Journal of Financial Economics 83 (2007) 321365

Corporate capital structure and the characteristics ofsuppliers and customers$

Jayant R. Kalea, Husayn Shahrurb,

r 2006 Published by Elsevier B.V.

ARTICLE IN PRESS

www.elsevier.com/locate/jfec

ParisUniversite Paris (Sorbonne), Laval University, the Lebanese American University, and University of

Pittsburgh. We appreciate the research assistance of Lin Hai and Jian Wen. We thank Joseph Fan and Larry Lang

for providing the conversion table for the IO-SIC codes.0304-405X/$ - see front matter r 2006 Published by Elsevier B.V.

doi:10.1016/j.jneco.2005.12.007

Corresponding author.

E-mail address: hshahrur@bentley.edu (H. Shahrur).JEL classifications: G32; G33; L13; L14; L22; L24

Keywords: Capital structure; Relationship-specic investments; Implicit contracts; Market power; Buyer power

$We have beneted from discussions with Vikas Agarwal, Anwar Boumosleh, Sudip Datta, Gerry Gay, Marty

Grace, Atul Gupta, Srini Krishnamurthy, Kartik Raman, Stefan Ruenzi, Chip Ryan, Ajay Subramanian, Anand

Venkateswaran, Lingling Wang, Chip Wiggins, an anonymous referee, and seminar participants at the 2004

Financial Management Association meetings, the 2005 European Financial Management Association meetings,

Bentley College, the Indian Institute of ManagementBangalore, Institut dAdministration des Entreprises deaJ. Mack Robinson College of Business, Georgia State University, Atlanta, GA 30303, USAbDepartment of Finance, Bentley College, Waltham, MA 02452, USA

Received 2 June 2005; received in revised form 29 November 2005; accepted 19 December 2005

Available online 20 November 2006

Abstract

We investigate the link between a rms leverage and the characteristics of its suppliers and

customers. Specically, we examine whether rms use decreased leverage as a commitment

mechanism to induce suppliers/customers to undertake relationship-specic investments. We nd

that the rms leverage is negatively related to the R&D intensities of its suppliers and customers. We

also nd lower debt levels for rms operating in industries in which strategic alliances and joint

ventures with rms in supplier and customer industries are more prevalent. Consistent with a

bargaining role for debt, we nd a positive relation between rm debt level and the degree of

concentration in supplier/customer industries.

ARTICLE IN PRESS1. Introduction

Since Modigliani and Millers (1958) capital structure irrelevance result, researchershave searched for capital structure explanations primarily within the context of rmboundaries that are determined by explicit contracts among stakeholders includingshareholders, debtholders, managers, and the government. The research in this stream ofliterature provides important insights into the effects of taxes, bankruptcy costs,information asymmetries, agency issues, and other frictions on corporate leveragedecisions. Building on this work, another body of research (see, e.g., Titman, 1984;Maksimovic and Titman, 1991) analyzes a rms capital structure decision in a setting inwhich the rms boundaries include implicit as well as explicit contracts. We contribute tothis latter stream by investigating how the inclusion of suppliers and customers asstakeholders affects a rms leverage choice.We focus on two aspects of the relation between a rms debt level and its dealings in the

input and output markets. First, we hypothesize that a rm can use a lower level of debt inits capital structure to induce its suppliers and customers to undertake relationship-specic(RS) investments. Our hypothesis is based on the work by Titman (1984) andMaksimovic and Titman (1991). Titman (1984) suggests that a rm with a unique productmay require its customers to undertake investments that lose value if the rm goes intoliquidation. In this setting, lower leverage commits the rm to a liquidation policy thattakes into account the effects on its customers. Further, customers may not be willing todeal with a highly levered rm, which is less likely to worry about its reputation(Maksimovic and Titman, 1991). We apply this intuition to RS investments by suppliersand customers and hypothesize that rms that expect their suppliers/customers toundertake R-S investments will carry lower levels of debt.Our second hypothesis considers the relation between a rms choice of debt level and its

bargaining position relative to its suppliers/customers. The intuition for our hypothesisfollows from the extant literature on the role of debt in managementlabor unionbargaining (see, e.g., Bronars and Deere, 1991), which suggests that raising the debt levelincreases the managements bargaining power vis-a`-vis a labor union by reducing theamount of rm surplus available for sharing with labor. Specically, we hypothesize that arm may choose a higher debt level when it faces suppliers/customers who have relativelyhigher bargaining power. The empirical implication of this hypothesis is a positive relationbetween a rms debt level and measures of supplier/customer negotiation power.In order to test our hypotheses, we construct two separate data sets. The rst data set

identies suppliers and customers at the industry level and the second consists of keycustomer and supplier rms. The industry-level data offer three distinct benets relative tothe rm-level data. First, the sample of rms in the industry-level data set is much larger.Second, endogeneity issues that are endemic to corporate nance research are likely to besignicantly less severe in tests that relate a rms capital structure to variables measuredfor supplier and customer industries than to variables measured for supplier and customerfirms. Third, industry-level data allow us to relate rm nancing decisions to importantvariables such as the levels of buyer and supplier power that need to be measured at theindustry level. The main advantage of the rm-level data set, on the other hand, is that itidenties supplier and customer rms more precisely and thus the inferences based on thendings from this data are cleaner. Further, it allows us to examine the effect of a rms

J.R. Kale, H. Shahrur / Journal of Financial Economics 83 (2007) 321365322leverage on the RS investments of its key suppliers and customers.

ARTICLE IN PRESSIn order to test our rst hypothesis, we measure RS investment by two sets of variables.Allen and Phillips (2000) suggest that R&D-intensive industries are more likely to createrelationship-specic assets. Accordingly, we base our rst set of variables for RSinvestments on supplier and customer R&D investments. Next, as in Fee, Hadlock, andThomas (2005), we conjecture that rms are more likely to establish strategic alliances(SAs) and joint ventures (JVs) with suppliers/customers when there is a greater need forRS investments. Thus, the intensity of SAs and JVs that are established between thesample rms and suppliers/customers forms our second set of RS investments variables.We document signicant support for the negative relation between rm debt levels and

the above measures of RS investments by suppliers/customers. We nd that a rmsleverage is decreasing in the R&D intensities of its supplier and customer industries. Wealso nd that rms operating in industries characterized by high intensities of SAs and JVswith rms in supplier and customer industries tend to have lower debt levels. We show thatthe economic signicance of these relations is comparable to that of traditionaldeterminants of capital structure. Next, we nd that the negative relations betweenleverage and the supplier and customer R&D variables are weaker for vertically integratedrms, which provides additional support for the RS investments hypothesis. Further, weshow that the negative relation between customer R&D investments and rm leverage ismore pronounced for rms that belong to a concentrated industry and for rms with highmarket share. This nding implies that the effect of customer RS investments on rmleverage is stronger when the rm produces an input for which there are few alternatesuppliers.The analysis using rm-level data further supports our hypothesis that rms lower debt

levels to induce RS investments. We nd that rm leverage is negatively related to theR&D intensity of its key suppliers and customers and that leverage is lower for rms thathave established SAs or JVs with their key suppliers and customers.We then estimate simultaneous equation models that treat the rms leverage and the

R&D of key suppliers and customers as endogenous variables. In addition to addressingendogeneity concerns, this specication also tests whether the rms leverage and the R&Dinvestment decisions of its suppliers and customers are simultaneously determined. We ndthat the negative relation between rm leverage and the R&D intensities of its suppliersand customers is robust to the simultaneous equation specication. In the R&Dregressions, we nd negative relations between the supplier and customer R&D andexpected leverage. Our ndings therefore add to the literature that studies the effect ofleverage on the rms dealings in the product markets (see, e.g., Opler and Titman, 1994;Phillips, 1995).Our second set of hypotheses relates to the use of debt in improving a rms bargaining

position vis-a`-vis its suppliers/customers, where we use industry concentration to measurethe negotiation power of suppliers/customers. Consistent with a bargaining role for debt,we nd that rms that face concentrated supplier and customer industries tend to havehigher levels of debt. Further, the positive relation between leverage and the concentrationof supplier/customer industries is weaker for rms with high market shares in their ownindustry. The latter nding implies that when a rm has a higher market share in its ownindustry, its bargaining power vis-a`-vis its suppliers/customers is greater and, hence, it hasa lower incentive to use debt as a bargaining mechanism.The rest of the paper proceeds as follows. In Section 2, we review the literature and

J.R. Kale, H. Shahrur / Journal of Financial Economics 83 (2007) 321365 323develop the empirical hypotheses. In Section 3, we provide details of the sample and the

ARTICLE IN PRESSmethodology we use to identify suppliers and corporate customers. We present the resultsin Section 4. Section 5 concludes the paper.

2. Related literature and testable hypotheses

Our work is part of a growing body of research that links capital structure and productmarket strategy; see Maksimovic (1995) for a review of this literature. Theoretical studiessuggest that leverage can affect the intensity of product market competition (see, e.g.,Brander and Lewis, 1986; Maksimovic, 1988; Chevalier and Scharfstein, 1996; Dasguptaand Titman, 1998). Empirical studies such as Opler and Titman (1994), Chevalier (1995a,b), Phillips (1995), Kovenock and Phillips (1997), Zingales (1998), Khanna and Tice (2000,2005), and Campello (2003, 2005) nd evidence consistent with leverage affecting productmarket strategy. Our study contributes to this literature by examining the relation betweenthe rms capital structure and the characteristics of its key suppliers and customers.Understanding this link is important since the ability of a rm to compete in the productmarket depends on its relations with its suppliers and customers.

