Capital Allocation and Timely Accounting Recognition of Economic Losses: International Evidence
Robert M. Bushman The University of North Carolina at Chapel Hill
Kenan-Flagler Business School Chapel Hill, North Carolina 27599
Joseph D. Piotroski∗The University of Chicago
Graduate School of Business Chicago, Illinois 60637
Abbie J. Smith The University of Chicago
Graduate School of Business Chicago, Illinois 60637
Current draft: 18 July 2005
∗ Corresponding author. We appreciate the useful comments of Phil Berger, an anonymous referee, and seminar participants at Chinese University of Hong Kong, Cornell University and New York University’s Accounting Summer Camp. We also appreciate the financial support of the Kenan-Flagler Business School, University of North Carolina at Chapel Hill, and the Graduate School of Business at the University of Chicago. We also appreciate the support of the William Ladany Faculty Research Fund at the Graduate School of Business, the University of Chicago.
Capital Allocation and Timely Accounting Recognition of Economic Losses: International Evidence
Abstract Does the efficiency of firms’ investment decisions around the world vary with financial accounting regimes? We investigate whether firms in countries with accounting regimes characterized by more timely accounting recognition of economic losses (TLR) respond more quickly to declining investment opportunities by reducing flows of capital to new investments and withdrawing capital from losing projects relatively faster than firms in countries with less timely loss recognition. We document a positive cross-country relation between the relative speed with which managers’ investments respond to declining investment opportunities and TLR. These results are robust to alternative estimates of TLR, alternative estimates of responses of investment to changing investment opportunities, and controls for financial development, per capita wealth, investor rights, state ownership of enterprise, and stock market synchronicity. The role of TLR in disciplining over-investment appears to be more pronounced in countries characterized by more diffuse ownership of firms, suggesting a substitution effect across alternative governance mechanisms.
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1. Introduction
A fundamental factor in wealth creation in an economy is the efficiency with which
capital is allocated to investment opportunities. Efficiency is a function of the extent to which
firms’ managers allocate capital to positive net present value (NPV) projects, avoid negative
NPV projects, and promptly withdraw capital from projects discovered to be losers at some point
after project initiation. Economic theories posit that formal financial markets and associated
institutions improve the capital allocation process and thus contribute to economic growth.1 One
key economic institution associated with capital markets is the financial accounting system.
How the efficiency of corporate investment decisions around the world varies with
properties of financial accounting information is an important, open question. Financial
accounting information can facilitate efficient resource allocation decisions by signaling
changing investment opportunities to managers and outside investors, disciplining self-interested
managers to maximize value, and reducing firms’ cost of capital.2
Wurgler (2000) directly examines relations between aspects of countries’ financial
development and the efficiency of firms’ investment decisions, including the possibility of over-
investment by poorly disciplined managers facing declining opportunities. Motivated by the
agency conflict considered in Jensen’s (1986) free cash flow theory, Wurgler (2000) predicts that
managers in countries with relatively weak investor rights over-invest in deteriorating industries,
as evidenced by a sluggish response of investment to a deterioration in investment opportunities
relative to the response to an improvement in investment opportunities. He finds that weak
1 Such theories include, among others, that efficient market prices help investors distinguish good investments from bad ones, that lenders and intermediaries screen out bad projects (e.g., Diamond (1984)), that pressures from external investors, as well as managerial ownership, encourage managers to pursue value-maximizing investment policies (Jensen (1986)), and that effective laws protecting minority investors facilitate the flow of finance to good projects (La Porta et al. (1997)). 2 See Bushman and Smith (2001). Also Kanodia (1980) and Kanodia and Lee (1998) for interesting models of the relation between investment decisions and publicly available accounting information.
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investor rights are associated with his country-level estimates of overinvestment in declining
industries, interpreted as support for Jensen’s free cash flow theory.
Given the significant international variation in the severity of the control problem
documented in Wurgler (2000), the question arises as to whether accounting regimes play a role
in disciplining against overinvestment in declining sectors, after controlling for other aspects of
financial development examined in previous research. In this paper we investigate whether firms
in countries with accounting regimes characterized by more timely accounting recognition of
economic losses respond more quickly to a deterioration in investment opportunities by reducing
the flow of capital to new investments and by withdrawing capital from losing projects faster
than firms in countries with less timely loss recognition.3
Our interest in whether timely loss recognition (TLR) limits overinvestment in activities
with deteriorating opportunities is motivated by arguments articulated most completely in Ball
(2001) and Ball and Shivakumar (2005). First, if managers know ex ante that losses will be
recognized during their tenure, they are less likely to make negative-NPV investments if they
cannot defer loss recognition to later periods and dump the negative earnings consequences of
bad investments on subsequent generations of managers. Second, managers are more inclined to
abandon bad investments quickly if firms are required to reveal economic losses at the same time
expectations are revised downward. Third, TLR provides lenders with more timely signals of
deteriorating performance through the tightening of covenants and/or triggering covenant
violations. 4 This gives lenders the option to impose contractual restrictions on covenant
3 As discussed in detail below, we are not able to completely disentangle these two distinct responses to a deterioration in investment opportunities. In some of our specifications, investment is measured as new investment flow net of sales of old assets, while in others we measure investment as new investment flow only. 4 Several papers empirically examine the efficiency gains from accounting conservatism in the debt contracting process. See for example, Ahmed et al. (2002), Zhang (2004), and Ball, Robin and Sadka (2005). Also, Beatty and Weber (2002) report that performance pricing, under which interest rates vary inversely with accounting
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violators and more quickly transfers decision rights from loss-making managers to lenders,
allowing earlier intervention by lenders. The prospects of this intervention by lenders can
discipline managers to both avoid ex ante negative NPV investments and to more quickly
abandon investments determined ex post to be negative NPV.
Our results support the hypothesized positive cross-country relation between the relative
speed with which managers’ investments respond to deteriorating investment opportunities and
timely loss recognition practices. These results are robust to alternative measures of TLR,
alternative measures of the speed with which managers’ investments respond to deteriorating
investment opportunities relative to the speed of investment response to growth opportunities,
and controls for financial development, per capita wealth, investors rights, state ownership of
enterprise, and the total amount of information impounded in a country’s stock prices as
measured (inversely) by the synchronicity of the country’s stock price movements. We also find
that the disciplining role of TLR is more pronounced in countries characterized by more diffuse
ownership of firms, suggesting a substitution effect across alternative governance mechanisms.
While the associations documented in our analyses are consistent with TLR playing a
role in disciplining managers’ exit decisions, we have not established causal relations. As shown
in Ball, Kothari and Robin (2000), Bushman and Piotroski (2005), and Ball, Robin, and Sadka
(2005), TLR is influenced by key aspects of a country’s institutional structure. However,
whether our results are caused by TLR or the underlying institutions that create a demand for or
facilitate the supply of TLR reporting behavior, it is indeed interesting that TLR appears as part
of equilibrium outcomes associated with timely exit from losing projects. Moreover, ex ante our
empirical design has the potential to cast meaningful doubt on the empirical validity of the
performance measures, is a new feature of US debt contracts. While this provides incentives for timely recognition of economic gains, we are not aware of this being practiced widely outside the U.S. during our sample period.
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theory that timely recognition of economic losses decreases the ex ante likelihood that managers
undertake negative-NPV projects and increases the incentive of current managers to abandon
investments and strategies that have ex post negative NPVs.
The TLR-investment relation documented here is the first empirical evidence of which
we are aware of how resource allocation decisions by firms around the world vary with
properties of income measurement. This represents an early step in pursuing a research agenda
outlined in Bushman and Smith (2001) investigating the relation between economic performance
and financial accounting information through the governance and other channels. Although our
results do not establish causality, they are consistent with predicted first-order economic effects
of variations in financial accounting information around the world.
The remainder of the paper is organized as follows. Section 2 discusses our hypothesis,
presents a conceptual framework, and relates our study to the prior literature. Section 3
describes the data, the sample, and the research design. Section 4 presents empirical results on
associations between timely loss recognition and the relative speed with which managers respond
to deteriorating investment opportunities. Section 5 provides a summary, conclusions, and a
discussion of future related research opportunities.
2. Conceptual framework and prior literature
In this section, we describe the conceptual framework underlying our hypotheses and
relate our study to the extant literature. Our main hypothesis can be stated as:
Hypothesis: Firms in countries characterized by more timely accounting recognition of economic losses respond more quickly to a deterioration in investment opportunities by reducing the flow of capital to new investments and by withdrawing capital from losing projects ( than firms in countries with less timely loss recognition practices).
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The importance of efficient exit for productivity and economic vitality in an economy has
long been recognized, expressed eloquently in Schumpeter’s (1942) notion of creative
destruction. Jensen (2000), for example, emphasizes the importance of devising control systems
to effectively deal with the requirement for exit and downsizing in the face of declining costs,
increasing average (but decreasing marginal) productivity of labor, reduced growth rates of labor
income, and excess capacity. But why are managers reluctant to exit losing projects and why at
times do they pursue negative NPV projects?
While no definitive answer exists, a number of potential explanations have been offered
in the economics, psychology and organizational behavior literatures. These include, among
others, perquisite consumption (Jensen and Meckling (1976)), free cash flow problems (Jensen
(1986)), pain avoidance (Jensen (1994)), principal-agent problems (Holmstrom (1979)),
signaling (Spence (1974)) the sunk cost phenomenon and escalation of commitment (e.g., Staw
(1981), Kanodia, Bushman and Dickhaut (1989), Heath (1995), Prendergast and Stole (1996),
and Camerer and Weber (1999)). For example, “escalation of commitment" is said to occur when
managers who have committed resources to a project are inclined to "throw good money after
bad" and maintain or increase their commitment to the project, even when its marginal costs
exceed marginal benefits (Camerer and Weber (1999)). An additional potential explanation is
that agendas other than economic efficiency, such as full and stable employment practices, take
precedent resulting from political, regulatory, or other pressures within specific economies (See
Rajan and Zingales (2003)).
We do not attempt to distinguish among these theories, treating the reluctance of
managers to exit losing projects and a tendency to pursue negative NPV projects as inherent
problems faced by firms characterized by a separation of ownership and control. We also
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recognize that accounting is only one of many potential mechanisms useful for aligning the
interests of managers with investors and other key stakeholders (see Shleifer and Vishny (1997)
and Bushman and Smith (2001) for extensive reviews of the governance literature). As
discussed in section 4.4, we allow for alternative mechanisms by investigating the potential
substitution of concentrated ownership structure for TLR in disciplining managers’ responses to
declining investment opportunities.
