of sales made by the firm outside its primary industry (Industry
Other) to control for any relation between industrial diversification and excess value as in Berger
and Ofek (1995) For this purpose we obtain industry sales using Compustat Segment data19 A
firmrsquos primary industry is the industry in which the firm generates the majority of its sales and
we determine industry sales at the business segment level We set Industry Other equal to zero
for firms that operate in a single business segment
Consistent with prior research we also include controls for firm size (Log Size) the ratio of
long term debt to total market value (Debt Firm Value) the ratio of capital expenditures to total
sales (CAPX Sales) the ratio of earnings before interest and taxes to total sales (EBIT Sales)
the ratio of research and development expenses to total sales (RampD Sales) and the ratio of 19 Our results are unaffected by using BEA data to measure Industry Other
19
advertising expenses to total sales (Ad Sales) Table 2 Panel B provides the distribution of the
control variables All independent variables are Winsorized at 1 In our OLS regressions we
use heteroskedasticity and autocorrelation consistent robust standard errors with clustering at the
firm level
42 Primary regression analysis
In this section we examine the relation between firm excess value and the amount of foreign
activity at US MNCs Table 4 contains the results of regressions of excess value on Foreign
Sales for multiple specifications In column (1) Excess Value is regressed on both the percent of
foreign sales (Foreign Sales) and the percent of sales outside the firmrsquos primary industry
(Industry Other) We add the control variables to the regression in column (2) and year
indicators in column (3) In all three specifications the coefficient on the percent of foreign sales
remains significantly positive (with a p-value lt 001) For a sense of economic magnitude in
column (3) a one standard deviation (197) increase in the percent of foreign sales (Foreign
Sales) is associated with a 41 (equal to (e0349-1) times 0197) increase in firm excess value The
effect is larger for the other two columns ndash 82 increase in firm excess value in column (1) and
46 in column (2)20
Our finding of a premium reconciles with the international trade literature which finds that
firms engaging in international trade are more productive than those that do not (see Helpman
2006 and Syverson 2011 for reviews) Due to data limitations it is difficult to accurately
measure TFP at the firm level especially for firms which operate in multiple industries andor
20 Using the alternative strategy that employs US domiciled single-segment firms as the benchmark for the imputed value of the foreign operations Denis et al (2002) find that greater foreign activity results in a discount to firm value Using the Denis et al (2002) method to calculate excess value in our sample we obtain a significantly negative regression coefficient on Foreign Sales using the models in Table 4 columns (2) and (3) (the coefficient is not significant using the model in column (1) results untabulated)
20
countries As an alternative method to evaluate whether our finding of a premium is simply the
result of more productive firms also having more multinational operations we investigate
whether the premium is simply an artifact of innate characteristics of firms that choose to invest
abroad For example Helpman et al (2004) present a simple model with heterogeneous
productivity endowments The firms that receive the highest productivity endowments are the
ones capable of paying the fixed costs to establish a foreign subsidiary
To the extent that productivity advantages create excess value one would expect the type of
firms that choose to invest more abroad would be valued at a premium even in the absence of
their foreign investments To control for time invariant firm characteristics (eg innate
productivity advantages) and hence a potential source of endogeneity we add firm-level fixed
effects in column (4) of Table 4 We continue to find that the extent of foreign operations
(Foreign Sales) is positively associated with Excess Value Across all specifications the sign
on all significant control variables is consistent with prior research (eg Berger and Ofek 1995
and Denis et al 2002)
The finding of a premium makes a significant contribution to the relatively sparse literature
on the value effects of multinational activity Our measure of imputed value expands upon the
methods used by Berger and Ofek (1995) and Denis et al (2002) but allows us to answer a
different question than Denis et al (2002) Holding constant the extent of industrial
diversification we ask whether MNCs are more valuable than a portfolio of benchmark firms of
a similar geographic footprint In contrast Denis et al (2002) ask whether holding constant the
extent of industrial diversification MNCs are more valuable than a portfolio of domestic (US)
benchmark firms We believe that asking whether an MNCrsquos foreign (domestic) operations are
21
more valuable than those of a foreign (domestic) firm is a more appropriate way to assess the
value implications of a multinational network
5 Robustness
51 Endogeneity related to dynamic relations
Prior research argues that the relation between industrial diversification and excess firm
value could be endogenous through dynamic relationships based on observing past outcomes
Managers of firms likely choose to enter or exit industries or geographic regions based upon
their previous performance For example Campa and Kedia (2002) find that firms are more
likely to enter new industries when prospects in their current lines of business are deteriorating
Similar dynamics could also drive a relation between excess value and FDI We assess the
robustness of the previous results by estimating a dynamic panel data model which allows us to
control for dynamic endogeneity in addition to firm level unobserved heterogeneity We employ
the GMM estimator for dynamic panel models developed by Arellano and Bond (1991) Arellano
and Bover (1995) and Blundell and Bond (1998) Our results from this analysis confirm our
previous findings that firms with a larger degree of foreign operations have positive excess
values
Dynamic panel data models are generalizations of the traditional fixed effects model where
lags of the dependent variable are added to the right hand side of the equation These additional
lags control for the impact a firmrsquos past performance has on its current performance through the
channels discussed above The GMM estimator for dynamic panel models developed by
Arellano and Bond (1991) starts by differencing the data at the firm level in neighboring time
periods to eliminate any firm specific time invariant unobserved heterogeneity that may be
22
present Eliminating the firm specific fixed effect by differencing the data with lags of the
dependent variable on the right hand side introduces correlation between the new error term and
the differenced explanatory variables This problem is solved by applying the standard GMM
estimator using lags of both the original explanatory variables and excess value as instruments
We note that the dynamic panel data framework allows us to treat both international and
industrial diversification (as well as the other firm-specific control variables) as endogenous
Following Hoechle et al (2012) we also allow contemporaneous values of these variables to be
treated as endogenous
Lagged values of the explanatory variables are valid instruments in a dynamic panel data
model when the model is dynamically complete (eg Wooldridge 2010) A dynamic panel data
model is dynamically complete conditional on the unobserved effect if enough lags of both the
explanatory and dependent variable are included to make the resulting error term uncorrelated
with all the right hand side variables Under this assumption any lags for the explanatory
variables beyond this can be used within the GMM estimator as instruments Like Hoechle et al
(2012) we add two years of lags of a firmrsquos excess value to the right hand side assuming that
this is enough lags to control for dynamic feedback effects We then use past values of all
variables beyond the second lag up to a total of seven years to act as instruments
The results of the dynamic panel data estimator are reported in Table 5 These results confirm
the previous finding of a positive relation between the degree of international operations and
excess value We note that the size and significance level of the coefficient on Foreign Sales
diversification are robust to changes in the number of lags used as instruments The Arellano and
Bond (1991) estimator includes testing diagnostics for correct specification of the model If the
model is correctly specified differences in the residuals from the levels equation should be
23
serially uncorrelated at lag one but correlated at lag two The p-values for tests of serial
correlation of the residuals at lags 1 and 2 are reported in Table 5 and these indicate that the
model is not rejected We also report in Table 5 the Hansen test for overidentification which is a
test of the null hypothesis that the instruments are valid The p-value for this test is 030
indicating that we fail to reject this null hypothesis Finally we report another test for exogeneity
known as the difference-in-Hansen test which tests the null hypothesis that the instruments in
the original equation are valid We fail to reject this null hypothesis as well
52 Identifying value effects of multinational operations via cross-border acquisitions
Another alternative to investigating a causal link between foreign direct investment and firm
value is an event study of foreign acquisitions21 Acquired foreign targets represent only a small
fraction of MNCsrsquo worldwide sales in the year of the acquisition ndash less than 1 percent in our
sample Additionally cross-border acquisitions are not the primary source of international
expansion for the average firm We find that foreign acquisitions account for between 18 percent
and 55 percent of the year-over-year change in foreign sales with the remainder of the growth
coming from either newly established entities (ie greenfield investments) or growth in existing
operations22 These results suggest that acquisitions are not a material source of growth for the
21 Doukas and Travlos (1988) find positive returns when the acquirer is not operating in the target firmrsquos country and insignificant returns when the acquirer is operating in the target firmrsquos country or expanding internationally for the first time Morck and Yeung (1991) find marginal evidence of positive returns for firms with intangible assets Finally Doukas (1995) finds positive returns only for firms with average Tobinrsquos q ratios greater than one More recent work also using an event study methodology provides mixed evidence on valuation effects of global diversification through acquisitions Dos Santos Errunza and Miller (2008) find insignificant returns for firms with already established foreign operations firms establishing initial foreign operations and firms acquiring targets in related industries Doukas and Kan (2006) and Moeller and Schlingemann (2005) find reductions to shareholder value while Francis Hasan and Sun (2008) find positive returns for acquirers of targets in segmented financial markets 22 New foreign affiliates to an MNC group each year report on the BEA survey whether they entered the group as an acquisition or a newly established entity (ie greenfield investment) Affiliates under the lowest reporting threshold do not report this information resulting in our inability to precisely identify the source of 33 percent of the increase in firm sales as either acquisition or greenfield
24
typical US MNC suggesting that an event study would lack power and generalizability For
these reasons we do not implement an event study
53 Control premium
A control premium or private benefits of control occurs when ldquosome value whatever the
source is not shared among all the shareholders in proportion of the shares owned but it is
enjoyed exclusively by the party in controlrdquo (Dyck and Zingales 2004 p 541) Using stock
prices of traded shares held by minority shareholders in determining the market value to sales
ratios of our benchmark firms could underestimate the true value of our benchmark firms if those
firms face large control premiums This in turn could induce an excess value premium because
the imputed value of these country-industry segments would be artificially low The existence of
such benefits is more prevalent in some countries than others and could affect the value of an
MNCrsquos operations in such domains We use the country-level control premiums from Dyck and
Zingales (2004) and using each firmrsquos country sales as a percent of total firm sales we obtain a
weighted average control premium that each MNC faces (Control Premium) For countries not
included in the Dyck and Zingales (2004) analysis we set the control premium equal to zero23 In
Table 6 we show that Foreign Sales remains positive and highly significant (p-value lt 001)
when we include the proxy for control premium As a result it does not appear that the excess
value premium is driven by MNCs operating in countries where majority shareholders are able to
extract large control premiums
23 Results are robust to replacing missing values with the mean control premium of the respective GDP quartile
25
54 Cost of capital
As discussed in Section 2 when countries have shallow capital markets a multinational
network may provide the benefit of a lower cost of capital for investments through an internal
capital market as well as through better access to the US capital market Prior research
establishes that there is substantial variation in the cost of capital across countries (Erb Harvey
and Viskanta 1996 and Hail and Leuz 2009) To determine whether the documented premium
is due to cost of capital advantages we include a control variable to capture the difference
between the MNCrsquos cost of capital and the cost of capital faced by its competitors in the global
markets in which it operates
We compute our Cost of Capital proxy in two steps First we obtain the country-year credit
rating published by Institutional Investor24 The rating ranges from 0 to 100 where higher values
imply that a country has a higher default risk Country-level credit risk is a reasonable predictor
of expected equity market returns and volatility (Erb et al 1996) and exhibits a highly
significant correlation with international accounting-based estimates of imputed cost of capital
(Hail and Leuz 2009) Second using each firmrsquos country sales as a percent of total firm sales
we obtain a weighted average credit risk rating across the countries in which each firm operates
A higher value implies an MNC operates in countries with higher cost of capital In Table 7 we
do not find a result consistent with MNCs gaining an advantage from their access to low cost
capitalndash Cost of Capital is not significantly associated with Excess Value25
24 We thank Cam Harvey for providing these data 25 Results are consistent using cost of capital as defined in Erb et al 1996
26
55 Corporate governance
In light of the recent finding that corporate governance mitigates the diversification discount
we examine whether the premium to multinational activity is sensitive to controls for corporate
governance Hoechle et al (2012) find that the magnitude of the industrial diversification
discount is decreased and in several specifications no longer significant in the presence of
governance proxies In the first column of Table 8 we include the five governance proxies that
are consistently significantly associated with excess value in Hoechle et al (2012) ndash the percent
of CEO ownership (CEO Ownership and CEO Ownership squared) the percent of institutional
ownership (Institution Ownership) whether the firm has a powerful or influential CEO
(Powerful CEO) and the governance index from Gompers Ishii and Metrick (2003)
(Governance Index)
As documented in Table 8 including these control variables reduces our sample size by
almost 50 (to 2550 observations) and tends to exclude the smaller firms from the analysis
None of the governance proxies are significant Relative to column (4) of Table 4 the coefficient
on Foreign Sales is similar in magnitude but is no longer statistically significant at
conventional levels To investigate whether the decreased significance is due to the smaller
sample or the inclusion of the governance variables we re-estimate the regression for the same
2550 observations after removing the governance controls The coefficient and t-statistic are
virtually identical suggesting that the loss of significance is due to the reduced sample and not
the governance controls We do not find evidence to suggest that variation in corporate
governance practices drives variation in excess value for US MNCs
27
6 Conclusion
Using a new method to quantify the valuation effects of foreign direct investment by US
domiciled firms we find robust evidence of a premium in excess value associated with the
degree of multinational operations Our method builds on that used in previous studies
examining firm excess value (eg Berger and Ofek 1995 and Denis et al 2002) by
incorporating the location in addition to the industry of each corporate affiliate This allows
important factors such as growth and cost of capital to vary across countries as well as industries
Examining a sample of multinational firms we find that a one-standard deviation (197)
increase in foreign operations is associated with an increase of between 41 and 82 in excess
value minus both a statistically and economically significant finding
This result complements the finding of the international trade literature that more productive
firms are more likely to operate internationally However the premium remains after including
firm fixed effects to control for persistent firm traits (eg productivity)
The finding of a premium stands up to a succession of robustness tests designed to evaluate
whether the result arises from alternative methods or explanations First we estimate a dynamic
panel data model using the generalized method of moments (GMM) estimator to simultaneously
account for the potential endogeneity of FDI industrial diversification and firm value Second
we include a control variable to proxy for whether the firm operates in countries with larger
control premiums which could result in understated multiples and mechanically induce a
premium Third we include a control variable to measure the cost of capital across the countries
in which an MNC operates to evaluate whether a relatively low cost of capital borne by
multinational firms is a meaningful source of the premium Fourth we control for several
corporate governance proxies which have been found to be correlated with excess values (and
28
industrial diversification) We fail to find evidence supporting any of these alternative
explanations ndash the premium in excess value arising at firms with greater multinational operations
remains significant
Overall our new method of estimating excess values for multinational firms provides strong
evidence of a statistically and economically significant premium associated with increased
international operations The premium is robust to a host of specifications and controls designed
to evaluate alternative explanations We leave to future research the questions of whether the
premium to US multinational firms applies more broadly to other countries of domicile and
what factors influence the premium
29
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Arellano M and Bover O 1995 Another look at instrumental variables estimation of error
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Berger P G and E Ofek 1995 Diversificationrsquos effect on firm value Journal of Financial
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Banking and Finance 19 1285-1303 Doukas J and N G Travlos 1988 The effect of corporate multinationalism on shareholdersrsquorsquo
wealth evidence from international acquisitions Journal of Finance 43(5) 1161-1175 Doukas JA and O B Kan 2006 Does global diversification destroy firm value Journal of
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Multinational and local firm responses to currency depreciations Review of Financial Studies 21 (6) 2857-2888
Francis B I Hasan and X Sun 2008 Financial market integration and the value of global
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Journal of Economics 118(1) 107-156 Hail L and C Leuz 2009 Cost of capital effects and changes in growth expectations around
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44(3) 289-630 Helpman E Melitz M J and S R Yeaple 2004 Export versus FDI with Heterogeneous Firms
American Economic Review 94(1) 300-316 Hoechle D Schmid M Walter W and D Yermack 2012 How much of the diversification
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Economic Review Papers and Proceedings 76 323-329
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Laeven L and R Levine 2007 Is there a diversification discount in financial conglomerates
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Moeller SB and F P Schlingemann 2005 Global diversification and bidder gains A
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32
Appendix A Variable Definitions
We obtain information regarding firmsrsquo operations by country and industry from the Bureau of Economic Analysis (BEA) unless otherwise noted Acronyms used below reflect Compustat mnemonics and thus indicate when we obtain data from the Compustat North America Fundamentals Annual database All independent variables are winsorized at 1
Dependent variable
Excess Value = The log of the ratio of actual firm value to imputed firm value Actual firm value is equal to market capitalization (MVE) + book value of total assets (AT) ndash book value of shareholdersrsquo equity (SEQ) Imputed firm value is calculated as the sum of the imputed values of a firmrsquos operations in each country-industry The imputed value for each country-industry is equal to sales generated in that country-industry times a multiple equal to the ratio of a benchmark firmrsquos actual value to total sales A benchmark firm is defined as a median single-segment firm operating in the same country-industry All multiples require at least five firms listed in the respective country-industry on Compustat (domestic) or Worldscope (foreign) with less than 10 of sales income and assets outside the country of domicile Similar to Berger and Ofek (1995) we eliminate extreme values where the ratio of actual to imputed firm value exceeds 400 or is less than 025
Independent variables
Foreign Sales = Sales made by the foreign operations of US MNCs as a percent of total firm sales
Industry Other = Sales outside a firmrsquos primary industry as a percent of total sales (SALE) Equal to zero for firms operating in a single business segment
Log Size = Log of total sales (SALE)
Debt Firm Value = Ratio of long-term debt (DLTT) to total market value (market capitalization (MVE) + book value of total assets (AT) ndash book value of shareholdersrsquo equity (SEQ))
CAPX Sales = Ratio of capital expenditures (CAPX) to total sales (SALE)
EBIT Sales = Ratio of earnings before interest and taxes (EBIT) to total sales (SALE)
RampD Sales = Ratio of research and development expenses (XRD) to total sales (SALE)
Ad Sales = Ratio of advertising expenses (XAD) to total sales (SALE)
Control Premium = A weighted-average (based on the sales in a given country as a percent of total firm sales) of the country-level control premium from Dyck and Zingales (2004)
Cost of Capital = A weighted-average (based on the sales in a given country as a percent of total firm sales) of the country-level credit rating from Institutional Investor (httpwwwinstitutionalinvestorcom) The country-level rating is available for 160 countries and ranges from 0 to 100 Higher values imply greater credit risk
33
CEO Ownership = Percent of shares owned by the CEO from Execucomp
Institution Ownership = Percent of shares owned by institutional investors from Thomson Reuterrsquos Institutional Holdings database
Powerful CEO = Indicator variable equal to one if the CEO is the only insider on the board of directors and also serves as the chairman and president based on data from RiskMetrics Directors database Equal to zero otherwise
Governance Index = Count measure of takeover defenses used in Gompers Ishii and Metrick (2003) from RiskMetrics Governance database As the governance data are not available for every year we follow Gompers Ishii and Metrick (2003) and assume that the firmsrsquo governance attributes are unchanged until publication of the subsequent series
34
Figure 1 Trends in multinational activity and industrial diversification from 1984-2008 This figure presents a plot of the mean percent of foreign sales and the mean percent of sales outside the firmrsquos primary industry (industrial diversification) from 1984 through 2008 Dotted lines above and below the primary lines represent 95 confidence intervals To compile the plot we include the full population of firms in Compustat
35
Table 1 Analysis of accounting data for firms reporting under multiple accounting standards This table provides a comparison of sales net income and assets for firms that changed accounting standards to or from US GAAP Comparisons are provided for the 66 firms that changed to or from US GAAP and for which we could obtain accounting data electronically and in English under both standards for the same fiscal year (eg the firm presented the accounting numbers originally under one set of standards and then restated the prior year numbers in the subsequent year under the new set of standards) Panel A shows the distribution of firms by year Panel B shows the other accounting standards to which US GAAP is compared Panel C shows the mean and median percent that the other accounting standard differs from US GAAP for the same fiscal year and accounting number Two samples are shown in Panel C The first is the full sample of firms with a change in accounting standards to or from US GAAP for which data was available The second is the subset of these firms that occurred during the sample window used in this study (ie excluding changes post-2008) The percent difference in sales is compared to the percent difference in net income and assets Tests of means are based on matched-pair t-tests Tests of medians are based on Wilcoxon matched-pair signed-rank tests Panel A Accounting standard changes by year
2003
4 6
2004
25 38
2005
8 12
2006
6 9
2007
7 11
2008
5 8
2009
5 8
2010
5 8
2011
1 2
66
Panel B Accounting standards compared to US GAAP
IFRS
47 71
Canadian GAAP
12 18
Israeli GAAP
2 3
Dutch GAAP
1 2
Indian GAAP
1 2
Japanese GAAP
1 2
Norwegian GAAP
1 2
Swiss GAAP
1 2
66
36
(Table 1 continued) Panel C Tests of differences between US GAAP and other accounting standards
Percent different from US GAAP
Comparison to sales (p-values)
N Mean Median
Mean Median
Full Sample (2001-2011)
Sales
66 236 000
Net Income
66 10653 647
005 lt001
Assets
66 1162 126
lt001 lt001
Study Sample (2001-2008)
Sales
55 257 000
Net Income
55 10303 589
011 lt001
Assets
55 1047 099
003 lt001
37
Table 2 Descriptive statistics Panel A reports the mean and medians of Excess Value for the multinational firms and single-segment benchmark firms as well as univariate tests across the subsamples Panel B reports summary statistics for the independent variables for the multinational firms The p-values of the tests of differences in means are based on t-tests The p-values of tests of differences in the medians are for a Wilcoxon rank-sum test All variables are defined in Appendix A Panel A Excess Value by sample Multinational All Benchmark Benchmark Subgroups Sample Firms US Foreign
Value Value Test of Diff (p-value) Value
Test of Diff (p-value) Value
Test of Diff (p-value)
Mean 0067 0006 lt001 -0028 lt001 0014 lt001 Median 0076 0000 lt001 -0025 lt001 0000 lt001 N 4950 33074 5998 27076 Panel B Distribution of independent variables for the multinational firms N Mean 25th 50th 75th StDev Foreign Sales 4950 0238 0074 0190 0363 0197 Industry Other 4950 0212 0000 0166 0414 0215 Log Size 4950 6955 5823 6989 8140 1687 Debt Firm Value 4950 0136 0009 0097 0212 0145 CAPX Sales 4950 0054 0020 0033 0054 0101 EBIT Sales 4950 0065 0031 0077 0121 0138 RampD Sales 4950 0049 0000 0013 0059 0083 Ad Sales 4950 0008 0000 0000 0005 0018
38
Table 3 BEA and Worldscope samples
This table reports details on affiliate sales for significant countries pooled from 1998-2008 Significant countries are defined as those in which the operations of US MNCs represent at least 02 of total firm sales (per the BEA) For significant countries the table compares their representation in terms of MNC affiliate sales in the BEA data to the distribution of firm observations in Worldscope US Foreign Affiliates (BEA) Foreign Firms (Worldscope)
Country
N (1)
Sales (millions)
(2)
Total Sales (3)
N (4)
Sales (million)
(5)
Total Sales (6)
Australia 1344 73305 056 859 255865 210 Belgium 894 52755 040 169 26136 021 Brazil 959 60245 046 1143 683167 560 Canada 2730 536767 407 1181 526750 432 China 922 75907 057 822 276541 227 France 1852 183942 139 540 105447 086 Germany 2182 275577 209 480 103888 085 Hong Kong 867 48100 036 157 34094 028 Ireland 478 36754 028 34 3467 003 Italy 1178 82116 062 111 47948 039 Japan 1494 148032 112 2281 1119223 917 Korea South 659 34500 026 3530 2752791 2255 Malaysia 386 26027 020 837 168683 138 Mexico 1405 113623 086 362 237173 194 Netherlands 1149 85105 064 47 22631 019 Singapore 1098 94917 072 139 32913 027 Spain 871 63094 048 129 37419 031 Sweden 712 32682 025 235 23090 019 Switzerland 726 60288 046 74 20242 017 Taiwan 592 30313 023 2949 787049 645 United Kingdom 3054 423934 321 1997 882423 723 United States (domicile) 4950 10460880 7923 na na na Other - Covered by WorldScope 6893 199394 151 9000 4057894 33 Other -Not Covered by WorldScope 713 5352 004 - - - Total 38108 13203609 1000 27076 12204834 100
39
Table 4 OLS regressions of firm excess values on the percent of foreign sales This table reports the results of OLS regressions of firm excess value on the percent of foreign sales (Foreign Sales) the percent of sales outside a firmrsquos primary industry (Industry Other) and control variables All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed) Dep Variable = Excess Value Coefficient t-stat Coefficient t-stat Coefficient t-stat Coefficient t-stat (1) (2) (3) (4) Foreign Sales 0349 419 0209 293 0189 266 0278 210 Industry Other -0134 -186 -0047 -072 -0053 -081 -0326 -325 Log Size 0066 634 0065 624 -0044 -096 Debt Firm Value -0196 -213 -0241 -260 -0685 -588 CAPX Sales 0901 425 0893 435 0488 227 EBIT Sales 1154 542 1170 547 0408 189 RampD Sales 2908 1242 2932 1237 1544 379 Ad Sales 1869 263 2003 280 -1696 -119 Year indicators No No Yes Yes Firm indicators No No No Yes R2 0017 0212 0226 0768 Observations 4950 4950 4950 4950
40
Table 5 Dynamic panel GMM model regressing firm excess values on the percent of foreign sales This table reports the result from a regression of the excess value on the percent of foreign sales (Foreign Sales) the percent of non-primary industry sales (Industry Other) and control variables using a dynamic panel GMM estimator All control variables are considered to be endogenous with the exception of the year indicator variables AR(1) and AR(2) are tests for first-order and second-order serial correlation in the first differenced residuals with the null hypothesis of no serial correlation The null hypothesis of the Hansen test of overidentification is that all instruments are valid The null hypothesis of the difference-in-Hansen test of exogeneity is that the instruments used for the equation in levels are exogenous All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed) Dep Variable = Excess Value Coef t-stat Excess Value (one lag) -0018 -082 Excess Value (two lags) -0048 -272 Foreign Sales 0371 270 Industry Other -0163 -171 Log Size 0010 123 Debt FirmValue -1135 -820 CAPX Sales 0590 342 EBIT Sales -0047 -300 RampD Sales 1376 531 Ad Sales -0751 -040 AR(1) test (p-value) 000 AR(2) test (p-value) 017 Hansen test of over-identification (p-value) 030 Difference-in-Hansen test of exogeneity (p-value) 093 Year indicators Yes Number of firms 1016 Number of observations 4090
41
Table 6 OLS regression of firm excess values on the percent of foreign sales controlling for a control premium This table reports the result of the OLS regression of firm excess value including firm and year fixed effects with an additional control variable to proxy for a country-level ownership control premium (Control Premium) All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed) Dep Variable = Excess Value Coef t -stat Foreign Sales 0303 224 Industry Other -0327 -325 Log Size -0045 -100 Debt FirmValue -0683 -585 CAPX Sales 0490 226 EBIT Sales 0409 189 RampD Sales 1545 379 Ad Sales -1689 -119 Control Premium -1004 -062 Year indicators Yes Firm indicators Yes R2 0768 Observations 4950
42
Table 7 OLS regression of firm excess values on the percent of foreign sales controlling for the cost of capital This table contains the results of an OLS regression of firm excess value including firm and year fixed effects with an additional control variable to proxy for a country-level cost of capital (Cost of Capital) All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed) Dep Variable = Excess Value Coef t -stat Foreign Sales 0255 178 Industry Other -0326 -324 Log Size -0043 -094 Debt FirmValue -0687 -589 CAPX Sales 0485 225 EBIT Sales 0406 188 RampD Sales 1539 379 Ad Sales -1703 -120 Cost of Capital 0004 039 Year indicators Yes Firm indicators Yes R2 0768 Observations 4950
43
Table 8 OLS regression of firm excess values on the percent of foreign sales controlling for aspects of corporate governance This table reports the results of two OLS regressions In the first column are the results of the firm excess value regression including firm and year fixed effect with five proxies for corporate governance included ndash the percent of shares owned by the CEO and the percent squared (CEO Ownership and CEO Ownership squared) the percent owned by institutional investors (Institution Ownership) whether there is a powerful CEO (Powerful CEO) and the Gompers Ishii and Metrick (2003) governance index (Governance Index) In the second column the regression includes the same sample and variables with the exception of the governance proxies which are excluded All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed)
Governance
No Governance (same sample)
Dep Variable = Excess Value Coef t-stat Coef t -stat Foreign Sales 0259 159 0251 154 Industry Other -0099 -080 -0099 -079 Log Size -0105 -185 -0102 -180 Debt FirmValue -0813 -478 -0817 -482 CAPX Sales 0440 252 0438 249 EBIT Sales 0457 247 0457 246 RampD Sales 1717 284 1716 286 Ad Sales -2210 -143 -2190 -143 CEO Ownership 0010 007
CEO Ownership squared -0029 -017 Institution Ownership 0047 067 Powerful CEO -0003 -014 Governance Index 0009 103
Year indicators Yes Yes Firm indicators Yes Yes R2 0811 0810 Observations 2550 2550
- Cover for paperpdf
- crrz_20130603_SMUpdf
-
12
The actual firm values are observable for all firms in our sample The hypothetical firm
values for multi-segment firms are imputed using market value to sales ratios herein referred to
as lsquomultiplesrsquo of the median single-segment firm operating in the same country-industry Thus
these single country-industry firms serve as benchmarks for an MNCrsquos operations within that
same country-industry The imputed value of the country-industry component can be viewed as
an estimate of what that component would be worth if it operated independently The sum of the
imputed values across all country-industry components represents the hypothetical value that the
multi-segment firm would be worth if it were broken apart
32 Data
Four primary data sources play a distinct role in carrying out our analysis i) Bureau of
Economic Analysis (BEA) data ii) Compustat Segment data iii) Worldscope data and iv)
Compustat Fundamentals Annual data Broadly speaking BEA data provide information about
the diversified nature of MNCs Compustat Segment data provide information about the
industrial diversification of US domiciled firms Worldscope data are used to compute
multiples for single-segment foreign firms that serve as benchmarks for the foreign operations of
MNCs (ie foreign benchmarks) and Compustat Fundamentals Annual data are used to
compute multiples for single-segment domestic firms that serve as benchmarks for the domestic
operations of multinational firms We also use the latter data source to construct our primary
control variables
13
Key to the execution of our study is our access to BEA data Federal law requires US-
domiciled MNCs to report certain financial and operating data to the BEA10 With regard to
observing the country-industry operations of MNCs use of the BEA data overcomes two
important limitations of Compustat Segment data First Compustat does not consistently identify
the specific location or scale of MNCsrsquo foreign operations11 Second Compustat does not
identify the industry activities in each country of operation This latter point is particularly
limiting for MNCs because domestic industry activity need not mirror foreign industry activity
An MNC generating an equal proportion of sales in two industries in the domestic market need
not generate industry sales in equal proportions in every foreign market12 The lack of detail and
consistency in Compustat Segment data arises because segment reporting is highly aggregated
and firms exercise substantial discretion in defining segments under Accounting Standards
Codification 280 (ASC 280) (eg Villalonga 2004a and Bens Berger and Monahan 2011)
The BEA defines an MNC as the combination of a single US entity called the US parent
and at least one foreign affiliate ndash that is these firms have a physical presence outside the US
due to FDI Since 1982 MNCs have completed a mandatory and confidential lsquoSurvey of US
Direct Investment Abroadrsquo for the domestic operation and the operations of each foreign
affiliate defined as a foreign entity in which the US parent holds at least a 10 percent equity
interest (directly or indirectly) The data are reported in US dollars on a fiscal year basis and in
10 BEA surveys are conducted pursuant to the International Investment and Trade in Services Survey Act (PL 94-472 90 Stat 2059 22 USC 3101-3108) See httpwwwbeagovsurveysdiasurvhtm and Mataloni (2003) for more detailed information on the BEA data 11 Exhibit 21 disclosures of material subsidiaries and their locations in SEC 10-K filings could potentially help overcome the inability to observe the location of the firmrsquos foreign operations from segment disclosures However at least three limitations remain (1) for industrially diversified multinational firms the researcher would be required to make an assumption about the proportion of industrial diversification in each country (2) Exhibit 21 provides no information about the scale of activity in each jurisdiction and (3) managerial discretion is used to determine a lsquomaterialrsquo subsidiary under Section 601 of SEC Regulation S-K 12 In fact the expectation that a multinational firm would exhibit different industry membership in different countries is consistent with the theory of vertical foreign direct investment where firms separate their various value chain activities and locate them in the most favorable country (see eg Hanson Mataloni and Slaughter 2005)
14
accordance with US Generally Accepted Accounting Principles (US GAAP) For each year
we observe the sales industry composition and location of not only the parent but also each
affiliate13
The procedures we use to construct our sample are similar to those used by Berger and Ofek
(1995) and Denis et al (2002) Using firm-years appearing in Compustat Fundamentals Annual
and Compustat Segment data we obtain a sample of firms domiciled in the US with total sales
exceeding $20 million We exclude firms in the financial service (SIC codes 6000 to 6999) and
utility (SIC codes 4000 to 4999) industries and eliminate firm-years when the sum of segment
sales (Compustat geographic or Compustat business) is not within 1 of total firm sales for that
year We restrict our sample period to include fiscal years beginning after December 15 1997 as
ASC 280 Segment Reporting substantially altered the definition of a reporting segment under
US GAAP This restriction ensures that Compustat Segment data reflects the use of a consistent
accounting standard Finally we require that our MNCs appear in both Compustat and BEA
data The requirement that our sample firms appear in the BEA database ensures that the firms
have observable international operations In sum these procedures result in 1166 multinational
firms incorporated in the US with 4950 observations between 1998 and 2008 that meet all of
our data requirements
33 Measuring excess value
The dependent variable in our study is excess firm value (Excess Value) defined as the
logarithm of the ratio of actual firm value to imputed firm value (Berger and Ofek 1995)
13 We obtain sales industry membership and country location for affiliates from one of two sources When affiliates exceed the BEA-determined reporting thresholds (ie if their assets sales or net income (loss) exceed $7 million in 1999 $30 million in 2000 through 2003 $10 million in 2004 and $40 million in 2005 through 2008) they must file their own detailed survey When affiliates fall below the thresholds we obtain our data from an attachment to the parent survey
15
Appendix A provides the definition of this and all other variables used in our analysis We
observe actual firm value equal to a firmrsquos market value of equity plus book value of liabilities
(ie book value of total assets minus book value of total equity) using Compustat Fundamentals
Annual data We calculate imputed firm value as the sum of the imputed values of a firmrsquos
operations in each country-industry Our method for imputing the value of the separate
components of a firm can best be described in three steps First we obtain total sales generated
by a firm for each country-industry in which it operates14 Second we obtain multiples (market
value to sales ratios) for benchmark firms operating in those same country-industries Third we
multiply a firmrsquos country-industry sales by the applicable median country-industry multiple to
obtain the imputed market value for each country-industry operation We perform each step
annually
Conceptually imputed values can be based on assets (ie Tobinrsquos Q) net income or sales
multiples We restrict ourselves to sales multiples for two reasons First using value to sales
ratios maintains consistency with the prior research on the excess value implications of
multinational operations (eg Denis et al 2002) Second as our method uses accounting data
for foreign companies to compute country-industry multiples the accounting numbers we use
need to be consistently measured across firms using different accounting standards We find that
sales data are measured most consistently
To assess the comparability of sales relative to either net income or assets across various
accounting standards we examine all firms listed on Worldscope as changing to or from US
14 Specifically we allocate total sales from Compustat to each component based on the proportion of BEA sales (after eliminating intercompany sales) occurring in each country-industry This step ensures that our total firm sales do not deviate from a firmrsquos total sales as reported in the 10-K
16
GAAP between 2005 and 201115 We obtain accounting data for the year of the change and the
prior year where electronically available in English We are able to obtain data for 66 firms
changing standards as detailed in Table 1 The distribution of years for which accounting data
are available under two different accounting standards for the same fiscal year is shown in Panel
A The years shown are the year prior to the change as these accounting numbers were provided
under the original standard and then subsequently restated in the following year under the new
standard for comparative purposes The majority of the shifts were to or from IFRS (71)
though seven other accounting standards are noted (Panel B) We examine the percent that the
non-US GAAP reported number differs from the US GAAP reported number in Panel C to
assess the comparability of these three summary accounting numbers across various accounting
standards Examining both the full sample as well as the subset that overlaps our study we find
that sales are more consistently measured than net income or assets
The sum of the imputed values across the country-industry components of an MNC is an
estimate of the value of a portfolio of unrelated businesses that mirror the related businesses of
the firm Consequently a comparison of the actual firm value with the imputed firm value is a
measure of how a multinational network affects firm value We provide descriptive statistics for
Excess Value for our sample of 4950 MNC firm-years in Table 2 Panel A This table also
includes univariate comparisons of the values between the MNC firms and the domestic single-
segment and foreign single-segment benchmark firms The Excess Value measure is significantly
higher for MNCs suggesting a premium in firm value for multinational firms relative to the
benchmark firms in total as well as the domestic and foreign subgroups (p-values for tests of
15 Due to the hand collection efforts required and the limitation on electronic filings we began our search with changes documented in 2005 which generally require filings from 2004
17
means and medians are lt 001)16 These results provide some initial evidence that operating in
multiple countries is positively associated with firm value
To estimate country-industry multiples we rely on Worldscope financial data on foreign
firms and Compustat Fundamentals Annual for domestic firms We restrict the firms to include
only those with at least 90 of sales income and assets inside the country of domicile (ie
those that do not report significant multinational activity) and that operate in a single industry
We refer to these firms as benchmark firms (either foreign or domestic depending on the country
of domicile) these firms do not appear in any of our regressions We report the number of
benchmark firms during our sample period in Table 2 Panel A The industry criterion is based on
the two-digit SIC code17 For every country that has at least five firms in the respective industry
and year we use the median ratio of market value to sales18 This ratio is the country-industry
multiple ndash an input required to compute imputed values
To determine the country-industry composition of MNCs we rely on BEA data In Table 3
we report the aggregate number of foreign affiliates and total sales by country as provided by
the BEA data for our sample of 4950 MNCs The specific countries tabulated are those that
represent at least 02 percent of total firm sales (pooled across all years) in our sample The top
five foreign countries in which MNCs generate sales are Canada United Kingdom Germany
France and Japan Table 3 also provides a comparison of the number of foreign benchmark
firms available in Worldscope in the specific countries in which our sample of MNCs generate
16 Similar to Berger and Ofek (1995) the median values differ slightly from zero for the benchmark single-segment firms due to the truncation of extreme excess values 17 BEA reporting constrains us to the two-digit SIC code level 18 When five firms are not available for a given country and industry we follow Berger and Ofek (1995) and move to a more general industry definition (eg one-digit SIC code) This procedure results in a mean (median) 630 (757) of MNC sales matched at the two-digit level
18
the majority of their sales Country coverage in Worldscope is not available for countries
representing 004 percent of total firm sales (per BEA)
4 Empirical results
41 Independent variables
Recall that our objective is to examine the overall relation between excess firm value and
multinational operations Our proxy for the extent of multinational operations is the percentage
of total firm sales made by foreign operations (Foreign Sales) By this measure the extent of
multinational activity in our sample of MNCs is non-trivial ndash Table 2 Panel B shows that
approximately 24 percent of sales are generated by foreign operations on average Our first
analysis estimates ordinary least square regressions of Excess Value on Foreign Sales and
control variables (discussed below)
We include variables in our regression to control for other potential determinants of excess
value These are the percentage of sales made by the firm outside its primary industry (Industry
Other) to control for any relation between industrial diversification and excess value as in Berger
and Ofek (1995) For this purpose we obtain industry sales using Compustat Segment data19 A
firmrsquos primary industry is the industry in which the firm generates the majority of its sales and
we determine industry sales at the business segment level We set Industry Other equal to zero
for firms that operate in a single business segment
Consistent with prior research we also include controls for firm size (Log Size) the ratio of
long term debt to total market value (Debt Firm Value) the ratio of capital expenditures to total
sales (CAPX Sales) the ratio of earnings before interest and taxes to total sales (EBIT Sales)
the ratio of research and development expenses to total sales (RampD Sales) and the ratio of 19 Our results are unaffected by using BEA data to measure Industry Other
19
advertising expenses to total sales (Ad Sales) Table 2 Panel B provides the distribution of the
control variables All independent variables are Winsorized at 1 In our OLS regressions we
use heteroskedasticity and autocorrelation consistent robust standard errors with clustering at the
firm level
42 Primary regression analysis
In this section we examine the relation between firm excess value and the amount of foreign
activity at US MNCs Table 4 contains the results of regressions of excess value on Foreign
Sales for multiple specifications In column (1) Excess Value is regressed on both the percent of
foreign sales (Foreign Sales) and the percent of sales outside the firmrsquos primary industry
(Industry Other) We add the control variables to the regression in column (2) and year
indicators in column (3) In all three specifications the coefficient on the percent of foreign sales
remains significantly positive (with a p-value lt 001) For a sense of economic magnitude in
column (3) a one standard deviation (197) increase in the percent of foreign sales (Foreign
Sales) is associated with a 41 (equal to (e0349-1) times 0197) increase in firm excess value The
effect is larger for the other two columns ndash 82 increase in firm excess value in column (1) and
46 in column (2)20
Our finding of a premium reconciles with the international trade literature which finds that
firms engaging in international trade are more productive than those that do not (see Helpman
2006 and Syverson 2011 for reviews) Due to data limitations it is difficult to accurately
measure TFP at the firm level especially for firms which operate in multiple industries andor
20 Using the alternative strategy that employs US domiciled single-segment firms as the benchmark for the imputed value of the foreign operations Denis et al (2002) find that greater foreign activity results in a discount to firm value Using the Denis et al (2002) method to calculate excess value in our sample we obtain a significantly negative regression coefficient on Foreign Sales using the models in Table 4 columns (2) and (3) (the coefficient is not significant using the model in column (1) results untabulated)
20
countries As an alternative method to evaluate whether our finding of a premium is simply the
result of more productive firms also having more multinational operations we investigate
whether the premium is simply an artifact of innate characteristics of firms that choose to invest
abroad For example Helpman et al (2004) present a simple model with heterogeneous
productivity endowments The firms that receive the highest productivity endowments are the
ones capable of paying the fixed costs to establish a foreign subsidiary
To the extent that productivity advantages create excess value one would expect the type of
firms that choose to invest more abroad would be valued at a premium even in the absence of
their foreign investments To control for time invariant firm characteristics (eg innate
productivity advantages) and hence a potential source of endogeneity we add firm-level fixed
effects in column (4) of Table 4 We continue to find that the extent of foreign operations
(Foreign Sales) is positively associated with Excess Value Across all specifications the sign
on all significant control variables is consistent with prior research (eg Berger and Ofek 1995
and Denis et al 2002)
The finding of a premium makes a significant contribution to the relatively sparse literature
on the value effects of multinational activity Our measure of imputed value expands upon the
methods used by Berger and Ofek (1995) and Denis et al (2002) but allows us to answer a
different question than Denis et al (2002) Holding constant the extent of industrial
diversification we ask whether MNCs are more valuable than a portfolio of benchmark firms of
a similar geographic footprint In contrast Denis et al (2002) ask whether holding constant the
extent of industrial diversification MNCs are more valuable than a portfolio of domestic (US)
benchmark firms We believe that asking whether an MNCrsquos foreign (domestic) operations are
21
more valuable than those of a foreign (domestic) firm is a more appropriate way to assess the
value implications of a multinational network
5 Robustness
51 Endogeneity related to dynamic relations
Prior research argues that the relation between industrial diversification and excess firm
value could be endogenous through dynamic relationships based on observing past outcomes
Managers of firms likely choose to enter or exit industries or geographic regions based upon
their previous performance For example Campa and Kedia (2002) find that firms are more
likely to enter new industries when prospects in their current lines of business are deteriorating
Similar dynamics could also drive a relation between excess value and FDI We assess the
robustness of the previous results by estimating a dynamic panel data model which allows us to
control for dynamic endogeneity in addition to firm level unobserved heterogeneity We employ
the GMM estimator for dynamic panel models developed by Arellano and Bond (1991) Arellano
and Bover (1995) and Blundell and Bond (1998) Our results from this analysis confirm our
previous findings that firms with a larger degree of foreign operations have positive excess
values
Dynamic panel data models are generalizations of the traditional fixed effects model where
lags of the dependent variable are added to the right hand side of the equation These additional
lags control for the impact a firmrsquos past performance has on its current performance through the
channels discussed above The GMM estimator for dynamic panel models developed by
Arellano and Bond (1991) starts by differencing the data at the firm level in neighboring time
periods to eliminate any firm specific time invariant unobserved heterogeneity that may be
22
present Eliminating the firm specific fixed effect by differencing the data with lags of the
dependent variable on the right hand side introduces correlation between the new error term and
the differenced explanatory variables This problem is solved by applying the standard GMM
estimator using lags of both the original explanatory variables and excess value as instruments
We note that the dynamic panel data framework allows us to treat both international and
industrial diversification (as well as the other firm-specific control variables) as endogenous
Following Hoechle et al (2012) we also allow contemporaneous values of these variables to be
treated as endogenous
Lagged values of the explanatory variables are valid instruments in a dynamic panel data
model when the model is dynamically complete (eg Wooldridge 2010) A dynamic panel data
model is dynamically complete conditional on the unobserved effect if enough lags of both the
explanatory and dependent variable are included to make the resulting error term uncorrelated
with all the right hand side variables Under this assumption any lags for the explanatory
variables beyond this can be used within the GMM estimator as instruments Like Hoechle et al
(2012) we add two years of lags of a firmrsquos excess value to the right hand side assuming that
this is enough lags to control for dynamic feedback effects We then use past values of all
variables beyond the second lag up to a total of seven years to act as instruments
The results of the dynamic panel data estimator are reported in Table 5 These results confirm
the previous finding of a positive relation between the degree of international operations and
excess value We note that the size and significance level of the coefficient on Foreign Sales
diversification are robust to changes in the number of lags used as instruments The Arellano and
Bond (1991) estimator includes testing diagnostics for correct specification of the model If the
model is correctly specified differences in the residuals from the levels equation should be
23
serially uncorrelated at lag one but correlated at lag two The p-values for tests of serial
correlation of the residuals at lags 1 and 2 are reported in Table 5 and these indicate that the
model is not rejected We also report in Table 5 the Hansen test for overidentification which is a
test of the null hypothesis that the instruments are valid The p-value for this test is 030
indicating that we fail to reject this null hypothesis Finally we report another test for exogeneity
known as the difference-in-Hansen test which tests the null hypothesis that the instruments in
the original equation are valid We fail to reject this null hypothesis as well
52 Identifying value effects of multinational operations via cross-border acquisitions
Another alternative to investigating a causal link between foreign direct investment and firm
value is an event study of foreign acquisitions21 Acquired foreign targets represent only a small
fraction of MNCsrsquo worldwide sales in the year of the acquisition ndash less than 1 percent in our
sample Additionally cross-border acquisitions are not the primary source of international
expansion for the average firm We find that foreign acquisitions account for between 18 percent
and 55 percent of the year-over-year change in foreign sales with the remainder of the growth
coming from either newly established entities (ie greenfield investments) or growth in existing
operations22 These results suggest that acquisitions are not a material source of growth for the
21 Doukas and Travlos (1988) find positive returns when the acquirer is not operating in the target firmrsquos country and insignificant returns when the acquirer is operating in the target firmrsquos country or expanding internationally for the first time Morck and Yeung (1991) find marginal evidence of positive returns for firms with intangible assets Finally Doukas (1995) finds positive returns only for firms with average Tobinrsquos q ratios greater than one More recent work also using an event study methodology provides mixed evidence on valuation effects of global diversification through acquisitions Dos Santos Errunza and Miller (2008) find insignificant returns for firms with already established foreign operations firms establishing initial foreign operations and firms acquiring targets in related industries Doukas and Kan (2006) and Moeller and Schlingemann (2005) find reductions to shareholder value while Francis Hasan and Sun (2008) find positive returns for acquirers of targets in segmented financial markets 22 New foreign affiliates to an MNC group each year report on the BEA survey whether they entered the group as an acquisition or a newly established entity (ie greenfield investment) Affiliates under the lowest reporting threshold do not report this information resulting in our inability to precisely identify the source of 33 percent of the increase in firm sales as either acquisition or greenfield
24
typical US MNC suggesting that an event study would lack power and generalizability For
these reasons we do not implement an event study
53 Control premium
A control premium or private benefits of control occurs when ldquosome value whatever the
source is not shared among all the shareholders in proportion of the shares owned but it is
enjoyed exclusively by the party in controlrdquo (Dyck and Zingales 2004 p 541) Using stock
prices of traded shares held by minority shareholders in determining the market value to sales
ratios of our benchmark firms could underestimate the true value of our benchmark firms if those
firms face large control premiums This in turn could induce an excess value premium because
the imputed value of these country-industry segments would be artificially low The existence of
such benefits is more prevalent in some countries than others and could affect the value of an
MNCrsquos operations in such domains We use the country-level control premiums from Dyck and
Zingales (2004) and using each firmrsquos country sales as a percent of total firm sales we obtain a
weighted average control premium that each MNC faces (Control Premium) For countries not
included in the Dyck and Zingales (2004) analysis we set the control premium equal to zero23 In
Table 6 we show that Foreign Sales remains positive and highly significant (p-value lt 001)
when we include the proxy for control premium As a result it does not appear that the excess
value premium is driven by MNCs operating in countries where majority shareholders are able to
extract large control premiums
23 Results are robust to replacing missing values with the mean control premium of the respective GDP quartile
25
54 Cost of capital
As discussed in Section 2 when countries have shallow capital markets a multinational
network may provide the benefit of a lower cost of capital for investments through an internal
capital market as well as through better access to the US capital market Prior research
establishes that there is substantial variation in the cost of capital across countries (Erb Harvey
and Viskanta 1996 and Hail and Leuz 2009) To determine whether the documented premium
is due to cost of capital advantages we include a control variable to capture the difference
between the MNCrsquos cost of capital and the cost of capital faced by its competitors in the global
markets in which it operates
We compute our Cost of Capital proxy in two steps First we obtain the country-year credit
rating published by Institutional Investor24 The rating ranges from 0 to 100 where higher values
imply that a country has a higher default risk Country-level credit risk is a reasonable predictor
of expected equity market returns and volatility (Erb et al 1996) and exhibits a highly
significant correlation with international accounting-based estimates of imputed cost of capital
(Hail and Leuz 2009) Second using each firmrsquos country sales as a percent of total firm sales
we obtain a weighted average credit risk rating across the countries in which each firm operates
A higher value implies an MNC operates in countries with higher cost of capital In Table 7 we
do not find a result consistent with MNCs gaining an advantage from their access to low cost
capitalndash Cost of Capital is not significantly associated with Excess Value25
24 We thank Cam Harvey for providing these data 25 Results are consistent using cost of capital as defined in Erb et al 1996
26
55 Corporate governance
In light of the recent finding that corporate governance mitigates the diversification discount
we examine whether the premium to multinational activity is sensitive to controls for corporate
governance Hoechle et al (2012) find that the magnitude of the industrial diversification
discount is decreased and in several specifications no longer significant in the presence of
governance proxies In the first column of Table 8 we include the five governance proxies that
are consistently significantly associated with excess value in Hoechle et al (2012) ndash the percent
of CEO ownership (CEO Ownership and CEO Ownership squared) the percent of institutional
ownership (Institution Ownership) whether the firm has a powerful or influential CEO
(Powerful CEO) and the governance index from Gompers Ishii and Metrick (2003)
(Governance Index)
As documented in Table 8 including these control variables reduces our sample size by
almost 50 (to 2550 observations) and tends to exclude the smaller firms from the analysis
None of the governance proxies are significant Relative to column (4) of Table 4 the coefficient
on Foreign Sales is similar in magnitude but is no longer statistically significant at
conventional levels To investigate whether the decreased significance is due to the smaller
sample or the inclusion of the governance variables we re-estimate the regression for the same
2550 observations after removing the governance controls The coefficient and t-statistic are
virtually identical suggesting that the loss of significance is due to the reduced sample and not
the governance controls We do not find evidence to suggest that variation in corporate
governance practices drives variation in excess value for US MNCs
27
6 Conclusion
Using a new method to quantify the valuation effects of foreign direct investment by US
domiciled firms we find robust evidence of a premium in excess value associated with the
degree of multinational operations Our method builds on that used in previous studies
examining firm excess value (eg Berger and Ofek 1995 and Denis et al 2002) by
incorporating the location in addition to the industry of each corporate affiliate This allows
important factors such as growth and cost of capital to vary across countries as well as industries
Examining a sample of multinational firms we find that a one-standard deviation (197)
increase in foreign operations is associated with an increase of between 41 and 82 in excess
value minus both a statistically and economically significant finding
This result complements the finding of the international trade literature that more productive
firms are more likely to operate internationally However the premium remains after including
firm fixed effects to control for persistent firm traits (eg productivity)
The finding of a premium stands up to a succession of robustness tests designed to evaluate
whether the result arises from alternative methods or explanations First we estimate a dynamic
panel data model using the generalized method of moments (GMM) estimator to simultaneously
account for the potential endogeneity of FDI industrial diversification and firm value Second
we include a control variable to proxy for whether the firm operates in countries with larger
control premiums which could result in understated multiples and mechanically induce a
premium Third we include a control variable to measure the cost of capital across the countries
in which an MNC operates to evaluate whether a relatively low cost of capital borne by
multinational firms is a meaningful source of the premium Fourth we control for several
corporate governance proxies which have been found to be correlated with excess values (and
28
industrial diversification) We fail to find evidence supporting any of these alternative
explanations ndash the premium in excess value arising at firms with greater multinational operations
remains significant
Overall our new method of estimating excess values for multinational firms provides strong
evidence of a statistically and economically significant premium associated with increased
international operations The premium is robust to a host of specifications and controls designed
to evaluate alternative explanations We leave to future research the questions of whether the
premium to US multinational firms applies more broadly to other countries of domicile and
what factors influence the premium
29
References
Ammann M Hoeschel D and M Schmid 2012 Is there really no conglomerate discount
Journal of Business Finance and Accounting 39(12) 264-288 Arellano M and Bond S R 1991 Some tests of specification for panel data Monte Carlo
evidence and an application to employment equations Review of Economic Studies 58 (2) 277-298
Arellano M and Bover O 1995 Another look at instrumental variables estimation of error
components models Journal of Econometrics 68 (1) 29-61 Bens D A Berger P G and S J Monahan 2011 Discretionary disclosure in financial
reporting An examination comparing internal firm data to externally reported segment data The Accounting Review 86(2) 417-449
Berger P G and E Ofek 1995 Diversificationrsquos effect on firm value Journal of Financial
Economics 37 39-65 Blundell R and Bond S R 1998 Initial conditions and moment restrictions in dynamic panel
data models Journal of Econometrics 87(1) 115-144 Campa J M and S Kedia 2002 Explaining the diversification discount The Journal of
Finance 57 (4) 1731-1762 Denis D J Denis D K and K Yost 2002 Global diversification industrial diversification
and firm value The Journal of Finance 57 (5) 1951-1979 Desai M A Foley C F and J R Hines 2004 A multinational perspective on capital structure
choice and internal capital markets The Journal of Finance 59 (6) 2451-2487 Dos Santos MB V Errunza and D Miller 2008 Does corporate international diversification
destroy value Evidence from cross-border mergers and acquisitions Journal of Banking and Finance 32(12) 2716-2724
Doukas JA 1995 Overinvestment Tobinrsquos q and gains from foreign acquisitions Journal of
Banking and Finance 19 1285-1303 Doukas J and N G Travlos 1988 The effect of corporate multinationalism on shareholdersrsquorsquo
wealth evidence from international acquisitions Journal of Finance 43(5) 1161-1175 Doukas JA and O B Kan 2006 Does global diversification destroy firm value Journal of
International Business Studies 37(3) 352-371
30
Dunning J and A Rugman 1985 The influence of Hymerrsquos dissertation on the theory of foreign direct investment The American Economic Review 75(2) 228-232
Dyck A and L Zingales 2004 Private benefits of control An international comparison The
Journal of Finance 59 (2) 537-600 Erb C Harvey C and T Viskanta 1996 Expected returns and volatility in 135 countries The
Journal of Portfolio Management 46-58 Foley C F Desai M A and K J Forbes 2008 Financial constraints and growth
Multinational and local firm responses to currency depreciations Review of Financial Studies 21 (6) 2857-2888
Francis B I Hasan and X Sun 2008 Financial market integration and the value of global
diversification Evidence for US acquirers in cross-border mergers and acquisitions Journal of Banking and Finance 321522-1540
Gande A Schenzler C and L Senbet 2009 Valuation effects of global diversification
Journal of International Business Studies 1515-1532 Gompers P Ishii J and A Metrick 2003 Corporate governance and equity prices Quarterly
Journal of Economics 118(1) 107-156 Hail L and C Leuz 2009 Cost of capital effects and changes in growth expectations around
US cross-listings Journal of Financial Economics 93 428-454 Hanson G Mataloni R and M Slaughter 2005 Vertical production networks in multinational
firms Review of Economics and Statistics 87(4) 1-15 Helpman E 2006 Trade FDI and the Organization of Firms Journal of Economic Literature
44(3) 289-630 Helpman E Melitz M J and S R Yeaple 2004 Export versus FDI with Heterogeneous Firms
American Economic Review 94(1) 300-316 Hoechle D Schmid M Walter W and D Yermack 2012 How much of the diversification
discount can be explained by poor governance Journal of Financial Economics 103 41-60 Hope O-K and W B Thomas 2008 Managerial empire building and firm disclosure Journal
of Accounting Research 46 (June) 591-626 Huizinga H P and J Voget 2009 International taxation and the direction and volume of cross-
border MampAs Journal of Finance 64 (3) 1217-1249 Jensen M 1986 Agency cost of free cash flow corporate finance and takeovers American
Economic Review Papers and Proceedings 76 323-329
31
Laeven L and R Levine 2007 Is there a diversification discount in financial conglomerates
Journal of Financial Economics 85 331-367 Lang L and R Stulz 1994 Tobinrsquos Q corporate diversification and firm performance Journal
of Political Economy 102 1248-1280 Mataloni R 2003 US multinational companies Operations in 2001 Survey of Current
Business 85-105 Matvos G and A Seru 2011 The dynamic tradeoff between corporate socialism and financial
market dislocation Estimates from internal capital markets Working paper University of Chicago Booth School of Business
Moeller SB and F P Schlingemann 2005 Global diversification and bidder gains A
comparison between cross-border and domestic acquisitions Journal of Banking and Finance 29 533-564
Morck R and B Yeung 1991 Why investors value multinationality Journal of Business 64
165-186
Morck R and B Yeung 2001 Why firms diversify Internatization vs agency behavior In Intangibles B Lev (ed) Oxford University Press pp 269-302
Myers S C and N S Majluf 1984 Corporate financing and investment decisions when firms have information that investors do not have Journal of Financial Economics 13 187-221
Schmid M and I Walter 2009 Do financial conglomerates create or destroy economic value
Journal of Financial Intermediation 18 193-216 Syverson C 2011 What determines productivity Journal of Economic Literature 49(2) 326-
365 Villalonga B 2004a Diversification discount or premium New evidence from the Business
Information Tracking Series Journal of Finance 59 479-506 Villalonga B 2004b Does diversification cause the diversification discount Financial
Management 33 5-27 Wooldridge JM 2010 Econometric Analysis of Cross Section and Panel Data (Second
Edition) Cambridge MA MIT Press Yamin M 1991 A reassessment of Hymerrsquos contribution to the theory of the transnational
corporation C Pitelis and R Sugden (eds) The Nature of the Transnational Firm London Routledge
32
Appendix A Variable Definitions
We obtain information regarding firmsrsquo operations by country and industry from the Bureau of Economic Analysis (BEA) unless otherwise noted Acronyms used below reflect Compustat mnemonics and thus indicate when we obtain data from the Compustat North America Fundamentals Annual database All independent variables are winsorized at 1
Dependent variable
Excess Value = The log of the ratio of actual firm value to imputed firm value Actual firm value is equal to market capitalization (MVE) + book value of total assets (AT) ndash book value of shareholdersrsquo equity (SEQ) Imputed firm value is calculated as the sum of the imputed values of a firmrsquos operations in each country-industry The imputed value for each country-industry is equal to sales generated in that country-industry times a multiple equal to the ratio of a benchmark firmrsquos actual value to total sales A benchmark firm is defined as a median single-segment firm operating in the same country-industry All multiples require at least five firms listed in the respective country-industry on Compustat (domestic) or Worldscope (foreign) with less than 10 of sales income and assets outside the country of domicile Similar to Berger and Ofek (1995) we eliminate extreme values where the ratio of actual to imputed firm value exceeds 400 or is less than 025
Independent variables
Foreign Sales = Sales made by the foreign operations of US MNCs as a percent of total firm sales
Industry Other = Sales outside a firmrsquos primary industry as a percent of total sales (SALE) Equal to zero for firms operating in a single business segment
Log Size = Log of total sales (SALE)
Debt Firm Value = Ratio of long-term debt (DLTT) to total market value (market capitalization (MVE) + book value of total assets (AT) ndash book value of shareholdersrsquo equity (SEQ))
CAPX Sales = Ratio of capital expenditures (CAPX) to total sales (SALE)
EBIT Sales = Ratio of earnings before interest and taxes (EBIT) to total sales (SALE)
RampD Sales = Ratio of research and development expenses (XRD) to total sales (SALE)
Ad Sales = Ratio of advertising expenses (XAD) to total sales (SALE)
Control Premium = A weighted-average (based on the sales in a given country as a percent of total firm sales) of the country-level control premium from Dyck and Zingales (2004)
Cost of Capital = A weighted-average (based on the sales in a given country as a percent of total firm sales) of the country-level credit rating from Institutional Investor (httpwwwinstitutionalinvestorcom) The country-level rating is available for 160 countries and ranges from 0 to 100 Higher values imply greater credit risk
33
CEO Ownership = Percent of shares owned by the CEO from Execucomp
Institution Ownership = Percent of shares owned by institutional investors from Thomson Reuterrsquos Institutional Holdings database
Powerful CEO = Indicator variable equal to one if the CEO is the only insider on the board of directors and also serves as the chairman and president based on data from RiskMetrics Directors database Equal to zero otherwise
Governance Index = Count measure of takeover defenses used in Gompers Ishii and Metrick (2003) from RiskMetrics Governance database As the governance data are not available for every year we follow Gompers Ishii and Metrick (2003) and assume that the firmsrsquo governance attributes are unchanged until publication of the subsequent series
34
Figure 1 Trends in multinational activity and industrial diversification from 1984-2008 This figure presents a plot of the mean percent of foreign sales and the mean percent of sales outside the firmrsquos primary industry (industrial diversification) from 1984 through 2008 Dotted lines above and below the primary lines represent 95 confidence intervals To compile the plot we include the full population of firms in Compustat
35
Table 1 Analysis of accounting data for firms reporting under multiple accounting standards This table provides a comparison of sales net income and assets for firms that changed accounting standards to or from US GAAP Comparisons are provided for the 66 firms that changed to or from US GAAP and for which we could obtain accounting data electronically and in English under both standards for the same fiscal year (eg the firm presented the accounting numbers originally under one set of standards and then restated the prior year numbers in the subsequent year under the new set of standards) Panel A shows the distribution of firms by year Panel B shows the other accounting standards to which US GAAP is compared Panel C shows the mean and median percent that the other accounting standard differs from US GAAP for the same fiscal year and accounting number Two samples are shown in Panel C The first is the full sample of firms with a change in accounting standards to or from US GAAP for which data was available The second is the subset of these firms that occurred during the sample window used in this study (ie excluding changes post-2008) The percent difference in sales is compared to the percent difference in net income and assets Tests of means are based on matched-pair t-tests Tests of medians are based on Wilcoxon matched-pair signed-rank tests Panel A Accounting standard changes by year
2003
4 6
2004
25 38
2005
8 12
2006
6 9
2007
7 11
2008
5 8
2009
5 8
2010
5 8
2011
1 2
66
Panel B Accounting standards compared to US GAAP
IFRS
47 71
Canadian GAAP
12 18
Israeli GAAP
2 3
Dutch GAAP
1 2
Indian GAAP
1 2
Japanese GAAP
1 2
Norwegian GAAP
1 2
Swiss GAAP
1 2
66
36
(Table 1 continued) Panel C Tests of differences between US GAAP and other accounting standards
Percent different from US GAAP
Comparison to sales (p-values)
N Mean Median
Mean Median
Full Sample (2001-2011)
Sales
66 236 000
Net Income
66 10653 647
005 lt001
Assets
66 1162 126
lt001 lt001
Study Sample (2001-2008)
Sales
55 257 000
Net Income
55 10303 589
011 lt001
Assets
55 1047 099
003 lt001
37
Table 2 Descriptive statistics Panel A reports the mean and medians of Excess Value for the multinational firms and single-segment benchmark firms as well as univariate tests across the subsamples Panel B reports summary statistics for the independent variables for the multinational firms The p-values of the tests of differences in means are based on t-tests The p-values of tests of differences in the medians are for a Wilcoxon rank-sum test All variables are defined in Appendix A Panel A Excess Value by sample Multinational All Benchmark Benchmark Subgroups Sample Firms US Foreign
Value Value Test of Diff (p-value) Value
Test of Diff (p-value) Value
Test of Diff (p-value)
Mean 0067 0006 lt001 -0028 lt001 0014 lt001 Median 0076 0000 lt001 -0025 lt001 0000 lt001 N 4950 33074 5998 27076 Panel B Distribution of independent variables for the multinational firms N Mean 25th 50th 75th StDev Foreign Sales 4950 0238 0074 0190 0363 0197 Industry Other 4950 0212 0000 0166 0414 0215 Log Size 4950 6955 5823 6989 8140 1687 Debt Firm Value 4950 0136 0009 0097 0212 0145 CAPX Sales 4950 0054 0020 0033 0054 0101 EBIT Sales 4950 0065 0031 0077 0121 0138 RampD Sales 4950 0049 0000 0013 0059 0083 Ad Sales 4950 0008 0000 0000 0005 0018
38
Table 3 BEA and Worldscope samples
This table reports details on affiliate sales for significant countries pooled from 1998-2008 Significant countries are defined as those in which the operations of US MNCs represent at least 02 of total firm sales (per the BEA) For significant countries the table compares their representation in terms of MNC affiliate sales in the BEA data to the distribution of firm observations in Worldscope US Foreign Affiliates (BEA) Foreign Firms (Worldscope)
Country
N (1)
Sales (millions)
(2)
Total Sales (3)
N (4)
Sales (million)
(5)
Total Sales (6)
Australia 1344 73305 056 859 255865 210 Belgium 894 52755 040 169 26136 021 Brazil 959 60245 046 1143 683167 560 Canada 2730 536767 407 1181 526750 432 China 922 75907 057 822 276541 227 France 1852 183942 139 540 105447 086 Germany 2182 275577 209 480 103888 085 Hong Kong 867 48100 036 157 34094 028 Ireland 478 36754 028 34 3467 003 Italy 1178 82116 062 111 47948 039 Japan 1494 148032 112 2281 1119223 917 Korea South 659 34500 026 3530 2752791 2255 Malaysia 386 26027 020 837 168683 138 Mexico 1405 113623 086 362 237173 194 Netherlands 1149 85105 064 47 22631 019 Singapore 1098 94917 072 139 32913 027 Spain 871 63094 048 129 37419 031 Sweden 712 32682 025 235 23090 019 Switzerland 726 60288 046 74 20242 017 Taiwan 592 30313 023 2949 787049 645 United Kingdom 3054 423934 321 1997 882423 723 United States (domicile) 4950 10460880 7923 na na na Other - Covered by WorldScope 6893 199394 151 9000 4057894 33 Other -Not Covered by WorldScope 713 5352 004 - - - Total 38108 13203609 1000 27076 12204834 100
39
Table 4 OLS regressions of firm excess values on the percent of foreign sales This table reports the results of OLS regressions of firm excess value on the percent of foreign sales (Foreign Sales) the percent of sales outside a firmrsquos primary industry (Industry Other) and control variables All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed) Dep Variable = Excess Value Coefficient t-stat Coefficient t-stat Coefficient t-stat Coefficient t-stat (1) (2) (3) (4) Foreign Sales 0349 419 0209 293 0189 266 0278 210 Industry Other -0134 -186 -0047 -072 -0053 -081 -0326 -325 Log Size 0066 634 0065 624 -0044 -096 Debt Firm Value -0196 -213 -0241 -260 -0685 -588 CAPX Sales 0901 425 0893 435 0488 227 EBIT Sales 1154 542 1170 547 0408 189 RampD Sales 2908 1242 2932 1237 1544 379 Ad Sales 1869 263 2003 280 -1696 -119 Year indicators No No Yes Yes Firm indicators No No No Yes R2 0017 0212 0226 0768 Observations 4950 4950 4950 4950
40
Table 5 Dynamic panel GMM model regressing firm excess values on the percent of foreign sales This table reports the result from a regression of the excess value on the percent of foreign sales (Foreign Sales) the percent of non-primary industry sales (Industry Other) and control variables using a dynamic panel GMM estimator All control variables are considered to be endogenous with the exception of the year indicator variables AR(1) and AR(2) are tests for first-order and second-order serial correlation in the first differenced residuals with the null hypothesis of no serial correlation The null hypothesis of the Hansen test of overidentification is that all instruments are valid The null hypothesis of the difference-in-Hansen test of exogeneity is that the instruments used for the equation in levels are exogenous All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed) Dep Variable = Excess Value Coef t-stat Excess Value (one lag) -0018 -082 Excess Value (two lags) -0048 -272 Foreign Sales 0371 270 Industry Other -0163 -171 Log Size 0010 123 Debt FirmValue -1135 -820 CAPX Sales 0590 342 EBIT Sales -0047 -300 RampD Sales 1376 531 Ad Sales -0751 -040 AR(1) test (p-value) 000 AR(2) test (p-value) 017 Hansen test of over-identification (p-value) 030 Difference-in-Hansen test of exogeneity (p-value) 093 Year indicators Yes Number of firms 1016 Number of observations 4090
41
Table 6 OLS regression of firm excess values on the percent of foreign sales controlling for a control premium This table reports the result of the OLS regression of firm excess value including firm and year fixed effects with an additional control variable to proxy for a country-level ownership control premium (Control Premium) All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed) Dep Variable = Excess Value Coef t -stat Foreign Sales 0303 224 Industry Other -0327 -325 Log Size -0045 -100 Debt FirmValue -0683 -585 CAPX Sales 0490 226 EBIT Sales 0409 189 RampD Sales 1545 379 Ad Sales -1689 -119 Control Premium -1004 -062 Year indicators Yes Firm indicators Yes R2 0768 Observations 4950
42
Table 7 OLS regression of firm excess values on the percent of foreign sales controlling for the cost of capital This table contains the results of an OLS regression of firm excess value including firm and year fixed effects with an additional control variable to proxy for a country-level cost of capital (Cost of Capital) All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed) Dep Variable = Excess Value Coef t -stat Foreign Sales 0255 178 Industry Other -0326 -324 Log Size -0043 -094 Debt FirmValue -0687 -589 CAPX Sales 0485 225 EBIT Sales 0406 188 RampD Sales 1539 379 Ad Sales -1703 -120 Cost of Capital 0004 039 Year indicators Yes Firm indicators Yes R2 0768 Observations 4950
43
Table 8 OLS regression of firm excess values on the percent of foreign sales controlling for aspects of corporate governance This table reports the results of two OLS regressions In the first column are the results of the firm excess value regression including firm and year fixed effect with five proxies for corporate governance included ndash the percent of shares owned by the CEO and the percent squared (CEO Ownership and CEO Ownership squared) the percent owned by institutional investors (Institution Ownership) whether there is a powerful CEO (Powerful CEO) and the Gompers Ishii and Metrick (2003) governance index (Governance Index) In the second column the regression includes the same sample and variables with the exception of the governance proxies which are excluded All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed)
Governance
No Governance (same sample)
Dep Variable = Excess Value Coef t-stat Coef t -stat Foreign Sales 0259 159 0251 154 Industry Other -0099 -080 -0099 -079 Log Size -0105 -185 -0102 -180 Debt FirmValue -0813 -478 -0817 -482 CAPX Sales 0440 252 0438 249 EBIT Sales 0457 247 0457 246 RampD Sales 1717 284 1716 286 Ad Sales -2210 -143 -2190 -143 CEO Ownership 0010 007
CEO Ownership squared -0029 -017 Institution Ownership 0047 067 Powerful CEO -0003 -014 Governance Index 0009 103
Year indicators Yes Yes Firm indicators Yes Yes R2 0811 0810 Observations 2550 2550
- Cover for paperpdf
- crrz_20130603_SMUpdf
-
13
Key to the execution of our study is our access to BEA data Federal law requires US-
domiciled MNCs to report certain financial and operating data to the BEA10 With regard to
observing the country-industry operations of MNCs use of the BEA data overcomes two
important limitations of Compustat Segment data First Compustat does not consistently identify
the specific location or scale of MNCsrsquo foreign operations11 Second Compustat does not
identify the industry activities in each country of operation This latter point is particularly
limiting for MNCs because domestic industry activity need not mirror foreign industry activity
An MNC generating an equal proportion of sales in two industries in the domestic market need
not generate industry sales in equal proportions in every foreign market12 The lack of detail and
consistency in Compustat Segment data arises because segment reporting is highly aggregated
and firms exercise substantial discretion in defining segments under Accounting Standards
Codification 280 (ASC 280) (eg Villalonga 2004a and Bens Berger and Monahan 2011)
The BEA defines an MNC as the combination of a single US entity called the US parent
and at least one foreign affiliate ndash that is these firms have a physical presence outside the US
due to FDI Since 1982 MNCs have completed a mandatory and confidential lsquoSurvey of US
Direct Investment Abroadrsquo for the domestic operation and the operations of each foreign
affiliate defined as a foreign entity in which the US parent holds at least a 10 percent equity
interest (directly or indirectly) The data are reported in US dollars on a fiscal year basis and in
10 BEA surveys are conducted pursuant to the International Investment and Trade in Services Survey Act (PL 94-472 90 Stat 2059 22 USC 3101-3108) See httpwwwbeagovsurveysdiasurvhtm and Mataloni (2003) for more detailed information on the BEA data 11 Exhibit 21 disclosures of material subsidiaries and their locations in SEC 10-K filings could potentially help overcome the inability to observe the location of the firmrsquos foreign operations from segment disclosures However at least three limitations remain (1) for industrially diversified multinational firms the researcher would be required to make an assumption about the proportion of industrial diversification in each country (2) Exhibit 21 provides no information about the scale of activity in each jurisdiction and (3) managerial discretion is used to determine a lsquomaterialrsquo subsidiary under Section 601 of SEC Regulation S-K 12 In fact the expectation that a multinational firm would exhibit different industry membership in different countries is consistent with the theory of vertical foreign direct investment where firms separate their various value chain activities and locate them in the most favorable country (see eg Hanson Mataloni and Slaughter 2005)
14
accordance with US Generally Accepted Accounting Principles (US GAAP) For each year
we observe the sales industry composition and location of not only the parent but also each
affiliate13
The procedures we use to construct our sample are similar to those used by Berger and Ofek
(1995) and Denis et al (2002) Using firm-years appearing in Compustat Fundamentals Annual
and Compustat Segment data we obtain a sample of firms domiciled in the US with total sales
exceeding $20 million We exclude firms in the financial service (SIC codes 6000 to 6999) and
utility (SIC codes 4000 to 4999) industries and eliminate firm-years when the sum of segment
sales (Compustat geographic or Compustat business) is not within 1 of total firm sales for that
year We restrict our sample period to include fiscal years beginning after December 15 1997 as
ASC 280 Segment Reporting substantially altered the definition of a reporting segment under
US GAAP This restriction ensures that Compustat Segment data reflects the use of a consistent
accounting standard Finally we require that our MNCs appear in both Compustat and BEA
data The requirement that our sample firms appear in the BEA database ensures that the firms
have observable international operations In sum these procedures result in 1166 multinational
firms incorporated in the US with 4950 observations between 1998 and 2008 that meet all of
our data requirements
33 Measuring excess value
The dependent variable in our study is excess firm value (Excess Value) defined as the
logarithm of the ratio of actual firm value to imputed firm value (Berger and Ofek 1995)
13 We obtain sales industry membership and country location for affiliates from one of two sources When affiliates exceed the BEA-determined reporting thresholds (ie if their assets sales or net income (loss) exceed $7 million in 1999 $30 million in 2000 through 2003 $10 million in 2004 and $40 million in 2005 through 2008) they must file their own detailed survey When affiliates fall below the thresholds we obtain our data from an attachment to the parent survey
15
Appendix A provides the definition of this and all other variables used in our analysis We
observe actual firm value equal to a firmrsquos market value of equity plus book value of liabilities
(ie book value of total assets minus book value of total equity) using Compustat Fundamentals
Annual data We calculate imputed firm value as the sum of the imputed values of a firmrsquos
operations in each country-industry Our method for imputing the value of the separate
components of a firm can best be described in three steps First we obtain total sales generated
by a firm for each country-industry in which it operates14 Second we obtain multiples (market
value to sales ratios) for benchmark firms operating in those same country-industries Third we
multiply a firmrsquos country-industry sales by the applicable median country-industry multiple to
obtain the imputed market value for each country-industry operation We perform each step
annually
Conceptually imputed values can be based on assets (ie Tobinrsquos Q) net income or sales
multiples We restrict ourselves to sales multiples for two reasons First using value to sales
ratios maintains consistency with the prior research on the excess value implications of
multinational operations (eg Denis et al 2002) Second as our method uses accounting data
for foreign companies to compute country-industry multiples the accounting numbers we use
need to be consistently measured across firms using different accounting standards We find that
sales data are measured most consistently
To assess the comparability of sales relative to either net income or assets across various
accounting standards we examine all firms listed on Worldscope as changing to or from US
14 Specifically we allocate total sales from Compustat to each component based on the proportion of BEA sales (after eliminating intercompany sales) occurring in each country-industry This step ensures that our total firm sales do not deviate from a firmrsquos total sales as reported in the 10-K
16
GAAP between 2005 and 201115 We obtain accounting data for the year of the change and the
prior year where electronically available in English We are able to obtain data for 66 firms
changing standards as detailed in Table 1 The distribution of years for which accounting data
are available under two different accounting standards for the same fiscal year is shown in Panel
A The years shown are the year prior to the change as these accounting numbers were provided
under the original standard and then subsequently restated in the following year under the new
standard for comparative purposes The majority of the shifts were to or from IFRS (71)
though seven other accounting standards are noted (Panel B) We examine the percent that the
non-US GAAP reported number differs from the US GAAP reported number in Panel C to
assess the comparability of these three summary accounting numbers across various accounting
standards Examining both the full sample as well as the subset that overlaps our study we find
that sales are more consistently measured than net income or assets
The sum of the imputed values across the country-industry components of an MNC is an
estimate of the value of a portfolio of unrelated businesses that mirror the related businesses of
the firm Consequently a comparison of the actual firm value with the imputed firm value is a
measure of how a multinational network affects firm value We provide descriptive statistics for
Excess Value for our sample of 4950 MNC firm-years in Table 2 Panel A This table also
includes univariate comparisons of the values between the MNC firms and the domestic single-
segment and foreign single-segment benchmark firms The Excess Value measure is significantly
higher for MNCs suggesting a premium in firm value for multinational firms relative to the
benchmark firms in total as well as the domestic and foreign subgroups (p-values for tests of
15 Due to the hand collection efforts required and the limitation on electronic filings we began our search with changes documented in 2005 which generally require filings from 2004
17
means and medians are lt 001)16 These results provide some initial evidence that operating in
multiple countries is positively associated with firm value
To estimate country-industry multiples we rely on Worldscope financial data on foreign
firms and Compustat Fundamentals Annual for domestic firms We restrict the firms to include
only those with at least 90 of sales income and assets inside the country of domicile (ie
those that do not report significant multinational activity) and that operate in a single industry
We refer to these firms as benchmark firms (either foreign or domestic depending on the country
of domicile) these firms do not appear in any of our regressions We report the number of
benchmark firms during our sample period in Table 2 Panel A The industry criterion is based on
the two-digit SIC code17 For every country that has at least five firms in the respective industry
and year we use the median ratio of market value to sales18 This ratio is the country-industry
multiple ndash an input required to compute imputed values