2.1. Relationship-specific investments and capital structure

Our work closely relates to research that studies the relation between nancial structureand implicit contracting. Titman (1984) demonstrates that the rm can commit to aliquidation policy that takes into consideration the effect of rm liquidation on customersby choosing a lower debt level. Developing this idea further, Maksimovic and Titman(1991) show that customers may be unwilling to conduct business with a highly leveredrm because higher debt reduces the rms willingness to invest in its reputation andproduce high-quality products.We build on the insights from the implicit contracting studies to formulate our

hypothesis regarding the relation between a rms debt level and RS investments by itssuppliers and customers. Suppose that a rms operations depend on its ability to induceits suppliers or customers to undertake RS investments, and further, that theseinvestments lose value if the rm goes into liquidation. Then the rms capital structurewill affect the incentives of suppliers and customers to make RS investments as long asthe rms liquidation decision is causally linked to its bankruptcy status (Titman, 1984). Inaddition, because high debt can reduce the rms incentives to invest in its reputation(Maksimovic and Titman, 1991), and hence reduces the willingness of suppliers andcustomers to undertake RS investments, a rm that expects its customers/suppliers toundertake RS investments should choose lower debt levels. On the other hand, if somerms are expected to remain highly levered due to exogenous factors, one would expect thesuppliers and customers of such rms to undertake less RS investments.Further, the effect of RS investments by customers on the rms leverage can be more

pronounced if the rm produces an input for which there are few alternative suppliers. Forinstance, if a customer rm has many potential suppliers, it may decide to diversify its RSinvestments and deal with many suppliers that produce the same input, thereby reducingits exposure to a loss given the liquidation of any particular supplier. In addition, the lossin the value of RS investments undertaken with any given supplier may be lower if thereare similar rms in the supplier industry that can salvage some of these investments in the

J.R. Kale, H. Shahrur / Journal of Financial Economics 83 (2007) 321365324case of liquidation. Using the concentration of the rms industry and the rms market

share as proxies for the importance of the rm to its customer, the above argumentssuggest that the effect of RS investments on leverage will be weaker for rms in a dispersesupplier industry and for rms with lower market shares.The above discussions relate to the use of debt as a mechanism to induce RS

investments. We recognize that debt is not the only tool available to a rm for this

The intensity of R&D expenditure of suppliers and customers is our rst proxy for the

ARTICLE IN PRESSJ.R. Kale, H. Shahrur / Journal of Financial Economics 83 (2007) 321365 325intensive industries tend to involve specialized inputs that require transaction-specicinvestments by suppliers. In addition, vertical chains that are R&D intensive are likely tohave complex interstage interdependencies (Armour and Teece, 1980). Allen and Phillips(2000) suggest that R&D-intensive industries are more likely to create relationship-specicassets. In addition to the R&D-based measures, we construct variables from data on SAsand JVs as alternative measures of RS investments between a rm and its suppliers/customers. Fee, Hadlock, and Thomas (2005) nd that a rm is more likely to establishSAs with trading partners that are expected to undertake RS investments. As a result,RS investments are likely to be higher if the rm and its suppliers/customers haveestablished such alliances.1

To summarize, the central hypothesis described in this section is:

Hypothesis 1:. Firms that deal with R&D-intensive suppliers/customers and rms withhigh intensities of SAs and JVs with suppliers/customers choose lower leverage.

We also test several related subhypotheses, namely: (i) the negative relations between therms leverage and the proxies for RS investments by customers (customer R&D, and theintensity of SAs and JVs with customers) are weaker for rms in disperse industries and forthose with low market shares; (ii) the negative relations between debt levels and the proxiesfor RS investments by supplier/customers are weaker for vertically integrated rms; and(iii) suppliers and customers of a highly levered rm undertake lower levels of R&Dinvestments.

1An alternative proxy is one that only includes SAs. An argument for excluding JVs is that, once a JV is formed,

partners in the JV may be less concerned with nancial distress in the parent companies. On the other hand, if

establishing a JV indicates that the partners are likely to cooperate in the future, then nancial distress at the

parent level becomes more important. To address this issue, we repeat our analysis by excluding JVs from ourextent of RS investments. The use of R&D intensity as a proxy for asset specicity isprevalent in the empirical literature on transactions cost economics; see Boerner andMacher (2001) for a recent review of this literature. Levy (1985) argues that research-purpose, that is, that a rm can induce the desired behavior from its trading partnersthrough other mechanisms. For example, Williamson (1985) argues that rms contemplatevertical integration when incentive problems with suppliers/customers are severe. Thisobservation leads to an interesting test of our hypothesis. If vertical integration is asolution to the incentive problem, then debt is less likely to be used as the incentivemechanism for vertically integrated rms. We therefore expect that the negative relationsbetween the debt level and the proxies for RS investments will be weaker when rmsintegrate vertically.measures. The results of this analysis are qualitatively similar to those reported here.

2.2. Debt and bargaining

Bronars and Deere (1991) investigate the use of debt as a negotiating tool by rms in

surplus from workers and/or other input suppliers.2 Bronars and Deere (1991) present

ARTICLE IN PRESSJ.R. Kale, H. Shahrur / Journal of Financial Economics 83 (2007) 321365326suppliers/customers. Specically, we conjecture that a rm will choose a higher debt levelwhen it faces suppliers/customers who enjoy greater bargaining power. In our empiricaltests we proxy supplier/customer bargaining power by the degree of concentration in thesupplier/customer industry, where greater concentration indicates greater bargainingpower. The use of industry concentration as a proxy for bargaining power has signicantsupport in the literature. In the case of suppliers, researchers argue that more concentratedsuppliers have greater seller power over their customers (see Stigler, 1964; Scherer andRoss, 1990 for a review of this literature). Thus, a rm that faces a concentrated supplierindustry is at a bargaining disadvantage, whereas the presence of numerous alternativesuppliers empowers the rm because it can make a credible threat to withhold futurebusiness from its existing suppliers (see, e.g., Holmstrom and Roberts, 1998). Similararguments apply to the degree of concentration in the rms customer industry. Inparticular, Maskin and Riley (1984), Stole and Zwiebel (1996), and Snyder (1996) proposemodels that suggest that more concentrated customers enjoy greater buyer power overtheir suppliers. The buyer power theory predicts that a rms bargaining power vis-a`-vis itscustomers should be decreasing in the concentration of the downstream industry.To summarize, our second central hypothesis described in this section is:

Hypothesis 2:. A rms leverage is positively related to the concentration levels in itssupplier and customer industries.

Since bargaining power is increasing in industry concentration, a rm with a dominantposition in its industry should enjoy a bargaining advantage vis-a`-vis its trading partners.Therefore, a rm with a high market share should have less incentive to use debt as abargaining tool. The ensuing subhypothesis, therefore, is that the positive relation betweena rms debt level and supplier/customer industry concentration is weaker for rms withhigher market shares.

2The business press recognizes the ability of a rms suppliers and customers to expropriate some of the rms

prots. For example, Fitzgerald (1996) suggests that suppliers of US automakers resist opening their books toempirical evidence that rms facing greater threats of unionization have higher leverageratios (see also Hanka, 1998).We propose that debt can play a similar bargaining role in a rms dealings with itsreducing the rm surplus that workers can extract through union formation. Since a rm iscommitted to pay a portion of its future surplus to debtholders, debt obligations reduce thepart of the surplus that a union can extract without driving the rm into bankruptcy.Bronars and Deere show that an optimal capital structure obtains when the marginalwealth gain from the reduction in the union wage is equal to the marginal increase inbankruptcy costs. Dasgupta and Sengupta (1993), Perotti and Spier (1993), andSubramanian (1996) show that a rm can use debt to shield a portion of its futuretheir customers because of their fear that the customers would signicantly chip away at their prot margins.

ARTICLE IN PRESS3. Data, sample construction, and variable descriptions

To construct our sample, we identify all rms covered by Compustat during the period19842003. As is common in cross-sectional studies of capital structure determinants (see,e.g., Berger, Ofek, and Yermack, 1997), we exclude nancial rms (SIC codes between6000 and 6999) and utilities (SIC codes between 4900 and 4999). We also exclude all rmsthat are headquartered in a foreign country (Compustats state variable equals 99) sincethe supplier and customer characteristics we capture reect the US market. From thismaster sample, we construct different subsamples to examine supplier and customer effectsat both the industry and rm levels. In Section 3.1, we discuss the supplier and customervariables and the restrictions we employ to create the subsamples used in our industry-levelanalysis. In Section 3.2, we describe the variables and the subsamples used in our analysisof key suppliers and customers. Section 3.3 discusses our leverage measures and controlvariables.

3.1. Samples and variable construction at supplier- and customer-industry levels

As we mention earlier, we rst relate a rms debt level to the characteristics of the rmssupplier and customer industries. The advantages of the industry-level data, relative to therm-level data, are: signicantly larger sample size, less severe endogeneity problems, andgreater suitability for constructing the concentration measures.