The persistent influence of conservatism on accounting practice suggests that it confers
benefits to economic agents who use, prepare or regulate financial reports.5 A number of
researchers (e.g., Watts and Zimmerman (1986), Basu (1997), Ball (2001) and Watts (2003))
argue that conservative accounting is part of efficient contracting design useful for constraining
opportunistic behavior by managers, and that timely incorporation of losses in accounting
income leads managers to address losses more quickly, and allows debt covenants and dividend
restrictions to bind more quickly.6 Ball (2001) argues that the use of financial statement
information in debt agreements and in corporate governance creates a demand for recognizing
and reporting economic losses in a timely fashion. Because lenders and borrowers contract on
the financial reports through accounting-based covenants, timely recognition of losses in
accounting reports enable lenders to receive more timely signals of deteriorating performance
5 For example, Basu (1997) notes that conditional conservatism has influenced accounting practice for at least six hundred years, and Sterling (1970, p. 256) stresses the highly influential impact of conservatism on the principles of valuation in accounting. Unconditional conservatism is defined as an accounting bias toward reporting low book values of stockholders equity (and hence, if clean surplus accounting is being followed, low average net incomes). Conditional conservatism is an equivalent bias conditional on firms experiencing contemporaneous economic losses, expressed in Basu (1997), as the “tendency to require a higher degree of verification for recognizing good news than bad news in financial statements.” It is the conditional form of conservatism (timely loss recognition), the focus of our paper, that has the potential to improve contracting efficiency, as bias associated with unconditional conservatism can be unwound by market participants. Ball and Shivakumar (2005) discuss this at length. 6 There is also a literature that studies aspects of conservatism within formal principal-agent settings. These include Antle and Lambert (1988), Kwon et al. (2001), Reichelstein (1997), Gigler and Hemmer (2001) and Dutta and Reichelstein (2005).
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through the tightening of covenants or triggering of covenant violations. As such, timely loss
recognition accelerates the transfer of decision rights to debt holders, and thus promotes early
intervention behavior.
Ball (2001) also argues that corporate governance gives rise to a similar demand for
accounting asymmetry. If managers are reluctant to exit losing projects and at times pursue
negative NPV projects (from the shareholders’ perspective), this behavior potentially can be
mitigated by accounting rules that quickly identify economic losses and charge them against
reported income. If economic losses are charged against income, there is no incremental income
penalty to actual abandonment, reducing the incentives of managers to prolong losing
investments and strategies. Managers agree to an accounting system that incorporates economic
losses in a timely fashion as it allows them to bond themselves ex ante to act more in the
interests of the owners of the firm, making their employment contract more valuable. Ball
(2004) illustrates this phenomenon in a case study of Daimler-Benz, arguing that by listing its
stock on the New York Stock Exchange, Daimler bonded itself to publicly report its economic
losses in a timely fashion. Following this listing, Ball notes that Daimler drastically reduced its
workforce, closed several plants, and discarded several loss-making businesses.
Our research design exploits significant cross-country differences in economic efficiency
and accounting regimes to study connections between timely loss recognition practices and the
efficiency of capital allocation. These relations are posited to occur through TLR’s promotion of
the timely withdraw of capital from industries experiencing a deterioration in investment
opportunities. However, it is important to clarify that our emphasis on TLR disciplining
managers’ responses to negative changes in investment opportunities does not imply that there
are no incentive problems associated with managers’ responses to increases in investment
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opportunities. The fact that managers under-invest through the avoidance of positive NPV
projects has been widely discussed in the literature, and can derive, for example, from
asymmetric information (e.g., Myers (1977)), risk aversion (e.g., Holmstrom (1979)), and
differences in time horizons and discount rates (e.g., Reichelstein (1997)). We do not conjecture
a role for TLR in alleviating under-reactions to positive shocks to investment opportunities (this
ultimately is borne out by empirical evidence in section 4), but recognize that additional
investment problems exist. Our empirical design attempts to control for country characteristics
that impact firms’ responses to increases in investment opportunities, those that impact responses
to negative changes in investment opportunities in addition to TLR, and for omitted variables
that impact responses to both positive and negative changes in investment opportunities.
Finally, we recognize that our cross-country research design will not be able to
demonstrate causality. There is a large literature in economics examining the influence of legal
and political institutions on the efficient flows of capital in an economy and resultant rates of
economic growth (see Levine (1997) for an interesting synthesis of this literature). Similarly,
Ball, Kothari and Robin (2000), Ball, Robin and Wu (2003) and Bushman and Piotroski (2005),
among others, examine the impact of legal and political differences on equilibrium levels of
accounting conservatism and timely loss recognition practices, and find that many of the same
institutions that promote economic growth also facilitate the demand for conservative financial
reports. As such, despite our efforts to control for the institutional structure of the economy, we
cannot unequivocally attribute any observed investment effects to financial reporting system per
se.
We turn now to our empirical analysis.
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3. Data and research design
The primary objective of this paper is to identify whether a relation exists between timely
loss recognition accounting practices and capital allocation at the economy level. To address this
question, we measure four proxies for timely loss recognition at the country level, and focus on
one dimension of capital allocation that is likely to be influenced by timely loss recognition
practices: the elasticity of investment to value added.
3.1 Measurement of timely loss recognition in accounting earnings
To measure and classify each country’s accounting regime along the dimension of timely
loss recognition, notated generically as TLRk, we construct variables derived from two separate
methodologies: the non-linear earnings-return model of Ball, Kothari and Robin (2000) and Basu
(1997), and the non-linear accruals-cash flow model of Ball and Shivakumar (2005). Our four
country-level timely loss recognition attributes are estimated for 38 countries with sufficient
firm-level accounting and returns data on Global Vantage over the period 1992 to 2001 to
estimate the respective model. The methodologies for estimating each country’s accounting
properties are outlined below; the resultant country-level estimates of timely loss recognition
(TLRk) are presented in Appendix 2.7
3.1.1 Measuring time loss recognition practices: Data and variable definitions
Our measures of timely loss recognition practices are estimated using observable returns
and accounting realizations. As such, these measures will reflect realized reporting practices in a
country, not strictly the effect of the country’s accounting standards per se. Accounting income
7 Our timely-loss recognition variables are the same country-level estimates of bad news sensitivity and incremental bad news sensitivity of earnings and accruals estimated and reported in Bushman and Piotroski (2005).
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and dividends data are gathered from the Global Vantage Industrial / Commercial (IC) files.
Stock price data are gathered from the Global Vantage Issues file. Consistent with prior
international research (e.g., Ball, Robin and Kothari (2000); Ball Robin and Wu (2003);
Bushman and Piotroski (2005)), accounting earnings (NIi,t) is defined as net income before
extraordinary items (IC data 32), and dividends (DIVi,t) is defined as dividends paid (IC data
36).8 Operating cash flow (CFOi,t) is measured as net income plus depreciation (IC data 11),
increases in current operating liabilities (IC data 104 minus IC data 94) and decreases in current
operating assets (IC data 75 minus IC data 60). Operating accruals (ACCRUALSi,t) are
measured as the difference between NIi,t and CFOi,t. All accounting variables (in local currency)
are scaled by the beginning market value of equity (in local currency), defined as price times
number of shares outstanding, adjusted for stock splits and dividends using the Global
Advantage adjustment factor. Stock return (RETi,t) is the holding period return, including
dividends, over the firm’s fiscal year. We assign firm-year observations to countries based on
the ISO country-code of incorporation.
3.1.2 Country-level measures of timely loss recognition: Ball, Kothari and Robin (2000)
earnings-return estimations
Prior research (e.g., Ball, Kothari and Robin (2000); Pope and Walker (1999)) suggests
the timeliness of loss recognition in a country (i.e., bad news sensitivity of earnings), as well as
the incremental timeliness of loss recognition relative to timeliness of gain recognition in a
country, can be estimated using coefficients from the following cross-sectional Basu (1997)
model:
8 The primary results are robust to TLR measures estimated using net income after extraordinary items in lieu of net income before extraordinary items (see Pope and Walker (1999)).
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NIi,t = α+ β1NEGi,t + β2RETi,t + β3NEGi,t*RETi,t + εi,t (1)
where NIi,t is annual earnings, RETi,t is the annual holding period stock return over the firm’s
fiscal year, and NEGi,t is an indicator variable equal to one if RETi,t is less than zero, zero
otherwise. In this non-linear earnings-return framework, the timeliness of loss recognition in
earnings is equal to sum of estimated coefficients β2 + β3, while the incremental timeliness of
loss recognition (i.e., frequently conceptualized as one dimension of conservative accounting
practices) is measured by the estimated coefficient β3.
We estimate equation (1), by country, using pooled, cross-sectional data over the time
period 1992 to 2001 for all countries with at least 100 observations over this time period. Our
first measure of timely loss recognition, BKR_TIMEk, is defined as the sum of estimated
coefficients β2+ β3 from a country-level estimation of equation (1). Our second measure of
timely loss recognition is the incremental timeliness of loss recognition, BKR_INCRk, defined as
the estimated coefficient β3 from a respective country-level estimation of equation (1).
To implement the non-linear earnings-return model, we followed the sample selection guidelines
established in Ball, Robin and Wu (2003), as applied in Bushman and Piotroski (2005). First, we
exclude the extreme percentiles of each model variable (RETi,t, NIi,t) when estimating our
timely-loss recognition measures. Second, we exclude all countries with less than 100 firm-year
observations over the ten years. Third, we exclude China and Poland because these two
countries have a socialist legal origin, and we exclude Bermuda and Cayman Islands because we
lack various institutional or accounting variables for these three countries. This results in
estimates of BKR_TIMEk and BKR_INCRk for 38 countries.
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3.2.2 Country-level measures of timely loss recognition: Ball and Shivakumar (2005)
accruals-cash flow estimations
The measures of timely loss recognition derived from equation (1) rely on the implicit
assumption that price changes reflect economic gains and losses, and that the price formation
process is equally efficient across all markets. Evidence in Morck, Yeung and Yu (2000)
suggests that securities in different economies reflect different levels of firm-specific
information. Moreover, some countries place asymmetric restrictions on the price formation
process, such as short-selling constraints, that impede the impounding of bad news into prices in
a timely manner. To the extent that the information content of prices (over a long window)
varies across countries, our estimates of timely loss recognition could be biased. Finally,
Dietrich, Muller and Riedl (2004) argue that results from the traditional earnings-returns
asymmetric timeless estimations are induced by the research design itself.