To determine the country-industry composition of MNCs we rely on BEA data In Table 3
we report the aggregate number of foreign affiliates and total sales by country as provided by
the BEA data for our sample of 4950 MNCs The specific countries tabulated are those that
represent at least 02 percent of total firm sales (pooled across all years) in our sample The top
five foreign countries in which MNCs generate sales are Canada United Kingdom Germany
France and Japan Table 3 also provides a comparison of the number of foreign benchmark
firms available in Worldscope in the specific countries in which our sample of MNCs generate
16 Similar to Berger and Ofek (1995) the median values differ slightly from zero for the benchmark single-segment firms due to the truncation of extreme excess values 17 BEA reporting constrains us to the two-digit SIC code level 18 When five firms are not available for a given country and industry we follow Berger and Ofek (1995) and move to a more general industry definition (eg one-digit SIC code) This procedure results in a mean (median) 630 (757) of MNC sales matched at the two-digit level
18
the majority of their sales Country coverage in Worldscope is not available for countries
representing 004 percent of total firm sales (per BEA)
4 Empirical results
41 Independent variables
Recall that our objective is to examine the overall relation between excess firm value and
multinational operations Our proxy for the extent of multinational operations is the percentage
of total firm sales made by foreign operations (Foreign Sales) By this measure the extent of
multinational activity in our sample of MNCs is non-trivial ndash Table 2 Panel B shows that
approximately 24 percent of sales are generated by foreign operations on average Our first
analysis estimates ordinary least square regressions of Excess Value on Foreign Sales and
control variables (discussed below)
We include variables in our regression to control for other potential determinants of excess
value These are the percentage of sales made by the firm outside its primary industry (Industry
Other) to control for any relation between industrial diversification and excess value as in Berger
and Ofek (1995) For this purpose we obtain industry sales using Compustat Segment data19 A
firmrsquos primary industry is the industry in which the firm generates the majority of its sales and
we determine industry sales at the business segment level We set Industry Other equal to zero
for firms that operate in a single business segment
Consistent with prior research we also include controls for firm size (Log Size) the ratio of
long term debt to total market value (Debt Firm Value) the ratio of capital expenditures to total
sales (CAPX Sales) the ratio of earnings before interest and taxes to total sales (EBIT Sales)
the ratio of research and development expenses to total sales (RampD Sales) and the ratio of 19 Our results are unaffected by using BEA data to measure Industry Other
19
advertising expenses to total sales (Ad Sales) Table 2 Panel B provides the distribution of the
control variables All independent variables are Winsorized at 1 In our OLS regressions we
use heteroskedasticity and autocorrelation consistent robust standard errors with clustering at the
firm level
42 Primary regression analysis
In this section we examine the relation between firm excess value and the amount of foreign
activity at US MNCs Table 4 contains the results of regressions of excess value on Foreign
Sales for multiple specifications In column (1) Excess Value is regressed on both the percent of
foreign sales (Foreign Sales) and the percent of sales outside the firmrsquos primary industry
(Industry Other) We add the control variables to the regression in column (2) and year
indicators in column (3) In all three specifications the coefficient on the percent of foreign sales
remains significantly positive (with a p-value lt 001) For a sense of economic magnitude in
column (3) a one standard deviation (197) increase in the percent of foreign sales (Foreign
Sales) is associated with a 41 (equal to (e0349-1) times 0197) increase in firm excess value The
effect is larger for the other two columns ndash 82 increase in firm excess value in column (1) and
46 in column (2)20
Our finding of a premium reconciles with the international trade literature which finds that
firms engaging in international trade are more productive than those that do not (see Helpman
2006 and Syverson 2011 for reviews) Due to data limitations it is difficult to accurately
measure TFP at the firm level especially for firms which operate in multiple industries andor
20 Using the alternative strategy that employs US domiciled single-segment firms as the benchmark for the imputed value of the foreign operations Denis et al (2002) find that greater foreign activity results in a discount to firm value Using the Denis et al (2002) method to calculate excess value in our sample we obtain a significantly negative regression coefficient on Foreign Sales using the models in Table 4 columns (2) and (3) (the coefficient is not significant using the model in column (1) results untabulated)
20
countries As an alternative method to evaluate whether our finding of a premium is simply the
result of more productive firms also having more multinational operations we investigate
whether the premium is simply an artifact of innate characteristics of firms that choose to invest
abroad For example Helpman et al (2004) present a simple model with heterogeneous
productivity endowments The firms that receive the highest productivity endowments are the
ones capable of paying the fixed costs to establish a foreign subsidiary
To the extent that productivity advantages create excess value one would expect the type of
firms that choose to invest more abroad would be valued at a premium even in the absence of
their foreign investments To control for time invariant firm characteristics (eg innate
productivity advantages) and hence a potential source of endogeneity we add firm-level fixed
effects in column (4) of Table 4 We continue to find that the extent of foreign operations
(Foreign Sales) is positively associated with Excess Value Across all specifications the sign
on all significant control variables is consistent with prior research (eg Berger and Ofek 1995
and Denis et al 2002)
The finding of a premium makes a significant contribution to the relatively sparse literature
on the value effects of multinational activity Our measure of imputed value expands upon the
methods used by Berger and Ofek (1995) and Denis et al (2002) but allows us to answer a
different question than Denis et al (2002) Holding constant the extent of industrial
diversification we ask whether MNCs are more valuable than a portfolio of benchmark firms of
a similar geographic footprint In contrast Denis et al (2002) ask whether holding constant the
extent of industrial diversification MNCs are more valuable than a portfolio of domestic (US)
benchmark firms We believe that asking whether an MNCrsquos foreign (domestic) operations are
21
more valuable than those of a foreign (domestic) firm is a more appropriate way to assess the
value implications of a multinational network
5 Robustness
51 Endogeneity related to dynamic relations
Prior research argues that the relation between industrial diversification and excess firm
value could be endogenous through dynamic relationships based on observing past outcomes
Managers of firms likely choose to enter or exit industries or geographic regions based upon
their previous performance For example Campa and Kedia (2002) find that firms are more
likely to enter new industries when prospects in their current lines of business are deteriorating
Similar dynamics could also drive a relation between excess value and FDI We assess the
robustness of the previous results by estimating a dynamic panel data model which allows us to
control for dynamic endogeneity in addition to firm level unobserved heterogeneity We employ
the GMM estimator for dynamic panel models developed by Arellano and Bond (1991) Arellano
and Bover (1995) and Blundell and Bond (1998) Our results from this analysis confirm our
previous findings that firms with a larger degree of foreign operations have positive excess
values
Dynamic panel data models are generalizations of the traditional fixed effects model where
lags of the dependent variable are added to the right hand side of the equation These additional
lags control for the impact a firmrsquos past performance has on its current performance through the
channels discussed above The GMM estimator for dynamic panel models developed by
Arellano and Bond (1991) starts by differencing the data at the firm level in neighboring time
periods to eliminate any firm specific time invariant unobserved heterogeneity that may be
22
present Eliminating the firm specific fixed effect by differencing the data with lags of the
dependent variable on the right hand side introduces correlation between the new error term and
the differenced explanatory variables This problem is solved by applying the standard GMM
estimator using lags of both the original explanatory variables and excess value as instruments
We note that the dynamic panel data framework allows us to treat both international and
industrial diversification (as well as the other firm-specific control variables) as endogenous
Following Hoechle et al (2012) we also allow contemporaneous values of these variables to be
treated as endogenous
Lagged values of the explanatory variables are valid instruments in a dynamic panel data
model when the model is dynamically complete (eg Wooldridge 2010) A dynamic panel data
model is dynamically complete conditional on the unobserved effect if enough lags of both the
explanatory and dependent variable are included to make the resulting error term uncorrelated
with all the right hand side variables Under this assumption any lags for the explanatory
variables beyond this can be used within the GMM estimator as instruments Like Hoechle et al
(2012) we add two years of lags of a firmrsquos excess value to the right hand side assuming that
this is enough lags to control for dynamic feedback effects We then use past values of all
variables beyond the second lag up to a total of seven years to act as instruments
The results of the dynamic panel data estimator are reported in Table 5 These results confirm
the previous finding of a positive relation between the degree of international operations and
excess value We note that the size and significance level of the coefficient on Foreign Sales
diversification are robust to changes in the number of lags used as instruments The Arellano and
Bond (1991) estimator includes testing diagnostics for correct specification of the model If the
model is correctly specified differences in the residuals from the levels equation should be
23
serially uncorrelated at lag one but correlated at lag two The p-values for tests of serial
correlation of the residuals at lags 1 and 2 are reported in Table 5 and these indicate that the
model is not rejected We also report in Table 5 the Hansen test for overidentification which is a
test of the null hypothesis that the instruments are valid The p-value for this test is 030
indicating that we fail to reject this null hypothesis Finally we report another test for exogeneity
known as the difference-in-Hansen test which tests the null hypothesis that the instruments in
the original equation are valid We fail to reject this null hypothesis as well
52 Identifying value effects of multinational operations via cross-border acquisitions
Another alternative to investigating a causal link between foreign direct investment and firm
value is an event study of foreign acquisitions21 Acquired foreign targets represent only a small
fraction of MNCsrsquo worldwide sales in the year of the acquisition ndash less than 1 percent in our
sample Additionally cross-border acquisitions are not the primary source of international
expansion for the average firm We find that foreign acquisitions account for between 18 percent
and 55 percent of the year-over-year change in foreign sales with the remainder of the growth
coming from either newly established entities (ie greenfield investments) or growth in existing
operations22 These results suggest that acquisitions are not a material source of growth for the
21 Doukas and Travlos (1988) find positive returns when the acquirer is not operating in the target firmrsquos country and insignificant returns when the acquirer is operating in the target firmrsquos country or expanding internationally for the first time Morck and Yeung (1991) find marginal evidence of positive returns for firms with intangible assets Finally Doukas (1995) finds positive returns only for firms with average Tobinrsquos q ratios greater than one More recent work also using an event study methodology provides mixed evidence on valuation effects of global diversification through acquisitions Dos Santos Errunza and Miller (2008) find insignificant returns for firms with already established foreign operations firms establishing initial foreign operations and firms acquiring targets in related industries Doukas and Kan (2006) and Moeller and Schlingemann (2005) find reductions to shareholder value while Francis Hasan and Sun (2008) find positive returns for acquirers of targets in segmented financial markets 22 New foreign affiliates to an MNC group each year report on the BEA survey whether they entered the group as an acquisition or a newly established entity (ie greenfield investment) Affiliates under the lowest reporting threshold do not report this information resulting in our inability to precisely identify the source of 33 percent of the increase in firm sales as either acquisition or greenfield
24
typical US MNC suggesting that an event study would lack power and generalizability For
these reasons we do not implement an event study
53 Control premium
A control premium or private benefits of control occurs when ldquosome value whatever the
source is not shared among all the shareholders in proportion of the shares owned but it is
enjoyed exclusively by the party in controlrdquo (Dyck and Zingales 2004 p 541) Using stock
prices of traded shares held by minority shareholders in determining the market value to sales
ratios of our benchmark firms could underestimate the true value of our benchmark firms if those
firms face large control premiums This in turn could induce an excess value premium because
the imputed value of these country-industry segments would be artificially low The existence of
such benefits is more prevalent in some countries than others and could affect the value of an
MNCrsquos operations in such domains We use the country-level control premiums from Dyck and
Zingales (2004) and using each firmrsquos country sales as a percent of total firm sales we obtain a
weighted average control premium that each MNC faces (Control Premium) For countries not
included in the Dyck and Zingales (2004) analysis we set the control premium equal to zero23 In
Table 6 we show that Foreign Sales remains positive and highly significant (p-value lt 001)
when we include the proxy for control premium As a result it does not appear that the excess
value premium is driven by MNCs operating in countries where majority shareholders are able to
extract large control premiums
23 Results are robust to replacing missing values with the mean control premium of the respective GDP quartile
25
54 Cost of capital
As discussed in Section 2 when countries have shallow capital markets a multinational
network may provide the benefit of a lower cost of capital for investments through an internal
capital market as well as through better access to the US capital market Prior research
establishes that there is substantial variation in the cost of capital across countries (Erb Harvey
and Viskanta 1996 and Hail and Leuz 2009) To determine whether the documented premium
is due to cost of capital advantages we include a control variable to capture the difference
between the MNCrsquos cost of capital and the cost of capital faced by its competitors in the global
markets in which it operates
We compute our Cost of Capital proxy in two steps First we obtain the country-year credit
rating published by Institutional Investor24 The rating ranges from 0 to 100 where higher values
imply that a country has a higher default risk Country-level credit risk is a reasonable predictor
of expected equity market returns and volatility (Erb et al 1996) and exhibits a highly
significant correlation with international accounting-based estimates of imputed cost of capital
(Hail and Leuz 2009) Second using each firmrsquos country sales as a percent of total firm sales
we obtain a weighted average credit risk rating across the countries in which each firm operates
A higher value implies an MNC operates in countries with higher cost of capital In Table 7 we
do not find a result consistent with MNCs gaining an advantage from their access to low cost
capitalndash Cost of Capital is not significantly associated with Excess Value25
24 We thank Cam Harvey for providing these data 25 Results are consistent using cost of capital as defined in Erb et al 1996
26
55 Corporate governance
In light of the recent finding that corporate governance mitigates the diversification discount
we examine whether the premium to multinational activity is sensitive to controls for corporate
governance Hoechle et al (2012) find that the magnitude of the industrial diversification
discount is decreased and in several specifications no longer significant in the presence of
governance proxies In the first column of Table 8 we include the five governance proxies that
are consistently significantly associated with excess value in Hoechle et al (2012) ndash the percent
of CEO ownership (CEO Ownership and CEO Ownership squared) the percent of institutional
ownership (Institution Ownership) whether the firm has a powerful or influential CEO
(Powerful CEO) and the governance index from Gompers Ishii and Metrick (2003)
(Governance Index)
As documented in Table 8 including these control variables reduces our sample size by
almost 50 (to 2550 observations) and tends to exclude the smaller firms from the analysis
None of the governance proxies are significant Relative to column (4) of Table 4 the coefficient
on Foreign Sales is similar in magnitude but is no longer statistically significant at
conventional levels To investigate whether the decreased significance is due to the smaller
sample or the inclusion of the governance variables we re-estimate the regression for the same
2550 observations after removing the governance controls The coefficient and t-statistic are
virtually identical suggesting that the loss of significance is due to the reduced sample and not
the governance controls We do not find evidence to suggest that variation in corporate
governance practices drives variation in excess value for US MNCs
27
6 Conclusion
Using a new method to quantify the valuation effects of foreign direct investment by US
domiciled firms we find robust evidence of a premium in excess value associated with the
degree of multinational operations Our method builds on that used in previous studies
examining firm excess value (eg Berger and Ofek 1995 and Denis et al 2002) by
incorporating the location in addition to the industry of each corporate affiliate This allows
important factors such as growth and cost of capital to vary across countries as well as industries
Examining a sample of multinational firms we find that a one-standard deviation (197)
increase in foreign operations is associated with an increase of between 41 and 82 in excess
value minus both a statistically and economically significant finding
This result complements the finding of the international trade literature that more productive
firms are more likely to operate internationally However the premium remains after including
firm fixed effects to control for persistent firm traits (eg productivity)
The finding of a premium stands up to a succession of robustness tests designed to evaluate
whether the result arises from alternative methods or explanations First we estimate a dynamic
panel data model using the generalized method of moments (GMM) estimator to simultaneously
account for the potential endogeneity of FDI industrial diversification and firm value Second
we include a control variable to proxy for whether the firm operates in countries with larger
control premiums which could result in understated multiples and mechanically induce a
premium Third we include a control variable to measure the cost of capital across the countries
in which an MNC operates to evaluate whether a relatively low cost of capital borne by
multinational firms is a meaningful source of the premium Fourth we control for several
corporate governance proxies which have been found to be correlated with excess values (and
28
industrial diversification) We fail to find evidence supporting any of these alternative
explanations ndash the premium in excess value arising at firms with greater multinational operations
remains significant
Overall our new method of estimating excess values for multinational firms provides strong
evidence of a statistically and economically significant premium associated with increased
international operations The premium is robust to a host of specifications and controls designed
to evaluate alternative explanations We leave to future research the questions of whether the
premium to US multinational firms applies more broadly to other countries of domicile and
what factors influence the premium
29
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Ammann M Hoeschel D and M Schmid 2012 Is there really no conglomerate discount
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Arellano M and Bover O 1995 Another look at instrumental variables estimation of error
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Berger P G and E Ofek 1995 Diversificationrsquos effect on firm value Journal of Financial
Economics 37 39-65 Blundell R and Bond S R 1998 Initial conditions and moment restrictions in dynamic panel
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Doukas JA 1995 Overinvestment Tobinrsquos q and gains from foreign acquisitions Journal of
Banking and Finance 19 1285-1303 Doukas J and N G Travlos 1988 The effect of corporate multinationalism on shareholdersrsquorsquo
wealth evidence from international acquisitions Journal of Finance 43(5) 1161-1175 Doukas JA and O B Kan 2006 Does global diversification destroy firm value Journal of
International Business Studies 37(3) 352-371
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Journal of Finance 59 (2) 537-600 Erb C Harvey C and T Viskanta 1996 Expected returns and volatility in 135 countries The
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Multinational and local firm responses to currency depreciations Review of Financial Studies 21 (6) 2857-2888
Francis B I Hasan and X Sun 2008 Financial market integration and the value of global
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Journal of Economics 118(1) 107-156 Hail L and C Leuz 2009 Cost of capital effects and changes in growth expectations around
US cross-listings Journal of Financial Economics 93 428-454 Hanson G Mataloni R and M Slaughter 2005 Vertical production networks in multinational
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44(3) 289-630 Helpman E Melitz M J and S R Yeaple 2004 Export versus FDI with Heterogeneous Firms
American Economic Review 94(1) 300-316 Hoechle D Schmid M Walter W and D Yermack 2012 How much of the diversification
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of Accounting Research 46 (June) 591-626 Huizinga H P and J Voget 2009 International taxation and the direction and volume of cross-
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Economic Review Papers and Proceedings 76 323-329
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Laeven L and R Levine 2007 Is there a diversification discount in financial conglomerates
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Moeller SB and F P Schlingemann 2005 Global diversification and bidder gains A
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365 Villalonga B 2004a Diversification discount or premium New evidence from the Business
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corporation C Pitelis and R Sugden (eds) The Nature of the Transnational Firm London Routledge
32
Appendix A Variable Definitions
We obtain information regarding firmsrsquo operations by country and industry from the Bureau of Economic Analysis (BEA) unless otherwise noted Acronyms used below reflect Compustat mnemonics and thus indicate when we obtain data from the Compustat North America Fundamentals Annual database All independent variables are winsorized at 1
Dependent variable
Excess Value = The log of the ratio of actual firm value to imputed firm value Actual firm value is equal to market capitalization (MVE) + book value of total assets (AT) ndash book value of shareholdersrsquo equity (SEQ) Imputed firm value is calculated as the sum of the imputed values of a firmrsquos operations in each country-industry The imputed value for each country-industry is equal to sales generated in that country-industry times a multiple equal to the ratio of a benchmark firmrsquos actual value to total sales A benchmark firm is defined as a median single-segment firm operating in the same country-industry All multiples require at least five firms listed in the respective country-industry on Compustat (domestic) or Worldscope (foreign) with less than 10 of sales income and assets outside the country of domicile Similar to Berger and Ofek (1995) we eliminate extreme values where the ratio of actual to imputed firm value exceeds 400 or is less than 025
Independent variables
Foreign Sales = Sales made by the foreign operations of US MNCs as a percent of total firm sales
Industry Other = Sales outside a firmrsquos primary industry as a percent of total sales (SALE) Equal to zero for firms operating in a single business segment
Log Size = Log of total sales (SALE)
Debt Firm Value = Ratio of long-term debt (DLTT) to total market value (market capitalization (MVE) + book value of total assets (AT) ndash book value of shareholdersrsquo equity (SEQ))
CAPX Sales = Ratio of capital expenditures (CAPX) to total sales (SALE)
EBIT Sales = Ratio of earnings before interest and taxes (EBIT) to total sales (SALE)
RampD Sales = Ratio of research and development expenses (XRD) to total sales (SALE)
Ad Sales = Ratio of advertising expenses (XAD) to total sales (SALE)
Control Premium = A weighted-average (based on the sales in a given country as a percent of total firm sales) of the country-level control premium from Dyck and Zingales (2004)
Cost of Capital = A weighted-average (based on the sales in a given country as a percent of total firm sales) of the country-level credit rating from Institutional Investor (httpwwwinstitutionalinvestorcom) The country-level rating is available for 160 countries and ranges from 0 to 100 Higher values imply greater credit risk
33
CEO Ownership = Percent of shares owned by the CEO from Execucomp
Institution Ownership = Percent of shares owned by institutional investors from Thomson Reuterrsquos Institutional Holdings database
Powerful CEO = Indicator variable equal to one if the CEO is the only insider on the board of directors and also serves as the chairman and president based on data from RiskMetrics Directors database Equal to zero otherwise
Governance Index = Count measure of takeover defenses used in Gompers Ishii and Metrick (2003) from RiskMetrics Governance database As the governance data are not available for every year we follow Gompers Ishii and Metrick (2003) and assume that the firmsrsquo governance attributes are unchanged until publication of the subsequent series
34
Figure 1 Trends in multinational activity and industrial diversification from 1984-2008 This figure presents a plot of the mean percent of foreign sales and the mean percent of sales outside the firmrsquos primary industry (industrial diversification) from 1984 through 2008 Dotted lines above and below the primary lines represent 95 confidence intervals To compile the plot we include the full population of firms in Compustat
35
Table 1 Analysis of accounting data for firms reporting under multiple accounting standards This table provides a comparison of sales net income and assets for firms that changed accounting standards to or from US GAAP Comparisons are provided for the 66 firms that changed to or from US GAAP and for which we could obtain accounting data electronically and in English under both standards for the same fiscal year (eg the firm presented the accounting numbers originally under one set of standards and then restated the prior year numbers in the subsequent year under the new set of standards) Panel A shows the distribution of firms by year Panel B shows the other accounting standards to which US GAAP is compared Panel C shows the mean and median percent that the other accounting standard differs from US GAAP for the same fiscal year and accounting number Two samples are shown in Panel C The first is the full sample of firms with a change in accounting standards to or from US GAAP for which data was available The second is the subset of these firms that occurred during the sample window used in this study (ie excluding changes post-2008) The percent difference in sales is compared to the percent difference in net income and assets Tests of means are based on matched-pair t-tests Tests of medians are based on Wilcoxon matched-pair signed-rank tests Panel A Accounting standard changes by year
2003
4 6
2004
25 38
2005
8 12
2006
6 9
2007
7 11
2008
5 8
2009
5 8
2010
5 8
2011
1 2
66
Panel B Accounting standards compared to US GAAP
IFRS
47 71
Canadian GAAP
12 18
Israeli GAAP
2 3
Dutch GAAP
1 2
Indian GAAP
1 2
Japanese GAAP
1 2
Norwegian GAAP
1 2
Swiss GAAP
1 2
66
36
(Table 1 continued) Panel C Tests of differences between US GAAP and other accounting standards
Percent different from US GAAP
Comparison to sales (p-values)
N Mean Median
Mean Median
Full Sample (2001-2011)
Sales
66 236 000
Net Income
66 10653 647
005 lt001
Assets
66 1162 126
lt001 lt001
Study Sample (2001-2008)
Sales
55 257 000
Net Income
55 10303 589
011 lt001
Assets
55 1047 099
003 lt001
37
Table 2 Descriptive statistics Panel A reports the mean and medians of Excess Value for the multinational firms and single-segment benchmark firms as well as univariate tests across the subsamples Panel B reports summary statistics for the independent variables for the multinational firms The p-values of the tests of differences in means are based on t-tests The p-values of tests of differences in the medians are for a Wilcoxon rank-sum test All variables are defined in Appendix A Panel A Excess Value by sample Multinational All Benchmark Benchmark Subgroups Sample Firms US Foreign
Value Value Test of Diff (p-value) Value
Test of Diff (p-value) Value
Test of Diff (p-value)
Mean 0067 0006 lt001 -0028 lt001 0014 lt001 Median 0076 0000 lt001 -0025 lt001 0000 lt001 N 4950 33074 5998 27076 Panel B Distribution of independent variables for the multinational firms N Mean 25th 50th 75th StDev Foreign Sales 4950 0238 0074 0190 0363 0197 Industry Other 4950 0212 0000 0166 0414 0215 Log Size 4950 6955 5823 6989 8140 1687 Debt Firm Value 4950 0136 0009 0097 0212 0145 CAPX Sales 4950 0054 0020 0033 0054 0101 EBIT Sales 4950 0065 0031 0077 0121 0138 RampD Sales 4950 0049 0000 0013 0059 0083 Ad Sales 4950 0008 0000 0000 0005 0018
38
Table 3 BEA and Worldscope samples
This table reports details on affiliate sales for significant countries pooled from 1998-2008 Significant countries are defined as those in which the operations of US MNCs represent at least 02 of total firm sales (per the BEA) For significant countries the table compares their representation in terms of MNC affiliate sales in the BEA data to the distribution of firm observations in Worldscope US Foreign Affiliates (BEA) Foreign Firms (Worldscope)
Country
N (1)
Sales (millions)
(2)
Total Sales (3)
N (4)
Sales (million)
(5)
Total Sales (6)
Australia 1344 73305 056 859 255865 210 Belgium 894 52755 040 169 26136 021 Brazil 959 60245 046 1143 683167 560 Canada 2730 536767 407 1181 526750 432 China 922 75907 057 822 276541 227 France 1852 183942 139 540 105447 086 Germany 2182 275577 209 480 103888 085 Hong Kong 867 48100 036 157 34094 028 Ireland 478 36754 028 34 3467 003 Italy 1178 82116 062 111 47948 039 Japan 1494 148032 112 2281 1119223 917 Korea South 659 34500 026 3530 2752791 2255 Malaysia 386 26027 020 837 168683 138 Mexico 1405 113623 086 362 237173 194 Netherlands 1149 85105 064 47 22631 019 Singapore 1098 94917 072 139 32913 027 Spain 871 63094 048 129 37419 031 Sweden 712 32682 025 235 23090 019 Switzerland 726 60288 046 74 20242 017 Taiwan 592 30313 023 2949 787049 645 United Kingdom 3054 423934 321 1997 882423 723 United States (domicile) 4950 10460880 7923 na na na Other - Covered by WorldScope 6893 199394 151 9000 4057894 33 Other -Not Covered by WorldScope 713 5352 004 - - - Total 38108 13203609 1000 27076 12204834 100
39
Table 4 OLS regressions of firm excess values on the percent of foreign sales This table reports the results of OLS regressions of firm excess value on the percent of foreign sales (Foreign Sales) the percent of sales outside a firmrsquos primary industry (Industry Other) and control variables All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed) Dep Variable = Excess Value Coefficient t-stat Coefficient t-stat Coefficient t-stat Coefficient t-stat (1) (2) (3) (4) Foreign Sales 0349 419 0209 293 0189 266 0278 210 Industry Other -0134 -186 -0047 -072 -0053 -081 -0326 -325 Log Size 0066 634 0065 624 -0044 -096 Debt Firm Value -0196 -213 -0241 -260 -0685 -588 CAPX Sales 0901 425 0893 435 0488 227 EBIT Sales 1154 542 1170 547 0408 189 RampD Sales 2908 1242 2932 1237 1544 379 Ad Sales 1869 263 2003 280 -1696 -119 Year indicators No No Yes Yes Firm indicators No No No Yes R2 0017 0212 0226 0768 Observations 4950 4950 4950 4950
40
Table 5 Dynamic panel GMM model regressing firm excess values on the percent of foreign sales This table reports the result from a regression of the excess value on the percent of foreign sales (Foreign Sales) the percent of non-primary industry sales (Industry Other) and control variables using a dynamic panel GMM estimator All control variables are considered to be endogenous with the exception of the year indicator variables AR(1) and AR(2) are tests for first-order and second-order serial correlation in the first differenced residuals with the null hypothesis of no serial correlation The null hypothesis of the Hansen test of overidentification is that all instruments are valid The null hypothesis of the difference-in-Hansen test of exogeneity is that the instruments used for the equation in levels are exogenous All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed) Dep Variable = Excess Value Coef t-stat Excess Value (one lag) -0018 -082 Excess Value (two lags) -0048 -272 Foreign Sales 0371 270 Industry Other -0163 -171 Log Size 0010 123 Debt FirmValue -1135 -820 CAPX Sales 0590 342 EBIT Sales -0047 -300 RampD Sales 1376 531 Ad Sales -0751 -040 AR(1) test (p-value) 000 AR(2) test (p-value) 017 Hansen test of over-identification (p-value) 030 Difference-in-Hansen test of exogeneity (p-value) 093 Year indicators Yes Number of firms 1016 Number of observations 4090
41
Table 6 OLS regression of firm excess values on the percent of foreign sales controlling for a control premium This table reports the result of the OLS regression of firm excess value including firm and year fixed effects with an additional control variable to proxy for a country-level ownership control premium (Control Premium) All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed) Dep Variable = Excess Value Coef t -stat Foreign Sales 0303 224 Industry Other -0327 -325 Log Size -0045 -100 Debt FirmValue -0683 -585 CAPX Sales 0490 226 EBIT Sales 0409 189 RampD Sales 1545 379 Ad Sales -1689 -119 Control Premium -1004 -062 Year indicators Yes Firm indicators Yes R2 0768 Observations 4950
42
Table 7 OLS regression of firm excess values on the percent of foreign sales controlling for the cost of capital This table contains the results of an OLS regression of firm excess value including firm and year fixed effects with an additional control variable to proxy for a country-level cost of capital (Cost of Capital) All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed) Dep Variable = Excess Value Coef t -stat Foreign Sales 0255 178 Industry Other -0326 -324 Log Size -0043 -094 Debt FirmValue -0687 -589 CAPX Sales 0485 225 EBIT Sales 0406 188 RampD Sales 1539 379 Ad Sales -1703 -120 Cost of Capital 0004 039 Year indicators Yes Firm indicators Yes R2 0768 Observations 4950
43
Table 8 OLS regression of firm excess values on the percent of foreign sales controlling for aspects of corporate governance This table reports the results of two OLS regressions In the first column are the results of the firm excess value regression including firm and year fixed effect with five proxies for corporate governance included ndash the percent of shares owned by the CEO and the percent squared (CEO Ownership and CEO Ownership squared) the percent owned by institutional investors (Institution Ownership) whether there is a powerful CEO (Powerful CEO) and the Gompers Ishii and Metrick (2003) governance index (Governance Index) In the second column the regression includes the same sample and variables with the exception of the governance proxies which are excluded All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed)
Governance
No Governance (same sample)
Dep Variable = Excess Value Coef t-stat Coef t -stat Foreign Sales 0259 159 0251 154 Industry Other -0099 -080 -0099 -079 Log Size -0105 -185 -0102 -180 Debt FirmValue -0813 -478 -0817 -482 CAPX Sales 0440 252 0438 249 EBIT Sales 0457 247 0457 246 RampD Sales 1717 284 1716 286 Ad Sales -2210 -143 -2190 -143 CEO Ownership 0010 007
CEO Ownership squared -0029 -017 Institution Ownership 0047 067 Powerful CEO -0003 -014 Governance Index 0009 103
Year indicators Yes Yes Firm indicators Yes Yes R2 0811 0810 Observations 2550 2550
- Cover for paperpdf
- crrz_20130603_SMUpdf
-
14
accordance with US Generally Accepted Accounting Principles (US GAAP) For each year
we observe the sales industry composition and location of not only the parent but also each
affiliate13
The procedures we use to construct our sample are similar to those used by Berger and Ofek
(1995) and Denis et al (2002) Using firm-years appearing in Compustat Fundamentals Annual
and Compustat Segment data we obtain a sample of firms domiciled in the US with total sales
exceeding $20 million We exclude firms in the financial service (SIC codes 6000 to 6999) and
utility (SIC codes 4000 to 4999) industries and eliminate firm-years when the sum of segment
sales (Compustat geographic or Compustat business) is not within 1 of total firm sales for that
year We restrict our sample period to include fiscal years beginning after December 15 1997 as
ASC 280 Segment Reporting substantially altered the definition of a reporting segment under
US GAAP This restriction ensures that Compustat Segment data reflects the use of a consistent
accounting standard Finally we require that our MNCs appear in both Compustat and BEA
data The requirement that our sample firms appear in the BEA database ensures that the firms
have observable international operations In sum these procedures result in 1166 multinational
firms incorporated in the US with 4950 observations between 1998 and 2008 that meet all of
our data requirements
33 Measuring excess value
The dependent variable in our study is excess firm value (Excess Value) defined as the
logarithm of the ratio of actual firm value to imputed firm value (Berger and Ofek 1995)
13 We obtain sales industry membership and country location for affiliates from one of two sources When affiliates exceed the BEA-determined reporting thresholds (ie if their assets sales or net income (loss) exceed $7 million in 1999 $30 million in 2000 through 2003 $10 million in 2004 and $40 million in 2005 through 2008) they must file their own detailed survey When affiliates fall below the thresholds we obtain our data from an attachment to the parent survey
15
Appendix A provides the definition of this and all other variables used in our analysis We
observe actual firm value equal to a firmrsquos market value of equity plus book value of liabilities
(ie book value of total assets minus book value of total equity) using Compustat Fundamentals
Annual data We calculate imputed firm value as the sum of the imputed values of a firmrsquos
operations in each country-industry Our method for imputing the value of the separate
components of a firm can best be described in three steps First we obtain total sales generated
by a firm for each country-industry in which it operates14 Second we obtain multiples (market
value to sales ratios) for benchmark firms operating in those same country-industries Third we
multiply a firmrsquos country-industry sales by the applicable median country-industry multiple to
obtain the imputed market value for each country-industry operation We perform each step
annually
Conceptually imputed values can be based on assets (ie Tobinrsquos Q) net income or sales
multiples We restrict ourselves to sales multiples for two reasons First using value to sales
ratios maintains consistency with the prior research on the excess value implications of
multinational operations (eg Denis et al 2002) Second as our method uses accounting data
for foreign companies to compute country-industry multiples the accounting numbers we use
need to be consistently measured across firms using different accounting standards We find that
sales data are measured most consistently
To assess the comparability of sales relative to either net income or assets across various
accounting standards we examine all firms listed on Worldscope as changing to or from US
14 Specifically we allocate total sales from Compustat to each component based on the proportion of BEA sales (after eliminating intercompany sales) occurring in each country-industry This step ensures that our total firm sales do not deviate from a firmrsquos total sales as reported in the 10-K
16
GAAP between 2005 and 201115 We obtain accounting data for the year of the change and the
prior year where electronically available in English We are able to obtain data for 66 firms
changing standards as detailed in Table 1 The distribution of years for which accounting data
are available under two different accounting standards for the same fiscal year is shown in Panel
A The years shown are the year prior to the change as these accounting numbers were provided
under the original standard and then subsequently restated in the following year under the new
standard for comparative purposes The majority of the shifts were to or from IFRS (71)
though seven other accounting standards are noted (Panel B) We examine the percent that the
non-US GAAP reported number differs from the US GAAP reported number in Panel C to
assess the comparability of these three summary accounting numbers across various accounting
standards Examining both the full sample as well as the subset that overlaps our study we find
that sales are more consistently measured than net income or assets
The sum of the imputed values across the country-industry components of an MNC is an
estimate of the value of a portfolio of unrelated businesses that mirror the related businesses of
the firm Consequently a comparison of the actual firm value with the imputed firm value is a
measure of how a multinational network affects firm value We provide descriptive statistics for
Excess Value for our sample of 4950 MNC firm-years in Table 2 Panel A This table also
includes univariate comparisons of the values between the MNC firms and the domestic single-
segment and foreign single-segment benchmark firms The Excess Value measure is significantly
higher for MNCs suggesting a premium in firm value for multinational firms relative to the
benchmark firms in total as well as the domestic and foreign subgroups (p-values for tests of
15 Due to the hand collection efforts required and the limitation on electronic filings we began our search with changes documented in 2005 which generally require filings from 2004
17
means and medians are lt 001)16 These results provide some initial evidence that operating in
multiple countries is positively associated with firm value
To estimate country-industry multiples we rely on Worldscope financial data on foreign
firms and Compustat Fundamentals Annual for domestic firms We restrict the firms to include
only those with at least 90 of sales income and assets inside the country of domicile (ie
those that do not report significant multinational activity) and that operate in a single industry
We refer to these firms as benchmark firms (either foreign or domestic depending on the country
of domicile) these firms do not appear in any of our regressions We report the number of
benchmark firms during our sample period in Table 2 Panel A The industry criterion is based on
the two-digit SIC code17 For every country that has at least five firms in the respective industry
and year we use the median ratio of market value to sales18 This ratio is the country-industry
multiple ndash an input required to compute imputed values
To determine the country-industry composition of MNCs we rely on BEA data In Table 3
we report the aggregate number of foreign affiliates and total sales by country as provided by
the BEA data for our sample of 4950 MNCs The specific countries tabulated are those that
represent at least 02 percent of total firm sales (pooled across all years) in our sample The top
five foreign countries in which MNCs generate sales are Canada United Kingdom Germany
France and Japan Table 3 also provides a comparison of the number of foreign benchmark
firms available in Worldscope in the specific countries in which our sample of MNCs generate
16 Similar to Berger and Ofek (1995) the median values differ slightly from zero for the benchmark single-segment firms due to the truncation of extreme excess values 17 BEA reporting constrains us to the two-digit SIC code level 18 When five firms are not available for a given country and industry we follow Berger and Ofek (1995) and move to a more general industry definition (eg one-digit SIC code) This procedure results in a mean (median) 630 (757) of MNC sales matched at the two-digit level
18
the majority of their sales Country coverage in Worldscope is not available for countries
representing 004 percent of total firm sales (per BEA)
4 Empirical results
41 Independent variables
Recall that our objective is to examine the overall relation between excess firm value and
multinational operations Our proxy for the extent of multinational operations is the percentage
of total firm sales made by foreign operations (Foreign Sales) By this measure the extent of
multinational activity in our sample of MNCs is non-trivial ndash Table 2 Panel B shows that
approximately 24 percent of sales are generated by foreign operations on average Our first
analysis estimates ordinary least square regressions of Excess Value on Foreign Sales and
control variables (discussed below)
We include variables in our regression to control for other potential determinants of excess
value These are the percentage of sales made by the firm outside its primary industry (Industry
Other) to control for any relation between industrial diversification and excess value as in Berger
and Ofek (1995) For this purpose we obtain industry sales using Compustat Segment data19 A
firmrsquos primary industry is the industry in which the firm generates the majority of its sales and
we determine industry sales at the business segment level We set Industry Other equal to zero
for firms that operate in a single business segment
Consistent with prior research we also include controls for firm size (Log Size) the ratio of
long term debt to total market value (Debt Firm Value) the ratio of capital expenditures to total
sales (CAPX Sales) the ratio of earnings before interest and taxes to total sales (EBIT Sales)
the ratio of research and development expenses to total sales (RampD Sales) and the ratio of 19 Our results are unaffected by using BEA data to measure Industry Other
19
advertising expenses to total sales (Ad Sales) Table 2 Panel B provides the distribution of the
control variables All independent variables are Winsorized at 1 In our OLS regressions we
use heteroskedasticity and autocorrelation consistent robust standard errors with clustering at the
firm level
42 Primary regression analysis
In this section we examine the relation between firm excess value and the amount of foreign
activity at US MNCs Table 4 contains the results of regressions of excess value on Foreign
Sales for multiple specifications In column (1) Excess Value is regressed on both the percent of
foreign sales (Foreign Sales) and the percent of sales outside the firmrsquos primary industry
(Industry Other) We add the control variables to the regression in column (2) and year
indicators in column (3) In all three specifications the coefficient on the percent of foreign sales
remains significantly positive (with a p-value lt 001) For a sense of economic magnitude in
column (3) a one standard deviation (197) increase in the percent of foreign sales (Foreign
Sales) is associated with a 41 (equal to (e0349-1) times 0197) increase in firm excess value The
effect is larger for the other two columns ndash 82 increase in firm excess value in column (1) and
46 in column (2)20
Our finding of a premium reconciles with the international trade literature which finds that
firms engaging in international trade are more productive than those that do not (see Helpman
2006 and Syverson 2011 for reviews) Due to data limitations it is difficult to accurately
measure TFP at the firm level especially for firms which operate in multiple industries andor
20 Using the alternative strategy that employs US domiciled single-segment firms as the benchmark for the imputed value of the foreign operations Denis et al (2002) find that greater foreign activity results in a discount to firm value Using the Denis et al (2002) method to calculate excess value in our sample we obtain a significantly negative regression coefficient on Foreign Sales using the models in Table 4 columns (2) and (3) (the coefficient is not significant using the model in column (1) results untabulated)
20
countries As an alternative method to evaluate whether our finding of a premium is simply the
result of more productive firms also having more multinational operations we investigate
whether the premium is simply an artifact of innate characteristics of firms that choose to invest
abroad For example Helpman et al (2004) present a simple model with heterogeneous
productivity endowments The firms that receive the highest productivity endowments are the
ones capable of paying the fixed costs to establish a foreign subsidiary
To the extent that productivity advantages create excess value one would expect the type of
firms that choose to invest more abroad would be valued at a premium even in the absence of
their foreign investments To control for time invariant firm characteristics (eg innate
productivity advantages) and hence a potential source of endogeneity we add firm-level fixed
effects in column (4) of Table 4 We continue to find that the extent of foreign operations
(Foreign Sales) is positively associated with Excess Value Across all specifications the sign
on all significant control variables is consistent with prior research (eg Berger and Ofek 1995
and Denis et al 2002)
The finding of a premium makes a significant contribution to the relatively sparse literature
on the value effects of multinational activity Our measure of imputed value expands upon the
methods used by Berger and Ofek (1995) and Denis et al (2002) but allows us to answer a
different question than Denis et al (2002) Holding constant the extent of industrial
diversification we ask whether MNCs are more valuable than a portfolio of benchmark firms of
a similar geographic footprint In contrast Denis et al (2002) ask whether holding constant the
extent of industrial diversification MNCs are more valuable than a portfolio of domestic (US)
benchmark firms We believe that asking whether an MNCrsquos foreign (domestic) operations are
21
more valuable than those of a foreign (domestic) firm is a more appropriate way to assess the
value implications of a multinational network
5 Robustness
51 Endogeneity related to dynamic relations
Prior research argues that the relation between industrial diversification and excess firm
value could be endogenous through dynamic relationships based on observing past outcomes
Managers of firms likely choose to enter or exit industries or geographic regions based upon
their previous performance For example Campa and Kedia (2002) find that firms are more
likely to enter new industries when prospects in their current lines of business are deteriorating
Similar dynamics could also drive a relation between excess value and FDI We assess the
robustness of the previous results by estimating a dynamic panel data model which allows us to
control for dynamic endogeneity in addition to firm level unobserved heterogeneity We employ
the GMM estimator for dynamic panel models developed by Arellano and Bond (1991) Arellano
and Bover (1995) and Blundell and Bond (1998) Our results from this analysis confirm our
previous findings that firms with a larger degree of foreign operations have positive excess
values
Dynamic panel data models are generalizations of the traditional fixed effects model where
lags of the dependent variable are added to the right hand side of the equation These additional
lags control for the impact a firmrsquos past performance has on its current performance through the
channels discussed above The GMM estimator for dynamic panel models developed by
Arellano and Bond (1991) starts by differencing the data at the firm level in neighboring time
periods to eliminate any firm specific time invariant unobserved heterogeneity that may be
22
present Eliminating the firm specific fixed effect by differencing the data with lags of the
dependent variable on the right hand side introduces correlation between the new error term and
the differenced explanatory variables This problem is solved by applying the standard GMM
estimator using lags of both the original explanatory variables and excess value as instruments
We note that the dynamic panel data framework allows us to treat both international and
industrial diversification (as well as the other firm-specific control variables) as endogenous
Following Hoechle et al (2012) we also allow contemporaneous values of these variables to be
treated as endogenous
Lagged values of the explanatory variables are valid instruments in a dynamic panel data
model when the model is dynamically complete (eg Wooldridge 2010) A dynamic panel data
model is dynamically complete conditional on the unobserved effect if enough lags of both the
explanatory and dependent variable are included to make the resulting error term uncorrelated
with all the right hand side variables Under this assumption any lags for the explanatory
variables beyond this can be used within the GMM estimator as instruments Like Hoechle et al
(2012) we add two years of lags of a firmrsquos excess value to the right hand side assuming that
this is enough lags to control for dynamic feedback effects We then use past values of all
variables beyond the second lag up to a total of seven years to act as instruments
The results of the dynamic panel data estimator are reported in Table 5 These results confirm
the previous finding of a positive relation between the degree of international operations and
excess value We note that the size and significance level of the coefficient on Foreign Sales
diversification are robust to changes in the number of lags used as instruments The Arellano and
Bond (1991) estimator includes testing diagnostics for correct specification of the model If the
model is correctly specified differences in the residuals from the levels equation should be
23
serially uncorrelated at lag one but correlated at lag two The p-values for tests of serial
correlation of the residuals at lags 1 and 2 are reported in Table 5 and these indicate that the
model is not rejected We also report in Table 5 the Hansen test for overidentification which is a
test of the null hypothesis that the instruments are valid The p-value for this test is 030
indicating that we fail to reject this null hypothesis Finally we report another test for exogeneity
known as the difference-in-Hansen test which tests the null hypothesis that the instruments in
the original equation are valid We fail to reject this null hypothesis as well
52 Identifying value effects of multinational operations via cross-border acquisitions
Another alternative to investigating a causal link between foreign direct investment and firm
value is an event study of foreign acquisitions21 Acquired foreign targets represent only a small
fraction of MNCsrsquo worldwide sales in the year of the acquisition ndash less than 1 percent in our
sample Additionally cross-border acquisitions are not the primary source of international
expansion for the average firm We find that foreign acquisitions account for between 18 percent
and 55 percent of the year-over-year change in foreign sales with the remainder of the growth
coming from either newly established entities (ie greenfield investments) or growth in existing
operations22 These results suggest that acquisitions are not a material source of growth for the
21 Doukas and Travlos (1988) find positive returns when the acquirer is not operating in the target firmrsquos country and insignificant returns when the acquirer is operating in the target firmrsquos country or expanding internationally for the first time Morck and Yeung (1991) find marginal evidence of positive returns for firms with intangible assets Finally Doukas (1995) finds positive returns only for firms with average Tobinrsquos q ratios greater than one More recent work also using an event study methodology provides mixed evidence on valuation effects of global diversification through acquisitions Dos Santos Errunza and Miller (2008) find insignificant returns for firms with already established foreign operations firms establishing initial foreign operations and firms acquiring targets in related industries Doukas and Kan (2006) and Moeller and Schlingemann (2005) find reductions to shareholder value while Francis Hasan and Sun (2008) find positive returns for acquirers of targets in segmented financial markets 22 New foreign affiliates to an MNC group each year report on the BEA survey whether they entered the group as an acquisition or a newly established entity (ie greenfield investment) Affiliates under the lowest reporting threshold do not report this information resulting in our inability to precisely identify the source of 33 percent of the increase in firm sales as either acquisition or greenfield
24
typical US MNC suggesting that an event study would lack power and generalizability For
these reasons we do not implement an event study
53 Control premium
A control premium or private benefits of control occurs when ldquosome value whatever the
source is not shared among all the shareholders in proportion of the shares owned but it is
enjoyed exclusively by the party in controlrdquo (Dyck and Zingales 2004 p 541) Using stock
prices of traded shares held by minority shareholders in determining the market value to sales
ratios of our benchmark firms could underestimate the true value of our benchmark firms if those
firms face large control premiums This in turn could induce an excess value premium because
the imputed value of these country-industry segments would be artificially low The existence of
such benefits is more prevalent in some countries than others and could affect the value of an
MNCrsquos operations in such domains We use the country-level control premiums from Dyck and
Zingales (2004) and using each firmrsquos country sales as a percent of total firm sales we obtain a
weighted average control premium that each MNC faces (Control Premium) For countries not
included in the Dyck and Zingales (2004) analysis we set the control premium equal to zero23 In
Table 6 we show that Foreign Sales remains positive and highly significant (p-value lt 001)
when we include the proxy for control premium As a result it does not appear that the excess
value premium is driven by MNCs operating in countries where majority shareholders are able to
extract large control premiums
23 Results are robust to replacing missing values with the mean control premium of the respective GDP quartile
25
54 Cost of capital
As discussed in Section 2 when countries have shallow capital markets a multinational
network may provide the benefit of a lower cost of capital for investments through an internal
capital market as well as through better access to the US capital market Prior research
establishes that there is substantial variation in the cost of capital across countries (Erb Harvey
and Viskanta 1996 and Hail and Leuz 2009) To determine whether the documented premium
is due to cost of capital advantages we include a control variable to capture the difference
between the MNCrsquos cost of capital and the cost of capital faced by its competitors in the global
markets in which it operates
We compute our Cost of Capital proxy in two steps First we obtain the country-year credit
rating published by Institutional Investor24 The rating ranges from 0 to 100 where higher values
imply that a country has a higher default risk Country-level credit risk is a reasonable predictor
of expected equity market returns and volatility (Erb et al 1996) and exhibits a highly
significant correlation with international accounting-based estimates of imputed cost of capital
(Hail and Leuz 2009) Second using each firmrsquos country sales as a percent of total firm sales
we obtain a weighted average credit risk rating across the countries in which each firm operates
A higher value implies an MNC operates in countries with higher cost of capital In Table 7 we
do not find a result consistent with MNCs gaining an advantage from their access to low cost
capitalndash Cost of Capital is not significantly associated with Excess Value25
24 We thank Cam Harvey for providing these data 25 Results are consistent using cost of capital as defined in Erb et al 1996
26
55 Corporate governance
In light of the recent finding that corporate governance mitigates the diversification discount
we examine whether the premium to multinational activity is sensitive to controls for corporate
governance Hoechle et al (2012) find that the magnitude of the industrial diversification
discount is decreased and in several specifications no longer significant in the presence of
governance proxies In the first column of Table 8 we include the five governance proxies that
are consistently significantly associated with excess value in Hoechle et al (2012) ndash the percent
of CEO ownership (CEO Ownership and CEO Ownership squared) the percent of institutional
ownership (Institution Ownership) whether the firm has a powerful or influential CEO
(Powerful CEO) and the governance index from Gompers Ishii and Metrick (2003)
(Governance Index)
As documented in Table 8 including these control variables reduces our sample size by
almost 50 (to 2550 observations) and tends to exclude the smaller firms from the analysis
None of the governance proxies are significant Relative to column (4) of Table 4 the coefficient
on Foreign Sales is similar in magnitude but is no longer statistically significant at
conventional levels To investigate whether the decreased significance is due to the smaller
sample or the inclusion of the governance variables we re-estimate the regression for the same
2550 observations after removing the governance controls The coefficient and t-statistic are
virtually identical suggesting that the loss of significance is due to the reduced sample and not
the governance controls We do not find evidence to suggest that variation in corporate
governance practices drives variation in excess value for US MNCs
27
6 Conclusion
Using a new method to quantify the valuation effects of foreign direct investment by US
domiciled firms we find robust evidence of a premium in excess value associated with the
degree of multinational operations Our method builds on that used in previous studies
examining firm excess value (eg Berger and Ofek 1995 and Denis et al 2002) by
incorporating the location in addition to the industry of each corporate affiliate This allows
important factors such as growth and cost of capital to vary across countries as well as industries
Examining a sample of multinational firms we find that a one-standard deviation (197)
increase in foreign operations is associated with an increase of between 41 and 82 in excess
value minus both a statistically and economically significant finding
This result complements the finding of the international trade literature that more productive
firms are more likely to operate internationally However the premium remains after including
firm fixed effects to control for persistent firm traits (eg productivity)
The finding of a premium stands up to a succession of robustness tests designed to evaluate
whether the result arises from alternative methods or explanations First we estimate a dynamic
panel data model using the generalized method of moments (GMM) estimator to simultaneously
account for the potential endogeneity of FDI industrial diversification and firm value Second
we include a control variable to proxy for whether the firm operates in countries with larger
control premiums which could result in understated multiples and mechanically induce a
premium Third we include a control variable to measure the cost of capital across the countries
in which an MNC operates to evaluate whether a relatively low cost of capital borne by
multinational firms is a meaningful source of the premium Fourth we control for several
corporate governance proxies which have been found to be correlated with excess values (and
28
industrial diversification) We fail to find evidence supporting any of these alternative
explanations ndash the premium in excess value arising at firms with greater multinational operations
remains significant
Overall our new method of estimating excess values for multinational firms provides strong
evidence of a statistically and economically significant premium associated with increased
international operations The premium is robust to a host of specifications and controls designed
to evaluate alternative explanations We leave to future research the questions of whether the
premium to US multinational firms applies more broadly to other countries of domicile and
what factors influence the premium
29
References
Ammann M Hoeschel D and M Schmid 2012 Is there really no conglomerate discount
Journal of Business Finance and Accounting 39(12) 264-288 Arellano M and Bond S R 1991 Some tests of specification for panel data Monte Carlo
evidence and an application to employment equations Review of Economic Studies 58 (2) 277-298
Arellano M and Bover O 1995 Another look at instrumental variables estimation of error
components models Journal of Econometrics 68 (1) 29-61 Bens D A Berger P G and S J Monahan 2011 Discretionary disclosure in financial
reporting An examination comparing internal firm data to externally reported segment data The Accounting Review 86(2) 417-449
Berger P G and E Ofek 1995 Diversificationrsquos effect on firm value Journal of Financial
Economics 37 39-65 Blundell R and Bond S R 1998 Initial conditions and moment restrictions in dynamic panel
data models Journal of Econometrics 87(1) 115-144 Campa J M and S Kedia 2002 Explaining the diversification discount The Journal of
Finance 57 (4) 1731-1762 Denis D J Denis D K and K Yost 2002 Global diversification industrial diversification
and firm value The Journal of Finance 57 (5) 1951-1979 Desai M A Foley C F and J R Hines 2004 A multinational perspective on capital structure
choice and internal capital markets The Journal of Finance 59 (6) 2451-2487 Dos Santos MB V Errunza and D Miller 2008 Does corporate international diversification
destroy value Evidence from cross-border mergers and acquisitions Journal of Banking and Finance 32(12) 2716-2724
Doukas JA 1995 Overinvestment Tobinrsquos q and gains from foreign acquisitions Journal of
Banking and Finance 19 1285-1303 Doukas J and N G Travlos 1988 The effect of corporate multinationalism on shareholdersrsquorsquo
wealth evidence from international acquisitions Journal of Finance 43(5) 1161-1175 Doukas JA and O B Kan 2006 Does global diversification destroy firm value Journal of
International Business Studies 37(3) 352-371
30
Dunning J and A Rugman 1985 The influence of Hymerrsquos dissertation on the theory of foreign direct investment The American Economic Review 75(2) 228-232
Dyck A and L Zingales 2004 Private benefits of control An international comparison The
Journal of Finance 59 (2) 537-600 Erb C Harvey C and T Viskanta 1996 Expected returns and volatility in 135 countries The
Journal of Portfolio Management 46-58 Foley C F Desai M A and K J Forbes 2008 Financial constraints and growth
Multinational and local firm responses to currency depreciations Review of Financial Studies 21 (6) 2857-2888
Francis B I Hasan and X Sun 2008 Financial market integration and the value of global
diversification Evidence for US acquirers in cross-border mergers and acquisitions Journal of Banking and Finance 321522-1540
Gande A Schenzler C and L Senbet 2009 Valuation effects of global diversification
Journal of International Business Studies 1515-1532 Gompers P Ishii J and A Metrick 2003 Corporate governance and equity prices Quarterly
Journal of Economics 118(1) 107-156 Hail L and C Leuz 2009 Cost of capital effects and changes in growth expectations around
US cross-listings Journal of Financial Economics 93 428-454 Hanson G Mataloni R and M Slaughter 2005 Vertical production networks in multinational
firms Review of Economics and Statistics 87(4) 1-15 Helpman E 2006 Trade FDI and the Organization of Firms Journal of Economic Literature
44(3) 289-630 Helpman E Melitz M J and S R Yeaple 2004 Export versus FDI with Heterogeneous Firms
American Economic Review 94(1) 300-316 Hoechle D Schmid M Walter W and D Yermack 2012 How much of the diversification
discount can be explained by poor governance Journal of Financial Economics 103 41-60 Hope O-K and W B Thomas 2008 Managerial empire building and firm disclosure Journal
of Accounting Research 46 (June) 591-626 Huizinga H P and J Voget 2009 International taxation and the direction and volume of cross-
border MampAs Journal of Finance 64 (3) 1217-1249 Jensen M 1986 Agency cost of free cash flow corporate finance and takeovers American
Economic Review Papers and Proceedings 76 323-329
31
Laeven L and R Levine 2007 Is there a diversification discount in financial conglomerates
Journal of Financial Economics 85 331-367 Lang L and R Stulz 1994 Tobinrsquos Q corporate diversification and firm performance Journal
of Political Economy 102 1248-1280 Mataloni R 2003 US multinational companies Operations in 2001 Survey of Current
Business 85-105 Matvos G and A Seru 2011 The dynamic tradeoff between corporate socialism and financial
market dislocation Estimates from internal capital markets Working paper University of Chicago Booth School of Business
Moeller SB and F P Schlingemann 2005 Global diversification and bidder gains A
comparison between cross-border and domestic acquisitions Journal of Banking and Finance 29 533-564
Morck R and B Yeung 1991 Why investors value multinationality Journal of Business 64
165-186
Morck R and B Yeung 2001 Why firms diversify Internatization vs agency behavior In Intangibles B Lev (ed) Oxford University Press pp 269-302
Myers S C and N S Majluf 1984 Corporate financing and investment decisions when firms have information that investors do not have Journal of Financial Economics 13 187-221
Schmid M and I Walter 2009 Do financial conglomerates create or destroy economic value
Journal of Financial Intermediation 18 193-216 Syverson C 2011 What determines productivity Journal of Economic Literature 49(2) 326-
365 Villalonga B 2004a Diversification discount or premium New evidence from the Business
Information Tracking Series Journal of Finance 59 479-506 Villalonga B 2004b Does diversification cause the diversification discount Financial
Management 33 5-27 Wooldridge JM 2010 Econometric Analysis of Cross Section and Panel Data (Second
Edition) Cambridge MA MIT Press Yamin M 1991 A reassessment of Hymerrsquos contribution to the theory of the transnational
corporation C Pitelis and R Sugden (eds) The Nature of the Transnational Firm London Routledge
32
Appendix A Variable Definitions
We obtain information regarding firmsrsquo operations by country and industry from the Bureau of Economic Analysis (BEA) unless otherwise noted Acronyms used below reflect Compustat mnemonics and thus indicate when we obtain data from the Compustat North America Fundamentals Annual database All independent variables are winsorized at 1
Dependent variable
Excess Value = The log of the ratio of actual firm value to imputed firm value Actual firm value is equal to market capitalization (MVE) + book value of total assets (AT) ndash book value of shareholdersrsquo equity (SEQ) Imputed firm value is calculated as the sum of the imputed values of a firmrsquos operations in each country-industry The imputed value for each country-industry is equal to sales generated in that country-industry times a multiple equal to the ratio of a benchmark firmrsquos actual value to total sales A benchmark firm is defined as a median single-segment firm operating in the same country-industry All multiples require at least five firms listed in the respective country-industry on Compustat (domestic) or Worldscope (foreign) with less than 10 of sales income and assets outside the country of domicile Similar to Berger and Ofek (1995) we eliminate extreme values where the ratio of actual to imputed firm value exceeds 400 or is less than 025
Independent variables
Foreign Sales = Sales made by the foreign operations of US MNCs as a percent of total firm sales
Industry Other = Sales outside a firmrsquos primary industry as a percent of total sales (SALE) Equal to zero for firms operating in a single business segment
Log Size = Log of total sales (SALE)
Debt Firm Value = Ratio of long-term debt (DLTT) to total market value (market capitalization (MVE) + book value of total assets (AT) ndash book value of shareholdersrsquo equity (SEQ))
CAPX Sales = Ratio of capital expenditures (CAPX) to total sales (SALE)
EBIT Sales = Ratio of earnings before interest and taxes (EBIT) to total sales (SALE)
RampD Sales = Ratio of research and development expenses (XRD) to total sales (SALE)
Ad Sales = Ratio of advertising expenses (XAD) to total sales (SALE)
Control Premium = A weighted-average (based on the sales in a given country as a percent of total firm sales) of the country-level control premium from Dyck and Zingales (2004)
Cost of Capital = A weighted-average (based on the sales in a given country as a percent of total firm sales) of the country-level credit rating from Institutional Investor (httpwwwinstitutionalinvestorcom) The country-level rating is available for 160 countries and ranges from 0 to 100 Higher values imply greater credit risk
33
CEO Ownership = Percent of shares owned by the CEO from Execucomp
Institution Ownership = Percent of shares owned by institutional investors from Thomson Reuterrsquos Institutional Holdings database
Powerful CEO = Indicator variable equal to one if the CEO is the only insider on the board of directors and also serves as the chairman and president based on data from RiskMetrics Directors database Equal to zero otherwise
Governance Index = Count measure of takeover defenses used in Gompers Ishii and Metrick (2003) from RiskMetrics Governance database As the governance data are not available for every year we follow Gompers Ishii and Metrick (2003) and assume that the firmsrsquo governance attributes are unchanged until publication of the subsequent series
34
Figure 1 Trends in multinational activity and industrial diversification from 1984-2008 This figure presents a plot of the mean percent of foreign sales and the mean percent of sales outside the firmrsquos primary industry (industrial diversification) from 1984 through 2008 Dotted lines above and below the primary lines represent 95 confidence intervals To compile the plot we include the full population of firms in Compustat
35
Table 1 Analysis of accounting data for firms reporting under multiple accounting standards This table provides a comparison of sales net income and assets for firms that changed accounting standards to or from US GAAP Comparisons are provided for the 66 firms that changed to or from US GAAP and for which we could obtain accounting data electronically and in English under both standards for the same fiscal year (eg the firm presented the accounting numbers originally under one set of standards and then restated the prior year numbers in the subsequent year under the new set of standards) Panel A shows the distribution of firms by year Panel B shows the other accounting standards to which US GAAP is compared Panel C shows the mean and median percent that the other accounting standard differs from US GAAP for the same fiscal year and accounting number Two samples are shown in Panel C The first is the full sample of firms with a change in accounting standards to or from US GAAP for which data was available The second is the subset of these firms that occurred during the sample window used in this study (ie excluding changes post-2008) The percent difference in sales is compared to the percent difference in net income and assets Tests of means are based on matched-pair t-tests Tests of medians are based on Wilcoxon matched-pair signed-rank tests Panel A Accounting standard changes by year
2003
4 6
2004
25 38
2005
8 12
2006
6 9
2007
7 11
2008
5 8
2009
5 8
2010
5 8
2011
1 2
66
Panel B Accounting standards compared to US GAAP
IFRS
47 71
Canadian GAAP
12 18
Israeli GAAP
2 3
Dutch GAAP
1 2
Indian GAAP
1 2
Japanese GAAP
1 2
Norwegian GAAP
1 2
Swiss GAAP
1 2
66
36
(Table 1 continued) Panel C Tests of differences between US GAAP and other accounting standards
Percent different from US GAAP
Comparison to sales (p-values)
N Mean Median
Mean Median
Full Sample (2001-2011)
Sales
66 236 000
Net Income
66 10653 647
005 lt001
Assets
66 1162 126
lt001 lt001
Study Sample (2001-2008)
Sales
55 257 000
Net Income
55 10303 589
011 lt001
Assets
55 1047 099
003 lt001
37
Table 2 Descriptive statistics Panel A reports the mean and medians of Excess Value for the multinational firms and single-segment benchmark firms as well as univariate tests across the subsamples Panel B reports summary statistics for the independent variables for the multinational firms The p-values of the tests of differences in means are based on t-tests The p-values of tests of differences in the medians are for a Wilcoxon rank-sum test All variables are defined in Appendix A Panel A Excess Value by sample Multinational All Benchmark Benchmark Subgroups Sample Firms US Foreign
Value Value Test of Diff (p-value) Value
Test of Diff (p-value) Value
Test of Diff (p-value)
Mean 0067 0006 lt001 -0028 lt001 0014 lt001 Median 0076 0000 lt001 -0025 lt001 0000 lt001 N 4950 33074 5998 27076 Panel B Distribution of independent variables for the multinational firms N Mean 25th 50th 75th StDev Foreign Sales 4950 0238 0074 0190 0363 0197 Industry Other 4950 0212 0000 0166 0414 0215 Log Size 4950 6955 5823 6989 8140 1687 Debt Firm Value 4950 0136 0009 0097 0212 0145 CAPX Sales 4950 0054 0020 0033 0054 0101 EBIT Sales 4950 0065 0031 0077 0121 0138 RampD Sales 4950 0049 0000 0013 0059 0083 Ad Sales 4950 0008 0000 0000 0005 0018
38
Table 3 BEA and Worldscope samples
This table reports details on affiliate sales for significant countries pooled from 1998-2008 Significant countries are defined as those in which the operations of US MNCs represent at least 02 of total firm sales (per the BEA) For significant countries the table compares their representation in terms of MNC affiliate sales in the BEA data to the distribution of firm observations in Worldscope US Foreign Affiliates (BEA) Foreign Firms (Worldscope)
Country
N (1)
Sales (millions)
(2)
Total Sales (3)
N (4)
Sales (million)
(5)
Total Sales (6)
Australia 1344 73305 056 859 255865 210 Belgium 894 52755 040 169 26136 021 Brazil 959 60245 046 1143 683167 560 Canada 2730 536767 407 1181 526750 432 China 922 75907 057 822 276541 227 France 1852 183942 139 540 105447 086 Germany 2182 275577 209 480 103888 085 Hong Kong 867 48100 036 157 34094 028 Ireland 478 36754 028 34 3467 003 Italy 1178 82116 062 111 47948 039 Japan 1494 148032 112 2281 1119223 917 Korea South 659 34500 026 3530 2752791 2255 Malaysia 386 26027 020 837 168683 138 Mexico 1405 113623 086 362 237173 194 Netherlands 1149 85105 064 47 22631 019 Singapore 1098 94917 072 139 32913 027 Spain 871 63094 048 129 37419 031 Sweden 712 32682 025 235 23090 019 Switzerland 726 60288 046 74 20242 017 Taiwan 592 30313 023 2949 787049 645 United Kingdom 3054 423934 321 1997 882423 723 United States (domicile) 4950 10460880 7923 na na na Other - Covered by WorldScope 6893 199394 151 9000 4057894 33 Other -Not Covered by WorldScope 713 5352 004 - - - Total 38108 13203609 1000 27076 12204834 100
39
Table 4 OLS regressions of firm excess values on the percent of foreign sales This table reports the results of OLS regressions of firm excess value on the percent of foreign sales (Foreign Sales) the percent of sales outside a firmrsquos primary industry (Industry Other) and control variables All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed) Dep Variable = Excess Value Coefficient t-stat Coefficient t-stat Coefficient t-stat Coefficient t-stat (1) (2) (3) (4) Foreign Sales 0349 419 0209 293 0189 266 0278 210 Industry Other -0134 -186 -0047 -072 -0053 -081 -0326 -325 Log Size 0066 634 0065 624 -0044 -096 Debt Firm Value -0196 -213 -0241 -260 -0685 -588 CAPX Sales 0901 425 0893 435 0488 227 EBIT Sales 1154 542 1170 547 0408 189 RampD Sales 2908 1242 2932 1237 1544 379 Ad Sales 1869 263 2003 280 -1696 -119 Year indicators No No Yes Yes Firm indicators No No No Yes R2 0017 0212 0226 0768 Observations 4950 4950 4950 4950
40
Table 5 Dynamic panel GMM model regressing firm excess values on the percent of foreign sales This table reports the result from a regression of the excess value on the percent of foreign sales (Foreign Sales) the percent of non-primary industry sales (Industry Other) and control variables using a dynamic panel GMM estimator All control variables are considered to be endogenous with the exception of the year indicator variables AR(1) and AR(2) are tests for first-order and second-order serial correlation in the first differenced residuals with the null hypothesis of no serial correlation The null hypothesis of the Hansen test of overidentification is that all instruments are valid The null hypothesis of the difference-in-Hansen test of exogeneity is that the instruments used for the equation in levels are exogenous All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed) Dep Variable = Excess Value Coef t-stat Excess Value (one lag) -0018 -082 Excess Value (two lags) -0048 -272 Foreign Sales 0371 270 Industry Other -0163 -171 Log Size 0010 123 Debt FirmValue -1135 -820 CAPX Sales 0590 342 EBIT Sales -0047 -300 RampD Sales 1376 531 Ad Sales -0751 -040 AR(1) test (p-value) 000 AR(2) test (p-value) 017 Hansen test of over-identification (p-value) 030 Difference-in-Hansen test of exogeneity (p-value) 093 Year indicators Yes Number of firms 1016 Number of observations 4090
41
Table 6 OLS regression of firm excess values on the percent of foreign sales controlling for a control premium This table reports the result of the OLS regression of firm excess value including firm and year fixed effects with an additional control variable to proxy for a country-level ownership control premium (Control Premium) All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed) Dep Variable = Excess Value Coef t -stat Foreign Sales 0303 224 Industry Other -0327 -325 Log Size -0045 -100 Debt FirmValue -0683 -585 CAPX Sales 0490 226 EBIT Sales 0409 189 RampD Sales 1545 379 Ad Sales -1689 -119 Control Premium -1004 -062 Year indicators Yes Firm indicators Yes R2 0768 Observations 4950
42
Table 7 OLS regression of firm excess values on the percent of foreign sales controlling for the cost of capital This table contains the results of an OLS regression of firm excess value including firm and year fixed effects with an additional control variable to proxy for a country-level cost of capital (Cost of Capital) All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed) Dep Variable = Excess Value Coef t -stat Foreign Sales 0255 178 Industry Other -0326 -324 Log Size -0043 -094 Debt FirmValue -0687 -589 CAPX Sales 0485 225 EBIT Sales 0406 188 RampD Sales 1539 379 Ad Sales -1703 -120 Cost of Capital 0004 039 Year indicators Yes Firm indicators Yes R2 0768 Observations 4950
43
Table 8 OLS regression of firm excess values on the percent of foreign sales controlling for aspects of corporate governance This table reports the results of two OLS regressions In the first column are the results of the firm excess value regression including firm and year fixed effect with five proxies for corporate governance included ndash the percent of shares owned by the CEO and the percent squared (CEO Ownership and CEO Ownership squared) the percent owned by institutional investors (Institution Ownership) whether there is a powerful CEO (Powerful CEO) and the Gompers Ishii and Metrick (2003) governance index (Governance Index) In the second column the regression includes the same sample and variables with the exception of the governance proxies which are excluded All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed)
Governance
No Governance (same sample)
Dep Variable = Excess Value Coef t-stat Coef t -stat Foreign Sales 0259 159 0251 154 Industry Other -0099 -080 -0099 -079 Log Size -0105 -185 -0102 -180 Debt FirmValue -0813 -478 -0817 -482 CAPX Sales 0440 252 0438 249 EBIT Sales 0457 247 0457 246 RampD Sales 1717 284 1716 286 Ad Sales -2210 -143 -2190 -143 CEO Ownership 0010 007
CEO Ownership squared -0029 -017 Institution Ownership 0047 067 Powerful CEO -0003 -014 Governance Index 0009 103
Year indicators Yes Yes Firm indicators Yes Yes R2 0811 0810 Observations 2550 2550
- Cover for paperpdf
- crrz_20130603_SMUpdf
-
15
Appendix A provides the definition of this and all other variables used in our analysis We
observe actual firm value equal to a firmrsquos market value of equity plus book value of liabilities
(ie book value of total assets minus book value of total equity) using Compustat Fundamentals
Annual data We calculate imputed firm value as the sum of the imputed values of a firmrsquos
operations in each country-industry Our method for imputing the value of the separate
components of a firm can best be described in three steps First we obtain total sales generated
by a firm for each country-industry in which it operates14 Second we obtain multiples (market
value to sales ratios) for benchmark firms operating in those same country-industries Third we
multiply a firmrsquos country-industry sales by the applicable median country-industry multiple to
obtain the imputed market value for each country-industry operation We perform each step
annually
Conceptually imputed values can be based on assets (ie Tobinrsquos Q) net income or sales
multiples We restrict ourselves to sales multiples for two reasons First using value to sales
ratios maintains consistency with the prior research on the excess value implications of
multinational operations (eg Denis et al 2002) Second as our method uses accounting data
for foreign companies to compute country-industry multiples the accounting numbers we use
need to be consistently measured across firms using different accounting standards We find that
sales data are measured most consistently
To assess the comparability of sales relative to either net income or assets across various
accounting standards we examine all firms listed on Worldscope as changing to or from US
14 Specifically we allocate total sales from Compustat to each component based on the proportion of BEA sales (after eliminating intercompany sales) occurring in each country-industry This step ensures that our total firm sales do not deviate from a firmrsquos total sales as reported in the 10-K
16
GAAP between 2005 and 201115 We obtain accounting data for the year of the change and the
prior year where electronically available in English We are able to obtain data for 66 firms
changing standards as detailed in Table 1 The distribution of years for which accounting data
are available under two different accounting standards for the same fiscal year is shown in Panel
A The years shown are the year prior to the change as these accounting numbers were provided
under the original standard and then subsequently restated in the following year under the new
standard for comparative purposes The majority of the shifts were to or from IFRS (71)
though seven other accounting standards are noted (Panel B) We examine the percent that the
non-US GAAP reported number differs from the US GAAP reported number in Panel C to
assess the comparability of these three summary accounting numbers across various accounting
standards Examining both the full sample as well as the subset that overlaps our study we find
that sales are more consistently measured than net income or assets
The sum of the imputed values across the country-industry components of an MNC is an
estimate of the value of a portfolio of unrelated businesses that mirror the related businesses of
the firm Consequently a comparison of the actual firm value with the imputed firm value is a
measure of how a multinational network affects firm value We provide descriptive statistics for
Excess Value for our sample of 4950 MNC firm-years in Table 2 Panel A This table also
includes univariate comparisons of the values between the MNC firms and the domestic single-
segment and foreign single-segment benchmark firms The Excess Value measure is significantly
higher for MNCs suggesting a premium in firm value for multinational firms relative to the
benchmark firms in total as well as the domestic and foreign subgroups (p-values for tests of
15 Due to the hand collection efforts required and the limitation on electronic filings we began our search with changes documented in 2005 which generally require filings from 2004
17
means and medians are lt 001)16 These results provide some initial evidence that operating in
multiple countries is positively associated with firm value
To estimate country-industry multiples we rely on Worldscope financial data on foreign
firms and Compustat Fundamentals Annual for domestic firms We restrict the firms to include
only those with at least 90 of sales income and assets inside the country of domicile (ie
those that do not report significant multinational activity) and that operate in a single industry
We refer to these firms as benchmark firms (either foreign or domestic depending on the country
of domicile) these firms do not appear in any of our regressions We report the number of
benchmark firms during our sample period in Table 2 Panel A The industry criterion is based on
the two-digit SIC code17 For every country that has at least five firms in the respective industry
and year we use the median ratio of market value to sales18 This ratio is the country-industry
multiple ndash an input required to compute imputed values
To determine the country-industry composition of MNCs we rely on BEA data In Table 3
we report the aggregate number of foreign affiliates and total sales by country as provided by
the BEA data for our sample of 4950 MNCs The specific countries tabulated are those that
represent at least 02 percent of total firm sales (pooled across all years) in our sample The top
five foreign countries in which MNCs generate sales are Canada United Kingdom Germany
France and Japan Table 3 also provides a comparison of the number of foreign benchmark
firms available in Worldscope in the specific countries in which our sample of MNCs generate
16 Similar to Berger and Ofek (1995) the median values differ slightly from zero for the benchmark single-segment firms due to the truncation of extreme excess values 17 BEA reporting constrains us to the two-digit SIC code level 18 When five firms are not available for a given country and industry we follow Berger and Ofek (1995) and move to a more general industry definition (eg one-digit SIC code) This procedure results in a mean (median) 630 (757) of MNC sales matched at the two-digit level
18
the majority of their sales Country coverage in Worldscope is not available for countries
representing 004 percent of total firm sales (per BEA)
4 Empirical results
41 Independent variables
Recall that our objective is to examine the overall relation between excess firm value and
multinational operations Our proxy for the extent of multinational operations is the percentage
of total firm sales made by foreign operations (Foreign Sales) By this measure the extent of
multinational activity in our sample of MNCs is non-trivial ndash Table 2 Panel B shows that
approximately 24 percent of sales are generated by foreign operations on average Our first
analysis estimates ordinary least square regressions of Excess Value on Foreign Sales and
control variables (discussed below)
We include variables in our regression to control for other potential determinants of excess
value These are the percentage of sales made by the firm outside its primary industry (Industry
Other) to control for any relation between industrial diversification and excess value as in Berger
and Ofek (1995) For this purpose we obtain industry sales using Compustat Segment data19 A
firmrsquos primary industry is the industry in which the firm generates the majority of its sales and
we determine industry sales at the business segment level We set Industry Other equal to zero
for firms that operate in a single business segment
Consistent with prior research we also include controls for firm size (Log Size) the ratio of
long term debt to total market value (Debt Firm Value) the ratio of capital expenditures to total
sales (CAPX Sales) the ratio of earnings before interest and taxes to total sales (EBIT Sales)
the ratio of research and development expenses to total sales (RampD Sales) and the ratio of 19 Our results are unaffected by using BEA data to measure Industry Other
19
advertising expenses to total sales (Ad Sales) Table 2 Panel B provides the distribution of the
control variables All independent variables are Winsorized at 1 In our OLS regressions we
use heteroskedasticity and autocorrelation consistent robust standard errors with clustering at the
firm level
42 Primary regression analysis
In this section we examine the relation between firm excess value and the amount of foreign
activity at US MNCs Table 4 contains the results of regressions of excess value on Foreign
Sales for multiple specifications In column (1) Excess Value is regressed on both the percent of
foreign sales (Foreign Sales) and the percent of sales outside the firmrsquos primary industry
(Industry Other) We add the control variables to the regression in column (2) and year
indicators in column (3) In all three specifications the coefficient on the percent of foreign sales
remains significantly positive (with a p-value lt 001) For a sense of economic magnitude in
column (3) a one standard deviation (197) increase in the percent of foreign sales (Foreign
Sales) is associated with a 41 (equal to (e0349-1) times 0197) increase in firm excess value The
effect is larger for the other two columns ndash 82 increase in firm excess value in column (1) and
46 in column (2)20
Our finding of a premium reconciles with the international trade literature which finds that
firms engaging in international trade are more productive than those that do not (see Helpman
2006 and Syverson 2011 for reviews) Due to data limitations it is difficult to accurately
measure TFP at the firm level especially for firms which operate in multiple industries andor
20 Using the alternative strategy that employs US domiciled single-segment firms as the benchmark for the imputed value of the foreign operations Denis et al (2002) find that greater foreign activity results in a discount to firm value Using the Denis et al (2002) method to calculate excess value in our sample we obtain a significantly negative regression coefficient on Foreign Sales using the models in Table 4 columns (2) and (3) (the coefficient is not significant using the model in column (1) results untabulated)
20
countries As an alternative method to evaluate whether our finding of a premium is simply the
result of more productive firms also having more multinational operations we investigate
whether the premium is simply an artifact of innate characteristics of firms that choose to invest
abroad For example Helpman et al (2004) present a simple model with heterogeneous
productivity endowments The firms that receive the highest productivity endowments are the
ones capable of paying the fixed costs to establish a foreign subsidiary
To the extent that productivity advantages create excess value one would expect the type of
firms that choose to invest more abroad would be valued at a premium even in the absence of
their foreign investments To control for time invariant firm characteristics (eg innate
productivity advantages) and hence a potential source of endogeneity we add firm-level fixed
effects in column (4) of Table 4 We continue to find that the extent of foreign operations
(Foreign Sales) is positively associated with Excess Value Across all specifications the sign