3.1.1. Supplier- and customer-industry R&D intensities

Since most rms use a large number of inputs, we measure supplier R&D intensity as theweighted average of the R&D intensities of all supplier industries, where the weightrepresents the importance of the input bought from each supplier industry in theproduction of the rms output. Specically, the supplier R&D intensity for a rm in theith industry is given by

Supplier Industries R&D Xn

j1;jai

Supplier Industry R&Dj Industry Input Coefficientji; (1)

where n is the number of supplier industries, Supplier Industry R&Dj is the jth supplierindustrys R&D expenditures divided by its total assets, and Industry Input Coefficientji isthe dollar amount of the jth supplier industrys output used as an input to produce onedollar of the output of the ith industry. Note that the supplier R&D intensity measure willbe high if the rm outsources a signicant part of its inputs from R&D-intensive supplierindustries.Analogously, the R&D intensity of customer industries, Customer Industries R&D, is

given by

Customer Industries R&D Xn

j1jai

Customer Industry R&Dj Industry Percentage Soldji; (2)

where n is the number of customer industries, Customer Industry R&Dj is the R&Dintensity of the jth customer industry measured by the ratio of the industrys total R&Dexpenditures to its total assets, and Industry Percentage Soldji is the percentage of the ith

J.R. Kale, H. Shahrur / Journal of Financial Economics 83 (2007) 321365 327industrys output that is sold to the jth customer industry. Intuitively, Industry Percentage

Soldji measures the importance of the jth customer industry as a buyer of the output of therms industry. Thus, Customer Industries R&D is a weighted average of the R&D intensityof all customer industries, where the weight is the percentage of the output of the rmsindustry that is sold to each customer industry.

where n is the number of customer industries, Herfindahl Indexj is the Herndahl index of

ARTICLE IN PRESSJ.R. Kale, H. Shahrur / Journal of Financial Economics 83 (2007) 3213653283Our measures are more in line with the variables used in Bartelsman, Caballero, and Lyons (1994). Shea (1993)

uses a measure that is similar to Customer Change in Sales as an instrument for demand shock. However, Shea

imposes restrictions on the downstream industries that are included in his measure to ensure both the absence of

correlation between the variable and industry supply (exogeneity), and the presence of correlation between the

variable and industry output (relevance). While these restrictions are crucial to Sheas objective of estimating thethe jth customer industry, and Industry Percentage Soldji is as dened above.

3.1.3. Growth in supplier/customer industries

It is possible that the supplier and customer R&D variables can capture someaspects of the rms own growth that is related to growth in its upstream and down-stream industries. In addition to rm-specic control variables that we use to controlfor the rms growth opportunities (see Section 3.4), we construct variables that controlfor the effect of growth in supplier and customer industries on the rms leverage.Following the literature (see, e.g., Shea, 1993; Bartelsman, Caballero and Lyons, 1994), wedene the two variables Supplier Change in Sales and Customer Change in Sales asfollows:3

Supplier Change in Sales Xn

j1jai

Change in Salesj Industry Input Coefficientji; (5)

Customer Change in Sales Xn

j1jai

Change in Salesj Industry Percentage Soldji; (6)3.1.2. Supplier/customer industry concentration

To capture the concentration of supplier (customer) industries, we follow the literatureand use a weighted average of the concentrations of all supplier (customer) industries (see,e.g., Ravenscraft, 1983; Scherer and Ross, 1990). For each rm in the ith industry, thesupplier concentration measure is dened as:

Supplier Concentration Xn

j1iaj

Herfindahl Indexj Industry Input Coefficientji, (3)

where n is the number of supplier industries,Herfindahl Indexj is the sales-based Herndahlindex of the jth supplier industry, and Industry Input Coefficientji is as dened above.Similarly, for each rm in the ith industry, the concentration of customers is:

Customer Concentration Xn

j1jai

Herfindahl Indexj Industry Percentage Soldji; (4)elasticity of supply, they are less relevant to our study.

where n is the number of supplier (customer) industries, Change in Salesj is the one-yearchange in sales for the median rm in the jth supplier (customer) industry, and IndustryInput Coefficientji (Industry Percentage Soldji) is as dened above.

4

ARTICLE IN PRESSJ.R. Kale, H. Shahrur / Journal of Financial Economics 83 (2007) 321365 3293.1.4. Construction of supplier/customer R&D, concentration, and change in sales variables

We rely on two data sources, the Use table of the benchmark input-output (IO) accountsfor the US economy and the Compustat database, to construct the supplier and customerindustry variables described above (see Fan and Lang, 2000; Shahrur, 2005 for recentpapers that use this data set). For any pair of supplier and customer industries, the Usetable reports estimates of the dollar value of the supplier industrys output that is used asan input in the production of the customer industrys output. The Use table enables us toidentify the rms customer and supplier industries and the importance of each supplier/customer industry to the rm. We use the 1987, 1992, and 1997 Use tables for the periods19841989, 19901994, and 19952003, respectively. We compute the R&D intensities,industry concentrations, and change in sales of supplier/customer industries fromCompustat. Appendix A describes the variable construction process in greater detail.

3.1.5. Labor as an input

In addition to the inputs bought from suppliers, labor is also a major input to the rmsproduction process. The supplier R&D variable described above does not take intoconsideration differences across rms with respect to their reliance on labor. To control forthis factor, we construct the variable Compensation of Employees as the dollar amountspent on employee compensation in the rms industry divided by the industrys totaloutput. This variable is computed using data from the input-output accounts. Further,since some rms may require that their employees undertake rm-specic investments, weuse the product of Compensation of Employees and the rms own R&D intensity as aproxy for the importance of rm-specic investments by employees.

3.1.6. Industry-level strategic alliances and joint venture variables

We conjecture that industries with higher levels of RS investments between the industryrms and suppliers/customers are also likely to have a higher intensity of SAs and JVs withtheir suppliers/customers (see, e.g., Fee, Hadlock, and Thomas, 2005). For each rm in oursamples, we identify all the SAs and JVs announced over the sample period (19842003) inwhich the sample rm is a participant from the Securities Data Company (SDC) strategicalliance and joint venture database. Using the input-output tables and the rms four-digitSIC code, we then identify whether at least one of the other participants in the SA/JVarrangement operates in a supplier or a customer industry. For the purposes of thisselection, we include only those supplier industries that sell more than 1% of their totaloutput to the rms industry. We also include a customer industry if the total dollaramount spent on the input bought from the rms industry represents more than 1% of theindustrys total output. For each four-digit SIC code industry, we dene Supplier(Customer) Alliance and JV Intensity as the number of SAs and JVs formed by rms in

4Using a three-year change in sales (t3 to t) in the construction of Supplier and Customer Change in Sales yieldssimilar results. As an alternative measure for growth in supplier (customer) markets, we use a weighted average of

the market-to-book ratios of the median rms in supplier (customer) industries. The results from this specicationare similar to those reported below.

that industry with rms in a supplier (customer) industry divided by the number of rms inthe industry.

3.1.7. Vertical integration variables

Firms can also resort to vertical integration to address contracting problems that arisewith their suppliers and customers. As a result, the effect of supplier- and customer-relatedvariables on leverage should be less pronounced for vertically integrated rms. FromCompustats segment tapes, we determine whether a rm in our sample has a segment in asupplier/customer industry SIC code. In order to capture the extent of vertical integrationfor rms in our sample, we construct a Backward (Forward) Integration Dummy, whichequals one if the rm has at least one segment in a supplier (customer) industry, and zerootherwise. In the construction of these variables, we include only those supplier industries

ARTICLE IN PRESSJ.R. Kale, H. Shahrur / Journal of Financial Economics 83 (2007) 321365330that sell at least 1% of their total output to the rms industry, and those customerindustries whose total dollar amount spent on the input bought from the rms industryrepresents at least 1% of their total outputs.

3.1.8. Samples used in the analyses

Not every rm has identiable suppliers and customers whose characteristics can affectits leverage. In order to test our hypotheses on the largest number of rms possible, weexamine upstream and downstream effects separately on different data subsamples. Wealso discuss in Section 4.2.4 an alternative approach whereby we use the same sample toexamine supplier and customer effects.To construct the two subsamples, we impose the following restrictions. First, since the

inputs bought by rms in retail and wholesale industries (SIC codes between 5000 and5999) are sold to other customers without any signicant processing, that is, they are notused as intermediate inputs, we analyze supplier effects on a subsample that excludes rmsin these industries. This subsample consists of a panel of 76,290 rm-year observations for10,310 rms for which we can construct the industry-level supplier variables.5

We also impose restrictions to obtain a sample for examining customer effects. Somerms sell to nal users and have no identiable customer rms. Because customercharacteristics are not observable for these rms, in our analysis of customer effects a rmis included in the subsample if no more than 25% of its output is sold to nal users.6 Basedon the Use table, we dene sales to nal users as uses classied under the IO system as:

5We verify whether the inclusion of rms that mainly use raw materials, such as steel producers, adds noise to

our analysis. Noise may result, for instance, from including rms that buy raw materials from R&D-intensive

suppliers, whose R&D investments are not likely to be RS investments (in this case, the supplier R&D variable

will be a noisy measure of RS investments by suppliers). We nd that most rms in the agriculture and mining

industries do not undertake any R&D investments, and thus their R&D intensity is not a noisy measure of their

limited RS investments. Nonetheless, we repeat the analysis of supplier effects excluding all rms that buy at least

50% of their input from rms in the agriculture and mining industries; the results are qualitatively similar to those

reported here.6The Use table shows the ow of commodities as if they move directly to nal users, that is, even if they reach

nal users by means of wholesalers or retailers (which are treated as service industries whose primary service is the

distribution of goods). If trade were shown as buying and reselling commodities, then many industries would have

wholesalers and retailers as their suppliers and customers. This method of treating trade is crucial to our study

since we are able to capture the actual consuming industries or nal users even if a commodity is sold through

retailers and wholesalers. However, this leads to a downward bias in the customer concentration for industriesselling consumer products, since they sell some of their goods through concentrated wholesale and retail

Personal Consumption Expenditures, Gross Private Fixed Investments, and GovernmentConsumption Expenditures and Gross Investment.7 Appendix A contains the assumptionsmade in the construction of the customer variables regarding the characteristics of nalusers for rms included in the sample. Appendix B presents a list of selected industriesalong with the percentage of the industry output sold to nal users, which rangesfrom 100% to 0%. For example, the industry Motor Homes (SIC code 3716) sellsall its output to nal users (mainly individuals), and thus rms in this industry are notincluded in the customer subsample. On the other hand, a rm in the industry PrimaryProduction of Aluminum (SIC code 3334) is included in the subsample since all its output isused as intermediate input in the production of other products. The restrictions im-posed on the customer subsample result in an unbalanced panel of 26,300 rm-year

ARTICLE IN PRESSJ.R. Kale, H. Shahrur / Journal of Financial Economics 83 (2007) 321365 331observations for 4,065 rms for which we can construct the industry-level customervariables. The median industry included in this subsample sells approximately 6.5% of itsoutput to nal users.