To mitigate these concerns, we implement an alternative method outlined in Ball and
Shivakumar (2005) to measure the timeliness and incremental timeliness of loss recognition
properties in earnings without referencing security prices. Specifically, Ball and Shivakumar
estimate the following model:
ACCRUALSi,t = α+ β1NEGCFOi,t + β2CFOi,t + β3NEGCFOi,t*CFOi,t + εi,t (2)
where ACCRUALSi,t is current period accruals, CFOi,t is current period operating cash flows,
and NEGCFOi,t is an indicator variable equal to one if CFOi,t is less than zero. Dechow, Kothari
and Watts (1998) document a negative correlation between contemporaneous accruals and cash
flows. Ball and Shivakumar argue that if the cash flow effects of current news are persistent,
then timely accounting recognition will be a source of positive correlation between accruals and
current-period cash flows. Given the asymmetric treatment of gains and losses due to accounting
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conservatism, this asymmetric timeliness would suggest that a positive correlation effect would
be stronger for losses than gains, or in other words, the traditional negative association between
accruals and cash flows will be weaker in the presence of losses than for gains. By conditioning
the relation between accruals and operating cash flows on the sign of current cash flows, Ball
and Shivakumar find that the negative relation between accruals and operating cash flows is
attenuated when cash flows are negative (i.e., β3 > 0).9
We estimate equation (2) by country using pooled, cross-sectional data over the time
period 1992 to 2001 using all firm-years with sufficient cash flow and accrual information, and
after eliminating the top and bottom percentile of CFOi,t and ACCRUALi,t realizations annually.
To measure the timely incorporation of bad news property for a given country, we define
BS_TIMEk as the sum of estimated coefficients β2+ β3. Similarly, to measure the incremental
timeliness of bad news recognition for given country, we define BS_INCRk as the estimated
coefficient on NEGCFOi,t*CFOi,t (i.e., β3).
As noted earlier, our estimates of BS_TIMEk and BS_INCRk do not depend upon the
assumption of equally informed stock prices; however, this non-price-based technique of
measuring timely loss recognition also has limitations. First, this methodology relies on an
assumption that the cash flow implications from a current news event are present in the current
year and are persistent (i.e., not a transitory, current period expenditure). Second, these
innovations are assumed to be equally persistent across countries and regimes, which may not
hold because different economies face different risks and different investment opportunities.
Third, the methodology assumes that operating cash flows are unbiased. Existing research
suggests that managers also have an incentive to manipulate real activities; cross-country
9 Bushman and Piotroski (2005), using cross-country data, find similar relations.
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variation in the level of real activities manipulation could potentially induce spurious relations.
Lastly, unlike price changes, operating cash flows co-mingles the effects of both past and current
news events. Despite the respective limitations of both the non-linear earnings-returns model
and the non-linear accruals-cash model, consistent results using timely loss recognition measures
(i.e., TLRk’s) derived from both of these techniques will mitigate concerns about the potential
limitations of each methodology.
3.2 Measuring Investment Flow Efficiency
Our primary country-level measures of investment behavior rely on Wurgler (2000).10
Specifically, Wurgler estimates the elasticity of gross investment to value added through
country-level estimations of the following model:
ln( Ijkt / Ijkt-1 ) = αk + ηk ln( Vjkt / Vjkt-1 ) + ε , (3)
where Ijkt is gross fixed capital formation in industry j, country k, year t, and Vjkt is value added
in industry j, country k, year t.11 Wurgler interprets the resultant elasticity coefficient, ηk , for
each country k, as a measure of the extent to which country k reduces investment in response to
declining investment opportunities and increases investment in response to increasing
10 Beck and Levine (2002) also use Wurgler’s elasticity measures to examine the relative importance of banks versus markets in promoting efficient capital allocation. They find that while financial development in general facilitates efficient capital allocation, having a bank-based or market-based system per se does not seem to matter. 11 The underlying data are drawn from the 1997 United Nations' General Industrial Statistics panel (the INDSTAT-3 CD-ROM) which reports gross fixed capital formation and value added for up to 28 three-digit ISIC manufacturing industries (an international classification standard that corresponds approximately to two-digit SIC industries), Value added is defined as the value of shipments of goods produced (output) minus the cost of intermediate goods and required services (but not including labor), with appropriate adjustments made for inventories of finished goods, work-in-progress, and raw materials. In other words, this value added measure reflects value added by labor as well as capital. Gross fixed capital formation is defined as the cost of new and used fixed assets minus the value of sales of used fixed assets, where fixed assets include land, buildings, and machinery and equipment. (The term gross is used to signify that the investments are not net of the replacement of expiring assets as measured by depreciation.) Wurgler also estimated (3) with additional lagged variables, finding a minimal increase in power.
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opportunities. In other words, ηk is interpreted as a summary measure of the efficiency of
resource allocation in an economy.
Further, and key to our study, Wurgler disaggregates ηk by separately estimating the
elasticity in country k for industry-year observations reflecting increasing value added (ηk+) and
those reflecting shrinking value added (ηk-).12 That is, ηk
+ captures the speed with which
investment flows increase in response to improved investment opportunities, and ηk- captures
the speed with which firms respond to a deterioration in investment opportunities by reducing the
flow of capital to new investments and withdrawing capital from losing projects.
In our multivariate analyses, we focus on the difference (ηk- - ηk
+). Wurgler notes that
this difference can be viewed as an inverse measure of the severity of the control problems in a
country, as self-serving managers are less likely to downsize investments in declining sectors
than they are to increase investments in growth opportunities (e.g., Jensen (1986)). By focusing
on (ηk- - ηk
+), we are comparing the propensity to decrease investment in poorly performing
sectors relative to the elasticity to invest in strong sectors within a given country, effectively
holding the effect of country-specific institutions constant (in the spirit of a fixed effects design).
To understand our design, it is important to understand the economics underlying
equation (3). It is common in the investment literature to employ a model of firm investment
with quadratic capital adjustment costs, where these adjustment costs are meant to capture a
range of potential frictions in the investment process. In this model, the optimal response of
investment to marginal q depends inversely on a multiplicative adjustment cost parameter,
12 It is important to recall that gross fixed capital formation is defined as the cost of new and used fixed assets minus the value of sales of used fixed assets. Thus, η- does not distinguish between withdrawals of capital versus reductions in new investment in response to deteriorating investment opportunities. In robustness analysis in section 4.3 below, we are able to at least partially separate these effects.
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indicating that investment is more responsive to investment opportunities when adjustment costs
are low (see e.g., Hubbard (1998)). By analogy, the country-specific slope coefficients estimated
from equation (3) reflect aspects of capital adjustment costs. These adjustment costs could result
from financing frictions, technological frictions, political frictions (such as barriers to entry or
downsizing), and agency cost frictions (such empire-building incentives, underinvestment
incentives), etc.
While we hypothesize that TLR practices primarily influence ηk-, it is the case that ηk
-
and ηk+ could be influenced asymmetrically by a range of other country-level characteristics.
Thus, we focus on the differenced variable (ηk- - ηk
+) to control for country-level aspects that
impact the absolute levels of ηk- and ηk
+, but not the asymmetry between them, and control
separately for a range of country-level characteristics that could affect the two sides
asymmetrically (e.g., financial development, per capita wealth, investors rights, state ownership
of enterprise, and synchronicity).
3.3 Descriptive statistics
Combining Wurgler’s elasticity data with our country-level TLR measures yields a
maximum sample of 32 country-level observations.13 Descriptive statistics for these measures
are presented in Table 1. Consistent with prior cross-country research (e.g., Ball, Kothari and
Robin, 2000; Bushman and Piotroski, 2005), our estimates of timely loss recognition practices
display considerable cross-country variation. For example, BKR_TIMEk ranges from -0.024
(Austria) to 0.575 (Mexico), with a mean timeliness coefficient of 0.278 (see Appendix 2 for
TLRk estimates by country). Similarly, the elasticity measures drawn from Wurlger (2000)
13 The merging of Wurgler’s elasticity database to Bushman and Piotroski’s TLR database results in the elimination of the following six countries with TLR data: Argentina, Brazil, Switzerland, Thailand, Taiwan and South Africa.
17
display considerable cross-country variation. The average country-level elasticity statistic, η, is
0.599, with a standard deviation of 0.253; country-specific differences in elasticity between
declining and growing industries, (ηk- - ηk
+), range from -0.415 (Netherlands) to 0.654 (Sweden),
with a mean realization of 0.005 and a standard deviation of 0.269.
The table also presents descriptive statistics for country-level variables measuring the
country’s general level of financial development (FD), fraction of the economy’s output
attributable to state owned enterprises (SOE), protection of investor rights (RIGHTS), per capita
wealth (GDP) and information flow proxied by stock return synchronicity (SYNCH). These
variables are the same set of institutional variables used in Wurgler’s (2000) original study to
explain cross-country variation in investment elasticity. Summary statistics for these variables
are presented at the bottom of Table 1 for completeness; see Appendix 1 for a complete
definition of each variable and its measurement.
4. Empirical results: Investment flow efficiency and timely loss recognition practices
We hypothesize that countries characterized by more timely accounting recognition of
economic losses will respond more quickly to declines in investment opportunities than firms in
countries with less timely loss recognition. We predict a positive relation between TLR and both
ηk- and (ηk
- - ηk
+), but make no prediction about the relation between TLR and ηk+. Recall that
ηk- proxies for the speed with which country k withdraws capital from shrinking sectors, while
the difference (ηk- - ηk
+) proxies for the severity of the control problem captured by the degree to
which managers are less likely to downsize investments in declining projects than they are to
increase investments in expanding industries.
18
4.1 Univariate evidence
Table 2 presents a correlation matrix of the primary variables used in our study. This
table provides a first glimpse of the basic relation between TLR practices and investment
behavior.
First, consider the correlations among our alternative measures of TLRk. Despite the
different methodologies employed, the two unique sets of timely loss recognition measures are
strongly correlated. For example, BKR_INCRk and BS_INCRk have a spearman (pearson)
correlation of 0.708 (0.669), while BKR_TIMEk and BS_TIMEk have a correlation of 0.744
(0.541). Thus, despite the different empirical assumptions underlying the two TLR estimation
techniques, the relative ranking of countries into timely loss recognition regimes across these
metrics are quite similar. Furthermore, these correlations illustrate that TLR practices are
positively related to the overall protection of investor rights and per capita wealth in the country,
and inversely related to stock return synchronicity (i.e., positively related to information flow in
the economy). Untabulated results also confirm that TLR practices are lower in code law
countries, consistent with prior research.