on all significant control variables is consistent with prior research (eg Berger and Ofek 1995
and Denis et al 2002)
The finding of a premium makes a significant contribution to the relatively sparse literature
on the value effects of multinational activity Our measure of imputed value expands upon the
methods used by Berger and Ofek (1995) and Denis et al (2002) but allows us to answer a
different question than Denis et al (2002) Holding constant the extent of industrial
diversification we ask whether MNCs are more valuable than a portfolio of benchmark firms of
a similar geographic footprint In contrast Denis et al (2002) ask whether holding constant the
extent of industrial diversification MNCs are more valuable than a portfolio of domestic (US)
benchmark firms We believe that asking whether an MNCrsquos foreign (domestic) operations are
21
more valuable than those of a foreign (domestic) firm is a more appropriate way to assess the
value implications of a multinational network
5 Robustness
51 Endogeneity related to dynamic relations
Prior research argues that the relation between industrial diversification and excess firm
value could be endogenous through dynamic relationships based on observing past outcomes
Managers of firms likely choose to enter or exit industries or geographic regions based upon
their previous performance For example Campa and Kedia (2002) find that firms are more
likely to enter new industries when prospects in their current lines of business are deteriorating
Similar dynamics could also drive a relation between excess value and FDI We assess the
robustness of the previous results by estimating a dynamic panel data model which allows us to
control for dynamic endogeneity in addition to firm level unobserved heterogeneity We employ
the GMM estimator for dynamic panel models developed by Arellano and Bond (1991) Arellano
and Bover (1995) and Blundell and Bond (1998) Our results from this analysis confirm our
previous findings that firms with a larger degree of foreign operations have positive excess
values
Dynamic panel data models are generalizations of the traditional fixed effects model where
lags of the dependent variable are added to the right hand side of the equation These additional
lags control for the impact a firmrsquos past performance has on its current performance through the
channels discussed above The GMM estimator for dynamic panel models developed by
Arellano and Bond (1991) starts by differencing the data at the firm level in neighboring time
periods to eliminate any firm specific time invariant unobserved heterogeneity that may be
22
present Eliminating the firm specific fixed effect by differencing the data with lags of the
dependent variable on the right hand side introduces correlation between the new error term and
the differenced explanatory variables This problem is solved by applying the standard GMM
estimator using lags of both the original explanatory variables and excess value as instruments
We note that the dynamic panel data framework allows us to treat both international and
industrial diversification (as well as the other firm-specific control variables) as endogenous
Following Hoechle et al (2012) we also allow contemporaneous values of these variables to be
treated as endogenous
Lagged values of the explanatory variables are valid instruments in a dynamic panel data
model when the model is dynamically complete (eg Wooldridge 2010) A dynamic panel data
model is dynamically complete conditional on the unobserved effect if enough lags of both the
explanatory and dependent variable are included to make the resulting error term uncorrelated
with all the right hand side variables Under this assumption any lags for the explanatory
variables beyond this can be used within the GMM estimator as instruments Like Hoechle et al
(2012) we add two years of lags of a firmrsquos excess value to the right hand side assuming that
this is enough lags to control for dynamic feedback effects We then use past values of all
variables beyond the second lag up to a total of seven years to act as instruments
The results of the dynamic panel data estimator are reported in Table 5 These results confirm
the previous finding of a positive relation between the degree of international operations and
excess value We note that the size and significance level of the coefficient on Foreign Sales
diversification are robust to changes in the number of lags used as instruments The Arellano and
Bond (1991) estimator includes testing diagnostics for correct specification of the model If the
model is correctly specified differences in the residuals from the levels equation should be
23
serially uncorrelated at lag one but correlated at lag two The p-values for tests of serial
correlation of the residuals at lags 1 and 2 are reported in Table 5 and these indicate that the
model is not rejected We also report in Table 5 the Hansen test for overidentification which is a
test of the null hypothesis that the instruments are valid The p-value for this test is 030
indicating that we fail to reject this null hypothesis Finally we report another test for exogeneity
known as the difference-in-Hansen test which tests the null hypothesis that the instruments in
the original equation are valid We fail to reject this null hypothesis as well
52 Identifying value effects of multinational operations via cross-border acquisitions
Another alternative to investigating a causal link between foreign direct investment and firm
value is an event study of foreign acquisitions21 Acquired foreign targets represent only a small
fraction of MNCsrsquo worldwide sales in the year of the acquisition ndash less than 1 percent in our
sample Additionally cross-border acquisitions are not the primary source of international
expansion for the average firm We find that foreign acquisitions account for between 18 percent
and 55 percent of the year-over-year change in foreign sales with the remainder of the growth
coming from either newly established entities (ie greenfield investments) or growth in existing
operations22 These results suggest that acquisitions are not a material source of growth for the
21 Doukas and Travlos (1988) find positive returns when the acquirer is not operating in the target firmrsquos country and insignificant returns when the acquirer is operating in the target firmrsquos country or expanding internationally for the first time Morck and Yeung (1991) find marginal evidence of positive returns for firms with intangible assets Finally Doukas (1995) finds positive returns only for firms with average Tobinrsquos q ratios greater than one More recent work also using an event study methodology provides mixed evidence on valuation effects of global diversification through acquisitions Dos Santos Errunza and Miller (2008) find insignificant returns for firms with already established foreign operations firms establishing initial foreign operations and firms acquiring targets in related industries Doukas and Kan (2006) and Moeller and Schlingemann (2005) find reductions to shareholder value while Francis Hasan and Sun (2008) find positive returns for acquirers of targets in segmented financial markets 22 New foreign affiliates to an MNC group each year report on the BEA survey whether they entered the group as an acquisition or a newly established entity (ie greenfield investment) Affiliates under the lowest reporting threshold do not report this information resulting in our inability to precisely identify the source of 33 percent of the increase in firm sales as either acquisition or greenfield
24
typical US MNC suggesting that an event study would lack power and generalizability For
these reasons we do not implement an event study
53 Control premium
A control premium or private benefits of control occurs when ldquosome value whatever the
source is not shared among all the shareholders in proportion of the shares owned but it is
enjoyed exclusively by the party in controlrdquo (Dyck and Zingales 2004 p 541) Using stock
prices of traded shares held by minority shareholders in determining the market value to sales
ratios of our benchmark firms could underestimate the true value of our benchmark firms if those
firms face large control premiums This in turn could induce an excess value premium because
the imputed value of these country-industry segments would be artificially low The existence of
such benefits is more prevalent in some countries than others and could affect the value of an
MNCrsquos operations in such domains We use the country-level control premiums from Dyck and
Zingales (2004) and using each firmrsquos country sales as a percent of total firm sales we obtain a
weighted average control premium that each MNC faces (Control Premium) For countries not
included in the Dyck and Zingales (2004) analysis we set the control premium equal to zero23 In
Table 6 we show that Foreign Sales remains positive and highly significant (p-value lt 001)
when we include the proxy for control premium As a result it does not appear that the excess
value premium is driven by MNCs operating in countries where majority shareholders are able to
extract large control premiums
23 Results are robust to replacing missing values with the mean control premium of the respective GDP quartile
25
54 Cost of capital
As discussed in Section 2 when countries have shallow capital markets a multinational
network may provide the benefit of a lower cost of capital for investments through an internal
capital market as well as through better access to the US capital market Prior research
establishes that there is substantial variation in the cost of capital across countries (Erb Harvey
and Viskanta 1996 and Hail and Leuz 2009) To determine whether the documented premium
is due to cost of capital advantages we include a control variable to capture the difference
between the MNCrsquos cost of capital and the cost of capital faced by its competitors in the global
markets in which it operates
We compute our Cost of Capital proxy in two steps First we obtain the country-year credit
rating published by Institutional Investor24 The rating ranges from 0 to 100 where higher values
imply that a country has a higher default risk Country-level credit risk is a reasonable predictor
of expected equity market returns and volatility (Erb et al 1996) and exhibits a highly
significant correlation with international accounting-based estimates of imputed cost of capital
(Hail and Leuz 2009) Second using each firmrsquos country sales as a percent of total firm sales
we obtain a weighted average credit risk rating across the countries in which each firm operates
A higher value implies an MNC operates in countries with higher cost of capital In Table 7 we
do not find a result consistent with MNCs gaining an advantage from their access to low cost
capitalndash Cost of Capital is not significantly associated with Excess Value25
24 We thank Cam Harvey for providing these data 25 Results are consistent using cost of capital as defined in Erb et al 1996
26
55 Corporate governance
In light of the recent finding that corporate governance mitigates the diversification discount
we examine whether the premium to multinational activity is sensitive to controls for corporate
governance Hoechle et al (2012) find that the magnitude of the industrial diversification
discount is decreased and in several specifications no longer significant in the presence of
governance proxies In the first column of Table 8 we include the five governance proxies that
are consistently significantly associated with excess value in Hoechle et al (2012) ndash the percent
of CEO ownership (CEO Ownership and CEO Ownership squared) the percent of institutional
ownership (Institution Ownership) whether the firm has a powerful or influential CEO
(Powerful CEO) and the governance index from Gompers Ishii and Metrick (2003)
(Governance Index)
As documented in Table 8 including these control variables reduces our sample size by
almost 50 (to 2550 observations) and tends to exclude the smaller firms from the analysis
None of the governance proxies are significant Relative to column (4) of Table 4 the coefficient
on Foreign Sales is similar in magnitude but is no longer statistically significant at
conventional levels To investigate whether the decreased significance is due to the smaller
sample or the inclusion of the governance variables we re-estimate the regression for the same
2550 observations after removing the governance controls The coefficient and t-statistic are
virtually identical suggesting that the loss of significance is due to the reduced sample and not
the governance controls We do not find evidence to suggest that variation in corporate
governance practices drives variation in excess value for US MNCs
27
6 Conclusion
Using a new method to quantify the valuation effects of foreign direct investment by US
domiciled firms we find robust evidence of a premium in excess value associated with the
degree of multinational operations Our method builds on that used in previous studies
examining firm excess value (eg Berger and Ofek 1995 and Denis et al 2002) by
incorporating the location in addition to the industry of each corporate affiliate This allows
important factors such as growth and cost of capital to vary across countries as well as industries
Examining a sample of multinational firms we find that a one-standard deviation (197)
increase in foreign operations is associated with an increase of between 41 and 82 in excess
value minus both a statistically and economically significant finding
This result complements the finding of the international trade literature that more productive
firms are more likely to operate internationally However the premium remains after including
firm fixed effects to control for persistent firm traits (eg productivity)
The finding of a premium stands up to a succession of robustness tests designed to evaluate
whether the result arises from alternative methods or explanations First we estimate a dynamic
panel data model using the generalized method of moments (GMM) estimator to simultaneously
account for the potential endogeneity of FDI industrial diversification and firm value Second
we include a control variable to proxy for whether the firm operates in countries with larger
control premiums which could result in understated multiples and mechanically induce a
premium Third we include a control variable to measure the cost of capital across the countries
in which an MNC operates to evaluate whether a relatively low cost of capital borne by
multinational firms is a meaningful source of the premium Fourth we control for several
corporate governance proxies which have been found to be correlated with excess values (and
28
industrial diversification) We fail to find evidence supporting any of these alternative
explanations ndash the premium in excess value arising at firms with greater multinational operations
remains significant
Overall our new method of estimating excess values for multinational firms provides strong
evidence of a statistically and economically significant premium associated with increased
international operations The premium is robust to a host of specifications and controls designed
to evaluate alternative explanations We leave to future research the questions of whether the
premium to US multinational firms applies more broadly to other countries of domicile and
what factors influence the premium
29
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Ammann M Hoeschel D and M Schmid 2012 Is there really no conglomerate discount
Journal of Business Finance and Accounting 39(12) 264-288 Arellano M and Bond S R 1991 Some tests of specification for panel data Monte Carlo
evidence and an application to employment equations Review of Economic Studies 58 (2) 277-298
Arellano M and Bover O 1995 Another look at instrumental variables estimation of error
components models Journal of Econometrics 68 (1) 29-61 Bens D A Berger P G and S J Monahan 2011 Discretionary disclosure in financial
reporting An examination comparing internal firm data to externally reported segment data The Accounting Review 86(2) 417-449
Berger P G and E Ofek 1995 Diversificationrsquos effect on firm value Journal of Financial
Economics 37 39-65 Blundell R and Bond S R 1998 Initial conditions and moment restrictions in dynamic panel
data models Journal of Econometrics 87(1) 115-144 Campa J M and S Kedia 2002 Explaining the diversification discount The Journal of
Finance 57 (4) 1731-1762 Denis D J Denis D K and K Yost 2002 Global diversification industrial diversification
and firm value The Journal of Finance 57 (5) 1951-1979 Desai M A Foley C F and J R Hines 2004 A multinational perspective on capital structure
choice and internal capital markets The Journal of Finance 59 (6) 2451-2487 Dos Santos MB V Errunza and D Miller 2008 Does corporate international diversification
destroy value Evidence from cross-border mergers and acquisitions Journal of Banking and Finance 32(12) 2716-2724
Doukas JA 1995 Overinvestment Tobinrsquos q and gains from foreign acquisitions Journal of
Banking and Finance 19 1285-1303 Doukas J and N G Travlos 1988 The effect of corporate multinationalism on shareholdersrsquorsquo
wealth evidence from international acquisitions Journal of Finance 43(5) 1161-1175 Doukas JA and O B Kan 2006 Does global diversification destroy firm value Journal of
International Business Studies 37(3) 352-371
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Dunning J and A Rugman 1985 The influence of Hymerrsquos dissertation on the theory of foreign direct investment The American Economic Review 75(2) 228-232
Dyck A and L Zingales 2004 Private benefits of control An international comparison The
Journal of Finance 59 (2) 537-600 Erb C Harvey C and T Viskanta 1996 Expected returns and volatility in 135 countries The
Journal of Portfolio Management 46-58 Foley C F Desai M A and K J Forbes 2008 Financial constraints and growth
Multinational and local firm responses to currency depreciations Review of Financial Studies 21 (6) 2857-2888
Francis B I Hasan and X Sun 2008 Financial market integration and the value of global
diversification Evidence for US acquirers in cross-border mergers and acquisitions Journal of Banking and Finance 321522-1540
Gande A Schenzler C and L Senbet 2009 Valuation effects of global diversification
Journal of International Business Studies 1515-1532 Gompers P Ishii J and A Metrick 2003 Corporate governance and equity prices Quarterly
Journal of Economics 118(1) 107-156 Hail L and C Leuz 2009 Cost of capital effects and changes in growth expectations around
US cross-listings Journal of Financial Economics 93 428-454 Hanson G Mataloni R and M Slaughter 2005 Vertical production networks in multinational
firms Review of Economics and Statistics 87(4) 1-15 Helpman E 2006 Trade FDI and the Organization of Firms Journal of Economic Literature
44(3) 289-630 Helpman E Melitz M J and S R Yeaple 2004 Export versus FDI with Heterogeneous Firms
American Economic Review 94(1) 300-316 Hoechle D Schmid M Walter W and D Yermack 2012 How much of the diversification
discount can be explained by poor governance Journal of Financial Economics 103 41-60 Hope O-K and W B Thomas 2008 Managerial empire building and firm disclosure Journal
of Accounting Research 46 (June) 591-626 Huizinga H P and J Voget 2009 International taxation and the direction and volume of cross-
border MampAs Journal of Finance 64 (3) 1217-1249 Jensen M 1986 Agency cost of free cash flow corporate finance and takeovers American
Economic Review Papers and Proceedings 76 323-329
31
Laeven L and R Levine 2007 Is there a diversification discount in financial conglomerates
Journal of Financial Economics 85 331-367 Lang L and R Stulz 1994 Tobinrsquos Q corporate diversification and firm performance Journal
of Political Economy 102 1248-1280 Mataloni R 2003 US multinational companies Operations in 2001 Survey of Current
Business 85-105 Matvos G and A Seru 2011 The dynamic tradeoff between corporate socialism and financial
market dislocation Estimates from internal capital markets Working paper University of Chicago Booth School of Business
Moeller SB and F P Schlingemann 2005 Global diversification and bidder gains A
comparison between cross-border and domestic acquisitions Journal of Banking and Finance 29 533-564
Morck R and B Yeung 1991 Why investors value multinationality Journal of Business 64
165-186
Morck R and B Yeung 2001 Why firms diversify Internatization vs agency behavior In Intangibles B Lev (ed) Oxford University Press pp 269-302
Myers S C and N S Majluf 1984 Corporate financing and investment decisions when firms have information that investors do not have Journal of Financial Economics 13 187-221
Schmid M and I Walter 2009 Do financial conglomerates create or destroy economic value
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365 Villalonga B 2004a Diversification discount or premium New evidence from the Business
Information Tracking Series Journal of Finance 59 479-506 Villalonga B 2004b Does diversification cause the diversification discount Financial
Management 33 5-27 Wooldridge JM 2010 Econometric Analysis of Cross Section and Panel Data (Second
Edition) Cambridge MA MIT Press Yamin M 1991 A reassessment of Hymerrsquos contribution to the theory of the transnational
corporation C Pitelis and R Sugden (eds) The Nature of the Transnational Firm London Routledge
32
Appendix A Variable Definitions
We obtain information regarding firmsrsquo operations by country and industry from the Bureau of Economic Analysis (BEA) unless otherwise noted Acronyms used below reflect Compustat mnemonics and thus indicate when we obtain data from the Compustat North America Fundamentals Annual database All independent variables are winsorized at 1
Dependent variable
Excess Value = The log of the ratio of actual firm value to imputed firm value Actual firm value is equal to market capitalization (MVE) + book value of total assets (AT) ndash book value of shareholdersrsquo equity (SEQ) Imputed firm value is calculated as the sum of the imputed values of a firmrsquos operations in each country-industry The imputed value for each country-industry is equal to sales generated in that country-industry times a multiple equal to the ratio of a benchmark firmrsquos actual value to total sales A benchmark firm is defined as a median single-segment firm operating in the same country-industry All multiples require at least five firms listed in the respective country-industry on Compustat (domestic) or Worldscope (foreign) with less than 10 of sales income and assets outside the country of domicile Similar to Berger and Ofek (1995) we eliminate extreme values where the ratio of actual to imputed firm value exceeds 400 or is less than 025
Independent variables
Foreign Sales = Sales made by the foreign operations of US MNCs as a percent of total firm sales
Industry Other = Sales outside a firmrsquos primary industry as a percent of total sales (SALE) Equal to zero for firms operating in a single business segment
Log Size = Log of total sales (SALE)
Debt Firm Value = Ratio of long-term debt (DLTT) to total market value (market capitalization (MVE) + book value of total assets (AT) ndash book value of shareholdersrsquo equity (SEQ))
CAPX Sales = Ratio of capital expenditures (CAPX) to total sales (SALE)
EBIT Sales = Ratio of earnings before interest and taxes (EBIT) to total sales (SALE)
RampD Sales = Ratio of research and development expenses (XRD) to total sales (SALE)
Ad Sales = Ratio of advertising expenses (XAD) to total sales (SALE)
Control Premium = A weighted-average (based on the sales in a given country as a percent of total firm sales) of the country-level control premium from Dyck and Zingales (2004)
Cost of Capital = A weighted-average (based on the sales in a given country as a percent of total firm sales) of the country-level credit rating from Institutional Investor (httpwwwinstitutionalinvestorcom) The country-level rating is available for 160 countries and ranges from 0 to 100 Higher values imply greater credit risk
33
CEO Ownership = Percent of shares owned by the CEO from Execucomp
Institution Ownership = Percent of shares owned by institutional investors from Thomson Reuterrsquos Institutional Holdings database
Powerful CEO = Indicator variable equal to one if the CEO is the only insider on the board of directors and also serves as the chairman and president based on data from RiskMetrics Directors database Equal to zero otherwise
Governance Index = Count measure of takeover defenses used in Gompers Ishii and Metrick (2003) from RiskMetrics Governance database As the governance data are not available for every year we follow Gompers Ishii and Metrick (2003) and assume that the firmsrsquo governance attributes are unchanged until publication of the subsequent series
34
Figure 1 Trends in multinational activity and industrial diversification from 1984-2008 This figure presents a plot of the mean percent of foreign sales and the mean percent of sales outside the firmrsquos primary industry (industrial diversification) from 1984 through 2008 Dotted lines above and below the primary lines represent 95 confidence intervals To compile the plot we include the full population of firms in Compustat
35
Table 1 Analysis of accounting data for firms reporting under multiple accounting standards This table provides a comparison of sales net income and assets for firms that changed accounting standards to or from US GAAP Comparisons are provided for the 66 firms that changed to or from US GAAP and for which we could obtain accounting data electronically and in English under both standards for the same fiscal year (eg the firm presented the accounting numbers originally under one set of standards and then restated the prior year numbers in the subsequent year under the new set of standards) Panel A shows the distribution of firms by year Panel B shows the other accounting standards to which US GAAP is compared Panel C shows the mean and median percent that the other accounting standard differs from US GAAP for the same fiscal year and accounting number Two samples are shown in Panel C The first is the full sample of firms with a change in accounting standards to or from US GAAP for which data was available The second is the subset of these firms that occurred during the sample window used in this study (ie excluding changes post-2008) The percent difference in sales is compared to the percent difference in net income and assets Tests of means are based on matched-pair t-tests Tests of medians are based on Wilcoxon matched-pair signed-rank tests Panel A Accounting standard changes by year
2003
4 6
2004
25 38
2005
8 12
2006
6 9
2007
7 11
2008
5 8
2009
5 8
2010
5 8
2011
1 2
66
Panel B Accounting standards compared to US GAAP
IFRS
47 71
Canadian GAAP
12 18
Israeli GAAP
2 3
Dutch GAAP
1 2
Indian GAAP
1 2
Japanese GAAP
1 2
Norwegian GAAP
1 2
Swiss GAAP
1 2
66
36
(Table 1 continued) Panel C Tests of differences between US GAAP and other accounting standards
Percent different from US GAAP
Comparison to sales (p-values)
N Mean Median
Mean Median
Full Sample (2001-2011)
Sales
66 236 000
Net Income
66 10653 647
005 lt001
Assets
66 1162 126
lt001 lt001
Study Sample (2001-2008)
Sales
55 257 000
Net Income
55 10303 589
011 lt001
Assets
55 1047 099
003 lt001
37
Table 2 Descriptive statistics Panel A reports the mean and medians of Excess Value for the multinational firms and single-segment benchmark firms as well as univariate tests across the subsamples Panel B reports summary statistics for the independent variables for the multinational firms The p-values of the tests of differences in means are based on t-tests The p-values of tests of differences in the medians are for a Wilcoxon rank-sum test All variables are defined in Appendix A Panel A Excess Value by sample Multinational All Benchmark Benchmark Subgroups Sample Firms US Foreign
Value Value Test of Diff (p-value) Value
Test of Diff (p-value) Value
Test of Diff (p-value)
Mean 0067 0006 lt001 -0028 lt001 0014 lt001 Median 0076 0000 lt001 -0025 lt001 0000 lt001 N 4950 33074 5998 27076 Panel B Distribution of independent variables for the multinational firms N Mean 25th 50th 75th StDev Foreign Sales 4950 0238 0074 0190 0363 0197 Industry Other 4950 0212 0000 0166 0414 0215 Log Size 4950 6955 5823 6989 8140 1687 Debt Firm Value 4950 0136 0009 0097 0212 0145 CAPX Sales 4950 0054 0020 0033 0054 0101 EBIT Sales 4950 0065 0031 0077 0121 0138 RampD Sales 4950 0049 0000 0013 0059 0083 Ad Sales 4950 0008 0000 0000 0005 0018
38
Table 3 BEA and Worldscope samples
This table reports details on affiliate sales for significant countries pooled from 1998-2008 Significant countries are defined as those in which the operations of US MNCs represent at least 02 of total firm sales (per the BEA) For significant countries the table compares their representation in terms of MNC affiliate sales in the BEA data to the distribution of firm observations in Worldscope US Foreign Affiliates (BEA) Foreign Firms (Worldscope)
Country
N (1)
Sales (millions)
(2)
Total Sales (3)
N (4)
Sales (million)
(5)
Total Sales (6)
Australia 1344 73305 056 859 255865 210 Belgium 894 52755 040 169 26136 021 Brazil 959 60245 046 1143 683167 560 Canada 2730 536767 407 1181 526750 432 China 922 75907 057 822 276541 227 France 1852 183942 139 540 105447 086 Germany 2182 275577 209 480 103888 085 Hong Kong 867 48100 036 157 34094 028 Ireland 478 36754 028 34 3467 003 Italy 1178 82116 062 111 47948 039 Japan 1494 148032 112 2281 1119223 917 Korea South 659 34500 026 3530 2752791 2255 Malaysia 386 26027 020 837 168683 138 Mexico 1405 113623 086 362 237173 194 Netherlands 1149 85105 064 47 22631 019 Singapore 1098 94917 072 139 32913 027 Spain 871 63094 048 129 37419 031 Sweden 712 32682 025 235 23090 019 Switzerland 726 60288 046 74 20242 017 Taiwan 592 30313 023 2949 787049 645 United Kingdom 3054 423934 321 1997 882423 723 United States (domicile) 4950 10460880 7923 na na na Other - Covered by WorldScope 6893 199394 151 9000 4057894 33 Other -Not Covered by WorldScope 713 5352 004 - - - Total 38108 13203609 1000 27076 12204834 100
39
Table 4 OLS regressions of firm excess values on the percent of foreign sales This table reports the results of OLS regressions of firm excess value on the percent of foreign sales (Foreign Sales) the percent of sales outside a firmrsquos primary industry (Industry Other) and control variables All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed) Dep Variable = Excess Value Coefficient t-stat Coefficient t-stat Coefficient t-stat Coefficient t-stat (1) (2) (3) (4) Foreign Sales 0349 419 0209 293 0189 266 0278 210 Industry Other -0134 -186 -0047 -072 -0053 -081 -0326 -325 Log Size 0066 634 0065 624 -0044 -096 Debt Firm Value -0196 -213 -0241 -260 -0685 -588 CAPX Sales 0901 425 0893 435 0488 227 EBIT Sales 1154 542 1170 547 0408 189 RampD Sales 2908 1242 2932 1237 1544 379 Ad Sales 1869 263 2003 280 -1696 -119 Year indicators No No Yes Yes Firm indicators No No No Yes R2 0017 0212 0226 0768 Observations 4950 4950 4950 4950
40
Table 5 Dynamic panel GMM model regressing firm excess values on the percent of foreign sales This table reports the result from a regression of the excess value on the percent of foreign sales (Foreign Sales) the percent of non-primary industry sales (Industry Other) and control variables using a dynamic panel GMM estimator All control variables are considered to be endogenous with the exception of the year indicator variables AR(1) and AR(2) are tests for first-order and second-order serial correlation in the first differenced residuals with the null hypothesis of no serial correlation The null hypothesis of the Hansen test of overidentification is that all instruments are valid The null hypothesis of the difference-in-Hansen test of exogeneity is that the instruments used for the equation in levels are exogenous All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed) Dep Variable = Excess Value Coef t-stat Excess Value (one lag) -0018 -082 Excess Value (two lags) -0048 -272 Foreign Sales 0371 270 Industry Other -0163 -171 Log Size 0010 123 Debt FirmValue -1135 -820 CAPX Sales 0590 342 EBIT Sales -0047 -300 RampD Sales 1376 531 Ad Sales -0751 -040 AR(1) test (p-value) 000 AR(2) test (p-value) 017 Hansen test of over-identification (p-value) 030 Difference-in-Hansen test of exogeneity (p-value) 093 Year indicators Yes Number of firms 1016 Number of observations 4090
41
Table 6 OLS regression of firm excess values on the percent of foreign sales controlling for a control premium This table reports the result of the OLS regression of firm excess value including firm and year fixed effects with an additional control variable to proxy for a country-level ownership control premium (Control Premium) All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed) Dep Variable = Excess Value Coef t -stat Foreign Sales 0303 224 Industry Other -0327 -325 Log Size -0045 -100 Debt FirmValue -0683 -585 CAPX Sales 0490 226 EBIT Sales 0409 189 RampD Sales 1545 379 Ad Sales -1689 -119 Control Premium -1004 -062 Year indicators Yes Firm indicators Yes R2 0768 Observations 4950
42
Table 7 OLS regression of firm excess values on the percent of foreign sales controlling for the cost of capital This table contains the results of an OLS regression of firm excess value including firm and year fixed effects with an additional control variable to proxy for a country-level cost of capital (Cost of Capital) All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed) Dep Variable = Excess Value Coef t -stat Foreign Sales 0255 178 Industry Other -0326 -324 Log Size -0043 -094 Debt FirmValue -0687 -589 CAPX Sales 0485 225 EBIT Sales 0406 188 RampD Sales 1539 379 Ad Sales -1703 -120 Cost of Capital 0004 039 Year indicators Yes Firm indicators Yes R2 0768 Observations 4950
43
Table 8 OLS regression of firm excess values on the percent of foreign sales controlling for aspects of corporate governance This table reports the results of two OLS regressions In the first column are the results of the firm excess value regression including firm and year fixed effect with five proxies for corporate governance included ndash the percent of shares owned by the CEO and the percent squared (CEO Ownership and CEO Ownership squared) the percent owned by institutional investors (Institution Ownership) whether there is a powerful CEO (Powerful CEO) and the Gompers Ishii and Metrick (2003) governance index (Governance Index) In the second column the regression includes the same sample and variables with the exception of the governance proxies which are excluded All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed)
Governance
No Governance (same sample)
Dep Variable = Excess Value Coef t-stat Coef t -stat Foreign Sales 0259 159 0251 154 Industry Other -0099 -080 -0099 -079 Log Size -0105 -185 -0102 -180 Debt FirmValue -0813 -478 -0817 -482 CAPX Sales 0440 252 0438 249 EBIT Sales 0457 247 0457 246 RampD Sales 1717 284 1716 286 Ad Sales -2210 -143 -2190 -143 CEO Ownership 0010 007
CEO Ownership squared -0029 -017 Institution Ownership 0047 067 Powerful CEO -0003 -014 Governance Index 0009 103
Year indicators Yes Yes Firm indicators Yes Yes R2 0811 0810 Observations 2550 2550
- Cover for paperpdf
- crrz_20130603_SMUpdf
-
16
GAAP between 2005 and 201115 We obtain accounting data for the year of the change and the
prior year where electronically available in English We are able to obtain data for 66 firms
changing standards as detailed in Table 1 The distribution of years for which accounting data
are available under two different accounting standards for the same fiscal year is shown in Panel
A The years shown are the year prior to the change as these accounting numbers were provided
under the original standard and then subsequently restated in the following year under the new
standard for comparative purposes The majority of the shifts were to or from IFRS (71)
though seven other accounting standards are noted (Panel B) We examine the percent that the
non-US GAAP reported number differs from the US GAAP reported number in Panel C to
assess the comparability of these three summary accounting numbers across various accounting
standards Examining both the full sample as well as the subset that overlaps our study we find
that sales are more consistently measured than net income or assets
The sum of the imputed values across the country-industry components of an MNC is an
estimate of the value of a portfolio of unrelated businesses that mirror the related businesses of
the firm Consequently a comparison of the actual firm value with the imputed firm value is a
measure of how a multinational network affects firm value We provide descriptive statistics for
Excess Value for our sample of 4950 MNC firm-years in Table 2 Panel A This table also
includes univariate comparisons of the values between the MNC firms and the domestic single-
segment and foreign single-segment benchmark firms The Excess Value measure is significantly
higher for MNCs suggesting a premium in firm value for multinational firms relative to the
benchmark firms in total as well as the domestic and foreign subgroups (p-values for tests of
15 Due to the hand collection efforts required and the limitation on electronic filings we began our search with changes documented in 2005 which generally require filings from 2004
17
means and medians are lt 001)16 These results provide some initial evidence that operating in
multiple countries is positively associated with firm value
To estimate country-industry multiples we rely on Worldscope financial data on foreign
firms and Compustat Fundamentals Annual for domestic firms We restrict the firms to include
only those with at least 90 of sales income and assets inside the country of domicile (ie
those that do not report significant multinational activity) and that operate in a single industry
We refer to these firms as benchmark firms (either foreign or domestic depending on the country
of domicile) these firms do not appear in any of our regressions We report the number of
benchmark firms during our sample period in Table 2 Panel A The industry criterion is based on
the two-digit SIC code17 For every country that has at least five firms in the respective industry
and year we use the median ratio of market value to sales18 This ratio is the country-industry
multiple ndash an input required to compute imputed values
To determine the country-industry composition of MNCs we rely on BEA data In Table 3
we report the aggregate number of foreign affiliates and total sales by country as provided by
the BEA data for our sample of 4950 MNCs The specific countries tabulated are those that
represent at least 02 percent of total firm sales (pooled across all years) in our sample The top
five foreign countries in which MNCs generate sales are Canada United Kingdom Germany
France and Japan Table 3 also provides a comparison of the number of foreign benchmark
firms available in Worldscope in the specific countries in which our sample of MNCs generate
16 Similar to Berger and Ofek (1995) the median values differ slightly from zero for the benchmark single-segment firms due to the truncation of extreme excess values 17 BEA reporting constrains us to the two-digit SIC code level 18 When five firms are not available for a given country and industry we follow Berger and Ofek (1995) and move to a more general industry definition (eg one-digit SIC code) This procedure results in a mean (median) 630 (757) of MNC sales matched at the two-digit level
18
the majority of their sales Country coverage in Worldscope is not available for countries
representing 004 percent of total firm sales (per BEA)
4 Empirical results
41 Independent variables
Recall that our objective is to examine the overall relation between excess firm value and
multinational operations Our proxy for the extent of multinational operations is the percentage
of total firm sales made by foreign operations (Foreign Sales) By this measure the extent of
multinational activity in our sample of MNCs is non-trivial ndash Table 2 Panel B shows that
approximately 24 percent of sales are generated by foreign operations on average Our first
analysis estimates ordinary least square regressions of Excess Value on Foreign Sales and
control variables (discussed below)
We include variables in our regression to control for other potential determinants of excess
value These are the percentage of sales made by the firm outside its primary industry (Industry
Other) to control for any relation between industrial diversification and excess value as in Berger
and Ofek (1995) For this purpose we obtain industry sales using Compustat Segment data19 A
firmrsquos primary industry is the industry in which the firm generates the majority of its sales and
we determine industry sales at the business segment level We set Industry Other equal to zero
for firms that operate in a single business segment
Consistent with prior research we also include controls for firm size (Log Size) the ratio of
long term debt to total market value (Debt Firm Value) the ratio of capital expenditures to total
sales (CAPX Sales) the ratio of earnings before interest and taxes to total sales (EBIT Sales)
the ratio of research and development expenses to total sales (RampD Sales) and the ratio of 19 Our results are unaffected by using BEA data to measure Industry Other
19
advertising expenses to total sales (Ad Sales) Table 2 Panel B provides the distribution of the
control variables All independent variables are Winsorized at 1 In our OLS regressions we
use heteroskedasticity and autocorrelation consistent robust standard errors with clustering at the
firm level
42 Primary regression analysis
In this section we examine the relation between firm excess value and the amount of foreign
activity at US MNCs Table 4 contains the results of regressions of excess value on Foreign
Sales for multiple specifications In column (1) Excess Value is regressed on both the percent of
foreign sales (Foreign Sales) and the percent of sales outside the firmrsquos primary industry
(Industry Other) We add the control variables to the regression in column (2) and year
indicators in column (3) In all three specifications the coefficient on the percent of foreign sales
remains significantly positive (with a p-value lt 001) For a sense of economic magnitude in
column (3) a one standard deviation (197) increase in the percent of foreign sales (Foreign
Sales) is associated with a 41 (equal to (e0349-1) times 0197) increase in firm excess value The
effect is larger for the other two columns ndash 82 increase in firm excess value in column (1) and
46 in column (2)20
Our finding of a premium reconciles with the international trade literature which finds that
firms engaging in international trade are more productive than those that do not (see Helpman
2006 and Syverson 2011 for reviews) Due to data limitations it is difficult to accurately
measure TFP at the firm level especially for firms which operate in multiple industries andor
20 Using the alternative strategy that employs US domiciled single-segment firms as the benchmark for the imputed value of the foreign operations Denis et al (2002) find that greater foreign activity results in a discount to firm value Using the Denis et al (2002) method to calculate excess value in our sample we obtain a significantly negative regression coefficient on Foreign Sales using the models in Table 4 columns (2) and (3) (the coefficient is not significant using the model in column (1) results untabulated)
20
countries As an alternative method to evaluate whether our finding of a premium is simply the
result of more productive firms also having more multinational operations we investigate
whether the premium is simply an artifact of innate characteristics of firms that choose to invest
abroad For example Helpman et al (2004) present a simple model with heterogeneous
productivity endowments The firms that receive the highest productivity endowments are the
ones capable of paying the fixed costs to establish a foreign subsidiary
To the extent that productivity advantages create excess value one would expect the type of
firms that choose to invest more abroad would be valued at a premium even in the absence of
their foreign investments To control for time invariant firm characteristics (eg innate
productivity advantages) and hence a potential source of endogeneity we add firm-level fixed
effects in column (4) of Table 4 We continue to find that the extent of foreign operations
(Foreign Sales) is positively associated with Excess Value Across all specifications the sign
on all significant control variables is consistent with prior research (eg Berger and Ofek 1995
and Denis et al 2002)
The finding of a premium makes a significant contribution to the relatively sparse literature
on the value effects of multinational activity Our measure of imputed value expands upon the
methods used by Berger and Ofek (1995) and Denis et al (2002) but allows us to answer a
different question than Denis et al (2002) Holding constant the extent of industrial
diversification we ask whether MNCs are more valuable than a portfolio of benchmark firms of
a similar geographic footprint In contrast Denis et al (2002) ask whether holding constant the
extent of industrial diversification MNCs are more valuable than a portfolio of domestic (US)
benchmark firms We believe that asking whether an MNCrsquos foreign (domestic) operations are
21
more valuable than those of a foreign (domestic) firm is a more appropriate way to assess the
value implications of a multinational network
5 Robustness
51 Endogeneity related to dynamic relations
Prior research argues that the relation between industrial diversification and excess firm
value could be endogenous through dynamic relationships based on observing past outcomes
Managers of firms likely choose to enter or exit industries or geographic regions based upon
their previous performance For example Campa and Kedia (2002) find that firms are more
likely to enter new industries when prospects in their current lines of business are deteriorating
Similar dynamics could also drive a relation between excess value and FDI We assess the
robustness of the previous results by estimating a dynamic panel data model which allows us to
control for dynamic endogeneity in addition to firm level unobserved heterogeneity We employ
the GMM estimator for dynamic panel models developed by Arellano and Bond (1991) Arellano
and Bover (1995) and Blundell and Bond (1998) Our results from this analysis confirm our
previous findings that firms with a larger degree of foreign operations have positive excess
values
Dynamic panel data models are generalizations of the traditional fixed effects model where
lags of the dependent variable are added to the right hand side of the equation These additional
lags control for the impact a firmrsquos past performance has on its current performance through the
channels discussed above The GMM estimator for dynamic panel models developed by
Arellano and Bond (1991) starts by differencing the data at the firm level in neighboring time
periods to eliminate any firm specific time invariant unobserved heterogeneity that may be
22
present Eliminating the firm specific fixed effect by differencing the data with lags of the
dependent variable on the right hand side introduces correlation between the new error term and
the differenced explanatory variables This problem is solved by applying the standard GMM
estimator using lags of both the original explanatory variables and excess value as instruments
We note that the dynamic panel data framework allows us to treat both international and
industrial diversification (as well as the other firm-specific control variables) as endogenous
Following Hoechle et al (2012) we also allow contemporaneous values of these variables to be
treated as endogenous
Lagged values of the explanatory variables are valid instruments in a dynamic panel data
model when the model is dynamically complete (eg Wooldridge 2010) A dynamic panel data
model is dynamically complete conditional on the unobserved effect if enough lags of both the
explanatory and dependent variable are included to make the resulting error term uncorrelated
with all the right hand side variables Under this assumption any lags for the explanatory
variables beyond this can be used within the GMM estimator as instruments Like Hoechle et al
(2012) we add two years of lags of a firmrsquos excess value to the right hand side assuming that
this is enough lags to control for dynamic feedback effects We then use past values of all
variables beyond the second lag up to a total of seven years to act as instruments
The results of the dynamic panel data estimator are reported in Table 5 These results confirm
the previous finding of a positive relation between the degree of international operations and
excess value We note that the size and significance level of the coefficient on Foreign Sales
diversification are robust to changes in the number of lags used as instruments The Arellano and
Bond (1991) estimator includes testing diagnostics for correct specification of the model If the
model is correctly specified differences in the residuals from the levels equation should be
23
serially uncorrelated at lag one but correlated at lag two The p-values for tests of serial
correlation of the residuals at lags 1 and 2 are reported in Table 5 and these indicate that the
model is not rejected We also report in Table 5 the Hansen test for overidentification which is a
test of the null hypothesis that the instruments are valid The p-value for this test is 030
indicating that we fail to reject this null hypothesis Finally we report another test for exogeneity
known as the difference-in-Hansen test which tests the null hypothesis that the instruments in
the original equation are valid We fail to reject this null hypothesis as well
52 Identifying value effects of multinational operations via cross-border acquisitions
Another alternative to investigating a causal link between foreign direct investment and firm
value is an event study of foreign acquisitions21 Acquired foreign targets represent only a small
fraction of MNCsrsquo worldwide sales in the year of the acquisition ndash less than 1 percent in our
sample Additionally cross-border acquisitions are not the primary source of international
expansion for the average firm We find that foreign acquisitions account for between 18 percent
and 55 percent of the year-over-year change in foreign sales with the remainder of the growth
coming from either newly established entities (ie greenfield investments) or growth in existing
operations22 These results suggest that acquisitions are not a material source of growth for the
21 Doukas and Travlos (1988) find positive returns when the acquirer is not operating in the target firmrsquos country and insignificant returns when the acquirer is operating in the target firmrsquos country or expanding internationally for the first time Morck and Yeung (1991) find marginal evidence of positive returns for firms with intangible assets Finally Doukas (1995) finds positive returns only for firms with average Tobinrsquos q ratios greater than one More recent work also using an event study methodology provides mixed evidence on valuation effects of global diversification through acquisitions Dos Santos Errunza and Miller (2008) find insignificant returns for firms with already established foreign operations firms establishing initial foreign operations and firms acquiring targets in related industries Doukas and Kan (2006) and Moeller and Schlingemann (2005) find reductions to shareholder value while Francis Hasan and Sun (2008) find positive returns for acquirers of targets in segmented financial markets 22 New foreign affiliates to an MNC group each year report on the BEA survey whether they entered the group as an acquisition or a newly established entity (ie greenfield investment) Affiliates under the lowest reporting threshold do not report this information resulting in our inability to precisely identify the source of 33 percent of the increase in firm sales as either acquisition or greenfield
24
typical US MNC suggesting that an event study would lack power and generalizability For
these reasons we do not implement an event study
53 Control premium
A control premium or private benefits of control occurs when ldquosome value whatever the
source is not shared among all the shareholders in proportion of the shares owned but it is
enjoyed exclusively by the party in controlrdquo (Dyck and Zingales 2004 p 541) Using stock
prices of traded shares held by minority shareholders in determining the market value to sales
ratios of our benchmark firms could underestimate the true value of our benchmark firms if those
firms face large control premiums This in turn could induce an excess value premium because
the imputed value of these country-industry segments would be artificially low The existence of
such benefits is more prevalent in some countries than others and could affect the value of an
MNCrsquos operations in such domains We use the country-level control premiums from Dyck and
Zingales (2004) and using each firmrsquos country sales as a percent of total firm sales we obtain a
weighted average control premium that each MNC faces (Control Premium) For countries not
included in the Dyck and Zingales (2004) analysis we set the control premium equal to zero23 In
Table 6 we show that Foreign Sales remains positive and highly significant (p-value lt 001)
when we include the proxy for control premium As a result it does not appear that the excess
value premium is driven by MNCs operating in countries where majority shareholders are able to
extract large control premiums
23 Results are robust to replacing missing values with the mean control premium of the respective GDP quartile
25
54 Cost of capital
As discussed in Section 2 when countries have shallow capital markets a multinational
network may provide the benefit of a lower cost of capital for investments through an internal
capital market as well as through better access to the US capital market Prior research
establishes that there is substantial variation in the cost of capital across countries (Erb Harvey
and Viskanta 1996 and Hail and Leuz 2009) To determine whether the documented premium
is due to cost of capital advantages we include a control variable to capture the difference
between the MNCrsquos cost of capital and the cost of capital faced by its competitors in the global
markets in which it operates
We compute our Cost of Capital proxy in two steps First we obtain the country-year credit
rating published by Institutional Investor24 The rating ranges from 0 to 100 where higher values
imply that a country has a higher default risk Country-level credit risk is a reasonable predictor
of expected equity market returns and volatility (Erb et al 1996) and exhibits a highly
significant correlation with international accounting-based estimates of imputed cost of capital
(Hail and Leuz 2009) Second using each firmrsquos country sales as a percent of total firm sales
we obtain a weighted average credit risk rating across the countries in which each firm operates
A higher value implies an MNC operates in countries with higher cost of capital In Table 7 we
do not find a result consistent with MNCs gaining an advantage from their access to low cost
capitalndash Cost of Capital is not significantly associated with Excess Value25
24 We thank Cam Harvey for providing these data 25 Results are consistent using cost of capital as defined in Erb et al 1996
26
55 Corporate governance
In light of the recent finding that corporate governance mitigates the diversification discount
we examine whether the premium to multinational activity is sensitive to controls for corporate
governance Hoechle et al (2012) find that the magnitude of the industrial diversification
discount is decreased and in several specifications no longer significant in the presence of
governance proxies In the first column of Table 8 we include the five governance proxies that
are consistently significantly associated with excess value in Hoechle et al (2012) ndash the percent
of CEO ownership (CEO Ownership and CEO Ownership squared) the percent of institutional
ownership (Institution Ownership) whether the firm has a powerful or influential CEO
(Powerful CEO) and the governance index from Gompers Ishii and Metrick (2003)
(Governance Index)
As documented in Table 8 including these control variables reduces our sample size by
almost 50 (to 2550 observations) and tends to exclude the smaller firms from the analysis
None of the governance proxies are significant Relative to column (4) of Table 4 the coefficient
on Foreign Sales is similar in magnitude but is no longer statistically significant at
conventional levels To investigate whether the decreased significance is due to the smaller
sample or the inclusion of the governance variables we re-estimate the regression for the same
2550 observations after removing the governance controls The coefficient and t-statistic are
virtually identical suggesting that the loss of significance is due to the reduced sample and not
the governance controls We do not find evidence to suggest that variation in corporate
governance practices drives variation in excess value for US MNCs
27
6 Conclusion
Using a new method to quantify the valuation effects of foreign direct investment by US
domiciled firms we find robust evidence of a premium in excess value associated with the
degree of multinational operations Our method builds on that used in previous studies
examining firm excess value (eg Berger and Ofek 1995 and Denis et al 2002) by
incorporating the location in addition to the industry of each corporate affiliate This allows
important factors such as growth and cost of capital to vary across countries as well as industries
Examining a sample of multinational firms we find that a one-standard deviation (197)
increase in foreign operations is associated with an increase of between 41 and 82 in excess
value minus both a statistically and economically significant finding
This result complements the finding of the international trade literature that more productive
firms are more likely to operate internationally However the premium remains after including
firm fixed effects to control for persistent firm traits (eg productivity)
The finding of a premium stands up to a succession of robustness tests designed to evaluate
whether the result arises from alternative methods or explanations First we estimate a dynamic
panel data model using the generalized method of moments (GMM) estimator to simultaneously
account for the potential endogeneity of FDI industrial diversification and firm value Second
we include a control variable to proxy for whether the firm operates in countries with larger
control premiums which could result in understated multiples and mechanically induce a
premium Third we include a control variable to measure the cost of capital across the countries
in which an MNC operates to evaluate whether a relatively low cost of capital borne by
multinational firms is a meaningful source of the premium Fourth we control for several
corporate governance proxies which have been found to be correlated with excess values (and
28
industrial diversification) We fail to find evidence supporting any of these alternative
explanations ndash the premium in excess value arising at firms with greater multinational operations
remains significant
Overall our new method of estimating excess values for multinational firms provides strong
evidence of a statistically and economically significant premium associated with increased
international operations The premium is robust to a host of specifications and controls designed
to evaluate alternative explanations We leave to future research the questions of whether the
premium to US multinational firms applies more broadly to other countries of domicile and
what factors influence the premium
29
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31
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Moeller SB and F P Schlingemann 2005 Global diversification and bidder gains A
comparison between cross-border and domestic acquisitions Journal of Banking and Finance 29 533-564
Morck R and B Yeung 1991 Why investors value multinationality Journal of Business 64
165-186
Morck R and B Yeung 2001 Why firms diversify Internatization vs agency behavior In Intangibles B Lev (ed) Oxford University Press pp 269-302
Myers S C and N S Majluf 1984 Corporate financing and investment decisions when firms have information that investors do not have Journal of Financial Economics 13 187-221
Schmid M and I Walter 2009 Do financial conglomerates create or destroy economic value
Journal of Financial Intermediation 18 193-216 Syverson C 2011 What determines productivity Journal of Economic Literature 49(2) 326-
365 Villalonga B 2004a Diversification discount or premium New evidence from the Business
Information Tracking Series Journal of Finance 59 479-506 Villalonga B 2004b Does diversification cause the diversification discount Financial
Management 33 5-27 Wooldridge JM 2010 Econometric Analysis of Cross Section and Panel Data (Second
Edition) Cambridge MA MIT Press Yamin M 1991 A reassessment of Hymerrsquos contribution to the theory of the transnational
corporation C Pitelis and R Sugden (eds) The Nature of the Transnational Firm London Routledge
32
Appendix A Variable Definitions
We obtain information regarding firmsrsquo operations by country and industry from the Bureau of Economic Analysis (BEA) unless otherwise noted Acronyms used below reflect Compustat mnemonics and thus indicate when we obtain data from the Compustat North America Fundamentals Annual database All independent variables are winsorized at 1
Dependent variable
Excess Value = The log of the ratio of actual firm value to imputed firm value Actual firm value is equal to market capitalization (MVE) + book value of total assets (AT) ndash book value of shareholdersrsquo equity (SEQ) Imputed firm value is calculated as the sum of the imputed values of a firmrsquos operations in each country-industry The imputed value for each country-industry is equal to sales generated in that country-industry times a multiple equal to the ratio of a benchmark firmrsquos actual value to total sales A benchmark firm is defined as a median single-segment firm operating in the same country-industry All multiples require at least five firms listed in the respective country-industry on Compustat (domestic) or Worldscope (foreign) with less than 10 of sales income and assets outside the country of domicile Similar to Berger and Ofek (1995) we eliminate extreme values where the ratio of actual to imputed firm value exceeds 400 or is less than 025
Independent variables
Foreign Sales = Sales made by the foreign operations of US MNCs as a percent of total firm sales
Industry Other = Sales outside a firmrsquos primary industry as a percent of total sales (SALE) Equal to zero for firms operating in a single business segment
Log Size = Log of total sales (SALE)
Debt Firm Value = Ratio of long-term debt (DLTT) to total market value (market capitalization (MVE) + book value of total assets (AT) ndash book value of shareholdersrsquo equity (SEQ))
CAPX Sales = Ratio of capital expenditures (CAPX) to total sales (SALE)
EBIT Sales = Ratio of earnings before interest and taxes (EBIT) to total sales (SALE)
RampD Sales = Ratio of research and development expenses (XRD) to total sales (SALE)
Ad Sales = Ratio of advertising expenses (XAD) to total sales (SALE)
Control Premium = A weighted-average (based on the sales in a given country as a percent of total firm sales) of the country-level control premium from Dyck and Zingales (2004)
Cost of Capital = A weighted-average (based on the sales in a given country as a percent of total firm sales) of the country-level credit rating from Institutional Investor (httpwwwinstitutionalinvestorcom) The country-level rating is available for 160 countries and ranges from 0 to 100 Higher values imply greater credit risk
33
CEO Ownership = Percent of shares owned by the CEO from Execucomp
Institution Ownership = Percent of shares owned by institutional investors from Thomson Reuterrsquos Institutional Holdings database
Powerful CEO = Indicator variable equal to one if the CEO is the only insider on the board of directors and also serves as the chairman and president based on data from RiskMetrics Directors database Equal to zero otherwise
Governance Index = Count measure of takeover defenses used in Gompers Ishii and Metrick (2003) from RiskMetrics Governance database As the governance data are not available for every year we follow Gompers Ishii and Metrick (2003) and assume that the firmsrsquo governance attributes are unchanged until publication of the subsequent series
34
Figure 1 Trends in multinational activity and industrial diversification from 1984-2008 This figure presents a plot of the mean percent of foreign sales and the mean percent of sales outside the firmrsquos primary industry (industrial diversification) from 1984 through 2008 Dotted lines above and below the primary lines represent 95 confidence intervals To compile the plot we include the full population of firms in Compustat
35
Table 1 Analysis of accounting data for firms reporting under multiple accounting standards This table provides a comparison of sales net income and assets for firms that changed accounting standards to or from US GAAP Comparisons are provided for the 66 firms that changed to or from US GAAP and for which we could obtain accounting data electronically and in English under both standards for the same fiscal year (eg the firm presented the accounting numbers originally under one set of standards and then restated the prior year numbers in the subsequent year under the new set of standards) Panel A shows the distribution of firms by year Panel B shows the other accounting standards to which US GAAP is compared Panel C shows the mean and median percent that the other accounting standard differs from US GAAP for the same fiscal year and accounting number Two samples are shown in Panel C The first is the full sample of firms with a change in accounting standards to or from US GAAP for which data was available The second is the subset of these firms that occurred during the sample window used in this study (ie excluding changes post-2008) The percent difference in sales is compared to the percent difference in net income and assets Tests of means are based on matched-pair t-tests Tests of medians are based on Wilcoxon matched-pair signed-rank tests Panel A Accounting standard changes by year
2003
4 6
2004
25 38
2005
8 12
2006
6 9
2007
7 11
2008
5 8
2009
5 8
2010
5 8
2011
1 2
66
Panel B Accounting standards compared to US GAAP
IFRS
47 71
Canadian GAAP
12 18
Israeli GAAP
2 3
Dutch GAAP
1 2
Indian GAAP
1 2
Japanese GAAP
1 2
Norwegian GAAP
1 2
Swiss GAAP
1 2
66
36
(Table 1 continued) Panel C Tests of differences between US GAAP and other accounting standards
Percent different from US GAAP
Comparison to sales (p-values)
N Mean Median
Mean Median
Full Sample (2001-2011)
Sales
66 236 000
Net Income
66 10653 647
005 lt001
Assets
66 1162 126
lt001 lt001
Study Sample (2001-2008)
Sales
55 257 000
Net Income
55 10303 589
011 lt001
Assets
55 1047 099
003 lt001
37
Table 2 Descriptive statistics Panel A reports the mean and medians of Excess Value for the multinational firms and single-segment benchmark firms as well as univariate tests across the subsamples Panel B reports summary statistics for the independent variables for the multinational firms The p-values of the tests of differences in means are based on t-tests The p-values of tests of differences in the medians are for a Wilcoxon rank-sum test All variables are defined in Appendix A Panel A Excess Value by sample Multinational All Benchmark Benchmark Subgroups Sample Firms US Foreign
Value Value Test of Diff (p-value) Value
Test of Diff (p-value) Value
Test of Diff (p-value)
Mean 0067 0006 lt001 -0028 lt001 0014 lt001 Median 0076 0000 lt001 -0025 lt001 0000 lt001 N 4950 33074 5998 27076 Panel B Distribution of independent variables for the multinational firms N Mean 25th 50th 75th StDev Foreign Sales 4950 0238 0074 0190 0363 0197 Industry Other 4950 0212 0000 0166 0414 0215 Log Size 4950 6955 5823 6989 8140 1687 Debt Firm Value 4950 0136 0009 0097 0212 0145 CAPX Sales 4950 0054 0020 0033 0054 0101 EBIT Sales 4950 0065 0031 0077 0121 0138 RampD Sales 4950 0049 0000 0013 0059 0083 Ad Sales 4950 0008 0000 0000 0005 0018
38
Table 3 BEA and Worldscope samples
This table reports details on affiliate sales for significant countries pooled from 1998-2008 Significant countries are defined as those in which the operations of US MNCs represent at least 02 of total firm sales (per the BEA) For significant countries the table compares their representation in terms of MNC affiliate sales in the BEA data to the distribution of firm observations in Worldscope US Foreign Affiliates (BEA) Foreign Firms (Worldscope)
Country
N (1)
Sales (millions)
(2)
Total Sales (3)
N (4)
Sales (million)
(5)
Total Sales (6)
Australia 1344 73305 056 859 255865 210 Belgium 894 52755 040 169 26136 021 Brazil 959 60245 046 1143 683167 560 Canada 2730 536767 407 1181 526750 432 China 922 75907 057 822 276541 227 France 1852 183942 139 540 105447 086 Germany 2182 275577 209 480 103888 085 Hong Kong 867 48100 036 157 34094 028 Ireland 478 36754 028 34 3467 003 Italy 1178 82116 062 111 47948 039 Japan 1494 148032 112 2281 1119223 917 Korea South 659 34500 026 3530 2752791 2255 Malaysia 386 26027 020 837 168683 138 Mexico 1405 113623 086 362 237173 194 Netherlands 1149 85105 064 47 22631 019 Singapore 1098 94917 072 139 32913 027 Spain 871 63094 048 129 37419 031 Sweden 712 32682 025 235 23090 019 Switzerland 726 60288 046 74 20242 017 Taiwan 592 30313 023 2949 787049 645 United Kingdom 3054 423934 321 1997 882423 723 United States (domicile) 4950 10460880 7923 na na na Other - Covered by WorldScope 6893 199394 151 9000 4057894 33 Other -Not Covered by WorldScope 713 5352 004 - - - Total 38108 13203609 1000 27076 12204834 100
39
Table 4 OLS regressions of firm excess values on the percent of foreign sales This table reports the results of OLS regressions of firm excess value on the percent of foreign sales (Foreign Sales) the percent of sales outside a firmrsquos primary industry (Industry Other) and control variables All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed) Dep Variable = Excess Value Coefficient t-stat Coefficient t-stat Coefficient t-stat Coefficient t-stat (1) (2) (3) (4) Foreign Sales 0349 419 0209 293 0189 266 0278 210 Industry Other -0134 -186 -0047 -072 -0053 -081 -0326 -325 Log Size 0066 634 0065 624 -0044 -096 Debt Firm Value -0196 -213 -0241 -260 -0685 -588 CAPX Sales 0901 425 0893 435 0488 227 EBIT Sales 1154 542 1170 547 0408 189 RampD Sales 2908 1242 2932 1237 1544 379 Ad Sales 1869 263 2003 280 -1696 -119 Year indicators No No Yes Yes Firm indicators No No No Yes R2 0017 0212 0226 0768 Observations 4950 4950 4950 4950
40
Table 5 Dynamic panel GMM model regressing firm excess values on the percent of foreign sales This table reports the result from a regression of the excess value on the percent of foreign sales (Foreign Sales) the percent of non-primary industry sales (Industry Other) and control variables using a dynamic panel GMM estimator All control variables are considered to be endogenous with the exception of the year indicator variables AR(1) and AR(2) are tests for first-order and second-order serial correlation in the first differenced residuals with the null hypothesis of no serial correlation The null hypothesis of the Hansen test of overidentification is that all instruments are valid The null hypothesis of the difference-in-Hansen test of exogeneity is that the instruments used for the equation in levels are exogenous All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed) Dep Variable = Excess Value Coef t-stat Excess Value (one lag) -0018 -082 Excess Value (two lags) -0048 -272 Foreign Sales 0371 270 Industry Other -0163 -171 Log Size 0010 123 Debt FirmValue -1135 -820 CAPX Sales 0590 342 EBIT Sales -0047 -300 RampD Sales 1376 531 Ad Sales -0751 -040 AR(1) test (p-value) 000 AR(2) test (p-value) 017 Hansen test of over-identification (p-value) 030 Difference-in-Hansen test of exogeneity (p-value) 093 Year indicators Yes Number of firms 1016 Number of observations 4090
41
Table 6 OLS regression of firm excess values on the percent of foreign sales controlling for a control premium This table reports the result of the OLS regression of firm excess value including firm and year fixed effects with an additional control variable to proxy for a country-level ownership control premium (Control Premium) All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed) Dep Variable = Excess Value Coef t -stat Foreign Sales 0303 224 Industry Other -0327 -325 Log Size -0045 -100 Debt FirmValue -0683 -585 CAPX Sales 0490 226 EBIT Sales 0409 189 RampD Sales 1545 379 Ad Sales -1689 -119 Control Premium -1004 -062 Year indicators Yes Firm indicators Yes R2 0768 Observations 4950
42
Table 7 OLS regression of firm excess values on the percent of foreign sales controlling for the cost of capital This table contains the results of an OLS regression of firm excess value including firm and year fixed effects with an additional control variable to proxy for a country-level cost of capital (Cost of Capital) All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed) Dep Variable = Excess Value Coef t -stat Foreign Sales 0255 178 Industry Other -0326 -324 Log Size -0043 -094 Debt FirmValue -0687 -589 CAPX Sales 0485 225 EBIT Sales 0406 188 RampD Sales 1539 379 Ad Sales -1703 -120 Cost of Capital 0004 039 Year indicators Yes Firm indicators Yes R2 0768 Observations 4950
43
Table 8 OLS regression of firm excess values on the percent of foreign sales controlling for aspects of corporate governance This table reports the results of two OLS regressions In the first column are the results of the firm excess value regression including firm and year fixed effect with five proxies for corporate governance included ndash the percent of shares owned by the CEO and the percent squared (CEO Ownership and CEO Ownership squared) the percent owned by institutional investors (Institution Ownership) whether there is a powerful CEO (Powerful CEO) and the Gompers Ishii and Metrick (2003) governance index (Governance Index) In the second column the regression includes the same sample and variables with the exception of the governance proxies which are excluded All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed)
Governance
No Governance (same sample)
Dep Variable = Excess Value Coef t-stat Coef t -stat Foreign Sales 0259 159 0251 154 Industry Other -0099 -080 -0099 -079 Log Size -0105 -185 -0102 -180 Debt FirmValue -0813 -478 -0817 -482 CAPX Sales 0440 252 0438 249 EBIT Sales 0457 247 0457 246 RampD Sales 1717 284 1716 286 Ad Sales -2210 -143 -2190 -143 CEO Ownership 0010 007
CEO Ownership squared -0029 -017 Institution Ownership 0047 067 Powerful CEO -0003 -014 Governance Index 0009 103
Year indicators Yes Yes Firm indicators Yes Yes R2 0811 0810 Observations 2550 2550
- Cover for paperpdf
- crrz_20130603_SMUpdf
-
17
means and medians are lt 001)16 These results provide some initial evidence that operating in
multiple countries is positively associated with firm value
To estimate country-industry multiples we rely on Worldscope financial data on foreign
firms and Compustat Fundamentals Annual for domestic firms We restrict the firms to include
only those with at least 90 of sales income and assets inside the country of domicile (ie
those that do not report significant multinational activity) and that operate in a single industry
We refer to these firms as benchmark firms (either foreign or domestic depending on the country
of domicile) these firms do not appear in any of our regressions We report the number of
benchmark firms during our sample period in Table 2 Panel A The industry criterion is based on
the two-digit SIC code17 For every country that has at least five firms in the respective industry
and year we use the median ratio of market value to sales18 This ratio is the country-industry
multiple ndash an input required to compute imputed values
To determine the country-industry composition of MNCs we rely on BEA data In Table 3
we report the aggregate number of foreign affiliates and total sales by country as provided by
the BEA data for our sample of 4950 MNCs The specific countries tabulated are those that
represent at least 02 percent of total firm sales (pooled across all years) in our sample The top
five foreign countries in which MNCs generate sales are Canada United Kingdom Germany
France and Japan Table 3 also provides a comparison of the number of foreign benchmark
firms available in Worldscope in the specific countries in which our sample of MNCs generate
16 Similar to Berger and Ofek (1995) the median values differ slightly from zero for the benchmark single-segment firms due to the truncation of extreme excess values 17 BEA reporting constrains us to the two-digit SIC code level 18 When five firms are not available for a given country and industry we follow Berger and Ofek (1995) and move to a more general industry definition (eg one-digit SIC code) This procedure results in a mean (median) 630 (757) of MNC sales matched at the two-digit level
18
the majority of their sales Country coverage in Worldscope is not available for countries
representing 004 percent of total firm sales (per BEA)
4 Empirical results
41 Independent variables
Recall that our objective is to examine the overall relation between excess firm value and
multinational operations Our proxy for the extent of multinational operations is the percentage
of total firm sales made by foreign operations (Foreign Sales) By this measure the extent of
multinational activity in our sample of MNCs is non-trivial ndash Table 2 Panel B shows that
approximately 24 percent of sales are generated by foreign operations on average Our first
analysis estimates ordinary least square regressions of Excess Value on Foreign Sales and
control variables (discussed below)
We include variables in our regression to control for other potential determinants of excess
value These are the percentage of sales made by the firm outside its primary industry (Industry
Other) to control for any relation between industrial diversification and excess value as in Berger
and Ofek (1995) For this purpose we obtain industry sales using Compustat Segment data19 A
firmrsquos primary industry is the industry in which the firm generates the majority of its sales and
we determine industry sales at the business segment level We set Industry Other equal to zero
for firms that operate in a single business segment
Consistent with prior research we also include controls for firm size (Log Size) the ratio of
long term debt to total market value (Debt Firm Value) the ratio of capital expenditures to total
sales (CAPX Sales) the ratio of earnings before interest and taxes to total sales (EBIT Sales)
the ratio of research and development expenses to total sales (RampD Sales) and the ratio of 19 Our results are unaffected by using BEA data to measure Industry Other
19
advertising expenses to total sales (Ad Sales) Table 2 Panel B provides the distribution of the
control variables All independent variables are Winsorized at 1 In our OLS regressions we
use heteroskedasticity and autocorrelation consistent robust standard errors with clustering at the
firm level
42 Primary regression analysis
In this section we examine the relation between firm excess value and the amount of foreign
activity at US MNCs Table 4 contains the results of regressions of excess value on Foreign
Sales for multiple specifications In column (1) Excess Value is regressed on both the percent of
foreign sales (Foreign Sales) and the percent of sales outside the firmrsquos primary industry
(Industry Other) We add the control variables to the regression in column (2) and year
indicators in column (3) In all three specifications the coefficient on the percent of foreign sales
remains significantly positive (with a p-value lt 001) For a sense of economic magnitude in
column (3) a one standard deviation (197) increase in the percent of foreign sales (Foreign
Sales) is associated with a 41 (equal to (e0349-1) times 0197) increase in firm excess value The
effect is larger for the other two columns ndash 82 increase in firm excess value in column (1) and
46 in column (2)20
Our finding of a premium reconciles with the international trade literature which finds that
firms engaging in international trade are more productive than those that do not (see Helpman
2006 and Syverson 2011 for reviews) Due to data limitations it is difficult to accurately
measure TFP at the firm level especially for firms which operate in multiple industries andor
20 Using the alternative strategy that employs US domiciled single-segment firms as the benchmark for the imputed value of the foreign operations Denis et al (2002) find that greater foreign activity results in a discount to firm value Using the Denis et al (2002) method to calculate excess value in our sample we obtain a significantly negative regression coefficient on Foreign Sales using the models in Table 4 columns (2) and (3) (the coefficient is not significant using the model in column (1) results untabulated)
20
countries As an alternative method to evaluate whether our finding of a premium is simply the
result of more productive firms also having more multinational operations we investigate
whether the premium is simply an artifact of innate characteristics of firms that choose to invest
abroad For example Helpman et al (2004) present a simple model with heterogeneous
productivity endowments The firms that receive the highest productivity endowments are the
ones capable of paying the fixed costs to establish a foreign subsidiary
To the extent that productivity advantages create excess value one would expect the type of
firms that choose to invest more abroad would be valued at a premium even in the absence of
their foreign investments To control for time invariant firm characteristics (eg innate
productivity advantages) and hence a potential source of endogeneity we add firm-level fixed
effects in column (4) of Table 4 We continue to find that the extent of foreign operations
(Foreign Sales) is positively associated with Excess Value Across all specifications the sign
on all significant control variables is consistent with prior research (eg Berger and Ofek 1995
and Denis et al 2002)
The finding of a premium makes a significant contribution to the relatively sparse literature
on the value effects of multinational activity Our measure of imputed value expands upon the
methods used by Berger and Ofek (1995) and Denis et al (2002) but allows us to answer a
different question than Denis et al (2002) Holding constant the extent of industrial
diversification we ask whether MNCs are more valuable than a portfolio of benchmark firms of
a similar geographic footprint In contrast Denis et al (2002) ask whether holding constant the
extent of industrial diversification MNCs are more valuable than a portfolio of domestic (US)
benchmark firms We believe that asking whether an MNCrsquos foreign (domestic) operations are
21
more valuable than those of a foreign (domestic) firm is a more appropriate way to assess the
value implications of a multinational network
5 Robustness
51 Endogeneity related to dynamic relations
Prior research argues that the relation between industrial diversification and excess firm
value could be endogenous through dynamic relationships based on observing past outcomes
Managers of firms likely choose to enter or exit industries or geographic regions based upon
their previous performance For example Campa and Kedia (2002) find that firms are more
likely to enter new industries when prospects in their current lines of business are deteriorating
Similar dynamics could also drive a relation between excess value and FDI We assess the
robustness of the previous results by estimating a dynamic panel data model which allows us to
control for dynamic endogeneity in addition to firm level unobserved heterogeneity We employ
the GMM estimator for dynamic panel models developed by Arellano and Bond (1991) Arellano
and Bover (1995) and Blundell and Bond (1998) Our results from this analysis confirm our
previous findings that firms with a larger degree of foreign operations have positive excess
values
Dynamic panel data models are generalizations of the traditional fixed effects model where
lags of the dependent variable are added to the right hand side of the equation These additional
lags control for the impact a firmrsquos past performance has on its current performance through the
channels discussed above The GMM estimator for dynamic panel models developed by
Arellano and Bond (1991) starts by differencing the data at the firm level in neighboring time
periods to eliminate any firm specific time invariant unobserved heterogeneity that may be
22
present Eliminating the firm specific fixed effect by differencing the data with lags of the
dependent variable on the right hand side introduces correlation between the new error term and
the differenced explanatory variables This problem is solved by applying the standard GMM
estimator using lags of both the original explanatory variables and excess value as instruments
We note that the dynamic panel data framework allows us to treat both international and
industrial diversification (as well as the other firm-specific control variables) as endogenous
Following Hoechle et al (2012) we also allow contemporaneous values of these variables to be
treated as endogenous
Lagged values of the explanatory variables are valid instruments in a dynamic panel data
model when the model is dynamically complete (eg Wooldridge 2010) A dynamic panel data
model is dynamically complete conditional on the unobserved effect if enough lags of both the
explanatory and dependent variable are included to make the resulting error term uncorrelated
with all the right hand side variables Under this assumption any lags for the explanatory
variables beyond this can be used within the GMM estimator as instruments Like Hoechle et al
(2012) we add two years of lags of a firmrsquos excess value to the right hand side assuming that
this is enough lags to control for dynamic feedback effects We then use past values of all
variables beyond the second lag up to a total of seven years to act as instruments
The results of the dynamic panel data estimator are reported in Table 5 These results confirm
the previous finding of a positive relation between the degree of international operations and
excess value We note that the size and significance level of the coefficient on Foreign Sales
diversification are robust to changes in the number of lags used as instruments The Arellano and
Bond (1991) estimator includes testing diagnostics for correct specification of the model If the
model is correctly specified differences in the residuals from the levels equation should be
23
serially uncorrelated at lag one but correlated at lag two The p-values for tests of serial
correlation of the residuals at lags 1 and 2 are reported in Table 5 and these indicate that the
model is not rejected We also report in Table 5 the Hansen test for overidentification which is a
test of the null hypothesis that the instruments are valid The p-value for this test is 030
indicating that we fail to reject this null hypothesis Finally we report another test for exogeneity
known as the difference-in-Hansen test which tests the null hypothesis that the instruments in
the original equation are valid We fail to reject this null hypothesis as well
52 Identifying value effects of multinational operations via cross-border acquisitions
Another alternative to investigating a causal link between foreign direct investment and firm
value is an event study of foreign acquisitions21 Acquired foreign targets represent only a small
fraction of MNCsrsquo worldwide sales in the year of the acquisition ndash less than 1 percent in our
sample Additionally cross-border acquisitions are not the primary source of international
expansion for the average firm We find that foreign acquisitions account for between 18 percent
and 55 percent of the year-over-year change in foreign sales with the remainder of the growth
coming from either newly established entities (ie greenfield investments) or growth in existing
operations22 These results suggest that acquisitions are not a material source of growth for the
21 Doukas and Travlos (1988) find positive returns when the acquirer is not operating in the target firmrsquos country and insignificant returns when the acquirer is operating in the target firmrsquos country or expanding internationally for the first time Morck and Yeung (1991) find marginal evidence of positive returns for firms with intangible assets Finally Doukas (1995) finds positive returns only for firms with average Tobinrsquos q ratios greater than one More recent work also using an event study methodology provides mixed evidence on valuation effects of global diversification through acquisitions Dos Santos Errunza and Miller (2008) find insignificant returns for firms with already established foreign operations firms establishing initial foreign operations and firms acquiring targets in related industries Doukas and Kan (2006) and Moeller and Schlingemann (2005) find reductions to shareholder value while Francis Hasan and Sun (2008) find positive returns for acquirers of targets in segmented financial markets 22 New foreign affiliates to an MNC group each year report on the BEA survey whether they entered the group as an acquisition or a newly established entity (ie greenfield investment) Affiliates under the lowest reporting threshold do not report this information resulting in our inability to precisely identify the source of 33 percent of the increase in firm sales as either acquisition or greenfield
24
typical US MNC suggesting that an event study would lack power and generalizability For
these reasons we do not implement an event study
53 Control premium
A control premium or private benefits of control occurs when ldquosome value whatever the
source is not shared among all the shareholders in proportion of the shares owned but it is
enjoyed exclusively by the party in controlrdquo (Dyck and Zingales 2004 p 541) Using stock
prices of traded shares held by minority shareholders in determining the market value to sales
ratios of our benchmark firms could underestimate the true value of our benchmark firms if those
firms face large control premiums This in turn could induce an excess value premium because
the imputed value of these country-industry segments would be artificially low The existence of
such benefits is more prevalent in some countries than others and could affect the value of an
MNCrsquos operations in such domains We use the country-level control premiums from Dyck and
Zingales (2004) and using each firmrsquos country sales as a percent of total firm sales we obtain a
weighted average control premium that each MNC faces (Control Premium) For countries not
included in the Dyck and Zingales (2004) analysis we set the control premium equal to zero23 In
Table 6 we show that Foreign Sales remains positive and highly significant (p-value lt 001)
when we include the proxy for control premium As a result it does not appear that the excess
value premium is driven by MNCs operating in countries where majority shareholders are able to
extract large control premiums
23 Results are robust to replacing missing values with the mean control premium of the respective GDP quartile
25
54 Cost of capital
As discussed in Section 2 when countries have shallow capital markets a multinational
network may provide the benefit of a lower cost of capital for investments through an internal
capital market as well as through better access to the US capital market Prior research
establishes that there is substantial variation in the cost of capital across countries (Erb Harvey
and Viskanta 1996 and Hail and Leuz 2009) To determine whether the documented premium
is due to cost of capital advantages we include a control variable to capture the difference
between the MNCrsquos cost of capital and the cost of capital faced by its competitors in the global
markets in which it operates
We compute our Cost of Capital proxy in two steps First we obtain the country-year credit
rating published by Institutional Investor24 The rating ranges from 0 to 100 where higher values
imply that a country has a higher default risk Country-level credit risk is a reasonable predictor
of expected equity market returns and volatility (Erb et al 1996) and exhibits a highly
significant correlation with international accounting-based estimates of imputed cost of capital
(Hail and Leuz 2009) Second using each firmrsquos country sales as a percent of total firm sales
we obtain a weighted average credit risk rating across the countries in which each firm operates
A higher value implies an MNC operates in countries with higher cost of capital In Table 7 we
do not find a result consistent with MNCs gaining an advantage from their access to low cost
capitalndash Cost of Capital is not significantly associated with Excess Value25
24 We thank Cam Harvey for providing these data 25 Results are consistent using cost of capital as defined in Erb et al 1996
26
55 Corporate governance
In light of the recent finding that corporate governance mitigates the diversification discount
we examine whether the premium to multinational activity is sensitive to controls for corporate
governance Hoechle et al (2012) find that the magnitude of the industrial diversification
discount is decreased and in several specifications no longer significant in the presence of
governance proxies In the first column of Table 8 we include the five governance proxies that
are consistently significantly associated with excess value in Hoechle et al (2012) ndash the percent
of CEO ownership (CEO Ownership and CEO Ownership squared) the percent of institutional
ownership (Institution Ownership) whether the firm has a powerful or influential CEO
(Powerful CEO) and the governance index from Gompers Ishii and Metrick (2003)
(Governance Index)
As documented in Table 8 including these control variables reduces our sample size by
almost 50 (to 2550 observations) and tends to exclude the smaller firms from the analysis
None of the governance proxies are significant Relative to column (4) of Table 4 the coefficient
on Foreign Sales is similar in magnitude but is no longer statistically significant at
conventional levels To investigate whether the decreased significance is due to the smaller
sample or the inclusion of the governance variables we re-estimate the regression for the same
2550 observations after removing the governance controls The coefficient and t-statistic are
virtually identical suggesting that the loss of significance is due to the reduced sample and not
the governance controls We do not find evidence to suggest that variation in corporate
governance practices drives variation in excess value for US MNCs
27
6 Conclusion
Using a new method to quantify the valuation effects of foreign direct investment by US
domiciled firms we find robust evidence of a premium in excess value associated with the
degree of multinational operations Our method builds on that used in previous studies
examining firm excess value (eg Berger and Ofek 1995 and Denis et al 2002) by
incorporating the location in addition to the industry of each corporate affiliate This allows
important factors such as growth and cost of capital to vary across countries as well as industries
Examining a sample of multinational firms we find that a one-standard deviation (197)
increase in foreign operations is associated with an increase of between 41 and 82 in excess
value minus both a statistically and economically significant finding
This result complements the finding of the international trade literature that more productive
firms are more likely to operate internationally However the premium remains after including
firm fixed effects to control for persistent firm traits (eg productivity)
The finding of a premium stands up to a succession of robustness tests designed to evaluate
whether the result arises from alternative methods or explanations First we estimate a dynamic
panel data model using the generalized method of moments (GMM) estimator to simultaneously
account for the potential endogeneity of FDI industrial diversification and firm value Second
we include a control variable to proxy for whether the firm operates in countries with larger
control premiums which could result in understated multiples and mechanically induce a
premium Third we include a control variable to measure the cost of capital across the countries
in which an MNC operates to evaluate whether a relatively low cost of capital borne by
multinational firms is a meaningful source of the premium Fourth we control for several
corporate governance proxies which have been found to be correlated with excess values (and
28
industrial diversification) We fail to find evidence supporting any of these alternative
explanations ndash the premium in excess value arising at firms with greater multinational operations
remains significant
Overall our new method of estimating excess values for multinational firms provides strong
evidence of a statistically and economically significant premium associated with increased
international operations The premium is robust to a host of specifications and controls designed
to evaluate alternative explanations We leave to future research the questions of whether the
premium to US multinational firms applies more broadly to other countries of domicile and
what factors influence the premium
29
References
Ammann M Hoeschel D and M Schmid 2012 Is there really no conglomerate discount
Journal of Business Finance and Accounting 39(12) 264-288 Arellano M and Bond S R 1991 Some tests of specification for panel data Monte Carlo
evidence and an application to employment equations Review of Economic Studies 58 (2) 277-298
Arellano M and Bover O 1995 Another look at instrumental variables estimation of error