3.2. Customer and supplier variables using firm-level data

The supplier and customer variables described above are at the industry level. We alsoinvestigate the relation between corporate debt levels and supplier/customer characteristicsthat are measured at the rm level. We compute these rm-level characteristics from thesubsamples of rms for which we can identify the key suppliers and customers. Inaccordance with the Statement of Financial Accounting Standards (SFAS) no. 14 and 131,public rms have to disclose the identity of any customer that contributes at least 10% tothe rms revenues, although some rms choose to report customers that contribute lessthan 10%. While these data are available on Compustats industry segment les, thedatabase reports only the name of the customer (not CUSIP or other identiers) and,further adding to the difculty, sometimes it reports only the abbreviated versions of thenames. As a result, we use a combination of automated and manual procedures to identifythe customer rms for our sample rms.8 We impose the same restrictions used toconstruct the industry-level subsamples. We exclude nancial rms and utilities, rms inthe retail and wholesale industries, and rms headquartered in a foreign country.9 Further,we exclude rms whose main supplier (main customer) is a retailer or a wholesaler from thesample used to examine supplier (customer) effects. Appendix C shows a list of selectedsupplier-customer industry pairs along with an example from each pair.

(footnote continued)

industries. Since such industries are not included in the sample used to examine customer effects, the effect of this

bias on the relation between customer concentration and leverage is greatly mitigated.7Personal Consumption Expenditures represent purchases by individual consumers. Gross Private Fixed

Investments consist of purchases of residential and nonresidential structures and of equipment and software by

private businesses. Government Consumption Expenditures and Gross Investment consist of consumption

expenditures and investments by federal, state, and local governments.8The procedure we follow to identify the customer rms is similar to that used in Fee and Thomas (2004) (see

pp. 436437 of their study for more details).9Recall that in the industry-level analysis, a rm is included in the sample if at most 25% of the outputs of the

rms industry are sales to nal users. We do not impose this restriction in this part of the analysis since we have

detailed data about actual customers. Thus, a rm that sells its output to other rm is include in the sample even if

the rms industry sells most of its output to nal users. Imposing the 25% restriction does signicantly affect ourresults.

Since some rms report many customers for any given year, we construct a weightedaverage of the R&D intensities of the rms key customers as follows:

Key Customers R&D Xn

j1Key Customer R&Dj Key Customer Percentage Soldj ; (7)

ARTICLE IN PRESSJ.R. Kale, H. Shahrur / Journal of Financial Economics 83 (2007) 321365332sample is a panel of 9,452 supplier-year observations for which we are able to compute Keycustomers R&D and the other control variables.A signicant number of the customers in our sample are also the key customers to more

than one supplier rms. Thus, for each customer rm in the sample, we compute the R&Dintensity of its suppliers as follows:

Key Suppliers R&D Xn

j1Key Supplier R&Dj Key Customer Input Coefficientj ; (8)

where n is the number of suppliers, Key Supplier R&Dj is equal to the ratio of the R&Dexpense of the jth supplier to its total assets, and Key Customer Input Coefficientj is theratio of the customers purchases from the jth supplier to the customer total sales.10 Forthe period 19842002, this procedure results in a panel of 4,122 customer-yearobservations for which Key Suppliers R&D and the other control variables are available.Finally, using the data from the SDC database, we construct a dummy variable for SAs

and JVs between a rm and its suppliers/customers. This dummy variable, Customer(Supplier) SA and JV Dummy, equals one if the rm has at least one SA or JV establishedwith one of its key customers (key suppliers), and zero otherwise.

3.3. Leverage measures and control variables

We use two measures of nancial leverage, Market Leverage and Book Leverage, whereMarket Leverage is equal to the sum of the book values of long-term debt and debt incurrent liabilities (Compustat items 9 and 34) divided by the sum of the book value of debtand the market value of common equity (item 25item 199), and Book Leverage is the sumof the book values of long-term debt and debt in current liabilities divided by the bookvalue of assets (item 6). Following the literature (see, e.g., Kale, Noe, and Ramirez, 1991;Berger, Ofek, and Yermack, 1997; Mackay and Phillips, 2006), we use the followingcontrol variables:

1. Firm Size is the log of total assets (item 6).2. Return on Assets is operating income (item 13) divided by total assets.

10Note that unlike the respective variables that are constructed using industry-level supplier and customer data,

the Key Customer Percentage Sold and Key Customer Input Coefficientj variables do not sum to one for each rm

since not all customer and supplier rms are reported. Thus, by construction, the key supplier and customer R&D

variables are biased downward. To ensure that our results are not affected by this bias, we repeat our analysis of

supplier (customer) effects after using the R&D intensity of the main supplier (customer). The results of this

specication are qualitatively similar to those reported below. Section 4.3 show estimates of simultaneousof tpercequahe jth customer divided by its total assets, and Key Customer Percentage Soldj is theentage of the rms sales to the jth customer. For the period 19842002, the resultingwhere n is the number of customer rms, Key Customer R&Dj is equal to the R&D expensetion models that use this specication.

3. Asset Collateral Value is net property, plant, and equipment (item 8) divided by totalassets.

4. Volatility is the standard deviation of operating income divided by total assets. Werequire at least three consecutive observations to construct this variable.

5. Nondebt Tax Shields is equal to investment tax credits (item 51) divided by total assets.6. R&D Intensity is equal to total research and development expenditures (item 46)

divided by total assets (item 12).

4. E

ARTICLE IN PRESSJ.R. Kale, H. Shahrur / Journal of Financial Economics 83 (2007) 321365 33314O

winsovalut retailers had missing values for this variable.

ur results are not especially sensitive to winsorization. We nd qualitatively similar results without

rizing the variables, although some of the results pertaining to the control variables that have extreme outlierbiote

mosWe discuss the effect of including rm xed effects on our results in Section 4.5.

We follow Loughran and Ritter (1997) and verify the validity of this assumption by checking the R&D of

ch rms and retailers. We nd that almost all biotech rms had a nonzero value for the R&D variable whileLyan12

13dres (2006) also nds that leverage is negatively related to the number of rivals in the industry.11Mackay and Phillips (2006) nd that rms in concentrated industries tend to rely more on debt nancing.the m

pare the debt levels of above-median rms with the debt levels of rms that are belowedian and present the ndings in Table 2. The average values ofMarket Leverage andtwocomn the univariate analysis, for each variable of interest we divide our sample of rms intogroupsthose with variable values greater than the median and those below. We thenIUnivariate analysis4.1.mpirical ndings7. SE Intensity is equal to selling, general, and administrative expenses (SE) (item 189)divided by total assets.

8. Tobins q is equal to the book value of assets plus the market value of common equityminus the book value of common equity (item 60) divided by the book value of assets.

9. Industry Concentration is the sales-based Herndahl Index of the rms primaryindustry.11

10. Industry and year dummy variables. The industry dummy variables are based on therms two-digit historical SIC code.12

In the construction of our variables, we assume that the rm spends zero dollarson R&D (SE) if the R&D (SE) expense for a rm is missing.13 We nd that for mostof the control variables, some rms have extremely high values. For example, while themedian value of the selling expense variable is 0.23, its maximum value is 2,343. Wewinsorize all the dependent and independent variables (including the supplier andcustomer variables) at the 1st and 99th percentiles in order to reduce the effect of outlierson our results.14

We present descriptive statistics for the dependent and independent variables in Table 1.The mean and median values for Market Leverage are 0.187 and 0.132, respectively. Themean and median values for Book Leverage are 0.268 and 0.210, respectively. Thedescriptive statistics of the independent variables presented in the table show wide rangesof values for all these variables, suggesting that the rms in our sample differ considerablywith regard to the characteristics of their suppliers and customers.es (such as R&D Intensity and SE Intensity) become statistically insignicant.

ARTICLE IN PRESS

Table 1

Descriptive statistics

This table reports descriptive statistics for the dependent and independent variables. The sample period is from

1984 to 2003. Market Leverage is the book value of long-term debt and debt in current liabilities divided by the

sum of the the book value of debt and the market value of common equity. Book Leverage is the book value of

long-term debt and debt in current liabilities divided by the book value of assets. Supplier Industries R&D

(Customer Industries R&D) is the weighted average of the R&D intensities of all supplier (customer) industries.