Second, consider our measures of investment elasticity. Consistent with the results in
Wurgler (2000) and Beck and Levine (2002), these country-level investment elasticity measures
are strongly related to the primitive institutional characteristics of the economy. In particular, all
three of the primary, country-level elasticity measures, ηk , ηk+ and ηk
-, are strongly positively
related to the country’s level of financial development, per capita wealth, general level of
investor protections, and information environment. Thus, how efficiently capital is allocated
across growing and shrinking sectors of the economy is closely related to the economy’s
underlying structure. In contrast, none of these primitive institutions have a significant
19
correlation with (ηk- - ηk
+). These insignificant relations highlight an important point: From a
measurement perspective, taking the difference between the two elasticities appears to be fairly
effective at removing the impact of country-level institutional effects, such as financial
development, wealth, and other factors, that influence the level of each individual elasticity
variable (i.e., removed the influence of country fixed effects that affect ηk- and ηk
+
symmetrically). This will allow us to more cleanly isolate any asymmetries in the firm’s
investment response to deteriorating conditions.
Finally, the correlation matrix reveals an interesting pattern of relations between these
elasticity measures and TLR practices. Specifically, all four TLR measures (BKR_TIME,
BKR_INCR, BS_TIME and BS_INCR) are positively and significantly (in 7 out of 8 cases)
correlated with the downside elasticity measures of ηk- and (ηk
- - ηk
+). In contrast, we find that
none of the four measures of timely loss recognition are significantly correlated with the
elasticities capturing the flow of capital to growth opportunities (ηk and ηk+). In other words,
TLR is positively associated with the timely withdrawal of capital from shrinking sectors (ηk-),
and inversely related to the severity of the corresponding control problem in a country (i.e.,
positively related to (ηk- - ηk
+)), but unrelated with the flow of capital to growing sectors. From
an interpretational perspective, this pattern of relations suggests that any documented
associations between TRL practices and (ηk- - ηk
+) are likely to be driven by TLR’s impact on
downside investment activities through ηk-, not through a correlated influence on capturing
growth opportunities (i.e., ηk+).
20
4.2 Multivariate analyses
In this section, we extend the analyses presented in Wurgler (2000) and Beck and Levine
(2002), and explore the association between timely accounting recognition of economic losses
and the efficient allocation of capital. As discussed earlier, our analysis will focus on the
difference (ηk- - ηk
+) for both methodological and conceptual reasons. Methodologically, this
difference statistic removes the effects of country-level institutions and frictions that
symmetrically impede or accelerate the flow capital in an economy, allowing us to more cleanly
isolate the sign and magnitude of the asymmetric investment problems associated with the
withdrawal of capital. Conceptually, this asymmetry in the underlying firm’s investment
responses to deteriorating versus expanding investment opportunities is the type of self-
interested managerial behavior discussed in the vast theoretical literature on empire building,
escalation of commitment, traditional principal-agency problems, et cetera which TLR is
hypothesized to mitigate.
4.2.1 Relations between (ηk--ηk
+) and TLR after controlling for financial development
To assess the relation between TLR practices and investment behavior after controlling
for the country’s general level of financial development, we estimate the following cross-
sectional model:
(ηk--ηk
+) = α + β1FDk + β2GDP1960k + β3TLRk + εk
In this model, FDk is our measure of financial development in country k, GDP1960k is the log of
country k’s per capital GDP in 1960, and TLRk is one of our measures of timely loss recognition
practices. This model, absent our TLR variable, is a replication of the baseline empirical model
used in Wurgler (2000).
21
Table 3 presents estimated coefficients from three different specifications of this model.
Panel A presents coefficients using the raw realizations of all of our variables. Panel B presents
coefficients from a specification using a ranked version of each TLR variable, where
RANK_TLR is the fractional rank (between zero and one) of the respective country-level TLR
measure. Panel C presents coefficients from a specification that uses the fractional rank for both
TLR and (ηk--ηk
+) in lieu of their raw realizations.
As evidenced across all three panels, the four measures of TLRk practices continue to
display a positive relation with (ηk--ηk
+) after controlling for financial development and GDP.
All coefficients, with the exception of BS_TIME in our raw variables specification, are
significantly positive at the ten percent level (one-tailed); in contrast, our two measures of
financial development (FD) and wealth (GDP1960) continue to have no relation with (ηk--ηk
+).
Given the consistency of results across these specifications, subsequent tests and tabulated
results focus on estimations using our raw data (for parsimony, and without loss of generality).
4.2.2 Relation between (ηk--ηk
+) and TLR after controlling for the level of financial
development in an economy and alternative governance and investor protection regimes
Table 4 presents a more demanding test of the relation between (ηk--ηk
+) and TLR by
controlling for both the level of financial development in the economy, per capita wealth, and
three alternative country-level attributes that could either magnify or abate a control problem in
the economy. Specifically, Table 4 presents coefficients from various estimations of the
following cross-sectional model:
(ηk--ηk
+) = α + β1FDk + β2GDP1960k + β3RIGHTSk + β4SOEk + β5SYNCHk + β6TLRk + εk
22
where RIGHTSk is a measure of investor rights in the country (see LaPorta et al (1998)), SOEk is
a measure of the extent of state owned enterprises in the economy, and SYNCHk is Morck,
Young and Yu’s (2000) stock return synchronicity variable, an inverse measure of the amount of
information impounded in stock prices in the country. By stacking TLR against other variables
that could influence the extent to which firms suffer from control problems, this specification is
designed to mitigate concerns that TLR was simply serving as a surrogate for a more primitive
set of governance practices in the economy.
Table 4 presents various estimations of this model, and reveals several interesting
relations. First, consistent with Wurgler, RIGHTS has a significant positive relation with (ηk--
ηk+), suggesting that countries adopting strong investor protections have a less severe control
problem associated with sluggish withdrawal of capital from deteriorating opportunities.
Second, after controlling for RIGHTS, there is an inverse relation between (ηk--ηk
+) and financial
development, which is also consistent with results reported in Wurgler (2000). This inverse
relation suggests that, for a given level of investor protections and wealth, greater access to
external finance exacerbates the control problem, possibly because managers either find it easier
to fund over-investment behavior or face less incentive to withdraw capital from poor investment
opportunities. In contrast, neither SOE nor SYNCH has explanatory power for the difference in
investment elasticities. Finally, after controlling for these alternative mechanisms, we continue
to find a significant positive relation between (ηk--ηk
+) and three of our TLR measures at the ten-
percent level, suggesting that financial reporting practices have an incremental relation to
investment behavior beyond the effects generated by traditional investor protection mechanisms.
23
4.3 Alternative methodology for measuring investment behavior The preceding section documents robust relations between timely loss recognition
practices and the speed with which investment flow responds to a decline in investment
opportunities. These findings are consistent with arguments in Ball (2001), Ball and Shivakumar
(2005), and others that TLR can serve as a mechanism to induce managers to withdraw capital
from and/or curtail investment in poor projects in a timely manner. To verify the robustness of
these results, in this section we address several potential limitations of our previous analysis.
First, the elasticity measures included in the Wurgler dataset are estimated over the thirty
two year period 1964 to 1995. In contrast, our TLR measures are estimated over the time period
1992 to 2001. Given that the elasticity measures effectively pre-date our measures of reporting
practices, causality is difficult to establish. Second, the proxy used for change in investment
opportunities, growth in value added, is likely to be an imperfect proxy for the underlying
theoretical construct, marginal q. Third, given the small number of country observations,
concerns that the results are simply a manifestation of “random chance” cannot be overlooked.
To mitigate concerns about the robustness of results using Wurgler’s elasticity data, we
employ an alternative technique to estimate differences in the sensitivity of investment to
expanding and deteriorating investment opportunities at the country level. As discussed in
section 3.2 above, the assumption of quadratic adjustment costs leads to an equilibrium relation
between optimal investment and investment opportunities as measured by marginal q. However,
marginal q is unobservable, and so the researcher must choose a proxy variable with which to
measure investment opportunities. A commonly used proxy is average Q, an estimate of the
ratio of market value to replacement value (see Hubbard (1998) for a discussion of limitations to
this measure). However, some papers relate investment to lagged stock returns instead of Q,
24
because stock returns perform better in empirical investment equations (see e.g., Lamont (2000),
Barro (1990) and Morck, Shleifer, and Vishny (1990)).
Following this latter approach, we estimate the response of investment to changes in
investment opportunities by regressing investment behavior in time t on lagged stock returns in
time t-1. A one year lag for returns is motivated by Lamont (2000) who, exploiting investment
plan data, provides convincing evidence of such a time lag between change in investment
opportunities and the investment response. Given such a lagged response, investment and lagged
stock returns should positively co-vary, because when discount rates fall, stock prices rise (as the
discounted sum of future cash flows rises) and firms should increase investment as the hurdle
rate on investment falls. A similar argument holds when hurdle rates increase.
To estimate investment efficiency in the spirit of (ηk--ηk
+), we estimate the regression:
log(It/It-1)j,t = αpPOSj,t-1 + αNNEGj,t-1 + λ+ POSj,t-1*log(1+RETj,t-1) + λ- NEGj,t-1*log(1+RETj,t-1) + εj,t , (4)
where investment growth is measured as the ratio of current to lagged additions to fixed assets
(Global Vantage data item 145), NEG (POS) is an indicator variable equal to one if RETj,t-1 is
less than (greater than or equal to) zero in year t-1, zero otherwise, and RETj,t-1 is the average
stock return of firms in industry j in country k in year t-1. Analogous to (ηk--ηk
+), we extract the
quantity (λ- - λ+) from equation (4). It is noteworthy that in this specification, investment only
captures new investment flows (which is consistent with Lamont’s (2000) model), not new
investment flows net of disinvestment as is the case with the gross investment formation variable
used by Wurgler.