components models Journal of Econometrics 68 (1) 29-61 Bens D A Berger P G and S J Monahan 2011 Discretionary disclosure in financial
reporting An examination comparing internal firm data to externally reported segment data The Accounting Review 86(2) 417-449
Berger P G and E Ofek 1995 Diversificationrsquos effect on firm value Journal of Financial
Economics 37 39-65 Blundell R and Bond S R 1998 Initial conditions and moment restrictions in dynamic panel
data models Journal of Econometrics 87(1) 115-144 Campa J M and S Kedia 2002 Explaining the diversification discount The Journal of
Finance 57 (4) 1731-1762 Denis D J Denis D K and K Yost 2002 Global diversification industrial diversification
and firm value The Journal of Finance 57 (5) 1951-1979 Desai M A Foley C F and J R Hines 2004 A multinational perspective on capital structure
choice and internal capital markets The Journal of Finance 59 (6) 2451-2487 Dos Santos MB V Errunza and D Miller 2008 Does corporate international diversification
destroy value Evidence from cross-border mergers and acquisitions Journal of Banking and Finance 32(12) 2716-2724
Doukas JA 1995 Overinvestment Tobinrsquos q and gains from foreign acquisitions Journal of
Banking and Finance 19 1285-1303 Doukas J and N G Travlos 1988 The effect of corporate multinationalism on shareholdersrsquorsquo
wealth evidence from international acquisitions Journal of Finance 43(5) 1161-1175 Doukas JA and O B Kan 2006 Does global diversification destroy firm value Journal of
International Business Studies 37(3) 352-371
30
Dunning J and A Rugman 1985 The influence of Hymerrsquos dissertation on the theory of foreign direct investment The American Economic Review 75(2) 228-232
Dyck A and L Zingales 2004 Private benefits of control An international comparison The
Journal of Finance 59 (2) 537-600 Erb C Harvey C and T Viskanta 1996 Expected returns and volatility in 135 countries The
Journal of Portfolio Management 46-58 Foley C F Desai M A and K J Forbes 2008 Financial constraints and growth
Multinational and local firm responses to currency depreciations Review of Financial Studies 21 (6) 2857-2888
Francis B I Hasan and X Sun 2008 Financial market integration and the value of global
diversification Evidence for US acquirers in cross-border mergers and acquisitions Journal of Banking and Finance 321522-1540
Gande A Schenzler C and L Senbet 2009 Valuation effects of global diversification
Journal of International Business Studies 1515-1532 Gompers P Ishii J and A Metrick 2003 Corporate governance and equity prices Quarterly
Journal of Economics 118(1) 107-156 Hail L and C Leuz 2009 Cost of capital effects and changes in growth expectations around
US cross-listings Journal of Financial Economics 93 428-454 Hanson G Mataloni R and M Slaughter 2005 Vertical production networks in multinational
firms Review of Economics and Statistics 87(4) 1-15 Helpman E 2006 Trade FDI and the Organization of Firms Journal of Economic Literature
44(3) 289-630 Helpman E Melitz M J and S R Yeaple 2004 Export versus FDI with Heterogeneous Firms
American Economic Review 94(1) 300-316 Hoechle D Schmid M Walter W and D Yermack 2012 How much of the diversification
discount can be explained by poor governance Journal of Financial Economics 103 41-60 Hope O-K and W B Thomas 2008 Managerial empire building and firm disclosure Journal
of Accounting Research 46 (June) 591-626 Huizinga H P and J Voget 2009 International taxation and the direction and volume of cross-
border MampAs Journal of Finance 64 (3) 1217-1249 Jensen M 1986 Agency cost of free cash flow corporate finance and takeovers American
Economic Review Papers and Proceedings 76 323-329
31
Laeven L and R Levine 2007 Is there a diversification discount in financial conglomerates
Journal of Financial Economics 85 331-367 Lang L and R Stulz 1994 Tobinrsquos Q corporate diversification and firm performance Journal
of Political Economy 102 1248-1280 Mataloni R 2003 US multinational companies Operations in 2001 Survey of Current
Business 85-105 Matvos G and A Seru 2011 The dynamic tradeoff between corporate socialism and financial
market dislocation Estimates from internal capital markets Working paper University of Chicago Booth School of Business
Moeller SB and F P Schlingemann 2005 Global diversification and bidder gains A
comparison between cross-border and domestic acquisitions Journal of Banking and Finance 29 533-564
Morck R and B Yeung 1991 Why investors value multinationality Journal of Business 64
165-186
Morck R and B Yeung 2001 Why firms diversify Internatization vs agency behavior In Intangibles B Lev (ed) Oxford University Press pp 269-302
Myers S C and N S Majluf 1984 Corporate financing and investment decisions when firms have information that investors do not have Journal of Financial Economics 13 187-221
Schmid M and I Walter 2009 Do financial conglomerates create or destroy economic value
Journal of Financial Intermediation 18 193-216 Syverson C 2011 What determines productivity Journal of Economic Literature 49(2) 326-
365 Villalonga B 2004a Diversification discount or premium New evidence from the Business
Information Tracking Series Journal of Finance 59 479-506 Villalonga B 2004b Does diversification cause the diversification discount Financial
Management 33 5-27 Wooldridge JM 2010 Econometric Analysis of Cross Section and Panel Data (Second
Edition) Cambridge MA MIT Press Yamin M 1991 A reassessment of Hymerrsquos contribution to the theory of the transnational
corporation C Pitelis and R Sugden (eds) The Nature of the Transnational Firm London Routledge
32
Appendix A Variable Definitions
We obtain information regarding firmsrsquo operations by country and industry from the Bureau of Economic Analysis (BEA) unless otherwise noted Acronyms used below reflect Compustat mnemonics and thus indicate when we obtain data from the Compustat North America Fundamentals Annual database All independent variables are winsorized at 1
Dependent variable
Excess Value = The log of the ratio of actual firm value to imputed firm value Actual firm value is equal to market capitalization (MVE) + book value of total assets (AT) ndash book value of shareholdersrsquo equity (SEQ) Imputed firm value is calculated as the sum of the imputed values of a firmrsquos operations in each country-industry The imputed value for each country-industry is equal to sales generated in that country-industry times a multiple equal to the ratio of a benchmark firmrsquos actual value to total sales A benchmark firm is defined as a median single-segment firm operating in the same country-industry All multiples require at least five firms listed in the respective country-industry on Compustat (domestic) or Worldscope (foreign) with less than 10 of sales income and assets outside the country of domicile Similar to Berger and Ofek (1995) we eliminate extreme values where the ratio of actual to imputed firm value exceeds 400 or is less than 025
Independent variables
Foreign Sales = Sales made by the foreign operations of US MNCs as a percent of total firm sales
Industry Other = Sales outside a firmrsquos primary industry as a percent of total sales (SALE) Equal to zero for firms operating in a single business segment
Log Size = Log of total sales (SALE)
Debt Firm Value = Ratio of long-term debt (DLTT) to total market value (market capitalization (MVE) + book value of total assets (AT) ndash book value of shareholdersrsquo equity (SEQ))
CAPX Sales = Ratio of capital expenditures (CAPX) to total sales (SALE)
EBIT Sales = Ratio of earnings before interest and taxes (EBIT) to total sales (SALE)
RampD Sales = Ratio of research and development expenses (XRD) to total sales (SALE)
Ad Sales = Ratio of advertising expenses (XAD) to total sales (SALE)
Control Premium = A weighted-average (based on the sales in a given country as a percent of total firm sales) of the country-level control premium from Dyck and Zingales (2004)
Cost of Capital = A weighted-average (based on the sales in a given country as a percent of total firm sales) of the country-level credit rating from Institutional Investor (httpwwwinstitutionalinvestorcom) The country-level rating is available for 160 countries and ranges from 0 to 100 Higher values imply greater credit risk
33
CEO Ownership = Percent of shares owned by the CEO from Execucomp
Institution Ownership = Percent of shares owned by institutional investors from Thomson Reuterrsquos Institutional Holdings database
Powerful CEO = Indicator variable equal to one if the CEO is the only insider on the board of directors and also serves as the chairman and president based on data from RiskMetrics Directors database Equal to zero otherwise
Governance Index = Count measure of takeover defenses used in Gompers Ishii and Metrick (2003) from RiskMetrics Governance database As the governance data are not available for every year we follow Gompers Ishii and Metrick (2003) and assume that the firmsrsquo governance attributes are unchanged until publication of the subsequent series
34
Figure 1 Trends in multinational activity and industrial diversification from 1984-2008 This figure presents a plot of the mean percent of foreign sales and the mean percent of sales outside the firmrsquos primary industry (industrial diversification) from 1984 through 2008 Dotted lines above and below the primary lines represent 95 confidence intervals To compile the plot we include the full population of firms in Compustat
35
Table 1 Analysis of accounting data for firms reporting under multiple accounting standards This table provides a comparison of sales net income and assets for firms that changed accounting standards to or from US GAAP Comparisons are provided for the 66 firms that changed to or from US GAAP and for which we could obtain accounting data electronically and in English under both standards for the same fiscal year (eg the firm presented the accounting numbers originally under one set of standards and then restated the prior year numbers in the subsequent year under the new set of standards) Panel A shows the distribution of firms by year Panel B shows the other accounting standards to which US GAAP is compared Panel C shows the mean and median percent that the other accounting standard differs from US GAAP for the same fiscal year and accounting number Two samples are shown in Panel C The first is the full sample of firms with a change in accounting standards to or from US GAAP for which data was available The second is the subset of these firms that occurred during the sample window used in this study (ie excluding changes post-2008) The percent difference in sales is compared to the percent difference in net income and assets Tests of means are based on matched-pair t-tests Tests of medians are based on Wilcoxon matched-pair signed-rank tests Panel A Accounting standard changes by year
2003
4 6
2004
25 38
2005
8 12
2006
6 9
2007
7 11
2008
5 8
2009
5 8
2010
5 8
2011
1 2
66
Panel B Accounting standards compared to US GAAP
IFRS
47 71
Canadian GAAP
12 18
Israeli GAAP
2 3
Dutch GAAP
1 2
Indian GAAP
1 2
Japanese GAAP
1 2
Norwegian GAAP
1 2
Swiss GAAP
1 2
66
36
(Table 1 continued) Panel C Tests of differences between US GAAP and other accounting standards
Percent different from US GAAP
Comparison to sales (p-values)
N Mean Median
Mean Median
Full Sample (2001-2011)
Sales
66 236 000
Net Income
66 10653 647
005 lt001
Assets
66 1162 126
lt001 lt001
Study Sample (2001-2008)
Sales
55 257 000
Net Income
55 10303 589
011 lt001
Assets
55 1047 099
003 lt001
37
Table 2 Descriptive statistics Panel A reports the mean and medians of Excess Value for the multinational firms and single-segment benchmark firms as well as univariate tests across the subsamples Panel B reports summary statistics for the independent variables for the multinational firms The p-values of the tests of differences in means are based on t-tests The p-values of tests of differences in the medians are for a Wilcoxon rank-sum test All variables are defined in Appendix A Panel A Excess Value by sample Multinational All Benchmark Benchmark Subgroups Sample Firms US Foreign
Value Value Test of Diff (p-value) Value
Test of Diff (p-value) Value
Test of Diff (p-value)
Mean 0067 0006 lt001 -0028 lt001 0014 lt001 Median 0076 0000 lt001 -0025 lt001 0000 lt001 N 4950 33074 5998 27076 Panel B Distribution of independent variables for the multinational firms N Mean 25th 50th 75th StDev Foreign Sales 4950 0238 0074 0190 0363 0197 Industry Other 4950 0212 0000 0166 0414 0215 Log Size 4950 6955 5823 6989 8140 1687 Debt Firm Value 4950 0136 0009 0097 0212 0145 CAPX Sales 4950 0054 0020 0033 0054 0101 EBIT Sales 4950 0065 0031 0077 0121 0138 RampD Sales 4950 0049 0000 0013 0059 0083 Ad Sales 4950 0008 0000 0000 0005 0018
38
Table 3 BEA and Worldscope samples
This table reports details on affiliate sales for significant countries pooled from 1998-2008 Significant countries are defined as those in which the operations of US MNCs represent at least 02 of total firm sales (per the BEA) For significant countries the table compares their representation in terms of MNC affiliate sales in the BEA data to the distribution of firm observations in Worldscope US Foreign Affiliates (BEA) Foreign Firms (Worldscope)
Country
N (1)
Sales (millions)
(2)
Total Sales (3)
N (4)
Sales (million)
(5)
Total Sales (6)
Australia 1344 73305 056 859 255865 210 Belgium 894 52755 040 169 26136 021 Brazil 959 60245 046 1143 683167 560 Canada 2730 536767 407 1181 526750 432 China 922 75907 057 822 276541 227 France 1852 183942 139 540 105447 086 Germany 2182 275577 209 480 103888 085 Hong Kong 867 48100 036 157 34094 028 Ireland 478 36754 028 34 3467 003 Italy 1178 82116 062 111 47948 039 Japan 1494 148032 112 2281 1119223 917 Korea South 659 34500 026 3530 2752791 2255 Malaysia 386 26027 020 837 168683 138 Mexico 1405 113623 086 362 237173 194 Netherlands 1149 85105 064 47 22631 019 Singapore 1098 94917 072 139 32913 027 Spain 871 63094 048 129 37419 031 Sweden 712 32682 025 235 23090 019 Switzerland 726 60288 046 74 20242 017 Taiwan 592 30313 023 2949 787049 645 United Kingdom 3054 423934 321 1997 882423 723 United States (domicile) 4950 10460880 7923 na na na Other - Covered by WorldScope 6893 199394 151 9000 4057894 33 Other -Not Covered by WorldScope 713 5352 004 - - - Total 38108 13203609 1000 27076 12204834 100
39
Table 4 OLS regressions of firm excess values on the percent of foreign sales This table reports the results of OLS regressions of firm excess value on the percent of foreign sales (Foreign Sales) the percent of sales outside a firmrsquos primary industry (Industry Other) and control variables All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed) Dep Variable = Excess Value Coefficient t-stat Coefficient t-stat Coefficient t-stat Coefficient t-stat (1) (2) (3) (4) Foreign Sales 0349 419 0209 293 0189 266 0278 210 Industry Other -0134 -186 -0047 -072 -0053 -081 -0326 -325 Log Size 0066 634 0065 624 -0044 -096 Debt Firm Value -0196 -213 -0241 -260 -0685 -588 CAPX Sales 0901 425 0893 435 0488 227 EBIT Sales 1154 542 1170 547 0408 189 RampD Sales 2908 1242 2932 1237 1544 379 Ad Sales 1869 263 2003 280 -1696 -119 Year indicators No No Yes Yes Firm indicators No No No Yes R2 0017 0212 0226 0768 Observations 4950 4950 4950 4950
40
Table 5 Dynamic panel GMM model regressing firm excess values on the percent of foreign sales This table reports the result from a regression of the excess value on the percent of foreign sales (Foreign Sales) the percent of non-primary industry sales (Industry Other) and control variables using a dynamic panel GMM estimator All control variables are considered to be endogenous with the exception of the year indicator variables AR(1) and AR(2) are tests for first-order and second-order serial correlation in the first differenced residuals with the null hypothesis of no serial correlation The null hypothesis of the Hansen test of overidentification is that all instruments are valid The null hypothesis of the difference-in-Hansen test of exogeneity is that the instruments used for the equation in levels are exogenous All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed) Dep Variable = Excess Value Coef t-stat Excess Value (one lag) -0018 -082 Excess Value (two lags) -0048 -272 Foreign Sales 0371 270 Industry Other -0163 -171 Log Size 0010 123 Debt FirmValue -1135 -820 CAPX Sales 0590 342 EBIT Sales -0047 -300 RampD Sales 1376 531 Ad Sales -0751 -040 AR(1) test (p-value) 000 AR(2) test (p-value) 017 Hansen test of over-identification (p-value) 030 Difference-in-Hansen test of exogeneity (p-value) 093 Year indicators Yes Number of firms 1016 Number of observations 4090
41
Table 6 OLS regression of firm excess values on the percent of foreign sales controlling for a control premium This table reports the result of the OLS regression of firm excess value including firm and year fixed effects with an additional control variable to proxy for a country-level ownership control premium (Control Premium) All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed) Dep Variable = Excess Value Coef t -stat Foreign Sales 0303 224 Industry Other -0327 -325 Log Size -0045 -100 Debt FirmValue -0683 -585 CAPX Sales 0490 226 EBIT Sales 0409 189 RampD Sales 1545 379 Ad Sales -1689 -119 Control Premium -1004 -062 Year indicators Yes Firm indicators Yes R2 0768 Observations 4950
42
Table 7 OLS regression of firm excess values on the percent of foreign sales controlling for the cost of capital This table contains the results of an OLS regression of firm excess value including firm and year fixed effects with an additional control variable to proxy for a country-level cost of capital (Cost of Capital) All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed) Dep Variable = Excess Value Coef t -stat Foreign Sales 0255 178 Industry Other -0326 -324 Log Size -0043 -094 Debt FirmValue -0687 -589 CAPX Sales 0485 225 EBIT Sales 0406 188 RampD Sales 1539 379 Ad Sales -1703 -120 Cost of Capital 0004 039 Year indicators Yes Firm indicators Yes R2 0768 Observations 4950
43
Table 8 OLS regression of firm excess values on the percent of foreign sales controlling for aspects of corporate governance This table reports the results of two OLS regressions In the first column are the results of the firm excess value regression including firm and year fixed effect with five proxies for corporate governance included ndash the percent of shares owned by the CEO and the percent squared (CEO Ownership and CEO Ownership squared) the percent owned by institutional investors (Institution Ownership) whether there is a powerful CEO (Powerful CEO) and the Gompers Ishii and Metrick (2003) governance index (Governance Index) In the second column the regression includes the same sample and variables with the exception of the governance proxies which are excluded All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed)
Governance
No Governance (same sample)
Dep Variable = Excess Value Coef t-stat Coef t -stat Foreign Sales 0259 159 0251 154 Industry Other -0099 -080 -0099 -079 Log Size -0105 -185 -0102 -180 Debt FirmValue -0813 -478 -0817 -482 CAPX Sales 0440 252 0438 249 EBIT Sales 0457 247 0457 246 RampD Sales 1717 284 1716 286 Ad Sales -2210 -143 -2190 -143 CEO Ownership 0010 007
CEO Ownership squared -0029 -017 Institution Ownership 0047 067 Powerful CEO -0003 -014 Governance Index 0009 103
Year indicators Yes Yes Firm indicators Yes Yes R2 0811 0810 Observations 2550 2550
- Cover for paperpdf
- crrz_20130603_SMUpdf
-
18
the majority of their sales Country coverage in Worldscope is not available for countries
representing 004 percent of total firm sales (per BEA)
4 Empirical results
41 Independent variables
Recall that our objective is to examine the overall relation between excess firm value and
multinational operations Our proxy for the extent of multinational operations is the percentage
of total firm sales made by foreign operations (Foreign Sales) By this measure the extent of
multinational activity in our sample of MNCs is non-trivial ndash Table 2 Panel B shows that
approximately 24 percent of sales are generated by foreign operations on average Our first
analysis estimates ordinary least square regressions of Excess Value on Foreign Sales and
control variables (discussed below)
We include variables in our regression to control for other potential determinants of excess
value These are the percentage of sales made by the firm outside its primary industry (Industry
Other) to control for any relation between industrial diversification and excess value as in Berger
and Ofek (1995) For this purpose we obtain industry sales using Compustat Segment data19 A
firmrsquos primary industry is the industry in which the firm generates the majority of its sales and
we determine industry sales at the business segment level We set Industry Other equal to zero
for firms that operate in a single business segment
Consistent with prior research we also include controls for firm size (Log Size) the ratio of
long term debt to total market value (Debt Firm Value) the ratio of capital expenditures to total
sales (CAPX Sales) the ratio of earnings before interest and taxes to total sales (EBIT Sales)
the ratio of research and development expenses to total sales (RampD Sales) and the ratio of 19 Our results are unaffected by using BEA data to measure Industry Other
19
advertising expenses to total sales (Ad Sales) Table 2 Panel B provides the distribution of the
control variables All independent variables are Winsorized at 1 In our OLS regressions we
use heteroskedasticity and autocorrelation consistent robust standard errors with clustering at the
firm level
42 Primary regression analysis
In this section we examine the relation between firm excess value and the amount of foreign
activity at US MNCs Table 4 contains the results of regressions of excess value on Foreign
Sales for multiple specifications In column (1) Excess Value is regressed on both the percent of
foreign sales (Foreign Sales) and the percent of sales outside the firmrsquos primary industry
(Industry Other) We add the control variables to the regression in column (2) and year
indicators in column (3) In all three specifications the coefficient on the percent of foreign sales
remains significantly positive (with a p-value lt 001) For a sense of economic magnitude in
column (3) a one standard deviation (197) increase in the percent of foreign sales (Foreign
Sales) is associated with a 41 (equal to (e0349-1) times 0197) increase in firm excess value The
effect is larger for the other two columns ndash 82 increase in firm excess value in column (1) and
46 in column (2)20
Our finding of a premium reconciles with the international trade literature which finds that
firms engaging in international trade are more productive than those that do not (see Helpman
2006 and Syverson 2011 for reviews) Due to data limitations it is difficult to accurately
measure TFP at the firm level especially for firms which operate in multiple industries andor
20 Using the alternative strategy that employs US domiciled single-segment firms as the benchmark for the imputed value of the foreign operations Denis et al (2002) find that greater foreign activity results in a discount to firm value Using the Denis et al (2002) method to calculate excess value in our sample we obtain a significantly negative regression coefficient on Foreign Sales using the models in Table 4 columns (2) and (3) (the coefficient is not significant using the model in column (1) results untabulated)
20
countries As an alternative method to evaluate whether our finding of a premium is simply the
result of more productive firms also having more multinational operations we investigate
whether the premium is simply an artifact of innate characteristics of firms that choose to invest
abroad For example Helpman et al (2004) present a simple model with heterogeneous
productivity endowments The firms that receive the highest productivity endowments are the
ones capable of paying the fixed costs to establish a foreign subsidiary
To the extent that productivity advantages create excess value one would expect the type of
firms that choose to invest more abroad would be valued at a premium even in the absence of
their foreign investments To control for time invariant firm characteristics (eg innate
productivity advantages) and hence a potential source of endogeneity we add firm-level fixed
effects in column (4) of Table 4 We continue to find that the extent of foreign operations
(Foreign Sales) is positively associated with Excess Value Across all specifications the sign
on all significant control variables is consistent with prior research (eg Berger and Ofek 1995
and Denis et al 2002)
The finding of a premium makes a significant contribution to the relatively sparse literature
on the value effects of multinational activity Our measure of imputed value expands upon the
methods used by Berger and Ofek (1995) and Denis et al (2002) but allows us to answer a
different question than Denis et al (2002) Holding constant the extent of industrial
diversification we ask whether MNCs are more valuable than a portfolio of benchmark firms of
a similar geographic footprint In contrast Denis et al (2002) ask whether holding constant the
extent of industrial diversification MNCs are more valuable than a portfolio of domestic (US)
benchmark firms We believe that asking whether an MNCrsquos foreign (domestic) operations are
21
more valuable than those of a foreign (domestic) firm is a more appropriate way to assess the
value implications of a multinational network
5 Robustness
51 Endogeneity related to dynamic relations
Prior research argues that the relation between industrial diversification and excess firm
value could be endogenous through dynamic relationships based on observing past outcomes
Managers of firms likely choose to enter or exit industries or geographic regions based upon
their previous performance For example Campa and Kedia (2002) find that firms are more
likely to enter new industries when prospects in their current lines of business are deteriorating
Similar dynamics could also drive a relation between excess value and FDI We assess the
robustness of the previous results by estimating a dynamic panel data model which allows us to
control for dynamic endogeneity in addition to firm level unobserved heterogeneity We employ
the GMM estimator for dynamic panel models developed by Arellano and Bond (1991) Arellano
and Bover (1995) and Blundell and Bond (1998) Our results from this analysis confirm our
previous findings that firms with a larger degree of foreign operations have positive excess
values
Dynamic panel data models are generalizations of the traditional fixed effects model where
lags of the dependent variable are added to the right hand side of the equation These additional
lags control for the impact a firmrsquos past performance has on its current performance through the
channels discussed above The GMM estimator for dynamic panel models developed by
Arellano and Bond (1991) starts by differencing the data at the firm level in neighboring time
periods to eliminate any firm specific time invariant unobserved heterogeneity that may be
22
present Eliminating the firm specific fixed effect by differencing the data with lags of the
dependent variable on the right hand side introduces correlation between the new error term and
the differenced explanatory variables This problem is solved by applying the standard GMM
estimator using lags of both the original explanatory variables and excess value as instruments
We note that the dynamic panel data framework allows us to treat both international and
industrial diversification (as well as the other firm-specific control variables) as endogenous
Following Hoechle et al (2012) we also allow contemporaneous values of these variables to be
treated as endogenous
Lagged values of the explanatory variables are valid instruments in a dynamic panel data
model when the model is dynamically complete (eg Wooldridge 2010) A dynamic panel data
model is dynamically complete conditional on the unobserved effect if enough lags of both the
explanatory and dependent variable are included to make the resulting error term uncorrelated
with all the right hand side variables Under this assumption any lags for the explanatory
variables beyond this can be used within the GMM estimator as instruments Like Hoechle et al
(2012) we add two years of lags of a firmrsquos excess value to the right hand side assuming that
this is enough lags to control for dynamic feedback effects We then use past values of all
variables beyond the second lag up to a total of seven years to act as instruments
The results of the dynamic panel data estimator are reported in Table 5 These results confirm
the previous finding of a positive relation between the degree of international operations and
excess value We note that the size and significance level of the coefficient on Foreign Sales
diversification are robust to changes in the number of lags used as instruments The Arellano and
Bond (1991) estimator includes testing diagnostics for correct specification of the model If the
model is correctly specified differences in the residuals from the levels equation should be
23
serially uncorrelated at lag one but correlated at lag two The p-values for tests of serial
correlation of the residuals at lags 1 and 2 are reported in Table 5 and these indicate that the
model is not rejected We also report in Table 5 the Hansen test for overidentification which is a
test of the null hypothesis that the instruments are valid The p-value for this test is 030
indicating that we fail to reject this null hypothesis Finally we report another test for exogeneity
known as the difference-in-Hansen test which tests the null hypothesis that the instruments in
the original equation are valid We fail to reject this null hypothesis as well
52 Identifying value effects of multinational operations via cross-border acquisitions
Another alternative to investigating a causal link between foreign direct investment and firm
value is an event study of foreign acquisitions21 Acquired foreign targets represent only a small
fraction of MNCsrsquo worldwide sales in the year of the acquisition ndash less than 1 percent in our
sample Additionally cross-border acquisitions are not the primary source of international
expansion for the average firm We find that foreign acquisitions account for between 18 percent
and 55 percent of the year-over-year change in foreign sales with the remainder of the growth
coming from either newly established entities (ie greenfield investments) or growth in existing
operations22 These results suggest that acquisitions are not a material source of growth for the
21 Doukas and Travlos (1988) find positive returns when the acquirer is not operating in the target firmrsquos country and insignificant returns when the acquirer is operating in the target firmrsquos country or expanding internationally for the first time Morck and Yeung (1991) find marginal evidence of positive returns for firms with intangible assets Finally Doukas (1995) finds positive returns only for firms with average Tobinrsquos q ratios greater than one More recent work also using an event study methodology provides mixed evidence on valuation effects of global diversification through acquisitions Dos Santos Errunza and Miller (2008) find insignificant returns for firms with already established foreign operations firms establishing initial foreign operations and firms acquiring targets in related industries Doukas and Kan (2006) and Moeller and Schlingemann (2005) find reductions to shareholder value while Francis Hasan and Sun (2008) find positive returns for acquirers of targets in segmented financial markets 22 New foreign affiliates to an MNC group each year report on the BEA survey whether they entered the group as an acquisition or a newly established entity (ie greenfield investment) Affiliates under the lowest reporting threshold do not report this information resulting in our inability to precisely identify the source of 33 percent of the increase in firm sales as either acquisition or greenfield
24
typical US MNC suggesting that an event study would lack power and generalizability For
these reasons we do not implement an event study
53 Control premium
A control premium or private benefits of control occurs when ldquosome value whatever the
source is not shared among all the shareholders in proportion of the shares owned but it is
enjoyed exclusively by the party in controlrdquo (Dyck and Zingales 2004 p 541) Using stock
prices of traded shares held by minority shareholders in determining the market value to sales
ratios of our benchmark firms could underestimate the true value of our benchmark firms if those
firms face large control premiums This in turn could induce an excess value premium because
the imputed value of these country-industry segments would be artificially low The existence of
such benefits is more prevalent in some countries than others and could affect the value of an
MNCrsquos operations in such domains We use the country-level control premiums from Dyck and
Zingales (2004) and using each firmrsquos country sales as a percent of total firm sales we obtain a
weighted average control premium that each MNC faces (Control Premium) For countries not
included in the Dyck and Zingales (2004) analysis we set the control premium equal to zero23 In
Table 6 we show that Foreign Sales remains positive and highly significant (p-value lt 001)
when we include the proxy for control premium As a result it does not appear that the excess
value premium is driven by MNCs operating in countries where majority shareholders are able to
extract large control premiums
23 Results are robust to replacing missing values with the mean control premium of the respective GDP quartile
25
54 Cost of capital
As discussed in Section 2 when countries have shallow capital markets a multinational
network may provide the benefit of a lower cost of capital for investments through an internal
capital market as well as through better access to the US capital market Prior research
establishes that there is substantial variation in the cost of capital across countries (Erb Harvey
and Viskanta 1996 and Hail and Leuz 2009) To determine whether the documented premium
is due to cost of capital advantages we include a control variable to capture the difference
between the MNCrsquos cost of capital and the cost of capital faced by its competitors in the global
markets in which it operates
We compute our Cost of Capital proxy in two steps First we obtain the country-year credit
rating published by Institutional Investor24 The rating ranges from 0 to 100 where higher values
imply that a country has a higher default risk Country-level credit risk is a reasonable predictor
of expected equity market returns and volatility (Erb et al 1996) and exhibits a highly
significant correlation with international accounting-based estimates of imputed cost of capital
(Hail and Leuz 2009) Second using each firmrsquos country sales as a percent of total firm sales
we obtain a weighted average credit risk rating across the countries in which each firm operates
A higher value implies an MNC operates in countries with higher cost of capital In Table 7 we
do not find a result consistent with MNCs gaining an advantage from their access to low cost
capitalndash Cost of Capital is not significantly associated with Excess Value25
24 We thank Cam Harvey for providing these data 25 Results are consistent using cost of capital as defined in Erb et al 1996
26
55 Corporate governance
In light of the recent finding that corporate governance mitigates the diversification discount
we examine whether the premium to multinational activity is sensitive to controls for corporate
governance Hoechle et al (2012) find that the magnitude of the industrial diversification
discount is decreased and in several specifications no longer significant in the presence of
governance proxies In the first column of Table 8 we include the five governance proxies that
are consistently significantly associated with excess value in Hoechle et al (2012) ndash the percent
of CEO ownership (CEO Ownership and CEO Ownership squared) the percent of institutional
ownership (Institution Ownership) whether the firm has a powerful or influential CEO
(Powerful CEO) and the governance index from Gompers Ishii and Metrick (2003)
(Governance Index)
As documented in Table 8 including these control variables reduces our sample size by
almost 50 (to 2550 observations) and tends to exclude the smaller firms from the analysis
None of the governance proxies are significant Relative to column (4) of Table 4 the coefficient
on Foreign Sales is similar in magnitude but is no longer statistically significant at
conventional levels To investigate whether the decreased significance is due to the smaller
sample or the inclusion of the governance variables we re-estimate the regression for the same
2550 observations after removing the governance controls The coefficient and t-statistic are
virtually identical suggesting that the loss of significance is due to the reduced sample and not
the governance controls We do not find evidence to suggest that variation in corporate
governance practices drives variation in excess value for US MNCs
27
6 Conclusion
Using a new method to quantify the valuation effects of foreign direct investment by US
domiciled firms we find robust evidence of a premium in excess value associated with the
degree of multinational operations Our method builds on that used in previous studies
examining firm excess value (eg Berger and Ofek 1995 and Denis et al 2002) by
incorporating the location in addition to the industry of each corporate affiliate This allows
important factors such as growth and cost of capital to vary across countries as well as industries
Examining a sample of multinational firms we find that a one-standard deviation (197)
increase in foreign operations is associated with an increase of between 41 and 82 in excess
value minus both a statistically and economically significant finding
This result complements the finding of the international trade literature that more productive
firms are more likely to operate internationally However the premium remains after including
firm fixed effects to control for persistent firm traits (eg productivity)
The finding of a premium stands up to a succession of robustness tests designed to evaluate
whether the result arises from alternative methods or explanations First we estimate a dynamic
panel data model using the generalized method of moments (GMM) estimator to simultaneously
account for the potential endogeneity of FDI industrial diversification and firm value Second
we include a control variable to proxy for whether the firm operates in countries with larger
control premiums which could result in understated multiples and mechanically induce a
premium Third we include a control variable to measure the cost of capital across the countries
in which an MNC operates to evaluate whether a relatively low cost of capital borne by
multinational firms is a meaningful source of the premium Fourth we control for several
corporate governance proxies which have been found to be correlated with excess values (and
28
industrial diversification) We fail to find evidence supporting any of these alternative
explanations ndash the premium in excess value arising at firms with greater multinational operations
remains significant
Overall our new method of estimating excess values for multinational firms provides strong
evidence of a statistically and economically significant premium associated with increased
international operations The premium is robust to a host of specifications and controls designed
to evaluate alternative explanations We leave to future research the questions of whether the
premium to US multinational firms applies more broadly to other countries of domicile and
what factors influence the premium
29
References
Ammann M Hoeschel D and M Schmid 2012 Is there really no conglomerate discount
Journal of Business Finance and Accounting 39(12) 264-288 Arellano M and Bond S R 1991 Some tests of specification for panel data Monte Carlo
evidence and an application to employment equations Review of Economic Studies 58 (2) 277-298
Arellano M and Bover O 1995 Another look at instrumental variables estimation of error
components models Journal of Econometrics 68 (1) 29-61 Bens D A Berger P G and S J Monahan 2011 Discretionary disclosure in financial
reporting An examination comparing internal firm data to externally reported segment data The Accounting Review 86(2) 417-449
Berger P G and E Ofek 1995 Diversificationrsquos effect on firm value Journal of Financial
Economics 37 39-65 Blundell R and Bond S R 1998 Initial conditions and moment restrictions in dynamic panel
data models Journal of Econometrics 87(1) 115-144 Campa J M and S Kedia 2002 Explaining the diversification discount The Journal of
Finance 57 (4) 1731-1762 Denis D J Denis D K and K Yost 2002 Global diversification industrial diversification
and firm value The Journal of Finance 57 (5) 1951-1979 Desai M A Foley C F and J R Hines 2004 A multinational perspective on capital structure
choice and internal capital markets The Journal of Finance 59 (6) 2451-2487 Dos Santos MB V Errunza and D Miller 2008 Does corporate international diversification
destroy value Evidence from cross-border mergers and acquisitions Journal of Banking and Finance 32(12) 2716-2724
Doukas JA 1995 Overinvestment Tobinrsquos q and gains from foreign acquisitions Journal of
Banking and Finance 19 1285-1303 Doukas J and N G Travlos 1988 The effect of corporate multinationalism on shareholdersrsquorsquo
wealth evidence from international acquisitions Journal of Finance 43(5) 1161-1175 Doukas JA and O B Kan 2006 Does global diversification destroy firm value Journal of
International Business Studies 37(3) 352-371
30
Dunning J and A Rugman 1985 The influence of Hymerrsquos dissertation on the theory of foreign direct investment The American Economic Review 75(2) 228-232
Dyck A and L Zingales 2004 Private benefits of control An international comparison The
Journal of Finance 59 (2) 537-600 Erb C Harvey C and T Viskanta 1996 Expected returns and volatility in 135 countries The
Journal of Portfolio Management 46-58 Foley C F Desai M A and K J Forbes 2008 Financial constraints and growth
Multinational and local firm responses to currency depreciations Review of Financial Studies 21 (6) 2857-2888
Francis B I Hasan and X Sun 2008 Financial market integration and the value of global
diversification Evidence for US acquirers in cross-border mergers and acquisitions Journal of Banking and Finance 321522-1540
Gande A Schenzler C and L Senbet 2009 Valuation effects of global diversification
Journal of International Business Studies 1515-1532 Gompers P Ishii J and A Metrick 2003 Corporate governance and equity prices Quarterly
Journal of Economics 118(1) 107-156 Hail L and C Leuz 2009 Cost of capital effects and changes in growth expectations around
US cross-listings Journal of Financial Economics 93 428-454 Hanson G Mataloni R and M Slaughter 2005 Vertical production networks in multinational
firms Review of Economics and Statistics 87(4) 1-15 Helpman E 2006 Trade FDI and the Organization of Firms Journal of Economic Literature
44(3) 289-630 Helpman E Melitz M J and S R Yeaple 2004 Export versus FDI with Heterogeneous Firms
American Economic Review 94(1) 300-316 Hoechle D Schmid M Walter W and D Yermack 2012 How much of the diversification
discount can be explained by poor governance Journal of Financial Economics 103 41-60 Hope O-K and W B Thomas 2008 Managerial empire building and firm disclosure Journal
of Accounting Research 46 (June) 591-626 Huizinga H P and J Voget 2009 International taxation and the direction and volume of cross-
border MampAs Journal of Finance 64 (3) 1217-1249 Jensen M 1986 Agency cost of free cash flow corporate finance and takeovers American
Economic Review Papers and Proceedings 76 323-329
31
Laeven L and R Levine 2007 Is there a diversification discount in financial conglomerates
Journal of Financial Economics 85 331-367 Lang L and R Stulz 1994 Tobinrsquos Q corporate diversification and firm performance Journal
of Political Economy 102 1248-1280 Mataloni R 2003 US multinational companies Operations in 2001 Survey of Current
Business 85-105 Matvos G and A Seru 2011 The dynamic tradeoff between corporate socialism and financial
market dislocation Estimates from internal capital markets Working paper University of Chicago Booth School of Business
Moeller SB and F P Schlingemann 2005 Global diversification and bidder gains A
comparison between cross-border and domestic acquisitions Journal of Banking and Finance 29 533-564
Morck R and B Yeung 1991 Why investors value multinationality Journal of Business 64
165-186
Morck R and B Yeung 2001 Why firms diversify Internatization vs agency behavior In Intangibles B Lev (ed) Oxford University Press pp 269-302
Myers S C and N S Majluf 1984 Corporate financing and investment decisions when firms have information that investors do not have Journal of Financial Economics 13 187-221
Schmid M and I Walter 2009 Do financial conglomerates create or destroy economic value
Journal of Financial Intermediation 18 193-216 Syverson C 2011 What determines productivity Journal of Economic Literature 49(2) 326-
365 Villalonga B 2004a Diversification discount or premium New evidence from the Business
Information Tracking Series Journal of Finance 59 479-506 Villalonga B 2004b Does diversification cause the diversification discount Financial
Management 33 5-27 Wooldridge JM 2010 Econometric Analysis of Cross Section and Panel Data (Second
Edition) Cambridge MA MIT Press Yamin M 1991 A reassessment of Hymerrsquos contribution to the theory of the transnational
corporation C Pitelis and R Sugden (eds) The Nature of the Transnational Firm London Routledge
32
Appendix A Variable Definitions
We obtain information regarding firmsrsquo operations by country and industry from the Bureau of Economic Analysis (BEA) unless otherwise noted Acronyms used below reflect Compustat mnemonics and thus indicate when we obtain data from the Compustat North America Fundamentals Annual database All independent variables are winsorized at 1
Dependent variable
Excess Value = The log of the ratio of actual firm value to imputed firm value Actual firm value is equal to market capitalization (MVE) + book value of total assets (AT) ndash book value of shareholdersrsquo equity (SEQ) Imputed firm value is calculated as the sum of the imputed values of a firmrsquos operations in each country-industry The imputed value for each country-industry is equal to sales generated in that country-industry times a multiple equal to the ratio of a benchmark firmrsquos actual value to total sales A benchmark firm is defined as a median single-segment firm operating in the same country-industry All multiples require at least five firms listed in the respective country-industry on Compustat (domestic) or Worldscope (foreign) with less than 10 of sales income and assets outside the country of domicile Similar to Berger and Ofek (1995) we eliminate extreme values where the ratio of actual to imputed firm value exceeds 400 or is less than 025
Independent variables
Foreign Sales = Sales made by the foreign operations of US MNCs as a percent of total firm sales
Industry Other = Sales outside a firmrsquos primary industry as a percent of total sales (SALE) Equal to zero for firms operating in a single business segment
Log Size = Log of total sales (SALE)
Debt Firm Value = Ratio of long-term debt (DLTT) to total market value (market capitalization (MVE) + book value of total assets (AT) ndash book value of shareholdersrsquo equity (SEQ))
CAPX Sales = Ratio of capital expenditures (CAPX) to total sales (SALE)
EBIT Sales = Ratio of earnings before interest and taxes (EBIT) to total sales (SALE)
RampD Sales = Ratio of research and development expenses (XRD) to total sales (SALE)
Ad Sales = Ratio of advertising expenses (XAD) to total sales (SALE)
Control Premium = A weighted-average (based on the sales in a given country as a percent of total firm sales) of the country-level control premium from Dyck and Zingales (2004)
Cost of Capital = A weighted-average (based on the sales in a given country as a percent of total firm sales) of the country-level credit rating from Institutional Investor (httpwwwinstitutionalinvestorcom) The country-level rating is available for 160 countries and ranges from 0 to 100 Higher values imply greater credit risk
33
CEO Ownership = Percent of shares owned by the CEO from Execucomp
Institution Ownership = Percent of shares owned by institutional investors from Thomson Reuterrsquos Institutional Holdings database
Powerful CEO = Indicator variable equal to one if the CEO is the only insider on the board of directors and also serves as the chairman and president based on data from RiskMetrics Directors database Equal to zero otherwise
Governance Index = Count measure of takeover defenses used in Gompers Ishii and Metrick (2003) from RiskMetrics Governance database As the governance data are not available for every year we follow Gompers Ishii and Metrick (2003) and assume that the firmsrsquo governance attributes are unchanged until publication of the subsequent series
34
Figure 1 Trends in multinational activity and industrial diversification from 1984-2008 This figure presents a plot of the mean percent of foreign sales and the mean percent of sales outside the firmrsquos primary industry (industrial diversification) from 1984 through 2008 Dotted lines above and below the primary lines represent 95 confidence intervals To compile the plot we include the full population of firms in Compustat
35
Table 1 Analysis of accounting data for firms reporting under multiple accounting standards This table provides a comparison of sales net income and assets for firms that changed accounting standards to or from US GAAP Comparisons are provided for the 66 firms that changed to or from US GAAP and for which we could obtain accounting data electronically and in English under both standards for the same fiscal year (eg the firm presented the accounting numbers originally under one set of standards and then restated the prior year numbers in the subsequent year under the new set of standards) Panel A shows the distribution of firms by year Panel B shows the other accounting standards to which US GAAP is compared Panel C shows the mean and median percent that the other accounting standard differs from US GAAP for the same fiscal year and accounting number Two samples are shown in Panel C The first is the full sample of firms with a change in accounting standards to or from US GAAP for which data was available The second is the subset of these firms that occurred during the sample window used in this study (ie excluding changes post-2008) The percent difference in sales is compared to the percent difference in net income and assets Tests of means are based on matched-pair t-tests Tests of medians are based on Wilcoxon matched-pair signed-rank tests Panel A Accounting standard changes by year
2003
4 6
2004
25 38
2005
8 12
2006
6 9
2007
7 11
2008
5 8
2009
5 8
2010
5 8
2011
1 2
66
Panel B Accounting standards compared to US GAAP
IFRS
47 71
Canadian GAAP
12 18
Israeli GAAP
2 3
Dutch GAAP
1 2
Indian GAAP
1 2
Japanese GAAP
1 2
Norwegian GAAP
1 2
Swiss GAAP
1 2
66
36
(Table 1 continued) Panel C Tests of differences between US GAAP and other accounting standards
Percent different from US GAAP
Comparison to sales (p-values)
N Mean Median
Mean Median
Full Sample (2001-2011)
Sales
66 236 000
Net Income
66 10653 647
005 lt001
Assets
66 1162 126
lt001 lt001
Study Sample (2001-2008)
Sales
55 257 000
Net Income
55 10303 589
011 lt001
Assets
55 1047 099
003 lt001
37
Table 2 Descriptive statistics Panel A reports the mean and medians of Excess Value for the multinational firms and single-segment benchmark firms as well as univariate tests across the subsamples Panel B reports summary statistics for the independent variables for the multinational firms The p-values of the tests of differences in means are based on t-tests The p-values of tests of differences in the medians are for a Wilcoxon rank-sum test All variables are defined in Appendix A Panel A Excess Value by sample Multinational All Benchmark Benchmark Subgroups Sample Firms US Foreign
Value Value Test of Diff (p-value) Value
Test of Diff (p-value) Value
Test of Diff (p-value)
Mean 0067 0006 lt001 -0028 lt001 0014 lt001 Median 0076 0000 lt001 -0025 lt001 0000 lt001 N 4950 33074 5998 27076 Panel B Distribution of independent variables for the multinational firms N Mean 25th 50th 75th StDev Foreign Sales 4950 0238 0074 0190 0363 0197 Industry Other 4950 0212 0000 0166 0414 0215 Log Size 4950 6955 5823 6989 8140 1687 Debt Firm Value 4950 0136 0009 0097 0212 0145 CAPX Sales 4950 0054 0020 0033 0054 0101 EBIT Sales 4950 0065 0031 0077 0121 0138 RampD Sales 4950 0049 0000 0013 0059 0083 Ad Sales 4950 0008 0000 0000 0005 0018
38
Table 3 BEA and Worldscope samples
This table reports details on affiliate sales for significant countries pooled from 1998-2008 Significant countries are defined as those in which the operations of US MNCs represent at least 02 of total firm sales (per the BEA) For significant countries the table compares their representation in terms of MNC affiliate sales in the BEA data to the distribution of firm observations in Worldscope US Foreign Affiliates (BEA) Foreign Firms (Worldscope)
Country
N (1)
Sales (millions)
(2)
Total Sales (3)
N (4)
Sales (million)
(5)
Total Sales (6)
Australia 1344 73305 056 859 255865 210 Belgium 894 52755 040 169 26136 021 Brazil 959 60245 046 1143 683167 560 Canada 2730 536767 407 1181 526750 432 China 922 75907 057 822 276541 227 France 1852 183942 139 540 105447 086 Germany 2182 275577 209 480 103888 085 Hong Kong 867 48100 036 157 34094 028 Ireland 478 36754 028 34 3467 003 Italy 1178 82116 062 111 47948 039 Japan 1494 148032 112 2281 1119223 917 Korea South 659 34500 026 3530 2752791 2255 Malaysia 386 26027 020 837 168683 138 Mexico 1405 113623 086 362 237173 194 Netherlands 1149 85105 064 47 22631 019 Singapore 1098 94917 072 139 32913 027 Spain 871 63094 048 129 37419 031 Sweden 712 32682 025 235 23090 019 Switzerland 726 60288 046 74 20242 017 Taiwan 592 30313 023 2949 787049 645 United Kingdom 3054 423934 321 1997 882423 723 United States (domicile) 4950 10460880 7923 na na na Other - Covered by WorldScope 6893 199394 151 9000 4057894 33 Other -Not Covered by WorldScope 713 5352 004 - - - Total 38108 13203609 1000 27076 12204834 100
39
Table 4 OLS regressions of firm excess values on the percent of foreign sales This table reports the results of OLS regressions of firm excess value on the percent of foreign sales (Foreign Sales) the percent of sales outside a firmrsquos primary industry (Industry Other) and control variables All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed) Dep Variable = Excess Value Coefficient t-stat Coefficient t-stat Coefficient t-stat Coefficient t-stat (1) (2) (3) (4) Foreign Sales 0349 419 0209 293 0189 266 0278 210 Industry Other -0134 -186 -0047 -072 -0053 -081 -0326 -325 Log Size 0066 634 0065 624 -0044 -096 Debt Firm Value -0196 -213 -0241 -260 -0685 -588 CAPX Sales 0901 425 0893 435 0488 227 EBIT Sales 1154 542 1170 547 0408 189 RampD Sales 2908 1242 2932 1237 1544 379 Ad Sales 1869 263 2003 280 -1696 -119 Year indicators No No Yes Yes Firm indicators No No No Yes R2 0017 0212 0226 0768 Observations 4950 4950 4950 4950
40
Table 5 Dynamic panel GMM model regressing firm excess values on the percent of foreign sales This table reports the result from a regression of the excess value on the percent of foreign sales (Foreign Sales) the percent of non-primary industry sales (Industry Other) and control variables using a dynamic panel GMM estimator All control variables are considered to be endogenous with the exception of the year indicator variables AR(1) and AR(2) are tests for first-order and second-order serial correlation in the first differenced residuals with the null hypothesis of no serial correlation The null hypothesis of the Hansen test of overidentification is that all instruments are valid The null hypothesis of the difference-in-Hansen test of exogeneity is that the instruments used for the equation in levels are exogenous All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed) Dep Variable = Excess Value Coef t-stat Excess Value (one lag) -0018 -082 Excess Value (two lags) -0048 -272 Foreign Sales 0371 270 Industry Other -0163 -171 Log Size 0010 123 Debt FirmValue -1135 -820 CAPX Sales 0590 342 EBIT Sales -0047 -300 RampD Sales 1376 531 Ad Sales -0751 -040 AR(1) test (p-value) 000 AR(2) test (p-value) 017 Hansen test of over-identification (p-value) 030 Difference-in-Hansen test of exogeneity (p-value) 093 Year indicators Yes Number of firms 1016 Number of observations 4090
41
Table 6 OLS regression of firm excess values on the percent of foreign sales controlling for a control premium This table reports the result of the OLS regression of firm excess value including firm and year fixed effects with an additional control variable to proxy for a country-level ownership control premium (Control Premium) All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed) Dep Variable = Excess Value Coef t -stat Foreign Sales 0303 224 Industry Other -0327 -325 Log Size -0045 -100 Debt FirmValue -0683 -585 CAPX Sales 0490 226 EBIT Sales 0409 189 RampD Sales 1545 379 Ad Sales -1689 -119 Control Premium -1004 -062 Year indicators Yes Firm indicators Yes R2 0768 Observations 4950
42
Table 7 OLS regression of firm excess values on the percent of foreign sales controlling for the cost of capital This table contains the results of an OLS regression of firm excess value including firm and year fixed effects with an additional control variable to proxy for a country-level cost of capital (Cost of Capital) All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed) Dep Variable = Excess Value Coef t -stat Foreign Sales 0255 178 Industry Other -0326 -324 Log Size -0043 -094 Debt FirmValue -0687 -589 CAPX Sales 0485 225 EBIT Sales 0406 188 RampD Sales 1539 379 Ad Sales -1703 -120 Cost of Capital 0004 039 Year indicators Yes Firm indicators Yes R2 0768 Observations 4950
43
Table 8 OLS regression of firm excess values on the percent of foreign sales controlling for aspects of corporate governance This table reports the results of two OLS regressions In the first column are the results of the firm excess value regression including firm and year fixed effect with five proxies for corporate governance included ndash the percent of shares owned by the CEO and the percent squared (CEO Ownership and CEO Ownership squared) the percent owned by institutional investors (Institution Ownership) whether there is a powerful CEO (Powerful CEO) and the Gompers Ishii and Metrick (2003) governance index (Governance Index) In the second column the regression includes the same sample and variables with the exception of the governance proxies which are excluded All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed)
Governance
No Governance (same sample)
Dep Variable = Excess Value Coef t-stat Coef t -stat Foreign Sales 0259 159 0251 154 Industry Other -0099 -080 -0099 -079 Log Size -0105 -185 -0102 -180 Debt FirmValue -0813 -478 -0817 -482 CAPX Sales 0440 252 0438 249 EBIT Sales 0457 247 0457 246 RampD Sales 1717 284 1716 286 Ad Sales -2210 -143 -2190 -143 CEO Ownership 0010 007
CEO Ownership squared -0029 -017 Institution Ownership 0047 067 Powerful CEO -0003 -014 Governance Index 0009 103
Year indicators Yes Yes Firm indicators Yes Yes R2 0811 0810 Observations 2550 2550
- Cover for paperpdf
- crrz_20130603_SMUpdf
-
19
advertising expenses to total sales (Ad Sales) Table 2 Panel B provides the distribution of the
control variables All independent variables are Winsorized at 1 In our OLS regressions we
use heteroskedasticity and autocorrelation consistent robust standard errors with clustering at the
firm level
42 Primary regression analysis
In this section we examine the relation between firm excess value and the amount of foreign
activity at US MNCs Table 4 contains the results of regressions of excess value on Foreign
Sales for multiple specifications In column (1) Excess Value is regressed on both the percent of
foreign sales (Foreign Sales) and the percent of sales outside the firmrsquos primary industry
(Industry Other) We add the control variables to the regression in column (2) and year
indicators in column (3) In all three specifications the coefficient on the percent of foreign sales
remains significantly positive (with a p-value lt 001) For a sense of economic magnitude in
column (3) a one standard deviation (197) increase in the percent of foreign sales (Foreign
Sales) is associated with a 41 (equal to (e0349-1) times 0197) increase in firm excess value The
effect is larger for the other two columns ndash 82 increase in firm excess value in column (1) and
46 in column (2)20
Our finding of a premium reconciles with the international trade literature which finds that
firms engaging in international trade are more productive than those that do not (see Helpman
2006 and Syverson 2011 for reviews) Due to data limitations it is difficult to accurately
measure TFP at the firm level especially for firms which operate in multiple industries andor
20 Using the alternative strategy that employs US domiciled single-segment firms as the benchmark for the imputed value of the foreign operations Denis et al (2002) find that greater foreign activity results in a discount to firm value Using the Denis et al (2002) method to calculate excess value in our sample we obtain a significantly negative regression coefficient on Foreign Sales using the models in Table 4 columns (2) and (3) (the coefficient is not significant using the model in column (1) results untabulated)
20
countries As an alternative method to evaluate whether our finding of a premium is simply the
result of more productive firms also having more multinational operations we investigate
whether the premium is simply an artifact of innate characteristics of firms that choose to invest
abroad For example Helpman et al (2004) present a simple model with heterogeneous
productivity endowments The firms that receive the highest productivity endowments are the
ones capable of paying the fixed costs to establish a foreign subsidiary
To the extent that productivity advantages create excess value one would expect the type of
firms that choose to invest more abroad would be valued at a premium even in the absence of
their foreign investments To control for time invariant firm characteristics (eg innate
productivity advantages) and hence a potential source of endogeneity we add firm-level fixed
effects in column (4) of Table 4 We continue to find that the extent of foreign operations
(Foreign Sales) is positively associated with Excess Value Across all specifications the sign
on all significant control variables is consistent with prior research (eg Berger and Ofek 1995
and Denis et al 2002)
The finding of a premium makes a significant contribution to the relatively sparse literature
on the value effects of multinational activity Our measure of imputed value expands upon the
methods used by Berger and Ofek (1995) and Denis et al (2002) but allows us to answer a
different question than Denis et al (2002) Holding constant the extent of industrial
diversification we ask whether MNCs are more valuable than a portfolio of benchmark firms of
a similar geographic footprint In contrast Denis et al (2002) ask whether holding constant the
extent of industrial diversification MNCs are more valuable than a portfolio of domestic (US)
benchmark firms We believe that asking whether an MNCrsquos foreign (domestic) operations are
21
more valuable than those of a foreign (domestic) firm is a more appropriate way to assess the
value implications of a multinational network
5 Robustness
51 Endogeneity related to dynamic relations
Prior research argues that the relation between industrial diversification and excess firm
value could be endogenous through dynamic relationships based on observing past outcomes
Managers of firms likely choose to enter or exit industries or geographic regions based upon
their previous performance For example Campa and Kedia (2002) find that firms are more
likely to enter new industries when prospects in their current lines of business are deteriorating
Similar dynamics could also drive a relation between excess value and FDI We assess the
robustness of the previous results by estimating a dynamic panel data model which allows us to
control for dynamic endogeneity in addition to firm level unobserved heterogeneity We employ
the GMM estimator for dynamic panel models developed by Arellano and Bond (1991) Arellano
and Bover (1995) and Blundell and Bond (1998) Our results from this analysis confirm our
previous findings that firms with a larger degree of foreign operations have positive excess
values
Dynamic panel data models are generalizations of the traditional fixed effects model where
lags of the dependent variable are added to the right hand side of the equation These additional
lags control for the impact a firmrsquos past performance has on its current performance through the
channels discussed above The GMM estimator for dynamic panel models developed by
Arellano and Bond (1991) starts by differencing the data at the firm level in neighboring time
periods to eliminate any firm specific time invariant unobserved heterogeneity that may be
22
present Eliminating the firm specific fixed effect by differencing the data with lags of the
dependent variable on the right hand side introduces correlation between the new error term and
the differenced explanatory variables This problem is solved by applying the standard GMM
estimator using lags of both the original explanatory variables and excess value as instruments
We note that the dynamic panel data framework allows us to treat both international and
industrial diversification (as well as the other firm-specific control variables) as endogenous
Following Hoechle et al (2012) we also allow contemporaneous values of these variables to be
treated as endogenous
Lagged values of the explanatory variables are valid instruments in a dynamic panel data
model when the model is dynamically complete (eg Wooldridge 2010) A dynamic panel data
model is dynamically complete conditional on the unobserved effect if enough lags of both the
explanatory and dependent variable are included to make the resulting error term uncorrelated
with all the right hand side variables Under this assumption any lags for the explanatory
variables beyond this can be used within the GMM estimator as instruments Like Hoechle et al
(2012) we add two years of lags of a firmrsquos excess value to the right hand side assuming that
this is enough lags to control for dynamic feedback effects We then use past values of all
variables beyond the second lag up to a total of seven years to act as instruments
The results of the dynamic panel data estimator are reported in Table 5 These results confirm
the previous finding of a positive relation between the degree of international operations and
excess value We note that the size and significance level of the coefficient on Foreign Sales
diversification are robust to changes in the number of lags used as instruments The Arellano and
Bond (1991) estimator includes testing diagnostics for correct specification of the model If the
model is correctly specified differences in the residuals from the levels equation should be
23
serially uncorrelated at lag one but correlated at lag two The p-values for tests of serial
correlation of the residuals at lags 1 and 2 are reported in Table 5 and these indicate that the
model is not rejected We also report in Table 5 the Hansen test for overidentification which is a
test of the null hypothesis that the instruments are valid The p-value for this test is 030
indicating that we fail to reject this null hypothesis Finally we report another test for exogeneity
known as the difference-in-Hansen test which tests the null hypothesis that the instruments in
the original equation are valid We fail to reject this null hypothesis as well
52 Identifying value effects of multinational operations via cross-border acquisitions
Another alternative to investigating a causal link between foreign direct investment and firm
value is an event study of foreign acquisitions21 Acquired foreign targets represent only a small
fraction of MNCsrsquo worldwide sales in the year of the acquisition ndash less than 1 percent in our
sample Additionally cross-border acquisitions are not the primary source of international
expansion for the average firm We find that foreign acquisitions account for between 18 percent
and 55 percent of the year-over-year change in foreign sales with the remainder of the growth
coming from either newly established entities (ie greenfield investments) or growth in existing
operations22 These results suggest that acquisitions are not a material source of growth for the
21 Doukas and Travlos (1988) find positive returns when the acquirer is not operating in the target firmrsquos country and insignificant returns when the acquirer is operating in the target firmrsquos country or expanding internationally for the first time Morck and Yeung (1991) find marginal evidence of positive returns for firms with intangible assets Finally Doukas (1995) finds positive returns only for firms with average Tobinrsquos q ratios greater than one More recent work also using an event study methodology provides mixed evidence on valuation effects of global diversification through acquisitions Dos Santos Errunza and Miller (2008) find insignificant returns for firms with already established foreign operations firms establishing initial foreign operations and firms acquiring targets in related industries Doukas and Kan (2006) and Moeller and Schlingemann (2005) find reductions to shareholder value while Francis Hasan and Sun (2008) find positive returns for acquirers of targets in segmented financial markets 22 New foreign affiliates to an MNC group each year report on the BEA survey whether they entered the group as an acquisition or a newly established entity (ie greenfield investment) Affiliates under the lowest reporting threshold do not report this information resulting in our inability to precisely identify the source of 33 percent of the increase in firm sales as either acquisition or greenfield
24
typical US MNC suggesting that an event study would lack power and generalizability For
these reasons we do not implement an event study
53 Control premium
A control premium or private benefits of control occurs when ldquosome value whatever the
source is not shared among all the shareholders in proportion of the shares owned but it is
enjoyed exclusively by the party in controlrdquo (Dyck and Zingales 2004 p 541) Using stock
prices of traded shares held by minority shareholders in determining the market value to sales
ratios of our benchmark firms could underestimate the true value of our benchmark firms if those
firms face large control premiums This in turn could induce an excess value premium because
the imputed value of these country-industry segments would be artificially low The existence of
such benefits is more prevalent in some countries than others and could affect the value of an
MNCrsquos operations in such domains We use the country-level control premiums from Dyck and
Zingales (2004) and using each firmrsquos country sales as a percent of total firm sales we obtain a
weighted average control premium that each MNC faces (Control Premium) For countries not
included in the Dyck and Zingales (2004) analysis we set the control premium equal to zero23 In
Table 6 we show that Foreign Sales remains positive and highly significant (p-value lt 001)
when we include the proxy for control premium As a result it does not appear that the excess
value premium is driven by MNCs operating in countries where majority shareholders are able to
extract large control premiums
23 Results are robust to replacing missing values with the mean control premium of the respective GDP quartile
25
54 Cost of capital
As discussed in Section 2 when countries have shallow capital markets a multinational
network may provide the benefit of a lower cost of capital for investments through an internal
capital market as well as through better access to the US capital market Prior research
establishes that there is substantial variation in the cost of capital across countries (Erb Harvey
and Viskanta 1996 and Hail and Leuz 2009) To determine whether the documented premium
is due to cost of capital advantages we include a control variable to capture the difference
between the MNCrsquos cost of capital and the cost of capital faced by its competitors in the global
markets in which it operates
We compute our Cost of Capital proxy in two steps First we obtain the country-year credit
rating published by Institutional Investor24 The rating ranges from 0 to 100 where higher values
imply that a country has a higher default risk Country-level credit risk is a reasonable predictor
of expected equity market returns and volatility (Erb et al 1996) and exhibits a highly
significant correlation with international accounting-based estimates of imputed cost of capital
(Hail and Leuz 2009) Second using each firmrsquos country sales as a percent of total firm sales
we obtain a weighted average credit risk rating across the countries in which each firm operates
A higher value implies an MNC operates in countries with higher cost of capital In Table 7 we
do not find a result consistent with MNCs gaining an advantage from their access to low cost
capitalndash Cost of Capital is not significantly associated with Excess Value25
24 We thank Cam Harvey for providing these data 25 Results are consistent using cost of capital as defined in Erb et al 1996
26
55 Corporate governance
In light of the recent finding that corporate governance mitigates the diversification discount
we examine whether the premium to multinational activity is sensitive to controls for corporate
governance Hoechle et al (2012) find that the magnitude of the industrial diversification
discount is decreased and in several specifications no longer significant in the presence of
governance proxies In the first column of Table 8 we include the five governance proxies that
are consistently significantly associated with excess value in Hoechle et al (2012) ndash the percent
of CEO ownership (CEO Ownership and CEO Ownership squared) the percent of institutional
ownership (Institution Ownership) whether the firm has a powerful or influential CEO
(Powerful CEO) and the governance index from Gompers Ishii and Metrick (2003)
(Governance Index)
As documented in Table 8 including these control variables reduces our sample size by
almost 50 (to 2550 observations) and tends to exclude the smaller firms from the analysis
None of the governance proxies are significant Relative to column (4) of Table 4 the coefficient
on Foreign Sales is similar in magnitude but is no longer statistically significant at
conventional levels To investigate whether the decreased significance is due to the smaller
sample or the inclusion of the governance variables we re-estimate the regression for the same
2550 observations after removing the governance controls The coefficient and t-statistic are
virtually identical suggesting that the loss of significance is due to the reduced sample and not
the governance controls We do not find evidence to suggest that variation in corporate
governance practices drives variation in excess value for US MNCs
27
6 Conclusion
Using a new method to quantify the valuation effects of foreign direct investment by US
domiciled firms we find robust evidence of a premium in excess value associated with the
degree of multinational operations Our method builds on that used in previous studies
examining firm excess value (eg Berger and Ofek 1995 and Denis et al 2002) by
incorporating the location in addition to the industry of each corporate affiliate This allows
important factors such as growth and cost of capital to vary across countries as well as industries
Examining a sample of multinational firms we find that a one-standard deviation (197)
increase in foreign operations is associated with an increase of between 41 and 82 in excess
value minus both a statistically and economically significant finding
This result complements the finding of the international trade literature that more productive
firms are more likely to operate internationally However the premium remains after including
firm fixed effects to control for persistent firm traits (eg productivity)
The finding of a premium stands up to a succession of robustness tests designed to evaluate
whether the result arises from alternative methods or explanations First we estimate a dynamic
panel data model using the generalized method of moments (GMM) estimator to simultaneously
account for the potential endogeneity of FDI industrial diversification and firm value Second
we include a control variable to proxy for whether the firm operates in countries with larger
control premiums which could result in understated multiples and mechanically induce a
premium Third we include a control variable to measure the cost of capital across the countries
in which an MNC operates to evaluate whether a relatively low cost of capital borne by
multinational firms is a meaningful source of the premium Fourth we control for several
corporate governance proxies which have been found to be correlated with excess values (and
28
industrial diversification) We fail to find evidence supporting any of these alternative
explanations ndash the premium in excess value arising at firms with greater multinational operations
remains significant
Overall our new method of estimating excess values for multinational firms provides strong
evidence of a statistically and economically significant premium associated with increased
international operations The premium is robust to a host of specifications and controls designed
to evaluate alternative explanations We leave to future research the questions of whether the
premium to US multinational firms applies more broadly to other countries of domicile and
what factors influence the premium
29
References
Ammann M Hoeschel D and M Schmid 2012 Is there really no conglomerate discount
Journal of Business Finance and Accounting 39(12) 264-288 Arellano M and Bond S R 1991 Some tests of specification for panel data Monte Carlo
evidence and an application to employment equations Review of Economic Studies 58 (2) 277-298
Arellano M and Bover O 1995 Another look at instrumental variables estimation of error
components models Journal of Econometrics 68 (1) 29-61 Bens D A Berger P G and S J Monahan 2011 Discretionary disclosure in financial
reporting An examination comparing internal firm data to externally reported segment data The Accounting Review 86(2) 417-449
Berger P G and E Ofek 1995 Diversificationrsquos effect on firm value Journal of Financial
Economics 37 39-65 Blundell R and Bond S R 1998 Initial conditions and moment restrictions in dynamic panel
data models Journal of Econometrics 87(1) 115-144 Campa J M and S Kedia 2002 Explaining the diversification discount The Journal of
Finance 57 (4) 1731-1762 Denis D J Denis D K and K Yost 2002 Global diversification industrial diversification
and firm value The Journal of Finance 57 (5) 1951-1979 Desai M A Foley C F and J R Hines 2004 A multinational perspective on capital structure
choice and internal capital markets The Journal of Finance 59 (6) 2451-2487 Dos Santos MB V Errunza and D Miller 2008 Does corporate international diversification
destroy value Evidence from cross-border mergers and acquisitions Journal of Banking and Finance 32(12) 2716-2724
Doukas JA 1995 Overinvestment Tobinrsquos q and gains from foreign acquisitions Journal of
Banking and Finance 19 1285-1303 Doukas J and N G Travlos 1988 The effect of corporate multinationalism on shareholdersrsquorsquo
wealth evidence from international acquisitions Journal of Finance 43(5) 1161-1175 Doukas JA and O B Kan 2006 Does global diversification destroy firm value Journal of
International Business Studies 37(3) 352-371
30
Dunning J and A Rugman 1985 The influence of Hymerrsquos dissertation on the theory of foreign direct investment The American Economic Review 75(2) 228-232
Dyck A and L Zingales 2004 Private benefits of control An international comparison The
Journal of Finance 59 (2) 537-600 Erb C Harvey C and T Viskanta 1996 Expected returns and volatility in 135 countries The
Journal of Portfolio Management 46-58 Foley C F Desai M A and K J Forbes 2008 Financial constraints and growth
Multinational and local firm responses to currency depreciations Review of Financial Studies 21 (6) 2857-2888
Francis B I Hasan and X Sun 2008 Financial market integration and the value of global
diversification Evidence for US acquirers in cross-border mergers and acquisitions Journal of Banking and Finance 321522-1540
Gande A Schenzler C and L Senbet 2009 Valuation effects of global diversification
Journal of International Business Studies 1515-1532 Gompers P Ishii J and A Metrick 2003 Corporate governance and equity prices Quarterly
Journal of Economics 118(1) 107-156 Hail L and C Leuz 2009 Cost of capital effects and changes in growth expectations around
US cross-listings Journal of Financial Economics 93 428-454 Hanson G Mataloni R and M Slaughter 2005 Vertical production networks in multinational
firms Review of Economics and Statistics 87(4) 1-15 Helpman E 2006 Trade FDI and the Organization of Firms Journal of Economic Literature
44(3) 289-630 Helpman E Melitz M J and S R Yeaple 2004 Export versus FDI with Heterogeneous Firms
American Economic Review 94(1) 300-316 Hoechle D Schmid M Walter W and D Yermack 2012 How much of the diversification
discount can be explained by poor governance Journal of Financial Economics 103 41-60 Hope O-K and W B Thomas 2008 Managerial empire building and firm disclosure Journal
of Accounting Research 46 (June) 591-626 Huizinga H P and J Voget 2009 International taxation and the direction and volume of cross-
border MampAs Journal of Finance 64 (3) 1217-1249 Jensen M 1986 Agency cost of free cash flow corporate finance and takeovers American
Economic Review Papers and Proceedings 76 323-329
31
Laeven L and R Levine 2007 Is there a diversification discount in financial conglomerates
Journal of Financial Economics 85 331-367 Lang L and R Stulz 1994 Tobinrsquos Q corporate diversification and firm performance Journal
of Political Economy 102 1248-1280 Mataloni R 2003 US multinational companies Operations in 2001 Survey of Current
Business 85-105 Matvos G and A Seru 2011 The dynamic tradeoff between corporate socialism and financial
market dislocation Estimates from internal capital markets Working paper University of Chicago Booth School of Business
Moeller SB and F P Schlingemann 2005 Global diversification and bidder gains A
comparison between cross-border and domestic acquisitions Journal of Banking and Finance 29 533-564
Morck R and B Yeung 1991 Why investors value multinationality Journal of Business 64
165-186
Morck R and B Yeung 2001 Why firms diversify Internatization vs agency behavior In Intangibles B Lev (ed) Oxford University Press pp 269-302
Myers S C and N S Majluf 1984 Corporate financing and investment decisions when firms have information that investors do not have Journal of Financial Economics 13 187-221
Schmid M and I Walter 2009 Do financial conglomerates create or destroy economic value
Journal of Financial Intermediation 18 193-216 Syverson C 2011 What determines productivity Journal of Economic Literature 49(2) 326-
365 Villalonga B 2004a Diversification discount or premium New evidence from the Business
Information Tracking Series Journal of Finance 59 479-506 Villalonga B 2004b Does diversification cause the diversification discount Financial
Management 33 5-27 Wooldridge JM 2010 Econometric Analysis of Cross Section and Panel Data (Second
Edition) Cambridge MA MIT Press Yamin M 1991 A reassessment of Hymerrsquos contribution to the theory of the transnational
corporation C Pitelis and R Sugden (eds) The Nature of the Transnational Firm London Routledge
32
Appendix A Variable Definitions
We obtain information regarding firmsrsquo operations by country and industry from the Bureau of Economic Analysis (BEA) unless otherwise noted Acronyms used below reflect Compustat mnemonics and thus indicate when we obtain data from the Compustat North America Fundamentals Annual database All independent variables are winsorized at 1
Dependent variable
Excess Value = The log of the ratio of actual firm value to imputed firm value Actual firm value is equal to market capitalization (MVE) + book value of total assets (AT) ndash book value of shareholdersrsquo equity (SEQ) Imputed firm value is calculated as the sum of the imputed values of a firmrsquos operations in each country-industry The imputed value for each country-industry is equal to sales generated in that country-industry times a multiple equal to the ratio of a benchmark firmrsquos actual value to total sales A benchmark firm is defined as a median single-segment firm operating in the same country-industry All multiples require at least five firms listed in the respective country-industry on Compustat (domestic) or Worldscope (foreign) with less than 10 of sales income and assets outside the country of domicile Similar to Berger and Ofek (1995) we eliminate extreme values where the ratio of actual to imputed firm value exceeds 400 or is less than 025
Independent variables
Foreign Sales = Sales made by the foreign operations of US MNCs as a percent of total firm sales
Industry Other = Sales outside a firmrsquos primary industry as a percent of total sales (SALE) Equal to zero for firms operating in a single business segment
Log Size = Log of total sales (SALE)
Debt Firm Value = Ratio of long-term debt (DLTT) to total market value (market capitalization (MVE) + book value of total assets (AT) ndash book value of shareholdersrsquo equity (SEQ))
CAPX Sales = Ratio of capital expenditures (CAPX) to total sales (SALE)
EBIT Sales = Ratio of earnings before interest and taxes (EBIT) to total sales (SALE)
RampD Sales = Ratio of research and development expenses (XRD) to total sales (SALE)
Ad Sales = Ratio of advertising expenses (XAD) to total sales (SALE)
Control Premium = A weighted-average (based on the sales in a given country as a percent of total firm sales) of the country-level control premium from Dyck and Zingales (2004)
Cost of Capital = A weighted-average (based on the sales in a given country as a percent of total firm sales) of the country-level credit rating from Institutional Investor (httpwwwinstitutionalinvestorcom) The country-level rating is available for 160 countries and ranges from 0 to 100 Higher values imply greater credit risk
33
CEO Ownership = Percent of shares owned by the CEO from Execucomp
Institution Ownership = Percent of shares owned by institutional investors from Thomson Reuterrsquos Institutional Holdings database
Powerful CEO = Indicator variable equal to one if the CEO is the only insider on the board of directors and also serves as the chairman and president based on data from RiskMetrics Directors database Equal to zero otherwise
Governance Index = Count measure of takeover defenses used in Gompers Ishii and Metrick (2003) from RiskMetrics Governance database As the governance data are not available for every year we follow Gompers Ishii and Metrick (2003) and assume that the firmsrsquo governance attributes are unchanged until publication of the subsequent series
34
Figure 1 Trends in multinational activity and industrial diversification from 1984-2008 This figure presents a plot of the mean percent of foreign sales and the mean percent of sales outside the firmrsquos primary industry (industrial diversification) from 1984 through 2008 Dotted lines above and below the primary lines represent 95 confidence intervals To compile the plot we include the full population of firms in Compustat
35
Table 1 Analysis of accounting data for firms reporting under multiple accounting standards This table provides a comparison of sales net income and assets for firms that changed accounting standards to or from US GAAP Comparisons are provided for the 66 firms that changed to or from US GAAP and for which we could obtain accounting data electronically and in English under both standards for the same fiscal year (eg the firm presented the accounting numbers originally under one set of standards and then restated the prior year numbers in the subsequent year under the new set of standards) Panel A shows the distribution of firms by year Panel B shows the other accounting standards to which US GAAP is compared Panel C shows the mean and median percent that the other accounting standard differs from US GAAP for the same fiscal year and accounting number Two samples are shown in Panel C The first is the full sample of firms with a change in accounting standards to or from US GAAP for which data was available The second is the subset of these firms that occurred during the sample window used in this study (ie excluding changes post-2008) The percent difference in sales is compared to the percent difference in net income and assets Tests of means are based on matched-pair t-tests Tests of medians are based on Wilcoxon matched-pair signed-rank tests Panel A Accounting standard changes by year
2003
4 6
2004
25 38
2005
8 12
2006
6 9
2007
7 11
2008
5 8
2009
5 8
2010
5 8
2011
1 2
66
Panel B Accounting standards compared to US GAAP
IFRS
47 71
Canadian GAAP
12 18
Israeli GAAP
2 3
Dutch GAAP
1 2
Indian GAAP
1 2
Japanese GAAP
1 2
Norwegian GAAP
1 2
Swiss GAAP
1 2
66
36
(Table 1 continued) Panel C Tests of differences between US GAAP and other accounting standards
Percent different from US GAAP
Comparison to sales (p-values)
N Mean Median
Mean Median
Full Sample (2001-2011)
Sales
66 236 000
Net Income
66 10653 647
005 lt001
Assets
66 1162 126
lt001 lt001
Study Sample (2001-2008)
Sales
55 257 000
Net Income
55 10303 589
011 lt001
Assets
55 1047 099
003 lt001
37
Table 2 Descriptive statistics Panel A reports the mean and medians of Excess Value for the multinational firms and single-segment benchmark firms as well as univariate tests across the subsamples Panel B reports summary statistics for the independent variables for the multinational firms The p-values of the tests of differences in means are based on t-tests The p-values of tests of differences in the medians are for a Wilcoxon rank-sum test All variables are defined in Appendix A Panel A Excess Value by sample Multinational All Benchmark Benchmark Subgroups Sample Firms US Foreign
Value Value Test of Diff (p-value) Value
Test of Diff (p-value) Value
Test of Diff (p-value)
Mean 0067 0006 lt001 -0028 lt001 0014 lt001 Median 0076 0000 lt001 -0025 lt001 0000 lt001 N 4950 33074 5998 27076 Panel B Distribution of independent variables for the multinational firms N Mean 25th 50th 75th StDev Foreign Sales 4950 0238 0074 0190 0363 0197 Industry Other 4950 0212 0000 0166 0414 0215 Log Size 4950 6955 5823 6989 8140 1687 Debt Firm Value 4950 0136 0009 0097 0212 0145 CAPX Sales 4950 0054 0020 0033 0054 0101 EBIT Sales 4950 0065 0031 0077 0121 0138 RampD Sales 4950 0049 0000 0013 0059 0083 Ad Sales 4950 0008 0000 0000 0005 0018
38
Table 3 BEA and Worldscope samples
This table reports details on affiliate sales for significant countries pooled from 1998-2008 Significant countries are defined as those in which the operations of US MNCs represent at least 02 of total firm sales (per the BEA) For significant countries the table compares their representation in terms of MNC affiliate sales in the BEA data to the distribution of firm observations in Worldscope US Foreign Affiliates (BEA) Foreign Firms (Worldscope)
Country
N (1)
Sales (millions)
(2)
Total Sales (3)
N (4)
Sales (million)
(5)
Total Sales (6)
Australia 1344 73305 056 859 255865 210 Belgium 894 52755 040 169 26136 021 Brazil 959 60245 046 1143 683167 560 Canada 2730 536767 407 1181 526750 432 China 922 75907 057 822 276541 227 France 1852 183942 139 540 105447 086 Germany 2182 275577 209 480 103888 085 Hong Kong 867 48100 036 157 34094 028 Ireland 478 36754 028 34 3467 003 Italy 1178 82116 062 111 47948 039 Japan 1494 148032 112 2281 1119223 917 Korea South 659 34500 026 3530 2752791 2255 Malaysia 386 26027 020 837 168683 138 Mexico 1405 113623 086 362 237173 194 Netherlands 1149 85105 064 47 22631 019 Singapore 1098 94917 072 139 32913 027 Spain 871 63094 048 129 37419 031 Sweden 712 32682 025 235 23090 019 Switzerland 726 60288 046 74 20242 017 Taiwan 592 30313 023 2949 787049 645 United Kingdom 3054 423934 321 1997 882423 723 United States (domicile) 4950 10460880 7923 na na na Other - Covered by WorldScope 6893 199394 151 9000 4057894 33 Other -Not Covered by WorldScope 713 5352 004 - - - Total 38108 13203609 1000 27076 12204834 100
39
Table 4 OLS regressions of firm excess values on the percent of foreign sales This table reports the results of OLS regressions of firm excess value on the percent of foreign sales (Foreign Sales) the percent of sales outside a firmrsquos primary industry (Industry Other) and control variables All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed) Dep Variable = Excess Value Coefficient t-stat Coefficient t-stat Coefficient t-stat Coefficient t-stat (1) (2) (3) (4) Foreign Sales 0349 419 0209 293 0189 266 0278 210 Industry Other -0134 -186 -0047 -072 -0053 -081 -0326 -325 Log Size 0066 634 0065 624 -0044 -096 Debt Firm Value -0196 -213 -0241 -260 -0685 -588 CAPX Sales 0901 425 0893 435 0488 227 EBIT Sales 1154 542 1170 547 0408 189 RampD Sales 2908 1242 2932 1237 1544 379 Ad Sales 1869 263 2003 280 -1696 -119 Year indicators No No Yes Yes Firm indicators No No No Yes R2 0017 0212 0226 0768 Observations 4950 4950 4950 4950
40
Table 5 Dynamic panel GMM model regressing firm excess values on the percent of foreign sales This table reports the result from a regression of the excess value on the percent of foreign sales (Foreign Sales) the percent of non-primary industry sales (Industry Other) and control variables using a dynamic panel GMM estimator All control variables are considered to be endogenous with the exception of the year indicator variables AR(1) and AR(2) are tests for first-order and second-order serial correlation in the first differenced residuals with the null hypothesis of no serial correlation The null hypothesis of the Hansen test of overidentification is that all instruments are valid The null hypothesis of the difference-in-Hansen test of exogeneity is that the instruments used for the equation in levels are exogenous All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed) Dep Variable = Excess Value Coef t-stat Excess Value (one lag) -0018 -082 Excess Value (two lags) -0048 -272 Foreign Sales 0371 270 Industry Other -0163 -171 Log Size 0010 123 Debt FirmValue -1135 -820 CAPX Sales 0590 342 EBIT Sales -0047 -300 RampD Sales 1376 531 Ad Sales -0751 -040 AR(1) test (p-value) 000 AR(2) test (p-value) 017 Hansen test of over-identification (p-value) 030 Difference-in-Hansen test of exogeneity (p-value) 093 Year indicators Yes Number of firms 1016 Number of observations 4090
41
Table 6 OLS regression of firm excess values on the percent of foreign sales controlling for a control premium This table reports the result of the OLS regression of firm excess value including firm and year fixed effects with an additional control variable to proxy for a country-level ownership control premium (Control Premium) All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed) Dep Variable = Excess Value Coef t -stat Foreign Sales 0303 224 Industry Other -0327 -325 Log Size -0045 -100 Debt FirmValue -0683 -585 CAPX Sales 0490 226 EBIT Sales 0409 189 RampD Sales 1545 379 Ad Sales -1689 -119 Control Premium -1004 -062 Year indicators Yes Firm indicators Yes R2 0768 Observations 4950
42
Table 7 OLS regression of firm excess values on the percent of foreign sales controlling for the cost of capital This table contains the results of an OLS regression of firm excess value including firm and year fixed effects with an additional control variable to proxy for a country-level cost of capital (Cost of Capital) All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed) Dep Variable = Excess Value Coef t -stat Foreign Sales 0255 178 Industry Other -0326 -324 Log Size -0043 -094 Debt FirmValue -0687 -589 CAPX Sales 0485 225 EBIT Sales 0406 188 RampD Sales 1539 379 Ad Sales -1703 -120 Cost of Capital 0004 039 Year indicators Yes Firm indicators Yes R2 0768 Observations 4950
43
Table 8 OLS regression of firm excess values on the percent of foreign sales controlling for aspects of corporate governance This table reports the results of two OLS regressions In the first column are the results of the firm excess value regression including firm and year fixed effect with five proxies for corporate governance included ndash the percent of shares owned by the CEO and the percent squared (CEO Ownership and CEO Ownership squared) the percent owned by institutional investors (Institution Ownership) whether there is a powerful CEO (Powerful CEO) and the Gompers Ishii and Metrick (2003) governance index (Governance Index) In the second column the regression includes the same sample and variables with the exception of the governance proxies which are excluded All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed)
Governance
No Governance (same sample)
Dep Variable = Excess Value Coef t-stat Coef t -stat Foreign Sales 0259 159 0251 154 Industry Other -0099 -080 -0099 -079 Log Size -0105 -185 -0102 -180 Debt FirmValue -0813 -478 -0817 -482 CAPX Sales 0440 252 0438 249 EBIT Sales 0457 247 0457 246 RampD Sales 1717 284 1716 286 Ad Sales -2210 -143 -2190 -143 CEO Ownership 0010 007
CEO Ownership squared -0029 -017 Institution Ownership 0047 067 Powerful CEO -0003 -014 Governance Index 0009 103
Year indicators Yes Yes Firm indicators Yes Yes R2 0811 0810 Observations 2550 2550
- Cover for paperpdf
- crrz_20130603_SMUpdf
-
20
countries As an alternative method to evaluate whether our finding of a premium is simply the