Supplier (Customer) SA and JV Intensity is the number of SAs and JVs established between industry rms and

rms in supplier (customer) industries, divided by the number of rms in the industry. Compensation of Employees

is total compensation paid to employees divided by total output, constructed at the industry level. Supplier

Concentration (Customer Concentration) is the weighted average of the Herndahl indices of all supplier

(customer) industries. Supplier (Customer) Change in Sales is a weighted average of the change in sales for

supplier (customer) industries. Key Suppliers R&D (Key Customers R&D) is the weighted average of the R&D

intensities of key suppliers (key customers). Suppliers(Customer) SA and JV Dummy is a dummy variable that

equals one if the rm has at least one alliance or a SA established with one of its suppliers (customers), and zero

otherwise. Industry concentration is the sales-based Herndahl index of the rms industry. Firm Size is the log of

total assets. Asset Collateral Value is net property, plant, and equipment divided by total assets. Volatility is the

standard deviation of operation income divided by total assets. Nondebt Tax Shields is investment tax credits

divided by total assets. Return on Assets is operation income divided by total assets. Tobins q is book value of

assets minus book value of common equity plus market value of common equity divided by book value of assets.

R&D Intensity is R&D expenditures divided by total assets. SE Intensity is selling, general, and administrative

expenses divided by total assets. All variables are winsorized at the 1st and 99th percentiles.

Variable Mean Median Maximum Minimum Std dev.

Market Leverage (overall sample) 0.187 0.132 0.750 0.000 0.189

Book Leverage (overall sample) 0.268 0.210 1.882 0.000 0.290

Supplier Industry-Level Variables (76,290 obs.)

Supplier Industries R&D 0.009 0.006 0.040 0.000 0.007

Supplier SA and JV Intensity 0.486 0.218 5.166 0.000 0.795

Compensation of Employees 0.303 0.299 0.818 0.022 0.116

Supplier Concentration 0.090 0.085 0.250 0.029 0.039

Supplier Change in Sales 0.098 0.096 0.308 0.050 0.066Customer Industry-Level Variables (26,300 obs)

Customer Industries R&D 0.017 0.009 0.081 0.000 0.017

Customer SA and JV Intensity 0.493 0.176 6.469 0.000 0.910

Customer Concentration 0.167 0.167 0.321 0.068 0.058

Customer Change in Sales 0.081 0.082 0.217 0.030 0.047

Supplier Firm-Level Variables(4,122 obs.)

Key Suppliers R&D 0.002 0.000 0.059 0.000 0.008

Supplier SA and JV Dummy 0.283 0.000 1.000 0.000 0.450

Customer Firm-Level Variables(9,452 obs)

Key Customers R&D 0.015 0.007 0.104 0.000 0.021

Customer SA and JV Dummy 0.162 0.000 1.000 0.000 0.368

Control Variables(overall sample)

Industry Concentration 0.223 0.179 0.865 0.043 0.163

Firm Size (log of total assets) 4.280 4.146 10.115 1.139 2.188Asset Collateral Value 0.294 0.235 0.917 0.001 0.226

Volatility 0.201 0.091 2.963 0.010 0.381

Nondebt Tax Shield 0.001 0.000 0.017 0.000 0.002

Return on Assets 0.015 0.104 0.439 2.284 0.344Tobins q 2.283 1.459 18.561 0.528 2.558

R&D Intensity 0.058 0.005 0.732 0.000 0.115

SE Intensity 0.332 0.250 2.374 0.000 0.354

J.R. Kale, H. Shahrur / Journal of Financial Economics 83 (2007) 321365334

Book Leverage for rms with above median values for Supplier and Customer Industries

ARTICLE IN PRESSJ.R. Kale, H. Shahrur / Journal of Financial Economics 83 (2007) 321365 335R&D, Key Suppliers and Customers R&D, Supplier and Customer SA and JV Intensities,and Supplier and Customer SA and JV Dummies are lower than those for rms with below-median values. All these ndings offer preliminary evidence consistent with the RSinvestments hypotheses (Hypothesis 1). For example, the averageMarket (Book) Leveragefor rms with above-median values for Supplier Industries R&D is 0.143 (0.226) and forthose with values below the median, the averageMarket Leverage is 0.231 (0.311). Further,both Market and Book Leverage appear to be lower for rms in the sample with above-median Compensation of Employees than for rms with below-median values for thisvariable. Consistent with Hypothesis 2, the mean Market Leverage for rms with above-median Supplier (Customer) Concentration is greater than the mean leverage for rmswhose suppliers (customers) are less concentrated. Overall, the results of the univariateanalysis support our hypotheses that relate the rms leverage to the characteristics ofsuppliers and customers.

4.2. Multivariate analysis with industry-level supplier and customer variables

4.2.1. Analysis of supplier effects

Table 3 shows the results of three ordinary least squares (OLS) regressions of MarketLeverage for the subsample with industry-level supplier variables. In regression Models 1 and2, we test Hypothesis 1 by examining the relation between debt level and supplier R-Sinvestment by Supplier Industry R&D and Supplier SA and JV Intensity, respectively. In Model3, we include both the RS variables as well as interaction variables. Consistent withHypothesis 1, the coefcients on Supplier Industries R&D and Supplier SA and JV Intensityreported in Models 1 and 2 are both negative and statistically signicant. For example, thecoefcient on Supplier SA and JV Intensity is equal to 0.011, signicant at the 1% level. Inthe all-inclusive specication in Model 3, the coefcients on both the RS investment variablescontinue to be negative. Further, the coefcient on Backward Integration DummySupplierIndustries R&D is positive and signicant, which suggests that the negative relation betweensupplier R&D and leverage is weaker for rms with some level of backward integration.15

The coefcient on Compensation of Employees in each reported model is negative andsignicant. Further, in Model 3, the interaction variable Compensation of EmployeesR&DIntensity is also negative and signicant. It appears that R&D-intensive rms with highemployee compensation are likely to require signicant rm-specic investments by theiremployees, and thus they have lower debt levels. The coefcients of Supplier Concentrationare positive and signicant in Models 1 and 3, while the coefcients of SupplierConcentrationMarket Share is negative and signicant in Model 3. These ndings areconsistent with Hypothesis 2 and indicate that leverage is greater when rms face supplierswho are at a bargaining advantage, and this relation is weaker if the rm is a dominantplayer in its own industry.The coefcient on Suppliers Change in Sales is negative and signicant at the 1% level.

As mentioned earlier, we include this variable to capture aspects of rm growth that arerelated to its input markets but are not captured by the rm-specic variables that measuregrowth opportunities such as Tobins q. Among the other control variables, the coefcients

15In unreported results, we include Backward Integration DummySupplier SA and JV Intensity and nd a

positive but statistically insignicant coefcient on this variable.

ARTICLE IN PRESSTable2

Univariateanalysis

Thistablereportsunivariate

analysisforthedifference

inmeansoftheleveragevariablesfordifferentsubsamplesform

edbasedonthemedianvalues

ofthe

supplierandcustomervariables.Thesampleperiodisfrom1984to

2003.MarketLeverageissumofthebookvaluesoflong-termdebtanddebtincurrentliabilities

divided

bythesumofthebookvalueofdebtandthemarketvalueofcommonequity.BookLeverageisthebookvalueoflong-termdebtanddebtincurrentliabilities

divided

bythebookvalueofassets.Supplier

Industries

R&D(Customer

Industries

R&D)istheweightedaverageoftheR&D

intensities

ofallsupplier

(customer)

industries.Key

SuppliersR&D(K

eyCustomersR&D)istheweightedaverageoftheR&Dintensitiesofkey

suppliers(key

customers).Supplier(Customer)SAandJV

IntensityisthenumberofSAsandJV

sestablished

betweenindustry

rm

sandrm

sinsupplier(customer)industries,divided

bythenumberofrm

sintheindustry.

Suppliers(Customer)SA

andJV

Dummyisadummyvariablethatequalsoneiftherm

hasatleastonealliance

oraSA

established

withoneofitssuppliers

(customers),andzero

otherwise.

CompensationofEmployeesistotalcompensationpaid

toem

ployeesdivided

bytotaloutput.Supplier

Concentration(Customer

Concentration)istheweightedaverageoftheHerndahlindicesofallsupplier(customer)industries.Thesymbol***indicatesstatisticalsignicance

atthe1%

level.

Variables

MeanMarketLeverage

MeanBookLeverage

N

Abovemedian

Belowmedian

Difference

(t-value)

Abovemedian

Belowmedian

Difference

(t-value)

Supplier

Industries

R&D

0.143

0.231

0.089***

0.226

0.311

0.085***

76,290

(66.42)

(40.92)

Customer

Industries

R&D

0.211

0.219

0.009***

0.284

0.289

0.005

26,300

(3.77)

(1.36)

Key

SuppliersR&D

0.141

0.207

0.065***

0.222

0.282

0.06***

4,122

(14.10)

(9.66)

Key

CustomersR&D

0.196

0.236

0.04***

0.262

0.298

0.036***

9,452

(7.09)

(7.09)

Supplier

SAandJV

Intensity

0.157

0.217

0.06***

0.246

0.290

0.045***

76,290

(43.97)

(21.21)

Customer

SAandJV

Intensity

0.183

0.244

0.061***

0.265

0.306

0.042***

26,300

(25.99)

(12.25)

Supplier

SAandJV

Dummy

0.135

0.189

0.054***

0.211

0.268

0.057***

4,122

(10.46)

(8.23)

Customer

SAandJV

Dummy

0.093

0.172

0.079***

0.168

0.238

0.070***

9,452

(16.28)

(9.82)

CompensationofEmployees

0.176

0.198

0.021***

0.253

0.283

0.03***

76,290

(15.38)

(14.37)

Supplier

Concentration

0.196

0.178

0.019***

0.265

0.271

0.006***

76,290

(13.80)

(2.77)

Customer

Concentration

0.221

0.209

0.011***

0.288

0.286

0.002

26,300

(4.99)

(0.67)

J.R. Kale, H. Shahrur / Journal of Financial Economics 83 (2007) 321365336

ARTICLE IN PRESS

Table3

Ordinary

leastsquaresregressionsofmarketleverageonsupplier

industry-levelvariables

Thesampleperiodisfrom1984to

2003.ThedependentvariableisMarket

Leverage,whichisthesumofthebookvalues

oflong-termdebtanddebtin

current

liabilitiesdivided

bythesum

ofthebookvalueofdebtandthemarket

valueofcommonequity.Supplier

Industries

R&D

istheweightedaverageoftheR&D

intensitiesofallsupplierindustries.Backward

IntegrationDummyisadummyvariablethatequalsoneiftherm

hasasegmentinasupplierindustry.SupplierSAand

JV

Intensity

isthenumber

ofSAsandJV

sestablished

betweenindustry

rm

sandrm

sin

supplier

industries,divided

bythenumber

ofrm

sin

theindustry.