In table 5, panel A we examine the robustness of the results from the Wurgler
methodology (tables 2-4) by estimating the following model:
(λ- – λ+)k = α + β1FD1995k + β2GDP1992k + β3RIGHTSk + β4SOEk + β5SYNCHk + β6TLRk + εk , (5)
25
where (λ- – λ+)k is the country-level difference in the investment growth response to industries
with lagged negative and positive mean industry returns respectively, estimated over the period
1994 to 2003, GDP1992 is GDP per capita in 1992, and FD1995 is financial development
measured in 1995. All other variables are the same as in table 4 (see appendix 1 for a description
of all variables). Although the sample in this specification is reduced to only 21 countries due to
limited investment flow data, these estimations support the inferences gleaned from our Wurlger
analysis. In particular, all four of our measures of TLR have positive and significant coefficients
in these estimations, similar to the results in table 4 using (ηk--ηk
+) instead of (λ- - λ+).
To complete the robustness analysis, in table 5, panels B we re-estimate equation (4), and
thus our estimates of (λ- - λ+), using an alternative measure of investment. In this specification,
investment growth is measured as growth in fixed assets in excess of depreciation, defined as the
ratio of current to lagged net fixed assets (Global Vantage data item 76). This investment
variable is analogous to the gross investment formation variable used in Wurgler as it nets new
investment and disinvestment, and is available for the complete sample of countries. These
results are reported in table 5, panel B. We find that the coefficients on TLR are positive and
significant in all estimations using TLR measures derived from Ball, Robin and Kothari’s
earnings-return methodology (i.e., BKRTIME and BKRINCR), supporting inferences gained
from the preceding sets of analysis. In contrast, our TLR measures from Ball and Shivakumar’s
accruals-cash flow technique (BS_TIME and BS_INCR) have significant positive relation with
this measure of asymmetric investment response when RIGHTS is excluded from the regression.
26
4.4 Cross-sectional variation in the benefits of timely loss recognition practices
The early-intervention benefits associated with the timely accounting recognition of
economic losses should not be expected to accrue uniformly across all countries. Instead, timely
loss recognition, as a governance mechanism, is likely to offer its greatest benefits in the
presence of a diffuse ownership structure that allows for potential conflicts between managers,
creditors and shareholders. Absent these potential agency-related conflicts (or in the presence of
alternative governance mechanisms that arise in the presence of a concentrated ownership
structure), changes in TLR practices may have minimal impact on economic outcomes among
firms with concentrated ownership. To test these arguments, as well as validate the preceding
sets of results, we annually estimate variations of the following cross-sectional model:
(ηk--ηk
+) or (λk--λk
+) = α0 + α1CONCOWNk + β1FDk + β2GDP1960k + β3RIGHTSk + β4TLRk
+ β5TLRk*CONCOWNk + εk (6)
where CONCOWNk is an indicator variable equal to one if the average percentage of common
shares owned by the three largest shareholders in the ten largest non-financial, privately owned
firms in the country is greater than or equal to the median country percentage of concentrated
ownership, zero otherwise. If TLR practices are effective at improving capital allocation and
investment efficiency, then the relations between TLR and both (ηk--ηk
+) and (λk--λk
+) should be
stronger in those countries with low concentration of ownership, and weaker in countries with
highly concentrated ownership.
Table 6 presents coefficients from an estimation of equation (6). We find that the
relations between TLR and both (ηk--ηk
+) and (λk--λk
+) and are consistently positive and
statistically significant in diffuse ownership regimes, regardless of our measure of TLR. In
contrast, the positive relation between TLR and investment elasticities are attenuated, or
complete eliminated, in concentrated ownership regimes, as signified by the sum of the
27
coefficients on TLR and TLR*CONCOWN (and related p-values). Effectively, absent the
classical agency conflicts associated with diffuse ownership, or in the presence of alternative
governance arrangements, the governance benefit of TLR is limited. These results are also
robust to both techniques for measuring investment behavior.
Together, this systematic variation in the relation between TLR and (ηk- - ηk
+) across an
institutional regime correlated with the likely benefits of TLR suggests that the relations
documented in Tables 2 through 5 are supportive of an underlying economic phenomenon and,
to some extent, mitigate concerns that the relations are an artifact of random chance or a research
design bias.
5. Summary, conclusions, and related future research opportunities
We test the hypothesis that timely accounting recognition of economic losses (TLR)
discourages managers from allocating capital to negative NPV projects and encourages relatively
rapid withdrawal of capital from failing projects. We investigate whether firms in countries
whose accounting regimes are characterized by TLR respond more quickly to deteriorating
investment opportunities than firms in countries with less timely loss recognition.
We document a significant positive relation between the relative speed of managerial
response to deteriorating opportunities and TLR. This relation is robust to two techniques for
estimating TLR, to two measures of investment sensitivity to changing investment opportunities,
and to controls for financial development, per capita wealth, investor rights, state ownership of
enterprise, and stock price synchronicity. Additional analysis suggests that the relation between
TLR and investment behavior is stronger in countries characterized by diffuse corporate
ownership structures.
28
While the associations documented in our analyses are suggestive of TLR playing a role
in disciplining managers’ investment decisions, we have not empirically established a causal
relation. As shown in Bushman and Piotroski (2005), Ball, Kothari and Robin (2000), and
others, TLR is influenced by the key aspects of a country’s institutional structure. However,
whether our results are caused by TLR or the underlying institutions that create a demand for or
facilitate the supply of TLR reporting behavior, it is interesting that TLR appears as part of
equilibrium outcomes associated with timely withdrawal of capital from losing projects. A lack
of association between TLR and a rapid response to deteriorating opportunities would have cast
doubt on the theory that TLR arises to play this disciplining role in firms’ capital allocation.
The relation between TLR and firms’ resource allocation decisions (i.e. inputs)
documented here leads naturally to the question of whether TLR contributes to superior firm and
economy-wide performance (i.e. outputs). At the firm level, does timely loss recognition lead to
stronger firm performance and greater wealth creation? At the economy level, does timely loss
recognition lead to higher productivity, economic growth, and greater wealth per capita?
In a related follow-up paper, we conjecture that realized stock return distributions
systematically vary across different timely loss recognition regimes, assuming that stock returns
capture unexpected economic gains or losses. If timely loss recognition discourages managers
from allocating capital to negative NPV projects, we expect a lower frequency of stockholder
losses in countries with timely loss recognition. And if timely accounting recognition of
economic losses leads to more rapid abandonment of deteriorating projects by firms’ managers
(mitigating the negative future cash flow consequences), we expect less severe stockholder
losses (i.e. “crashes”) in countries with timely loss recognition, extending the crash hypotheses
presented in Jin and Myers (2005). We find preliminary support for these conjectures.
29
Many related research opportunities remain for future research. The relation between
TLR and a variety of measures of firm and economy-wide performance can be investigated. In
addition, how firms’ resource allocation decisions and economic performance vary with other
fundamental properties of accounting and disclosure regimes merits investigation. And finally,
how a multitude of factors (e.g. media penetration, analyst following, insider trading rules,
importance of debt vs. equity markets, corporate ownership concentration etc.) amplify or
dampen the “real effects” of variations in financial accounting and disclosure systems can be
investigated. Such a research agenda has much potential to contribute to a more complete
understanding of the role financial accounting information and its economic consequences.
30
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Appendix 1 Variable Definitions
Variable Definition of variable Data Source RETt Holding period stock return, including dividends, over the
firm’s fiscal accounting year. Standard and Poor’s Global Vantage Issues file.
NEGt An indicator variable equal to one if RET is less than zero; zero otherwise.
PROPNEG Percentage of firms in the country in a given fiscal year with negative economic income. Negative economic income is defined as an annual stock return (i.e., RET) less than zero.
η Country-level estimates of the elasticity of gross investment to value-added over the period 1963 to 1995, as a measure of the efficiency of resource allocation.
Wurgler (2000)
BKR_TIME A measure of the timeliness of the recognition of bad economic news into earnings, based on the methodology in Ball, Kothari and Robin (2000). Defined as the sum of β2 + β3, where β2 and β3 are the estimated coefficients from country-level estimations of the following model over the period 1992 to 2001: NI = α+ β1NEG + β2RET + β3NEG*RET
Bushman and Piotroski (2005)
BKR_INCR A measure of the asymmetric timeliness of the recognition of bad economic news into earnings, based on the methodology in Ball, Kothari and Robin (2000). Defined as the incremental coefficient on bad economic news (i.e., β3) , where β3 is the estimated coefficient from country-level estimations of the following model over the period 1992 to 2001: NI = α+ β1NEG + β2RET + β3NEG*RET
Bushman and Piotroski (2005)
BS_TIME A measure of the timeliness of the recognition of bad economic news into earnings, based on the methodology in Ball and Shivakumar (2004). Defined as the sum of β2 + β3, where β2 and β3 are the estimated coefficients from country-level estimations of the following model over the period 1992 to 2001: ACCRUALS = α+ β1NEGCFO + β2CFO + β3NEGCFO*CF
Bushman and Piotroski (2005)
BS_INCR A measure of the asymmetric timeliness of the recognition of bad economic news into earnings, based on the methodology in Ball and Shivakumar (2004). Defined as the incremental coefficient on bad economic news (i.e., β3) , where β3 is the estimated coefficient from country-level estimations of the following model over the period 1992 to 2001: ACCRUALS = α+ β1NEGCFO + β2CFO + β3NEGCFO*CF
Bushman and Piotroski (2005)
RANK_TLR The fractional rank (i.e., between 0 and 1) of the respective country-level timely loss recognition measure.
NIt Net income before extraordinary items, deflated by beginning of period prices (MVEt-1).
Standard and Poor’s Global Vantage Industrial / Commercial file.
CFOt Operating cash flow, deflated by beginning of period prices (MVEt-1).
Standard and Poor’s Global Vantage Industrial / Commercial file.
ACCRUALSt Total accruals, deflated by beginning of period prices, defined as NIt - CFOt.
Standard and Poor’s Global Vantage Industrial / Commercial file.
NEGCFOt An indicator variable equal to one if CFOt is less than zero; zero otherwise.
CLOSEHLD
The average percentage of common shares owned by the three largest shareholders in the 10 largest non-financial, privately-owned domestic firms in a given country. A firm is considered privately owned if the state is not a known shareholder in it.
LaPorta, Lopez-de-Silanes, Shleifer and Vishny (1998)
CONCOWN An indicator variable equal to one if the country’s CLOSEHLD
35
realization is greater than or equal to the sample’s median realization, zero otherwise.
FD A summary measure of "nancial development, FD, is the log of one plus the average sum of stock market capitalization and credit to GDP.
Wurgler (2000)
GDP1960 1960 value of per capita GDP; the date is chosen to minimize the potential for endogeneity when this variable isused as a control in cross-country regressions.