result of more productive firms also having more multinational operations we investigate
whether the premium is simply an artifact of innate characteristics of firms that choose to invest
abroad For example Helpman et al (2004) present a simple model with heterogeneous
productivity endowments The firms that receive the highest productivity endowments are the
ones capable of paying the fixed costs to establish a foreign subsidiary
To the extent that productivity advantages create excess value one would expect the type of
firms that choose to invest more abroad would be valued at a premium even in the absence of
their foreign investments To control for time invariant firm characteristics (eg innate
productivity advantages) and hence a potential source of endogeneity we add firm-level fixed
effects in column (4) of Table 4 We continue to find that the extent of foreign operations
(Foreign Sales) is positively associated with Excess Value Across all specifications the sign
on all significant control variables is consistent with prior research (eg Berger and Ofek 1995
and Denis et al 2002)
The finding of a premium makes a significant contribution to the relatively sparse literature
on the value effects of multinational activity Our measure of imputed value expands upon the
methods used by Berger and Ofek (1995) and Denis et al (2002) but allows us to answer a
different question than Denis et al (2002) Holding constant the extent of industrial
diversification we ask whether MNCs are more valuable than a portfolio of benchmark firms of
a similar geographic footprint In contrast Denis et al (2002) ask whether holding constant the
extent of industrial diversification MNCs are more valuable than a portfolio of domestic (US)
benchmark firms We believe that asking whether an MNCrsquos foreign (domestic) operations are
21
more valuable than those of a foreign (domestic) firm is a more appropriate way to assess the
value implications of a multinational network
5 Robustness
51 Endogeneity related to dynamic relations
Prior research argues that the relation between industrial diversification and excess firm
value could be endogenous through dynamic relationships based on observing past outcomes
Managers of firms likely choose to enter or exit industries or geographic regions based upon
their previous performance For example Campa and Kedia (2002) find that firms are more
likely to enter new industries when prospects in their current lines of business are deteriorating
Similar dynamics could also drive a relation between excess value and FDI We assess the
robustness of the previous results by estimating a dynamic panel data model which allows us to
control for dynamic endogeneity in addition to firm level unobserved heterogeneity We employ
the GMM estimator for dynamic panel models developed by Arellano and Bond (1991) Arellano
and Bover (1995) and Blundell and Bond (1998) Our results from this analysis confirm our
previous findings that firms with a larger degree of foreign operations have positive excess
values
Dynamic panel data models are generalizations of the traditional fixed effects model where
lags of the dependent variable are added to the right hand side of the equation These additional
lags control for the impact a firmrsquos past performance has on its current performance through the
channels discussed above The GMM estimator for dynamic panel models developed by
Arellano and Bond (1991) starts by differencing the data at the firm level in neighboring time
periods to eliminate any firm specific time invariant unobserved heterogeneity that may be
22
present Eliminating the firm specific fixed effect by differencing the data with lags of the
dependent variable on the right hand side introduces correlation between the new error term and
the differenced explanatory variables This problem is solved by applying the standard GMM
estimator using lags of both the original explanatory variables and excess value as instruments
We note that the dynamic panel data framework allows us to treat both international and
industrial diversification (as well as the other firm-specific control variables) as endogenous
Following Hoechle et al (2012) we also allow contemporaneous values of these variables to be
treated as endogenous
Lagged values of the explanatory variables are valid instruments in a dynamic panel data
model when the model is dynamically complete (eg Wooldridge 2010) A dynamic panel data
model is dynamically complete conditional on the unobserved effect if enough lags of both the
explanatory and dependent variable are included to make the resulting error term uncorrelated
with all the right hand side variables Under this assumption any lags for the explanatory
variables beyond this can be used within the GMM estimator as instruments Like Hoechle et al
(2012) we add two years of lags of a firmrsquos excess value to the right hand side assuming that
this is enough lags to control for dynamic feedback effects We then use past values of all
variables beyond the second lag up to a total of seven years to act as instruments
The results of the dynamic panel data estimator are reported in Table 5 These results confirm
the previous finding of a positive relation between the degree of international operations and
excess value We note that the size and significance level of the coefficient on Foreign Sales
diversification are robust to changes in the number of lags used as instruments The Arellano and
Bond (1991) estimator includes testing diagnostics for correct specification of the model If the
model is correctly specified differences in the residuals from the levels equation should be
23
serially uncorrelated at lag one but correlated at lag two The p-values for tests of serial
correlation of the residuals at lags 1 and 2 are reported in Table 5 and these indicate that the
model is not rejected We also report in Table 5 the Hansen test for overidentification which is a
test of the null hypothesis that the instruments are valid The p-value for this test is 030
indicating that we fail to reject this null hypothesis Finally we report another test for exogeneity
known as the difference-in-Hansen test which tests the null hypothesis that the instruments in
the original equation are valid We fail to reject this null hypothesis as well
52 Identifying value effects of multinational operations via cross-border acquisitions
Another alternative to investigating a causal link between foreign direct investment and firm
value is an event study of foreign acquisitions21 Acquired foreign targets represent only a small
fraction of MNCsrsquo worldwide sales in the year of the acquisition ndash less than 1 percent in our
sample Additionally cross-border acquisitions are not the primary source of international
expansion for the average firm We find that foreign acquisitions account for between 18 percent
and 55 percent of the year-over-year change in foreign sales with the remainder of the growth
coming from either newly established entities (ie greenfield investments) or growth in existing
operations22 These results suggest that acquisitions are not a material source of growth for the
21 Doukas and Travlos (1988) find positive returns when the acquirer is not operating in the target firmrsquos country and insignificant returns when the acquirer is operating in the target firmrsquos country or expanding internationally for the first time Morck and Yeung (1991) find marginal evidence of positive returns for firms with intangible assets Finally Doukas (1995) finds positive returns only for firms with average Tobinrsquos q ratios greater than one More recent work also using an event study methodology provides mixed evidence on valuation effects of global diversification through acquisitions Dos Santos Errunza and Miller (2008) find insignificant returns for firms with already established foreign operations firms establishing initial foreign operations and firms acquiring targets in related industries Doukas and Kan (2006) and Moeller and Schlingemann (2005) find reductions to shareholder value while Francis Hasan and Sun (2008) find positive returns for acquirers of targets in segmented financial markets 22 New foreign affiliates to an MNC group each year report on the BEA survey whether they entered the group as an acquisition or a newly established entity (ie greenfield investment) Affiliates under the lowest reporting threshold do not report this information resulting in our inability to precisely identify the source of 33 percent of the increase in firm sales as either acquisition or greenfield
24
typical US MNC suggesting that an event study would lack power and generalizability For
these reasons we do not implement an event study
53 Control premium
A control premium or private benefits of control occurs when ldquosome value whatever the
source is not shared among all the shareholders in proportion of the shares owned but it is
enjoyed exclusively by the party in controlrdquo (Dyck and Zingales 2004 p 541) Using stock
prices of traded shares held by minority shareholders in determining the market value to sales
ratios of our benchmark firms could underestimate the true value of our benchmark firms if those
firms face large control premiums This in turn could induce an excess value premium because
the imputed value of these country-industry segments would be artificially low The existence of
such benefits is more prevalent in some countries than others and could affect the value of an
MNCrsquos operations in such domains We use the country-level control premiums from Dyck and
Zingales (2004) and using each firmrsquos country sales as a percent of total firm sales we obtain a
weighted average control premium that each MNC faces (Control Premium) For countries not
included in the Dyck and Zingales (2004) analysis we set the control premium equal to zero23 In
Table 6 we show that Foreign Sales remains positive and highly significant (p-value lt 001)
when we include the proxy for control premium As a result it does not appear that the excess
value premium is driven by MNCs operating in countries where majority shareholders are able to
extract large control premiums
23 Results are robust to replacing missing values with the mean control premium of the respective GDP quartile
25
54 Cost of capital
As discussed in Section 2 when countries have shallow capital markets a multinational
network may provide the benefit of a lower cost of capital for investments through an internal
capital market as well as through better access to the US capital market Prior research
establishes that there is substantial variation in the cost of capital across countries (Erb Harvey
and Viskanta 1996 and Hail and Leuz 2009) To determine whether the documented premium
is due to cost of capital advantages we include a control variable to capture the difference
between the MNCrsquos cost of capital and the cost of capital faced by its competitors in the global
markets in which it operates
We compute our Cost of Capital proxy in two steps First we obtain the country-year credit
rating published by Institutional Investor24 The rating ranges from 0 to 100 where higher values
imply that a country has a higher default risk Country-level credit risk is a reasonable predictor
of expected equity market returns and volatility (Erb et al 1996) and exhibits a highly
significant correlation with international accounting-based estimates of imputed cost of capital
(Hail and Leuz 2009) Second using each firmrsquos country sales as a percent of total firm sales
we obtain a weighted average credit risk rating across the countries in which each firm operates
A higher value implies an MNC operates in countries with higher cost of capital In Table 7 we
do not find a result consistent with MNCs gaining an advantage from their access to low cost
capitalndash Cost of Capital is not significantly associated with Excess Value25
24 We thank Cam Harvey for providing these data 25 Results are consistent using cost of capital as defined in Erb et al 1996
26
55 Corporate governance
In light of the recent finding that corporate governance mitigates the diversification discount
we examine whether the premium to multinational activity is sensitive to controls for corporate
governance Hoechle et al (2012) find that the magnitude of the industrial diversification
discount is decreased and in several specifications no longer significant in the presence of
governance proxies In the first column of Table 8 we include the five governance proxies that
are consistently significantly associated with excess value in Hoechle et al (2012) ndash the percent
of CEO ownership (CEO Ownership and CEO Ownership squared) the percent of institutional
ownership (Institution Ownership) whether the firm has a powerful or influential CEO
(Powerful CEO) and the governance index from Gompers Ishii and Metrick (2003)
(Governance Index)
As documented in Table 8 including these control variables reduces our sample size by
almost 50 (to 2550 observations) and tends to exclude the smaller firms from the analysis
None of the governance proxies are significant Relative to column (4) of Table 4 the coefficient
on Foreign Sales is similar in magnitude but is no longer statistically significant at
conventional levels To investigate whether the decreased significance is due to the smaller
sample or the inclusion of the governance variables we re-estimate the regression for the same
2550 observations after removing the governance controls The coefficient and t-statistic are
virtually identical suggesting that the loss of significance is due to the reduced sample and not
the governance controls We do not find evidence to suggest that variation in corporate
governance practices drives variation in excess value for US MNCs
27
6 Conclusion
Using a new method to quantify the valuation effects of foreign direct investment by US
domiciled firms we find robust evidence of a premium in excess value associated with the
degree of multinational operations Our method builds on that used in previous studies
examining firm excess value (eg Berger and Ofek 1995 and Denis et al 2002) by
incorporating the location in addition to the industry of each corporate affiliate This allows
important factors such as growth and cost of capital to vary across countries as well as industries
Examining a sample of multinational firms we find that a one-standard deviation (197)
increase in foreign operations is associated with an increase of between 41 and 82 in excess
value minus both a statistically and economically significant finding
This result complements the finding of the international trade literature that more productive
firms are more likely to operate internationally However the premium remains after including
firm fixed effects to control for persistent firm traits (eg productivity)
The finding of a premium stands up to a succession of robustness tests designed to evaluate
whether the result arises from alternative methods or explanations First we estimate a dynamic
panel data model using the generalized method of moments (GMM) estimator to simultaneously
account for the potential endogeneity of FDI industrial diversification and firm value Second
we include a control variable to proxy for whether the firm operates in countries with larger
control premiums which could result in understated multiples and mechanically induce a
premium Third we include a control variable to measure the cost of capital across the countries
in which an MNC operates to evaluate whether a relatively low cost of capital borne by
multinational firms is a meaningful source of the premium Fourth we control for several
corporate governance proxies which have been found to be correlated with excess values (and
28
industrial diversification) We fail to find evidence supporting any of these alternative
explanations ndash the premium in excess value arising at firms with greater multinational operations
remains significant
Overall our new method of estimating excess values for multinational firms provides strong
evidence of a statistically and economically significant premium associated with increased
international operations The premium is robust to a host of specifications and controls designed
to evaluate alternative explanations We leave to future research the questions of whether the
premium to US multinational firms applies more broadly to other countries of domicile and
what factors influence the premium
29
References
Ammann M Hoeschel D and M Schmid 2012 Is there really no conglomerate discount
Journal of Business Finance and Accounting 39(12) 264-288 Arellano M and Bond S R 1991 Some tests of specification for panel data Monte Carlo
evidence and an application to employment equations Review of Economic Studies 58 (2) 277-298
Arellano M and Bover O 1995 Another look at instrumental variables estimation of error
components models Journal of Econometrics 68 (1) 29-61 Bens D A Berger P G and S J Monahan 2011 Discretionary disclosure in financial
reporting An examination comparing internal firm data to externally reported segment data The Accounting Review 86(2) 417-449
Berger P G and E Ofek 1995 Diversificationrsquos effect on firm value Journal of Financial
Economics 37 39-65 Blundell R and Bond S R 1998 Initial conditions and moment restrictions in dynamic panel
data models Journal of Econometrics 87(1) 115-144 Campa J M and S Kedia 2002 Explaining the diversification discount The Journal of
Finance 57 (4) 1731-1762 Denis D J Denis D K and K Yost 2002 Global diversification industrial diversification
and firm value The Journal of Finance 57 (5) 1951-1979 Desai M A Foley C F and J R Hines 2004 A multinational perspective on capital structure
choice and internal capital markets The Journal of Finance 59 (6) 2451-2487 Dos Santos MB V Errunza and D Miller 2008 Does corporate international diversification
destroy value Evidence from cross-border mergers and acquisitions Journal of Banking and Finance 32(12) 2716-2724
Doukas JA 1995 Overinvestment Tobinrsquos q and gains from foreign acquisitions Journal of
Banking and Finance 19 1285-1303 Doukas J and N G Travlos 1988 The effect of corporate multinationalism on shareholdersrsquorsquo
wealth evidence from international acquisitions Journal of Finance 43(5) 1161-1175 Doukas JA and O B Kan 2006 Does global diversification destroy firm value Journal of
International Business Studies 37(3) 352-371
30
Dunning J and A Rugman 1985 The influence of Hymerrsquos dissertation on the theory of foreign direct investment The American Economic Review 75(2) 228-232
Dyck A and L Zingales 2004 Private benefits of control An international comparison The
Journal of Finance 59 (2) 537-600 Erb C Harvey C and T Viskanta 1996 Expected returns and volatility in 135 countries The
Journal of Portfolio Management 46-58 Foley C F Desai M A and K J Forbes 2008 Financial constraints and growth
Multinational and local firm responses to currency depreciations Review of Financial Studies 21 (6) 2857-2888
Francis B I Hasan and X Sun 2008 Financial market integration and the value of global
diversification Evidence for US acquirers in cross-border mergers and acquisitions Journal of Banking and Finance 321522-1540
Gande A Schenzler C and L Senbet 2009 Valuation effects of global diversification
Journal of International Business Studies 1515-1532 Gompers P Ishii J and A Metrick 2003 Corporate governance and equity prices Quarterly
Journal of Economics 118(1) 107-156 Hail L and C Leuz 2009 Cost of capital effects and changes in growth expectations around
US cross-listings Journal of Financial Economics 93 428-454 Hanson G Mataloni R and M Slaughter 2005 Vertical production networks in multinational
firms Review of Economics and Statistics 87(4) 1-15 Helpman E 2006 Trade FDI and the Organization of Firms Journal of Economic Literature
44(3) 289-630 Helpman E Melitz M J and S R Yeaple 2004 Export versus FDI with Heterogeneous Firms
American Economic Review 94(1) 300-316 Hoechle D Schmid M Walter W and D Yermack 2012 How much of the diversification
discount can be explained by poor governance Journal of Financial Economics 103 41-60 Hope O-K and W B Thomas 2008 Managerial empire building and firm disclosure Journal
of Accounting Research 46 (June) 591-626 Huizinga H P and J Voget 2009 International taxation and the direction and volume of cross-
border MampAs Journal of Finance 64 (3) 1217-1249 Jensen M 1986 Agency cost of free cash flow corporate finance and takeovers American
Economic Review Papers and Proceedings 76 323-329
31
Laeven L and R Levine 2007 Is there a diversification discount in financial conglomerates
Journal of Financial Economics 85 331-367 Lang L and R Stulz 1994 Tobinrsquos Q corporate diversification and firm performance Journal
of Political Economy 102 1248-1280 Mataloni R 2003 US multinational companies Operations in 2001 Survey of Current
Business 85-105 Matvos G and A Seru 2011 The dynamic tradeoff between corporate socialism and financial
market dislocation Estimates from internal capital markets Working paper University of Chicago Booth School of Business
Moeller SB and F P Schlingemann 2005 Global diversification and bidder gains A
comparison between cross-border and domestic acquisitions Journal of Banking and Finance 29 533-564
Morck R and B Yeung 1991 Why investors value multinationality Journal of Business 64
165-186
Morck R and B Yeung 2001 Why firms diversify Internatization vs agency behavior In Intangibles B Lev (ed) Oxford University Press pp 269-302
Myers S C and N S Majluf 1984 Corporate financing and investment decisions when firms have information that investors do not have Journal of Financial Economics 13 187-221
Schmid M and I Walter 2009 Do financial conglomerates create or destroy economic value
Journal of Financial Intermediation 18 193-216 Syverson C 2011 What determines productivity Journal of Economic Literature 49(2) 326-
365 Villalonga B 2004a Diversification discount or premium New evidence from the Business
Information Tracking Series Journal of Finance 59 479-506 Villalonga B 2004b Does diversification cause the diversification discount Financial
Management 33 5-27 Wooldridge JM 2010 Econometric Analysis of Cross Section and Panel Data (Second
Edition) Cambridge MA MIT Press Yamin M 1991 A reassessment of Hymerrsquos contribution to the theory of the transnational
corporation C Pitelis and R Sugden (eds) The Nature of the Transnational Firm London Routledge
32
Appendix A Variable Definitions
We obtain information regarding firmsrsquo operations by country and industry from the Bureau of Economic Analysis (BEA) unless otherwise noted Acronyms used below reflect Compustat mnemonics and thus indicate when we obtain data from the Compustat North America Fundamentals Annual database All independent variables are winsorized at 1
Dependent variable
Excess Value = The log of the ratio of actual firm value to imputed firm value Actual firm value is equal to market capitalization (MVE) + book value of total assets (AT) ndash book value of shareholdersrsquo equity (SEQ) Imputed firm value is calculated as the sum of the imputed values of a firmrsquos operations in each country-industry The imputed value for each country-industry is equal to sales generated in that country-industry times a multiple equal to the ratio of a benchmark firmrsquos actual value to total sales A benchmark firm is defined as a median single-segment firm operating in the same country-industry All multiples require at least five firms listed in the respective country-industry on Compustat (domestic) or Worldscope (foreign) with less than 10 of sales income and assets outside the country of domicile Similar to Berger and Ofek (1995) we eliminate extreme values where the ratio of actual to imputed firm value exceeds 400 or is less than 025
Independent variables
Foreign Sales = Sales made by the foreign operations of US MNCs as a percent of total firm sales
Industry Other = Sales outside a firmrsquos primary industry as a percent of total sales (SALE) Equal to zero for firms operating in a single business segment
Log Size = Log of total sales (SALE)
Debt Firm Value = Ratio of long-term debt (DLTT) to total market value (market capitalization (MVE) + book value of total assets (AT) ndash book value of shareholdersrsquo equity (SEQ))
CAPX Sales = Ratio of capital expenditures (CAPX) to total sales (SALE)
EBIT Sales = Ratio of earnings before interest and taxes (EBIT) to total sales (SALE)
RampD Sales = Ratio of research and development expenses (XRD) to total sales (SALE)
Ad Sales = Ratio of advertising expenses (XAD) to total sales (SALE)
Control Premium = A weighted-average (based on the sales in a given country as a percent of total firm sales) of the country-level control premium from Dyck and Zingales (2004)
Cost of Capital = A weighted-average (based on the sales in a given country as a percent of total firm sales) of the country-level credit rating from Institutional Investor (httpwwwinstitutionalinvestorcom) The country-level rating is available for 160 countries and ranges from 0 to 100 Higher values imply greater credit risk
33
CEO Ownership = Percent of shares owned by the CEO from Execucomp
Institution Ownership = Percent of shares owned by institutional investors from Thomson Reuterrsquos Institutional Holdings database
Powerful CEO = Indicator variable equal to one if the CEO is the only insider on the board of directors and also serves as the chairman and president based on data from RiskMetrics Directors database Equal to zero otherwise
Governance Index = Count measure of takeover defenses used in Gompers Ishii and Metrick (2003) from RiskMetrics Governance database As the governance data are not available for every year we follow Gompers Ishii and Metrick (2003) and assume that the firmsrsquo governance attributes are unchanged until publication of the subsequent series
34
Figure 1 Trends in multinational activity and industrial diversification from 1984-2008 This figure presents a plot of the mean percent of foreign sales and the mean percent of sales outside the firmrsquos primary industry (industrial diversification) from 1984 through 2008 Dotted lines above and below the primary lines represent 95 confidence intervals To compile the plot we include the full population of firms in Compustat
35
Table 1 Analysis of accounting data for firms reporting under multiple accounting standards This table provides a comparison of sales net income and assets for firms that changed accounting standards to or from US GAAP Comparisons are provided for the 66 firms that changed to or from US GAAP and for which we could obtain accounting data electronically and in English under both standards for the same fiscal year (eg the firm presented the accounting numbers originally under one set of standards and then restated the prior year numbers in the subsequent year under the new set of standards) Panel A shows the distribution of firms by year Panel B shows the other accounting standards to which US GAAP is compared Panel C shows the mean and median percent that the other accounting standard differs from US GAAP for the same fiscal year and accounting number Two samples are shown in Panel C The first is the full sample of firms with a change in accounting standards to or from US GAAP for which data was available The second is the subset of these firms that occurred during the sample window used in this study (ie excluding changes post-2008) The percent difference in sales is compared to the percent difference in net income and assets Tests of means are based on matched-pair t-tests Tests of medians are based on Wilcoxon matched-pair signed-rank tests Panel A Accounting standard changes by year
2003
4 6
2004
25 38
2005
8 12
2006
6 9
2007
7 11
2008
5 8
2009
5 8
2010
5 8
2011
1 2
66
Panel B Accounting standards compared to US GAAP
IFRS
47 71
Canadian GAAP
12 18
Israeli GAAP
2 3
Dutch GAAP
1 2
Indian GAAP
1 2
Japanese GAAP
1 2
Norwegian GAAP
1 2
Swiss GAAP
1 2
66
36
(Table 1 continued) Panel C Tests of differences between US GAAP and other accounting standards
Percent different from US GAAP
Comparison to sales (p-values)
N Mean Median
Mean Median
Full Sample (2001-2011)
Sales
66 236 000
Net Income
66 10653 647
005 lt001
Assets
66 1162 126
lt001 lt001
Study Sample (2001-2008)
Sales
55 257 000
Net Income
55 10303 589
011 lt001
Assets
55 1047 099
003 lt001
37
Table 2 Descriptive statistics Panel A reports the mean and medians of Excess Value for the multinational firms and single-segment benchmark firms as well as univariate tests across the subsamples Panel B reports summary statistics for the independent variables for the multinational firms The p-values of the tests of differences in means are based on t-tests The p-values of tests of differences in the medians are for a Wilcoxon rank-sum test All variables are defined in Appendix A Panel A Excess Value by sample Multinational All Benchmark Benchmark Subgroups Sample Firms US Foreign
Value Value Test of Diff (p-value) Value
Test of Diff (p-value) Value
Test of Diff (p-value)
Mean 0067 0006 lt001 -0028 lt001 0014 lt001 Median 0076 0000 lt001 -0025 lt001 0000 lt001 N 4950 33074 5998 27076 Panel B Distribution of independent variables for the multinational firms N Mean 25th 50th 75th StDev Foreign Sales 4950 0238 0074 0190 0363 0197 Industry Other 4950 0212 0000 0166 0414 0215 Log Size 4950 6955 5823 6989 8140 1687 Debt Firm Value 4950 0136 0009 0097 0212 0145 CAPX Sales 4950 0054 0020 0033 0054 0101 EBIT Sales 4950 0065 0031 0077 0121 0138 RampD Sales 4950 0049 0000 0013 0059 0083 Ad Sales 4950 0008 0000 0000 0005 0018
38
Table 3 BEA and Worldscope samples
This table reports details on affiliate sales for significant countries pooled from 1998-2008 Significant countries are defined as those in which the operations of US MNCs represent at least 02 of total firm sales (per the BEA) For significant countries the table compares their representation in terms of MNC affiliate sales in the BEA data to the distribution of firm observations in Worldscope US Foreign Affiliates (BEA) Foreign Firms (Worldscope)
Country
N (1)
Sales (millions)
(2)
Total Sales (3)
N (4)
Sales (million)
(5)
Total Sales (6)
Australia 1344 73305 056 859 255865 210 Belgium 894 52755 040 169 26136 021 Brazil 959 60245 046 1143 683167 560 Canada 2730 536767 407 1181 526750 432 China 922 75907 057 822 276541 227 France 1852 183942 139 540 105447 086 Germany 2182 275577 209 480 103888 085 Hong Kong 867 48100 036 157 34094 028 Ireland 478 36754 028 34 3467 003 Italy 1178 82116 062 111 47948 039 Japan 1494 148032 112 2281 1119223 917 Korea South 659 34500 026 3530 2752791 2255 Malaysia 386 26027 020 837 168683 138 Mexico 1405 113623 086 362 237173 194 Netherlands 1149 85105 064 47 22631 019 Singapore 1098 94917 072 139 32913 027 Spain 871 63094 048 129 37419 031 Sweden 712 32682 025 235 23090 019 Switzerland 726 60288 046 74 20242 017 Taiwan 592 30313 023 2949 787049 645 United Kingdom 3054 423934 321 1997 882423 723 United States (domicile) 4950 10460880 7923 na na na Other - Covered by WorldScope 6893 199394 151 9000 4057894 33 Other -Not Covered by WorldScope 713 5352 004 - - - Total 38108 13203609 1000 27076 12204834 100
39
Table 4 OLS regressions of firm excess values on the percent of foreign sales This table reports the results of OLS regressions of firm excess value on the percent of foreign sales (Foreign Sales) the percent of sales outside a firmrsquos primary industry (Industry Other) and control variables All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed) Dep Variable = Excess Value Coefficient t-stat Coefficient t-stat Coefficient t-stat Coefficient t-stat (1) (2) (3) (4) Foreign Sales 0349 419 0209 293 0189 266 0278 210 Industry Other -0134 -186 -0047 -072 -0053 -081 -0326 -325 Log Size 0066 634 0065 624 -0044 -096 Debt Firm Value -0196 -213 -0241 -260 -0685 -588 CAPX Sales 0901 425 0893 435 0488 227 EBIT Sales 1154 542 1170 547 0408 189 RampD Sales 2908 1242 2932 1237 1544 379 Ad Sales 1869 263 2003 280 -1696 -119 Year indicators No No Yes Yes Firm indicators No No No Yes R2 0017 0212 0226 0768 Observations 4950 4950 4950 4950
40
Table 5 Dynamic panel GMM model regressing firm excess values on the percent of foreign sales This table reports the result from a regression of the excess value on the percent of foreign sales (Foreign Sales) the percent of non-primary industry sales (Industry Other) and control variables using a dynamic panel GMM estimator All control variables are considered to be endogenous with the exception of the year indicator variables AR(1) and AR(2) are tests for first-order and second-order serial correlation in the first differenced residuals with the null hypothesis of no serial correlation The null hypothesis of the Hansen test of overidentification is that all instruments are valid The null hypothesis of the difference-in-Hansen test of exogeneity is that the instruments used for the equation in levels are exogenous All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed) Dep Variable = Excess Value Coef t-stat Excess Value (one lag) -0018 -082 Excess Value (two lags) -0048 -272 Foreign Sales 0371 270 Industry Other -0163 -171 Log Size 0010 123 Debt FirmValue -1135 -820 CAPX Sales 0590 342 EBIT Sales -0047 -300 RampD Sales 1376 531 Ad Sales -0751 -040 AR(1) test (p-value) 000 AR(2) test (p-value) 017 Hansen test of over-identification (p-value) 030 Difference-in-Hansen test of exogeneity (p-value) 093 Year indicators Yes Number of firms 1016 Number of observations 4090
41
Table 6 OLS regression of firm excess values on the percent of foreign sales controlling for a control premium This table reports the result of the OLS regression of firm excess value including firm and year fixed effects with an additional control variable to proxy for a country-level ownership control premium (Control Premium) All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed) Dep Variable = Excess Value Coef t -stat Foreign Sales 0303 224 Industry Other -0327 -325 Log Size -0045 -100 Debt FirmValue -0683 -585 CAPX Sales 0490 226 EBIT Sales 0409 189 RampD Sales 1545 379 Ad Sales -1689 -119 Control Premium -1004 -062 Year indicators Yes Firm indicators Yes R2 0768 Observations 4950
42
Table 7 OLS regression of firm excess values on the percent of foreign sales controlling for the cost of capital This table contains the results of an OLS regression of firm excess value including firm and year fixed effects with an additional control variable to proxy for a country-level cost of capital (Cost of Capital) All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed) Dep Variable = Excess Value Coef t -stat Foreign Sales 0255 178 Industry Other -0326 -324 Log Size -0043 -094 Debt FirmValue -0687 -589 CAPX Sales 0485 225 EBIT Sales 0406 188 RampD Sales 1539 379 Ad Sales -1703 -120 Cost of Capital 0004 039 Year indicators Yes Firm indicators Yes R2 0768 Observations 4950
43
Table 8 OLS regression of firm excess values on the percent of foreign sales controlling for aspects of corporate governance This table reports the results of two OLS regressions In the first column are the results of the firm excess value regression including firm and year fixed effect with five proxies for corporate governance included ndash the percent of shares owned by the CEO and the percent squared (CEO Ownership and CEO Ownership squared) the percent owned by institutional investors (Institution Ownership) whether there is a powerful CEO (Powerful CEO) and the Gompers Ishii and Metrick (2003) governance index (Governance Index) In the second column the regression includes the same sample and variables with the exception of the governance proxies which are excluded All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed)
Governance
No Governance (same sample)
Dep Variable = Excess Value Coef t-stat Coef t -stat Foreign Sales 0259 159 0251 154 Industry Other -0099 -080 -0099 -079 Log Size -0105 -185 -0102 -180 Debt FirmValue -0813 -478 -0817 -482 CAPX Sales 0440 252 0438 249 EBIT Sales 0457 247 0457 246 RampD Sales 1717 284 1716 286 Ad Sales -2210 -143 -2190 -143 CEO Ownership 0010 007
CEO Ownership squared -0029 -017 Institution Ownership 0047 067 Powerful CEO -0003 -014 Governance Index 0009 103
Year indicators Yes Yes Firm indicators Yes Yes R2 0811 0810 Observations 2550 2550
- Cover for paperpdf
- crrz_20130603_SMUpdf
-
21
more valuable than those of a foreign (domestic) firm is a more appropriate way to assess the
value implications of a multinational network
5 Robustness
51 Endogeneity related to dynamic relations
Prior research argues that the relation between industrial diversification and excess firm
value could be endogenous through dynamic relationships based on observing past outcomes
Managers of firms likely choose to enter or exit industries or geographic regions based upon
their previous performance For example Campa and Kedia (2002) find that firms are more
likely to enter new industries when prospects in their current lines of business are deteriorating
Similar dynamics could also drive a relation between excess value and FDI We assess the
robustness of the previous results by estimating a dynamic panel data model which allows us to
control for dynamic endogeneity in addition to firm level unobserved heterogeneity We employ
the GMM estimator for dynamic panel models developed by Arellano and Bond (1991) Arellano
and Bover (1995) and Blundell and Bond (1998) Our results from this analysis confirm our
previous findings that firms with a larger degree of foreign operations have positive excess
values
Dynamic panel data models are generalizations of the traditional fixed effects model where
lags of the dependent variable are added to the right hand side of the equation These additional
lags control for the impact a firmrsquos past performance has on its current performance through the
channels discussed above The GMM estimator for dynamic panel models developed by
Arellano and Bond (1991) starts by differencing the data at the firm level in neighboring time
periods to eliminate any firm specific time invariant unobserved heterogeneity that may be
22
present Eliminating the firm specific fixed effect by differencing the data with lags of the
dependent variable on the right hand side introduces correlation between the new error term and
the differenced explanatory variables This problem is solved by applying the standard GMM
estimator using lags of both the original explanatory variables and excess value as instruments
We note that the dynamic panel data framework allows us to treat both international and
industrial diversification (as well as the other firm-specific control variables) as endogenous
Following Hoechle et al (2012) we also allow contemporaneous values of these variables to be
treated as endogenous
Lagged values of the explanatory variables are valid instruments in a dynamic panel data
model when the model is dynamically complete (eg Wooldridge 2010) A dynamic panel data
model is dynamically complete conditional on the unobserved effect if enough lags of both the
explanatory and dependent variable are included to make the resulting error term uncorrelated
with all the right hand side variables Under this assumption any lags for the explanatory
variables beyond this can be used within the GMM estimator as instruments Like Hoechle et al
(2012) we add two years of lags of a firmrsquos excess value to the right hand side assuming that
this is enough lags to control for dynamic feedback effects We then use past values of all
variables beyond the second lag up to a total of seven years to act as instruments
The results of the dynamic panel data estimator are reported in Table 5 These results confirm
the previous finding of a positive relation between the degree of international operations and
excess value We note that the size and significance level of the coefficient on Foreign Sales
diversification are robust to changes in the number of lags used as instruments The Arellano and
Bond (1991) estimator includes testing diagnostics for correct specification of the model If the
model is correctly specified differences in the residuals from the levels equation should be
23
serially uncorrelated at lag one but correlated at lag two The p-values for tests of serial
correlation of the residuals at lags 1 and 2 are reported in Table 5 and these indicate that the
model is not rejected We also report in Table 5 the Hansen test for overidentification which is a
test of the null hypothesis that the instruments are valid The p-value for this test is 030
indicating that we fail to reject this null hypothesis Finally we report another test for exogeneity
known as the difference-in-Hansen test which tests the null hypothesis that the instruments in
the original equation are valid We fail to reject this null hypothesis as well
52 Identifying value effects of multinational operations via cross-border acquisitions
Another alternative to investigating a causal link between foreign direct investment and firm
value is an event study of foreign acquisitions21 Acquired foreign targets represent only a small
fraction of MNCsrsquo worldwide sales in the year of the acquisition ndash less than 1 percent in our
sample Additionally cross-border acquisitions are not the primary source of international
expansion for the average firm We find that foreign acquisitions account for between 18 percent
and 55 percent of the year-over-year change in foreign sales with the remainder of the growth
coming from either newly established entities (ie greenfield investments) or growth in existing
operations22 These results suggest that acquisitions are not a material source of growth for the
21 Doukas and Travlos (1988) find positive returns when the acquirer is not operating in the target firmrsquos country and insignificant returns when the acquirer is operating in the target firmrsquos country or expanding internationally for the first time Morck and Yeung (1991) find marginal evidence of positive returns for firms with intangible assets Finally Doukas (1995) finds positive returns only for firms with average Tobinrsquos q ratios greater than one More recent work also using an event study methodology provides mixed evidence on valuation effects of global diversification through acquisitions Dos Santos Errunza and Miller (2008) find insignificant returns for firms with already established foreign operations firms establishing initial foreign operations and firms acquiring targets in related industries Doukas and Kan (2006) and Moeller and Schlingemann (2005) find reductions to shareholder value while Francis Hasan and Sun (2008) find positive returns for acquirers of targets in segmented financial markets 22 New foreign affiliates to an MNC group each year report on the BEA survey whether they entered the group as an acquisition or a newly established entity (ie greenfield investment) Affiliates under the lowest reporting threshold do not report this information resulting in our inability to precisely identify the source of 33 percent of the increase in firm sales as either acquisition or greenfield
24
typical US MNC suggesting that an event study would lack power and generalizability For
these reasons we do not implement an event study
53 Control premium
A control premium or private benefits of control occurs when ldquosome value whatever the
source is not shared among all the shareholders in proportion of the shares owned but it is
enjoyed exclusively by the party in controlrdquo (Dyck and Zingales 2004 p 541) Using stock
prices of traded shares held by minority shareholders in determining the market value to sales
ratios of our benchmark firms could underestimate the true value of our benchmark firms if those
firms face large control premiums This in turn could induce an excess value premium because
the imputed value of these country-industry segments would be artificially low The existence of
such benefits is more prevalent in some countries than others and could affect the value of an
MNCrsquos operations in such domains We use the country-level control premiums from Dyck and
Zingales (2004) and using each firmrsquos country sales as a percent of total firm sales we obtain a
weighted average control premium that each MNC faces (Control Premium) For countries not
included in the Dyck and Zingales (2004) analysis we set the control premium equal to zero23 In
Table 6 we show that Foreign Sales remains positive and highly significant (p-value lt 001)
when we include the proxy for control premium As a result it does not appear that the excess
value premium is driven by MNCs operating in countries where majority shareholders are able to
extract large control premiums
23 Results are robust to replacing missing values with the mean control premium of the respective GDP quartile
25
54 Cost of capital
As discussed in Section 2 when countries have shallow capital markets a multinational
network may provide the benefit of a lower cost of capital for investments through an internal
capital market as well as through better access to the US capital market Prior research
establishes that there is substantial variation in the cost of capital across countries (Erb Harvey
and Viskanta 1996 and Hail and Leuz 2009) To determine whether the documented premium
is due to cost of capital advantages we include a control variable to capture the difference
between the MNCrsquos cost of capital and the cost of capital faced by its competitors in the global
markets in which it operates
We compute our Cost of Capital proxy in two steps First we obtain the country-year credit
rating published by Institutional Investor24 The rating ranges from 0 to 100 where higher values
imply that a country has a higher default risk Country-level credit risk is a reasonable predictor
of expected equity market returns and volatility (Erb et al 1996) and exhibits a highly
significant correlation with international accounting-based estimates of imputed cost of capital
(Hail and Leuz 2009) Second using each firmrsquos country sales as a percent of total firm sales
we obtain a weighted average credit risk rating across the countries in which each firm operates
A higher value implies an MNC operates in countries with higher cost of capital In Table 7 we
do not find a result consistent with MNCs gaining an advantage from their access to low cost
capitalndash Cost of Capital is not significantly associated with Excess Value25
24 We thank Cam Harvey for providing these data 25 Results are consistent using cost of capital as defined in Erb et al 1996
26
55 Corporate governance
In light of the recent finding that corporate governance mitigates the diversification discount
we examine whether the premium to multinational activity is sensitive to controls for corporate
governance Hoechle et al (2012) find that the magnitude of the industrial diversification
discount is decreased and in several specifications no longer significant in the presence of
governance proxies In the first column of Table 8 we include the five governance proxies that
are consistently significantly associated with excess value in Hoechle et al (2012) ndash the percent
of CEO ownership (CEO Ownership and CEO Ownership squared) the percent of institutional
ownership (Institution Ownership) whether the firm has a powerful or influential CEO
(Powerful CEO) and the governance index from Gompers Ishii and Metrick (2003)
(Governance Index)
As documented in Table 8 including these control variables reduces our sample size by
almost 50 (to 2550 observations) and tends to exclude the smaller firms from the analysis
None of the governance proxies are significant Relative to column (4) of Table 4 the coefficient
on Foreign Sales is similar in magnitude but is no longer statistically significant at
conventional levels To investigate whether the decreased significance is due to the smaller
sample or the inclusion of the governance variables we re-estimate the regression for the same
2550 observations after removing the governance controls The coefficient and t-statistic are
virtually identical suggesting that the loss of significance is due to the reduced sample and not
the governance controls We do not find evidence to suggest that variation in corporate
governance practices drives variation in excess value for US MNCs
27
6 Conclusion
Using a new method to quantify the valuation effects of foreign direct investment by US
domiciled firms we find robust evidence of a premium in excess value associated with the
degree of multinational operations Our method builds on that used in previous studies
examining firm excess value (eg Berger and Ofek 1995 and Denis et al 2002) by
incorporating the location in addition to the industry of each corporate affiliate This allows
important factors such as growth and cost of capital to vary across countries as well as industries
Examining a sample of multinational firms we find that a one-standard deviation (197)
increase in foreign operations is associated with an increase of between 41 and 82 in excess
value minus both a statistically and economically significant finding
This result complements the finding of the international trade literature that more productive
firms are more likely to operate internationally However the premium remains after including
firm fixed effects to control for persistent firm traits (eg productivity)
The finding of a premium stands up to a succession of robustness tests designed to evaluate
whether the result arises from alternative methods or explanations First we estimate a dynamic
panel data model using the generalized method of moments (GMM) estimator to simultaneously
account for the potential endogeneity of FDI industrial diversification and firm value Second
we include a control variable to proxy for whether the firm operates in countries with larger
control premiums which could result in understated multiples and mechanically induce a
premium Third we include a control variable to measure the cost of capital across the countries
in which an MNC operates to evaluate whether a relatively low cost of capital borne by
multinational firms is a meaningful source of the premium Fourth we control for several
corporate governance proxies which have been found to be correlated with excess values (and
28
industrial diversification) We fail to find evidence supporting any of these alternative
explanations ndash the premium in excess value arising at firms with greater multinational operations
remains significant
Overall our new method of estimating excess values for multinational firms provides strong
evidence of a statistically and economically significant premium associated with increased
international operations The premium is robust to a host of specifications and controls designed
to evaluate alternative explanations We leave to future research the questions of whether the
premium to US multinational firms applies more broadly to other countries of domicile and
what factors influence the premium
29
References
Ammann M Hoeschel D and M Schmid 2012 Is there really no conglomerate discount
Journal of Business Finance and Accounting 39(12) 264-288 Arellano M and Bond S R 1991 Some tests of specification for panel data Monte Carlo
evidence and an application to employment equations Review of Economic Studies 58 (2) 277-298
Arellano M and Bover O 1995 Another look at instrumental variables estimation of error
components models Journal of Econometrics 68 (1) 29-61 Bens D A Berger P G and S J Monahan 2011 Discretionary disclosure in financial
reporting An examination comparing internal firm data to externally reported segment data The Accounting Review 86(2) 417-449
Berger P G and E Ofek 1995 Diversificationrsquos effect on firm value Journal of Financial
Economics 37 39-65 Blundell R and Bond S R 1998 Initial conditions and moment restrictions in dynamic panel
data models Journal of Econometrics 87(1) 115-144 Campa J M and S Kedia 2002 Explaining the diversification discount The Journal of
Finance 57 (4) 1731-1762 Denis D J Denis D K and K Yost 2002 Global diversification industrial diversification
and firm value The Journal of Finance 57 (5) 1951-1979 Desai M A Foley C F and J R Hines 2004 A multinational perspective on capital structure
choice and internal capital markets The Journal of Finance 59 (6) 2451-2487 Dos Santos MB V Errunza and D Miller 2008 Does corporate international diversification
destroy value Evidence from cross-border mergers and acquisitions Journal of Banking and Finance 32(12) 2716-2724
Doukas JA 1995 Overinvestment Tobinrsquos q and gains from foreign acquisitions Journal of
Banking and Finance 19 1285-1303 Doukas J and N G Travlos 1988 The effect of corporate multinationalism on shareholdersrsquorsquo
wealth evidence from international acquisitions Journal of Finance 43(5) 1161-1175 Doukas JA and O B Kan 2006 Does global diversification destroy firm value Journal of
International Business Studies 37(3) 352-371
30
Dunning J and A Rugman 1985 The influence of Hymerrsquos dissertation on the theory of foreign direct investment The American Economic Review 75(2) 228-232
Dyck A and L Zingales 2004 Private benefits of control An international comparison The
Journal of Finance 59 (2) 537-600 Erb C Harvey C and T Viskanta 1996 Expected returns and volatility in 135 countries The
Journal of Portfolio Management 46-58 Foley C F Desai M A and K J Forbes 2008 Financial constraints and growth
Multinational and local firm responses to currency depreciations Review of Financial Studies 21 (6) 2857-2888
Francis B I Hasan and X Sun 2008 Financial market integration and the value of global
diversification Evidence for US acquirers in cross-border mergers and acquisitions Journal of Banking and Finance 321522-1540
Gande A Schenzler C and L Senbet 2009 Valuation effects of global diversification
Journal of International Business Studies 1515-1532 Gompers P Ishii J and A Metrick 2003 Corporate governance and equity prices Quarterly
Journal of Economics 118(1) 107-156 Hail L and C Leuz 2009 Cost of capital effects and changes in growth expectations around
US cross-listings Journal of Financial Economics 93 428-454 Hanson G Mataloni R and M Slaughter 2005 Vertical production networks in multinational
firms Review of Economics and Statistics 87(4) 1-15 Helpman E 2006 Trade FDI and the Organization of Firms Journal of Economic Literature
44(3) 289-630 Helpman E Melitz M J and S R Yeaple 2004 Export versus FDI with Heterogeneous Firms
American Economic Review 94(1) 300-316 Hoechle D Schmid M Walter W and D Yermack 2012 How much of the diversification
discount can be explained by poor governance Journal of Financial Economics 103 41-60 Hope O-K and W B Thomas 2008 Managerial empire building and firm disclosure Journal
of Accounting Research 46 (June) 591-626 Huizinga H P and J Voget 2009 International taxation and the direction and volume of cross-
border MampAs Journal of Finance 64 (3) 1217-1249 Jensen M 1986 Agency cost of free cash flow corporate finance and takeovers American
Economic Review Papers and Proceedings 76 323-329
31
Laeven L and R Levine 2007 Is there a diversification discount in financial conglomerates
Journal of Financial Economics 85 331-367 Lang L and R Stulz 1994 Tobinrsquos Q corporate diversification and firm performance Journal
of Political Economy 102 1248-1280 Mataloni R 2003 US multinational companies Operations in 2001 Survey of Current
Business 85-105 Matvos G and A Seru 2011 The dynamic tradeoff between corporate socialism and financial
market dislocation Estimates from internal capital markets Working paper University of Chicago Booth School of Business
Moeller SB and F P Schlingemann 2005 Global diversification and bidder gains A
comparison between cross-border and domestic acquisitions Journal of Banking and Finance 29 533-564
Morck R and B Yeung 1991 Why investors value multinationality Journal of Business 64
165-186
Morck R and B Yeung 2001 Why firms diversify Internatization vs agency behavior In Intangibles B Lev (ed) Oxford University Press pp 269-302
Myers S C and N S Majluf 1984 Corporate financing and investment decisions when firms have information that investors do not have Journal of Financial Economics 13 187-221
Schmid M and I Walter 2009 Do financial conglomerates create or destroy economic value
Journal of Financial Intermediation 18 193-216 Syverson C 2011 What determines productivity Journal of Economic Literature 49(2) 326-
365 Villalonga B 2004a Diversification discount or premium New evidence from the Business
Information Tracking Series Journal of Finance 59 479-506 Villalonga B 2004b Does diversification cause the diversification discount Financial
Management 33 5-27 Wooldridge JM 2010 Econometric Analysis of Cross Section and Panel Data (Second
Edition) Cambridge MA MIT Press Yamin M 1991 A reassessment of Hymerrsquos contribution to the theory of the transnational
corporation C Pitelis and R Sugden (eds) The Nature of the Transnational Firm London Routledge
32
Appendix A Variable Definitions
We obtain information regarding firmsrsquo operations by country and industry from the Bureau of Economic Analysis (BEA) unless otherwise noted Acronyms used below reflect Compustat mnemonics and thus indicate when we obtain data from the Compustat North America Fundamentals Annual database All independent variables are winsorized at 1
Dependent variable
Excess Value = The log of the ratio of actual firm value to imputed firm value Actual firm value is equal to market capitalization (MVE) + book value of total assets (AT) ndash book value of shareholdersrsquo equity (SEQ) Imputed firm value is calculated as the sum of the imputed values of a firmrsquos operations in each country-industry The imputed value for each country-industry is equal to sales generated in that country-industry times a multiple equal to the ratio of a benchmark firmrsquos actual value to total sales A benchmark firm is defined as a median single-segment firm operating in the same country-industry All multiples require at least five firms listed in the respective country-industry on Compustat (domestic) or Worldscope (foreign) with less than 10 of sales income and assets outside the country of domicile Similar to Berger and Ofek (1995) we eliminate extreme values where the ratio of actual to imputed firm value exceeds 400 or is less than 025
Independent variables
Foreign Sales = Sales made by the foreign operations of US MNCs as a percent of total firm sales
Industry Other = Sales outside a firmrsquos primary industry as a percent of total sales (SALE) Equal to zero for firms operating in a single business segment
Log Size = Log of total sales (SALE)
Debt Firm Value = Ratio of long-term debt (DLTT) to total market value (market capitalization (MVE) + book value of total assets (AT) ndash book value of shareholdersrsquo equity (SEQ))
CAPX Sales = Ratio of capital expenditures (CAPX) to total sales (SALE)
EBIT Sales = Ratio of earnings before interest and taxes (EBIT) to total sales (SALE)
RampD Sales = Ratio of research and development expenses (XRD) to total sales (SALE)
Ad Sales = Ratio of advertising expenses (XAD) to total sales (SALE)
Control Premium = A weighted-average (based on the sales in a given country as a percent of total firm sales) of the country-level control premium from Dyck and Zingales (2004)
Cost of Capital = A weighted-average (based on the sales in a given country as a percent of total firm sales) of the country-level credit rating from Institutional Investor (httpwwwinstitutionalinvestorcom) The country-level rating is available for 160 countries and ranges from 0 to 100 Higher values imply greater credit risk
33
CEO Ownership = Percent of shares owned by the CEO from Execucomp
Institution Ownership = Percent of shares owned by institutional investors from Thomson Reuterrsquos Institutional Holdings database
Powerful CEO = Indicator variable equal to one if the CEO is the only insider on the board of directors and also serves as the chairman and president based on data from RiskMetrics Directors database Equal to zero otherwise
Governance Index = Count measure of takeover defenses used in Gompers Ishii and Metrick (2003) from RiskMetrics Governance database As the governance data are not available for every year we follow Gompers Ishii and Metrick (2003) and assume that the firmsrsquo governance attributes are unchanged until publication of the subsequent series
34
Figure 1 Trends in multinational activity and industrial diversification from 1984-2008 This figure presents a plot of the mean percent of foreign sales and the mean percent of sales outside the firmrsquos primary industry (industrial diversification) from 1984 through 2008 Dotted lines above and below the primary lines represent 95 confidence intervals To compile the plot we include the full population of firms in Compustat
35
Table 1 Analysis of accounting data for firms reporting under multiple accounting standards This table provides a comparison of sales net income and assets for firms that changed accounting standards to or from US GAAP Comparisons are provided for the 66 firms that changed to or from US GAAP and for which we could obtain accounting data electronically and in English under both standards for the same fiscal year (eg the firm presented the accounting numbers originally under one set of standards and then restated the prior year numbers in the subsequent year under the new set of standards) Panel A shows the distribution of firms by year Panel B shows the other accounting standards to which US GAAP is compared Panel C shows the mean and median percent that the other accounting standard differs from US GAAP for the same fiscal year and accounting number Two samples are shown in Panel C The first is the full sample of firms with a change in accounting standards to or from US GAAP for which data was available The second is the subset of these firms that occurred during the sample window used in this study (ie excluding changes post-2008) The percent difference in sales is compared to the percent difference in net income and assets Tests of means are based on matched-pair t-tests Tests of medians are based on Wilcoxon matched-pair signed-rank tests Panel A Accounting standard changes by year
2003
4 6
2004
25 38
2005
8 12
2006
6 9
2007
7 11
2008
5 8
2009
5 8
2010
5 8
2011
1 2
66
Panel B Accounting standards compared to US GAAP
IFRS
47 71
Canadian GAAP
12 18
Israeli GAAP
2 3
Dutch GAAP
1 2
Indian GAAP
1 2
Japanese GAAP
1 2
Norwegian GAAP
1 2
Swiss GAAP
1 2
66
36
(Table 1 continued) Panel C Tests of differences between US GAAP and other accounting standards
Percent different from US GAAP
Comparison to sales (p-values)
N Mean Median
Mean Median
Full Sample (2001-2011)
Sales
66 236 000
Net Income
66 10653 647
005 lt001
Assets
66 1162 126
lt001 lt001
Study Sample (2001-2008)
Sales
55 257 000
Net Income
55 10303 589
011 lt001
Assets
55 1047 099
003 lt001
37
Table 2 Descriptive statistics Panel A reports the mean and medians of Excess Value for the multinational firms and single-segment benchmark firms as well as univariate tests across the subsamples Panel B reports summary statistics for the independent variables for the multinational firms The p-values of the tests of differences in means are based on t-tests The p-values of tests of differences in the medians are for a Wilcoxon rank-sum test All variables are defined in Appendix A Panel A Excess Value by sample Multinational All Benchmark Benchmark Subgroups Sample Firms US Foreign
Value Value Test of Diff (p-value) Value
Test of Diff (p-value) Value
Test of Diff (p-value)
Mean 0067 0006 lt001 -0028 lt001 0014 lt001 Median 0076 0000 lt001 -0025 lt001 0000 lt001 N 4950 33074 5998 27076 Panel B Distribution of independent variables for the multinational firms N Mean 25th 50th 75th StDev Foreign Sales 4950 0238 0074 0190 0363 0197 Industry Other 4950 0212 0000 0166 0414 0215 Log Size 4950 6955 5823 6989 8140 1687 Debt Firm Value 4950 0136 0009 0097 0212 0145 CAPX Sales 4950 0054 0020 0033 0054 0101 EBIT Sales 4950 0065 0031 0077 0121 0138 RampD Sales 4950 0049 0000 0013 0059 0083 Ad Sales 4950 0008 0000 0000 0005 0018
38
Table 3 BEA and Worldscope samples
This table reports details on affiliate sales for significant countries pooled from 1998-2008 Significant countries are defined as those in which the operations of US MNCs represent at least 02 of total firm sales (per the BEA) For significant countries the table compares their representation in terms of MNC affiliate sales in the BEA data to the distribution of firm observations in Worldscope US Foreign Affiliates (BEA) Foreign Firms (Worldscope)
Country
N (1)
Sales (millions)
(2)
Total Sales (3)
N (4)
Sales (million)
(5)
Total Sales (6)
Australia 1344 73305 056 859 255865 210 Belgium 894 52755 040 169 26136 021 Brazil 959 60245 046 1143 683167 560 Canada 2730 536767 407 1181 526750 432 China 922 75907 057 822 276541 227 France 1852 183942 139 540 105447 086 Germany 2182 275577 209 480 103888 085 Hong Kong 867 48100 036 157 34094 028 Ireland 478 36754 028 34 3467 003 Italy 1178 82116 062 111 47948 039 Japan 1494 148032 112 2281 1119223 917 Korea South 659 34500 026 3530 2752791 2255 Malaysia 386 26027 020 837 168683 138 Mexico 1405 113623 086 362 237173 194 Netherlands 1149 85105 064 47 22631 019 Singapore 1098 94917 072 139 32913 027 Spain 871 63094 048 129 37419 031 Sweden 712 32682 025 235 23090 019 Switzerland 726 60288 046 74 20242 017 Taiwan 592 30313 023 2949 787049 645 United Kingdom 3054 423934 321 1997 882423 723 United States (domicile) 4950 10460880 7923 na na na Other - Covered by WorldScope 6893 199394 151 9000 4057894 33 Other -Not Covered by WorldScope 713 5352 004 - - - Total 38108 13203609 1000 27076 12204834 100
39
Table 4 OLS regressions of firm excess values on the percent of foreign sales This table reports the results of OLS regressions of firm excess value on the percent of foreign sales (Foreign Sales) the percent of sales outside a firmrsquos primary industry (Industry Other) and control variables All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed) Dep Variable = Excess Value Coefficient t-stat Coefficient t-stat Coefficient t-stat Coefficient t-stat (1) (2) (3) (4) Foreign Sales 0349 419 0209 293 0189 266 0278 210 Industry Other -0134 -186 -0047 -072 -0053 -081 -0326 -325 Log Size 0066 634 0065 624 -0044 -096 Debt Firm Value -0196 -213 -0241 -260 -0685 -588 CAPX Sales 0901 425 0893 435 0488 227 EBIT Sales 1154 542 1170 547 0408 189 RampD Sales 2908 1242 2932 1237 1544 379 Ad Sales 1869 263 2003 280 -1696 -119 Year indicators No No Yes Yes Firm indicators No No No Yes R2 0017 0212 0226 0768 Observations 4950 4950 4950 4950
40
Table 5 Dynamic panel GMM model regressing firm excess values on the percent of foreign sales This table reports the result from a regression of the excess value on the percent of foreign sales (Foreign Sales) the percent of non-primary industry sales (Industry Other) and control variables using a dynamic panel GMM estimator All control variables are considered to be endogenous with the exception of the year indicator variables AR(1) and AR(2) are tests for first-order and second-order serial correlation in the first differenced residuals with the null hypothesis of no serial correlation The null hypothesis of the Hansen test of overidentification is that all instruments are valid The null hypothesis of the difference-in-Hansen test of exogeneity is that the instruments used for the equation in levels are exogenous All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed) Dep Variable = Excess Value Coef t-stat Excess Value (one lag) -0018 -082 Excess Value (two lags) -0048 -272 Foreign Sales 0371 270 Industry Other -0163 -171 Log Size 0010 123 Debt FirmValue -1135 -820 CAPX Sales 0590 342 EBIT Sales -0047 -300 RampD Sales 1376 531 Ad Sales -0751 -040 AR(1) test (p-value) 000 AR(2) test (p-value) 017 Hansen test of over-identification (p-value) 030 Difference-in-Hansen test of exogeneity (p-value) 093 Year indicators Yes Number of firms 1016 Number of observations 4090
41
Table 6 OLS regression of firm excess values on the percent of foreign sales controlling for a control premium This table reports the result of the OLS regression of firm excess value including firm and year fixed effects with an additional control variable to proxy for a country-level ownership control premium (Control Premium) All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed) Dep Variable = Excess Value Coef t -stat Foreign Sales 0303 224 Industry Other -0327 -325 Log Size -0045 -100 Debt FirmValue -0683 -585 CAPX Sales 0490 226 EBIT Sales 0409 189 RampD Sales 1545 379 Ad Sales -1689 -119 Control Premium -1004 -062 Year indicators Yes Firm indicators Yes R2 0768 Observations 4950
42
Table 7 OLS regression of firm excess values on the percent of foreign sales controlling for the cost of capital This table contains the results of an OLS regression of firm excess value including firm and year fixed effects with an additional control variable to proxy for a country-level cost of capital (Cost of Capital) All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed) Dep Variable = Excess Value Coef t -stat Foreign Sales 0255 178 Industry Other -0326 -324 Log Size -0043 -094 Debt FirmValue -0687 -589 CAPX Sales 0485 225 EBIT Sales 0406 188 RampD Sales 1539 379 Ad Sales -1703 -120 Cost of Capital 0004 039 Year indicators Yes Firm indicators Yes R2 0768 Observations 4950
43
Table 8 OLS regression of firm excess values on the percent of foreign sales controlling for aspects of corporate governance This table reports the results of two OLS regressions In the first column are the results of the firm excess value regression including firm and year fixed effect with five proxies for corporate governance included ndash the percent of shares owned by the CEO and the percent squared (CEO Ownership and CEO Ownership squared) the percent owned by institutional investors (Institution Ownership) whether there is a powerful CEO (Powerful CEO) and the Gompers Ishii and Metrick (2003) governance index (Governance Index) In the second column the regression includes the same sample and variables with the exception of the governance proxies which are excluded All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed)
Governance
No Governance (same sample)
Dep Variable = Excess Value Coef t-stat Coef t -stat Foreign Sales 0259 159 0251 154 Industry Other -0099 -080 -0099 -079 Log Size -0105 -185 -0102 -180 Debt FirmValue -0813 -478 -0817 -482 CAPX Sales 0440 252 0438 249 EBIT Sales 0457 247 0457 246 RampD Sales 1717 284 1716 286 Ad Sales -2210 -143 -2190 -143 CEO Ownership 0010 007
CEO Ownership squared -0029 -017 Institution Ownership 0047 067 Powerful CEO -0003 -014 Governance Index 0009 103
Year indicators Yes Yes Firm indicators Yes Yes R2 0811 0810 Observations 2550 2550
- Cover for paperpdf
- crrz_20130603_SMUpdf
-
22
present Eliminating the firm specific fixed effect by differencing the data with lags of the
dependent variable on the right hand side introduces correlation between the new error term and
the differenced explanatory variables This problem is solved by applying the standard GMM
estimator using lags of both the original explanatory variables and excess value as instruments
We note that the dynamic panel data framework allows us to treat both international and
industrial diversification (as well as the other firm-specific control variables) as endogenous
Following Hoechle et al (2012) we also allow contemporaneous values of these variables to be
treated as endogenous
Lagged values of the explanatory variables are valid instruments in a dynamic panel data
model when the model is dynamically complete (eg Wooldridge 2010) A dynamic panel data
model is dynamically complete conditional on the unobserved effect if enough lags of both the
explanatory and dependent variable are included to make the resulting error term uncorrelated
with all the right hand side variables Under this assumption any lags for the explanatory
variables beyond this can be used within the GMM estimator as instruments Like Hoechle et al
(2012) we add two years of lags of a firmrsquos excess value to the right hand side assuming that
this is enough lags to control for dynamic feedback effects We then use past values of all
variables beyond the second lag up to a total of seven years to act as instruments
The results of the dynamic panel data estimator are reported in Table 5 These results confirm
the previous finding of a positive relation between the degree of international operations and
excess value We note that the size and significance level of the coefficient on Foreign Sales
diversification are robust to changes in the number of lags used as instruments The Arellano and
Bond (1991) estimator includes testing diagnostics for correct specification of the model If the
model is correctly specified differences in the residuals from the levels equation should be
23
serially uncorrelated at lag one but correlated at lag two The p-values for tests of serial
correlation of the residuals at lags 1 and 2 are reported in Table 5 and these indicate that the
model is not rejected We also report in Table 5 the Hansen test for overidentification which is a
test of the null hypothesis that the instruments are valid The p-value for this test is 030
indicating that we fail to reject this null hypothesis Finally we report another test for exogeneity
known as the difference-in-Hansen test which tests the null hypothesis that the instruments in
the original equation are valid We fail to reject this null hypothesis as well
52 Identifying value effects of multinational operations via cross-border acquisitions
Another alternative to investigating a causal link between foreign direct investment and firm
value is an event study of foreign acquisitions21 Acquired foreign targets represent only a small
fraction of MNCsrsquo worldwide sales in the year of the acquisition ndash less than 1 percent in our
sample Additionally cross-border acquisitions are not the primary source of international
expansion for the average firm We find that foreign acquisitions account for between 18 percent
and 55 percent of the year-over-year change in foreign sales with the remainder of the growth
coming from either newly established entities (ie greenfield investments) or growth in existing
operations22 These results suggest that acquisitions are not a material source of growth for the
21 Doukas and Travlos (1988) find positive returns when the acquirer is not operating in the target firmrsquos country and insignificant returns when the acquirer is operating in the target firmrsquos country or expanding internationally for the first time Morck and Yeung (1991) find marginal evidence of positive returns for firms with intangible assets Finally Doukas (1995) finds positive returns only for firms with average Tobinrsquos q ratios greater than one More recent work also using an event study methodology provides mixed evidence on valuation effects of global diversification through acquisitions Dos Santos Errunza and Miller (2008) find insignificant returns for firms with already established foreign operations firms establishing initial foreign operations and firms acquiring targets in related industries Doukas and Kan (2006) and Moeller and Schlingemann (2005) find reductions to shareholder value while Francis Hasan and Sun (2008) find positive returns for acquirers of targets in segmented financial markets 22 New foreign affiliates to an MNC group each year report on the BEA survey whether they entered the group as an acquisition or a newly established entity (ie greenfield investment) Affiliates under the lowest reporting threshold do not report this information resulting in our inability to precisely identify the source of 33 percent of the increase in firm sales as either acquisition or greenfield
24
typical US MNC suggesting that an event study would lack power and generalizability For
these reasons we do not implement an event study
53 Control premium
A control premium or private benefits of control occurs when ldquosome value whatever the
source is not shared among all the shareholders in proportion of the shares owned but it is
enjoyed exclusively by the party in controlrdquo (Dyck and Zingales 2004 p 541) Using stock
prices of traded shares held by minority shareholders in determining the market value to sales
ratios of our benchmark firms could underestimate the true value of our benchmark firms if those
firms face large control premiums This in turn could induce an excess value premium because
the imputed value of these country-industry segments would be artificially low The existence of
such benefits is more prevalent in some countries than others and could affect the value of an
MNCrsquos operations in such domains We use the country-level control premiums from Dyck and
Zingales (2004) and using each firmrsquos country sales as a percent of total firm sales we obtain a
weighted average control premium that each MNC faces (Control Premium) For countries not
included in the Dyck and Zingales (2004) analysis we set the control premium equal to zero23 In
Table 6 we show that Foreign Sales remains positive and highly significant (p-value lt 001)
when we include the proxy for control premium As a result it does not appear that the excess
value premium is driven by MNCs operating in countries where majority shareholders are able to
extract large control premiums
23 Results are robust to replacing missing values with the mean control premium of the respective GDP quartile
25
54 Cost of capital
As discussed in Section 2 when countries have shallow capital markets a multinational
network may provide the benefit of a lower cost of capital for investments through an internal
capital market as well as through better access to the US capital market Prior research
establishes that there is substantial variation in the cost of capital across countries (Erb Harvey
and Viskanta 1996 and Hail and Leuz 2009) To determine whether the documented premium
is due to cost of capital advantages we include a control variable to capture the difference
between the MNCrsquos cost of capital and the cost of capital faced by its competitors in the global
markets in which it operates
We compute our Cost of Capital proxy in two steps First we obtain the country-year credit
rating published by Institutional Investor24 The rating ranges from 0 to 100 where higher values
imply that a country has a higher default risk Country-level credit risk is a reasonable predictor
of expected equity market returns and volatility (Erb et al 1996) and exhibits a highly
significant correlation with international accounting-based estimates of imputed cost of capital
(Hail and Leuz 2009) Second using each firmrsquos country sales as a percent of total firm sales
we obtain a weighted average credit risk rating across the countries in which each firm operates
A higher value implies an MNC operates in countries with higher cost of capital In Table 7 we
do not find a result consistent with MNCs gaining an advantage from their access to low cost
capitalndash Cost of Capital is not significantly associated with Excess Value25
24 We thank Cam Harvey for providing these data 25 Results are consistent using cost of capital as defined in Erb et al 1996
26
55 Corporate governance
In light of the recent finding that corporate governance mitigates the diversification discount
we examine whether the premium to multinational activity is sensitive to controls for corporate
governance Hoechle et al (2012) find that the magnitude of the industrial diversification
discount is decreased and in several specifications no longer significant in the presence of
governance proxies In the first column of Table 8 we include the five governance proxies that
are consistently significantly associated with excess value in Hoechle et al (2012) ndash the percent
of CEO ownership (CEO Ownership and CEO Ownership squared) the percent of institutional
ownership (Institution Ownership) whether the firm has a powerful or influential CEO
(Powerful CEO) and the governance index from Gompers Ishii and Metrick (2003)
(Governance Index)
As documented in Table 8 including these control variables reduces our sample size by
almost 50 (to 2550 observations) and tends to exclude the smaller firms from the analysis
None of the governance proxies are significant Relative to column (4) of Table 4 the coefficient
on Foreign Sales is similar in magnitude but is no longer statistically significant at
conventional levels To investigate whether the decreased significance is due to the smaller
sample or the inclusion of the governance variables we re-estimate the regression for the same
2550 observations after removing the governance controls The coefficient and t-statistic are
virtually identical suggesting that the loss of significance is due to the reduced sample and not
the governance controls We do not find evidence to suggest that variation in corporate
governance practices drives variation in excess value for US MNCs
27
6 Conclusion
Using a new method to quantify the valuation effects of foreign direct investment by US
domiciled firms we find robust evidence of a premium in excess value associated with the
degree of multinational operations Our method builds on that used in previous studies
examining firm excess value (eg Berger and Ofek 1995 and Denis et al 2002) by
incorporating the location in addition to the industry of each corporate affiliate This allows
important factors such as growth and cost of capital to vary across countries as well as industries
Examining a sample of multinational firms we find that a one-standard deviation (197)
increase in foreign operations is associated with an increase of between 41 and 82 in excess
value minus both a statistically and economically significant finding
This result complements the finding of the international trade literature that more productive
firms are more likely to operate internationally However the premium remains after including
firm fixed effects to control for persistent firm traits (eg productivity)
The finding of a premium stands up to a succession of robustness tests designed to evaluate
whether the result arises from alternative methods or explanations First we estimate a dynamic
panel data model using the generalized method of moments (GMM) estimator to simultaneously
account for the potential endogeneity of FDI industrial diversification and firm value Second
we include a control variable to proxy for whether the firm operates in countries with larger
control premiums which could result in understated multiples and mechanically induce a
premium Third we include a control variable to measure the cost of capital across the countries
in which an MNC operates to evaluate whether a relatively low cost of capital borne by
multinational firms is a meaningful source of the premium Fourth we control for several
corporate governance proxies which have been found to be correlated with excess values (and
28
industrial diversification) We fail to find evidence supporting any of these alternative
explanations ndash the premium in excess value arising at firms with greater multinational operations
remains significant
Overall our new method of estimating excess values for multinational firms provides strong
evidence of a statistically and economically significant premium associated with increased
international operations The premium is robust to a host of specifications and controls designed
to evaluate alternative explanations We leave to future research the questions of whether the
premium to US multinational firms applies more broadly to other countries of domicile and
what factors influence the premium
29
References
Ammann M Hoeschel D and M Schmid 2012 Is there really no conglomerate discount
Journal of Business Finance and Accounting 39(12) 264-288 Arellano M and Bond S R 1991 Some tests of specification for panel data Monte Carlo
evidence and an application to employment equations Review of Economic Studies 58 (2) 277-298
Arellano M and Bover O 1995 Another look at instrumental variables estimation of error
components models Journal of Econometrics 68 (1) 29-61 Bens D A Berger P G and S J Monahan 2011 Discretionary disclosure in financial
reporting An examination comparing internal firm data to externally reported segment data The Accounting Review 86(2) 417-449
Berger P G and E Ofek 1995 Diversificationrsquos effect on firm value Journal of Financial
Economics 37 39-65 Blundell R and Bond S R 1998 Initial conditions and moment restrictions in dynamic panel
data models Journal of Econometrics 87(1) 115-144 Campa J M and S Kedia 2002 Explaining the diversification discount The Journal of
Finance 57 (4) 1731-1762 Denis D J Denis D K and K Yost 2002 Global diversification industrial diversification
and firm value The Journal of Finance 57 (5) 1951-1979 Desai M A Foley C F and J R Hines 2004 A multinational perspective on capital structure
choice and internal capital markets The Journal of Finance 59 (6) 2451-2487 Dos Santos MB V Errunza and D Miller 2008 Does corporate international diversification
destroy value Evidence from cross-border mergers and acquisitions Journal of Banking and Finance 32(12) 2716-2724
Doukas JA 1995 Overinvestment Tobinrsquos q and gains from foreign acquisitions Journal of
Banking and Finance 19 1285-1303 Doukas J and N G Travlos 1988 The effect of corporate multinationalism on shareholdersrsquorsquo
wealth evidence from international acquisitions Journal of Finance 43(5) 1161-1175 Doukas JA and O B Kan 2006 Does global diversification destroy firm value Journal of
International Business Studies 37(3) 352-371
30
Dunning J and A Rugman 1985 The influence of Hymerrsquos dissertation on the theory of foreign direct investment The American Economic Review 75(2) 228-232
Dyck A and L Zingales 2004 Private benefits of control An international comparison The
Journal of Finance 59 (2) 537-600 Erb C Harvey C and T Viskanta 1996 Expected returns and volatility in 135 countries The
Journal of Portfolio Management 46-58 Foley C F Desai M A and K J Forbes 2008 Financial constraints and growth
Multinational and local firm responses to currency depreciations Review of Financial Studies 21 (6) 2857-2888
Francis B I Hasan and X Sun 2008 Financial market integration and the value of global
diversification Evidence for US acquirers in cross-border mergers and acquisitions Journal of Banking and Finance 321522-1540
Gande A Schenzler C and L Senbet 2009 Valuation effects of global diversification
Journal of International Business Studies 1515-1532 Gompers P Ishii J and A Metrick 2003 Corporate governance and equity prices Quarterly
Journal of Economics 118(1) 107-156 Hail L and C Leuz 2009 Cost of capital effects and changes in growth expectations around
US cross-listings Journal of Financial Economics 93 428-454 Hanson G Mataloni R and M Slaughter 2005 Vertical production networks in multinational
firms Review of Economics and Statistics 87(4) 1-15 Helpman E 2006 Trade FDI and the Organization of Firms Journal of Economic Literature
44(3) 289-630 Helpman E Melitz M J and S R Yeaple 2004 Export versus FDI with Heterogeneous Firms
American Economic Review 94(1) 300-316 Hoechle D Schmid M Walter W and D Yermack 2012 How much of the diversification
discount can be explained by poor governance Journal of Financial Economics 103 41-60 Hope O-K and W B Thomas 2008 Managerial empire building and firm disclosure Journal
of Accounting Research 46 (June) 591-626 Huizinga H P and J Voget 2009 International taxation and the direction and volume of cross-
border MampAs Journal of Finance 64 (3) 1217-1249 Jensen M 1986 Agency cost of free cash flow corporate finance and takeovers American
Economic Review Papers and Proceedings 76 323-329
31
Laeven L and R Levine 2007 Is there a diversification discount in financial conglomerates
Journal of Financial Economics 85 331-367 Lang L and R Stulz 1994 Tobinrsquos Q corporate diversification and firm performance Journal
of Political Economy 102 1248-1280 Mataloni R 2003 US multinational companies Operations in 2001 Survey of Current
Business 85-105 Matvos G and A Seru 2011 The dynamic tradeoff between corporate socialism and financial
market dislocation Estimates from internal capital markets Working paper University of Chicago Booth School of Business
Moeller SB and F P Schlingemann 2005 Global diversification and bidder gains A
comparison between cross-border and domestic acquisitions Journal of Banking and Finance 29 533-564
Morck R and B Yeung 1991 Why investors value multinationality Journal of Business 64
165-186
Morck R and B Yeung 2001 Why firms diversify Internatization vs agency behavior In Intangibles B Lev (ed) Oxford University Press pp 269-302
Myers S C and N S Majluf 1984 Corporate financing and investment decisions when firms have information that investors do not have Journal of Financial Economics 13 187-221
Schmid M and I Walter 2009 Do financial conglomerates create or destroy economic value
Journal of Financial Intermediation 18 193-216 Syverson C 2011 What determines productivity Journal of Economic Literature 49(2) 326-
365 Villalonga B 2004a Diversification discount or premium New evidence from the Business
Information Tracking Series Journal of Finance 59 479-506 Villalonga B 2004b Does diversification cause the diversification discount Financial
Management 33 5-27 Wooldridge JM 2010 Econometric Analysis of Cross Section and Panel Data (Second
Edition) Cambridge MA MIT Press Yamin M 1991 A reassessment of Hymerrsquos contribution to the theory of the transnational
corporation C Pitelis and R Sugden (eds) The Nature of the Transnational Firm London Routledge
32
Appendix A Variable Definitions
We obtain information regarding firmsrsquo operations by country and industry from the Bureau of Economic Analysis (BEA) unless otherwise noted Acronyms used below reflect Compustat mnemonics and thus indicate when we obtain data from the Compustat North America Fundamentals Annual database All independent variables are winsorized at 1
Dependent variable
Excess Value = The log of the ratio of actual firm value to imputed firm value Actual firm value is equal to market capitalization (MVE) + book value of total assets (AT) ndash book value of shareholdersrsquo equity (SEQ) Imputed firm value is calculated as the sum of the imputed values of a firmrsquos operations in each country-industry The imputed value for each country-industry is equal to sales generated in that country-industry times a multiple equal to the ratio of a benchmark firmrsquos actual value to total sales A benchmark firm is defined as a median single-segment firm operating in the same country-industry All multiples require at least five firms listed in the respective country-industry on Compustat (domestic) or Worldscope (foreign) with less than 10 of sales income and assets outside the country of domicile Similar to Berger and Ofek (1995) we eliminate extreme values where the ratio of actual to imputed firm value exceeds 400 or is less than 025
Independent variables
Foreign Sales = Sales made by the foreign operations of US MNCs as a percent of total firm sales
Industry Other = Sales outside a firmrsquos primary industry as a percent of total sales (SALE) Equal to zero for firms operating in a single business segment
Log Size = Log of total sales (SALE)
Debt Firm Value = Ratio of long-term debt (DLTT) to total market value (market capitalization (MVE) + book value of total assets (AT) ndash book value of shareholdersrsquo equity (SEQ))
CAPX Sales = Ratio of capital expenditures (CAPX) to total sales (SALE)
EBIT Sales = Ratio of earnings before interest and taxes (EBIT) to total sales (SALE)
RampD Sales = Ratio of research and development expenses (XRD) to total sales (SALE)
Ad Sales = Ratio of advertising expenses (XAD) to total sales (SALE)
Control Premium = A weighted-average (based on the sales in a given country as a percent of total firm sales) of the country-level control premium from Dyck and Zingales (2004)
Cost of Capital = A weighted-average (based on the sales in a given country as a percent of total firm sales) of the country-level credit rating from Institutional Investor (httpwwwinstitutionalinvestorcom) The country-level rating is available for 160 countries and ranges from 0 to 100 Higher values imply greater credit risk
33
CEO Ownership = Percent of shares owned by the CEO from Execucomp
Institution Ownership = Percent of shares owned by institutional investors from Thomson Reuterrsquos Institutional Holdings database
Powerful CEO = Indicator variable equal to one if the CEO is the only insider on the board of directors and also serves as the chairman and president based on data from RiskMetrics Directors database Equal to zero otherwise
Governance Index = Count measure of takeover defenses used in Gompers Ishii and Metrick (2003) from RiskMetrics Governance database As the governance data are not available for every year we follow Gompers Ishii and Metrick (2003) and assume that the firmsrsquo governance attributes are unchanged until publication of the subsequent series
34
Figure 1 Trends in multinational activity and industrial diversification from 1984-2008 This figure presents a plot of the mean percent of foreign sales and the mean percent of sales outside the firmrsquos primary industry (industrial diversification) from 1984 through 2008 Dotted lines above and below the primary lines represent 95 confidence intervals To compile the plot we include the full population of firms in Compustat
35
Table 1 Analysis of accounting data for firms reporting under multiple accounting standards This table provides a comparison of sales net income and assets for firms that changed accounting standards to or from US GAAP Comparisons are provided for the 66 firms that changed to or from US GAAP and for which we could obtain accounting data electronically and in English under both standards for the same fiscal year (eg the firm presented the accounting numbers originally under one set of standards and then restated the prior year numbers in the subsequent year under the new set of standards) Panel A shows the distribution of firms by year Panel B shows the other accounting standards to which US GAAP is compared Panel C shows the mean and median percent that the other accounting standard differs from US GAAP for the same fiscal year and accounting number Two samples are shown in Panel C The first is the full sample of firms with a change in accounting standards to or from US GAAP for which data was available The second is the subset of these firms that occurred during the sample window used in this study (ie excluding changes post-2008) The percent difference in sales is compared to the percent difference in net income and assets Tests of means are based on matched-pair t-tests Tests of medians are based on Wilcoxon matched-pair signed-rank tests Panel A Accounting standard changes by year
2003
4 6
2004
25 38
2005
8 12
2006
6 9
2007
7 11
2008
5 8
2009
5 8
2010
5 8
2011
1 2
66
Panel B Accounting standards compared to US GAAP
IFRS
47 71
Canadian GAAP
12 18
Israeli GAAP
2 3
Dutch GAAP
1 2
Indian GAAP
1 2
Japanese GAAP
1 2
Norwegian GAAP
1 2
Swiss GAAP
1 2
66
36
(Table 1 continued) Panel C Tests of differences between US GAAP and other accounting standards
Percent different from US GAAP
Comparison to sales (p-values)
N Mean Median
Mean Median
Full Sample (2001-2011)
Sales
66 236 000
Net Income
66 10653 647
005 lt001
Assets
66 1162 126
lt001 lt001
Study Sample (2001-2008)
Sales
55 257 000
Net Income
55 10303 589
011 lt001
Assets
55 1047 099
003 lt001
37
Table 2 Descriptive statistics Panel A reports the mean and medians of Excess Value for the multinational firms and single-segment benchmark firms as well as univariate tests across the subsamples Panel B reports summary statistics for the independent variables for the multinational firms The p-values of the tests of differences in means are based on t-tests The p-values of tests of differences in the medians are for a Wilcoxon rank-sum test All variables are defined in Appendix A Panel A Excess Value by sample Multinational All Benchmark Benchmark Subgroups Sample Firms US Foreign
Value Value Test of Diff (p-value) Value
Test of Diff (p-value) Value
Test of Diff (p-value)
Mean 0067 0006 lt001 -0028 lt001 0014 lt001 Median 0076 0000 lt001 -0025 lt001 0000 lt001 N 4950 33074 5998 27076 Panel B Distribution of independent variables for the multinational firms N Mean 25th 50th 75th StDev Foreign Sales 4950 0238 0074 0190 0363 0197 Industry Other 4950 0212 0000 0166 0414 0215 Log Size 4950 6955 5823 6989 8140 1687 Debt Firm Value 4950 0136 0009 0097 0212 0145 CAPX Sales 4950 0054 0020 0033 0054 0101 EBIT Sales 4950 0065 0031 0077 0121 0138 RampD Sales 4950 0049 0000 0013 0059 0083 Ad Sales 4950 0008 0000 0000 0005 0018
38
Table 3 BEA and Worldscope samples
This table reports details on affiliate sales for significant countries pooled from 1998-2008 Significant countries are defined as those in which the operations of US MNCs represent at least 02 of total firm sales (per the BEA) For significant countries the table compares their representation in terms of MNC affiliate sales in the BEA data to the distribution of firm observations in Worldscope US Foreign Affiliates (BEA) Foreign Firms (Worldscope)
Country
N (1)
Sales (millions)
(2)
Total Sales (3)
N (4)
Sales (million)
(5)
Total Sales (6)
Australia 1344 73305 056 859 255865 210 Belgium 894 52755 040 169 26136 021 Brazil 959 60245 046 1143 683167 560 Canada 2730 536767 407 1181 526750 432 China 922 75907 057 822 276541 227 France 1852 183942 139 540 105447 086 Germany 2182 275577 209 480 103888 085 Hong Kong 867 48100 036 157 34094 028 Ireland 478 36754 028 34 3467 003 Italy 1178 82116 062 111 47948 039 Japan 1494 148032 112 2281 1119223 917 Korea South 659 34500 026 3530 2752791 2255 Malaysia 386 26027 020 837 168683 138 Mexico 1405 113623 086 362 237173 194 Netherlands 1149 85105 064 47 22631 019 Singapore 1098 94917 072 139 32913 027 Spain 871 63094 048 129 37419 031 Sweden 712 32682 025 235 23090 019 Switzerland 726 60288 046 74 20242 017 Taiwan 592 30313 023 2949 787049 645 United Kingdom 3054 423934 321 1997 882423 723 United States (domicile) 4950 10460880 7923 na na na Other - Covered by WorldScope 6893 199394 151 9000 4057894 33 Other -Not Covered by WorldScope 713 5352 004 - - - Total 38108 13203609 1000 27076 12204834 100
39