CompensationofEmployeesistotalcompensationpaidto

employeesdivided

bytotaloutput.Supplier

ConcentrationistheweightedaverageoftheHerndahlindices

ofallsupplierindustries.Supplier

ChangeinSalesisaweightedaverageofthechangeinsalesforsupplierindustries.MarketShareistheratiooftherm

ssalesto

totalsalesofitsprimary

industry.Industry

concentrationisthesales-basedHerndahlindex

oftherm

sindustry.FirmSizeisthelogoftotalassets.AssetCollateral

Valueisnetproperty,plant,andequipmentdivided

bytotalassets.Volatility

isthestandard

deviationofoperationincomedivided

bytotalassets.NondebtTax

Shieldsisinvestm

enttaxcreditsdivided

bytotalassets.Return

onAssetsisoperationincomedivided

bytotalassets.Tobinsqisbookvalueofassetsminusbookvalue

ofcommonequityplusmarketvalueofcommonequitydivided

bybookvalueofassets.R&DIntensityisR&Dexpendituresdivided

bytotalassets.SEIntensityis

selling,general,andadministrativeexpensesdivided

bytotalassets.Allvariablesarewinsorizedatthe1stand99th

percentiles.Lagged

valuesofrm

-speciccontrol

variablesare

used.Thereported

t-values

reectWhitesheteroskedasticitycorrection.Thesymbols**and***indicatestatisticalsignicance

atthe5%,and1%

levels,respectively.

Model1

Model2

Model3

Coeff.

t-value

Coeff.

t-value

Coeff.

t-value

Intercept

0.183***

25.66

0.184***

25.59

0.181***

24.86

Supplier

Industries

R&D

1.472***

13.99

1.208***

10.48

Supplier

Industries

R&DB

ackward

Integration

Dummy

0.786***

3.90

Supplier

SAandJV

Intensity

0.011***

12.09

0.009***

9.03

CompensationofEmployees

0.024***

2.70

0.025***

2.79

0.031***

3.41

Comp.OfEmployees R&D

Intensity

0.161***

6.85

Supplier

Concentration

0.074***

3.00

0.035

1.42

0.085***

3.14

Supplier

ConcentrationM

arketShare

0.354***

3.66

SuppliersChangein

Sales

0.102***

6.40

0.105***

6.58

0.098***

6.14

J.R. Kale, H. Shahrur / Journal of Financial Economics 83 (2007) 321365 337

ARTICLE IN PRESSTable3(continued)

Model1

Model2

Model3

Coeff.

t-value

Coeff.

t-value

Coeff.

t-value

Backward

IntegrationDummy

0.002

0.66

MarketShare

0.026**

2.07

Other

ControlVariables

Industry

Concentration

0.009**

2.24

0.011***

2.69

0.020***

4.62

Firm

Size

0.004***

12.71

0.005***

14.25

0.007***

15.73

AssetCollateralValue

0.193***

47.31

0.196***

48.61

0.191***

46.80

Volatility

0.008***

4.06

0.008***

4.00

0.006***

2.71

NondebtTaxShield

4.435***

18.83

4.451***

18.86

4.364***

18.54

Return

onAssets

0.110***

37.96

0.111***

38.48

0.109***

37.71

Tobinsq

0.015***

56.08

0.015***

56.20

0.015***

55.61

R&D

Intensity

0.232***

34.63

0.244***

36.95

0.188***

21.33

SEIntensity

0.032***

15.14

0.031***

14.76

0.029***

13.69

YearandIndustry

Dummies

Yes

Yes

Yes

Number

ofobservations

76,290

76,290

76,290

Adjusted

R-squared

25.57%

25.54%

26.01%

J.R. Kale, H. Shahrur / Journal of Financial Economics 83 (2007) 321365338

on Size, Asset Collateral Value, and Industry Concentration are signicantly positive. The

ARTICLE IN PRESSJ.R. Kale, H. Shahrur / Journal of Financial Economics 83 (2007) 321365 339positive coefcient on Industry Concentration is in keeping with the evidence in Mackayand Phillips (2006) and the predictions in Brander and Lewis (1986) and Maksimovic(1988). The signicantly negative coefcients on the remaining control variables areconsistent with the literature on the determinants of capital structure. Finally, the negativeand signicant coefcients on the rms R&D and SE Intensity measures are similar tothose documented in Titman and Wessels (1988) and are consistent with the predictions inTitman (1984) and Maksimovic and Titman (1991).Armour and Teece (1980) suggest that vertical chains that are R&D intensive are likely

to have complex interstage interdependencies (see also Williamson, 1975). Therefore, it ismore likely that the R&D intensity of suppliers will capture RS investments by supplierswhen the rm itself is R&D intensive. To investigate this possibility, we estimate ourregressions separately on the subsample of rms with positive R&D expense and thesubsample for which it is zero. Table 4 presents the ndings. Consistent with ourexpectation, we nd that the negative relation between Supplier Industries R&D and arms debt level is signicant only for rms with positive R&D intensity. Further, thecoefcient on the supplier concentration variable is positive and signicant only for thesubsample of rms with positive R&D. This result suggests that the effect of bargaining onleverage is only pronounced when there are relationship-specic investments between therm and its suppliers.

4.2.2. Analysis of customer effects

Table 5 presents the results of the regression analysis for Market Leverage for thesubsample with industry-level customer variables. Consistent with the RS investmentshypothesis (Hypothesis 1), in Models 1 and 2, the coefcients on the variables CustomerIndustries R&D and Customer SA and JV Intensity, respectively, are both negative andsignicant. Further, the coefcients on Customer Industries R&DMarket Share andCustomer SA and JV IntensityMarket Share are negative and signicant. These ndingsare consistent with the hypothesis that the effect of RS investments by customers onleverage is stronger for rms that are important to their customers. In unreportedregressions, we nd insignicant coefcients on Customer Industries R&DIndustryConcentration and Customer SA and JV IntensityIndustry Concentration. While thecoefcient on Customer Industries R&DForward Integration Dummy is insignicant inModel 1, the coefcient on Customer SA and JV IntensityForward Integration Dummy ispositive and signicant in Model 2. Thus, the effect of RS investments on leverageappears to be less pronounced for vertically integrated rms.In Models 1 and 3, the coefcients on Customer Concentration are positive and

signicant, consistent with the bargaining role of debt (Hypothesis 2). However, we ndthat the coefcient on Customer ConcentrationMarket Share, while negative, is notstatistically signicant at conventional levels. Next, we nd a negative relation betweenleverage and Customer Change in Sales, the variable that measures growth in downstreamindustries. Finally, except for the volatility measure, the results pertaining to the controlvariables are in keeping with our results in the supplier regressions and with the extantliterature on capital structure.16

16Unlike the specication for investigating supplier effects, we do not include R&D and SA/JV variablestogether in Model 3 in our analysis of customer effects because these two variables are highly positively correlated

ARTICLE IN PRESS

Table 4

Ordinary least squares regressions of market leverage on supplier industry-level variables: R&D-based subsamples

The sample period is from 1984 to 2003. The dependent variable is Market Leverage, which is the sum of the

J.R. Kale, H. Shahrur / Journal of Financial Economics 83 (2007) 321365340book values of long-term debt and debt in current liabilities divided by the sum of the book value of debt and the

market value of common equity. Column 1 (2) shows OLS regression results for rms with positive (zero) R&D

Intensity, which is the rms R&D expenditures divided by its total assets. Supplier Industries R&D is the weighted

average of the R&D intensities of all supplier industries. Compensation of Employees is total compensation paid to

employees divided by total output. Supplier Concentration is the weighted average of the Herndahl indices of all

supplier industries. Supplier Change in Sales is a weighted average of the change in sales for supplier industries.

Industry concentration is the sales-based Herndahl index of the rms industry. Firm Size is the log of total assets.

Asset Collateral Value is net property, plant, and equipment divided by total assets. Volatility is the standardIn Table 6, we show the results of regression analysis that examines the relation betweenleverage and the R&D of customers for different subsamples of rms. In the rst columnof the table, we report results for rms in the Agriculture & Mining (SIC codes between0100 and 1499) industries. For these rms we nd an insignicant relation betweenCustomer Industries R&D and market leverage. This result is expected given that many ofthese rms sell raw materials and are unlikely to engage in RS investments with their

deviation of operation income divided by total assets. Nondebt Tax Shields is investment tax credits divided by

total assets. Return on Assets is operation income divided by total assets. Tobins q is book value of assets minus

book value of common equity plus market value of common equity divided by book value of assets. R&D

Intensity is R&D expenditures divided by total assets. SE Intensity is selling, general, and administrative expenses

divided by total assets. All variables are winsorized at the 1st and 99th percentiles. Lagged values of rm-specic

control variables are used. The reported t-values reect Whites heteroskedasticity correction. The symbols *, **,

and *** indicate statistical signicance at the 10%, 5%, and 1% levels, respectively.