Wurgler (2000)
RIGHTS A summary measure of effective legal rights. RIGHTS is computed by multiplying the number of important shareholder and creditor rights that exist in the country’s legal code (0 to 10, integer) by a measure of the domestic `rule of law’ (0 to 1continuous). Both variables are from La Porta et al. (1998).
LaPorta, Lopez-de-Silanes, Shleifer and Vishny (1998)
SOE A rating (0 to 10) of the State’s involvement in a country’s economy, based on the fraction of the economy's output due to state-owned enterprises.
Economic Freedom of the World (2003)
SYNCH A measure of stock price synchronicity, equaling the average fraction of stocks moving in the same direction in a given week during 1995.
Morck, Yeung and Yu (2000)
36
Appendix 2 Country-level measures of timely loss recognition practices Country BKR_TIME BKR_INCR BS_TIME BS_INCR n Argentina 0.360 0.401 0.443 1.340 118 Australia 0.376 0.343 0.029 0.781 1,591 Austria -0.024 -0.093 -0.382 0.589 401 Belgium 0.210 0.181 -0.985 0.002 386 Brazil 0.131 0.152 -0.330 0.357 364 Canada 0.373 0.377 -0.067 0.762 3,569 Switzerland 0.357 0.303 -0.237 0.662 772 Chile 0.027 0.017 -0.394 0.436 366 Germany 0.255 0.220 -0.487 0.469 2,824 Denmark 0.283 0.244 -0.387 0.439 495 Spain 0.370 0.314 -0.425 0.515 620 Finland 0.116 0.110 -0.464 0.474 436 France 0.086 0.040 -0.447 0.492 2,275 Great Britain 0.278 0.276 -0.058 0.808 5,993 Greece 0.085 0.087 -1.010 -0.352 176 Hong Kong 0.300 0.256 -0.097 0.859 638 Indonesia 0.040 0.046 -0.633 0.231 534 India 0.187 0.156 -0.425 0.489 1,076 Ireland 0.464 0.495 0.198 0.859 239 Israel 0.261 0.230 -0.237 0.533 188 Italy 0.140 0.135 -0.994 -0.060 480 Japan 0.120 0.107 -0.328 0.639 22,417 South Korea 0.023 0.026 -1.110 -0.211 423 Mexico 0.575 0.466 -0.306 0.687 238 Malaysia 0.138 0.125 -0.254 0.707 2,099 Netherlands 0.186 0.177 -0.551 0.393 915 Norway 0.493 0.459 -0.088 0.695 437 New Zealand 0.513 0.419 0.500 1.277 284 Pakistan 0.071 -0.085 -0.389 0.551 165 Philippines 0.195 0.231 0.099 1.006 308 Portugal 0.281 0.263 -0.126 0.761 171 Singapore 0.149 0.130 -0.482 0.409 1,315 Sweden 0.529 0.486 0.054 0.759 679 Thailand 0.386 0.337 -0.228 0.658 1250 Turkey -0.005 -0.006 -1.214 -1.326 146 Taiwan 0.213 0.158 -0.330 0.785 651 United States 0.307 0.312 -0.022 0.910 22,983 South Africa 0.100 0.051 -0.165 0.655 457
37
Table 1 Descriptive Statistics This table presents descriptive statistics for the full set of countries with both Wurgler’s elasticity measures and Bushman and Piotroski’s timely loss recognition measures.
Variable N Mean Std Dev Median Minimum Maximum Elasticity of investments (Source: Wurgler 2000) ηk 32 0.5987 0.2531 0.6405 0.1000 0.9880 ηk
+ 32 0.5038 0.3551 0.5190 -0.3880 1.0570 ηk
- 32 0.5091 0.3547 0.4645 -0.1050 1.3010 ηk
- - ηk+ 32 0.0052 0.2694 0.0070 -0.4150 0.6540
TLR: Ball Kothari and Robin (2000) earnings-returns measures (Source: Bushman and Piotroski 2005) BKR_INCR 32 0.2045 0.1625 0.2005 -0.0930 0.4950 BKR_TIME 32 0.2313 0.1660 0.2025 -0.0240 0.5750 TLR: Ball and Shivakumar (2004) accruals-cash flows measures (Source: Bushman and Piotroski 2005) BS_INCR 32 0.4869 0.4763 0.5420 -1.3260 1.2770 BS_TIME 32 -0.3588 0.3928 -0.3845 -1.2140 0.5000 Measures of financial development FD 31 0.9761 0.5551 0.8500 0.2600 2.6700 GDP 1960 32 4.1241 2.6052 3.3750 0.6400 9.9100 Mechanisms/environmental variables SYNCH 31 0.6614 0.0420 0.6660 0.5790 0.7540 SOE 32 3.7813 2.4982 4.0000 0.0000 10.0000 RIGHTS 32 4.1821 1.8656 4.0000 0.5350 7.7130
The elasticity of investment measures are taken from Wurgler (2000), and represent the estimated elasticity of gross manufacturing investment to value-added from all industry-year observations in country k (ηk), from all industry-year observations where value-added is growing in country k (ηk
+), from all industry-year observations where value added is shrinking in country k (ηk
-), and the difference between the elasticity estimate for declining industry-year observations and the elasticity estimate for growing industry-year observations (ηk
- - ηk+). Country level measures
of timely loss recognition (TLR) practices are taken from Bushman and Piotroski (2005). BKR_TIME and BKR_INCR are measures of the timeliness of loss recognition and incremental timely loss recognition based on coefficients from country-level earnings-returns estimations. BS_TIME and BS_INCR are measures of the timeliness of loss recognition and incremental timely loss recognition based on coefficients from country-level accrual-cash flow estimations. FD is a summary measure of financial development, measured as the log of one plus the average sum of stock market capitalization and credit to GDP for country k. GDP1960 is the value of log per capital GDP for country k in 1960. Both FD and GDP1960 are taken from Wurgler (2000). SYNCH is a measure of stock price synchronicity, and is measured as the average fraction of stocks moving in the same direction in a given week during 1995, from Morck et al. (2000). SOE is index (0 to 10) of the State’s involvement in the country’s economy, based on the fraction of an economy's output due to state-owned enterprises. RIGHTS is an index of investor rights. It is the product of a measure of the rule of law and the number of important shareholder and creditor in the country's legal code.
38
Table 2 Correlation matrix Pearson (Spearman) correlations above (below) the diagonal. Two-tailed p-values are presented in parentheses. All variables are defined in Appendix 1.
ηk ηk+ ηk
- ηk- - ηk
+ BKRTIME BKRINCR BS_TIME BS_INCR FD GDP1960 SYNCH SOE RIGHTS
ηk 1.000 0.9009 0.8350 -0.0882 0.2881 0.2745 0.1752 0.2379 0.4092 0.6180 -0.5169 -0.2634 0.4093- (0.000) (0.000) (0.631) (0.110) (0.128) (0.338) (0.190) (0.022) (0.000) (0.003) (0.145) (0.020)
ηk+ 0.8907 1.000 0.7118 -0.3810 0.1770 0.1301 0.0818 0.2043 0.3826 0.4886 -0.4145 -0.1299 0.3125
(0.000) - (0.000) (0.032) (0.333) (0.478) (0.656) (0.262) (0.034) (0.005) (0.020) (0.479) (0.082)
ηk- 0.8805 0.7666 1.000 0.3782 0.4243 0.3562 0.3764 0.3431 0.3162 0.6255 -0.5204 -0.1959 0.5041
(0.000) (0.000) - (0.033) (0.016) (0.045) (0.034) (0.055) (0.083) (0.000) (0.002) (0.283) (0.003)
ηk- - ηk
+ -0.0752 -0.3670 0.2720 1.000
0.3252 0.2974 0.3877 0.1824 -0.0786 0.1795 -0.1340 -0.0867 0.2517 (0.683) (0.039) (0.132) - (0.069) (0.098) (0.028) (0.318) (0.674) (0.326) (0.472) (0.637) (0.165)
BKRTIME
0.3460 0.1754 0.4120 0.2727 1.000
0.9720 0.6690 0.5783 0.0887 0.4667 -0.2799 -0.2554 0.2730(0.052) (0.337) (0.019) (0.131) - (0.000) (0.000) (0.001) (0.635) (0.007) (0.127) (0.158) (0.131)
BKRINCR
0.3138 0.1232 0.3640 0.2625 0.9831 1.000 0.6495 0.5412 0.1511 0.4687 -0.2827 -0.3756 0.2749(0.080) (0.502) (0.041) (0.147) (0.000) - (0.000) (0.001) (0.417) (0.007) (0.123) (0.034) (0.128)
BS_TIME
0.2060 0.0017 0.2918 0.3442 0.7009 0.7071 1.000 0.9137 0.2359 0.3861 -0.3844 -0.2098 0.5238(0.258) (0.993) (0.105) (0.054) (0.000) (0.000) - (0.000) (0.201) (0.029) (0.033) (0.249) (0.002)
BS_INCR
0.2489 0.0810 0.3314 0.2804 0.6463 0.6474 0.9567 1.000
0.2848 0.3504 -0.4322 -0.0820 0.4836(0.169) (0.659) (0.064) (0.120) (0.000) (0.000) (0.000) - (0.121) (0.049) (0.015) (0.655) (0.005)
FD
0.4184 0.3808 0.3956 -0.0694 0.1899 0.2166 0.2791 0.2986 1.000
0.2481 -0.1791 -0.2680 0.6218(0.019) (0.035) (0.028) (0.711) (0.306) (0.242) (0.128) (0.103) - (0.178) (0.344) (0.145) (0.000)
GDP1960
0.6445 0.5071 0.6467 0.1461 0.5450 0.5292 0.4060 0.3404 0.4092 1.000 -0.7028 -0.2668 0.4362(0.000) (0.003) (0.000) (0.425) (0.001) (0.002) (0.021) (0.057) (0.022) - (0.000) (0.140) (0.013)
SYNCH
-0.5098 -0.4222 -0.5643 -0.1239 -0.3753 -0.3573 -0.3793 -0.3757 -0.2995 -0.7360 1.000
0.1391 -0.2795(0.003) (0.018) (0.001) (0.507) (0.038) (0.049) (0.035) (0.037) (0.108) (0.000) - (0.456) (0.128)
SOE
-0.1415 0.0110 -0.1298 -0.1919 -0.2387 -0.3047 -0.2445 -0.2317 -0.2038 -0.2527 0.1695 1.000
-0.2671(0.440) (0.953) (0.479) (0.293) (0.188) (0.090) (0.178) (0.202) (0.272) (0.163) (0.362) - (0.140)
RIGHTS
0.3816 0.2912 0.4666 0.2437 0.3564 0.3328 0.5240 0.5201 0.6939 0.4758 -0.2991 -0.2864 1.000 (0.031) (0.106) (0.007) (0.179) (0.045) (0.063) (0.002) (0.002) (0.000) (0.006) (0.102) (0.112) -
39
Table 3 Impact of timely loss recognition practices on the difference in elasticity of investment between declining and growing industries (ηk
--ηk+) after controlling for the level of
financial development in an economy This table presents coefficients from various estimations of the following model:
(ηk--ηk
+) = α + β1FDk + β2GDP1960k + β3TLRk + εk where (ηk
--ηk+) is the difference between the elasticity of manufacturing investment to value-added estimate for
declining industry-year observations and the elasticity of manufacturing investment to value-added estimate for growing industry-year observations in country k, FDk is a summary measure of financial development, measured as the log of one plus the average sum of stock market capitalization and credit to GDP, GDP1960k is the value of log per capital GDP for 1960, TLRk is a country-level measure of timely loss recognition practices, and RANK_TLR is the fractional rank (0 to 1) of the respective country-level TLR measure. All variables are defined in Appendix 1. N=31 in all estimations. Country-level accounting timely loss
recognition measures using coefficients from country-level estimations of the model:
NI = α+ β1NEG + β2RET + β3NEG*RET + ε
Country-level accounting timely loss recognition measures using coefficients from
country-level estimations of the model: ACCRUALS = α+ β1NEGCFO + β2CFO
+ β3NEGCFO*CFO + ε
Wurgler
Ball Kothari and Robin (2000)
Ball and Shivakumar (2005)
Incremental Incremental Bad new Bad news Bad new Bad news Measure of TLRk: Timeliness Timeliness Timeliness Timeliness Panel A: Estimations using estimated TLR coefficients Intercept -0.016 -0.073 -0.050 0.194 -0.022 (0.894) (0.552) (0.678) (0.190) (0.855) FDk -0.063 -0.058 -0.068 -0.094 -0.083 (0.506) (0.532) (0.466) (0.300) (0.397) GDP(1960)k 0.021 0.004 0.006 0.004 0.014 (0.301) (0.847) (0.777) (0.840) (0.504) TLRk - 0.528c 0.491c 0.298b 0.111 - (0.062) (0.082) (0.016) (0.174) R2 0.0440 0.1253 0.1116 0.1967 0.0753 Adj. R2 -0.0243 0.0281 0.0128 0.1075 -0.0274 a,b,c Significant at the one, five and ten percent level, respectively (one-sided test for predicted TLR relation; two-sided test otherwise). P-values are presented in parentheses.