Table 4 OLS regressions of firm excess values on the percent of foreign sales This table reports the results of OLS regressions of firm excess value on the percent of foreign sales (Foreign Sales) the percent of sales outside a firmrsquos primary industry (Industry Other) and control variables All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed) Dep Variable = Excess Value Coefficient t-stat Coefficient t-stat Coefficient t-stat Coefficient t-stat (1) (2) (3) (4) Foreign Sales 0349 419 0209 293 0189 266 0278 210 Industry Other -0134 -186 -0047 -072 -0053 -081 -0326 -325 Log Size 0066 634 0065 624 -0044 -096 Debt Firm Value -0196 -213 -0241 -260 -0685 -588 CAPX Sales 0901 425 0893 435 0488 227 EBIT Sales 1154 542 1170 547 0408 189 RampD Sales 2908 1242 2932 1237 1544 379 Ad Sales 1869 263 2003 280 -1696 -119 Year indicators No No Yes Yes Firm indicators No No No Yes R2 0017 0212 0226 0768 Observations 4950 4950 4950 4950
40
Table 5 Dynamic panel GMM model regressing firm excess values on the percent of foreign sales This table reports the result from a regression of the excess value on the percent of foreign sales (Foreign Sales) the percent of non-primary industry sales (Industry Other) and control variables using a dynamic panel GMM estimator All control variables are considered to be endogenous with the exception of the year indicator variables AR(1) and AR(2) are tests for first-order and second-order serial correlation in the first differenced residuals with the null hypothesis of no serial correlation The null hypothesis of the Hansen test of overidentification is that all instruments are valid The null hypothesis of the difference-in-Hansen test of exogeneity is that the instruments used for the equation in levels are exogenous All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed) Dep Variable = Excess Value Coef t-stat Excess Value (one lag) -0018 -082 Excess Value (two lags) -0048 -272 Foreign Sales 0371 270 Industry Other -0163 -171 Log Size 0010 123 Debt FirmValue -1135 -820 CAPX Sales 0590 342 EBIT Sales -0047 -300 RampD Sales 1376 531 Ad Sales -0751 -040 AR(1) test (p-value) 000 AR(2) test (p-value) 017 Hansen test of over-identification (p-value) 030 Difference-in-Hansen test of exogeneity (p-value) 093 Year indicators Yes Number of firms 1016 Number of observations 4090
41
Table 6 OLS regression of firm excess values on the percent of foreign sales controlling for a control premium This table reports the result of the OLS regression of firm excess value including firm and year fixed effects with an additional control variable to proxy for a country-level ownership control premium (Control Premium) All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed) Dep Variable = Excess Value Coef t -stat Foreign Sales 0303 224 Industry Other -0327 -325 Log Size -0045 -100 Debt FirmValue -0683 -585 CAPX Sales 0490 226 EBIT Sales 0409 189 RampD Sales 1545 379 Ad Sales -1689 -119 Control Premium -1004 -062 Year indicators Yes Firm indicators Yes R2 0768 Observations 4950
42
Table 7 OLS regression of firm excess values on the percent of foreign sales controlling for the cost of capital This table contains the results of an OLS regression of firm excess value including firm and year fixed effects with an additional control variable to proxy for a country-level cost of capital (Cost of Capital) All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed) Dep Variable = Excess Value Coef t -stat Foreign Sales 0255 178 Industry Other -0326 -324 Log Size -0043 -094 Debt FirmValue -0687 -589 CAPX Sales 0485 225 EBIT Sales 0406 188 RampD Sales 1539 379 Ad Sales -1703 -120 Cost of Capital 0004 039 Year indicators Yes Firm indicators Yes R2 0768 Observations 4950
43
Table 8 OLS regression of firm excess values on the percent of foreign sales controlling for aspects of corporate governance This table reports the results of two OLS regressions In the first column are the results of the firm excess value regression including firm and year fixed effect with five proxies for corporate governance included ndash the percent of shares owned by the CEO and the percent squared (CEO Ownership and CEO Ownership squared) the percent owned by institutional investors (Institution Ownership) whether there is a powerful CEO (Powerful CEO) and the Gompers Ishii and Metrick (2003) governance index (Governance Index) In the second column the regression includes the same sample and variables with the exception of the governance proxies which are excluded All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed)
Governance
No Governance (same sample)
Dep Variable = Excess Value Coef t-stat Coef t -stat Foreign Sales 0259 159 0251 154 Industry Other -0099 -080 -0099 -079 Log Size -0105 -185 -0102 -180 Debt FirmValue -0813 -478 -0817 -482 CAPX Sales 0440 252 0438 249 EBIT Sales 0457 247 0457 246 RampD Sales 1717 284 1716 286 Ad Sales -2210 -143 -2190 -143 CEO Ownership 0010 007
CEO Ownership squared -0029 -017 Institution Ownership 0047 067 Powerful CEO -0003 -014 Governance Index 0009 103
Year indicators Yes Yes Firm indicators Yes Yes R2 0811 0810 Observations 2550 2550
- Cover for paperpdf
- crrz_20130603_SMUpdf
-
23
serially uncorrelated at lag one but correlated at lag two The p-values for tests of serial
correlation of the residuals at lags 1 and 2 are reported in Table 5 and these indicate that the
model is not rejected We also report in Table 5 the Hansen test for overidentification which is a
test of the null hypothesis that the instruments are valid The p-value for this test is 030
indicating that we fail to reject this null hypothesis Finally we report another test for exogeneity
known as the difference-in-Hansen test which tests the null hypothesis that the instruments in
the original equation are valid We fail to reject this null hypothesis as well
52 Identifying value effects of multinational operations via cross-border acquisitions
Another alternative to investigating a causal link between foreign direct investment and firm
value is an event study of foreign acquisitions21 Acquired foreign targets represent only a small
fraction of MNCsrsquo worldwide sales in the year of the acquisition ndash less than 1 percent in our
sample Additionally cross-border acquisitions are not the primary source of international
expansion for the average firm We find that foreign acquisitions account for between 18 percent
and 55 percent of the year-over-year change in foreign sales with the remainder of the growth
coming from either newly established entities (ie greenfield investments) or growth in existing
operations22 These results suggest that acquisitions are not a material source of growth for the
21 Doukas and Travlos (1988) find positive returns when the acquirer is not operating in the target firmrsquos country and insignificant returns when the acquirer is operating in the target firmrsquos country or expanding internationally for the first time Morck and Yeung (1991) find marginal evidence of positive returns for firms with intangible assets Finally Doukas (1995) finds positive returns only for firms with average Tobinrsquos q ratios greater than one More recent work also using an event study methodology provides mixed evidence on valuation effects of global diversification through acquisitions Dos Santos Errunza and Miller (2008) find insignificant returns for firms with already established foreign operations firms establishing initial foreign operations and firms acquiring targets in related industries Doukas and Kan (2006) and Moeller and Schlingemann (2005) find reductions to shareholder value while Francis Hasan and Sun (2008) find positive returns for acquirers of targets in segmented financial markets 22 New foreign affiliates to an MNC group each year report on the BEA survey whether they entered the group as an acquisition or a newly established entity (ie greenfield investment) Affiliates under the lowest reporting threshold do not report this information resulting in our inability to precisely identify the source of 33 percent of the increase in firm sales as either acquisition or greenfield
24
typical US MNC suggesting that an event study would lack power and generalizability For
these reasons we do not implement an event study
53 Control premium
A control premium or private benefits of control occurs when ldquosome value whatever the
source is not shared among all the shareholders in proportion of the shares owned but it is
enjoyed exclusively by the party in controlrdquo (Dyck and Zingales 2004 p 541) Using stock
prices of traded shares held by minority shareholders in determining the market value to sales
ratios of our benchmark firms could underestimate the true value of our benchmark firms if those
firms face large control premiums This in turn could induce an excess value premium because
the imputed value of these country-industry segments would be artificially low The existence of
such benefits is more prevalent in some countries than others and could affect the value of an
MNCrsquos operations in such domains We use the country-level control premiums from Dyck and
Zingales (2004) and using each firmrsquos country sales as a percent of total firm sales we obtain a
weighted average control premium that each MNC faces (Control Premium) For countries not
included in the Dyck and Zingales (2004) analysis we set the control premium equal to zero23 In
Table 6 we show that Foreign Sales remains positive and highly significant (p-value lt 001)
when we include the proxy for control premium As a result it does not appear that the excess
value premium is driven by MNCs operating in countries where majority shareholders are able to
extract large control premiums
23 Results are robust to replacing missing values with the mean control premium of the respective GDP quartile
25
54 Cost of capital
As discussed in Section 2 when countries have shallow capital markets a multinational
network may provide the benefit of a lower cost of capital for investments through an internal
capital market as well as through better access to the US capital market Prior research
establishes that there is substantial variation in the cost of capital across countries (Erb Harvey
and Viskanta 1996 and Hail and Leuz 2009) To determine whether the documented premium
is due to cost of capital advantages we include a control variable to capture the difference
between the MNCrsquos cost of capital and the cost of capital faced by its competitors in the global
markets in which it operates
We compute our Cost of Capital proxy in two steps First we obtain the country-year credit
rating published by Institutional Investor24 The rating ranges from 0 to 100 where higher values
imply that a country has a higher default risk Country-level credit risk is a reasonable predictor
of expected equity market returns and volatility (Erb et al 1996) and exhibits a highly
significant correlation with international accounting-based estimates of imputed cost of capital
(Hail and Leuz 2009) Second using each firmrsquos country sales as a percent of total firm sales
we obtain a weighted average credit risk rating across the countries in which each firm operates
A higher value implies an MNC operates in countries with higher cost of capital In Table 7 we
do not find a result consistent with MNCs gaining an advantage from their access to low cost
capitalndash Cost of Capital is not significantly associated with Excess Value25
24 We thank Cam Harvey for providing these data 25 Results are consistent using cost of capital as defined in Erb et al 1996
26
55 Corporate governance
In light of the recent finding that corporate governance mitigates the diversification discount
we examine whether the premium to multinational activity is sensitive to controls for corporate
governance Hoechle et al (2012) find that the magnitude of the industrial diversification
discount is decreased and in several specifications no longer significant in the presence of
governance proxies In the first column of Table 8 we include the five governance proxies that
are consistently significantly associated with excess value in Hoechle et al (2012) ndash the percent
of CEO ownership (CEO Ownership and CEO Ownership squared) the percent of institutional
ownership (Institution Ownership) whether the firm has a powerful or influential CEO
(Powerful CEO) and the governance index from Gompers Ishii and Metrick (2003)
(Governance Index)
As documented in Table 8 including these control variables reduces our sample size by
almost 50 (to 2550 observations) and tends to exclude the smaller firms from the analysis
None of the governance proxies are significant Relative to column (4) of Table 4 the coefficient
on Foreign Sales is similar in magnitude but is no longer statistically significant at
conventional levels To investigate whether the decreased significance is due to the smaller
sample or the inclusion of the governance variables we re-estimate the regression for the same
2550 observations after removing the governance controls The coefficient and t-statistic are
virtually identical suggesting that the loss of significance is due to the reduced sample and not
the governance controls We do not find evidence to suggest that variation in corporate
governance practices drives variation in excess value for US MNCs
27
6 Conclusion
Using a new method to quantify the valuation effects of foreign direct investment by US
domiciled firms we find robust evidence of a premium in excess value associated with the
degree of multinational operations Our method builds on that used in previous studies
examining firm excess value (eg Berger and Ofek 1995 and Denis et al 2002) by
incorporating the location in addition to the industry of each corporate affiliate This allows
important factors such as growth and cost of capital to vary across countries as well as industries
Examining a sample of multinational firms we find that a one-standard deviation (197)
increase in foreign operations is associated with an increase of between 41 and 82 in excess
value minus both a statistically and economically significant finding
This result complements the finding of the international trade literature that more productive
firms are more likely to operate internationally However the premium remains after including
firm fixed effects to control for persistent firm traits (eg productivity)
The finding of a premium stands up to a succession of robustness tests designed to evaluate
whether the result arises from alternative methods or explanations First we estimate a dynamic
panel data model using the generalized method of moments (GMM) estimator to simultaneously
account for the potential endogeneity of FDI industrial diversification and firm value Second
we include a control variable to proxy for whether the firm operates in countries with larger
control premiums which could result in understated multiples and mechanically induce a
premium Third we include a control variable to measure the cost of capital across the countries
in which an MNC operates to evaluate whether a relatively low cost of capital borne by
multinational firms is a meaningful source of the premium Fourth we control for several
corporate governance proxies which have been found to be correlated with excess values (and
28
industrial diversification) We fail to find evidence supporting any of these alternative
explanations ndash the premium in excess value arising at firms with greater multinational operations
remains significant
Overall our new method of estimating excess values for multinational firms provides strong
evidence of a statistically and economically significant premium associated with increased
international operations The premium is robust to a host of specifications and controls designed
to evaluate alternative explanations We leave to future research the questions of whether the
premium to US multinational firms applies more broadly to other countries of domicile and
what factors influence the premium
29
References
Ammann M Hoeschel D and M Schmid 2012 Is there really no conglomerate discount
Journal of Business Finance and Accounting 39(12) 264-288 Arellano M and Bond S R 1991 Some tests of specification for panel data Monte Carlo
evidence and an application to employment equations Review of Economic Studies 58 (2) 277-298
Arellano M and Bover O 1995 Another look at instrumental variables estimation of error
components models Journal of Econometrics 68 (1) 29-61 Bens D A Berger P G and S J Monahan 2011 Discretionary disclosure in financial
reporting An examination comparing internal firm data to externally reported segment data The Accounting Review 86(2) 417-449
Berger P G and E Ofek 1995 Diversificationrsquos effect on firm value Journal of Financial
Economics 37 39-65 Blundell R and Bond S R 1998 Initial conditions and moment restrictions in dynamic panel
data models Journal of Econometrics 87(1) 115-144 Campa J M and S Kedia 2002 Explaining the diversification discount The Journal of
Finance 57 (4) 1731-1762 Denis D J Denis D K and K Yost 2002 Global diversification industrial diversification
and firm value The Journal of Finance 57 (5) 1951-1979 Desai M A Foley C F and J R Hines 2004 A multinational perspective on capital structure
choice and internal capital markets The Journal of Finance 59 (6) 2451-2487 Dos Santos MB V Errunza and D Miller 2008 Does corporate international diversification
destroy value Evidence from cross-border mergers and acquisitions Journal of Banking and Finance 32(12) 2716-2724
Doukas JA 1995 Overinvestment Tobinrsquos q and gains from foreign acquisitions Journal of
Banking and Finance 19 1285-1303 Doukas J and N G Travlos 1988 The effect of corporate multinationalism on shareholdersrsquorsquo
wealth evidence from international acquisitions Journal of Finance 43(5) 1161-1175 Doukas JA and O B Kan 2006 Does global diversification destroy firm value Journal of
International Business Studies 37(3) 352-371
30
Dunning J and A Rugman 1985 The influence of Hymerrsquos dissertation on the theory of foreign direct investment The American Economic Review 75(2) 228-232
Dyck A and L Zingales 2004 Private benefits of control An international comparison The
Journal of Finance 59 (2) 537-600 Erb C Harvey C and T Viskanta 1996 Expected returns and volatility in 135 countries The
Journal of Portfolio Management 46-58 Foley C F Desai M A and K J Forbes 2008 Financial constraints and growth
Multinational and local firm responses to currency depreciations Review of Financial Studies 21 (6) 2857-2888
Francis B I Hasan and X Sun 2008 Financial market integration and the value of global
diversification Evidence for US acquirers in cross-border mergers and acquisitions Journal of Banking and Finance 321522-1540
Gande A Schenzler C and L Senbet 2009 Valuation effects of global diversification
Journal of International Business Studies 1515-1532 Gompers P Ishii J and A Metrick 2003 Corporate governance and equity prices Quarterly
Journal of Economics 118(1) 107-156 Hail L and C Leuz 2009 Cost of capital effects and changes in growth expectations around
US cross-listings Journal of Financial Economics 93 428-454 Hanson G Mataloni R and M Slaughter 2005 Vertical production networks in multinational
firms Review of Economics and Statistics 87(4) 1-15 Helpman E 2006 Trade FDI and the Organization of Firms Journal of Economic Literature
44(3) 289-630 Helpman E Melitz M J and S R Yeaple 2004 Export versus FDI with Heterogeneous Firms
American Economic Review 94(1) 300-316 Hoechle D Schmid M Walter W and D Yermack 2012 How much of the diversification
discount can be explained by poor governance Journal of Financial Economics 103 41-60 Hope O-K and W B Thomas 2008 Managerial empire building and firm disclosure Journal
of Accounting Research 46 (June) 591-626 Huizinga H P and J Voget 2009 International taxation and the direction and volume of cross-
border MampAs Journal of Finance 64 (3) 1217-1249 Jensen M 1986 Agency cost of free cash flow corporate finance and takeovers American
Economic Review Papers and Proceedings 76 323-329
31
Laeven L and R Levine 2007 Is there a diversification discount in financial conglomerates
Journal of Financial Economics 85 331-367 Lang L and R Stulz 1994 Tobinrsquos Q corporate diversification and firm performance Journal
of Political Economy 102 1248-1280 Mataloni R 2003 US multinational companies Operations in 2001 Survey of Current
Business 85-105 Matvos G and A Seru 2011 The dynamic tradeoff between corporate socialism and financial
market dislocation Estimates from internal capital markets Working paper University of Chicago Booth School of Business
Moeller SB and F P Schlingemann 2005 Global diversification and bidder gains A
comparison between cross-border and domestic acquisitions Journal of Banking and Finance 29 533-564
Morck R and B Yeung 1991 Why investors value multinationality Journal of Business 64
165-186
Morck R and B Yeung 2001 Why firms diversify Internatization vs agency behavior In Intangibles B Lev (ed) Oxford University Press pp 269-302
Myers S C and N S Majluf 1984 Corporate financing and investment decisions when firms have information that investors do not have Journal of Financial Economics 13 187-221
Schmid M and I Walter 2009 Do financial conglomerates create or destroy economic value
Journal of Financial Intermediation 18 193-216 Syverson C 2011 What determines productivity Journal of Economic Literature 49(2) 326-
365 Villalonga B 2004a Diversification discount or premium New evidence from the Business
Information Tracking Series Journal of Finance 59 479-506 Villalonga B 2004b Does diversification cause the diversification discount Financial
Management 33 5-27 Wooldridge JM 2010 Econometric Analysis of Cross Section and Panel Data (Second
Edition) Cambridge MA MIT Press Yamin M 1991 A reassessment of Hymerrsquos contribution to the theory of the transnational
corporation C Pitelis and R Sugden (eds) The Nature of the Transnational Firm London Routledge
32
Appendix A Variable Definitions
We obtain information regarding firmsrsquo operations by country and industry from the Bureau of Economic Analysis (BEA) unless otherwise noted Acronyms used below reflect Compustat mnemonics and thus indicate when we obtain data from the Compustat North America Fundamentals Annual database All independent variables are winsorized at 1
Dependent variable
Excess Value = The log of the ratio of actual firm value to imputed firm value Actual firm value is equal to market capitalization (MVE) + book value of total assets (AT) ndash book value of shareholdersrsquo equity (SEQ) Imputed firm value is calculated as the sum of the imputed values of a firmrsquos operations in each country-industry The imputed value for each country-industry is equal to sales generated in that country-industry times a multiple equal to the ratio of a benchmark firmrsquos actual value to total sales A benchmark firm is defined as a median single-segment firm operating in the same country-industry All multiples require at least five firms listed in the respective country-industry on Compustat (domestic) or Worldscope (foreign) with less than 10 of sales income and assets outside the country of domicile Similar to Berger and Ofek (1995) we eliminate extreme values where the ratio of actual to imputed firm value exceeds 400 or is less than 025
Independent variables
Foreign Sales = Sales made by the foreign operations of US MNCs as a percent of total firm sales
Industry Other = Sales outside a firmrsquos primary industry as a percent of total sales (SALE) Equal to zero for firms operating in a single business segment
Log Size = Log of total sales (SALE)
Debt Firm Value = Ratio of long-term debt (DLTT) to total market value (market capitalization (MVE) + book value of total assets (AT) ndash book value of shareholdersrsquo equity (SEQ))
CAPX Sales = Ratio of capital expenditures (CAPX) to total sales (SALE)
EBIT Sales = Ratio of earnings before interest and taxes (EBIT) to total sales (SALE)
RampD Sales = Ratio of research and development expenses (XRD) to total sales (SALE)
Ad Sales = Ratio of advertising expenses (XAD) to total sales (SALE)
Control Premium = A weighted-average (based on the sales in a given country as a percent of total firm sales) of the country-level control premium from Dyck and Zingales (2004)
Cost of Capital = A weighted-average (based on the sales in a given country as a percent of total firm sales) of the country-level credit rating from Institutional Investor (httpwwwinstitutionalinvestorcom) The country-level rating is available for 160 countries and ranges from 0 to 100 Higher values imply greater credit risk
33
CEO Ownership = Percent of shares owned by the CEO from Execucomp
Institution Ownership = Percent of shares owned by institutional investors from Thomson Reuterrsquos Institutional Holdings database
Powerful CEO = Indicator variable equal to one if the CEO is the only insider on the board of directors and also serves as the chairman and president based on data from RiskMetrics Directors database Equal to zero otherwise
Governance Index = Count measure of takeover defenses used in Gompers Ishii and Metrick (2003) from RiskMetrics Governance database As the governance data are not available for every year we follow Gompers Ishii and Metrick (2003) and assume that the firmsrsquo governance attributes are unchanged until publication of the subsequent series
34
Figure 1 Trends in multinational activity and industrial diversification from 1984-2008 This figure presents a plot of the mean percent of foreign sales and the mean percent of sales outside the firmrsquos primary industry (industrial diversification) from 1984 through 2008 Dotted lines above and below the primary lines represent 95 confidence intervals To compile the plot we include the full population of firms in Compustat
35
Table 1 Analysis of accounting data for firms reporting under multiple accounting standards This table provides a comparison of sales net income and assets for firms that changed accounting standards to or from US GAAP Comparisons are provided for the 66 firms that changed to or from US GAAP and for which we could obtain accounting data electronically and in English under both standards for the same fiscal year (eg the firm presented the accounting numbers originally under one set of standards and then restated the prior year numbers in the subsequent year under the new set of standards) Panel A shows the distribution of firms by year Panel B shows the other accounting standards to which US GAAP is compared Panel C shows the mean and median percent that the other accounting standard differs from US GAAP for the same fiscal year and accounting number Two samples are shown in Panel C The first is the full sample of firms with a change in accounting standards to or from US GAAP for which data was available The second is the subset of these firms that occurred during the sample window used in this study (ie excluding changes post-2008) The percent difference in sales is compared to the percent difference in net income and assets Tests of means are based on matched-pair t-tests Tests of medians are based on Wilcoxon matched-pair signed-rank tests Panel A Accounting standard changes by year
2003
4 6
2004
25 38
2005
8 12
2006
6 9
2007
7 11
2008
5 8
2009
5 8
2010
5 8
2011
1 2
66
Panel B Accounting standards compared to US GAAP
IFRS
47 71
Canadian GAAP
12 18
Israeli GAAP
2 3
Dutch GAAP
1 2
Indian GAAP
1 2
Japanese GAAP
1 2
Norwegian GAAP
1 2
Swiss GAAP
1 2
66
36
(Table 1 continued) Panel C Tests of differences between US GAAP and other accounting standards
Percent different from US GAAP
Comparison to sales (p-values)
N Mean Median
Mean Median
Full Sample (2001-2011)
Sales
66 236 000
Net Income
66 10653 647
005 lt001
Assets
66 1162 126
lt001 lt001
Study Sample (2001-2008)
Sales
55 257 000
Net Income
55 10303 589
011 lt001
Assets
55 1047 099
003 lt001
37
Table 2 Descriptive statistics Panel A reports the mean and medians of Excess Value for the multinational firms and single-segment benchmark firms as well as univariate tests across the subsamples Panel B reports summary statistics for the independent variables for the multinational firms The p-values of the tests of differences in means are based on t-tests The p-values of tests of differences in the medians are for a Wilcoxon rank-sum test All variables are defined in Appendix A Panel A Excess Value by sample Multinational All Benchmark Benchmark Subgroups Sample Firms US Foreign
Value Value Test of Diff (p-value) Value
Test of Diff (p-value) Value
Test of Diff (p-value)
Mean 0067 0006 lt001 -0028 lt001 0014 lt001 Median 0076 0000 lt001 -0025 lt001 0000 lt001 N 4950 33074 5998 27076 Panel B Distribution of independent variables for the multinational firms N Mean 25th 50th 75th StDev Foreign Sales 4950 0238 0074 0190 0363 0197 Industry Other 4950 0212 0000 0166 0414 0215 Log Size 4950 6955 5823 6989 8140 1687 Debt Firm Value 4950 0136 0009 0097 0212 0145 CAPX Sales 4950 0054 0020 0033 0054 0101 EBIT Sales 4950 0065 0031 0077 0121 0138 RampD Sales 4950 0049 0000 0013 0059 0083 Ad Sales 4950 0008 0000 0000 0005 0018
38
Table 3 BEA and Worldscope samples
This table reports details on affiliate sales for significant countries pooled from 1998-2008 Significant countries are defined as those in which the operations of US MNCs represent at least 02 of total firm sales (per the BEA) For significant countries the table compares their representation in terms of MNC affiliate sales in the BEA data to the distribution of firm observations in Worldscope US Foreign Affiliates (BEA) Foreign Firms (Worldscope)
Country
N (1)
Sales (millions)
(2)
Total Sales (3)
N (4)
Sales (million)
(5)
Total Sales (6)
Australia 1344 73305 056 859 255865 210 Belgium 894 52755 040 169 26136 021 Brazil 959 60245 046 1143 683167 560 Canada 2730 536767 407 1181 526750 432 China 922 75907 057 822 276541 227 France 1852 183942 139 540 105447 086 Germany 2182 275577 209 480 103888 085 Hong Kong 867 48100 036 157 34094 028 Ireland 478 36754 028 34 3467 003 Italy 1178 82116 062 111 47948 039 Japan 1494 148032 112 2281 1119223 917 Korea South 659 34500 026 3530 2752791 2255 Malaysia 386 26027 020 837 168683 138 Mexico 1405 113623 086 362 237173 194 Netherlands 1149 85105 064 47 22631 019 Singapore 1098 94917 072 139 32913 027 Spain 871 63094 048 129 37419 031 Sweden 712 32682 025 235 23090 019 Switzerland 726 60288 046 74 20242 017 Taiwan 592 30313 023 2949 787049 645 United Kingdom 3054 423934 321 1997 882423 723 United States (domicile) 4950 10460880 7923 na na na Other - Covered by WorldScope 6893 199394 151 9000 4057894 33 Other -Not Covered by WorldScope 713 5352 004 - - - Total 38108 13203609 1000 27076 12204834 100
39
Table 4 OLS regressions of firm excess values on the percent of foreign sales This table reports the results of OLS regressions of firm excess value on the percent of foreign sales (Foreign Sales) the percent of sales outside a firmrsquos primary industry (Industry Other) and control variables All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed) Dep Variable = Excess Value Coefficient t-stat Coefficient t-stat Coefficient t-stat Coefficient t-stat (1) (2) (3) (4) Foreign Sales 0349 419 0209 293 0189 266 0278 210 Industry Other -0134 -186 -0047 -072 -0053 -081 -0326 -325 Log Size 0066 634 0065 624 -0044 -096 Debt Firm Value -0196 -213 -0241 -260 -0685 -588 CAPX Sales 0901 425 0893 435 0488 227 EBIT Sales 1154 542 1170 547 0408 189 RampD Sales 2908 1242 2932 1237 1544 379 Ad Sales 1869 263 2003 280 -1696 -119 Year indicators No No Yes Yes Firm indicators No No No Yes R2 0017 0212 0226 0768 Observations 4950 4950 4950 4950
40
Table 5 Dynamic panel GMM model regressing firm excess values on the percent of foreign sales This table reports the result from a regression of the excess value on the percent of foreign sales (Foreign Sales) the percent of non-primary industry sales (Industry Other) and control variables using a dynamic panel GMM estimator All control variables are considered to be endogenous with the exception of the year indicator variables AR(1) and AR(2) are tests for first-order and second-order serial correlation in the first differenced residuals with the null hypothesis of no serial correlation The null hypothesis of the Hansen test of overidentification is that all instruments are valid The null hypothesis of the difference-in-Hansen test of exogeneity is that the instruments used for the equation in levels are exogenous All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed) Dep Variable = Excess Value Coef t-stat Excess Value (one lag) -0018 -082 Excess Value (two lags) -0048 -272 Foreign Sales 0371 270 Industry Other -0163 -171 Log Size 0010 123 Debt FirmValue -1135 -820 CAPX Sales 0590 342 EBIT Sales -0047 -300 RampD Sales 1376 531 Ad Sales -0751 -040 AR(1) test (p-value) 000 AR(2) test (p-value) 017 Hansen test of over-identification (p-value) 030 Difference-in-Hansen test of exogeneity (p-value) 093 Year indicators Yes Number of firms 1016 Number of observations 4090
41
Table 6 OLS regression of firm excess values on the percent of foreign sales controlling for a control premium This table reports the result of the OLS regression of firm excess value including firm and year fixed effects with an additional control variable to proxy for a country-level ownership control premium (Control Premium) All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed) Dep Variable = Excess Value Coef t -stat Foreign Sales 0303 224 Industry Other -0327 -325 Log Size -0045 -100 Debt FirmValue -0683 -585 CAPX Sales 0490 226 EBIT Sales 0409 189 RampD Sales 1545 379 Ad Sales -1689 -119 Control Premium -1004 -062 Year indicators Yes Firm indicators Yes R2 0768 Observations 4950
42
Table 7 OLS regression of firm excess values on the percent of foreign sales controlling for the cost of capital This table contains the results of an OLS regression of firm excess value including firm and year fixed effects with an additional control variable to proxy for a country-level cost of capital (Cost of Capital) All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed) Dep Variable = Excess Value Coef t -stat Foreign Sales 0255 178 Industry Other -0326 -324 Log Size -0043 -094 Debt FirmValue -0687 -589 CAPX Sales 0485 225 EBIT Sales 0406 188 RampD Sales 1539 379 Ad Sales -1703 -120 Cost of Capital 0004 039 Year indicators Yes Firm indicators Yes R2 0768 Observations 4950
43
Table 8 OLS regression of firm excess values on the percent of foreign sales controlling for aspects of corporate governance This table reports the results of two OLS regressions In the first column are the results of the firm excess value regression including firm and year fixed effect with five proxies for corporate governance included ndash the percent of shares owned by the CEO and the percent squared (CEO Ownership and CEO Ownership squared) the percent owned by institutional investors (Institution Ownership) whether there is a powerful CEO (Powerful CEO) and the Gompers Ishii and Metrick (2003) governance index (Governance Index) In the second column the regression includes the same sample and variables with the exception of the governance proxies which are excluded All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed)
Governance
No Governance (same sample)
Dep Variable = Excess Value Coef t-stat Coef t -stat Foreign Sales 0259 159 0251 154 Industry Other -0099 -080 -0099 -079 Log Size -0105 -185 -0102 -180 Debt FirmValue -0813 -478 -0817 -482 CAPX Sales 0440 252 0438 249 EBIT Sales 0457 247 0457 246 RampD Sales 1717 284 1716 286 Ad Sales -2210 -143 -2190 -143 CEO Ownership 0010 007
CEO Ownership squared -0029 -017 Institution Ownership 0047 067 Powerful CEO -0003 -014 Governance Index 0009 103
Year indicators Yes Yes Firm indicators Yes Yes R2 0811 0810 Observations 2550 2550
- Cover for paperpdf
- crrz_20130603_SMUpdf
-
24
typical US MNC suggesting that an event study would lack power and generalizability For
these reasons we do not implement an event study
53 Control premium
A control premium or private benefits of control occurs when ldquosome value whatever the
source is not shared among all the shareholders in proportion of the shares owned but it is
enjoyed exclusively by the party in controlrdquo (Dyck and Zingales 2004 p 541) Using stock
prices of traded shares held by minority shareholders in determining the market value to sales
ratios of our benchmark firms could underestimate the true value of our benchmark firms if those
firms face large control premiums This in turn could induce an excess value premium because
the imputed value of these country-industry segments would be artificially low The existence of
such benefits is more prevalent in some countries than others and could affect the value of an
MNCrsquos operations in such domains We use the country-level control premiums from Dyck and
Zingales (2004) and using each firmrsquos country sales as a percent of total firm sales we obtain a
weighted average control premium that each MNC faces (Control Premium) For countries not
included in the Dyck and Zingales (2004) analysis we set the control premium equal to zero23 In
Table 6 we show that Foreign Sales remains positive and highly significant (p-value lt 001)
when we include the proxy for control premium As a result it does not appear that the excess
value premium is driven by MNCs operating in countries where majority shareholders are able to
extract large control premiums
23 Results are robust to replacing missing values with the mean control premium of the respective GDP quartile
25
54 Cost of capital
As discussed in Section 2 when countries have shallow capital markets a multinational
network may provide the benefit of a lower cost of capital for investments through an internal
capital market as well as through better access to the US capital market Prior research
establishes that there is substantial variation in the cost of capital across countries (Erb Harvey
and Viskanta 1996 and Hail and Leuz 2009) To determine whether the documented premium
is due to cost of capital advantages we include a control variable to capture the difference
between the MNCrsquos cost of capital and the cost of capital faced by its competitors in the global
markets in which it operates
We compute our Cost of Capital proxy in two steps First we obtain the country-year credit
rating published by Institutional Investor24 The rating ranges from 0 to 100 where higher values
imply that a country has a higher default risk Country-level credit risk is a reasonable predictor
of expected equity market returns and volatility (Erb et al 1996) and exhibits a highly
significant correlation with international accounting-based estimates of imputed cost of capital
(Hail and Leuz 2009) Second using each firmrsquos country sales as a percent of total firm sales
we obtain a weighted average credit risk rating across the countries in which each firm operates
A higher value implies an MNC operates in countries with higher cost of capital In Table 7 we
do not find a result consistent with MNCs gaining an advantage from their access to low cost
capitalndash Cost of Capital is not significantly associated with Excess Value25
24 We thank Cam Harvey for providing these data 25 Results are consistent using cost of capital as defined in Erb et al 1996
26
55 Corporate governance
In light of the recent finding that corporate governance mitigates the diversification discount
we examine whether the premium to multinational activity is sensitive to controls for corporate
governance Hoechle et al (2012) find that the magnitude of the industrial diversification
discount is decreased and in several specifications no longer significant in the presence of
governance proxies In the first column of Table 8 we include the five governance proxies that
are consistently significantly associated with excess value in Hoechle et al (2012) ndash the percent
of CEO ownership (CEO Ownership and CEO Ownership squared) the percent of institutional
ownership (Institution Ownership) whether the firm has a powerful or influential CEO
(Powerful CEO) and the governance index from Gompers Ishii and Metrick (2003)
(Governance Index)
As documented in Table 8 including these control variables reduces our sample size by
almost 50 (to 2550 observations) and tends to exclude the smaller firms from the analysis
None of the governance proxies are significant Relative to column (4) of Table 4 the coefficient
on Foreign Sales is similar in magnitude but is no longer statistically significant at
conventional levels To investigate whether the decreased significance is due to the smaller
sample or the inclusion of the governance variables we re-estimate the regression for the same
2550 observations after removing the governance controls The coefficient and t-statistic are
virtually identical suggesting that the loss of significance is due to the reduced sample and not
the governance controls We do not find evidence to suggest that variation in corporate
governance practices drives variation in excess value for US MNCs
27
6 Conclusion
Using a new method to quantify the valuation effects of foreign direct investment by US
domiciled firms we find robust evidence of a premium in excess value associated with the
degree of multinational operations Our method builds on that used in previous studies
examining firm excess value (eg Berger and Ofek 1995 and Denis et al 2002) by
incorporating the location in addition to the industry of each corporate affiliate This allows
important factors such as growth and cost of capital to vary across countries as well as industries
Examining a sample of multinational firms we find that a one-standard deviation (197)
increase in foreign operations is associated with an increase of between 41 and 82 in excess
value minus both a statistically and economically significant finding
This result complements the finding of the international trade literature that more productive
firms are more likely to operate internationally However the premium remains after including
firm fixed effects to control for persistent firm traits (eg productivity)
The finding of a premium stands up to a succession of robustness tests designed to evaluate
whether the result arises from alternative methods or explanations First we estimate a dynamic
panel data model using the generalized method of moments (GMM) estimator to simultaneously
account for the potential endogeneity of FDI industrial diversification and firm value Second
we include a control variable to proxy for whether the firm operates in countries with larger
control premiums which could result in understated multiples and mechanically induce a
premium Third we include a control variable to measure the cost of capital across the countries
in which an MNC operates to evaluate whether a relatively low cost of capital borne by
multinational firms is a meaningful source of the premium Fourth we control for several
corporate governance proxies which have been found to be correlated with excess values (and
28
industrial diversification) We fail to find evidence supporting any of these alternative
explanations ndash the premium in excess value arising at firms with greater multinational operations
remains significant
Overall our new method of estimating excess values for multinational firms provides strong
evidence of a statistically and economically significant premium associated with increased
international operations The premium is robust to a host of specifications and controls designed
to evaluate alternative explanations We leave to future research the questions of whether the
premium to US multinational firms applies more broadly to other countries of domicile and
what factors influence the premium
29
References
Ammann M Hoeschel D and M Schmid 2012 Is there really no conglomerate discount
Journal of Business Finance and Accounting 39(12) 264-288 Arellano M and Bond S R 1991 Some tests of specification for panel data Monte Carlo
evidence and an application to employment equations Review of Economic Studies 58 (2) 277-298
Arellano M and Bover O 1995 Another look at instrumental variables estimation of error
components models Journal of Econometrics 68 (1) 29-61 Bens D A Berger P G and S J Monahan 2011 Discretionary disclosure in financial
reporting An examination comparing internal firm data to externally reported segment data The Accounting Review 86(2) 417-449
Berger P G and E Ofek 1995 Diversificationrsquos effect on firm value Journal of Financial
Economics 37 39-65 Blundell R and Bond S R 1998 Initial conditions and moment restrictions in dynamic panel
data models Journal of Econometrics 87(1) 115-144 Campa J M and S Kedia 2002 Explaining the diversification discount The Journal of
Finance 57 (4) 1731-1762 Denis D J Denis D K and K Yost 2002 Global diversification industrial diversification
and firm value The Journal of Finance 57 (5) 1951-1979 Desai M A Foley C F and J R Hines 2004 A multinational perspective on capital structure
choice and internal capital markets The Journal of Finance 59 (6) 2451-2487 Dos Santos MB V Errunza and D Miller 2008 Does corporate international diversification
destroy value Evidence from cross-border mergers and acquisitions Journal of Banking and Finance 32(12) 2716-2724
Doukas JA 1995 Overinvestment Tobinrsquos q and gains from foreign acquisitions Journal of
Banking and Finance 19 1285-1303 Doukas J and N G Travlos 1988 The effect of corporate multinationalism on shareholdersrsquorsquo
wealth evidence from international acquisitions Journal of Finance 43(5) 1161-1175 Doukas JA and O B Kan 2006 Does global diversification destroy firm value Journal of
International Business Studies 37(3) 352-371
30
Dunning J and A Rugman 1985 The influence of Hymerrsquos dissertation on the theory of foreign direct investment The American Economic Review 75(2) 228-232
Dyck A and L Zingales 2004 Private benefits of control An international comparison The
Journal of Finance 59 (2) 537-600 Erb C Harvey C and T Viskanta 1996 Expected returns and volatility in 135 countries The
Journal of Portfolio Management 46-58 Foley C F Desai M A and K J Forbes 2008 Financial constraints and growth
Multinational and local firm responses to currency depreciations Review of Financial Studies 21 (6) 2857-2888
Francis B I Hasan and X Sun 2008 Financial market integration and the value of global
diversification Evidence for US acquirers in cross-border mergers and acquisitions Journal of Banking and Finance 321522-1540
Gande A Schenzler C and L Senbet 2009 Valuation effects of global diversification
Journal of International Business Studies 1515-1532 Gompers P Ishii J and A Metrick 2003 Corporate governance and equity prices Quarterly
Journal of Economics 118(1) 107-156 Hail L and C Leuz 2009 Cost of capital effects and changes in growth expectations around
US cross-listings Journal of Financial Economics 93 428-454 Hanson G Mataloni R and M Slaughter 2005 Vertical production networks in multinational
firms Review of Economics and Statistics 87(4) 1-15 Helpman E 2006 Trade FDI and the Organization of Firms Journal of Economic Literature
44(3) 289-630 Helpman E Melitz M J and S R Yeaple 2004 Export versus FDI with Heterogeneous Firms
American Economic Review 94(1) 300-316 Hoechle D Schmid M Walter W and D Yermack 2012 How much of the diversification
discount can be explained by poor governance Journal of Financial Economics 103 41-60 Hope O-K and W B Thomas 2008 Managerial empire building and firm disclosure Journal
of Accounting Research 46 (June) 591-626 Huizinga H P and J Voget 2009 International taxation and the direction and volume of cross-
border MampAs Journal of Finance 64 (3) 1217-1249 Jensen M 1986 Agency cost of free cash flow corporate finance and takeovers American
Economic Review Papers and Proceedings 76 323-329
31
Laeven L and R Levine 2007 Is there a diversification discount in financial conglomerates
Journal of Financial Economics 85 331-367 Lang L and R Stulz 1994 Tobinrsquos Q corporate diversification and firm performance Journal
of Political Economy 102 1248-1280 Mataloni R 2003 US multinational companies Operations in 2001 Survey of Current
Business 85-105 Matvos G and A Seru 2011 The dynamic tradeoff between corporate socialism and financial
market dislocation Estimates from internal capital markets Working paper University of Chicago Booth School of Business
Moeller SB and F P Schlingemann 2005 Global diversification and bidder gains A
comparison between cross-border and domestic acquisitions Journal of Banking and Finance 29 533-564
Morck R and B Yeung 1991 Why investors value multinationality Journal of Business 64
165-186
Morck R and B Yeung 2001 Why firms diversify Internatization vs agency behavior In Intangibles B Lev (ed) Oxford University Press pp 269-302
Myers S C and N S Majluf 1984 Corporate financing and investment decisions when firms have information that investors do not have Journal of Financial Economics 13 187-221
Schmid M and I Walter 2009 Do financial conglomerates create or destroy economic value
Journal of Financial Intermediation 18 193-216 Syverson C 2011 What determines productivity Journal of Economic Literature 49(2) 326-
365 Villalonga B 2004a Diversification discount or premium New evidence from the Business
Information Tracking Series Journal of Finance 59 479-506 Villalonga B 2004b Does diversification cause the diversification discount Financial
Management 33 5-27 Wooldridge JM 2010 Econometric Analysis of Cross Section and Panel Data (Second
Edition) Cambridge MA MIT Press Yamin M 1991 A reassessment of Hymerrsquos contribution to the theory of the transnational
corporation C Pitelis and R Sugden (eds) The Nature of the Transnational Firm London Routledge
32
Appendix A Variable Definitions
We obtain information regarding firmsrsquo operations by country and industry from the Bureau of Economic Analysis (BEA) unless otherwise noted Acronyms used below reflect Compustat mnemonics and thus indicate when we obtain data from the Compustat North America Fundamentals Annual database All independent variables are winsorized at 1
Dependent variable
Excess Value = The log of the ratio of actual firm value to imputed firm value Actual firm value is equal to market capitalization (MVE) + book value of total assets (AT) ndash book value of shareholdersrsquo equity (SEQ) Imputed firm value is calculated as the sum of the imputed values of a firmrsquos operations in each country-industry The imputed value for each country-industry is equal to sales generated in that country-industry times a multiple equal to the ratio of a benchmark firmrsquos actual value to total sales A benchmark firm is defined as a median single-segment firm operating in the same country-industry All multiples require at least five firms listed in the respective country-industry on Compustat (domestic) or Worldscope (foreign) with less than 10 of sales income and assets outside the country of domicile Similar to Berger and Ofek (1995) we eliminate extreme values where the ratio of actual to imputed firm value exceeds 400 or is less than 025
Independent variables
Foreign Sales = Sales made by the foreign operations of US MNCs as a percent of total firm sales
Industry Other = Sales outside a firmrsquos primary industry as a percent of total sales (SALE) Equal to zero for firms operating in a single business segment
Log Size = Log of total sales (SALE)
Debt Firm Value = Ratio of long-term debt (DLTT) to total market value (market capitalization (MVE) + book value of total assets (AT) ndash book value of shareholdersrsquo equity (SEQ))
CAPX Sales = Ratio of capital expenditures (CAPX) to total sales (SALE)
EBIT Sales = Ratio of earnings before interest and taxes (EBIT) to total sales (SALE)
RampD Sales = Ratio of research and development expenses (XRD) to total sales (SALE)
Ad Sales = Ratio of advertising expenses (XAD) to total sales (SALE)
Control Premium = A weighted-average (based on the sales in a given country as a percent of total firm sales) of the country-level control premium from Dyck and Zingales (2004)
Cost of Capital = A weighted-average (based on the sales in a given country as a percent of total firm sales) of the country-level credit rating from Institutional Investor (httpwwwinstitutionalinvestorcom) The country-level rating is available for 160 countries and ranges from 0 to 100 Higher values imply greater credit risk
33
CEO Ownership = Percent of shares owned by the CEO from Execucomp
Institution Ownership = Percent of shares owned by institutional investors from Thomson Reuterrsquos Institutional Holdings database
Powerful CEO = Indicator variable equal to one if the CEO is the only insider on the board of directors and also serves as the chairman and president based on data from RiskMetrics Directors database Equal to zero otherwise
Governance Index = Count measure of takeover defenses used in Gompers Ishii and Metrick (2003) from RiskMetrics Governance database As the governance data are not available for every year we follow Gompers Ishii and Metrick (2003) and assume that the firmsrsquo governance attributes are unchanged until publication of the subsequent series
34
Figure 1 Trends in multinational activity and industrial diversification from 1984-2008 This figure presents a plot of the mean percent of foreign sales and the mean percent of sales outside the firmrsquos primary industry (industrial diversification) from 1984 through 2008 Dotted lines above and below the primary lines represent 95 confidence intervals To compile the plot we include the full population of firms in Compustat
35
Table 1 Analysis of accounting data for firms reporting under multiple accounting standards This table provides a comparison of sales net income and assets for firms that changed accounting standards to or from US GAAP Comparisons are provided for the 66 firms that changed to or from US GAAP and for which we could obtain accounting data electronically and in English under both standards for the same fiscal year (eg the firm presented the accounting numbers originally under one set of standards and then restated the prior year numbers in the subsequent year under the new set of standards) Panel A shows the distribution of firms by year Panel B shows the other accounting standards to which US GAAP is compared Panel C shows the mean and median percent that the other accounting standard differs from US GAAP for the same fiscal year and accounting number Two samples are shown in Panel C The first is the full sample of firms with a change in accounting standards to or from US GAAP for which data was available The second is the subset of these firms that occurred during the sample window used in this study (ie excluding changes post-2008) The percent difference in sales is compared to the percent difference in net income and assets Tests of means are based on matched-pair t-tests Tests of medians are based on Wilcoxon matched-pair signed-rank tests Panel A Accounting standard changes by year
2003
4 6
2004
25 38
2005
8 12
2006
6 9
2007
7 11
2008
5 8
2009
5 8
2010
5 8
2011
1 2
66
Panel B Accounting standards compared to US GAAP
IFRS
47 71
Canadian GAAP
12 18
Israeli GAAP
2 3
Dutch GAAP
1 2
Indian GAAP
1 2
Japanese GAAP
1 2
Norwegian GAAP
1 2
Swiss GAAP
1 2
66
36
(Table 1 continued) Panel C Tests of differences between US GAAP and other accounting standards
Percent different from US GAAP
Comparison to sales (p-values)
N Mean Median
Mean Median
Full Sample (2001-2011)
Sales
66 236 000
Net Income
66 10653 647
005 lt001
Assets
66 1162 126
lt001 lt001
Study Sample (2001-2008)
Sales
55 257 000
Net Income
55 10303 589
011 lt001
Assets
55 1047 099
003 lt001
37
Table 2 Descriptive statistics Panel A reports the mean and medians of Excess Value for the multinational firms and single-segment benchmark firms as well as univariate tests across the subsamples Panel B reports summary statistics for the independent variables for the multinational firms The p-values of the tests of differences in means are based on t-tests The p-values of tests of differences in the medians are for a Wilcoxon rank-sum test All variables are defined in Appendix A Panel A Excess Value by sample Multinational All Benchmark Benchmark Subgroups Sample Firms US Foreign
Value Value Test of Diff (p-value) Value
Test of Diff (p-value) Value
Test of Diff (p-value)
Mean 0067 0006 lt001 -0028 lt001 0014 lt001 Median 0076 0000 lt001 -0025 lt001 0000 lt001 N 4950 33074 5998 27076 Panel B Distribution of independent variables for the multinational firms N Mean 25th 50th 75th StDev Foreign Sales 4950 0238 0074 0190 0363 0197 Industry Other 4950 0212 0000 0166 0414 0215 Log Size 4950 6955 5823 6989 8140 1687 Debt Firm Value 4950 0136 0009 0097 0212 0145 CAPX Sales 4950 0054 0020 0033 0054 0101 EBIT Sales 4950 0065 0031 0077 0121 0138 RampD Sales 4950 0049 0000 0013 0059 0083 Ad Sales 4950 0008 0000 0000 0005 0018
38
Table 3 BEA and Worldscope samples
This table reports details on affiliate sales for significant countries pooled from 1998-2008 Significant countries are defined as those in which the operations of US MNCs represent at least 02 of total firm sales (per the BEA) For significant countries the table compares their representation in terms of MNC affiliate sales in the BEA data to the distribution of firm observations in Worldscope US Foreign Affiliates (BEA) Foreign Firms (Worldscope)
Country
N (1)
Sales (millions)
(2)
Total Sales (3)
N (4)
Sales (million)
(5)
Total Sales (6)
Australia 1344 73305 056 859 255865 210 Belgium 894 52755 040 169 26136 021 Brazil 959 60245 046 1143 683167 560 Canada 2730 536767 407 1181 526750 432 China 922 75907 057 822 276541 227 France 1852 183942 139 540 105447 086 Germany 2182 275577 209 480 103888 085 Hong Kong 867 48100 036 157 34094 028 Ireland 478 36754 028 34 3467 003 Italy 1178 82116 062 111 47948 039 Japan 1494 148032 112 2281 1119223 917 Korea South 659 34500 026 3530 2752791 2255 Malaysia 386 26027 020 837 168683 138 Mexico 1405 113623 086 362 237173 194 Netherlands 1149 85105 064 47 22631 019 Singapore 1098 94917 072 139 32913 027 Spain 871 63094 048 129 37419 031 Sweden 712 32682 025 235 23090 019 Switzerland 726 60288 046 74 20242 017 Taiwan 592 30313 023 2949 787049 645 United Kingdom 3054 423934 321 1997 882423 723 United States (domicile) 4950 10460880 7923 na na na Other - Covered by WorldScope 6893 199394 151 9000 4057894 33 Other -Not Covered by WorldScope 713 5352 004 - - - Total 38108 13203609 1000 27076 12204834 100
39
Table 4 OLS regressions of firm excess values on the percent of foreign sales This table reports the results of OLS regressions of firm excess value on the percent of foreign sales (Foreign Sales) the percent of sales outside a firmrsquos primary industry (Industry Other) and control variables All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed) Dep Variable = Excess Value Coefficient t-stat Coefficient t-stat Coefficient t-stat Coefficient t-stat (1) (2) (3) (4) Foreign Sales 0349 419 0209 293 0189 266 0278 210 Industry Other -0134 -186 -0047 -072 -0053 -081 -0326 -325 Log Size 0066 634 0065 624 -0044 -096 Debt Firm Value -0196 -213 -0241 -260 -0685 -588 CAPX Sales 0901 425 0893 435 0488 227 EBIT Sales 1154 542 1170 547 0408 189 RampD Sales 2908 1242 2932 1237 1544 379 Ad Sales 1869 263 2003 280 -1696 -119 Year indicators No No Yes Yes Firm indicators No No No Yes R2 0017 0212 0226 0768 Observations 4950 4950 4950 4950
40
Table 5 Dynamic panel GMM model regressing firm excess values on the percent of foreign sales This table reports the result from a regression of the excess value on the percent of foreign sales (Foreign Sales) the percent of non-primary industry sales (Industry Other) and control variables using a dynamic panel GMM estimator All control variables are considered to be endogenous with the exception of the year indicator variables AR(1) and AR(2) are tests for first-order and second-order serial correlation in the first differenced residuals with the null hypothesis of no serial correlation The null hypothesis of the Hansen test of overidentification is that all instruments are valid The null hypothesis of the difference-in-Hansen test of exogeneity is that the instruments used for the equation in levels are exogenous All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed) Dep Variable = Excess Value Coef t-stat Excess Value (one lag) -0018 -082 Excess Value (two lags) -0048 -272 Foreign Sales 0371 270 Industry Other -0163 -171 Log Size 0010 123 Debt FirmValue -1135 -820 CAPX Sales 0590 342 EBIT Sales -0047 -300 RampD Sales 1376 531 Ad Sales -0751 -040 AR(1) test (p-value) 000 AR(2) test (p-value) 017 Hansen test of over-identification (p-value) 030 Difference-in-Hansen test of exogeneity (p-value) 093 Year indicators Yes Number of firms 1016 Number of observations 4090
41
Table 6 OLS regression of firm excess values on the percent of foreign sales controlling for a control premium This table reports the result of the OLS regression of firm excess value including firm and year fixed effects with an additional control variable to proxy for a country-level ownership control premium (Control Premium) All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed) Dep Variable = Excess Value Coef t -stat Foreign Sales 0303 224 Industry Other -0327 -325 Log Size -0045 -100 Debt FirmValue -0683 -585 CAPX Sales 0490 226 EBIT Sales 0409 189 RampD Sales 1545 379 Ad Sales -1689 -119 Control Premium -1004 -062 Year indicators Yes Firm indicators Yes R2 0768 Observations 4950
42
Table 7 OLS regression of firm excess values on the percent of foreign sales controlling for the cost of capital This table contains the results of an OLS regression of firm excess value including firm and year fixed effects with an additional control variable to proxy for a country-level cost of capital (Cost of Capital) All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed) Dep Variable = Excess Value Coef t -stat Foreign Sales 0255 178 Industry Other -0326 -324 Log Size -0043 -094 Debt FirmValue -0687 -589 CAPX Sales 0485 225 EBIT Sales 0406 188 RampD Sales 1539 379 Ad Sales -1703 -120 Cost of Capital 0004 039 Year indicators Yes Firm indicators Yes R2 0768 Observations 4950
43
Table 8 OLS regression of firm excess values on the percent of foreign sales controlling for aspects of corporate governance This table reports the results of two OLS regressions In the first column are the results of the firm excess value regression including firm and year fixed effect with five proxies for corporate governance included ndash the percent of shares owned by the CEO and the percent squared (CEO Ownership and CEO Ownership squared) the percent owned by institutional investors (Institution Ownership) whether there is a powerful CEO (Powerful CEO) and the Gompers Ishii and Metrick (2003) governance index (Governance Index) In the second column the regression includes the same sample and variables with the exception of the governance proxies which are excluded All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed)
Governance
No Governance (same sample)
Dep Variable = Excess Value Coef t-stat Coef t -stat Foreign Sales 0259 159 0251 154 Industry Other -0099 -080 -0099 -079 Log Size -0105 -185 -0102 -180 Debt FirmValue -0813 -478 -0817 -482 CAPX Sales 0440 252 0438 249 EBIT Sales 0457 247 0457 246 RampD Sales 1717 284 1716 286 Ad Sales -2210 -143 -2190 -143 CEO Ownership 0010 007
CEO Ownership squared -0029 -017 Institution Ownership 0047 067 Powerful CEO -0003 -014 Governance Index 0009 103
Year indicators Yes Yes Firm indicators Yes Yes R2 0811 0810 Observations 2550 2550
- Cover for paperpdf
- crrz_20130603_SMUpdf
-
25
54 Cost of capital
As discussed in Section 2 when countries have shallow capital markets a multinational
network may provide the benefit of a lower cost of capital for investments through an internal
capital market as well as through better access to the US capital market Prior research
establishes that there is substantial variation in the cost of capital across countries (Erb Harvey
and Viskanta 1996 and Hail and Leuz 2009) To determine whether the documented premium
is due to cost of capital advantages we include a control variable to capture the difference
between the MNCrsquos cost of capital and the cost of capital faced by its competitors in the global
markets in which it operates
We compute our Cost of Capital proxy in two steps First we obtain the country-year credit
rating published by Institutional Investor24 The rating ranges from 0 to 100 where higher values
imply that a country has a higher default risk Country-level credit risk is a reasonable predictor
of expected equity market returns and volatility (Erb et al 1996) and exhibits a highly
significant correlation with international accounting-based estimates of imputed cost of capital
(Hail and Leuz 2009) Second using each firmrsquos country sales as a percent of total firm sales
we obtain a weighted average credit risk rating across the countries in which each firm operates
A higher value implies an MNC operates in countries with higher cost of capital In Table 7 we
do not find a result consistent with MNCs gaining an advantage from their access to low cost
capitalndash Cost of Capital is not significantly associated with Excess Value25
24 We thank Cam Harvey for providing these data 25 Results are consistent using cost of capital as defined in Erb et al 1996
26
55 Corporate governance
In light of the recent finding that corporate governance mitigates the diversification discount
we examine whether the premium to multinational activity is sensitive to controls for corporate
governance Hoechle et al (2012) find that the magnitude of the industrial diversification
discount is decreased and in several specifications no longer significant in the presence of
governance proxies In the first column of Table 8 we include the five governance proxies that
are consistently significantly associated with excess value in Hoechle et al (2012) ndash the percent
of CEO ownership (CEO Ownership and CEO Ownership squared) the percent of institutional
ownership (Institution Ownership) whether the firm has a powerful or influential CEO
(Powerful CEO) and the governance index from Gompers Ishii and Metrick (2003)
(Governance Index)
As documented in Table 8 including these control variables reduces our sample size by
almost 50 (to 2550 observations) and tends to exclude the smaller firms from the analysis
None of the governance proxies are significant Relative to column (4) of Table 4 the coefficient
on Foreign Sales is similar in magnitude but is no longer statistically significant at
conventional levels To investigate whether the decreased significance is due to the smaller
sample or the inclusion of the governance variables we re-estimate the regression for the same
2550 observations after removing the governance controls The coefficient and t-statistic are
virtually identical suggesting that the loss of significance is due to the reduced sample and not
the governance controls We do not find evidence to suggest that variation in corporate
governance practices drives variation in excess value for US MNCs
27
6 Conclusion
Using a new method to quantify the valuation effects of foreign direct investment by US
domiciled firms we find robust evidence of a premium in excess value associated with the
degree of multinational operations Our method builds on that used in previous studies
examining firm excess value (eg Berger and Ofek 1995 and Denis et al 2002) by
incorporating the location in addition to the industry of each corporate affiliate This allows
important factors such as growth and cost of capital to vary across countries as well as industries
Examining a sample of multinational firms we find that a one-standard deviation (197)
increase in foreign operations is associated with an increase of between 41 and 82 in excess
value minus both a statistically and economically significant finding
This result complements the finding of the international trade literature that more productive
firms are more likely to operate internationally However the premium remains after including
firm fixed effects to control for persistent firm traits (eg productivity)
The finding of a premium stands up to a succession of robustness tests designed to evaluate
whether the result arises from alternative methods or explanations First we estimate a dynamic
panel data model using the generalized method of moments (GMM) estimator to simultaneously
account for the potential endogeneity of FDI industrial diversification and firm value Second
we include a control variable to proxy for whether the firm operates in countries with larger
control premiums which could result in understated multiples and mechanically induce a
premium Third we include a control variable to measure the cost of capital across the countries
in which an MNC operates to evaluate whether a relatively low cost of capital borne by
multinational firms is a meaningful source of the premium Fourth we control for several
corporate governance proxies which have been found to be correlated with excess values (and
28
industrial diversification) We fail to find evidence supporting any of these alternative
explanations ndash the premium in excess value arising at firms with greater multinational operations
remains significant
Overall our new method of estimating excess values for multinational firms provides strong
evidence of a statistically and economically significant premium associated with increased
international operations The premium is robust to a host of specifications and controls designed
to evaluate alternative explanations We leave to future research the questions of whether the
premium to US multinational firms applies more broadly to other countries of domicile and
what factors influence the premium
29
References
Ammann M Hoeschel D and M Schmid 2012 Is there really no conglomerate discount
Journal of Business Finance and Accounting 39(12) 264-288 Arellano M and Bond S R 1991 Some tests of specification for panel data Monte Carlo
evidence and an application to employment equations Review of Economic Studies 58 (2) 277-298
Arellano M and Bover O 1995 Another look at instrumental variables estimation of error
components models Journal of Econometrics 68 (1) 29-61 Bens D A Berger P G and S J Monahan 2011 Discretionary disclosure in financial
reporting An examination comparing internal firm data to externally reported segment data The Accounting Review 86(2) 417-449
Berger P G and E Ofek 1995 Diversificationrsquos effect on firm value Journal of Financial
Economics 37 39-65 Blundell R and Bond S R 1998 Initial conditions and moment restrictions in dynamic panel
data models Journal of Econometrics 87(1) 115-144 Campa J M and S Kedia 2002 Explaining the diversification discount The Journal of
Finance 57 (4) 1731-1762 Denis D J Denis D K and K Yost 2002 Global diversification industrial diversification
and firm value The Journal of Finance 57 (5) 1951-1979 Desai M A Foley C F and J R Hines 2004 A multinational perspective on capital structure
choice and internal capital markets The Journal of Finance 59 (6) 2451-2487 Dos Santos MB V Errunza and D Miller 2008 Does corporate international diversification
destroy value Evidence from cross-border mergers and acquisitions Journal of Banking and Finance 32(12) 2716-2724
Doukas JA 1995 Overinvestment Tobinrsquos q and gains from foreign acquisitions Journal of
Banking and Finance 19 1285-1303 Doukas J and N G Travlos 1988 The effect of corporate multinationalism on shareholdersrsquorsquo
wealth evidence from international acquisitions Journal of Finance 43(5) 1161-1175 Doukas JA and O B Kan 2006 Does global diversification destroy firm value Journal of
International Business Studies 37(3) 352-371
30
Dunning J and A Rugman 1985 The influence of Hymerrsquos dissertation on the theory of foreign direct investment The American Economic Review 75(2) 228-232
Dyck A and L Zingales 2004 Private benefits of control An international comparison The
Journal of Finance 59 (2) 537-600 Erb C Harvey C and T Viskanta 1996 Expected returns and volatility in 135 countries The
Journal of Portfolio Management 46-58 Foley C F Desai M A and K J Forbes 2008 Financial constraints and growth
Multinational and local firm responses to currency depreciations Review of Financial Studies 21 (6) 2857-2888
Francis B I Hasan and X Sun 2008 Financial market integration and the value of global
diversification Evidence for US acquirers in cross-border mergers and acquisitions Journal of Banking and Finance 321522-1540
Gande A Schenzler C and L Senbet 2009 Valuation effects of global diversification
Journal of International Business Studies 1515-1532 Gompers P Ishii J and A Metrick 2003 Corporate governance and equity prices Quarterly
Journal of Economics 118(1) 107-156 Hail L and C Leuz 2009 Cost of capital effects and changes in growth expectations around
US cross-listings Journal of Financial Economics 93 428-454 Hanson G Mataloni R and M Slaughter 2005 Vertical production networks in multinational
firms Review of Economics and Statistics 87(4) 1-15 Helpman E 2006 Trade FDI and the Organization of Firms Journal of Economic Literature
44(3) 289-630 Helpman E Melitz M J and S R Yeaple 2004 Export versus FDI with Heterogeneous Firms
American Economic Review 94(1) 300-316 Hoechle D Schmid M Walter W and D Yermack 2012 How much of the diversification
discount can be explained by poor governance Journal of Financial Economics 103 41-60 Hope O-K and W B Thomas 2008 Managerial empire building and firm disclosure Journal
of Accounting Research 46 (June) 591-626 Huizinga H P and J Voget 2009 International taxation and the direction and volume of cross-
border MampAs Journal of Finance 64 (3) 1217-1249 Jensen M 1986 Agency cost of free cash flow corporate finance and takeovers American
Economic Review Papers and Proceedings 76 323-329
31
Laeven L and R Levine 2007 Is there a diversification discount in financial conglomerates
Journal of Financial Economics 85 331-367 Lang L and R Stulz 1994 Tobinrsquos Q corporate diversification and firm performance Journal
of Political Economy 102 1248-1280 Mataloni R 2003 US multinational companies Operations in 2001 Survey of Current
Business 85-105 Matvos G and A Seru 2011 The dynamic tradeoff between corporate socialism and financial
market dislocation Estimates from internal capital markets Working paper University of Chicago Booth School of Business
Moeller SB and F P Schlingemann 2005 Global diversification and bidder gains A
comparison between cross-border and domestic acquisitions Journal of Banking and Finance 29 533-564
Morck R and B Yeung 1991 Why investors value multinationality Journal of Business 64
165-186
Morck R and B Yeung 2001 Why firms diversify Internatization vs agency behavior In Intangibles B Lev (ed) Oxford University Press pp 269-302
Myers S C and N S Majluf 1984 Corporate financing and investment decisions when firms have information that investors do not have Journal of Financial Economics 13 187-221
Schmid M and I Walter 2009 Do financial conglomerates create or destroy economic value
Journal of Financial Intermediation 18 193-216 Syverson C 2011 What determines productivity Journal of Economic Literature 49(2) 326-
365 Villalonga B 2004a Diversification discount or premium New evidence from the Business
Information Tracking Series Journal of Finance 59 479-506 Villalonga B 2004b Does diversification cause the diversification discount Financial
Management 33 5-27 Wooldridge JM 2010 Econometric Analysis of Cross Section and Panel Data (Second
Edition) Cambridge MA MIT Press Yamin M 1991 A reassessment of Hymerrsquos contribution to the theory of the transnational
corporation C Pitelis and R Sugden (eds) The Nature of the Transnational Firm London Routledge
32
Appendix A Variable Definitions
We obtain information regarding firmsrsquo operations by country and industry from the Bureau of Economic Analysis (BEA) unless otherwise noted Acronyms used below reflect Compustat mnemonics and thus indicate when we obtain data from the Compustat North America Fundamentals Annual database All independent variables are winsorized at 1
Dependent variable
Excess Value = The log of the ratio of actual firm value to imputed firm value Actual firm value is equal to market capitalization (MVE) + book value of total assets (AT) ndash book value of shareholdersrsquo equity (SEQ) Imputed firm value is calculated as the sum of the imputed values of a firmrsquos operations in each country-industry The imputed value for each country-industry is equal to sales generated in that country-industry times a multiple equal to the ratio of a benchmark firmrsquos actual value to total sales A benchmark firm is defined as a median single-segment firm operating in the same country-industry All multiples require at least five firms listed in the respective country-industry on Compustat (domestic) or Worldscope (foreign) with less than 10 of sales income and assets outside the country of domicile Similar to Berger and Ofek (1995) we eliminate extreme values where the ratio of actual to imputed firm value exceeds 400 or is less than 025
Independent variables
Foreign Sales = Sales made by the foreign operations of US MNCs as a percent of total firm sales
Industry Other = Sales outside a firmrsquos primary industry as a percent of total sales (SALE) Equal to zero for firms operating in a single business segment
Log Size = Log of total sales (SALE)
Debt Firm Value = Ratio of long-term debt (DLTT) to total market value (market capitalization (MVE) + book value of total assets (AT) ndash book value of shareholdersrsquo equity (SEQ))
CAPX Sales = Ratio of capital expenditures (CAPX) to total sales (SALE)
EBIT Sales = Ratio of earnings before interest and taxes (EBIT) to total sales (SALE)
RampD Sales = Ratio of research and development expenses (XRD) to total sales (SALE)
Ad Sales = Ratio of advertising expenses (XAD) to total sales (SALE)
Control Premium = A weighted-average (based on the sales in a given country as a percent of total firm sales) of the country-level control premium from Dyck and Zingales (2004)
Cost of Capital = A weighted-average (based on the sales in a given country as a percent of total firm sales) of the country-level credit rating from Institutional Investor (httpwwwinstitutionalinvestorcom) The country-level rating is available for 160 countries and ranges from 0 to 100 Higher values imply greater credit risk
33
CEO Ownership = Percent of shares owned by the CEO from Execucomp
Institution Ownership = Percent of shares owned by institutional investors from Thomson Reuterrsquos Institutional Holdings database
Powerful CEO = Indicator variable equal to one if the CEO is the only insider on the board of directors and also serves as the chairman and president based on data from RiskMetrics Directors database Equal to zero otherwise
Governance Index = Count measure of takeover defenses used in Gompers Ishii and Metrick (2003) from RiskMetrics Governance database As the governance data are not available for every year we follow Gompers Ishii and Metrick (2003) and assume that the firmsrsquo governance attributes are unchanged until publication of the subsequent series
34
Figure 1 Trends in multinational activity and industrial diversification from 1984-2008 This figure presents a plot of the mean percent of foreign sales and the mean percent of sales outside the firmrsquos primary industry (industrial diversification) from 1984 through 2008 Dotted lines above and below the primary lines represent 95 confidence intervals To compile the plot we include the full population of firms in Compustat
35
Table 1 Analysis of accounting data for firms reporting under multiple accounting standards This table provides a comparison of sales net income and assets for firms that changed accounting standards to or from US GAAP Comparisons are provided for the 66 firms that changed to or from US GAAP and for which we could obtain accounting data electronically and in English under both standards for the same fiscal year (eg the firm presented the accounting numbers originally under one set of standards and then restated the prior year numbers in the subsequent year under the new set of standards) Panel A shows the distribution of firms by year Panel B shows the other accounting standards to which US GAAP is compared Panel C shows the mean and median percent that the other accounting standard differs from US GAAP for the same fiscal year and accounting number Two samples are shown in Panel C The first is the full sample of firms with a change in accounting standards to or from US GAAP for which data was available The second is the subset of these firms that occurred during the sample window used in this study (ie excluding changes post-2008) The percent difference in sales is compared to the percent difference in net income and assets Tests of means are based on matched-pair t-tests Tests of medians are based on Wilcoxon matched-pair signed-rank tests Panel A Accounting standard changes by year
2003
4 6
2004
25 38
2005
8 12
2006
6 9
2007
7 11
2008
5 8
2009
5 8
2010
5 8
2011
1 2
66
Panel B Accounting standards compared to US GAAP
IFRS
47 71
Canadian GAAP
12 18
Israeli GAAP
2 3
Dutch GAAP
1 2
Indian GAAP
1 2
Japanese GAAP
1 2
Norwegian GAAP
1 2
Swiss GAAP
1 2
66
36
(Table 1 continued) Panel C Tests of differences between US GAAP and other accounting standards
Percent different from US GAAP
Comparison to sales (p-values)
N Mean Median
Mean Median
Full Sample (2001-2011)
Sales
66 236 000
Net Income
66 10653 647
005 lt001
Assets
66 1162 126
lt001 lt001
Study Sample (2001-2008)
Sales
55 257 000
Net Income
55 10303 589
011 lt001
Assets
55 1047 099
003 lt001
37
Table 2 Descriptive statistics Panel A reports the mean and medians of Excess Value for the multinational firms and single-segment benchmark firms as well as univariate tests across the subsamples Panel B reports summary statistics for the independent variables for the multinational firms The p-values of the tests of differences in means are based on t-tests The p-values of tests of differences in the medians are for a Wilcoxon rank-sum test All variables are defined in Appendix A Panel A Excess Value by sample Multinational All Benchmark Benchmark Subgroups Sample Firms US Foreign
Value Value Test of Diff (p-value) Value
Test of Diff (p-value) Value
Test of Diff (p-value)
Mean 0067 0006 lt001 -0028 lt001 0014 lt001 Median 0076 0000 lt001 -0025 lt001 0000 lt001 N 4950 33074 5998 27076 Panel B Distribution of independent variables for the multinational firms N Mean 25th 50th 75th StDev Foreign Sales 4950 0238 0074 0190 0363 0197 Industry Other 4950 0212 0000 0166 0414 0215 Log Size 4950 6955 5823 6989 8140 1687 Debt Firm Value 4950 0136 0009 0097 0212 0145 CAPX Sales 4950 0054 0020 0033 0054 0101 EBIT Sales 4950 0065 0031 0077 0121 0138 RampD Sales 4950 0049 0000 0013 0059 0083 Ad Sales 4950 0008 0000 0000 0005 0018
38
Table 3 BEA and Worldscope samples
This table reports details on affiliate sales for significant countries pooled from 1998-2008 Significant countries are defined as those in which the operations of US MNCs represent at least 02 of total firm sales (per the BEA) For significant countries the table compares their representation in terms of MNC affiliate sales in the BEA data to the distribution of firm observations in Worldscope US Foreign Affiliates (BEA) Foreign Firms (Worldscope)
Country
N (1)
Sales (millions)
(2)
Total Sales (3)
N (4)
Sales (million)
(5)
Total Sales (6)
Australia 1344 73305 056 859 255865 210 Belgium 894 52755 040 169 26136 021 Brazil 959 60245 046 1143 683167 560 Canada 2730 536767 407 1181 526750 432 China 922 75907 057 822 276541 227 France 1852 183942 139 540 105447 086 Germany 2182 275577 209 480 103888 085 Hong Kong 867 48100 036 157 34094 028 Ireland 478 36754 028 34 3467 003 Italy 1178 82116 062 111 47948 039 Japan 1494 148032 112 2281 1119223 917 Korea South 659 34500 026 3530 2752791 2255 Malaysia 386 26027 020 837 168683 138 Mexico 1405 113623 086 362 237173 194 Netherlands 1149 85105 064 47 22631 019 Singapore 1098 94917 072 139 32913 027 Spain 871 63094 048 129 37419 031 Sweden 712 32682 025 235 23090 019 Switzerland 726 60288 046 74 20242 017 Taiwan 592 30313 023 2949 787049 645 United Kingdom 3054 423934 321 1997 882423 723 United States (domicile) 4950 10460880 7923 na na na Other - Covered by WorldScope 6893 199394 151 9000 4057894 33 Other -Not Covered by WorldScope 713 5352 004 - - - Total 38108 13203609 1000 27076 12204834 100
39
Table 4 OLS regressions of firm excess values on the percent of foreign sales This table reports the results of OLS regressions of firm excess value on the percent of foreign sales (Foreign Sales) the percent of sales outside a firmrsquos primary industry (Industry Other) and control variables All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed) Dep Variable = Excess Value Coefficient t-stat Coefficient t-stat Coefficient t-stat Coefficient t-stat (1) (2) (3) (4) Foreign Sales 0349 419 0209 293 0189 266 0278 210 Industry Other -0134 -186 -0047 -072 -0053 -081 -0326 -325 Log Size 0066 634 0065 624 -0044 -096 Debt Firm Value -0196 -213 -0241 -260 -0685 -588 CAPX Sales 0901 425 0893 435 0488 227 EBIT Sales 1154 542 1170 547 0408 189 RampD Sales 2908 1242 2932 1237 1544 379 Ad Sales 1869 263 2003 280 -1696 -119 Year indicators No No Yes Yes Firm indicators No No No Yes R2 0017 0212 0226 0768 Observations 4950 4950 4950 4950
40
Table 5 Dynamic panel GMM model regressing firm excess values on the percent of foreign sales This table reports the result from a regression of the excess value on the percent of foreign sales (Foreign Sales) the percent of non-primary industry sales (Industry Other) and control variables using a dynamic panel GMM estimator All control variables are considered to be endogenous with the exception of the year indicator variables AR(1) and AR(2) are tests for first-order and second-order serial correlation in the first differenced residuals with the null hypothesis of no serial correlation The null hypothesis of the Hansen test of overidentification is that all instruments are valid The null hypothesis of the difference-in-Hansen test of exogeneity is that the instruments used for the equation in levels are exogenous All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed) Dep Variable = Excess Value Coef t-stat Excess Value (one lag) -0018 -082 Excess Value (two lags) -0048 -272 Foreign Sales 0371 270 Industry Other -0163 -171 Log Size 0010 123 Debt FirmValue -1135 -820 CAPX Sales 0590 342 EBIT Sales -0047 -300 RampD Sales 1376 531 Ad Sales -0751 -040 AR(1) test (p-value) 000 AR(2) test (p-value) 017 Hansen test of over-identification (p-value) 030 Difference-in-Hansen test of exogeneity (p-value) 093 Year indicators Yes Number of firms 1016 Number of observations 4090
41
Table 6 OLS regression of firm excess values on the percent of foreign sales controlling for a control premium This table reports the result of the OLS regression of firm excess value including firm and year fixed effects with an additional control variable to proxy for a country-level ownership control premium (Control Premium) All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed) Dep Variable = Excess Value Coef t -stat Foreign Sales 0303 224 Industry Other -0327 -325 Log Size -0045 -100 Debt FirmValue -0683 -585 CAPX Sales 0490 226 EBIT Sales 0409 189 RampD Sales 1545 379 Ad Sales -1689 -119 Control Premium -1004 -062 Year indicators Yes Firm indicators Yes R2 0768 Observations 4950
42
Table 7 OLS regression of firm excess values on the percent of foreign sales controlling for the cost of capital This table contains the results of an OLS regression of firm excess value including firm and year fixed effects with an additional control variable to proxy for a country-level cost of capital (Cost of Capital) All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed) Dep Variable = Excess Value Coef t -stat Foreign Sales 0255 178 Industry Other -0326 -324 Log Size -0043 -094 Debt FirmValue -0687 -589 CAPX Sales 0485 225 EBIT Sales 0406 188 RampD Sales 1539 379 Ad Sales -1703 -120 Cost of Capital 0004 039 Year indicators Yes Firm indicators Yes R2 0768 Observations 4950
43
Table 8 OLS regression of firm excess values on the percent of foreign sales controlling for aspects of corporate governance This table reports the results of two OLS regressions In the first column are the results of the firm excess value regression including firm and year fixed effect with five proxies for corporate governance included ndash the percent of shares owned by the CEO and the percent squared (CEO Ownership and CEO Ownership squared) the percent owned by institutional investors (Institution Ownership) whether there is a powerful CEO (Powerful CEO) and the Gompers Ishii and Metrick (2003) governance index (Governance Index) In the second column the regression includes the same sample and variables with the exception of the governance proxies which are excluded All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed)
Governance
No Governance (same sample)
Dep Variable = Excess Value Coef t-stat Coef t -stat Foreign Sales 0259 159 0251 154 Industry Other -0099 -080 -0099 -079 Log Size -0105 -185 -0102 -180 Debt FirmValue -0813 -478 -0817 -482 CAPX Sales 0440 252 0438 249 EBIT Sales 0457 247 0457 246 RampD Sales 1717 284 1716 286 Ad Sales -2210 -143 -2190 -143 CEO Ownership 0010 007
CEO Ownership squared -0029 -017 Institution Ownership 0047 067 Powerful CEO -0003 -014 Governance Index 0009 103
Year indicators Yes Yes Firm indicators Yes Yes R2 0811 0810 Observations 2550 2550
- Cover for paperpdf
- crrz_20130603_SMUpdf
-
26
55 Corporate governance
In light of the recent finding that corporate governance mitigates the diversification discount
we examine whether the premium to multinational activity is sensitive to controls for corporate
governance Hoechle et al (2012) find that the magnitude of the industrial diversification
discount is decreased and in several specifications no longer significant in the presence of
governance proxies In the first column of Table 8 we include the five governance proxies that
are consistently significantly associated with excess value in Hoechle et al (2012) ndash the percent
of CEO ownership (CEO Ownership and CEO Ownership squared) the percent of institutional
ownership (Institution Ownership) whether the firm has a powerful or influential CEO
(Powerful CEO) and the governance index from Gompers Ishii and Metrick (2003)
(Governance Index)
As documented in Table 8 including these control variables reduces our sample size by
almost 50 (to 2550 observations) and tends to exclude the smaller firms from the analysis
None of the governance proxies are significant Relative to column (4) of Table 4 the coefficient
on Foreign Sales is similar in magnitude but is no longer statistically significant at
conventional levels To investigate whether the decreased significance is due to the smaller
sample or the inclusion of the governance variables we re-estimate the regression for the same
2550 observations after removing the governance controls The coefficient and t-statistic are
virtually identical suggesting that the loss of significance is due to the reduced sample and not
the governance controls We do not find evidence to suggest that variation in corporate
governance practices drives variation in excess value for US MNCs
27
6 Conclusion
Using a new method to quantify the valuation effects of foreign direct investment by US
domiciled firms we find robust evidence of a premium in excess value associated with the
degree of multinational operations Our method builds on that used in previous studies
examining firm excess value (eg Berger and Ofek 1995 and Denis et al 2002) by
incorporating the location in addition to the industry of each corporate affiliate This allows
important factors such as growth and cost of capital to vary across countries as well as industries
Examining a sample of multinational firms we find that a one-standard deviation (197)
increase in foreign operations is associated with an increase of between 41 and 82 in excess
value minus both a statistically and economically significant finding
This result complements the finding of the international trade literature that more productive
firms are more likely to operate internationally However the premium remains after including
firm fixed effects to control for persistent firm traits (eg productivity)
The finding of a premium stands up to a succession of robustness tests designed to evaluate
whether the result arises from alternative methods or explanations First we estimate a dynamic
panel data model using the generalized method of moments (GMM) estimator to simultaneously
account for the potential endogeneity of FDI industrial diversification and firm value Second
we include a control variable to proxy for whether the firm operates in countries with larger
control premiums which could result in understated multiples and mechanically induce a
premium Third we include a control variable to measure the cost of capital across the countries
in which an MNC operates to evaluate whether a relatively low cost of capital borne by
multinational firms is a meaningful source of the premium Fourth we control for several
corporate governance proxies which have been found to be correlated with excess values (and
28
industrial diversification) We fail to find evidence supporting any of these alternative
explanations ndash the premium in excess value arising at firms with greater multinational operations
remains significant
Overall our new method of estimating excess values for multinational firms provides strong
evidence of a statistically and economically significant premium associated with increased
international operations The premium is robust to a host of specifications and controls designed
to evaluate alternative explanations We leave to future research the questions of whether the
premium to US multinational firms applies more broadly to other countries of domicile and
what factors influence the premium
29
References
Ammann M Hoeschel D and M Schmid 2012 Is there really no conglomerate discount
Journal of Business Finance and Accounting 39(12) 264-288 Arellano M and Bond S R 1991 Some tests of specification for panel data Monte Carlo
evidence and an application to employment equations Review of Economic Studies 58 (2) 277-298
Arellano M and Bover O 1995 Another look at instrumental variables estimation of error
components models Journal of Econometrics 68 (1) 29-61 Bens D A Berger P G and S J Monahan 2011 Discretionary disclosure in financial
reporting An examination comparing internal firm data to externally reported segment data The Accounting Review 86(2) 417-449
Berger P G and E Ofek 1995 Diversificationrsquos effect on firm value Journal of Financial
Economics 37 39-65 Blundell R and Bond S R 1998 Initial conditions and moment restrictions in dynamic panel
data models Journal of Econometrics 87(1) 115-144 Campa J M and S Kedia 2002 Explaining the diversification discount The Journal of
Finance 57 (4) 1731-1762 Denis D J Denis D K and K Yost 2002 Global diversification industrial diversification
and firm value The Journal of Finance 57 (5) 1951-1979 Desai M A Foley C F and J R Hines 2004 A multinational perspective on capital structure
choice and internal capital markets The Journal of Finance 59 (6) 2451-2487 Dos Santos MB V Errunza and D Miller 2008 Does corporate international diversification
destroy value Evidence from cross-border mergers and acquisitions Journal of Banking and Finance 32(12) 2716-2724
Doukas JA 1995 Overinvestment Tobinrsquos q and gains from foreign acquisitions Journal of
Banking and Finance 19 1285-1303 Doukas J and N G Travlos 1988 The effect of corporate multinationalism on shareholdersrsquorsquo
wealth evidence from international acquisitions Journal of Finance 43(5) 1161-1175 Doukas JA and O B Kan 2006 Does global diversification destroy firm value Journal of
International Business Studies 37(3) 352-371
30
Dunning J and A Rugman 1985 The influence of Hymerrsquos dissertation on the theory of foreign direct investment The American Economic Review 75(2) 228-232
Dyck A and L Zingales 2004 Private benefits of control An international comparison The
Journal of Finance 59 (2) 537-600 Erb C Harvey C and T Viskanta 1996 Expected returns and volatility in 135 countries The
Journal of Portfolio Management 46-58 Foley C F Desai M A and K J Forbes 2008 Financial constraints and growth
Multinational and local firm responses to currency depreciations Review of Financial Studies 21 (6) 2857-2888
Francis B I Hasan and X Sun 2008 Financial market integration and the value of global
diversification Evidence for US acquirers in cross-border mergers and acquisitions Journal of Banking and Finance 321522-1540
Gande A Schenzler C and L Senbet 2009 Valuation effects of global diversification
Journal of International Business Studies 1515-1532 Gompers P Ishii J and A Metrick 2003 Corporate governance and equity prices Quarterly
Journal of Economics 118(1) 107-156 Hail L and C Leuz 2009 Cost of capital effects and changes in growth expectations around
US cross-listings Journal of Financial Economics 93 428-454 Hanson G Mataloni R and M Slaughter 2005 Vertical production networks in multinational
firms Review of Economics and Statistics 87(4) 1-15 Helpman E 2006 Trade FDI and the Organization of Firms Journal of Economic Literature
44(3) 289-630 Helpman E Melitz M J and S R Yeaple 2004 Export versus FDI with Heterogeneous Firms
American Economic Review 94(1) 300-316 Hoechle D Schmid M Walter W and D Yermack 2012 How much of the diversification
discount can be explained by poor governance Journal of Financial Economics 103 41-60 Hope O-K and W B Thomas 2008 Managerial empire building and firm disclosure Journal
of Accounting Research 46 (June) 591-626 Huizinga H P and J Voget 2009 International taxation and the direction and volume of cross-
border MampAs Journal of Finance 64 (3) 1217-1249 Jensen M 1986 Agency cost of free cash flow corporate finance and takeovers American
Economic Review Papers and Proceedings 76 323-329
31
Laeven L and R Levine 2007 Is there a diversification discount in financial conglomerates
Journal of Financial Economics 85 331-367 Lang L and R Stulz 1994 Tobinrsquos Q corporate diversification and firm performance Journal
of Political Economy 102 1248-1280 Mataloni R 2003 US multinational companies Operations in 2001 Survey of Current
Business 85-105 Matvos G and A Seru 2011 The dynamic tradeoff between corporate socialism and financial
market dislocation Estimates from internal capital markets Working paper University of Chicago Booth School of Business