Firms with Positive R&D

intensity

Firms with Zero R&D intensity

Coeff. t-value Coeff. t-value

Intercept 0.179*** 18.30 0.146*** 13.62

Supplier Industries R&D 1.395*** 12.11 0.08 0.26Compensation of Employees 0.047*** 3.42 0.021* 1.83Supplier Concentration 0.083** 2.56 0.001 0.04Suppliers Change in Sales 0.099*** 4.60 0.117*** 5.17Other control variables

Industry Concentration 0.007 1.46 0.016** 2.48

Firm Size 0.003*** 6.94 0.009*** 15.52

Asset Collateral Value 0.192*** 32.08 0.185*** 33.77

Volatility 0.001 0.58 0.016*** 4.21Nondebt Tax Shield 3.768*** 16.21 4.660*** 8.76Return on Assets 0.086*** 27.21 0.140*** 25.05Tobins q 0.011*** 43.41 0.023*** 33.55R&D Intensity 0.199*** 27.22 SE Intensity 0.012*** 5.12 0.050*** 12.67Year and Industry Dummies Yes Yes

Number of observations 40,685 35,605

Adjusted R-squared 22.12% 20.42%

(footnote continued)

in this sub-sample (correlation equals 0.5). Including both variables does not signicantly affect our results,

although it reduces the statistical signicance of both variables.

ARTICLE IN PRESS

Table5

Ordinary

leastsquaresregressionsofmarketleverageoncustomer

industry-levelvariables

Thesampleperiodisfrom1984to

2003.ThedependentvariableisMarket

Leverage,whichisthesumofthebookvalues

oflong-termdebtanddebtin

current

liabilitiesdivided

bythesum

ofthebookvalueofdebtandthemarket

valueofcommonequity.Customer

Industries

R&D

istheweightedaverageoftheR&D

intensitiesofallcustomerindustries.Customer

SAandJVIntensityisthenumberofSAsandJV

sestablished

betweenindustryrm

sandrm

sincustomerindustries,

divided

bythenumberofrm

sin

theindustry.Forward

IntegrationDummyisadummyvariablethatequalsoneiftherm

hasasegmentin

acustomer

industry.

Customer

ConcentrationistheweightedaverageoftheHerndahlindicesofallcustomerindustries.Customer

ChangeinSalesisaweightedaverageofthechangein

salesforcustomer

industries.Market

Shareistheratiooftherm

ssalesto

totalsalesofitsprimary

industry.Industry

concentrationisthesales-basedHerndahl

index

oftherm

sindustry.Firm

Sizeisthelogoftotalassets.Asset

CollateralValueisnetproperty,plant,andequipmentdivided

bytotalassets.Volatilityisthe

standard

deviationofoperationincomedivided

bytotalassets.NondebtTaxShieldsisinvestm

enttaxcreditsdivided

bytotalassets.Return

onAssetsisoperation

incomedivided

bytotalassets.Tobinsqisbookvalueofassetsminusbookvalueofcommonequityplusmarketvalueofcommonequitydivided

bybookvalueof

assets.R&DIntensityisR&Dexpendituresdivided

bytotalassets.SEIntensityisselling,general,andadministrativeexpensesdivided

bytotalassets.Allvariablesare

winsorizedatthe1stand99thpercentiles.Lagged

valuesofrm

-speciccontrolvariablesareused.Thereported

t-valuesreectWhitesheteroskedasticitycorrection.

Thesymbols**and***indicatestatisticalsignicance

atthe5%

and1%

levels,respectively.

Model1

Model2

Model3

Coeff.

t-value

Coeff.

t-value

Coeff.

t-value

Intercept

0.119***

13.13

0.123***

13.62

0.118***

12.79

Customer

Industries

R&D

0.832***

5.97

0.875***

6.38

Customer

Industries

R&DM

arketShare

1.442***

3.39

Customer

Industries

R&DF

orward

IntegrationDummy

0.043

0.30

Customer

SAandJV

Intensity

0.015***

9.18

Cust.SAandJV

IntensityM

arketShare

0.070***

7.43

Customer

SAandJV

IntensityF

orward

IntegrationDummy

0.012***

5.69

Customer

Concentration

0.101***

3.92

0.033

1.24

0.114***

4.01

Customer

ConcentrationM

arketShare

0.198

1.59

Customer

Changein

Sales

0.171***

8.45

0.168***

8.33

0.171***

8.48

Forward

IntegrationDummy

0.008**

2.26

0.004

1.51

MarketShare

0.047***

4.10

0.048***

4.62

0.026

1.03

J.R. Kale, H. Shahrur / Journal of Financial Economics 83 (2007) 321365 341

ARTICLE IN PRESSTable5(continued)

Model1

Model2

Model3

Coeff.

t-value

Coeff.

t-value

Coeff.

t-value

Other

controlvariables

Industry

Concentration

0.044***

5.47

0.050***

6.22

0.044***

5.43

Firm

Size

0.006***

8.39

0.007***

9.69

0.006***

8.98

AssetCollateralValue

0.187***

28.72

0.183***

28.29

0.187***

28.70

Volatility

0.006

1.31

0.006

1.18

0.006

1.33

NondebtTaxShield

3.663***

8.29

3.432***

7.80

3.661***

8.30

Return

onAssets

0.134***

21.89

0.133***

21.69

0.135***

21.98

Tobinsq

0.019***

29.68

0.018***

29.37

0.019***

29.69

R&D

Intensity

0.264***

15.76

0.251***

14.92

0.265***

15.85

SEIntensity

0.044***

9.25

0.043***

9.08

0.044***

9.18

YearandIndustry

Dummies

Yes

Yes

Yes

Number

ofobservations

26,300

26,300

26,300

Adjusted

R-squared

21.21%

21.40%

21.17%

J.R. Kale, H. Shahrur / Journal of Financial Economics 83 (2007) 321365342

customers. On the other hand, we nd (column two) that for rms producingdurable manufacturing products, the relation between leverage and customer R&D is

Supplier Industries R&D, Supplier SA and JV Intensity, and Supplier Concentration, we nd

ARTICLE IN PRESSJ.R. Kale, H. Shahrur / Journal of Financial Economics 83 (2007) 321365 343that moves from the 5th to 95th percentile change leverage by 0.026, 0.016, and 0.008(14.05%, 8.23%, 4.17%), respectively. For changes in the customer variables,Customer Industries R&D, Customer SA and JV Intensity, and Customer Concentration,leverage changes by 0.054, 0.056, and 0.023 (23.59%, 25.31%, and 11.44%),respectively. For the positive-R&D supplier (customer) subsample, the change in leveragefor similar moves in Supplier and Customer Industries R&D are 0.042 and 0.083(27.87% and 41.31%), respectively. The levels of economic signicance of the supplierand customer effects are even greater when we consider the effect of the interactedvariables. For example, among the reported results, the largest change in leverage of0.138 (or 67.73%) results from a move from the 5th to 95th percentile of CustomerIndustries R&D for a rm in the 95th percentile of market share in the positive R&Dsubsample.Are these changes in a rms leverage from changes in the supplier and customer

variables economically signicant? It turns out that an increase in rm size from the 5th tothe 95th percentile value is associated with a change in leverage of 0.051 (or 30.83%).Overall, it appears from the results reported in Table 7 that the levels of economic

17We follow the US Census Bureau and classify manufacturing industries into durable and nondurable good

industries. Durable (Nondurable) good industries are those with the following two-digit SIC codes:negative.17 In unreported results, we nd that this relation is statistically insignicant forrms producing nondurable manufacturing products. This result is consistent with theconjecture that there are more RS investments between rms in durable good industriesand their customers.The last three columns of the table report results on the subsample of rms that have

positive R&D expenses. In all three specications, we nd that the relation betweenleverage and customer R&D is negative and signicant. In untabulated results, we nd thatfor zero-R&D rms this relation is not signicant. Thus, as in the case of suppliers, we ndsupport for the conjecture that the R&D of customers is more likely to capture RSinvestments when the rm itself is R&D intensive. Results in columns four and ve alsooffer support for the hypothesis that the effect of RS investments by customers on a rmsdebt level is more pronounced if the rm produces output for which there are fewalternative suppliers. In column 4, the coefcient on the interaction variable CustomerIndustries R&DIndustry Concentration is negative and signicant. Further, in columnve, we nd that the coefcient on Customer Industries R&DMarket Share is alsonegative and signicant.

4.2.3. Economic significance

In order to determine the economic signicance of the effects of the supplier andcustomer variables, we use the estimated coefcients reported in Tables 36 to compute thechange in leverage if a rm moves from the 5th percentile value to the 95th percentile valueof a specic independent variable, holding all the other independent variables at theirsample means. In Table 7, we report the levels of economic signicance for selectedspecications and variables, owing to space constraints. For the supplier variables,24,25,32,33,34,35,36,37,38, and 39 (20,21,22,23,26,27, 28,29,30, and 31).