40
Table 3 (continued) Impact of timely loss recognition practices on the difference in elasticity of investment between declining and growing industries (ηk
--ηk+) after controlling for the level of
financial development in an economy Country-level accounting timely loss
recognition measures using coefficients from country-level estimations of the model:
NI = α+ β1NEG + β2RET + β3NEG*RET + ε
Country-level accounting timely loss recognition measures using coefficients from
country-level estimations of the model: ACCRUALS = α+ β1NEGCFO + β2CFO
+ β3NEGCFO*CFO + ε
Wurgler
Ball Kothari and Robin (2000)
Ball and Shivakumar (2005)
Incremental Incremental Bad new Bad news Bad new Bad news Measure of TLRk: Timeliness Timeliness Timeliness Timeliness Panel B: Estimations using ranked TRL regime variables Intercept -0.016 -0.088 -0.081 -0.098 -0.096 (0.894) (0.487) (0.524) (0.422) (0.447) FDk -0.063 -0.061 -0.062 -0.082 -0.080 (0.506) (0.512) (0.511) (0.370) (0.391) GDP(1960)k 0.021 0.004 0.006 0.005 0.009 (0.301) (0.873) (0.803) (0.795) (0.674) RANK_TLRk - 0.284c 0.252c 0.335b 0.298c
- (0.074) (0.097) (0.029) (0.054) R2 0.0440 0.1163 0.1030 0.1650 0.1335 Adj. R2 -0.0243 0.0181 0.0034 0.0722 0.0372 Panel C: Estimations using both ranked elasticity and ranked TLR regime variables Intercept 0.486a 0.422a 0.428a 0.410a 0.409a
(0.000) (0.001) (0.001) (0.001) (0.001) FDk -0.061 -0.059 -0.059 -0.078 -0.077 (0.491) (0.498) (0.497) (0.357) (0.373) GDP(1960)k 0.019 0.004 0.005 0.004 0.007 (0.319) (0.872) (0.804) (0.824) (0.717) RANK_TLRk: - 0.252c 0.223 0.313b 0.288b
- (0.085) (0.109) (0.029) (0.046) R2 0.0424 0.1081 0.0958 0.1641 0.1392 Adj. R2 -0.0259 0.0090 -0.0046 0.0712 0.0436 a,b,c Significant at the one, five and ten percent level, respectively (one-sided test for predicted TLR relation; two-sided test otherwise). P-values are presented in parentheses. N=31 in all estimations.
41
Table 4 Impact of timely loss recognition practices on the difference in elasticity of investment between declining and growing industries (ηk
--ηk+) after controlling for the level of
financial development in an economy and alternative governance/investor protection mechanisms This table presents coefficients from various estimations of the following model:
(ηk--ηk
+) = α + β1FDk + β2GDP1960k + β3RIGHTSk + β4SOEk + β5SYNCHk + β6TLRk + εk where (ηk
--ηk+) is the difference between the elasticity of manufacturing investment to value-added estimate for
declining industry-year observations and the elasticity of manufacturing investment to value-added estimate for growing industry-year observations in country k. FDk is a summary measure of financial development, measured as the log of one plus the average sum of stock market capitalization and credit to GDP. GDP1960k is the value of log per capital GDP for 1960. TLRk is a country-level measure of timely loss recognition practices. SYNCH is a measure of stock price synchronicity, and is measured as the average fraction of stocks moving in the same direction in a given week during 1995, from Morck et al. (2000). SOE is index (0 to 10) of the State’s involvement in the country’s economy, based on the fraction of an economy's output due to state-owned enterprises. RIGHTS is an index of investor rights. It is the product of a measure of the rule of law and the number of important shareholder and creditor in the country's legal code. All variables are defined in Appendix 1. Panel A: TLR measures based on Ball, Kothari and Robin (2000) non-linear earnings-returns estimation technique Bad News Timeliness Incremental Bad News Timeliness
Intercept -0.179 -0.027 0.196 0.401 -0.163 -0.028 0.201 0.367 (0.151) (0.882) (0.873) (0.734) (0.184) (0.880) (0.871) (0.758) FDk -0.202c -0.065 -0.060 -0.221c -0.216c -0.071 -0.071 -0.229c
(0.067) (0.502) (0.531) (0.066) (0.052) (0.465) (0.468) (0.058) GDP(1960)k -0.016 0.003 -0.001 -0.028 -0.016 0.006 0.002 -0.026 (0.475) (0.902) (0.987) (0.380) (0.489) (0.798) (0.953) (0.421) RIGHTSk 0.084b - - 0.088b 0.086b - - 0.089b
(0.030) - - (0.034) (0.027) - - (0.032) SOEk - -0.008 - -0.013 - -0.004 - -0.008 - (0.737) - (0.582) - (0.881) - (0.729) SYNCHk - - -0.373 -0.717 - - -0.347 -0.679 - - (0.829) (0.663) - - (0.842) (0.682) TLRk 0.487c 0.514c 0.541c 0.477c 0.179c 0.476c 0.504c 0.459 (0.065) (0.072) (0.066) (0.082) (0.071) (0.099) (0.085) (0.102) R2 0.2721 0.1291 0.1291 0.2884 0.2675 0.1123 0.1149 0.2778 Adj. R2 0.1601 -0.0048 -0.0103 0.1028 0.1548 -0.0242 -0.0267 0.0894 N 31 31 30 30 31 31 30 30 a,b,c Significant at the one, five and ten percent level, respectively (one-sided test for predicted TLR relation; two-sided test otherwise). P-values are presented in parentheses.
42
Table 4 (continued) Impact of timely loss recognition practices on the difference in elasticity of investment between declining and growing industries (ηk
--ηk+) after controlling for the level of
financial development in an economy and alternative governance/investor protection mechanisms Panel B: TLR measures based on Ball and Shivakumar (2005) non-linear accruals-cash flow estimation technique Bad News Timeliness Incremental Bad News Timeliness
Intercept 0.049 0.250 -0.122 0.148 -0.128 0.063 -0.261 0.223 (0.768) (0.196) (0.918) (0.902) (0.306) (0.726) (0.843) (0.862) FDk -0.195c -0.102 -0.098 -0.212c -0.213c -0.098 -0.086 -0.236c
(0.078) (0.275) (0.297) (0.080) (0.064) (0.338) (0.398) (0.059) GDP(1960)k -0.008 0.002 0.008 -0.012 -0.003 0.010 0.017 -0.013 (0.712) (0.937) (0.760) (0.690) (0.882) (0.658) (0.547) (0.682) RIGHTSk 0.064 - - 0.067 0.083b - - 0.087c
(0.118) - - (0.132) (0.047) - - (0.053) SOEk - -0.010 - -0.013 - -0.015 - -0.018 - (0.639) - (0.573) - (0.524) - (0.447) SYNCHk - - 0.470 -0.037 - - 0.344 -0.355 - - (0.779) (0.982) - - (0.852) (0.843) TLRk 0.213c 0.296b 0.311b 0.209c 0.045 0.121 0.120 0.048 (0.067) (0.018) (0.017) (0.090) (0.349) (0.158) (0.175) (0.356) R2 0.2702 0.2036 0.2046 0.2838 0.2078 0.0899 0.0778 0.2291 Adj. R2 0.1580 0.0811 0.0773 0.0970 0.0860 -0.0501 -0.0697 0.0280 N 31 31 30 30 31 31 30 30 a,b,c Significant at the one, five and ten percent level, respectively (one-sided test for predicted TLR relation; two-sided test otherwise). P-values are presented in parentheses.