Moeller SB and F P Schlingemann 2005 Global diversification and bidder gains A
comparison between cross-border and domestic acquisitions Journal of Banking and Finance 29 533-564
Morck R and B Yeung 1991 Why investors value multinationality Journal of Business 64
165-186
Morck R and B Yeung 2001 Why firms diversify Internatization vs agency behavior In Intangibles B Lev (ed) Oxford University Press pp 269-302
Myers S C and N S Majluf 1984 Corporate financing and investment decisions when firms have information that investors do not have Journal of Financial Economics 13 187-221
Schmid M and I Walter 2009 Do financial conglomerates create or destroy economic value
Journal of Financial Intermediation 18 193-216 Syverson C 2011 What determines productivity Journal of Economic Literature 49(2) 326-
365 Villalonga B 2004a Diversification discount or premium New evidence from the Business
Information Tracking Series Journal of Finance 59 479-506 Villalonga B 2004b Does diversification cause the diversification discount Financial
Management 33 5-27 Wooldridge JM 2010 Econometric Analysis of Cross Section and Panel Data (Second
Edition) Cambridge MA MIT Press Yamin M 1991 A reassessment of Hymerrsquos contribution to the theory of the transnational
corporation C Pitelis and R Sugden (eds) The Nature of the Transnational Firm London Routledge
32
Appendix A Variable Definitions
We obtain information regarding firmsrsquo operations by country and industry from the Bureau of Economic Analysis (BEA) unless otherwise noted Acronyms used below reflect Compustat mnemonics and thus indicate when we obtain data from the Compustat North America Fundamentals Annual database All independent variables are winsorized at 1
Dependent variable
Excess Value = The log of the ratio of actual firm value to imputed firm value Actual firm value is equal to market capitalization (MVE) + book value of total assets (AT) ndash book value of shareholdersrsquo equity (SEQ) Imputed firm value is calculated as the sum of the imputed values of a firmrsquos operations in each country-industry The imputed value for each country-industry is equal to sales generated in that country-industry times a multiple equal to the ratio of a benchmark firmrsquos actual value to total sales A benchmark firm is defined as a median single-segment firm operating in the same country-industry All multiples require at least five firms listed in the respective country-industry on Compustat (domestic) or Worldscope (foreign) with less than 10 of sales income and assets outside the country of domicile Similar to Berger and Ofek (1995) we eliminate extreme values where the ratio of actual to imputed firm value exceeds 400 or is less than 025
Independent variables
Foreign Sales = Sales made by the foreign operations of US MNCs as a percent of total firm sales
Industry Other = Sales outside a firmrsquos primary industry as a percent of total sales (SALE) Equal to zero for firms operating in a single business segment
Log Size = Log of total sales (SALE)
Debt Firm Value = Ratio of long-term debt (DLTT) to total market value (market capitalization (MVE) + book value of total assets (AT) ndash book value of shareholdersrsquo equity (SEQ))
CAPX Sales = Ratio of capital expenditures (CAPX) to total sales (SALE)
EBIT Sales = Ratio of earnings before interest and taxes (EBIT) to total sales (SALE)
RampD Sales = Ratio of research and development expenses (XRD) to total sales (SALE)
Ad Sales = Ratio of advertising expenses (XAD) to total sales (SALE)
Control Premium = A weighted-average (based on the sales in a given country as a percent of total firm sales) of the country-level control premium from Dyck and Zingales (2004)
Cost of Capital = A weighted-average (based on the sales in a given country as a percent of total firm sales) of the country-level credit rating from Institutional Investor (httpwwwinstitutionalinvestorcom) The country-level rating is available for 160 countries and ranges from 0 to 100 Higher values imply greater credit risk
33
CEO Ownership = Percent of shares owned by the CEO from Execucomp
Institution Ownership = Percent of shares owned by institutional investors from Thomson Reuterrsquos Institutional Holdings database
Powerful CEO = Indicator variable equal to one if the CEO is the only insider on the board of directors and also serves as the chairman and president based on data from RiskMetrics Directors database Equal to zero otherwise
Governance Index = Count measure of takeover defenses used in Gompers Ishii and Metrick (2003) from RiskMetrics Governance database As the governance data are not available for every year we follow Gompers Ishii and Metrick (2003) and assume that the firmsrsquo governance attributes are unchanged until publication of the subsequent series
34
Figure 1 Trends in multinational activity and industrial diversification from 1984-2008 This figure presents a plot of the mean percent of foreign sales and the mean percent of sales outside the firmrsquos primary industry (industrial diversification) from 1984 through 2008 Dotted lines above and below the primary lines represent 95 confidence intervals To compile the plot we include the full population of firms in Compustat
35
Table 1 Analysis of accounting data for firms reporting under multiple accounting standards This table provides a comparison of sales net income and assets for firms that changed accounting standards to or from US GAAP Comparisons are provided for the 66 firms that changed to or from US GAAP and for which we could obtain accounting data electronically and in English under both standards for the same fiscal year (eg the firm presented the accounting numbers originally under one set of standards and then restated the prior year numbers in the subsequent year under the new set of standards) Panel A shows the distribution of firms by year Panel B shows the other accounting standards to which US GAAP is compared Panel C shows the mean and median percent that the other accounting standard differs from US GAAP for the same fiscal year and accounting number Two samples are shown in Panel C The first is the full sample of firms with a change in accounting standards to or from US GAAP for which data was available The second is the subset of these firms that occurred during the sample window used in this study (ie excluding changes post-2008) The percent difference in sales is compared to the percent difference in net income and assets Tests of means are based on matched-pair t-tests Tests of medians are based on Wilcoxon matched-pair signed-rank tests Panel A Accounting standard changes by year
2003
4 6
2004
25 38
2005
8 12
2006
6 9
2007
7 11
2008
5 8
2009
5 8
2010
5 8
2011
1 2
66
Panel B Accounting standards compared to US GAAP
IFRS
47 71
Canadian GAAP
12 18
Israeli GAAP
2 3
Dutch GAAP
1 2
Indian GAAP
1 2
Japanese GAAP
1 2
Norwegian GAAP
1 2
Swiss GAAP
1 2
66
36
(Table 1 continued) Panel C Tests of differences between US GAAP and other accounting standards
Percent different from US GAAP
Comparison to sales (p-values)
N Mean Median
Mean Median
Full Sample (2001-2011)
Sales
66 236 000
Net Income
66 10653 647
005 lt001
Assets
66 1162 126
lt001 lt001
Study Sample (2001-2008)
Sales
55 257 000
Net Income
55 10303 589
011 lt001
Assets
55 1047 099
003 lt001
37
Table 2 Descriptive statistics Panel A reports the mean and medians of Excess Value for the multinational firms and single-segment benchmark firms as well as univariate tests across the subsamples Panel B reports summary statistics for the independent variables for the multinational firms The p-values of the tests of differences in means are based on t-tests The p-values of tests of differences in the medians are for a Wilcoxon rank-sum test All variables are defined in Appendix A Panel A Excess Value by sample Multinational All Benchmark Benchmark Subgroups Sample Firms US Foreign
Value Value Test of Diff (p-value) Value
Test of Diff (p-value) Value
Test of Diff (p-value)
Mean 0067 0006 lt001 -0028 lt001 0014 lt001 Median 0076 0000 lt001 -0025 lt001 0000 lt001 N 4950 33074 5998 27076 Panel B Distribution of independent variables for the multinational firms N Mean 25th 50th 75th StDev Foreign Sales 4950 0238 0074 0190 0363 0197 Industry Other 4950 0212 0000 0166 0414 0215 Log Size 4950 6955 5823 6989 8140 1687 Debt Firm Value 4950 0136 0009 0097 0212 0145 CAPX Sales 4950 0054 0020 0033 0054 0101 EBIT Sales 4950 0065 0031 0077 0121 0138 RampD Sales 4950 0049 0000 0013 0059 0083 Ad Sales 4950 0008 0000 0000 0005 0018
38
Table 3 BEA and Worldscope samples
This table reports details on affiliate sales for significant countries pooled from 1998-2008 Significant countries are defined as those in which the operations of US MNCs represent at least 02 of total firm sales (per the BEA) For significant countries the table compares their representation in terms of MNC affiliate sales in the BEA data to the distribution of firm observations in Worldscope US Foreign Affiliates (BEA) Foreign Firms (Worldscope)
Country
N (1)
Sales (millions)
(2)
Total Sales (3)
N (4)
Sales (million)
(5)
Total Sales (6)
Australia 1344 73305 056 859 255865 210 Belgium 894 52755 040 169 26136 021 Brazil 959 60245 046 1143 683167 560 Canada 2730 536767 407 1181 526750 432 China 922 75907 057 822 276541 227 France 1852 183942 139 540 105447 086 Germany 2182 275577 209 480 103888 085 Hong Kong 867 48100 036 157 34094 028 Ireland 478 36754 028 34 3467 003 Italy 1178 82116 062 111 47948 039 Japan 1494 148032 112 2281 1119223 917 Korea South 659 34500 026 3530 2752791 2255 Malaysia 386 26027 020 837 168683 138 Mexico 1405 113623 086 362 237173 194 Netherlands 1149 85105 064 47 22631 019 Singapore 1098 94917 072 139 32913 027 Spain 871 63094 048 129 37419 031 Sweden 712 32682 025 235 23090 019 Switzerland 726 60288 046 74 20242 017 Taiwan 592 30313 023 2949 787049 645 United Kingdom 3054 423934 321 1997 882423 723 United States (domicile) 4950 10460880 7923 na na na Other - Covered by WorldScope 6893 199394 151 9000 4057894 33 Other -Not Covered by WorldScope 713 5352 004 - - - Total 38108 13203609 1000 27076 12204834 100
39
Table 4 OLS regressions of firm excess values on the percent of foreign sales This table reports the results of OLS regressions of firm excess value on the percent of foreign sales (Foreign Sales) the percent of sales outside a firmrsquos primary industry (Industry Other) and control variables All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed) Dep Variable = Excess Value Coefficient t-stat Coefficient t-stat Coefficient t-stat Coefficient t-stat (1) (2) (3) (4) Foreign Sales 0349 419 0209 293 0189 266 0278 210 Industry Other -0134 -186 -0047 -072 -0053 -081 -0326 -325 Log Size 0066 634 0065 624 -0044 -096 Debt Firm Value -0196 -213 -0241 -260 -0685 -588 CAPX Sales 0901 425 0893 435 0488 227 EBIT Sales 1154 542 1170 547 0408 189 RampD Sales 2908 1242 2932 1237 1544 379 Ad Sales 1869 263 2003 280 -1696 -119 Year indicators No No Yes Yes Firm indicators No No No Yes R2 0017 0212 0226 0768 Observations 4950 4950 4950 4950
40
Table 5 Dynamic panel GMM model regressing firm excess values on the percent of foreign sales This table reports the result from a regression of the excess value on the percent of foreign sales (Foreign Sales) the percent of non-primary industry sales (Industry Other) and control variables using a dynamic panel GMM estimator All control variables are considered to be endogenous with the exception of the year indicator variables AR(1) and AR(2) are tests for first-order and second-order serial correlation in the first differenced residuals with the null hypothesis of no serial correlation The null hypothesis of the Hansen test of overidentification is that all instruments are valid The null hypothesis of the difference-in-Hansen test of exogeneity is that the instruments used for the equation in levels are exogenous All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed) Dep Variable = Excess Value Coef t-stat Excess Value (one lag) -0018 -082 Excess Value (two lags) -0048 -272 Foreign Sales 0371 270 Industry Other -0163 -171 Log Size 0010 123 Debt FirmValue -1135 -820 CAPX Sales 0590 342 EBIT Sales -0047 -300 RampD Sales 1376 531 Ad Sales -0751 -040 AR(1) test (p-value) 000 AR(2) test (p-value) 017 Hansen test of over-identification (p-value) 030 Difference-in-Hansen test of exogeneity (p-value) 093 Year indicators Yes Number of firms 1016 Number of observations 4090
41
Table 6 OLS regression of firm excess values on the percent of foreign sales controlling for a control premium This table reports the result of the OLS regression of firm excess value including firm and year fixed effects with an additional control variable to proxy for a country-level ownership control premium (Control Premium) All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed) Dep Variable = Excess Value Coef t -stat Foreign Sales 0303 224 Industry Other -0327 -325 Log Size -0045 -100 Debt FirmValue -0683 -585 CAPX Sales 0490 226 EBIT Sales 0409 189 RampD Sales 1545 379 Ad Sales -1689 -119 Control Premium -1004 -062 Year indicators Yes Firm indicators Yes R2 0768 Observations 4950
42
Table 7 OLS regression of firm excess values on the percent of foreign sales controlling for the cost of capital This table contains the results of an OLS regression of firm excess value including firm and year fixed effects with an additional control variable to proxy for a country-level cost of capital (Cost of Capital) All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed) Dep Variable = Excess Value Coef t -stat Foreign Sales 0255 178 Industry Other -0326 -324 Log Size -0043 -094 Debt FirmValue -0687 -589 CAPX Sales 0485 225 EBIT Sales 0406 188 RampD Sales 1539 379 Ad Sales -1703 -120 Cost of Capital 0004 039 Year indicators Yes Firm indicators Yes R2 0768 Observations 4950
43
Table 8 OLS regression of firm excess values on the percent of foreign sales controlling for aspects of corporate governance This table reports the results of two OLS regressions In the first column are the results of the firm excess value regression including firm and year fixed effect with five proxies for corporate governance included ndash the percent of shares owned by the CEO and the percent squared (CEO Ownership and CEO Ownership squared) the percent owned by institutional investors (Institution Ownership) whether there is a powerful CEO (Powerful CEO) and the Gompers Ishii and Metrick (2003) governance index (Governance Index) In the second column the regression includes the same sample and variables with the exception of the governance proxies which are excluded All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed)
Governance
No Governance (same sample)
Dep Variable = Excess Value Coef t-stat Coef t -stat Foreign Sales 0259 159 0251 154 Industry Other -0099 -080 -0099 -079 Log Size -0105 -185 -0102 -180 Debt FirmValue -0813 -478 -0817 -482 CAPX Sales 0440 252 0438 249 EBIT Sales 0457 247 0457 246 RampD Sales 1717 284 1716 286 Ad Sales -2210 -143 -2190 -143 CEO Ownership 0010 007
CEO Ownership squared -0029 -017 Institution Ownership 0047 067 Powerful CEO -0003 -014 Governance Index 0009 103
Year indicators Yes Yes Firm indicators Yes Yes R2 0811 0810 Observations 2550 2550
- Cover for paperpdf
- crrz_20130603_SMUpdf
-
27
6 Conclusion
Using a new method to quantify the valuation effects of foreign direct investment by US
domiciled firms we find robust evidence of a premium in excess value associated with the
degree of multinational operations Our method builds on that used in previous studies
examining firm excess value (eg Berger and Ofek 1995 and Denis et al 2002) by
incorporating the location in addition to the industry of each corporate affiliate This allows
important factors such as growth and cost of capital to vary across countries as well as industries
Examining a sample of multinational firms we find that a one-standard deviation (197)
increase in foreign operations is associated with an increase of between 41 and 82 in excess
value minus both a statistically and economically significant finding
This result complements the finding of the international trade literature that more productive
firms are more likely to operate internationally However the premium remains after including
firm fixed effects to control for persistent firm traits (eg productivity)
The finding of a premium stands up to a succession of robustness tests designed to evaluate
whether the result arises from alternative methods or explanations First we estimate a dynamic
panel data model using the generalized method of moments (GMM) estimator to simultaneously
account for the potential endogeneity of FDI industrial diversification and firm value Second
we include a control variable to proxy for whether the firm operates in countries with larger
control premiums which could result in understated multiples and mechanically induce a
premium Third we include a control variable to measure the cost of capital across the countries
in which an MNC operates to evaluate whether a relatively low cost of capital borne by
multinational firms is a meaningful source of the premium Fourth we control for several
corporate governance proxies which have been found to be correlated with excess values (and
28
industrial diversification) We fail to find evidence supporting any of these alternative
explanations ndash the premium in excess value arising at firms with greater multinational operations
remains significant
Overall our new method of estimating excess values for multinational firms provides strong
evidence of a statistically and economically significant premium associated with increased
international operations The premium is robust to a host of specifications and controls designed
to evaluate alternative explanations We leave to future research the questions of whether the
premium to US multinational firms applies more broadly to other countries of domicile and
what factors influence the premium
29
References
Ammann M Hoeschel D and M Schmid 2012 Is there really no conglomerate discount
Journal of Business Finance and Accounting 39(12) 264-288 Arellano M and Bond S R 1991 Some tests of specification for panel data Monte Carlo
evidence and an application to employment equations Review of Economic Studies 58 (2) 277-298
Arellano M and Bover O 1995 Another look at instrumental variables estimation of error
components models Journal of Econometrics 68 (1) 29-61 Bens D A Berger P G and S J Monahan 2011 Discretionary disclosure in financial
reporting An examination comparing internal firm data to externally reported segment data The Accounting Review 86(2) 417-449
Berger P G and E Ofek 1995 Diversificationrsquos effect on firm value Journal of Financial
Economics 37 39-65 Blundell R and Bond S R 1998 Initial conditions and moment restrictions in dynamic panel
data models Journal of Econometrics 87(1) 115-144 Campa J M and S Kedia 2002 Explaining the diversification discount The Journal of
Finance 57 (4) 1731-1762 Denis D J Denis D K and K Yost 2002 Global diversification industrial diversification
and firm value The Journal of Finance 57 (5) 1951-1979 Desai M A Foley C F and J R Hines 2004 A multinational perspective on capital structure
choice and internal capital markets The Journal of Finance 59 (6) 2451-2487 Dos Santos MB V Errunza and D Miller 2008 Does corporate international diversification
destroy value Evidence from cross-border mergers and acquisitions Journal of Banking and Finance 32(12) 2716-2724
Doukas JA 1995 Overinvestment Tobinrsquos q and gains from foreign acquisitions Journal of
Banking and Finance 19 1285-1303 Doukas J and N G Travlos 1988 The effect of corporate multinationalism on shareholdersrsquorsquo
wealth evidence from international acquisitions Journal of Finance 43(5) 1161-1175 Doukas JA and O B Kan 2006 Does global diversification destroy firm value Journal of
International Business Studies 37(3) 352-371
30
Dunning J and A Rugman 1985 The influence of Hymerrsquos dissertation on the theory of foreign direct investment The American Economic Review 75(2) 228-232
Dyck A and L Zingales 2004 Private benefits of control An international comparison The
Journal of Finance 59 (2) 537-600 Erb C Harvey C and T Viskanta 1996 Expected returns and volatility in 135 countries The
Journal of Portfolio Management 46-58 Foley C F Desai M A and K J Forbes 2008 Financial constraints and growth
Multinational and local firm responses to currency depreciations Review of Financial Studies 21 (6) 2857-2888
Francis B I Hasan and X Sun 2008 Financial market integration and the value of global
diversification Evidence for US acquirers in cross-border mergers and acquisitions Journal of Banking and Finance 321522-1540
Gande A Schenzler C and L Senbet 2009 Valuation effects of global diversification
Journal of International Business Studies 1515-1532 Gompers P Ishii J and A Metrick 2003 Corporate governance and equity prices Quarterly
Journal of Economics 118(1) 107-156 Hail L and C Leuz 2009 Cost of capital effects and changes in growth expectations around
US cross-listings Journal of Financial Economics 93 428-454 Hanson G Mataloni R and M Slaughter 2005 Vertical production networks in multinational
firms Review of Economics and Statistics 87(4) 1-15 Helpman E 2006 Trade FDI and the Organization of Firms Journal of Economic Literature
44(3) 289-630 Helpman E Melitz M J and S R Yeaple 2004 Export versus FDI with Heterogeneous Firms
American Economic Review 94(1) 300-316 Hoechle D Schmid M Walter W and D Yermack 2012 How much of the diversification
discount can be explained by poor governance Journal of Financial Economics 103 41-60 Hope O-K and W B Thomas 2008 Managerial empire building and firm disclosure Journal
of Accounting Research 46 (June) 591-626 Huizinga H P and J Voget 2009 International taxation and the direction and volume of cross-
border MampAs Journal of Finance 64 (3) 1217-1249 Jensen M 1986 Agency cost of free cash flow corporate finance and takeovers American
Economic Review Papers and Proceedings 76 323-329
31
Laeven L and R Levine 2007 Is there a diversification discount in financial conglomerates
Journal of Financial Economics 85 331-367 Lang L and R Stulz 1994 Tobinrsquos Q corporate diversification and firm performance Journal
of Political Economy 102 1248-1280 Mataloni R 2003 US multinational companies Operations in 2001 Survey of Current
Business 85-105 Matvos G and A Seru 2011 The dynamic tradeoff between corporate socialism and financial
market dislocation Estimates from internal capital markets Working paper University of Chicago Booth School of Business
Moeller SB and F P Schlingemann 2005 Global diversification and bidder gains A
comparison between cross-border and domestic acquisitions Journal of Banking and Finance 29 533-564
Morck R and B Yeung 1991 Why investors value multinationality Journal of Business 64
165-186
Morck R and B Yeung 2001 Why firms diversify Internatization vs agency behavior In Intangibles B Lev (ed) Oxford University Press pp 269-302
Myers S C and N S Majluf 1984 Corporate financing and investment decisions when firms have information that investors do not have Journal of Financial Economics 13 187-221
Schmid M and I Walter 2009 Do financial conglomerates create or destroy economic value
Journal of Financial Intermediation 18 193-216 Syverson C 2011 What determines productivity Journal of Economic Literature 49(2) 326-
365 Villalonga B 2004a Diversification discount or premium New evidence from the Business
Information Tracking Series Journal of Finance 59 479-506 Villalonga B 2004b Does diversification cause the diversification discount Financial
Management 33 5-27 Wooldridge JM 2010 Econometric Analysis of Cross Section and Panel Data (Second
Edition) Cambridge MA MIT Press Yamin M 1991 A reassessment of Hymerrsquos contribution to the theory of the transnational
corporation C Pitelis and R Sugden (eds) The Nature of the Transnational Firm London Routledge
32
Appendix A Variable Definitions
We obtain information regarding firmsrsquo operations by country and industry from the Bureau of Economic Analysis (BEA) unless otherwise noted Acronyms used below reflect Compustat mnemonics and thus indicate when we obtain data from the Compustat North America Fundamentals Annual database All independent variables are winsorized at 1
Dependent variable
Excess Value = The log of the ratio of actual firm value to imputed firm value Actual firm value is equal to market capitalization (MVE) + book value of total assets (AT) ndash book value of shareholdersrsquo equity (SEQ) Imputed firm value is calculated as the sum of the imputed values of a firmrsquos operations in each country-industry The imputed value for each country-industry is equal to sales generated in that country-industry times a multiple equal to the ratio of a benchmark firmrsquos actual value to total sales A benchmark firm is defined as a median single-segment firm operating in the same country-industry All multiples require at least five firms listed in the respective country-industry on Compustat (domestic) or Worldscope (foreign) with less than 10 of sales income and assets outside the country of domicile Similar to Berger and Ofek (1995) we eliminate extreme values where the ratio of actual to imputed firm value exceeds 400 or is less than 025
Independent variables
Foreign Sales = Sales made by the foreign operations of US MNCs as a percent of total firm sales
Industry Other = Sales outside a firmrsquos primary industry as a percent of total sales (SALE) Equal to zero for firms operating in a single business segment
Log Size = Log of total sales (SALE)
Debt Firm Value = Ratio of long-term debt (DLTT) to total market value (market capitalization (MVE) + book value of total assets (AT) ndash book value of shareholdersrsquo equity (SEQ))
CAPX Sales = Ratio of capital expenditures (CAPX) to total sales (SALE)
EBIT Sales = Ratio of earnings before interest and taxes (EBIT) to total sales (SALE)
RampD Sales = Ratio of research and development expenses (XRD) to total sales (SALE)
Ad Sales = Ratio of advertising expenses (XAD) to total sales (SALE)
Control Premium = A weighted-average (based on the sales in a given country as a percent of total firm sales) of the country-level control premium from Dyck and Zingales (2004)
Cost of Capital = A weighted-average (based on the sales in a given country as a percent of total firm sales) of the country-level credit rating from Institutional Investor (httpwwwinstitutionalinvestorcom) The country-level rating is available for 160 countries and ranges from 0 to 100 Higher values imply greater credit risk
33
CEO Ownership = Percent of shares owned by the CEO from Execucomp
Institution Ownership = Percent of shares owned by institutional investors from Thomson Reuterrsquos Institutional Holdings database
Powerful CEO = Indicator variable equal to one if the CEO is the only insider on the board of directors and also serves as the chairman and president based on data from RiskMetrics Directors database Equal to zero otherwise
Governance Index = Count measure of takeover defenses used in Gompers Ishii and Metrick (2003) from RiskMetrics Governance database As the governance data are not available for every year we follow Gompers Ishii and Metrick (2003) and assume that the firmsrsquo governance attributes are unchanged until publication of the subsequent series
34
Figure 1 Trends in multinational activity and industrial diversification from 1984-2008 This figure presents a plot of the mean percent of foreign sales and the mean percent of sales outside the firmrsquos primary industry (industrial diversification) from 1984 through 2008 Dotted lines above and below the primary lines represent 95 confidence intervals To compile the plot we include the full population of firms in Compustat
35
Table 1 Analysis of accounting data for firms reporting under multiple accounting standards This table provides a comparison of sales net income and assets for firms that changed accounting standards to or from US GAAP Comparisons are provided for the 66 firms that changed to or from US GAAP and for which we could obtain accounting data electronically and in English under both standards for the same fiscal year (eg the firm presented the accounting numbers originally under one set of standards and then restated the prior year numbers in the subsequent year under the new set of standards) Panel A shows the distribution of firms by year Panel B shows the other accounting standards to which US GAAP is compared Panel C shows the mean and median percent that the other accounting standard differs from US GAAP for the same fiscal year and accounting number Two samples are shown in Panel C The first is the full sample of firms with a change in accounting standards to or from US GAAP for which data was available The second is the subset of these firms that occurred during the sample window used in this study (ie excluding changes post-2008) The percent difference in sales is compared to the percent difference in net income and assets Tests of means are based on matched-pair t-tests Tests of medians are based on Wilcoxon matched-pair signed-rank tests Panel A Accounting standard changes by year
2003
4 6
2004
25 38
2005
8 12
2006
6 9
2007
7 11
2008
5 8
2009
5 8
2010
5 8
2011
1 2
66
Panel B Accounting standards compared to US GAAP
IFRS
47 71
Canadian GAAP
12 18
Israeli GAAP
2 3
Dutch GAAP
1 2
Indian GAAP
1 2
Japanese GAAP
1 2
Norwegian GAAP
1 2
Swiss GAAP
1 2
66
36
(Table 1 continued) Panel C Tests of differences between US GAAP and other accounting standards
Percent different from US GAAP
Comparison to sales (p-values)
N Mean Median
Mean Median
Full Sample (2001-2011)
Sales
66 236 000
Net Income
66 10653 647
005 lt001
Assets
66 1162 126
lt001 lt001
Study Sample (2001-2008)
Sales
55 257 000
Net Income
55 10303 589
011 lt001
Assets
55 1047 099
003 lt001
37
Table 2 Descriptive statistics Panel A reports the mean and medians of Excess Value for the multinational firms and single-segment benchmark firms as well as univariate tests across the subsamples Panel B reports summary statistics for the independent variables for the multinational firms The p-values of the tests of differences in means are based on t-tests The p-values of tests of differences in the medians are for a Wilcoxon rank-sum test All variables are defined in Appendix A Panel A Excess Value by sample Multinational All Benchmark Benchmark Subgroups Sample Firms US Foreign
Value Value Test of Diff (p-value) Value
Test of Diff (p-value) Value
Test of Diff (p-value)
Mean 0067 0006 lt001 -0028 lt001 0014 lt001 Median 0076 0000 lt001 -0025 lt001 0000 lt001 N 4950 33074 5998 27076 Panel B Distribution of independent variables for the multinational firms N Mean 25th 50th 75th StDev Foreign Sales 4950 0238 0074 0190 0363 0197 Industry Other 4950 0212 0000 0166 0414 0215 Log Size 4950 6955 5823 6989 8140 1687 Debt Firm Value 4950 0136 0009 0097 0212 0145 CAPX Sales 4950 0054 0020 0033 0054 0101 EBIT Sales 4950 0065 0031 0077 0121 0138 RampD Sales 4950 0049 0000 0013 0059 0083 Ad Sales 4950 0008 0000 0000 0005 0018
38
Table 3 BEA and Worldscope samples
This table reports details on affiliate sales for significant countries pooled from 1998-2008 Significant countries are defined as those in which the operations of US MNCs represent at least 02 of total firm sales (per the BEA) For significant countries the table compares their representation in terms of MNC affiliate sales in the BEA data to the distribution of firm observations in Worldscope US Foreign Affiliates (BEA) Foreign Firms (Worldscope)
Country
N (1)
Sales (millions)
(2)
Total Sales (3)
N (4)
Sales (million)
(5)
Total Sales (6)
Australia 1344 73305 056 859 255865 210 Belgium 894 52755 040 169 26136 021 Brazil 959 60245 046 1143 683167 560 Canada 2730 536767 407 1181 526750 432 China 922 75907 057 822 276541 227 France 1852 183942 139 540 105447 086 Germany 2182 275577 209 480 103888 085 Hong Kong 867 48100 036 157 34094 028 Ireland 478 36754 028 34 3467 003 Italy 1178 82116 062 111 47948 039 Japan 1494 148032 112 2281 1119223 917 Korea South 659 34500 026 3530 2752791 2255 Malaysia 386 26027 020 837 168683 138 Mexico 1405 113623 086 362 237173 194 Netherlands 1149 85105 064 47 22631 019 Singapore 1098 94917 072 139 32913 027 Spain 871 63094 048 129 37419 031 Sweden 712 32682 025 235 23090 019 Switzerland 726 60288 046 74 20242 017 Taiwan 592 30313 023 2949 787049 645 United Kingdom 3054 423934 321 1997 882423 723 United States (domicile) 4950 10460880 7923 na na na Other - Covered by WorldScope 6893 199394 151 9000 4057894 33 Other -Not Covered by WorldScope 713 5352 004 - - - Total 38108 13203609 1000 27076 12204834 100
39
Table 4 OLS regressions of firm excess values on the percent of foreign sales This table reports the results of OLS regressions of firm excess value on the percent of foreign sales (Foreign Sales) the percent of sales outside a firmrsquos primary industry (Industry Other) and control variables All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed) Dep Variable = Excess Value Coefficient t-stat Coefficient t-stat Coefficient t-stat Coefficient t-stat (1) (2) (3) (4) Foreign Sales 0349 419 0209 293 0189 266 0278 210 Industry Other -0134 -186 -0047 -072 -0053 -081 -0326 -325 Log Size 0066 634 0065 624 -0044 -096 Debt Firm Value -0196 -213 -0241 -260 -0685 -588 CAPX Sales 0901 425 0893 435 0488 227 EBIT Sales 1154 542 1170 547 0408 189 RampD Sales 2908 1242 2932 1237 1544 379 Ad Sales 1869 263 2003 280 -1696 -119 Year indicators No No Yes Yes Firm indicators No No No Yes R2 0017 0212 0226 0768 Observations 4950 4950 4950 4950
40
Table 5 Dynamic panel GMM model regressing firm excess values on the percent of foreign sales This table reports the result from a regression of the excess value on the percent of foreign sales (Foreign Sales) the percent of non-primary industry sales (Industry Other) and control variables using a dynamic panel GMM estimator All control variables are considered to be endogenous with the exception of the year indicator variables AR(1) and AR(2) are tests for first-order and second-order serial correlation in the first differenced residuals with the null hypothesis of no serial correlation The null hypothesis of the Hansen test of overidentification is that all instruments are valid The null hypothesis of the difference-in-Hansen test of exogeneity is that the instruments used for the equation in levels are exogenous All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed) Dep Variable = Excess Value Coef t-stat Excess Value (one lag) -0018 -082 Excess Value (two lags) -0048 -272 Foreign Sales 0371 270 Industry Other -0163 -171 Log Size 0010 123 Debt FirmValue -1135 -820 CAPX Sales 0590 342 EBIT Sales -0047 -300 RampD Sales 1376 531 Ad Sales -0751 -040 AR(1) test (p-value) 000 AR(2) test (p-value) 017 Hansen test of over-identification (p-value) 030 Difference-in-Hansen test of exogeneity (p-value) 093 Year indicators Yes Number of firms 1016 Number of observations 4090
41
Table 6 OLS regression of firm excess values on the percent of foreign sales controlling for a control premium This table reports the result of the OLS regression of firm excess value including firm and year fixed effects with an additional control variable to proxy for a country-level ownership control premium (Control Premium) All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed) Dep Variable = Excess Value Coef t -stat Foreign Sales 0303 224 Industry Other -0327 -325 Log Size -0045 -100 Debt FirmValue -0683 -585 CAPX Sales 0490 226 EBIT Sales 0409 189 RampD Sales 1545 379 Ad Sales -1689 -119 Control Premium -1004 -062 Year indicators Yes Firm indicators Yes R2 0768 Observations 4950
42
Table 7 OLS regression of firm excess values on the percent of foreign sales controlling for the cost of capital This table contains the results of an OLS regression of firm excess value including firm and year fixed effects with an additional control variable to proxy for a country-level cost of capital (Cost of Capital) All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed) Dep Variable = Excess Value Coef t -stat Foreign Sales 0255 178 Industry Other -0326 -324 Log Size -0043 -094 Debt FirmValue -0687 -589 CAPX Sales 0485 225 EBIT Sales 0406 188 RampD Sales 1539 379 Ad Sales -1703 -120 Cost of Capital 0004 039 Year indicators Yes Firm indicators Yes R2 0768 Observations 4950
43
Table 8 OLS regression of firm excess values on the percent of foreign sales controlling for aspects of corporate governance This table reports the results of two OLS regressions In the first column are the results of the firm excess value regression including firm and year fixed effect with five proxies for corporate governance included ndash the percent of shares owned by the CEO and the percent squared (CEO Ownership and CEO Ownership squared) the percent owned by institutional investors (Institution Ownership) whether there is a powerful CEO (Powerful CEO) and the Gompers Ishii and Metrick (2003) governance index (Governance Index) In the second column the regression includes the same sample and variables with the exception of the governance proxies which are excluded All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed)
Governance
No Governance (same sample)
Dep Variable = Excess Value Coef t-stat Coef t -stat Foreign Sales 0259 159 0251 154 Industry Other -0099 -080 -0099 -079 Log Size -0105 -185 -0102 -180 Debt FirmValue -0813 -478 -0817 -482 CAPX Sales 0440 252 0438 249 EBIT Sales 0457 247 0457 246 RampD Sales 1717 284 1716 286 Ad Sales -2210 -143 -2190 -143 CEO Ownership 0010 007
CEO Ownership squared -0029 -017 Institution Ownership 0047 067 Powerful CEO -0003 -014 Governance Index 0009 103
Year indicators Yes Yes Firm indicators Yes Yes R2 0811 0810 Observations 2550 2550
- Cover for paperpdf
- crrz_20130603_SMUpdf
-
28
industrial diversification) We fail to find evidence supporting any of these alternative
explanations ndash the premium in excess value arising at firms with greater multinational operations
remains significant
Overall our new method of estimating excess values for multinational firms provides strong
evidence of a statistically and economically significant premium associated with increased
international operations The premium is robust to a host of specifications and controls designed
to evaluate alternative explanations We leave to future research the questions of whether the
premium to US multinational firms applies more broadly to other countries of domicile and
what factors influence the premium
29
References
Ammann M Hoeschel D and M Schmid 2012 Is there really no conglomerate discount
Journal of Business Finance and Accounting 39(12) 264-288 Arellano M and Bond S R 1991 Some tests of specification for panel data Monte Carlo
evidence and an application to employment equations Review of Economic Studies 58 (2) 277-298
Arellano M and Bover O 1995 Another look at instrumental variables estimation of error
components models Journal of Econometrics 68 (1) 29-61 Bens D A Berger P G and S J Monahan 2011 Discretionary disclosure in financial
reporting An examination comparing internal firm data to externally reported segment data The Accounting Review 86(2) 417-449
Berger P G and E Ofek 1995 Diversificationrsquos effect on firm value Journal of Financial
Economics 37 39-65 Blundell R and Bond S R 1998 Initial conditions and moment restrictions in dynamic panel
data models Journal of Econometrics 87(1) 115-144 Campa J M and S Kedia 2002 Explaining the diversification discount The Journal of
Finance 57 (4) 1731-1762 Denis D J Denis D K and K Yost 2002 Global diversification industrial diversification
and firm value The Journal of Finance 57 (5) 1951-1979 Desai M A Foley C F and J R Hines 2004 A multinational perspective on capital structure
choice and internal capital markets The Journal of Finance 59 (6) 2451-2487 Dos Santos MB V Errunza and D Miller 2008 Does corporate international diversification
destroy value Evidence from cross-border mergers and acquisitions Journal of Banking and Finance 32(12) 2716-2724
Doukas JA 1995 Overinvestment Tobinrsquos q and gains from foreign acquisitions Journal of
Banking and Finance 19 1285-1303 Doukas J and N G Travlos 1988 The effect of corporate multinationalism on shareholdersrsquorsquo
wealth evidence from international acquisitions Journal of Finance 43(5) 1161-1175 Doukas JA and O B Kan 2006 Does global diversification destroy firm value Journal of
International Business Studies 37(3) 352-371
30
Dunning J and A Rugman 1985 The influence of Hymerrsquos dissertation on the theory of foreign direct investment The American Economic Review 75(2) 228-232
Dyck A and L Zingales 2004 Private benefits of control An international comparison The
Journal of Finance 59 (2) 537-600 Erb C Harvey C and T Viskanta 1996 Expected returns and volatility in 135 countries The
Journal of Portfolio Management 46-58 Foley C F Desai M A and K J Forbes 2008 Financial constraints and growth
Multinational and local firm responses to currency depreciations Review of Financial Studies 21 (6) 2857-2888
Francis B I Hasan and X Sun 2008 Financial market integration and the value of global
diversification Evidence for US acquirers in cross-border mergers and acquisitions Journal of Banking and Finance 321522-1540
Gande A Schenzler C and L Senbet 2009 Valuation effects of global diversification
Journal of International Business Studies 1515-1532 Gompers P Ishii J and A Metrick 2003 Corporate governance and equity prices Quarterly
Journal of Economics 118(1) 107-156 Hail L and C Leuz 2009 Cost of capital effects and changes in growth expectations around
US cross-listings Journal of Financial Economics 93 428-454 Hanson G Mataloni R and M Slaughter 2005 Vertical production networks in multinational
firms Review of Economics and Statistics 87(4) 1-15 Helpman E 2006 Trade FDI and the Organization of Firms Journal of Economic Literature
44(3) 289-630 Helpman E Melitz M J and S R Yeaple 2004 Export versus FDI with Heterogeneous Firms
American Economic Review 94(1) 300-316 Hoechle D Schmid M Walter W and D Yermack 2012 How much of the diversification
discount can be explained by poor governance Journal of Financial Economics 103 41-60 Hope O-K and W B Thomas 2008 Managerial empire building and firm disclosure Journal
of Accounting Research 46 (June) 591-626 Huizinga H P and J Voget 2009 International taxation and the direction and volume of cross-
border MampAs Journal of Finance 64 (3) 1217-1249 Jensen M 1986 Agency cost of free cash flow corporate finance and takeovers American
Economic Review Papers and Proceedings 76 323-329
31
Laeven L and R Levine 2007 Is there a diversification discount in financial conglomerates
Journal of Financial Economics 85 331-367 Lang L and R Stulz 1994 Tobinrsquos Q corporate diversification and firm performance Journal
of Political Economy 102 1248-1280 Mataloni R 2003 US multinational companies Operations in 2001 Survey of Current
Business 85-105 Matvos G and A Seru 2011 The dynamic tradeoff between corporate socialism and financial
market dislocation Estimates from internal capital markets Working paper University of Chicago Booth School of Business
Moeller SB and F P Schlingemann 2005 Global diversification and bidder gains A
comparison between cross-border and domestic acquisitions Journal of Banking and Finance 29 533-564
Morck R and B Yeung 1991 Why investors value multinationality Journal of Business 64
165-186
Morck R and B Yeung 2001 Why firms diversify Internatization vs agency behavior In Intangibles B Lev (ed) Oxford University Press pp 269-302
Myers S C and N S Majluf 1984 Corporate financing and investment decisions when firms have information that investors do not have Journal of Financial Economics 13 187-221
Schmid M and I Walter 2009 Do financial conglomerates create or destroy economic value
Journal of Financial Intermediation 18 193-216 Syverson C 2011 What determines productivity Journal of Economic Literature 49(2) 326-
365 Villalonga B 2004a Diversification discount or premium New evidence from the Business
Information Tracking Series Journal of Finance 59 479-506 Villalonga B 2004b Does diversification cause the diversification discount Financial
Management 33 5-27 Wooldridge JM 2010 Econometric Analysis of Cross Section and Panel Data (Second
Edition) Cambridge MA MIT Press Yamin M 1991 A reassessment of Hymerrsquos contribution to the theory of the transnational
corporation C Pitelis and R Sugden (eds) The Nature of the Transnational Firm London Routledge
32
Appendix A Variable Definitions
We obtain information regarding firmsrsquo operations by country and industry from the Bureau of Economic Analysis (BEA) unless otherwise noted Acronyms used below reflect Compustat mnemonics and thus indicate when we obtain data from the Compustat North America Fundamentals Annual database All independent variables are winsorized at 1
Dependent variable
Excess Value = The log of the ratio of actual firm value to imputed firm value Actual firm value is equal to market capitalization (MVE) + book value of total assets (AT) ndash book value of shareholdersrsquo equity (SEQ) Imputed firm value is calculated as the sum of the imputed values of a firmrsquos operations in each country-industry The imputed value for each country-industry is equal to sales generated in that country-industry times a multiple equal to the ratio of a benchmark firmrsquos actual value to total sales A benchmark firm is defined as a median single-segment firm operating in the same country-industry All multiples require at least five firms listed in the respective country-industry on Compustat (domestic) or Worldscope (foreign) with less than 10 of sales income and assets outside the country of domicile Similar to Berger and Ofek (1995) we eliminate extreme values where the ratio of actual to imputed firm value exceeds 400 or is less than 025
Independent variables
Foreign Sales = Sales made by the foreign operations of US MNCs as a percent of total firm sales
Industry Other = Sales outside a firmrsquos primary industry as a percent of total sales (SALE) Equal to zero for firms operating in a single business segment
Log Size = Log of total sales (SALE)
Debt Firm Value = Ratio of long-term debt (DLTT) to total market value (market capitalization (MVE) + book value of total assets (AT) ndash book value of shareholdersrsquo equity (SEQ))
CAPX Sales = Ratio of capital expenditures (CAPX) to total sales (SALE)
EBIT Sales = Ratio of earnings before interest and taxes (EBIT) to total sales (SALE)
RampD Sales = Ratio of research and development expenses (XRD) to total sales (SALE)
Ad Sales = Ratio of advertising expenses (XAD) to total sales (SALE)
Control Premium = A weighted-average (based on the sales in a given country as a percent of total firm sales) of the country-level control premium from Dyck and Zingales (2004)
Cost of Capital = A weighted-average (based on the sales in a given country as a percent of total firm sales) of the country-level credit rating from Institutional Investor (httpwwwinstitutionalinvestorcom) The country-level rating is available for 160 countries and ranges from 0 to 100 Higher values imply greater credit risk
33
CEO Ownership = Percent of shares owned by the CEO from Execucomp
Institution Ownership = Percent of shares owned by institutional investors from Thomson Reuterrsquos Institutional Holdings database
Powerful CEO = Indicator variable equal to one if the CEO is the only insider on the board of directors and also serves as the chairman and president based on data from RiskMetrics Directors database Equal to zero otherwise
Governance Index = Count measure of takeover defenses used in Gompers Ishii and Metrick (2003) from RiskMetrics Governance database As the governance data are not available for every year we follow Gompers Ishii and Metrick (2003) and assume that the firmsrsquo governance attributes are unchanged until publication of the subsequent series
34
Figure 1 Trends in multinational activity and industrial diversification from 1984-2008 This figure presents a plot of the mean percent of foreign sales and the mean percent of sales outside the firmrsquos primary industry (industrial diversification) from 1984 through 2008 Dotted lines above and below the primary lines represent 95 confidence intervals To compile the plot we include the full population of firms in Compustat
35
Table 1 Analysis of accounting data for firms reporting under multiple accounting standards This table provides a comparison of sales net income and assets for firms that changed accounting standards to or from US GAAP Comparisons are provided for the 66 firms that changed to or from US GAAP and for which we could obtain accounting data electronically and in English under both standards for the same fiscal year (eg the firm presented the accounting numbers originally under one set of standards and then restated the prior year numbers in the subsequent year under the new set of standards) Panel A shows the distribution of firms by year Panel B shows the other accounting standards to which US GAAP is compared Panel C shows the mean and median percent that the other accounting standard differs from US GAAP for the same fiscal year and accounting number Two samples are shown in Panel C The first is the full sample of firms with a change in accounting standards to or from US GAAP for which data was available The second is the subset of these firms that occurred during the sample window used in this study (ie excluding changes post-2008) The percent difference in sales is compared to the percent difference in net income and assets Tests of means are based on matched-pair t-tests Tests of medians are based on Wilcoxon matched-pair signed-rank tests Panel A Accounting standard changes by year
2003
4 6
2004
25 38
2005
8 12
2006
6 9
2007
7 11
2008
5 8
2009
5 8
2010
5 8
2011
1 2
66
Panel B Accounting standards compared to US GAAP
IFRS
47 71
Canadian GAAP
12 18
Israeli GAAP
2 3
Dutch GAAP
1 2
Indian GAAP
1 2
Japanese GAAP
1 2
Norwegian GAAP
1 2
Swiss GAAP
1 2
66
36
(Table 1 continued) Panel C Tests of differences between US GAAP and other accounting standards
Percent different from US GAAP
Comparison to sales (p-values)
N Mean Median
Mean Median
Full Sample (2001-2011)
Sales
66 236 000
Net Income
66 10653 647
005 lt001
Assets
66 1162 126
lt001 lt001
Study Sample (2001-2008)
Sales
55 257 000
Net Income
55 10303 589
011 lt001
Assets
55 1047 099
003 lt001
37
Table 2 Descriptive statistics Panel A reports the mean and medians of Excess Value for the multinational firms and single-segment benchmark firms as well as univariate tests across the subsamples Panel B reports summary statistics for the independent variables for the multinational firms The p-values of the tests of differences in means are based on t-tests The p-values of tests of differences in the medians are for a Wilcoxon rank-sum test All variables are defined in Appendix A Panel A Excess Value by sample Multinational All Benchmark Benchmark Subgroups Sample Firms US Foreign
Value Value Test of Diff (p-value) Value
Test of Diff (p-value) Value
Test of Diff (p-value)
Mean 0067 0006 lt001 -0028 lt001 0014 lt001 Median 0076 0000 lt001 -0025 lt001 0000 lt001 N 4950 33074 5998 27076 Panel B Distribution of independent variables for the multinational firms N Mean 25th 50th 75th StDev Foreign Sales 4950 0238 0074 0190 0363 0197 Industry Other 4950 0212 0000 0166 0414 0215 Log Size 4950 6955 5823 6989 8140 1687 Debt Firm Value 4950 0136 0009 0097 0212 0145 CAPX Sales 4950 0054 0020 0033 0054 0101 EBIT Sales 4950 0065 0031 0077 0121 0138 RampD Sales 4950 0049 0000 0013 0059 0083 Ad Sales 4950 0008 0000 0000 0005 0018
38
Table 3 BEA and Worldscope samples
This table reports details on affiliate sales for significant countries pooled from 1998-2008 Significant countries are defined as those in which the operations of US MNCs represent at least 02 of total firm sales (per the BEA) For significant countries the table compares their representation in terms of MNC affiliate sales in the BEA data to the distribution of firm observations in Worldscope US Foreign Affiliates (BEA) Foreign Firms (Worldscope)
Country
N (1)
Sales (millions)
(2)
Total Sales (3)
N (4)
Sales (million)
(5)
Total Sales (6)
Australia 1344 73305 056 859 255865 210 Belgium 894 52755 040 169 26136 021 Brazil 959 60245 046 1143 683167 560 Canada 2730 536767 407 1181 526750 432 China 922 75907 057 822 276541 227 France 1852 183942 139 540 105447 086 Germany 2182 275577 209 480 103888 085 Hong Kong 867 48100 036 157 34094 028 Ireland 478 36754 028 34 3467 003 Italy 1178 82116 062 111 47948 039 Japan 1494 148032 112 2281 1119223 917 Korea South 659 34500 026 3530 2752791 2255 Malaysia 386 26027 020 837 168683 138 Mexico 1405 113623 086 362 237173 194 Netherlands 1149 85105 064 47 22631 019 Singapore 1098 94917 072 139 32913 027 Spain 871 63094 048 129 37419 031 Sweden 712 32682 025 235 23090 019 Switzerland 726 60288 046 74 20242 017 Taiwan 592 30313 023 2949 787049 645 United Kingdom 3054 423934 321 1997 882423 723 United States (domicile) 4950 10460880 7923 na na na Other - Covered by WorldScope 6893 199394 151 9000 4057894 33 Other -Not Covered by WorldScope 713 5352 004 - - - Total 38108 13203609 1000 27076 12204834 100
39
Table 4 OLS regressions of firm excess values on the percent of foreign sales This table reports the results of OLS regressions of firm excess value on the percent of foreign sales (Foreign Sales) the percent of sales outside a firmrsquos primary industry (Industry Other) and control variables All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed) Dep Variable = Excess Value Coefficient t-stat Coefficient t-stat Coefficient t-stat Coefficient t-stat (1) (2) (3) (4) Foreign Sales 0349 419 0209 293 0189 266 0278 210 Industry Other -0134 -186 -0047 -072 -0053 -081 -0326 -325 Log Size 0066 634 0065 624 -0044 -096 Debt Firm Value -0196 -213 -0241 -260 -0685 -588 CAPX Sales 0901 425 0893 435 0488 227 EBIT Sales 1154 542 1170 547 0408 189 RampD Sales 2908 1242 2932 1237 1544 379 Ad Sales 1869 263 2003 280 -1696 -119 Year indicators No No Yes Yes Firm indicators No No No Yes R2 0017 0212 0226 0768 Observations 4950 4950 4950 4950
40
Table 5 Dynamic panel GMM model regressing firm excess values on the percent of foreign sales This table reports the result from a regression of the excess value on the percent of foreign sales (Foreign Sales) the percent of non-primary industry sales (Industry Other) and control variables using a dynamic panel GMM estimator All control variables are considered to be endogenous with the exception of the year indicator variables AR(1) and AR(2) are tests for first-order and second-order serial correlation in the first differenced residuals with the null hypothesis of no serial correlation The null hypothesis of the Hansen test of overidentification is that all instruments are valid The null hypothesis of the difference-in-Hansen test of exogeneity is that the instruments used for the equation in levels are exogenous All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed) Dep Variable = Excess Value Coef t-stat Excess Value (one lag) -0018 -082 Excess Value (two lags) -0048 -272 Foreign Sales 0371 270 Industry Other -0163 -171 Log Size 0010 123 Debt FirmValue -1135 -820 CAPX Sales 0590 342 EBIT Sales -0047 -300 RampD Sales 1376 531 Ad Sales -0751 -040 AR(1) test (p-value) 000 AR(2) test (p-value) 017 Hansen test of over-identification (p-value) 030 Difference-in-Hansen test of exogeneity (p-value) 093 Year indicators Yes Number of firms 1016 Number of observations 4090
41
Table 6 OLS regression of firm excess values on the percent of foreign sales controlling for a control premium This table reports the result of the OLS regression of firm excess value including firm and year fixed effects with an additional control variable to proxy for a country-level ownership control premium (Control Premium) All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed) Dep Variable = Excess Value Coef t -stat Foreign Sales 0303 224 Industry Other -0327 -325 Log Size -0045 -100 Debt FirmValue -0683 -585 CAPX Sales 0490 226 EBIT Sales 0409 189 RampD Sales 1545 379 Ad Sales -1689 -119 Control Premium -1004 -062 Year indicators Yes Firm indicators Yes R2 0768 Observations 4950
42
Table 7 OLS regression of firm excess values on the percent of foreign sales controlling for the cost of capital This table contains the results of an OLS regression of firm excess value including firm and year fixed effects with an additional control variable to proxy for a country-level cost of capital (Cost of Capital) All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed) Dep Variable = Excess Value Coef t -stat Foreign Sales 0255 178 Industry Other -0326 -324 Log Size -0043 -094 Debt FirmValue -0687 -589 CAPX Sales 0485 225 EBIT Sales 0406 188 RampD Sales 1539 379 Ad Sales -1703 -120 Cost of Capital 0004 039 Year indicators Yes Firm indicators Yes R2 0768 Observations 4950
43
Table 8 OLS regression of firm excess values on the percent of foreign sales controlling for aspects of corporate governance This table reports the results of two OLS regressions In the first column are the results of the firm excess value regression including firm and year fixed effect with five proxies for corporate governance included ndash the percent of shares owned by the CEO and the percent squared (CEO Ownership and CEO Ownership squared) the percent owned by institutional investors (Institution Ownership) whether there is a powerful CEO (Powerful CEO) and the Gompers Ishii and Metrick (2003) governance index (Governance Index) In the second column the regression includes the same sample and variables with the exception of the governance proxies which are excluded All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed)
Governance
No Governance (same sample)
Dep Variable = Excess Value Coef t-stat Coef t -stat Foreign Sales 0259 159 0251 154 Industry Other -0099 -080 -0099 -079 Log Size -0105 -185 -0102 -180 Debt FirmValue -0813 -478 -0817 -482 CAPX Sales 0440 252 0438 249 EBIT Sales 0457 247 0457 246 RampD Sales 1717 284 1716 286 Ad Sales -2210 -143 -2190 -143 CEO Ownership 0010 007
CEO Ownership squared -0029 -017 Institution Ownership 0047 067 Powerful CEO -0003 -014 Governance Index 0009 103
Year indicators Yes Yes Firm indicators Yes Yes R2 0811 0810 Observations 2550 2550
- Cover for paperpdf
- crrz_20130603_SMUpdf
-
29
References
Ammann M Hoeschel D and M Schmid 2012 Is there really no conglomerate discount
Journal of Business Finance and Accounting 39(12) 264-288 Arellano M and Bond S R 1991 Some tests of specification for panel data Monte Carlo
evidence and an application to employment equations Review of Economic Studies 58 (2) 277-298
Arellano M and Bover O 1995 Another look at instrumental variables estimation of error
components models Journal of Econometrics 68 (1) 29-61 Bens D A Berger P G and S J Monahan 2011 Discretionary disclosure in financial
reporting An examination comparing internal firm data to externally reported segment data The Accounting Review 86(2) 417-449
Berger P G and E Ofek 1995 Diversificationrsquos effect on firm value Journal of Financial
Economics 37 39-65 Blundell R and Bond S R 1998 Initial conditions and moment restrictions in dynamic panel
data models Journal of Econometrics 87(1) 115-144 Campa J M and S Kedia 2002 Explaining the diversification discount The Journal of
Finance 57 (4) 1731-1762 Denis D J Denis D K and K Yost 2002 Global diversification industrial diversification
and firm value The Journal of Finance 57 (5) 1951-1979 Desai M A Foley C F and J R Hines 2004 A multinational perspective on capital structure
choice and internal capital markets The Journal of Finance 59 (6) 2451-2487 Dos Santos MB V Errunza and D Miller 2008 Does corporate international diversification
destroy value Evidence from cross-border mergers and acquisitions Journal of Banking and Finance 32(12) 2716-2724
Doukas JA 1995 Overinvestment Tobinrsquos q and gains from foreign acquisitions Journal of
Banking and Finance 19 1285-1303 Doukas J and N G Travlos 1988 The effect of corporate multinationalism on shareholdersrsquorsquo
wealth evidence from international acquisitions Journal of Finance 43(5) 1161-1175 Doukas JA and O B Kan 2006 Does global diversification destroy firm value Journal of
International Business Studies 37(3) 352-371
30
Dunning J and A Rugman 1985 The influence of Hymerrsquos dissertation on the theory of foreign direct investment The American Economic Review 75(2) 228-232
Dyck A and L Zingales 2004 Private benefits of control An international comparison The
Journal of Finance 59 (2) 537-600 Erb C Harvey C and T Viskanta 1996 Expected returns and volatility in 135 countries The
Journal of Portfolio Management 46-58 Foley C F Desai M A and K J Forbes 2008 Financial constraints and growth
Multinational and local firm responses to currency depreciations Review of Financial Studies 21 (6) 2857-2888
Francis B I Hasan and X Sun 2008 Financial market integration and the value of global
diversification Evidence for US acquirers in cross-border mergers and acquisitions Journal of Banking and Finance 321522-1540
Gande A Schenzler C and L Senbet 2009 Valuation effects of global diversification
Journal of International Business Studies 1515-1532 Gompers P Ishii J and A Metrick 2003 Corporate governance and equity prices Quarterly
Journal of Economics 118(1) 107-156 Hail L and C Leuz 2009 Cost of capital effects and changes in growth expectations around
US cross-listings Journal of Financial Economics 93 428-454 Hanson G Mataloni R and M Slaughter 2005 Vertical production networks in multinational
firms Review of Economics and Statistics 87(4) 1-15 Helpman E 2006 Trade FDI and the Organization of Firms Journal of Economic Literature
44(3) 289-630 Helpman E Melitz M J and S R Yeaple 2004 Export versus FDI with Heterogeneous Firms
American Economic Review 94(1) 300-316 Hoechle D Schmid M Walter W and D Yermack 2012 How much of the diversification
discount can be explained by poor governance Journal of Financial Economics 103 41-60 Hope O-K and W B Thomas 2008 Managerial empire building and firm disclosure Journal
of Accounting Research 46 (June) 591-626 Huizinga H P and J Voget 2009 International taxation and the direction and volume of cross-
border MampAs Journal of Finance 64 (3) 1217-1249 Jensen M 1986 Agency cost of free cash flow corporate finance and takeovers American
Economic Review Papers and Proceedings 76 323-329
31
Laeven L and R Levine 2007 Is there a diversification discount in financial conglomerates
Journal of Financial Economics 85 331-367 Lang L and R Stulz 1994 Tobinrsquos Q corporate diversification and firm performance Journal
of Political Economy 102 1248-1280 Mataloni R 2003 US multinational companies Operations in 2001 Survey of Current
Business 85-105 Matvos G and A Seru 2011 The dynamic tradeoff between corporate socialism and financial
market dislocation Estimates from internal capital markets Working paper University of Chicago Booth School of Business
Moeller SB and F P Schlingemann 2005 Global diversification and bidder gains A
comparison between cross-border and domestic acquisitions Journal of Banking and Finance 29 533-564
Morck R and B Yeung 1991 Why investors value multinationality Journal of Business 64
165-186
Morck R and B Yeung 2001 Why firms diversify Internatization vs agency behavior In Intangibles B Lev (ed) Oxford University Press pp 269-302
Myers S C and N S Majluf 1984 Corporate financing and investment decisions when firms have information that investors do not have Journal of Financial Economics 13 187-221
Schmid M and I Walter 2009 Do financial conglomerates create or destroy economic value
Journal of Financial Intermediation 18 193-216 Syverson C 2011 What determines productivity Journal of Economic Literature 49(2) 326-
365 Villalonga B 2004a Diversification discount or premium New evidence from the Business
Information Tracking Series Journal of Finance 59 479-506 Villalonga B 2004b Does diversification cause the diversification discount Financial
Management 33 5-27 Wooldridge JM 2010 Econometric Analysis of Cross Section and Panel Data (Second
Edition) Cambridge MA MIT Press Yamin M 1991 A reassessment of Hymerrsquos contribution to the theory of the transnational
corporation C Pitelis and R Sugden (eds) The Nature of the Transnational Firm London Routledge
32
Appendix A Variable Definitions
We obtain information regarding firmsrsquo operations by country and industry from the Bureau of Economic Analysis (BEA) unless otherwise noted Acronyms used below reflect Compustat mnemonics and thus indicate when we obtain data from the Compustat North America Fundamentals Annual database All independent variables are winsorized at 1
Dependent variable
Excess Value = The log of the ratio of actual firm value to imputed firm value Actual firm value is equal to market capitalization (MVE) + book value of total assets (AT) ndash book value of shareholdersrsquo equity (SEQ) Imputed firm value is calculated as the sum of the imputed values of a firmrsquos operations in each country-industry The imputed value for each country-industry is equal to sales generated in that country-industry times a multiple equal to the ratio of a benchmark firmrsquos actual value to total sales A benchmark firm is defined as a median single-segment firm operating in the same country-industry All multiples require at least five firms listed in the respective country-industry on Compustat (domestic) or Worldscope (foreign) with less than 10 of sales income and assets outside the country of domicile Similar to Berger and Ofek (1995) we eliminate extreme values where the ratio of actual to imputed firm value exceeds 400 or is less than 025
Independent variables
Foreign Sales = Sales made by the foreign operations of US MNCs as a percent of total firm sales
Industry Other = Sales outside a firmrsquos primary industry as a percent of total sales (SALE) Equal to zero for firms operating in a single business segment
Log Size = Log of total sales (SALE)
Debt Firm Value = Ratio of long-term debt (DLTT) to total market value (market capitalization (MVE) + book value of total assets (AT) ndash book value of shareholdersrsquo equity (SEQ))
CAPX Sales = Ratio of capital expenditures (CAPX) to total sales (SALE)
EBIT Sales = Ratio of earnings before interest and taxes (EBIT) to total sales (SALE)
RampD Sales = Ratio of research and development expenses (XRD) to total sales (SALE)
Ad Sales = Ratio of advertising expenses (XAD) to total sales (SALE)
Control Premium = A weighted-average (based on the sales in a given country as a percent of total firm sales) of the country-level control premium from Dyck and Zingales (2004)
Cost of Capital = A weighted-average (based on the sales in a given country as a percent of total firm sales) of the country-level credit rating from Institutional Investor (httpwwwinstitutionalinvestorcom) The country-level rating is available for 160 countries and ranges from 0 to 100 Higher values imply greater credit risk
33
CEO Ownership = Percent of shares owned by the CEO from Execucomp
Institution Ownership = Percent of shares owned by institutional investors from Thomson Reuterrsquos Institutional Holdings database
Powerful CEO = Indicator variable equal to one if the CEO is the only insider on the board of directors and also serves as the chairman and president based on data from RiskMetrics Directors database Equal to zero otherwise
Governance Index = Count measure of takeover defenses used in Gompers Ishii and Metrick (2003) from RiskMetrics Governance database As the governance data are not available for every year we follow Gompers Ishii and Metrick (2003) and assume that the firmsrsquo governance attributes are unchanged until publication of the subsequent series
34
Figure 1 Trends in multinational activity and industrial diversification from 1984-2008 This figure presents a plot of the mean percent of foreign sales and the mean percent of sales outside the firmrsquos primary industry (industrial diversification) from 1984 through 2008 Dotted lines above and below the primary lines represent 95 confidence intervals To compile the plot we include the full population of firms in Compustat
35
Table 1 Analysis of accounting data for firms reporting under multiple accounting standards This table provides a comparison of sales net income and assets for firms that changed accounting standards to or from US GAAP Comparisons are provided for the 66 firms that changed to or from US GAAP and for which we could obtain accounting data electronically and in English under both standards for the same fiscal year (eg the firm presented the accounting numbers originally under one set of standards and then restated the prior year numbers in the subsequent year under the new set of standards) Panel A shows the distribution of firms by year Panel B shows the other accounting standards to which US GAAP is compared Panel C shows the mean and median percent that the other accounting standard differs from US GAAP for the same fiscal year and accounting number Two samples are shown in Panel C The first is the full sample of firms with a change in accounting standards to or from US GAAP for which data was available The second is the subset of these firms that occurred during the sample window used in this study (ie excluding changes post-2008) The percent difference in sales is compared to the percent difference in net income and assets Tests of means are based on matched-pair t-tests Tests of medians are based on Wilcoxon matched-pair signed-rank tests Panel A Accounting standard changes by year
2003
4 6
2004
25 38
2005
8 12
2006
6 9
2007
7 11
2008
5 8
2009
5 8
2010
5 8
2011
1 2
66
Panel B Accounting standards compared to US GAAP
IFRS
47 71
Canadian GAAP
12 18
Israeli GAAP
2 3
Dutch GAAP
1 2
Indian GAAP
1 2
Japanese GAAP
1 2
Norwegian GAAP
1 2
Swiss GAAP
1 2
66
36
(Table 1 continued) Panel C Tests of differences between US GAAP and other accounting standards
Percent different from US GAAP
Comparison to sales (p-values)
N Mean Median
Mean Median
Full Sample (2001-2011)
Sales
66 236 000
Net Income
66 10653 647
005 lt001
Assets
66 1162 126
lt001 lt001
Study Sample (2001-2008)
Sales
55 257 000
Net Income
55 10303 589
011 lt001
Assets
55 1047 099
003 lt001
37
Table 2 Descriptive statistics Panel A reports the mean and medians of Excess Value for the multinational firms and single-segment benchmark firms as well as univariate tests across the subsamples Panel B reports summary statistics for the independent variables for the multinational firms The p-values of the tests of differences in means are based on t-tests The p-values of tests of differences in the medians are for a Wilcoxon rank-sum test All variables are defined in Appendix A Panel A Excess Value by sample Multinational All Benchmark Benchmark Subgroups Sample Firms US Foreign
Value Value Test of Diff (p-value) Value
Test of Diff (p-value) Value
Test of Diff (p-value)
Mean 0067 0006 lt001 -0028 lt001 0014 lt001 Median 0076 0000 lt001 -0025 lt001 0000 lt001 N 4950 33074 5998 27076 Panel B Distribution of independent variables for the multinational firms N Mean 25th 50th 75th StDev Foreign Sales 4950 0238 0074 0190 0363 0197 Industry Other 4950 0212 0000 0166 0414 0215 Log Size 4950 6955 5823 6989 8140 1687 Debt Firm Value 4950 0136 0009 0097 0212 0145 CAPX Sales 4950 0054 0020 0033 0054 0101 EBIT Sales 4950 0065 0031 0077 0121 0138 RampD Sales 4950 0049 0000 0013 0059 0083 Ad Sales 4950 0008 0000 0000 0005 0018
38
Table 3 BEA and Worldscope samples
This table reports details on affiliate sales for significant countries pooled from 1998-2008 Significant countries are defined as those in which the operations of US MNCs represent at least 02 of total firm sales (per the BEA) For significant countries the table compares their representation in terms of MNC affiliate sales in the BEA data to the distribution of firm observations in Worldscope US Foreign Affiliates (BEA) Foreign Firms (Worldscope)
Country
N (1)
Sales (millions)
(2)
Total Sales (3)
N (4)
Sales (million)
(5)
Total Sales (6)
Australia 1344 73305 056 859 255865 210 Belgium 894 52755 040 169 26136 021 Brazil 959 60245 046 1143 683167 560 Canada 2730 536767 407 1181 526750 432 China 922 75907 057 822 276541 227 France 1852 183942 139 540 105447 086 Germany 2182 275577 209 480 103888 085 Hong Kong 867 48100 036 157 34094 028 Ireland 478 36754 028 34 3467 003 Italy 1178 82116 062 111 47948 039 Japan 1494 148032 112 2281 1119223 917 Korea South 659 34500 026 3530 2752791 2255 Malaysia 386 26027 020 837 168683 138 Mexico 1405 113623 086 362 237173 194 Netherlands 1149 85105 064 47 22631 019 Singapore 1098 94917 072 139 32913 027 Spain 871 63094 048 129 37419 031 Sweden 712 32682 025 235 23090 019 Switzerland 726 60288 046 74 20242 017 Taiwan 592 30313 023 2949 787049 645 United Kingdom 3054 423934 321 1997 882423 723 United States (domicile) 4950 10460880 7923 na na na Other - Covered by WorldScope 6893 199394 151 9000 4057894 33 Other -Not Covered by WorldScope 713 5352 004 - - - Total 38108 13203609 1000 27076 12204834 100
39
Table 4 OLS regressions of firm excess values on the percent of foreign sales This table reports the results of OLS regressions of firm excess value on the percent of foreign sales (Foreign Sales) the percent of sales outside a firmrsquos primary industry (Industry Other) and control variables All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed) Dep Variable = Excess Value Coefficient t-stat Coefficient t-stat Coefficient t-stat Coefficient t-stat (1) (2) (3) (4) Foreign Sales 0349 419 0209 293 0189 266 0278 210 Industry Other -0134 -186 -0047 -072 -0053 -081 -0326 -325 Log Size 0066 634 0065 624 -0044 -096 Debt Firm Value -0196 -213 -0241 -260 -0685 -588 CAPX Sales 0901 425 0893 435 0488 227 EBIT Sales 1154 542 1170 547 0408 189 RampD Sales 2908 1242 2932 1237 1544 379 Ad Sales 1869 263 2003 280 -1696 -119 Year indicators No No Yes Yes Firm indicators No No No Yes R2 0017 0212 0226 0768 Observations 4950 4950 4950 4950
40
Table 5 Dynamic panel GMM model regressing firm excess values on the percent of foreign sales This table reports the result from a regression of the excess value on the percent of foreign sales (Foreign Sales) the percent of non-primary industry sales (Industry Other) and control variables using a dynamic panel GMM estimator All control variables are considered to be endogenous with the exception of the year indicator variables AR(1) and AR(2) are tests for first-order and second-order serial correlation in the first differenced residuals with the null hypothesis of no serial correlation The null hypothesis of the Hansen test of overidentification is that all instruments are valid The null hypothesis of the difference-in-Hansen test of exogeneity is that the instruments used for the equation in levels are exogenous All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed) Dep Variable = Excess Value Coef t-stat Excess Value (one lag) -0018 -082 Excess Value (two lags) -0048 -272 Foreign Sales 0371 270 Industry Other -0163 -171 Log Size 0010 123 Debt FirmValue -1135 -820 CAPX Sales 0590 342 EBIT Sales -0047 -300 RampD Sales 1376 531 Ad Sales -0751 -040 AR(1) test (p-value) 000 AR(2) test (p-value) 017 Hansen test of over-identification (p-value) 030 Difference-in-Hansen test of exogeneity (p-value) 093 Year indicators Yes Number of firms 1016 Number of observations 4090
41
Table 6 OLS regression of firm excess values on the percent of foreign sales controlling for a control premium This table reports the result of the OLS regression of firm excess value including firm and year fixed effects with an additional control variable to proxy for a country-level ownership control premium (Control Premium) All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed) Dep Variable = Excess Value Coef t -stat Foreign Sales 0303 224 Industry Other -0327 -325 Log Size -0045 -100 Debt FirmValue -0683 -585 CAPX Sales 0490 226 EBIT Sales 0409 189 RampD Sales 1545 379 Ad Sales -1689 -119 Control Premium -1004 -062 Year indicators Yes Firm indicators Yes R2 0768 Observations 4950
42
Table 7 OLS regression of firm excess values on the percent of foreign sales controlling for the cost of capital This table contains the results of an OLS regression of firm excess value including firm and year fixed effects with an additional control variable to proxy for a country-level cost of capital (Cost of Capital) All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed) Dep Variable = Excess Value Coef t -stat Foreign Sales 0255 178 Industry Other -0326 -324 Log Size -0043 -094 Debt FirmValue -0687 -589 CAPX Sales 0485 225 EBIT Sales 0406 188 RampD Sales 1539 379 Ad Sales -1703 -120 Cost of Capital 0004 039 Year indicators Yes Firm indicators Yes R2 0768 Observations 4950
43
Table 8 OLS regression of firm excess values on the percent of foreign sales controlling for aspects of corporate governance This table reports the results of two OLS regressions In the first column are the results of the firm excess value regression including firm and year fixed effect with five proxies for corporate governance included ndash the percent of shares owned by the CEO and the percent squared (CEO Ownership and CEO Ownership squared) the percent owned by institutional investors (Institution Ownership) whether there is a powerful CEO (Powerful CEO) and the Gompers Ishii and Metrick (2003) governance index (Governance Index) In the second column the regression includes the same sample and variables with the exception of the governance proxies which are excluded All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed)
Governance
No Governance (same sample)
Dep Variable = Excess Value Coef t-stat Coef t -stat Foreign Sales 0259 159 0251 154 Industry Other -0099 -080 -0099 -079 Log Size -0105 -185 -0102 -180 Debt FirmValue -0813 -478 -0817 -482 CAPX Sales 0440 252 0438 249 EBIT Sales 0457 247 0457 246 RampD Sales 1717 284 1716 286 Ad Sales -2210 -143 -2190 -143 CEO Ownership 0010 007
CEO Ownership squared -0029 -017 Institution Ownership 0047 067 Powerful CEO -0003 -014 Governance Index 0009 103
Year indicators Yes Yes Firm indicators Yes Yes R2 0811 0810 Observations 2550 2550
- Cover for paperpdf
- crrz_20130603_SMUpdf
-
30
Dunning J and A Rugman 1985 The influence of Hymerrsquos dissertation on the theory of foreign direct investment The American Economic Review 75(2) 228-232
Dyck A and L Zingales 2004 Private benefits of control An international comparison The
Journal of Finance 59 (2) 537-600 Erb C Harvey C and T Viskanta 1996 Expected returns and volatility in 135 countries The
Journal of Portfolio Management 46-58 Foley C F Desai M A and K J Forbes 2008 Financial constraints and growth
Multinational and local firm responses to currency depreciations Review of Financial Studies 21 (6) 2857-2888
Francis B I Hasan and X Sun 2008 Financial market integration and the value of global
diversification Evidence for US acquirers in cross-border mergers and acquisitions Journal of Banking and Finance 321522-1540
Gande A Schenzler C and L Senbet 2009 Valuation effects of global diversification
Journal of International Business Studies 1515-1532 Gompers P Ishii J and A Metrick 2003 Corporate governance and equity prices Quarterly
Journal of Economics 118(1) 107-156 Hail L and C Leuz 2009 Cost of capital effects and changes in growth expectations around
US cross-listings Journal of Financial Economics 93 428-454 Hanson G Mataloni R and M Slaughter 2005 Vertical production networks in multinational
firms Review of Economics and Statistics 87(4) 1-15 Helpman E 2006 Trade FDI and the Organization of Firms Journal of Economic Literature
44(3) 289-630 Helpman E Melitz M J and S R Yeaple 2004 Export versus FDI with Heterogeneous Firms
American Economic Review 94(1) 300-316 Hoechle D Schmid M Walter W and D Yermack 2012 How much of the diversification
discount can be explained by poor governance Journal of Financial Economics 103 41-60 Hope O-K and W B Thomas 2008 Managerial empire building and firm disclosure Journal
of Accounting Research 46 (June) 591-626 Huizinga H P and J Voget 2009 International taxation and the direction and volume of cross-
border MampAs Journal of Finance 64 (3) 1217-1249 Jensen M 1986 Agency cost of free cash flow corporate finance and takeovers American
Economic Review Papers and Proceedings 76 323-329
31
Laeven L and R Levine 2007 Is there a diversification discount in financial conglomerates
Journal of Financial Economics 85 331-367 Lang L and R Stulz 1994 Tobinrsquos Q corporate diversification and firm performance Journal
of Political Economy 102 1248-1280 Mataloni R 2003 US multinational companies Operations in 2001 Survey of Current
Business 85-105 Matvos G and A Seru 2011 The dynamic tradeoff between corporate socialism and financial
market dislocation Estimates from internal capital markets Working paper University of Chicago Booth School of Business
Moeller SB and F P Schlingemann 2005 Global diversification and bidder gains A
comparison between cross-border and domestic acquisitions Journal of Banking and Finance 29 533-564
Morck R and B Yeung 1991 Why investors value multinationality Journal of Business 64
165-186
Morck R and B Yeung 2001 Why firms diversify Internatization vs agency behavior In Intangibles B Lev (ed) Oxford University Press pp 269-302
Myers S C and N S Majluf 1984 Corporate financing and investment decisions when firms have information that investors do not have Journal of Financial Economics 13 187-221
Schmid M and I Walter 2009 Do financial conglomerates create or destroy economic value
Journal of Financial Intermediation 18 193-216 Syverson C 2011 What determines productivity Journal of Economic Literature 49(2) 326-
365 Villalonga B 2004a Diversification discount or premium New evidence from the Business
Information Tracking Series Journal of Finance 59 479-506 Villalonga B 2004b Does diversification cause the diversification discount Financial
Management 33 5-27 Wooldridge JM 2010 Econometric Analysis of Cross Section and Panel Data (Second
Edition) Cambridge MA MIT Press Yamin M 1991 A reassessment of Hymerrsquos contribution to the theory of the transnational
corporation C Pitelis and R Sugden (eds) The Nature of the Transnational Firm London Routledge
32
Appendix A Variable Definitions
We obtain information regarding firmsrsquo operations by country and industry from the Bureau of Economic Analysis (BEA) unless otherwise noted Acronyms used below reflect Compustat mnemonics and thus indicate when we obtain data from the Compustat North America Fundamentals Annual database All independent variables are winsorized at 1
Dependent variable
Excess Value = The log of the ratio of actual firm value to imputed firm value Actual firm value is equal to market capitalization (MVE) + book value of total assets (AT) ndash book value of shareholdersrsquo equity (SEQ) Imputed firm value is calculated as the sum of the imputed values of a firmrsquos operations in each country-industry The imputed value for each country-industry is equal to sales generated in that country-industry times a multiple equal to the ratio of a benchmark firmrsquos actual value to total sales A benchmark firm is defined as a median single-segment firm operating in the same country-industry All multiples require at least five firms listed in the respective country-industry on Compustat (domestic) or Worldscope (foreign) with less than 10 of sales income and assets outside the country of domicile Similar to Berger and Ofek (1995) we eliminate extreme values where the ratio of actual to imputed firm value exceeds 400 or is less than 025
Independent variables
Foreign Sales = Sales made by the foreign operations of US MNCs as a percent of total firm sales
Industry Other = Sales outside a firmrsquos primary industry as a percent of total sales (SALE) Equal to zero for firms operating in a single business segment
Log Size = Log of total sales (SALE)
Debt Firm Value = Ratio of long-term debt (DLTT) to total market value (market capitalization (MVE) + book value of total assets (AT) ndash book value of shareholdersrsquo equity (SEQ))
CAPX Sales = Ratio of capital expenditures (CAPX) to total sales (SALE)
EBIT Sales = Ratio of earnings before interest and taxes (EBIT) to total sales (SALE)
RampD Sales = Ratio of research and development expenses (XRD) to total sales (SALE)
Ad Sales = Ratio of advertising expenses (XAD) to total sales (SALE)
Control Premium = A weighted-average (based on the sales in a given country as a percent of total firm sales) of the country-level control premium from Dyck and Zingales (2004)
Cost of Capital = A weighted-average (based on the sales in a given country as a percent of total firm sales) of the country-level credit rating from Institutional Investor (httpwwwinstitutionalinvestorcom) The country-level rating is available for 160 countries and ranges from 0 to 100 Higher values imply greater credit risk
33
CEO Ownership = Percent of shares owned by the CEO from Execucomp
Institution Ownership = Percent of shares owned by institutional investors from Thomson Reuterrsquos Institutional Holdings database
Powerful CEO = Indicator variable equal to one if the CEO is the only insider on the board of directors and also serves as the chairman and president based on data from RiskMetrics Directors database Equal to zero otherwise
Governance Index = Count measure of takeover defenses used in Gompers Ishii and Metrick (2003) from RiskMetrics Governance database As the governance data are not available for every year we follow Gompers Ishii and Metrick (2003) and assume that the firmsrsquo governance attributes are unchanged until publication of the subsequent series
34
Figure 1 Trends in multinational activity and industrial diversification from 1984-2008 This figure presents a plot of the mean percent of foreign sales and the mean percent of sales outside the firmrsquos primary industry (industrial diversification) from 1984 through 2008 Dotted lines above and below the primary lines represent 95 confidence intervals To compile the plot we include the full population of firms in Compustat
35
Table 1 Analysis of accounting data for firms reporting under multiple accounting standards This table provides a comparison of sales net income and assets for firms that changed accounting standards to or from US GAAP Comparisons are provided for the 66 firms that changed to or from US GAAP and for which we could obtain accounting data electronically and in English under both standards for the same fiscal year (eg the firm presented the accounting numbers originally under one set of standards and then restated the prior year numbers in the subsequent year under the new set of standards) Panel A shows the distribution of firms by year Panel B shows the other accounting standards to which US GAAP is compared Panel C shows the mean and median percent that the other accounting standard differs from US GAAP for the same fiscal year and accounting number Two samples are shown in Panel C The first is the full sample of firms with a change in accounting standards to or from US GAAP for which data was available The second is the subset of these firms that occurred during the sample window used in this study (ie excluding changes post-2008) The percent difference in sales is compared to the percent difference in net income and assets Tests of means are based on matched-pair t-tests Tests of medians are based on Wilcoxon matched-pair signed-rank tests Panel A Accounting standard changes by year
2003
4 6
2004
25 38
2005
8 12
2006
6 9
2007
7 11
2008
5 8
2009
5 8
2010
5 8
2011
1 2
66
Panel B Accounting standards compared to US GAAP
IFRS
47 71
Canadian GAAP
12 18
Israeli GAAP
2 3
Dutch GAAP
1 2
Indian GAAP
1 2
Japanese GAAP
1 2
Norwegian GAAP
1 2
Swiss GAAP
1 2
66
36
(Table 1 continued) Panel C Tests of differences between US GAAP and other accounting standards
Percent different from US GAAP
Comparison to sales (p-values)
N Mean Median
Mean Median
Full Sample (2001-2011)
Sales
66 236 000
Net Income
66 10653 647
005 lt001
Assets
66 1162 126
lt001 lt001
Study Sample (2001-2008)
Sales
55 257 000
Net Income
55 10303 589
011 lt001
Assets
55 1047 099
003 lt001
37
Table 2 Descriptive statistics Panel A reports the mean and medians of Excess Value for the multinational firms and single-segment benchmark firms as well as univariate tests across the subsamples Panel B reports summary statistics for the independent variables for the multinational firms The p-values of the tests of differences in means are based on t-tests The p-values of tests of differences in the medians are for a Wilcoxon rank-sum test All variables are defined in Appendix A Panel A Excess Value by sample Multinational All Benchmark Benchmark Subgroups Sample Firms US Foreign
Value Value Test of Diff (p-value) Value
Test of Diff (p-value) Value
Test of Diff (p-value)
Mean 0067 0006 lt001 -0028 lt001 0014 lt001 Median 0076 0000 lt001 -0025 lt001 0000 lt001 N 4950 33074 5998 27076 Panel B Distribution of independent variables for the multinational firms N Mean 25th 50th 75th StDev Foreign Sales 4950 0238 0074 0190 0363 0197 Industry Other 4950 0212 0000 0166 0414 0215 Log Size 4950 6955 5823 6989 8140 1687 Debt Firm Value 4950 0136 0009 0097 0212 0145 CAPX Sales 4950 0054 0020 0033 0054 0101 EBIT Sales 4950 0065 0031 0077 0121 0138 RampD Sales 4950 0049 0000 0013 0059 0083 Ad Sales 4950 0008 0000 0000 0005 0018
38
Table 3 BEA and Worldscope samples
This table reports details on affiliate sales for significant countries pooled from 1998-2008 Significant countries are defined as those in which the operations of US MNCs represent at least 02 of total firm sales (per the BEA) For significant countries the table compares their representation in terms of MNC affiliate sales in the BEA data to the distribution of firm observations in Worldscope US Foreign Affiliates (BEA) Foreign Firms (Worldscope)
Country
N (1)
Sales (millions)
(2)
Total Sales (3)
N (4)
Sales (million)
(5)
Total Sales (6)
Australia 1344 73305 056 859 255865 210 Belgium 894 52755 040 169 26136 021 Brazil 959 60245 046 1143 683167 560 Canada 2730 536767 407 1181 526750 432 China 922 75907 057 822 276541 227 France 1852 183942 139 540 105447 086 Germany 2182 275577 209 480 103888 085 Hong Kong 867 48100 036 157 34094 028 Ireland 478 36754 028 34 3467 003 Italy 1178 82116 062 111 47948 039 Japan 1494 148032 112 2281 1119223 917 Korea South 659 34500 026 3530 2752791 2255 Malaysia 386 26027 020 837 168683 138 Mexico 1405 113623 086 362 237173 194 Netherlands 1149 85105 064 47 22631 019 Singapore 1098 94917 072 139 32913 027 Spain 871 63094 048 129 37419 031 Sweden 712 32682 025 235 23090 019 Switzerland 726 60288 046 74 20242 017 Taiwan 592 30313 023 2949 787049 645 United Kingdom 3054 423934 321 1997 882423 723 United States (domicile) 4950 10460880 7923 na na na Other - Covered by WorldScope 6893 199394 151 9000 4057894 33 Other -Not Covered by WorldScope 713 5352 004 - - - Total 38108 13203609 1000 27076 12204834 100
39
Table 4 OLS regressions of firm excess values on the percent of foreign sales This table reports the results of OLS regressions of firm excess value on the percent of foreign sales (Foreign Sales) the percent of sales outside a firmrsquos primary industry (Industry Other) and control variables All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed) Dep Variable = Excess Value Coefficient t-stat Coefficient t-stat Coefficient t-stat Coefficient t-stat (1) (2) (3) (4) Foreign Sales 0349 419 0209 293 0189 266 0278 210 Industry Other -0134 -186 -0047 -072 -0053 -081 -0326 -325 Log Size 0066 634 0065 624 -0044 -096 Debt Firm Value -0196 -213 -0241 -260 -0685 -588 CAPX Sales 0901 425 0893 435 0488 227 EBIT Sales 1154 542 1170 547 0408 189 RampD Sales 2908 1242 2932 1237 1544 379 Ad Sales 1869 263 2003 280 -1696 -119 Year indicators No No Yes Yes Firm indicators No No No Yes R2 0017 0212 0226 0768 Observations 4950 4950 4950 4950
40
Table 5 Dynamic panel GMM model regressing firm excess values on the percent of foreign sales This table reports the result from a regression of the excess value on the percent of foreign sales (Foreign Sales) the percent of non-primary industry sales (Industry Other) and control variables using a dynamic panel GMM estimator All control variables are considered to be endogenous with the exception of the year indicator variables AR(1) and AR(2) are tests for first-order and second-order serial correlation in the first differenced residuals with the null hypothesis of no serial correlation The null hypothesis of the Hansen test of overidentification is that all instruments are valid The null hypothesis of the difference-in-Hansen test of exogeneity is that the instruments used for the equation in levels are exogenous All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed) Dep Variable = Excess Value Coef t-stat Excess Value (one lag) -0018 -082 Excess Value (two lags) -0048 -272 Foreign Sales 0371 270 Industry Other -0163 -171 Log Size 0010 123 Debt FirmValue -1135 -820 CAPX Sales 0590 342 EBIT Sales -0047 -300 RampD Sales 1376 531 Ad Sales -0751 -040 AR(1) test (p-value) 000 AR(2) test (p-value) 017 Hansen test of over-identification (p-value) 030 Difference-in-Hansen test of exogeneity (p-value) 093 Year indicators Yes Number of firms 1016 Number of observations 4090
41
Table 6 OLS regression of firm excess values on the percent of foreign sales controlling for a control premium This table reports the result of the OLS regression of firm excess value including firm and year fixed effects with an additional control variable to proxy for a country-level ownership control premium (Control Premium) All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed) Dep Variable = Excess Value Coef t -stat Foreign Sales 0303 224 Industry Other -0327 -325 Log Size -0045 -100 Debt FirmValue -0683 -585 CAPX Sales 0490 226 EBIT Sales 0409 189 RampD Sales 1545 379 Ad Sales -1689 -119 Control Premium -1004 -062 Year indicators Yes Firm indicators Yes R2 0768 Observations 4950
42
Table 7 OLS regression of firm excess values on the percent of foreign sales controlling for the cost of capital This table contains the results of an OLS regression of firm excess value including firm and year fixed effects with an additional control variable to proxy for a country-level cost of capital (Cost of Capital) All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed) Dep Variable = Excess Value Coef t -stat Foreign Sales 0255 178 Industry Other -0326 -324 Log Size -0043 -094 Debt FirmValue -0687 -589 CAPX Sales 0485 225 EBIT Sales 0406 188 RampD Sales 1539 379 Ad Sales -1703 -120 Cost of Capital 0004 039 Year indicators Yes Firm indicators Yes R2 0768 Observations 4950
43
Table 8 OLS regression of firm excess values on the percent of foreign sales controlling for aspects of corporate governance This table reports the results of two OLS regressions In the first column are the results of the firm excess value regression including firm and year fixed effect with five proxies for corporate governance included ndash the percent of shares owned by the CEO and the percent squared (CEO Ownership and CEO Ownership squared) the percent owned by institutional investors (Institution Ownership) whether there is a powerful CEO (Powerful CEO) and the Gompers Ishii and Metrick (2003) governance index (Governance Index) In the second column the regression includes the same sample and variables with the exception of the governance proxies which are excluded All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed)
Governance
No Governance (same sample)
Dep Variable = Excess Value Coef t-stat Coef t -stat Foreign Sales 0259 159 0251 154 Industry Other -0099 -080 -0099 -079 Log Size -0105 -185 -0102 -180 Debt FirmValue -0813 -478 -0817 -482 CAPX Sales 0440 252 0438 249 EBIT Sales 0457 247 0457 246 RampD Sales 1717 284 1716 286 Ad Sales -2210 -143 -2190 -143 CEO Ownership 0010 007
CEO Ownership squared -0029 -017 Institution Ownership 0047 067 Powerful CEO -0003 -014 Governance Index 0009 103
Year indicators Yes Yes Firm indicators Yes Yes R2 0811 0810 Observations 2550 2550
- Cover for paperpdf
- crrz_20130603_SMUpdf
-
31
Laeven L and R Levine 2007 Is there a diversification discount in financial conglomerates
Journal of Financial Economics 85 331-367 Lang L and R Stulz 1994 Tobinrsquos Q corporate diversification and firm performance Journal
of Political Economy 102 1248-1280 Mataloni R 2003 US multinational companies Operations in 2001 Survey of Current
Business 85-105 Matvos G and A Seru 2011 The dynamic tradeoff between corporate socialism and financial
market dislocation Estimates from internal capital markets Working paper University of Chicago Booth School of Business
Moeller SB and F P Schlingemann 2005 Global diversification and bidder gains A
comparison between cross-border and domestic acquisitions Journal of Banking and Finance 29 533-564
Morck R and B Yeung 1991 Why investors value multinationality Journal of Business 64
165-186
Morck R and B Yeung 2001 Why firms diversify Internatization vs agency behavior In Intangibles B Lev (ed) Oxford University Press pp 269-302
Myers S C and N S Majluf 1984 Corporate financing and investment decisions when firms have information that investors do not have Journal of Financial Economics 13 187-221
Schmid M and I Walter 2009 Do financial conglomerates create or destroy economic value
Journal of Financial Intermediation 18 193-216 Syverson C 2011 What determines productivity Journal of Economic Literature 49(2) 326-
365 Villalonga B 2004a Diversification discount or premium New evidence from the Business
Information Tracking Series Journal of Finance 59 479-506 Villalonga B 2004b Does diversification cause the diversification discount Financial
Management 33 5-27 Wooldridge JM 2010 Econometric Analysis of Cross Section and Panel Data (Second
Edition) Cambridge MA MIT Press Yamin M 1991 A reassessment of Hymerrsquos contribution to the theory of the transnational
corporation C Pitelis and R Sugden (eds) The Nature of the Transnational Firm London Routledge
32
Appendix A Variable Definitions
We obtain information regarding firmsrsquo operations by country and industry from the Bureau of Economic Analysis (BEA) unless otherwise noted Acronyms used below reflect Compustat mnemonics and thus indicate when we obtain data from the Compustat North America Fundamentals Annual database All independent variables are winsorized at 1
Dependent variable