ARTICLE IN PRESS

Table6

Ordinary

leastsquaresregressionsofmarketleverageoncustomer

industry-levelvariables:varioussubsamples

Thesampleperiodisfrom1984to

2003.ThedependentvariableisMarketLeverage.Column1showOLSregressionresultsforrm

swithhistoricalprimary

SIC

codebetween0100and1499,whileColumn2showOLSregressionresultsforrm

sproducingdurablegoods.Thelastthreecolumnsshowregressionresultsforrm

s

withpositiveR&DIntensity,whichistherm

sR&Dexpendituresdivided

byitstotalassets.Customer

IndustriesR&DistheweightedaverageoftheR&Dintensities

ofallcustomerindustries.CustomerConcentrationistheweightedaverageoftheHerndahlindicesofallcustomerindustries.CustomerChangeinSalesisaweighted

averageofthechangeinsalesforcustomerindustries.MarketShareistheratiooftherm

ssalesto

totalsalesofitsprimary

industry.Industry

concentrationisthe

sales-basedHerndahlindex

oftherm

sindustry.FirmSizeisthelogoftotalassets.AssetCollateralValueisnetproperty,plant,andequipmentdivided

bytotal

assets.Volatilityisthestandard

deviationofoperationincomedivided

bytotalassets.NondebtTaxShieldsisinvestm

enttaxcreditsdivided

bytotalassets.Return

on

Assetsisoperationincomedivided

bytotalassets.Tobinsqisbookvalueofassetsminusbookvalueofcommonequityplusmarketvalueofcommonequitydivided

bybookvalueofassets.R&D

Intensity

isR&D

expendituresdivided

bytotalassets.SEIntensity

isselling,general,andadministrativeexpensesdivided

bytotal

assets.Allvariablesarewinsorizedatthe1stand99thpercentiles.Lagged

valuesofrm

-speciccontrolvariablesareused.Thet-valuesreported

inparenthesesreect

Whitesheteroskedasticitycorrection.Thesymbols**and***indicatestatisticalsignicance

atthe5%,and1%

levels,respectively.

Agriculture

andmining

Durable

manufacturing

Positive

R&D

rm

s

Positive

R&D

rm

s

Positive

R&D

rm

s

Intercept

0.141***

0.288***

0.179***

0.168***

0.169***

(3.56)

(9.00)

(13.46)

(12.15)

(12.42)

Customer

Industries

R&D

0.982

0.841***

1.122***

0.807***

1.012***

(0.95)

(5.29)

(6.94)

(4.24)

(6.11)

Customer

Industries

R&DIndustry

Concentration

1.723***

(3.21)

Customer

Industries

R&DM

arketShare

2.362***

(5.36)

Customer

Concentration

0.219***

0.184***

0.137***

0.160***

0.164***

(2.66)

(5.19)

(3.62)

(4.11)

(4.23)

Customer

Changein

Sales

0.315***

0.041

0.118***

0.114***

0.117***

(5.43)

(1.24)

(3.70)

(3.58)

(3.67)

Industry

Concentration

0.007

0.015

0.048***

0.025**

(0.28)

(1.36)

(3.03)

(2.08)

MarketShare

0.014

0.005

(0.87)

J.R. Kale, H. Shahrur / Journal of Financial Economics 83 (2007) 321365344

ARTICLE IN PRESSOther

ControlVariables

(0.42)

Firm

Size

0.020***

0.00

0.001

0.001

0.000

(4.27)

(1.57)

(0.93)

(0.91)

(0.30)

AssetCollateralValue

0.199***

0.09***

0.193***

0.195***

0.194***

(14.25)

(7.22)

(18.26)

(18.36)

(18.27)

Volatility

0.018

0.03***

0.005

0.005

0.004

(1.43)

(3.27)

(0.87)

(0.94)

(0.79)

NondebtTaxShield

8.079***

2.15***

3.555***

3.616***

3.634***

(4.33)

(3.40)

(7.50)

(7.63)

(7.67)

Return

onAssets

0.087***

0.19***

0.120***

0.120***

0.120***

(5.41)

(15.83)

(15.38)

(15.39)

(15.47)

Tobinsq

0.015***

0.02***

0.015***

0.015***

0.015***

(8.44)

(15.35)

(21.28)

(21.29)

(21.19)

R&D

Intensity

0.117

0.35***

0.249***

0.248***

0.250***

(1.53)

(9.80)

(12.77)

(12.72)

(12.82)

SEIntensity

0.073***

0.04***

0.033***

0.033***

0.033***

(3.50)

(2.78)

(5.34)

(5.29)

(5.20)

YearandIndustry

Dummies

Yes

Yes

Yes

Yes

Yes

Number

ofobservations

4,593

9,046

10,883

10,883

10,883

Adjusted

R-squared

18.71%

20.06%

23.27%

22.54%

23.27%

J.R. Kale, H. Shahrur / Journal of Financial Economics 83 (2007) 321365 345

ARTICLE IN PRESS

Table7

Economicsignicance

ofregressionresults

Thetablereportsthepredictedvalues

ofMarket

Leverageatthe5th

and95th

percentilesofeach

oftheindependentvariables,holdingallother

independent

variablesattheirsamplemeans.Formodelswithinteractionvariables,theoveralleffectforeach

variablebyitself(theInteractionEffectscolumnissetto

None)is

computedbyusingthemeanoftheinteracted

variable(s).Forvariableswithinteractioneffects,thepredictedvaluesofleveragearealsocomputedatthe5thand95th

percentilesoftheinteracted

variable.Changeisthedifference

betweenthepredictedvalueatthe95th

percentileandthatatthe5th

percentile.Thelasttwocolumns

showthenumberofthetablethatcontainsthemodelusedto

computethepredictedvaluesandthenumberofthemodelintherespectivetable.Thesampleperiodis

from1984to

2003.Supplier

(Customer)Industries

R&DistheweightedaverageoftheR&Dintensitiesofallsupplier(customer)industries.Supplier

(Customer)SA

andJVIntensityisthenumberofSAsandJV

sestablished

betweenindustry

rm

sandrm

sinsupplier(customer)industries,divided

bythenumberofrm

sinthe

industry.CompensationofEmployeesistotalcompensationpaidto

employeesdivided

bytotaloutput.Supplier

(Customer)Concentrationistheweightedaverageof

theHerndahlindicesofallsupplier(customer)industries.Supplier

(Customer)ChangeinSalesisaweightedaverageofthechangeinsalesforsupplier(customer)

industries.Market

Shareistheratiooftherm

ssalesto

totalsalesofitsprimary

industry.Industry

concentrationisthesales-basedHerndahlindex

oftherm

s

industry.FirmSizeisthelogoftotalassets.AssetCollateralValueisnetproperty,plant,andequipmentdivided

bytotalassets.Volatilityisthestandard

deviationof

operationincomedivided

bytotalassets.NondebtTaxShieldsisinvestm

enttaxcreditsdivided

bytotalassets.Return

onAssetsisoperationincomedivided

bytotal

assets.Tobinsqisbookvalueofassetsminusbookvalueofcommonequityplusmarketvalueofcommonequitydivided

bybookvalueofassets.R&DIntensityis

R&D

expendituresdivided

bytotalassets.SEIntensity

isselling,general,andadministrativeexpensesdivided

bytotalassets.

PredictedLeverageatthe5th

and95th

PercentilesofIndependentVariable

Independentvariable

Interactioneffects

5th

perc.

95th

perc.

Change

%Change

Table

Model

Supplier

variables

Supplier

Industries

R&D

None

0.197

0.169

0.028

14.05

33

Supplier

Industries

R&D

VerticallyIntegratedFirms

0.191

0.181

0.010

5.07

33

Supplier

SAandJV

Int.

None

0.192

0.171

0.016

8.23

33

Supplier

Concentration

None

0.184

0.192

0.008

4.17

33

Supplier

Concentration

5th

perc.ofMarketShare

0.187

0.197

0.010

5.39

33

Supplier

Concentration

95th

perc.ofMarketShare

0.174

0.171

0.003

1.73

33

Comp.ofEmployees

None

0.194

0.181

0.013

6.70

33

Comp.ofEmployees

5th

perc.ofR&D

Intensity

0.209

0.199

0.010

4.77

33

Comp.ofEmployees

95th

perc.ofR&D

Intensity

0.140

0.116

0.024

17.20

33

Sup.Changein

Sales

None

0.199

0.177

0.022

11.27

33

J.R. Kale, H. Shahrur / Journal of Financial Economics 83 (2007) 321365346

ARTICLE IN PRESSCustomer

variables

Customer

Industries

R&D

None

0.229

0.175

0.054

23.59

51

Customer

Industries

R&D

5th

perc.ofMarketShare

0.232

0.184

0.049

20.95

51

Customer

Industries

R&D

95th

perc.ofMarketShare

0.215

0.138

0.078

36.18

51

Customer

SAandJV

Int.

None

0.223

0.167

0.056

25.31

52

Customer

SAandJV

Int.

VerticallyIntegratedFirms

0.226

0.201

0.025

11.04

52

Customer

SAandJV

Int.

5th

perc.ofMarketShare

0.225

0.184

0.041

18.36

52

Customer

SAandJV

Int.

95th

perc.ofMarketShare

0.210

0.087

0.123

58.49

52

Customer

Concentration

None

0.205

0.228

0.023

11.44

51

Positive-R&D

samples

Supplier

Industries

R&D

None

0.151

0.109

0.042

27.87

41

Customer

Industries

R&D

None

0.201

0.118

0.083

41.31

64

Customer

Industries

R&D

5th

perc.ofIndustry

Conc.

0.194

0.131

0.063

32.68

64

Customer

Industries

R&D

95th

perc.ofIndustry

Conc.

0.216

0.093

0.123

56.87

64

Customer

Industries

R&D

5th

perc.ofMarketShare

0.201

0.130

0.071

35.22

65

Customer

Industries

R&D

95th

perc.ofMarketShare

0.204

0.066

0.138

67.73

65

Controlvariables

Mark