43
Table 5 Impact of timely loss recognition practices on the difference in investment growth response to industries with lagged negative and lagged positive mean returns This table presents coefficients from various estimations of the following model:
(λ- – λ+)k = α + β1FD1995k + β2GDP1992k + β3RIGHTSk + β4SOEk + β5SYNCHk + β6TLRk + εk
where (λ- – λ+)k is the country-level difference in the investment growth response to industries with lagged negative and positive mean industry returns respectively, estimated over the period 1994 to 2003. In panel A, investment growth is measured as the ratio of current to lagged additions to fixed assets (Global Vantage data item 145). In panels B and C, investment growth is measured as the net investment in fixed assets (in excess of depreciation), defined as the ratio of current to lagged net fixed assets (Global Vantage data item 76). All variables are defined in Appendix 1. Panel A: Dependent variable: Differences in investment response coefficients generated from country-level estimations of the following equation:
log(It/It-1)j,t = αpPOSj,t-1 + αnNEGj,t-1 + λ+POSj,t-1*log(1+RETj,t-1) + λ-NEGj,t-1*log(1+RETj,t-1) + εj,t
where RETj,t-1 is the average return to firms in industry j in country k in year t-1. NEG (POS) is an indicator variable equal to one if RETj,t-1 is less than (greater than or equal to) zero in year t-1, zero otherwise. N=21 in all estimations. Ball, Kothari and Robin Ball and Shivakumar
TLRk: Bad News Timeliness Incremental Bad News Timeliness Bad News Timeliness Incremental Bad News Timeliness
Intercept
0.318 0.081 1.384 0.435 0.165 1.517 0.817 0.637 1.434 0.089 -0.027 0.769(0.238)
(0.784) (0.430) (0.129)
(0.601) (0.406) (0.019)
(0.159) (0.447) (0.779)
(0.935) (0.707)
FD(1995)k 0.083 0.024 0.135 0.067 -0.040 0.105 -0.047 0.130 0.170 -0.040 0.066 0.081 (0.675)
(0.942) (0.722) (0.751)
(0.909) (0.790) (0.829)
(0.725) (0.684) (0.864)
(0.858) (0.844)
GDP(1992)
-0.275b -0.305b -0.357b -0.273b -0.296b -0.349b -0.155 -0.221 -0.258 -0.149 -0.205 -0.247(0.027)
(0.020) (0.027) (0.037)
(0.030) (0.036) (0.188)
(0.078) (0.116) (0.224)
(0.105) (0.140)
RIGHTSk - 0.096 0.102 - 0.107 0.113 - 0.038 0.049 - 0.036 0.045 - (0.126) (0.125) - (0.102) (0.101) - (0.643) (0.595) - (0.689) (0.654)
SOEk - - 0.009 - - 0.019 - - 0.000 - - -0.009 - - (0.813) - - (0.652) - - (0.994) - - (0.827)
SYNCHk - - -2.015 - - -2.215 - - -1.226 - - -1.019 - - (0.425) - - (0.400) - - (0.661) - - (0.727)
TLRk 1.694a 1.465b 1.446b 1.416b 1.161b 1.220b 0.758a 0.701b 0.647c 0.826b 0.815c 0.742c
(0.005)
(0.014) (0.021) (0.014)
(0.034) (0.042) (0.010)
(0.039) (0.072) (0.021)
(0.056) (0.099)
R2 0.3957
0.4815 0.5059 0.3287
0.4290 0.4625 0.3515
0.4205 0.4288 0.2962
0.3988 0.4076
Adj. R2 0.2891
0.3519 0.2942 0.2103
0.2862 0.2322 0.2371
0.2757 0.1840 0.1720
0.2485 0.1536
44
Table 5 (continued) Impact of timely loss recognition on the difference in investment growth response to industries with lagged negative and positive returns Panel B: Dependent variable: Differences in investment response coefficients generated from country-level estimations of the following equation:
((netPPEt/netPPEt-1)-1)j,t = αpPOSj,t-1 + αnNEGj,t-1 + λ+POSj,t-1* RETj,t-1 + λ-NEGj,t-1* RETj,t-1 + εj,t where RETj,t-1 is the average return to firms in industry j in country k in year t-1. NEG (POS) is an indicator variable equal to one if RETj,t-1 is less than (greater than or equal to) zero in year t-1, zero otherwise. N=31 in all estimations. Ball, Kothari and Robin Ball and Shivakumar
TLRk: Bad News Timeliness Incremental Bad News Timeliness Bad News Timeliness Incremental Bad News Timeliness
Intercept
-0.105 -0.116 -0.158 -0.079 -0.093 -0.177 -0.013 -0.043 -0.104 -0.079 -0.090 -0.329(0.231)
(0.196) (0.774) (0.354)
(0.285) (0.743) (0.904)
(0.710) (0.871) (0.412)
(0.361) (0.619)
FDk 0.116 0.081 0.023 0.099 0.060 0.009 0.075 0.044 -0.023 0.053 0.026 -0.058 (0.226)
(0.440) (0.840) (0.293)
(0.552) (0.933) (0.484)
(0.704) (0.858) (0.622)
(0.820) (0.644)
GDP(1992)
-0.047 -0.053 -0.042 -0.0512 -0.058 -0.044 -0.013 -0.020 -0.010 -0.011 -0.019 -0.002(0.234)
(0.189) (0.371) (0.193)
(0.145) (0.339) (0.758)
(0.643) (0.858) (0.780)
(0.649) (0.970)
RIGHTSk - 0.014 0.014 - 0.016 0.016 - 0.014 0.013 - 0.014 0.011 - (0.409) (0.413) - (0.339) (0.342) - (0.511) (0.550) - (0.475) (0.581)
SOEk - - -0.007 - - -0.003 - - -0.011 - - -0.014 - - (0.544) - - (0.828) - - (0.400) - - (0.280)
SYNCHk - - 0.142 - - 0.156 - - 0.210 - - 0.477 - - (0.848) - - (0.830) - - (0.813) - - (0.596)
TLRk 0.480a 0.453a 0.431a 0.521a 0.497a 0.487a 0.097c 0.067 0.073 0.090c 0.070 0.099c
(0.003)
(0.005) (0.009) (0.002)
(0.003) (0.006) (0.095)
(0.219) (0.226) (0.074)
(0.153) (0.100)
R2 0.2719
0.2912 0.3038 0.2965
0.3213 0.3289 0.0875
0.1029 0.1323 0.1005
0.1184 0.1730
Adj. R2 0.1911
0.1821 0.1222 0.2183
0.2169 0.1538 -0.0138
-0.0351 -0.0940 0.0005
-0.0173 -0.0428 a,b,c Significant at the one, five and ten percent level, respectively (one-sided test for predicted TLR relation; two-sided test otherwise). P-values are presented in parentheses.
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Table 6 The influence of TLR practices conditional on the country’s ownership structure This table presents coefficients from various estimations of the following model:
(ηk--ηk
+) or (λk--λk
+) = α0 + α1CONCOWNk + β1FDk + β2GDP1960k + β3RIGHTSk + β4TLRk
+ β5TLRk*CONCOWNk + εk In these models, (ηk
--ηk+) is the difference between the elasticity of manufacturing investment to value-added
estimate for declining industry-year observations and the elasticity of manufacturing investment to value-added estimate for growing industry-year observations in country k. (λk
--λk+) is the difference in the investment response
(as measured by current year growth in net PPE) to lagged negative and positive industry returns. FDk is a summary measure of financial development, measured as the log of one plus the average sum of stock market capitalization and credit to GDP. In the second set of estimations, FD is measured as the sum of stock market capitalization and credit to GDP in 1992. In the first set of estimations, GDPk is the value of log per capital GDP for 1960; in the second set of estimations, GDPk is the value of log per capital GDP for 1992. TLRk is a country-level measure of timely loss recognition practices. RIGHTS is an index of investor rights. It is the product of a measure of the rule of law and the number of important shareholder and creditor in the country's legal code. CONCOWN is an indicator variable equal to one if the country is classified as having concentrated ownership, zero otherwise. Countries are classified as concentrated (diffuse) ownership structure if the country’s realization is above (less than or equal to) the median sample observation. The extent to which the country is characterized by concentrated ownership is based on the average percentage of common shares owned by the three largest shareholders in the ten largest non-financial, privately-owned domestic firms in the country. All variables are defined in Appendix 1. Dependent Variable: (ηk
--ηk+) (λk
--λk+)
TLR: BKR_TIME BKR_INCR BS_TIME BS_INCR BKR_TIME BKR_INCR BS_TIME BS_INCR
Intercept -0.121 -0.097 0.193 -0.200 -0.364 -0.324 0.005 -0.413 (0.462) (0.543) (0.363) (0.289) (0.007) (0.012) (0.977) (0.009) CONCOWNk -0.035 -0.043 -0.165 0.136 0.091 0.054 -0.101 0.254 (0.828) (0.778) (0.253) (0.175) (0.456) (0.642) (0.383) (0.084) FDk -0.214c -0.229b -0.209c -0.227c 0.090 0.069 0.087 0.072 (0.062) (0.048) (0.066) (0.051) (0.290) (0.420) (0.334) (0.414) GDP1960k -0.021 -0.020 -0.017 -0.016 -0.020 -0.019 -0.010 -0.011 (0.379) (0.399) (0.451) (0.493) (0.286) (0.314) (0.576) (0.549) RIGHTSk 0.080b 0.082b 0.064 0.077c 0.031 0.035 0.019 0.017 (0.047) (0.041) (0.126) (0.066) (0.300) (0.237) (0.563) (0.594) TLRk 0.618c 0.577c 0.410b 0.402c 0.912a 0.819a 0.486b 0.540b
(0.091) (0.093) (0.047) (0.087) (0.007) (0.009) (0.017) (0.013) TLRk*CONCOWNk -0.205 -0.186 -0.283 -0.403c -0.298 -0.130 -0.411b -0.404b
(0.362) (0.381) (0.149) (0.092) (0.250) (0.391) (0.034) (0.047) β4 + β5 0.413 0.391 0.127 -0.001 0.614c 0.689c 0.075 0.136 (0.323) (0.414) (0.432) (0.995) (0.060) (0.067) (0.567) (0.153) R2 0.2935 0.2870 0.3128 0.2761 0.4294 0.4200 0.3659 0.3749 Adj. R2 0.1168 0.1087 0.1410 0.0952 0.2867 0.2751 0.2073 0.2187 a,b,c Significant at the one, five and ten percent level, respectively (one-sided test for predicted TLR relation; two-sided test otherwise). P-values are presented in parentheses.
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