Excess Value = The log of the ratio of actual firm value to imputed firm value Actual firm value is equal to market capitalization (MVE) + book value of total assets (AT) ndash book value of shareholdersrsquo equity (SEQ) Imputed firm value is calculated as the sum of the imputed values of a firmrsquos operations in each country-industry The imputed value for each country-industry is equal to sales generated in that country-industry times a multiple equal to the ratio of a benchmark firmrsquos actual value to total sales A benchmark firm is defined as a median single-segment firm operating in the same country-industry All multiples require at least five firms listed in the respective country-industry on Compustat (domestic) or Worldscope (foreign) with less than 10 of sales income and assets outside the country of domicile Similar to Berger and Ofek (1995) we eliminate extreme values where the ratio of actual to imputed firm value exceeds 400 or is less than 025
Independent variables
Foreign Sales = Sales made by the foreign operations of US MNCs as a percent of total firm sales
Industry Other = Sales outside a firmrsquos primary industry as a percent of total sales (SALE) Equal to zero for firms operating in a single business segment
Log Size = Log of total sales (SALE)
Debt Firm Value = Ratio of long-term debt (DLTT) to total market value (market capitalization (MVE) + book value of total assets (AT) ndash book value of shareholdersrsquo equity (SEQ))
CAPX Sales = Ratio of capital expenditures (CAPX) to total sales (SALE)
EBIT Sales = Ratio of earnings before interest and taxes (EBIT) to total sales (SALE)
RampD Sales = Ratio of research and development expenses (XRD) to total sales (SALE)
Ad Sales = Ratio of advertising expenses (XAD) to total sales (SALE)
Control Premium = A weighted-average (based on the sales in a given country as a percent of total firm sales) of the country-level control premium from Dyck and Zingales (2004)
Cost of Capital = A weighted-average (based on the sales in a given country as a percent of total firm sales) of the country-level credit rating from Institutional Investor (httpwwwinstitutionalinvestorcom) The country-level rating is available for 160 countries and ranges from 0 to 100 Higher values imply greater credit risk
33
CEO Ownership = Percent of shares owned by the CEO from Execucomp
Institution Ownership = Percent of shares owned by institutional investors from Thomson Reuterrsquos Institutional Holdings database
Powerful CEO = Indicator variable equal to one if the CEO is the only insider on the board of directors and also serves as the chairman and president based on data from RiskMetrics Directors database Equal to zero otherwise
Governance Index = Count measure of takeover defenses used in Gompers Ishii and Metrick (2003) from RiskMetrics Governance database As the governance data are not available for every year we follow Gompers Ishii and Metrick (2003) and assume that the firmsrsquo governance attributes are unchanged until publication of the subsequent series
34
Figure 1 Trends in multinational activity and industrial diversification from 1984-2008 This figure presents a plot of the mean percent of foreign sales and the mean percent of sales outside the firmrsquos primary industry (industrial diversification) from 1984 through 2008 Dotted lines above and below the primary lines represent 95 confidence intervals To compile the plot we include the full population of firms in Compustat
35
Table 1 Analysis of accounting data for firms reporting under multiple accounting standards This table provides a comparison of sales net income and assets for firms that changed accounting standards to or from US GAAP Comparisons are provided for the 66 firms that changed to or from US GAAP and for which we could obtain accounting data electronically and in English under both standards for the same fiscal year (eg the firm presented the accounting numbers originally under one set of standards and then restated the prior year numbers in the subsequent year under the new set of standards) Panel A shows the distribution of firms by year Panel B shows the other accounting standards to which US GAAP is compared Panel C shows the mean and median percent that the other accounting standard differs from US GAAP for the same fiscal year and accounting number Two samples are shown in Panel C The first is the full sample of firms with a change in accounting standards to or from US GAAP for which data was available The second is the subset of these firms that occurred during the sample window used in this study (ie excluding changes post-2008) The percent difference in sales is compared to the percent difference in net income and assets Tests of means are based on matched-pair t-tests Tests of medians are based on Wilcoxon matched-pair signed-rank tests Panel A Accounting standard changes by year
2003
4 6
2004
25 38
2005
8 12
2006
6 9
2007
7 11
2008
5 8
2009
5 8
2010
5 8
2011
1 2
66
Panel B Accounting standards compared to US GAAP
IFRS
47 71
Canadian GAAP
12 18
Israeli GAAP
2 3
Dutch GAAP
1 2
Indian GAAP
1 2
Japanese GAAP
1 2
Norwegian GAAP
1 2
Swiss GAAP
1 2
66
36
(Table 1 continued) Panel C Tests of differences between US GAAP and other accounting standards
Percent different from US GAAP
Comparison to sales (p-values)
N Mean Median
Mean Median
Full Sample (2001-2011)
Sales
66 236 000
Net Income
66 10653 647
005 lt001
Assets
66 1162 126
lt001 lt001
Study Sample (2001-2008)
Sales
55 257 000
Net Income
55 10303 589
011 lt001
Assets
55 1047 099
003 lt001
37
Table 2 Descriptive statistics Panel A reports the mean and medians of Excess Value for the multinational firms and single-segment benchmark firms as well as univariate tests across the subsamples Panel B reports summary statistics for the independent variables for the multinational firms The p-values of the tests of differences in means are based on t-tests The p-values of tests of differences in the medians are for a Wilcoxon rank-sum test All variables are defined in Appendix A Panel A Excess Value by sample Multinational All Benchmark Benchmark Subgroups Sample Firms US Foreign
Value Value Test of Diff (p-value) Value
Test of Diff (p-value) Value
Test of Diff (p-value)
Mean 0067 0006 lt001 -0028 lt001 0014 lt001 Median 0076 0000 lt001 -0025 lt001 0000 lt001 N 4950 33074 5998 27076 Panel B Distribution of independent variables for the multinational firms N Mean 25th 50th 75th StDev Foreign Sales 4950 0238 0074 0190 0363 0197 Industry Other 4950 0212 0000 0166 0414 0215 Log Size 4950 6955 5823 6989 8140 1687 Debt Firm Value 4950 0136 0009 0097 0212 0145 CAPX Sales 4950 0054 0020 0033 0054 0101 EBIT Sales 4950 0065 0031 0077 0121 0138 RampD Sales 4950 0049 0000 0013 0059 0083 Ad Sales 4950 0008 0000 0000 0005 0018
38
Table 3 BEA and Worldscope samples
This table reports details on affiliate sales for significant countries pooled from 1998-2008 Significant countries are defined as those in which the operations of US MNCs represent at least 02 of total firm sales (per the BEA) For significant countries the table compares their representation in terms of MNC affiliate sales in the BEA data to the distribution of firm observations in Worldscope US Foreign Affiliates (BEA) Foreign Firms (Worldscope)
Country
N (1)
Sales (millions)
(2)
Total Sales (3)
N (4)
Sales (million)
(5)
Total Sales (6)
Australia 1344 73305 056 859 255865 210 Belgium 894 52755 040 169 26136 021 Brazil 959 60245 046 1143 683167 560 Canada 2730 536767 407 1181 526750 432 China 922 75907 057 822 276541 227 France 1852 183942 139 540 105447 086 Germany 2182 275577 209 480 103888 085 Hong Kong 867 48100 036 157 34094 028 Ireland 478 36754 028 34 3467 003 Italy 1178 82116 062 111 47948 039 Japan 1494 148032 112 2281 1119223 917 Korea South 659 34500 026 3530 2752791 2255 Malaysia 386 26027 020 837 168683 138 Mexico 1405 113623 086 362 237173 194 Netherlands 1149 85105 064 47 22631 019 Singapore 1098 94917 072 139 32913 027 Spain 871 63094 048 129 37419 031 Sweden 712 32682 025 235 23090 019 Switzerland 726 60288 046 74 20242 017 Taiwan 592 30313 023 2949 787049 645 United Kingdom 3054 423934 321 1997 882423 723 United States (domicile) 4950 10460880 7923 na na na Other - Covered by WorldScope 6893 199394 151 9000 4057894 33 Other -Not Covered by WorldScope 713 5352 004 - - - Total 38108 13203609 1000 27076 12204834 100
39
Table 4 OLS regressions of firm excess values on the percent of foreign sales This table reports the results of OLS regressions of firm excess value on the percent of foreign sales (Foreign Sales) the percent of sales outside a firmrsquos primary industry (Industry Other) and control variables All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed) Dep Variable = Excess Value Coefficient t-stat Coefficient t-stat Coefficient t-stat Coefficient t-stat (1) (2) (3) (4) Foreign Sales 0349 419 0209 293 0189 266 0278 210 Industry Other -0134 -186 -0047 -072 -0053 -081 -0326 -325 Log Size 0066 634 0065 624 -0044 -096 Debt Firm Value -0196 -213 -0241 -260 -0685 -588 CAPX Sales 0901 425 0893 435 0488 227 EBIT Sales 1154 542 1170 547 0408 189 RampD Sales 2908 1242 2932 1237 1544 379 Ad Sales 1869 263 2003 280 -1696 -119 Year indicators No No Yes Yes Firm indicators No No No Yes R2 0017 0212 0226 0768 Observations 4950 4950 4950 4950
40
Table 5 Dynamic panel GMM model regressing firm excess values on the percent of foreign sales This table reports the result from a regression of the excess value on the percent of foreign sales (Foreign Sales) the percent of non-primary industry sales (Industry Other) and control variables using a dynamic panel GMM estimator All control variables are considered to be endogenous with the exception of the year indicator variables AR(1) and AR(2) are tests for first-order and second-order serial correlation in the first differenced residuals with the null hypothesis of no serial correlation The null hypothesis of the Hansen test of overidentification is that all instruments are valid The null hypothesis of the difference-in-Hansen test of exogeneity is that the instruments used for the equation in levels are exogenous All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed) Dep Variable = Excess Value Coef t-stat Excess Value (one lag) -0018 -082 Excess Value (two lags) -0048 -272 Foreign Sales 0371 270 Industry Other -0163 -171 Log Size 0010 123 Debt FirmValue -1135 -820 CAPX Sales 0590 342 EBIT Sales -0047 -300 RampD Sales 1376 531 Ad Sales -0751 -040 AR(1) test (p-value) 000 AR(2) test (p-value) 017 Hansen test of over-identification (p-value) 030 Difference-in-Hansen test of exogeneity (p-value) 093 Year indicators Yes Number of firms 1016 Number of observations 4090
41
Table 6 OLS regression of firm excess values on the percent of foreign sales controlling for a control premium This table reports the result of the OLS regression of firm excess value including firm and year fixed effects with an additional control variable to proxy for a country-level ownership control premium (Control Premium) All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed) Dep Variable = Excess Value Coef t -stat Foreign Sales 0303 224 Industry Other -0327 -325 Log Size -0045 -100 Debt FirmValue -0683 -585 CAPX Sales 0490 226 EBIT Sales 0409 189 RampD Sales 1545 379 Ad Sales -1689 -119 Control Premium -1004 -062 Year indicators Yes Firm indicators Yes R2 0768 Observations 4950
42
Table 7 OLS regression of firm excess values on the percent of foreign sales controlling for the cost of capital This table contains the results of an OLS regression of firm excess value including firm and year fixed effects with an additional control variable to proxy for a country-level cost of capital (Cost of Capital) All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed) Dep Variable = Excess Value Coef t -stat Foreign Sales 0255 178 Industry Other -0326 -324 Log Size -0043 -094 Debt FirmValue -0687 -589 CAPX Sales 0485 225 EBIT Sales 0406 188 RampD Sales 1539 379 Ad Sales -1703 -120 Cost of Capital 0004 039 Year indicators Yes Firm indicators Yes R2 0768 Observations 4950
43
Table 8 OLS regression of firm excess values on the percent of foreign sales controlling for aspects of corporate governance This table reports the results of two OLS regressions In the first column are the results of the firm excess value regression including firm and year fixed effect with five proxies for corporate governance included ndash the percent of shares owned by the CEO and the percent squared (CEO Ownership and CEO Ownership squared) the percent owned by institutional investors (Institution Ownership) whether there is a powerful CEO (Powerful CEO) and the Gompers Ishii and Metrick (2003) governance index (Governance Index) In the second column the regression includes the same sample and variables with the exception of the governance proxies which are excluded All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed)
Governance
No Governance (same sample)
Dep Variable = Excess Value Coef t-stat Coef t -stat Foreign Sales 0259 159 0251 154 Industry Other -0099 -080 -0099 -079 Log Size -0105 -185 -0102 -180 Debt FirmValue -0813 -478 -0817 -482 CAPX Sales 0440 252 0438 249 EBIT Sales 0457 247 0457 246 RampD Sales 1717 284 1716 286 Ad Sales -2210 -143 -2190 -143 CEO Ownership 0010 007
CEO Ownership squared -0029 -017 Institution Ownership 0047 067 Powerful CEO -0003 -014 Governance Index 0009 103
Year indicators Yes Yes Firm indicators Yes Yes R2 0811 0810 Observations 2550 2550
- Cover for paperpdf
- crrz_20130603_SMUpdf
-
32
Appendix A Variable Definitions
We obtain information regarding firmsrsquo operations by country and industry from the Bureau of Economic Analysis (BEA) unless otherwise noted Acronyms used below reflect Compustat mnemonics and thus indicate when we obtain data from the Compustat North America Fundamentals Annual database All independent variables are winsorized at 1
Dependent variable
Excess Value = The log of the ratio of actual firm value to imputed firm value Actual firm value is equal to market capitalization (MVE) + book value of total assets (AT) ndash book value of shareholdersrsquo equity (SEQ) Imputed firm value is calculated as the sum of the imputed values of a firmrsquos operations in each country-industry The imputed value for each country-industry is equal to sales generated in that country-industry times a multiple equal to the ratio of a benchmark firmrsquos actual value to total sales A benchmark firm is defined as a median single-segment firm operating in the same country-industry All multiples require at least five firms listed in the respective country-industry on Compustat (domestic) or Worldscope (foreign) with less than 10 of sales income and assets outside the country of domicile Similar to Berger and Ofek (1995) we eliminate extreme values where the ratio of actual to imputed firm value exceeds 400 or is less than 025
Independent variables
Foreign Sales = Sales made by the foreign operations of US MNCs as a percent of total firm sales
Industry Other = Sales outside a firmrsquos primary industry as a percent of total sales (SALE) Equal to zero for firms operating in a single business segment
Log Size = Log of total sales (SALE)
Debt Firm Value = Ratio of long-term debt (DLTT) to total market value (market capitalization (MVE) + book value of total assets (AT) ndash book value of shareholdersrsquo equity (SEQ))
CAPX Sales = Ratio of capital expenditures (CAPX) to total sales (SALE)
EBIT Sales = Ratio of earnings before interest and taxes (EBIT) to total sales (SALE)
RampD Sales = Ratio of research and development expenses (XRD) to total sales (SALE)
Ad Sales = Ratio of advertising expenses (XAD) to total sales (SALE)
Control Premium = A weighted-average (based on the sales in a given country as a percent of total firm sales) of the country-level control premium from Dyck and Zingales (2004)
Cost of Capital = A weighted-average (based on the sales in a given country as a percent of total firm sales) of the country-level credit rating from Institutional Investor (httpwwwinstitutionalinvestorcom) The country-level rating is available for 160 countries and ranges from 0 to 100 Higher values imply greater credit risk
33
CEO Ownership = Percent of shares owned by the CEO from Execucomp
Institution Ownership = Percent of shares owned by institutional investors from Thomson Reuterrsquos Institutional Holdings database
Powerful CEO = Indicator variable equal to one if the CEO is the only insider on the board of directors and also serves as the chairman and president based on data from RiskMetrics Directors database Equal to zero otherwise
Governance Index = Count measure of takeover defenses used in Gompers Ishii and Metrick (2003) from RiskMetrics Governance database As the governance data are not available for every year we follow Gompers Ishii and Metrick (2003) and assume that the firmsrsquo governance attributes are unchanged until publication of the subsequent series
34
Figure 1 Trends in multinational activity and industrial diversification from 1984-2008 This figure presents a plot of the mean percent of foreign sales and the mean percent of sales outside the firmrsquos primary industry (industrial diversification) from 1984 through 2008 Dotted lines above and below the primary lines represent 95 confidence intervals To compile the plot we include the full population of firms in Compustat
35
Table 1 Analysis of accounting data for firms reporting under multiple accounting standards This table provides a comparison of sales net income and assets for firms that changed accounting standards to or from US GAAP Comparisons are provided for the 66 firms that changed to or from US GAAP and for which we could obtain accounting data electronically and in English under both standards for the same fiscal year (eg the firm presented the accounting numbers originally under one set of standards and then restated the prior year numbers in the subsequent year under the new set of standards) Panel A shows the distribution of firms by year Panel B shows the other accounting standards to which US GAAP is compared Panel C shows the mean and median percent that the other accounting standard differs from US GAAP for the same fiscal year and accounting number Two samples are shown in Panel C The first is the full sample of firms with a change in accounting standards to or from US GAAP for which data was available The second is the subset of these firms that occurred during the sample window used in this study (ie excluding changes post-2008) The percent difference in sales is compared to the percent difference in net income and assets Tests of means are based on matched-pair t-tests Tests of medians are based on Wilcoxon matched-pair signed-rank tests Panel A Accounting standard changes by year
2003
4 6
2004
25 38
2005
8 12
2006
6 9
2007
7 11
2008
5 8
2009
5 8
2010
5 8
2011
1 2
66
Panel B Accounting standards compared to US GAAP
IFRS
47 71
Canadian GAAP
12 18
Israeli GAAP
2 3
Dutch GAAP
1 2
Indian GAAP
1 2
Japanese GAAP
1 2
Norwegian GAAP
1 2
Swiss GAAP
1 2
66
36
(Table 1 continued) Panel C Tests of differences between US GAAP and other accounting standards
Percent different from US GAAP
Comparison to sales (p-values)
N Mean Median
Mean Median
Full Sample (2001-2011)
Sales
66 236 000
Net Income
66 10653 647
005 lt001
Assets
66 1162 126
lt001 lt001
Study Sample (2001-2008)
Sales
55 257 000
Net Income
55 10303 589
011 lt001
Assets
55 1047 099
003 lt001
37
Table 2 Descriptive statistics Panel A reports the mean and medians of Excess Value for the multinational firms and single-segment benchmark firms as well as univariate tests across the subsamples Panel B reports summary statistics for the independent variables for the multinational firms The p-values of the tests of differences in means are based on t-tests The p-values of tests of differences in the medians are for a Wilcoxon rank-sum test All variables are defined in Appendix A Panel A Excess Value by sample Multinational All Benchmark Benchmark Subgroups Sample Firms US Foreign
Value Value Test of Diff (p-value) Value
Test of Diff (p-value) Value
Test of Diff (p-value)
Mean 0067 0006 lt001 -0028 lt001 0014 lt001 Median 0076 0000 lt001 -0025 lt001 0000 lt001 N 4950 33074 5998 27076 Panel B Distribution of independent variables for the multinational firms N Mean 25th 50th 75th StDev Foreign Sales 4950 0238 0074 0190 0363 0197 Industry Other 4950 0212 0000 0166 0414 0215 Log Size 4950 6955 5823 6989 8140 1687 Debt Firm Value 4950 0136 0009 0097 0212 0145 CAPX Sales 4950 0054 0020 0033 0054 0101 EBIT Sales 4950 0065 0031 0077 0121 0138 RampD Sales 4950 0049 0000 0013 0059 0083 Ad Sales 4950 0008 0000 0000 0005 0018
38
Table 3 BEA and Worldscope samples
This table reports details on affiliate sales for significant countries pooled from 1998-2008 Significant countries are defined as those in which the operations of US MNCs represent at least 02 of total firm sales (per the BEA) For significant countries the table compares their representation in terms of MNC affiliate sales in the BEA data to the distribution of firm observations in Worldscope US Foreign Affiliates (BEA) Foreign Firms (Worldscope)
Country
N (1)
Sales (millions)
(2)
Total Sales (3)
N (4)
Sales (million)
(5)
Total Sales (6)
Australia 1344 73305 056 859 255865 210 Belgium 894 52755 040 169 26136 021 Brazil 959 60245 046 1143 683167 560 Canada 2730 536767 407 1181 526750 432 China 922 75907 057 822 276541 227 France 1852 183942 139 540 105447 086 Germany 2182 275577 209 480 103888 085 Hong Kong 867 48100 036 157 34094 028 Ireland 478 36754 028 34 3467 003 Italy 1178 82116 062 111 47948 039 Japan 1494 148032 112 2281 1119223 917 Korea South 659 34500 026 3530 2752791 2255 Malaysia 386 26027 020 837 168683 138 Mexico 1405 113623 086 362 237173 194 Netherlands 1149 85105 064 47 22631 019 Singapore 1098 94917 072 139 32913 027 Spain 871 63094 048 129 37419 031 Sweden 712 32682 025 235 23090 019 Switzerland 726 60288 046 74 20242 017 Taiwan 592 30313 023 2949 787049 645 United Kingdom 3054 423934 321 1997 882423 723 United States (domicile) 4950 10460880 7923 na na na Other - Covered by WorldScope 6893 199394 151 9000 4057894 33 Other -Not Covered by WorldScope 713 5352 004 - - - Total 38108 13203609 1000 27076 12204834 100
39
Table 4 OLS regressions of firm excess values on the percent of foreign sales This table reports the results of OLS regressions of firm excess value on the percent of foreign sales (Foreign Sales) the percent of sales outside a firmrsquos primary industry (Industry Other) and control variables All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed) Dep Variable = Excess Value Coefficient t-stat Coefficient t-stat Coefficient t-stat Coefficient t-stat (1) (2) (3) (4) Foreign Sales 0349 419 0209 293 0189 266 0278 210 Industry Other -0134 -186 -0047 -072 -0053 -081 -0326 -325 Log Size 0066 634 0065 624 -0044 -096 Debt Firm Value -0196 -213 -0241 -260 -0685 -588 CAPX Sales 0901 425 0893 435 0488 227 EBIT Sales 1154 542 1170 547 0408 189 RampD Sales 2908 1242 2932 1237 1544 379 Ad Sales 1869 263 2003 280 -1696 -119 Year indicators No No Yes Yes Firm indicators No No No Yes R2 0017 0212 0226 0768 Observations 4950 4950 4950 4950
40
Table 5 Dynamic panel GMM model regressing firm excess values on the percent of foreign sales This table reports the result from a regression of the excess value on the percent of foreign sales (Foreign Sales) the percent of non-primary industry sales (Industry Other) and control variables using a dynamic panel GMM estimator All control variables are considered to be endogenous with the exception of the year indicator variables AR(1) and AR(2) are tests for first-order and second-order serial correlation in the first differenced residuals with the null hypothesis of no serial correlation The null hypothesis of the Hansen test of overidentification is that all instruments are valid The null hypothesis of the difference-in-Hansen test of exogeneity is that the instruments used for the equation in levels are exogenous All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed) Dep Variable = Excess Value Coef t-stat Excess Value (one lag) -0018 -082 Excess Value (two lags) -0048 -272 Foreign Sales 0371 270 Industry Other -0163 -171 Log Size 0010 123 Debt FirmValue -1135 -820 CAPX Sales 0590 342 EBIT Sales -0047 -300 RampD Sales 1376 531 Ad Sales -0751 -040 AR(1) test (p-value) 000 AR(2) test (p-value) 017 Hansen test of over-identification (p-value) 030 Difference-in-Hansen test of exogeneity (p-value) 093 Year indicators Yes Number of firms 1016 Number of observations 4090
41
Table 6 OLS regression of firm excess values on the percent of foreign sales controlling for a control premium This table reports the result of the OLS regression of firm excess value including firm and year fixed effects with an additional control variable to proxy for a country-level ownership control premium (Control Premium) All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed) Dep Variable = Excess Value Coef t -stat Foreign Sales 0303 224 Industry Other -0327 -325 Log Size -0045 -100 Debt FirmValue -0683 -585 CAPX Sales 0490 226 EBIT Sales 0409 189 RampD Sales 1545 379 Ad Sales -1689 -119 Control Premium -1004 -062 Year indicators Yes Firm indicators Yes R2 0768 Observations 4950
42
Table 7 OLS regression of firm excess values on the percent of foreign sales controlling for the cost of capital This table contains the results of an OLS regression of firm excess value including firm and year fixed effects with an additional control variable to proxy for a country-level cost of capital (Cost of Capital) All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed) Dep Variable = Excess Value Coef t -stat Foreign Sales 0255 178 Industry Other -0326 -324 Log Size -0043 -094 Debt FirmValue -0687 -589 CAPX Sales 0485 225 EBIT Sales 0406 188 RampD Sales 1539 379 Ad Sales -1703 -120 Cost of Capital 0004 039 Year indicators Yes Firm indicators Yes R2 0768 Observations 4950
43
Table 8 OLS regression of firm excess values on the percent of foreign sales controlling for aspects of corporate governance This table reports the results of two OLS regressions In the first column are the results of the firm excess value regression including firm and year fixed effect with five proxies for corporate governance included ndash the percent of shares owned by the CEO and the percent squared (CEO Ownership and CEO Ownership squared) the percent owned by institutional investors (Institution Ownership) whether there is a powerful CEO (Powerful CEO) and the Gompers Ishii and Metrick (2003) governance index (Governance Index) In the second column the regression includes the same sample and variables with the exception of the governance proxies which are excluded All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed)
Governance
No Governance (same sample)
Dep Variable = Excess Value Coef t-stat Coef t -stat Foreign Sales 0259 159 0251 154 Industry Other -0099 -080 -0099 -079 Log Size -0105 -185 -0102 -180 Debt FirmValue -0813 -478 -0817 -482 CAPX Sales 0440 252 0438 249 EBIT Sales 0457 247 0457 246 RampD Sales 1717 284 1716 286 Ad Sales -2210 -143 -2190 -143 CEO Ownership 0010 007
CEO Ownership squared -0029 -017 Institution Ownership 0047 067 Powerful CEO -0003 -014 Governance Index 0009 103
Year indicators Yes Yes Firm indicators Yes Yes R2 0811 0810 Observations 2550 2550
- Cover for paperpdf
- crrz_20130603_SMUpdf
-
33
CEO Ownership = Percent of shares owned by the CEO from Execucomp
Institution Ownership = Percent of shares owned by institutional investors from Thomson Reuterrsquos Institutional Holdings database
Powerful CEO = Indicator variable equal to one if the CEO is the only insider on the board of directors and also serves as the chairman and president based on data from RiskMetrics Directors database Equal to zero otherwise
Governance Index = Count measure of takeover defenses used in Gompers Ishii and Metrick (2003) from RiskMetrics Governance database As the governance data are not available for every year we follow Gompers Ishii and Metrick (2003) and assume that the firmsrsquo governance attributes are unchanged until publication of the subsequent series
34
Figure 1 Trends in multinational activity and industrial diversification from 1984-2008 This figure presents a plot of the mean percent of foreign sales and the mean percent of sales outside the firmrsquos primary industry (industrial diversification) from 1984 through 2008 Dotted lines above and below the primary lines represent 95 confidence intervals To compile the plot we include the full population of firms in Compustat
35
Table 1 Analysis of accounting data for firms reporting under multiple accounting standards This table provides a comparison of sales net income and assets for firms that changed accounting standards to or from US GAAP Comparisons are provided for the 66 firms that changed to or from US GAAP and for which we could obtain accounting data electronically and in English under both standards for the same fiscal year (eg the firm presented the accounting numbers originally under one set of standards and then restated the prior year numbers in the subsequent year under the new set of standards) Panel A shows the distribution of firms by year Panel B shows the other accounting standards to which US GAAP is compared Panel C shows the mean and median percent that the other accounting standard differs from US GAAP for the same fiscal year and accounting number Two samples are shown in Panel C The first is the full sample of firms with a change in accounting standards to or from US GAAP for which data was available The second is the subset of these firms that occurred during the sample window used in this study (ie excluding changes post-2008) The percent difference in sales is compared to the percent difference in net income and assets Tests of means are based on matched-pair t-tests Tests of medians are based on Wilcoxon matched-pair signed-rank tests Panel A Accounting standard changes by year
2003
4 6
2004
25 38
2005
8 12
2006
6 9
2007
7 11
2008
5 8
2009
5 8
2010
5 8
2011
1 2
66
Panel B Accounting standards compared to US GAAP
IFRS
47 71
Canadian GAAP
12 18
Israeli GAAP
2 3
Dutch GAAP
1 2
Indian GAAP
1 2
Japanese GAAP
1 2
Norwegian GAAP
1 2
Swiss GAAP
1 2
66
36
(Table 1 continued) Panel C Tests of differences between US GAAP and other accounting standards
Percent different from US GAAP
Comparison to sales (p-values)
N Mean Median
Mean Median
Full Sample (2001-2011)
Sales
66 236 000
Net Income
66 10653 647
005 lt001
Assets
66 1162 126
lt001 lt001
Study Sample (2001-2008)
Sales
55 257 000
Net Income
55 10303 589
011 lt001
Assets
55 1047 099
003 lt001
37
Table 2 Descriptive statistics Panel A reports the mean and medians of Excess Value for the multinational firms and single-segment benchmark firms as well as univariate tests across the subsamples Panel B reports summary statistics for the independent variables for the multinational firms The p-values of the tests of differences in means are based on t-tests The p-values of tests of differences in the medians are for a Wilcoxon rank-sum test All variables are defined in Appendix A Panel A Excess Value by sample Multinational All Benchmark Benchmark Subgroups Sample Firms US Foreign
Value Value Test of Diff (p-value) Value
Test of Diff (p-value) Value
Test of Diff (p-value)
Mean 0067 0006 lt001 -0028 lt001 0014 lt001 Median 0076 0000 lt001 -0025 lt001 0000 lt001 N 4950 33074 5998 27076 Panel B Distribution of independent variables for the multinational firms N Mean 25th 50th 75th StDev Foreign Sales 4950 0238 0074 0190 0363 0197 Industry Other 4950 0212 0000 0166 0414 0215 Log Size 4950 6955 5823 6989 8140 1687 Debt Firm Value 4950 0136 0009 0097 0212 0145 CAPX Sales 4950 0054 0020 0033 0054 0101 EBIT Sales 4950 0065 0031 0077 0121 0138 RampD Sales 4950 0049 0000 0013 0059 0083 Ad Sales 4950 0008 0000 0000 0005 0018
38
Table 3 BEA and Worldscope samples
This table reports details on affiliate sales for significant countries pooled from 1998-2008 Significant countries are defined as those in which the operations of US MNCs represent at least 02 of total firm sales (per the BEA) For significant countries the table compares their representation in terms of MNC affiliate sales in the BEA data to the distribution of firm observations in Worldscope US Foreign Affiliates (BEA) Foreign Firms (Worldscope)
Country
N (1)
Sales (millions)
(2)
Total Sales (3)
N (4)
Sales (million)
(5)
Total Sales (6)
Australia 1344 73305 056 859 255865 210 Belgium 894 52755 040 169 26136 021 Brazil 959 60245 046 1143 683167 560 Canada 2730 536767 407 1181 526750 432 China 922 75907 057 822 276541 227 France 1852 183942 139 540 105447 086 Germany 2182 275577 209 480 103888 085 Hong Kong 867 48100 036 157 34094 028 Ireland 478 36754 028 34 3467 003 Italy 1178 82116 062 111 47948 039 Japan 1494 148032 112 2281 1119223 917 Korea South 659 34500 026 3530 2752791 2255 Malaysia 386 26027 020 837 168683 138 Mexico 1405 113623 086 362 237173 194 Netherlands 1149 85105 064 47 22631 019 Singapore 1098 94917 072 139 32913 027 Spain 871 63094 048 129 37419 031 Sweden 712 32682 025 235 23090 019 Switzerland 726 60288 046 74 20242 017 Taiwan 592 30313 023 2949 787049 645 United Kingdom 3054 423934 321 1997 882423 723 United States (domicile) 4950 10460880 7923 na na na Other - Covered by WorldScope 6893 199394 151 9000 4057894 33 Other -Not Covered by WorldScope 713 5352 004 - - - Total 38108 13203609 1000 27076 12204834 100
39
Table 4 OLS regressions of firm excess values on the percent of foreign sales This table reports the results of OLS regressions of firm excess value on the percent of foreign sales (Foreign Sales) the percent of sales outside a firmrsquos primary industry (Industry Other) and control variables All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed) Dep Variable = Excess Value Coefficient t-stat Coefficient t-stat Coefficient t-stat Coefficient t-stat (1) (2) (3) (4) Foreign Sales 0349 419 0209 293 0189 266 0278 210 Industry Other -0134 -186 -0047 -072 -0053 -081 -0326 -325 Log Size 0066 634 0065 624 -0044 -096 Debt Firm Value -0196 -213 -0241 -260 -0685 -588 CAPX Sales 0901 425 0893 435 0488 227 EBIT Sales 1154 542 1170 547 0408 189 RampD Sales 2908 1242 2932 1237 1544 379 Ad Sales 1869 263 2003 280 -1696 -119 Year indicators No No Yes Yes Firm indicators No No No Yes R2 0017 0212 0226 0768 Observations 4950 4950 4950 4950
40
Table 5 Dynamic panel GMM model regressing firm excess values on the percent of foreign sales This table reports the result from a regression of the excess value on the percent of foreign sales (Foreign Sales) the percent of non-primary industry sales (Industry Other) and control variables using a dynamic panel GMM estimator All control variables are considered to be endogenous with the exception of the year indicator variables AR(1) and AR(2) are tests for first-order and second-order serial correlation in the first differenced residuals with the null hypothesis of no serial correlation The null hypothesis of the Hansen test of overidentification is that all instruments are valid The null hypothesis of the difference-in-Hansen test of exogeneity is that the instruments used for the equation in levels are exogenous All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed) Dep Variable = Excess Value Coef t-stat Excess Value (one lag) -0018 -082 Excess Value (two lags) -0048 -272 Foreign Sales 0371 270 Industry Other -0163 -171 Log Size 0010 123 Debt FirmValue -1135 -820 CAPX Sales 0590 342 EBIT Sales -0047 -300 RampD Sales 1376 531 Ad Sales -0751 -040 AR(1) test (p-value) 000 AR(2) test (p-value) 017 Hansen test of over-identification (p-value) 030 Difference-in-Hansen test of exogeneity (p-value) 093 Year indicators Yes Number of firms 1016 Number of observations 4090
41
Table 6 OLS regression of firm excess values on the percent of foreign sales controlling for a control premium This table reports the result of the OLS regression of firm excess value including firm and year fixed effects with an additional control variable to proxy for a country-level ownership control premium (Control Premium) All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed) Dep Variable = Excess Value Coef t -stat Foreign Sales 0303 224 Industry Other -0327 -325 Log Size -0045 -100 Debt FirmValue -0683 -585 CAPX Sales 0490 226 EBIT Sales 0409 189 RampD Sales 1545 379 Ad Sales -1689 -119 Control Premium -1004 -062 Year indicators Yes Firm indicators Yes R2 0768 Observations 4950
42
Table 7 OLS regression of firm excess values on the percent of foreign sales controlling for the cost of capital This table contains the results of an OLS regression of firm excess value including firm and year fixed effects with an additional control variable to proxy for a country-level cost of capital (Cost of Capital) All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed) Dep Variable = Excess Value Coef t -stat Foreign Sales 0255 178 Industry Other -0326 -324 Log Size -0043 -094 Debt FirmValue -0687 -589 CAPX Sales 0485 225 EBIT Sales 0406 188 RampD Sales 1539 379 Ad Sales -1703 -120 Cost of Capital 0004 039 Year indicators Yes Firm indicators Yes R2 0768 Observations 4950
43
Table 8 OLS regression of firm excess values on the percent of foreign sales controlling for aspects of corporate governance This table reports the results of two OLS regressions In the first column are the results of the firm excess value regression including firm and year fixed effect with five proxies for corporate governance included ndash the percent of shares owned by the CEO and the percent squared (CEO Ownership and CEO Ownership squared) the percent owned by institutional investors (Institution Ownership) whether there is a powerful CEO (Powerful CEO) and the Gompers Ishii and Metrick (2003) governance index (Governance Index) In the second column the regression includes the same sample and variables with the exception of the governance proxies which are excluded All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed)
Governance
No Governance (same sample)
Dep Variable = Excess Value Coef t-stat Coef t -stat Foreign Sales 0259 159 0251 154 Industry Other -0099 -080 -0099 -079 Log Size -0105 -185 -0102 -180 Debt FirmValue -0813 -478 -0817 -482 CAPX Sales 0440 252 0438 249 EBIT Sales 0457 247 0457 246 RampD Sales 1717 284 1716 286 Ad Sales -2210 -143 -2190 -143 CEO Ownership 0010 007
CEO Ownership squared -0029 -017 Institution Ownership 0047 067 Powerful CEO -0003 -014 Governance Index 0009 103
Year indicators Yes Yes Firm indicators Yes Yes R2 0811 0810 Observations 2550 2550
- Cover for paperpdf
- crrz_20130603_SMUpdf
-
34
Figure 1 Trends in multinational activity and industrial diversification from 1984-2008 This figure presents a plot of the mean percent of foreign sales and the mean percent of sales outside the firmrsquos primary industry (industrial diversification) from 1984 through 2008 Dotted lines above and below the primary lines represent 95 confidence intervals To compile the plot we include the full population of firms in Compustat
35
Table 1 Analysis of accounting data for firms reporting under multiple accounting standards This table provides a comparison of sales net income and assets for firms that changed accounting standards to or from US GAAP Comparisons are provided for the 66 firms that changed to or from US GAAP and for which we could obtain accounting data electronically and in English under both standards for the same fiscal year (eg the firm presented the accounting numbers originally under one set of standards and then restated the prior year numbers in the subsequent year under the new set of standards) Panel A shows the distribution of firms by year Panel B shows the other accounting standards to which US GAAP is compared Panel C shows the mean and median percent that the other accounting standard differs from US GAAP for the same fiscal year and accounting number Two samples are shown in Panel C The first is the full sample of firms with a change in accounting standards to or from US GAAP for which data was available The second is the subset of these firms that occurred during the sample window used in this study (ie excluding changes post-2008) The percent difference in sales is compared to the percent difference in net income and assets Tests of means are based on matched-pair t-tests Tests of medians are based on Wilcoxon matched-pair signed-rank tests Panel A Accounting standard changes by year
2003
4 6
2004
25 38
2005
8 12
2006
6 9
2007
7 11
2008
5 8
2009
5 8
2010
5 8
2011
1 2
66
Panel B Accounting standards compared to US GAAP
IFRS
47 71
Canadian GAAP
12 18
Israeli GAAP
2 3
Dutch GAAP
1 2
Indian GAAP
1 2
Japanese GAAP
1 2
Norwegian GAAP
1 2
Swiss GAAP
1 2
66
36
(Table 1 continued) Panel C Tests of differences between US GAAP and other accounting standards
Percent different from US GAAP
Comparison to sales (p-values)
N Mean Median
Mean Median
Full Sample (2001-2011)
Sales
66 236 000
Net Income
66 10653 647
005 lt001
Assets
66 1162 126
lt001 lt001
Study Sample (2001-2008)
Sales
55 257 000
Net Income
55 10303 589
011 lt001
Assets
55 1047 099
003 lt001
37
Table 2 Descriptive statistics Panel A reports the mean and medians of Excess Value for the multinational firms and single-segment benchmark firms as well as univariate tests across the subsamples Panel B reports summary statistics for the independent variables for the multinational firms The p-values of the tests of differences in means are based on t-tests The p-values of tests of differences in the medians are for a Wilcoxon rank-sum test All variables are defined in Appendix A Panel A Excess Value by sample Multinational All Benchmark Benchmark Subgroups Sample Firms US Foreign
Value Value Test of Diff (p-value) Value
Test of Diff (p-value) Value
Test of Diff (p-value)
Mean 0067 0006 lt001 -0028 lt001 0014 lt001 Median 0076 0000 lt001 -0025 lt001 0000 lt001 N 4950 33074 5998 27076 Panel B Distribution of independent variables for the multinational firms N Mean 25th 50th 75th StDev Foreign Sales 4950 0238 0074 0190 0363 0197 Industry Other 4950 0212 0000 0166 0414 0215 Log Size 4950 6955 5823 6989 8140 1687 Debt Firm Value 4950 0136 0009 0097 0212 0145 CAPX Sales 4950 0054 0020 0033 0054 0101 EBIT Sales 4950 0065 0031 0077 0121 0138 RampD Sales 4950 0049 0000 0013 0059 0083 Ad Sales 4950 0008 0000 0000 0005 0018
38
Table 3 BEA and Worldscope samples
This table reports details on affiliate sales for significant countries pooled from 1998-2008 Significant countries are defined as those in which the operations of US MNCs represent at least 02 of total firm sales (per the BEA) For significant countries the table compares their representation in terms of MNC affiliate sales in the BEA data to the distribution of firm observations in Worldscope US Foreign Affiliates (BEA) Foreign Firms (Worldscope)
Country
N (1)
Sales (millions)
(2)
Total Sales (3)
N (4)
Sales (million)
(5)
Total Sales (6)
Australia 1344 73305 056 859 255865 210 Belgium 894 52755 040 169 26136 021 Brazil 959 60245 046 1143 683167 560 Canada 2730 536767 407 1181 526750 432 China 922 75907 057 822 276541 227 France 1852 183942 139 540 105447 086 Germany 2182 275577 209 480 103888 085 Hong Kong 867 48100 036 157 34094 028 Ireland 478 36754 028 34 3467 003 Italy 1178 82116 062 111 47948 039 Japan 1494 148032 112 2281 1119223 917 Korea South 659 34500 026 3530 2752791 2255 Malaysia 386 26027 020 837 168683 138 Mexico 1405 113623 086 362 237173 194 Netherlands 1149 85105 064 47 22631 019 Singapore 1098 94917 072 139 32913 027 Spain 871 63094 048 129 37419 031 Sweden 712 32682 025 235 23090 019 Switzerland 726 60288 046 74 20242 017 Taiwan 592 30313 023 2949 787049 645 United Kingdom 3054 423934 321 1997 882423 723 United States (domicile) 4950 10460880 7923 na na na Other - Covered by WorldScope 6893 199394 151 9000 4057894 33 Other -Not Covered by WorldScope 713 5352 004 - - - Total 38108 13203609 1000 27076 12204834 100
39
Table 4 OLS regressions of firm excess values on the percent of foreign sales This table reports the results of OLS regressions of firm excess value on the percent of foreign sales (Foreign Sales) the percent of sales outside a firmrsquos primary industry (Industry Other) and control variables All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed) Dep Variable = Excess Value Coefficient t-stat Coefficient t-stat Coefficient t-stat Coefficient t-stat (1) (2) (3) (4) Foreign Sales 0349 419 0209 293 0189 266 0278 210 Industry Other -0134 -186 -0047 -072 -0053 -081 -0326 -325 Log Size 0066 634 0065 624 -0044 -096 Debt Firm Value -0196 -213 -0241 -260 -0685 -588 CAPX Sales 0901 425 0893 435 0488 227 EBIT Sales 1154 542 1170 547 0408 189 RampD Sales 2908 1242 2932 1237 1544 379 Ad Sales 1869 263 2003 280 -1696 -119 Year indicators No No Yes Yes Firm indicators No No No Yes R2 0017 0212 0226 0768 Observations 4950 4950 4950 4950
40
Table 5 Dynamic panel GMM model regressing firm excess values on the percent of foreign sales This table reports the result from a regression of the excess value on the percent of foreign sales (Foreign Sales) the percent of non-primary industry sales (Industry Other) and control variables using a dynamic panel GMM estimator All control variables are considered to be endogenous with the exception of the year indicator variables AR(1) and AR(2) are tests for first-order and second-order serial correlation in the first differenced residuals with the null hypothesis of no serial correlation The null hypothesis of the Hansen test of overidentification is that all instruments are valid The null hypothesis of the difference-in-Hansen test of exogeneity is that the instruments used for the equation in levels are exogenous All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed) Dep Variable = Excess Value Coef t-stat Excess Value (one lag) -0018 -082 Excess Value (two lags) -0048 -272 Foreign Sales 0371 270 Industry Other -0163 -171 Log Size 0010 123 Debt FirmValue -1135 -820 CAPX Sales 0590 342 EBIT Sales -0047 -300 RampD Sales 1376 531 Ad Sales -0751 -040 AR(1) test (p-value) 000 AR(2) test (p-value) 017 Hansen test of over-identification (p-value) 030 Difference-in-Hansen test of exogeneity (p-value) 093 Year indicators Yes Number of firms 1016 Number of observations 4090
41
Table 6 OLS regression of firm excess values on the percent of foreign sales controlling for a control premium This table reports the result of the OLS regression of firm excess value including firm and year fixed effects with an additional control variable to proxy for a country-level ownership control premium (Control Premium) All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed) Dep Variable = Excess Value Coef t -stat Foreign Sales 0303 224 Industry Other -0327 -325 Log Size -0045 -100 Debt FirmValue -0683 -585 CAPX Sales 0490 226 EBIT Sales 0409 189 RampD Sales 1545 379 Ad Sales -1689 -119 Control Premium -1004 -062 Year indicators Yes Firm indicators Yes R2 0768 Observations 4950
42
Table 7 OLS regression of firm excess values on the percent of foreign sales controlling for the cost of capital This table contains the results of an OLS regression of firm excess value including firm and year fixed effects with an additional control variable to proxy for a country-level cost of capital (Cost of Capital) All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed) Dep Variable = Excess Value Coef t -stat Foreign Sales 0255 178 Industry Other -0326 -324 Log Size -0043 -094 Debt FirmValue -0687 -589 CAPX Sales 0485 225 EBIT Sales 0406 188 RampD Sales 1539 379 Ad Sales -1703 -120 Cost of Capital 0004 039 Year indicators Yes Firm indicators Yes R2 0768 Observations 4950
43
Table 8 OLS regression of firm excess values on the percent of foreign sales controlling for aspects of corporate governance This table reports the results of two OLS regressions In the first column are the results of the firm excess value regression including firm and year fixed effect with five proxies for corporate governance included ndash the percent of shares owned by the CEO and the percent squared (CEO Ownership and CEO Ownership squared) the percent owned by institutional investors (Institution Ownership) whether there is a powerful CEO (Powerful CEO) and the Gompers Ishii and Metrick (2003) governance index (Governance Index) In the second column the regression includes the same sample and variables with the exception of the governance proxies which are excluded All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed)
Governance
No Governance (same sample)
Dep Variable = Excess Value Coef t-stat Coef t -stat Foreign Sales 0259 159 0251 154 Industry Other -0099 -080 -0099 -079 Log Size -0105 -185 -0102 -180 Debt FirmValue -0813 -478 -0817 -482 CAPX Sales 0440 252 0438 249 EBIT Sales 0457 247 0457 246 RampD Sales 1717 284 1716 286 Ad Sales -2210 -143 -2190 -143 CEO Ownership 0010 007
CEO Ownership squared -0029 -017 Institution Ownership 0047 067 Powerful CEO -0003 -014 Governance Index 0009 103
Year indicators Yes Yes Firm indicators Yes Yes R2 0811 0810 Observations 2550 2550
- Cover for paperpdf
- crrz_20130603_SMUpdf
-
35
Table 1 Analysis of accounting data for firms reporting under multiple accounting standards This table provides a comparison of sales net income and assets for firms that changed accounting standards to or from US GAAP Comparisons are provided for the 66 firms that changed to or from US GAAP and for which we could obtain accounting data electronically and in English under both standards for the same fiscal year (eg the firm presented the accounting numbers originally under one set of standards and then restated the prior year numbers in the subsequent year under the new set of standards) Panel A shows the distribution of firms by year Panel B shows the other accounting standards to which US GAAP is compared Panel C shows the mean and median percent that the other accounting standard differs from US GAAP for the same fiscal year and accounting number Two samples are shown in Panel C The first is the full sample of firms with a change in accounting standards to or from US GAAP for which data was available The second is the subset of these firms that occurred during the sample window used in this study (ie excluding changes post-2008) The percent difference in sales is compared to the percent difference in net income and assets Tests of means are based on matched-pair t-tests Tests of medians are based on Wilcoxon matched-pair signed-rank tests Panel A Accounting standard changes by year
2003
4 6
2004
25 38
2005
8 12
2006
6 9
2007
7 11
2008
5 8
2009
5 8
2010
5 8
2011
1 2
66
Panel B Accounting standards compared to US GAAP
IFRS
47 71
Canadian GAAP
12 18
Israeli GAAP
2 3
Dutch GAAP
1 2
Indian GAAP
1 2
Japanese GAAP
1 2
Norwegian GAAP
1 2
Swiss GAAP
1 2
66
36
(Table 1 continued) Panel C Tests of differences between US GAAP and other accounting standards
Percent different from US GAAP
Comparison to sales (p-values)
N Mean Median
Mean Median
Full Sample (2001-2011)
Sales
66 236 000
Net Income
66 10653 647
005 lt001
Assets
66 1162 126
lt001 lt001
Study Sample (2001-2008)
Sales
55 257 000
Net Income
55 10303 589
011 lt001
Assets
55 1047 099
003 lt001
37
Table 2 Descriptive statistics Panel A reports the mean and medians of Excess Value for the multinational firms and single-segment benchmark firms as well as univariate tests across the subsamples Panel B reports summary statistics for the independent variables for the multinational firms The p-values of the tests of differences in means are based on t-tests The p-values of tests of differences in the medians are for a Wilcoxon rank-sum test All variables are defined in Appendix A Panel A Excess Value by sample Multinational All Benchmark Benchmark Subgroups Sample Firms US Foreign
Value Value Test of Diff (p-value) Value
Test of Diff (p-value) Value
Test of Diff (p-value)
Mean 0067 0006 lt001 -0028 lt001 0014 lt001 Median 0076 0000 lt001 -0025 lt001 0000 lt001 N 4950 33074 5998 27076 Panel B Distribution of independent variables for the multinational firms N Mean 25th 50th 75th StDev Foreign Sales 4950 0238 0074 0190 0363 0197 Industry Other 4950 0212 0000 0166 0414 0215 Log Size 4950 6955 5823 6989 8140 1687 Debt Firm Value 4950 0136 0009 0097 0212 0145 CAPX Sales 4950 0054 0020 0033 0054 0101 EBIT Sales 4950 0065 0031 0077 0121 0138 RampD Sales 4950 0049 0000 0013 0059 0083 Ad Sales 4950 0008 0000 0000 0005 0018
38
Table 3 BEA and Worldscope samples
This table reports details on affiliate sales for significant countries pooled from 1998-2008 Significant countries are defined as those in which the operations of US MNCs represent at least 02 of total firm sales (per the BEA) For significant countries the table compares their representation in terms of MNC affiliate sales in the BEA data to the distribution of firm observations in Worldscope US Foreign Affiliates (BEA) Foreign Firms (Worldscope)
Country
N (1)
Sales (millions)
(2)
Total Sales (3)
N (4)
Sales (million)
(5)
Total Sales (6)
Australia 1344 73305 056 859 255865 210 Belgium 894 52755 040 169 26136 021 Brazil 959 60245 046 1143 683167 560 Canada 2730 536767 407 1181 526750 432 China 922 75907 057 822 276541 227 France 1852 183942 139 540 105447 086 Germany 2182 275577 209 480 103888 085 Hong Kong 867 48100 036 157 34094 028 Ireland 478 36754 028 34 3467 003 Italy 1178 82116 062 111 47948 039 Japan 1494 148032 112 2281 1119223 917 Korea South 659 34500 026 3530 2752791 2255 Malaysia 386 26027 020 837 168683 138 Mexico 1405 113623 086 362 237173 194 Netherlands 1149 85105 064 47 22631 019 Singapore 1098 94917 072 139 32913 027 Spain 871 63094 048 129 37419 031 Sweden 712 32682 025 235 23090 019 Switzerland 726 60288 046 74 20242 017 Taiwan 592 30313 023 2949 787049 645 United Kingdom 3054 423934 321 1997 882423 723 United States (domicile) 4950 10460880 7923 na na na Other - Covered by WorldScope 6893 199394 151 9000 4057894 33 Other -Not Covered by WorldScope 713 5352 004 - - - Total 38108 13203609 1000 27076 12204834 100
39
Table 4 OLS regressions of firm excess values on the percent of foreign sales This table reports the results of OLS regressions of firm excess value on the percent of foreign sales (Foreign Sales) the percent of sales outside a firmrsquos primary industry (Industry Other) and control variables All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed) Dep Variable = Excess Value Coefficient t-stat Coefficient t-stat Coefficient t-stat Coefficient t-stat (1) (2) (3) (4) Foreign Sales 0349 419 0209 293 0189 266 0278 210 Industry Other -0134 -186 -0047 -072 -0053 -081 -0326 -325 Log Size 0066 634 0065 624 -0044 -096 Debt Firm Value -0196 -213 -0241 -260 -0685 -588 CAPX Sales 0901 425 0893 435 0488 227 EBIT Sales 1154 542 1170 547 0408 189 RampD Sales 2908 1242 2932 1237 1544 379 Ad Sales 1869 263 2003 280 -1696 -119 Year indicators No No Yes Yes Firm indicators No No No Yes R2 0017 0212 0226 0768 Observations 4950 4950 4950 4950
40
Table 5 Dynamic panel GMM model regressing firm excess values on the percent of foreign sales This table reports the result from a regression of the excess value on the percent of foreign sales (Foreign Sales) the percent of non-primary industry sales (Industry Other) and control variables using a dynamic panel GMM estimator All control variables are considered to be endogenous with the exception of the year indicator variables AR(1) and AR(2) are tests for first-order and second-order serial correlation in the first differenced residuals with the null hypothesis of no serial correlation The null hypothesis of the Hansen test of overidentification is that all instruments are valid The null hypothesis of the difference-in-Hansen test of exogeneity is that the instruments used for the equation in levels are exogenous All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed) Dep Variable = Excess Value Coef t-stat Excess Value (one lag) -0018 -082 Excess Value (two lags) -0048 -272 Foreign Sales 0371 270 Industry Other -0163 -171 Log Size 0010 123 Debt FirmValue -1135 -820 CAPX Sales 0590 342 EBIT Sales -0047 -300 RampD Sales 1376 531 Ad Sales -0751 -040 AR(1) test (p-value) 000 AR(2) test (p-value) 017 Hansen test of over-identification (p-value) 030 Difference-in-Hansen test of exogeneity (p-value) 093 Year indicators Yes Number of firms 1016 Number of observations 4090
41
Table 6 OLS regression of firm excess values on the percent of foreign sales controlling for a control premium This table reports the result of the OLS regression of firm excess value including firm and year fixed effects with an additional control variable to proxy for a country-level ownership control premium (Control Premium) All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed) Dep Variable = Excess Value Coef t -stat Foreign Sales 0303 224 Industry Other -0327 -325 Log Size -0045 -100 Debt FirmValue -0683 -585 CAPX Sales 0490 226 EBIT Sales 0409 189 RampD Sales 1545 379 Ad Sales -1689 -119 Control Premium -1004 -062 Year indicators Yes Firm indicators Yes R2 0768 Observations 4950
42
Table 7 OLS regression of firm excess values on the percent of foreign sales controlling for the cost of capital This table contains the results of an OLS regression of firm excess value including firm and year fixed effects with an additional control variable to proxy for a country-level cost of capital (Cost of Capital) All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed) Dep Variable = Excess Value Coef t -stat Foreign Sales 0255 178 Industry Other -0326 -324 Log Size -0043 -094 Debt FirmValue -0687 -589 CAPX Sales 0485 225 EBIT Sales 0406 188 RampD Sales 1539 379 Ad Sales -1703 -120 Cost of Capital 0004 039 Year indicators Yes Firm indicators Yes R2 0768 Observations 4950
43
Table 8 OLS regression of firm excess values on the percent of foreign sales controlling for aspects of corporate governance This table reports the results of two OLS regressions In the first column are the results of the firm excess value regression including firm and year fixed effect with five proxies for corporate governance included ndash the percent of shares owned by the CEO and the percent squared (CEO Ownership and CEO Ownership squared) the percent owned by institutional investors (Institution Ownership) whether there is a powerful CEO (Powerful CEO) and the Gompers Ishii and Metrick (2003) governance index (Governance Index) In the second column the regression includes the same sample and variables with the exception of the governance proxies which are excluded All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed)
Governance
No Governance (same sample)
Dep Variable = Excess Value Coef t-stat Coef t -stat Foreign Sales 0259 159 0251 154 Industry Other -0099 -080 -0099 -079 Log Size -0105 -185 -0102 -180 Debt FirmValue -0813 -478 -0817 -482 CAPX Sales 0440 252 0438 249 EBIT Sales 0457 247 0457 246 RampD Sales 1717 284 1716 286 Ad Sales -2210 -143 -2190 -143 CEO Ownership 0010 007
CEO Ownership squared -0029 -017 Institution Ownership 0047 067 Powerful CEO -0003 -014 Governance Index 0009 103
Year indicators Yes Yes Firm indicators Yes Yes R2 0811 0810 Observations 2550 2550
- Cover for paperpdf
- crrz_20130603_SMUpdf
-
36
(Table 1 continued) Panel C Tests of differences between US GAAP and other accounting standards
Percent different from US GAAP
Comparison to sales (p-values)
N Mean Median
Mean Median
Full Sample (2001-2011)
Sales
66 236 000
Net Income
66 10653 647
005 lt001
Assets
66 1162 126
lt001 lt001
Study Sample (2001-2008)
Sales
55 257 000
Net Income
55 10303 589
011 lt001
Assets
55 1047 099
003 lt001
37
Table 2 Descriptive statistics Panel A reports the mean and medians of Excess Value for the multinational firms and single-segment benchmark firms as well as univariate tests across the subsamples Panel B reports summary statistics for the independent variables for the multinational firms The p-values of the tests of differences in means are based on t-tests The p-values of tests of differences in the medians are for a Wilcoxon rank-sum test All variables are defined in Appendix A Panel A Excess Value by sample Multinational All Benchmark Benchmark Subgroups Sample Firms US Foreign
Value Value Test of Diff (p-value) Value
Test of Diff (p-value) Value
Test of Diff (p-value)
Mean 0067 0006 lt001 -0028 lt001 0014 lt001 Median 0076 0000 lt001 -0025 lt001 0000 lt001 N 4950 33074 5998 27076 Panel B Distribution of independent variables for the multinational firms N Mean 25th 50th 75th StDev Foreign Sales 4950 0238 0074 0190 0363 0197 Industry Other 4950 0212 0000 0166 0414 0215 Log Size 4950 6955 5823 6989 8140 1687 Debt Firm Value 4950 0136 0009 0097 0212 0145 CAPX Sales 4950 0054 0020 0033 0054 0101 EBIT Sales 4950 0065 0031 0077 0121 0138 RampD Sales 4950 0049 0000 0013 0059 0083 Ad Sales 4950 0008 0000 0000 0005 0018
38
Table 3 BEA and Worldscope samples
This table reports details on affiliate sales for significant countries pooled from 1998-2008 Significant countries are defined as those in which the operations of US MNCs represent at least 02 of total firm sales (per the BEA) For significant countries the table compares their representation in terms of MNC affiliate sales in the BEA data to the distribution of firm observations in Worldscope US Foreign Affiliates (BEA) Foreign Firms (Worldscope)
Country
N (1)
Sales (millions)
(2)
Total Sales (3)
N (4)
Sales (million)
(5)
Total Sales (6)
Australia 1344 73305 056 859 255865 210 Belgium 894 52755 040 169 26136 021 Brazil 959 60245 046 1143 683167 560 Canada 2730 536767 407 1181 526750 432 China 922 75907 057 822 276541 227 France 1852 183942 139 540 105447 086 Germany 2182 275577 209 480 103888 085 Hong Kong 867 48100 036 157 34094 028 Ireland 478 36754 028 34 3467 003 Italy 1178 82116 062 111 47948 039 Japan 1494 148032 112 2281 1119223 917 Korea South 659 34500 026 3530 2752791 2255 Malaysia 386 26027 020 837 168683 138 Mexico 1405 113623 086 362 237173 194 Netherlands 1149 85105 064 47 22631 019 Singapore 1098 94917 072 139 32913 027 Spain 871 63094 048 129 37419 031 Sweden 712 32682 025 235 23090 019 Switzerland 726 60288 046 74 20242 017 Taiwan 592 30313 023 2949 787049 645 United Kingdom 3054 423934 321 1997 882423 723 United States (domicile) 4950 10460880 7923 na na na Other - Covered by WorldScope 6893 199394 151 9000 4057894 33 Other -Not Covered by WorldScope 713 5352 004 - - - Total 38108 13203609 1000 27076 12204834 100
39
Table 4 OLS regressions of firm excess values on the percent of foreign sales This table reports the results of OLS regressions of firm excess value on the percent of foreign sales (Foreign Sales) the percent of sales outside a firmrsquos primary industry (Industry Other) and control variables All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed) Dep Variable = Excess Value Coefficient t-stat Coefficient t-stat Coefficient t-stat Coefficient t-stat (1) (2) (3) (4) Foreign Sales 0349 419 0209 293 0189 266 0278 210 Industry Other -0134 -186 -0047 -072 -0053 -081 -0326 -325 Log Size 0066 634 0065 624 -0044 -096 Debt Firm Value -0196 -213 -0241 -260 -0685 -588 CAPX Sales 0901 425 0893 435 0488 227 EBIT Sales 1154 542 1170 547 0408 189 RampD Sales 2908 1242 2932 1237 1544 379 Ad Sales 1869 263 2003 280 -1696 -119 Year indicators No No Yes Yes Firm indicators No No No Yes R2 0017 0212 0226 0768 Observations 4950 4950 4950 4950
40
Table 5 Dynamic panel GMM model regressing firm excess values on the percent of foreign sales This table reports the result from a regression of the excess value on the percent of foreign sales (Foreign Sales) the percent of non-primary industry sales (Industry Other) and control variables using a dynamic panel GMM estimator All control variables are considered to be endogenous with the exception of the year indicator variables AR(1) and AR(2) are tests for first-order and second-order serial correlation in the first differenced residuals with the null hypothesis of no serial correlation The null hypothesis of the Hansen test of overidentification is that all instruments are valid The null hypothesis of the difference-in-Hansen test of exogeneity is that the instruments used for the equation in levels are exogenous All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed) Dep Variable = Excess Value Coef t-stat Excess Value (one lag) -0018 -082 Excess Value (two lags) -0048 -272 Foreign Sales 0371 270 Industry Other -0163 -171 Log Size 0010 123 Debt FirmValue -1135 -820 CAPX Sales 0590 342 EBIT Sales -0047 -300 RampD Sales 1376 531 Ad Sales -0751 -040 AR(1) test (p-value) 000 AR(2) test (p-value) 017 Hansen test of over-identification (p-value) 030 Difference-in-Hansen test of exogeneity (p-value) 093 Year indicators Yes Number of firms 1016 Number of observations 4090
41
Table 6 OLS regression of firm excess values on the percent of foreign sales controlling for a control premium This table reports the result of the OLS regression of firm excess value including firm and year fixed effects with an additional control variable to proxy for a country-level ownership control premium (Control Premium) All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed) Dep Variable = Excess Value Coef t -stat Foreign Sales 0303 224 Industry Other -0327 -325 Log Size -0045 -100 Debt FirmValue -0683 -585 CAPX Sales 0490 226 EBIT Sales 0409 189 RampD Sales 1545 379 Ad Sales -1689 -119 Control Premium -1004 -062 Year indicators Yes Firm indicators Yes R2 0768 Observations 4950
42
Table 7 OLS regression of firm excess values on the percent of foreign sales controlling for the cost of capital This table contains the results of an OLS regression of firm excess value including firm and year fixed effects with an additional control variable to proxy for a country-level cost of capital (Cost of Capital) All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed) Dep Variable = Excess Value Coef t -stat Foreign Sales 0255 178 Industry Other -0326 -324 Log Size -0043 -094 Debt FirmValue -0687 -589 CAPX Sales 0485 225 EBIT Sales 0406 188 RampD Sales 1539 379 Ad Sales -1703 -120 Cost of Capital 0004 039 Year indicators Yes Firm indicators Yes R2 0768 Observations 4950
43
Table 8 OLS regression of firm excess values on the percent of foreign sales controlling for aspects of corporate governance This table reports the results of two OLS regressions In the first column are the results of the firm excess value regression including firm and year fixed effect with five proxies for corporate governance included ndash the percent of shares owned by the CEO and the percent squared (CEO Ownership and CEO Ownership squared) the percent owned by institutional investors (Institution Ownership) whether there is a powerful CEO (Powerful CEO) and the Gompers Ishii and Metrick (2003) governance index (Governance Index) In the second column the regression includes the same sample and variables with the exception of the governance proxies which are excluded All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed)
Governance
No Governance (same sample)
Dep Variable = Excess Value Coef t-stat Coef t -stat Foreign Sales 0259 159 0251 154 Industry Other -0099 -080 -0099 -079 Log Size -0105 -185 -0102 -180 Debt FirmValue -0813 -478 -0817 -482 CAPX Sales 0440 252 0438 249 EBIT Sales 0457 247 0457 246 RampD Sales 1717 284 1716 286 Ad Sales -2210 -143 -2190 -143 CEO Ownership 0010 007
CEO Ownership squared -0029 -017 Institution Ownership 0047 067 Powerful CEO -0003 -014 Governance Index 0009 103
Year indicators Yes Yes Firm indicators Yes Yes R2 0811 0810 Observations 2550 2550
- Cover for paperpdf
- crrz_20130603_SMUpdf
-
37
Table 2 Descriptive statistics Panel A reports the mean and medians of Excess Value for the multinational firms and single-segment benchmark firms as well as univariate tests across the subsamples Panel B reports summary statistics for the independent variables for the multinational firms The p-values of the tests of differences in means are based on t-tests The p-values of tests of differences in the medians are for a Wilcoxon rank-sum test All variables are defined in Appendix A Panel A Excess Value by sample Multinational All Benchmark Benchmark Subgroups Sample Firms US Foreign
Value Value Test of Diff (p-value) Value
Test of Diff (p-value) Value
Test of Diff (p-value)
Mean 0067 0006 lt001 -0028 lt001 0014 lt001 Median 0076 0000 lt001 -0025 lt001 0000 lt001 N 4950 33074 5998 27076 Panel B Distribution of independent variables for the multinational firms N Mean 25th 50th 75th StDev Foreign Sales 4950 0238 0074 0190 0363 0197 Industry Other 4950 0212 0000 0166 0414 0215 Log Size 4950 6955 5823 6989 8140 1687 Debt Firm Value 4950 0136 0009 0097 0212 0145 CAPX Sales 4950 0054 0020 0033 0054 0101 EBIT Sales 4950 0065 0031 0077 0121 0138 RampD Sales 4950 0049 0000 0013 0059 0083 Ad Sales 4950 0008 0000 0000 0005 0018
38
Table 3 BEA and Worldscope samples
This table reports details on affiliate sales for significant countries pooled from 1998-2008 Significant countries are defined as those in which the operations of US MNCs represent at least 02 of total firm sales (per the BEA) For significant countries the table compares their representation in terms of MNC affiliate sales in the BEA data to the distribution of firm observations in Worldscope US Foreign Affiliates (BEA) Foreign Firms (Worldscope)
Country
N (1)
Sales (millions)
(2)
Total Sales (3)
N (4)
Sales (million)
(5)
Total Sales (6)
Australia 1344 73305 056 859 255865 210 Belgium 894 52755 040 169 26136 021 Brazil 959 60245 046 1143 683167 560 Canada 2730 536767 407 1181 526750 432 China 922 75907 057 822 276541 227 France 1852 183942 139 540 105447 086 Germany 2182 275577 209 480 103888 085 Hong Kong 867 48100 036 157 34094 028 Ireland 478 36754 028 34 3467 003 Italy 1178 82116 062 111 47948 039 Japan 1494 148032 112 2281 1119223 917 Korea South 659 34500 026 3530 2752791 2255 Malaysia 386 26027 020 837 168683 138 Mexico 1405 113623 086 362 237173 194 Netherlands 1149 85105 064 47 22631 019 Singapore 1098 94917 072 139 32913 027 Spain 871 63094 048 129 37419 031 Sweden 712 32682 025 235 23090 019 Switzerland 726 60288 046 74 20242 017 Taiwan 592 30313 023 2949 787049 645 United Kingdom 3054 423934 321 1997 882423 723 United States (domicile) 4950 10460880 7923 na na na Other - Covered by WorldScope 6893 199394 151 9000 4057894 33 Other -Not Covered by WorldScope 713 5352 004 - - - Total 38108 13203609 1000 27076 12204834 100
39
Table 4 OLS regressions of firm excess values on the percent of foreign sales This table reports the results of OLS regressions of firm excess value on the percent of foreign sales (Foreign Sales) the percent of sales outside a firmrsquos primary industry (Industry Other) and control variables All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed) Dep Variable = Excess Value Coefficient t-stat Coefficient t-stat Coefficient t-stat Coefficient t-stat (1) (2) (3) (4) Foreign Sales 0349 419 0209 293 0189 266 0278 210 Industry Other -0134 -186 -0047 -072 -0053 -081 -0326 -325 Log Size 0066 634 0065 624 -0044 -096 Debt Firm Value -0196 -213 -0241 -260 -0685 -588 CAPX Sales 0901 425 0893 435 0488 227 EBIT Sales 1154 542 1170 547 0408 189 RampD Sales 2908 1242 2932 1237 1544 379 Ad Sales 1869 263 2003 280 -1696 -119 Year indicators No No Yes Yes Firm indicators No No No Yes R2 0017 0212 0226 0768 Observations 4950 4950 4950 4950
40
Table 5 Dynamic panel GMM model regressing firm excess values on the percent of foreign sales This table reports the result from a regression of the excess value on the percent of foreign sales (Foreign Sales) the percent of non-primary industry sales (Industry Other) and control variables using a dynamic panel GMM estimator All control variables are considered to be endogenous with the exception of the year indicator variables AR(1) and AR(2) are tests for first-order and second-order serial correlation in the first differenced residuals with the null hypothesis of no serial correlation The null hypothesis of the Hansen test of overidentification is that all instruments are valid The null hypothesis of the difference-in-Hansen test of exogeneity is that the instruments used for the equation in levels are exogenous All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed) Dep Variable = Excess Value Coef t-stat Excess Value (one lag) -0018 -082 Excess Value (two lags) -0048 -272 Foreign Sales 0371 270 Industry Other -0163 -171 Log Size 0010 123 Debt FirmValue -1135 -820 CAPX Sales 0590 342 EBIT Sales -0047 -300 RampD Sales 1376 531 Ad Sales -0751 -040 AR(1) test (p-value) 000 AR(2) test (p-value) 017 Hansen test of over-identification (p-value) 030 Difference-in-Hansen test of exogeneity (p-value) 093 Year indicators Yes Number of firms 1016 Number of observations 4090
41
Table 6 OLS regression of firm excess values on the percent of foreign sales controlling for a control premium This table reports the result of the OLS regression of firm excess value including firm and year fixed effects with an additional control variable to proxy for a country-level ownership control premium (Control Premium) All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed) Dep Variable = Excess Value Coef t -stat Foreign Sales 0303 224 Industry Other -0327 -325 Log Size -0045 -100 Debt FirmValue -0683 -585 CAPX Sales 0490 226 EBIT Sales 0409 189 RampD Sales 1545 379 Ad Sales -1689 -119 Control Premium -1004 -062 Year indicators Yes Firm indicators Yes R2 0768 Observations 4950
42
Table 7 OLS regression of firm excess values on the percent of foreign sales controlling for the cost of capital This table contains the results of an OLS regression of firm excess value including firm and year fixed effects with an additional control variable to proxy for a country-level cost of capital (Cost of Capital) All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed) Dep Variable = Excess Value Coef t -stat Foreign Sales 0255 178 Industry Other -0326 -324 Log Size -0043 -094 Debt FirmValue -0687 -589 CAPX Sales 0485 225 EBIT Sales 0406 188 RampD Sales 1539 379 Ad Sales -1703 -120 Cost of Capital 0004 039 Year indicators Yes Firm indicators Yes R2 0768 Observations 4950
43
Table 8 OLS regression of firm excess values on the percent of foreign sales controlling for aspects of corporate governance This table reports the results of two OLS regressions In the first column are the results of the firm excess value regression including firm and year fixed effect with five proxies for corporate governance included ndash the percent of shares owned by the CEO and the percent squared (CEO Ownership and CEO Ownership squared) the percent owned by institutional investors (Institution Ownership) whether there is a powerful CEO (Powerful CEO) and the Gompers Ishii and Metrick (2003) governance index (Governance Index) In the second column the regression includes the same sample and variables with the exception of the governance proxies which are excluded All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed)
Governance
No Governance (same sample)
Dep Variable = Excess Value Coef t-stat Coef t -stat Foreign Sales 0259 159 0251 154 Industry Other -0099 -080 -0099 -079 Log Size -0105 -185 -0102 -180 Debt FirmValue -0813 -478 -0817 -482 CAPX Sales 0440 252 0438 249 EBIT Sales 0457 247 0457 246 RampD Sales 1717 284 1716 286 Ad Sales -2210 -143 -2190 -143 CEO Ownership 0010 007
CEO Ownership squared -0029 -017 Institution Ownership 0047 067 Powerful CEO -0003 -014 Governance Index 0009 103
Year indicators Yes Yes Firm indicators Yes Yes R2 0811 0810 Observations 2550 2550
- Cover for paperpdf
- crrz_20130603_SMUpdf
-
38
Table 3 BEA and Worldscope samples
This table reports details on affiliate sales for significant countries pooled from 1998-2008 Significant countries are defined as those in which the operations of US MNCs represent at least 02 of total firm sales (per the BEA) For significant countries the table compares their representation in terms of MNC affiliate sales in the BEA data to the distribution of firm observations in Worldscope US Foreign Affiliates (BEA) Foreign Firms (Worldscope)
Country
N (1)
Sales (millions)
(2)
Total Sales (3)
N (4)
Sales (million)
(5)
Total Sales (6)
Australia 1344 73305 056 859 255865 210 Belgium 894 52755 040 169 26136 021 Brazil 959 60245 046 1143 683167 560 Canada 2730 536767 407 1181 526750 432 China 922 75907 057 822 276541 227 France 1852 183942 139 540 105447 086 Germany 2182 275577 209 480 103888 085 Hong Kong 867 48100 036 157 34094 028 Ireland 478 36754 028 34 3467 003 Italy 1178 82116 062 111 47948 039 Japan 1494 148032 112 2281 1119223 917 Korea South 659 34500 026 3530 2752791 2255 Malaysia 386 26027 020 837 168683 138 Mexico 1405 113623 086 362 237173 194 Netherlands 1149 85105 064 47 22631 019 Singapore 1098 94917 072 139 32913 027 Spain 871 63094 048 129 37419 031 Sweden 712 32682 025 235 23090 019 Switzerland 726 60288 046 74 20242 017 Taiwan 592 30313 023 2949 787049 645 United Kingdom 3054 423934 321 1997 882423 723 United States (domicile) 4950 10460880 7923 na na na Other - Covered by WorldScope 6893 199394 151 9000 4057894 33 Other -Not Covered by WorldScope 713 5352 004 - - - Total 38108 13203609 1000 27076 12204834 100
39
Table 4 OLS regressions of firm excess values on the percent of foreign sales This table reports the results of OLS regressions of firm excess value on the percent of foreign sales (Foreign Sales) the percent of sales outside a firmrsquos primary industry (Industry Other) and control variables All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed) Dep Variable = Excess Value Coefficient t-stat Coefficient t-stat Coefficient t-stat Coefficient t-stat (1) (2) (3) (4) Foreign Sales 0349 419 0209 293 0189 266 0278 210 Industry Other -0134 -186 -0047 -072 -0053 -081 -0326 -325 Log Size 0066 634 0065 624 -0044 -096 Debt Firm Value -0196 -213 -0241 -260 -0685 -588 CAPX Sales 0901 425 0893 435 0488 227 EBIT Sales 1154 542 1170 547 0408 189 RampD Sales 2908 1242 2932 1237 1544 379 Ad Sales 1869 263 2003 280 -1696 -119 Year indicators No No Yes Yes Firm indicators No No No Yes R2 0017 0212 0226 0768 Observations 4950 4950 4950 4950
40
Table 5 Dynamic panel GMM model regressing firm excess values on the percent of foreign sales This table reports the result from a regression of the excess value on the percent of foreign sales (Foreign Sales) the percent of non-primary industry sales (Industry Other) and control variables using a dynamic panel GMM estimator All control variables are considered to be endogenous with the exception of the year indicator variables AR(1) and AR(2) are tests for first-order and second-order serial correlation in the first differenced residuals with the null hypothesis of no serial correlation The null hypothesis of the Hansen test of overidentification is that all instruments are valid The null hypothesis of the difference-in-Hansen test of exogeneity is that the instruments used for the equation in levels are exogenous All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed) Dep Variable = Excess Value Coef t-stat Excess Value (one lag) -0018 -082 Excess Value (two lags) -0048 -272 Foreign Sales 0371 270 Industry Other -0163 -171 Log Size 0010 123 Debt FirmValue -1135 -820 CAPX Sales 0590 342 EBIT Sales -0047 -300 RampD Sales 1376 531 Ad Sales -0751 -040 AR(1) test (p-value) 000 AR(2) test (p-value) 017 Hansen test of over-identification (p-value) 030 Difference-in-Hansen test of exogeneity (p-value) 093 Year indicators Yes Number of firms 1016 Number of observations 4090
41
Table 6 OLS regression of firm excess values on the percent of foreign sales controlling for a control premium This table reports the result of the OLS regression of firm excess value including firm and year fixed effects with an additional control variable to proxy for a country-level ownership control premium (Control Premium) All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed) Dep Variable = Excess Value Coef t -stat Foreign Sales 0303 224 Industry Other -0327 -325 Log Size -0045 -100 Debt FirmValue -0683 -585 CAPX Sales 0490 226 EBIT Sales 0409 189 RampD Sales 1545 379 Ad Sales -1689 -119 Control Premium -1004 -062 Year indicators Yes Firm indicators Yes R2 0768 Observations 4950
42
Table 7 OLS regression of firm excess values on the percent of foreign sales controlling for the cost of capital This table contains the results of an OLS regression of firm excess value including firm and year fixed effects with an additional control variable to proxy for a country-level cost of capital (Cost of Capital) All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed) Dep Variable = Excess Value Coef t -stat Foreign Sales 0255 178 Industry Other -0326 -324 Log Size -0043 -094 Debt FirmValue -0687 -589 CAPX Sales 0485 225 EBIT Sales 0406 188 RampD Sales 1539 379 Ad Sales -1703 -120 Cost of Capital 0004 039 Year indicators Yes Firm indicators Yes R2 0768 Observations 4950
43
Table 8 OLS regression of firm excess values on the percent of foreign sales controlling for aspects of corporate governance This table reports the results of two OLS regressions In the first column are the results of the firm excess value regression including firm and year fixed effect with five proxies for corporate governance included ndash the percent of shares owned by the CEO and the percent squared (CEO Ownership and CEO Ownership squared) the percent owned by institutional investors (Institution Ownership) whether there is a powerful CEO (Powerful CEO) and the Gompers Ishii and Metrick (2003) governance index (Governance Index) In the second column the regression includes the same sample and variables with the exception of the governance proxies which are excluded All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed)
Governance
No Governance (same sample)
Dep Variable = Excess Value Coef t-stat Coef t -stat Foreign Sales 0259 159 0251 154 Industry Other -0099 -080 -0099 -079 Log Size -0105 -185 -0102 -180 Debt FirmValue -0813 -478 -0817 -482 CAPX Sales 0440 252 0438 249 EBIT Sales 0457 247 0457 246 RampD Sales 1717 284 1716 286 Ad Sales -2210 -143 -2190 -143 CEO Ownership 0010 007
CEO Ownership squared -0029 -017 Institution Ownership 0047 067 Powerful CEO -0003 -014 Governance Index 0009 103
Year indicators Yes Yes Firm indicators Yes Yes R2 0811 0810 Observations 2550 2550
- Cover for paperpdf
- crrz_20130603_SMUpdf
-
39
Table 4 OLS regressions of firm excess values on the percent of foreign sales This table reports the results of OLS regressions of firm excess value on the percent of foreign sales (Foreign Sales) the percent of sales outside a firmrsquos primary industry (Industry Other) and control variables All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed) Dep Variable = Excess Value Coefficient t-stat Coefficient t-stat Coefficient t-stat Coefficient t-stat (1) (2) (3) (4) Foreign Sales 0349 419 0209 293 0189 266 0278 210 Industry Other -0134 -186 -0047 -072 -0053 -081 -0326 -325 Log Size 0066 634 0065 624 -0044 -096 Debt Firm Value -0196 -213 -0241 -260 -0685 -588 CAPX Sales 0901 425 0893 435 0488 227 EBIT Sales 1154 542 1170 547 0408 189 RampD Sales 2908 1242 2932 1237 1544 379 Ad Sales 1869 263 2003 280 -1696 -119 Year indicators No No Yes Yes Firm indicators No No No Yes R2 0017 0212 0226 0768 Observations 4950 4950 4950 4950
40
Table 5 Dynamic panel GMM model regressing firm excess values on the percent of foreign sales This table reports the result from a regression of the excess value on the percent of foreign sales (Foreign Sales) the percent of non-primary industry sales (Industry Other) and control variables using a dynamic panel GMM estimator All control variables are considered to be endogenous with the exception of the year indicator variables AR(1) and AR(2) are tests for first-order and second-order serial correlation in the first differenced residuals with the null hypothesis of no serial correlation The null hypothesis of the Hansen test of overidentification is that all instruments are valid The null hypothesis of the difference-in-Hansen test of exogeneity is that the instruments used for the equation in levels are exogenous All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed) Dep Variable = Excess Value Coef t-stat Excess Value (one lag) -0018 -082 Excess Value (two lags) -0048 -272 Foreign Sales 0371 270 Industry Other -0163 -171 Log Size 0010 123 Debt FirmValue -1135 -820 CAPX Sales 0590 342 EBIT Sales -0047 -300 RampD Sales 1376 531 Ad Sales -0751 -040 AR(1) test (p-value) 000 AR(2) test (p-value) 017 Hansen test of over-identification (p-value) 030 Difference-in-Hansen test of exogeneity (p-value) 093 Year indicators Yes Number of firms 1016 Number of observations 4090
41
Table 6 OLS regression of firm excess values on the percent of foreign sales controlling for a control premium This table reports the result of the OLS regression of firm excess value including firm and year fixed effects with an additional control variable to proxy for a country-level ownership control premium (Control Premium) All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed) Dep Variable = Excess Value Coef t -stat Foreign Sales 0303 224 Industry Other -0327 -325 Log Size -0045 -100 Debt FirmValue -0683 -585 CAPX Sales 0490 226 EBIT Sales 0409 189 RampD Sales 1545 379 Ad Sales -1689 -119 Control Premium -1004 -062 Year indicators Yes Firm indicators Yes R2 0768 Observations 4950
42
Table 7 OLS regression of firm excess values on the percent of foreign sales controlling for the cost of capital This table contains the results of an OLS regression of firm excess value including firm and year fixed effects with an additional control variable to proxy for a country-level cost of capital (Cost of Capital) All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed) Dep Variable = Excess Value Coef t -stat Foreign Sales 0255 178 Industry Other -0326 -324 Log Size -0043 -094 Debt FirmValue -0687 -589 CAPX Sales 0485 225 EBIT Sales 0406 188 RampD Sales 1539 379 Ad Sales -1703 -120 Cost of Capital 0004 039 Year indicators Yes Firm indicators Yes R2 0768 Observations 4950
43
Table 8 OLS regression of firm excess values on the percent of foreign sales controlling for aspects of corporate governance This table reports the results of two OLS regressions In the first column are the results of the firm excess value regression including firm and year fixed effect with five proxies for corporate governance included ndash the percent of shares owned by the CEO and the percent squared (CEO Ownership and CEO Ownership squared) the percent owned by institutional investors (Institution Ownership) whether there is a powerful CEO (Powerful CEO) and the Gompers Ishii and Metrick (2003) governance index (Governance Index) In the second column the regression includes the same sample and variables with the exception of the governance proxies which are excluded All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed)
Governance
No Governance (same sample)
Dep Variable = Excess Value Coef t-stat Coef t -stat Foreign Sales 0259 159 0251 154 Industry Other -0099 -080 -0099 -079 Log Size -0105 -185 -0102 -180 Debt FirmValue -0813 -478 -0817 -482 CAPX Sales 0440 252 0438 249 EBIT Sales 0457 247 0457 246 RampD Sales 1717 284 1716 286 Ad Sales -2210 -143 -2190 -143 CEO Ownership 0010 007
CEO Ownership squared -0029 -017 Institution Ownership 0047 067 Powerful CEO -0003 -014 Governance Index 0009 103
Year indicators Yes Yes Firm indicators Yes Yes R2 0811 0810 Observations 2550 2550
- Cover for paperpdf
- crrz_20130603_SMUpdf
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40
Table 5 Dynamic panel GMM model regressing firm excess values on the percent of foreign sales This table reports the result from a regression of the excess value on the percent of foreign sales (Foreign Sales) the percent of non-primary industry sales (Industry Other) and control variables using a dynamic panel GMM estimator All control variables are considered to be endogenous with the exception of the year indicator variables AR(1) and AR(2) are tests for first-order and second-order serial correlation in the first differenced residuals with the null hypothesis of no serial correlation The null hypothesis of the Hansen test of overidentification is that all instruments are valid The null hypothesis of the difference-in-Hansen test of exogeneity is that the instruments used for the equation in levels are exogenous All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed) Dep Variable = Excess Value Coef t-stat Excess Value (one lag) -0018 -082 Excess Value (two lags) -0048 -272 Foreign Sales 0371 270 Industry Other -0163 -171 Log Size 0010 123 Debt FirmValue -1135 -820 CAPX Sales 0590 342 EBIT Sales -0047 -300 RampD Sales 1376 531 Ad Sales -0751 -040 AR(1) test (p-value) 000 AR(2) test (p-value) 017 Hansen test of over-identification (p-value) 030 Difference-in-Hansen test of exogeneity (p-value) 093 Year indicators Yes Number of firms 1016 Number of observations 4090
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Table 6 OLS regression of firm excess values on the percent of foreign sales controlling for a control premium This table reports the result of the OLS regression of firm excess value including firm and year fixed effects with an additional control variable to proxy for a country-level ownership control premium (Control Premium) All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed) Dep Variable = Excess Value Coef t -stat Foreign Sales 0303 224 Industry Other -0327 -325 Log Size -0045 -100 Debt FirmValue -0683 -585 CAPX Sales 0490 226 EBIT Sales 0409 189 RampD Sales 1545 379 Ad Sales -1689 -119 Control Premium -1004 -062 Year indicators Yes Firm indicators Yes R2 0768 Observations 4950
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Table 7 OLS regression of firm excess values on the percent of foreign sales controlling for the cost of capital This table contains the results of an OLS regression of firm excess value including firm and year fixed effects with an additional control variable to proxy for a country-level cost of capital (Cost of Capital) All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed) Dep Variable = Excess Value Coef t -stat Foreign Sales 0255 178 Industry Other -0326 -324 Log Size -0043 -094 Debt FirmValue -0687 -589 CAPX Sales 0485 225 EBIT Sales 0406 188 RampD Sales 1539 379 Ad Sales -1703 -120 Cost of Capital 0004 039 Year indicators Yes Firm indicators Yes R2 0768 Observations 4950
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Table 8 OLS regression of firm excess values on the percent of foreign sales controlling for aspects of corporate governance This table reports the results of two OLS regressions In the first column are the results of the firm excess value regression including firm and year fixed effect with five proxies for corporate governance included ndash the percent of shares owned by the CEO and the percent squared (CEO Ownership and CEO Ownership squared) the percent owned by institutional investors (Institution Ownership) whether there is a powerful CEO (Powerful CEO) and the Gompers Ishii and Metrick (2003) governance index (Governance Index) In the second column the regression includes the same sample and variables with the exception of the governance proxies which are excluded All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed)
Governance
No Governance (same sample)
Dep Variable = Excess Value Coef t-stat Coef t -stat Foreign Sales 0259 159 0251 154 Industry Other -0099 -080 -0099 -079 Log Size -0105 -185 -0102 -180 Debt FirmValue -0813 -478 -0817 -482 CAPX Sales 0440 252 0438 249 EBIT Sales 0457 247 0457 246 RampD Sales 1717 284 1716 286 Ad Sales -2210 -143 -2190 -143 CEO Ownership 0010 007
CEO Ownership squared -0029 -017 Institution Ownership 0047 067 Powerful CEO -0003 -014 Governance Index 0009 103
Year indicators Yes Yes Firm indicators Yes Yes R2 0811 0810 Observations 2550 2550
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Table 6 OLS regression of firm excess values on the percent of foreign sales controlling for a control premium This table reports the result of the OLS regression of firm excess value including firm and year fixed effects with an additional control variable to proxy for a country-level ownership control premium (Control Premium) All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed) Dep Variable = Excess Value Coef t -stat Foreign Sales 0303 224 Industry Other -0327 -325 Log Size -0045 -100 Debt FirmValue -0683 -585 CAPX Sales 0490 226 EBIT Sales 0409 189 RampD Sales 1545 379 Ad Sales -1689 -119 Control Premium -1004 -062 Year indicators Yes Firm indicators Yes R2 0768 Observations 4950
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Table 7 OLS regression of firm excess values on the percent of foreign sales controlling for the cost of capital This table contains the results of an OLS regression of firm excess value including firm and year fixed effects with an additional control variable to proxy for a country-level cost of capital (Cost of Capital) All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed) Dep Variable = Excess Value Coef t -stat Foreign Sales 0255 178 Industry Other -0326 -324 Log Size -0043 -094 Debt FirmValue -0687 -589 CAPX Sales 0485 225 EBIT Sales 0406 188 RampD Sales 1539 379 Ad Sales -1703 -120 Cost of Capital 0004 039 Year indicators Yes Firm indicators Yes R2 0768 Observations 4950
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Table 8 OLS regression of firm excess values on the percent of foreign sales controlling for aspects of corporate governance This table reports the results of two OLS regressions In the first column are the results of the firm excess value regression including firm and year fixed effect with five proxies for corporate governance included ndash the percent of shares owned by the CEO and the percent squared (CEO Ownership and CEO Ownership squared) the percent owned by institutional investors (Institution Ownership) whether there is a powerful CEO (Powerful CEO) and the Gompers Ishii and Metrick (2003) governance index (Governance Index) In the second column the regression includes the same sample and variables with the exception of the governance proxies which are excluded All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed)
Governance
No Governance (same sample)
Dep Variable = Excess Value Coef t-stat Coef t -stat Foreign Sales 0259 159 0251 154 Industry Other -0099 -080 -0099 -079 Log Size -0105 -185 -0102 -180 Debt FirmValue -0813 -478 -0817 -482 CAPX Sales 0440 252 0438 249 EBIT Sales 0457 247 0457 246 RampD Sales 1717 284 1716 286 Ad Sales -2210 -143 -2190 -143 CEO Ownership 0010 007
CEO Ownership squared -0029 -017 Institution Ownership 0047 067 Powerful CEO -0003 -014 Governance Index 0009 103
Year indicators Yes Yes Firm indicators Yes Yes R2 0811 0810 Observations 2550 2550
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Table 7 OLS regression of firm excess values on the percent of foreign sales controlling for the cost of capital This table contains the results of an OLS regression of firm excess value including firm and year fixed effects with an additional control variable to proxy for a country-level cost of capital (Cost of Capital) All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed) Dep Variable = Excess Value Coef t -stat Foreign Sales 0255 178 Industry Other -0326 -324 Log Size -0043 -094 Debt FirmValue -0687 -589 CAPX Sales 0485 225 EBIT Sales 0406 188 RampD Sales 1539 379 Ad Sales -1703 -120 Cost of Capital 0004 039 Year indicators Yes Firm indicators Yes R2 0768 Observations 4950
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Table 8 OLS regression of firm excess values on the percent of foreign sales controlling for aspects of corporate governance This table reports the results of two OLS regressions In the first column are the results of the firm excess value regression including firm and year fixed effect with five proxies for corporate governance included ndash the percent of shares owned by the CEO and the percent squared (CEO Ownership and CEO Ownership squared) the percent owned by institutional investors (Institution Ownership) whether there is a powerful CEO (Powerful CEO) and the Gompers Ishii and Metrick (2003) governance index (Governance Index) In the second column the regression includes the same sample and variables with the exception of the governance proxies which are excluded All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed)
Governance
No Governance (same sample)
Dep Variable = Excess Value Coef t-stat Coef t -stat Foreign Sales 0259 159 0251 154 Industry Other -0099 -080 -0099 -079 Log Size -0105 -185 -0102 -180 Debt FirmValue -0813 -478 -0817 -482 CAPX Sales 0440 252 0438 249 EBIT Sales 0457 247 0457 246 RampD Sales 1717 284 1716 286 Ad Sales -2210 -143 -2190 -143 CEO Ownership 0010 007
CEO Ownership squared -0029 -017 Institution Ownership 0047 067 Powerful CEO -0003 -014 Governance Index 0009 103
Year indicators Yes Yes Firm indicators Yes Yes R2 0811 0810 Observations 2550 2550
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Table 8 OLS regression of firm excess values on the percent of foreign sales controlling for aspects of corporate governance This table reports the results of two OLS regressions In the first column are the results of the firm excess value regression including firm and year fixed effect with five proxies for corporate governance included ndash the percent of shares owned by the CEO and the percent squared (CEO Ownership and CEO Ownership squared) the percent owned by institutional investors (Institution Ownership) whether there is a powerful CEO (Powerful CEO) and the Gompers Ishii and Metrick (2003) governance index (Governance Index) In the second column the regression includes the same sample and variables with the exception of the governance proxies which are excluded All variables are defined in Appendix A T-statistics and significance levels are computed using clustered standard errors with firm level clustering Significance levels are indicated by and representing 1 5 and 10 levels respectively (2-tailed)
Governance
No Governance (same sample)
Dep Variable = Excess Value Coef t-stat Coef t -stat Foreign Sales 0259 159 0251 154 Industry Other -0099 -080 -0099 -079 Log Size -0105 -185 -0102 -180 Debt FirmValue -0813 -478 -0817 -482 CAPX Sales 0440 252 0438 249 EBIT Sales 0457 247 0457 246 RampD Sales 1717 284 1716 286 Ad Sales -2210 -143 -2190 -143 CEO Ownership 0010 007
CEO Ownership squared -0029 -017 Institution Ownership 0047 067 Powerful CEO -0003 -014 Governance Index 0009 103
Year indicators Yes Yes Firm indicators Yes Yes R2 0811 0810 Observations 2550 2550
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