Journal of Corporate Finance 17 (2011) 1457–1474
Contents lists available at SciVerse ScienceDirect
Journal of Corporate Finance
journal homepage: www.elsevier.com/locate/jcorpfin
Firm- and country-level determinants of corporate leverage: Some new
international evidence
Ali Gungoraydinoglu a, 1, Özde Öztekin b,⁎
a
b
Economics Department, University of Mississippi, Oxford, MS 38677, United States
School of Business, University of Kansas, Lawrence, KS 66045-7585, United States
a r t i c l e
i n f o
Article history:
Received 23 December 2010
Received in revised form 23 August 2011
Accepted 25 August 2011
Available online 3 September 2011
JEL classification:
G20
G32
a b s t r a c t
This research analyzes the determinants of capital structure across 37 countries. Institutional
arrangements matter for capital structure decisions; however, firm-level covariates drive
two-thirds of the variation in capital structure across countries, while the country-level covariates explain the remaining one-third. The observed relationships between the country-level
determinants and leverage provide strong support to the predictions of both the trade-off
and the pecking-order theories. Country-level determinants serve as substitute mechanisms
for the firm-level, industry-level, and macroeconomic determinants by moderating their marginal impact on leverage.
© 2011 Elsevier B.V. All rights reserved.
Keywords:
Dynamic capital structure
International
Institutions
Trade-off
Pecking-order
1. Introduction
This paper aims to contribute to the knowledge of capital structure by examining the determinants of capital structure across a
large panel of firms and countries and by focusing on both the characteristics of the firm and its institutional environment. In the
first part of the study, we explore which determinants, at the firm and country level, reliably predict the variation in corporate
leverage. In the second half, we explore how these determinants relate to the capital structure theories. Overall, this paper
helps improve understanding of the capital structure choices around the world.
Traditional analysis of the determinants of corporate leverage has emphasized firm characteristics. For example, Frank and
Goyal (2009) and Lemmon et al. (2008) evaluate the contribution of firm-specific factors to leverage variation of U.S. firms. Recent
and growing research has incorporated country-level characteristics into the traditional firm-level determinants to explain
a firm's leverage (Booth et al., 2001; Demirguc-Kunt and Maksimovic, 1999; Hanousek and Shamshur, 2011; Rajan and Zingales,
1995). Country characteristics influence firms' costs and benefits in determining their capital structure. Countries differ in the
quality of institutions that may potentially affect the trade-off among the bankruptcy costs and tax benefits, agency costs, and information asymmetry costs imposed on firms. However, to our knowledge, no attempt has been made to assess the contribution
of country-specific factors to leverage variation. Furthermore, whether the firm or the country characteristics matter more for the
⁎ Corresponding author. Tel.: + 1 785 864 7545; fax: + 1 785 864 5328.
E-mail addresses: [email protected] (A. Gungoraydinoglu), [email protected] (Ö. Öztekin).
1
Tel.: + 1 662 915 1589.
0929-1199/$ – see front matter © 2011 Elsevier B.V. All rights reserved.
doi:10.1016/j.jcorpfin.2011.08.004
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A. Gungoraydinoglu, Ö. Öztekin / Journal of Corporate Finance 17 (2011) 1457–1474
capital structure choices is still an open empirical question. Therefore, our first contribution comes from assessing the importance
of each type of country-level factor for capital structure decisions and evaluating the relative importance of the country-specific
factors in determining a firm's leverage compared with the firm-specific factors.
We examine how well several firm-level and institutional factors explain corporate leverage and assess the relative importance of the various determinants in providing information about firms' capital structure decisions using variance decomposition
analysis. We find that institutional arrangements matter for the capital structure decisions; however, the firm-level covariates
drive most of the variation in capital structure across countries (66%), not the country-level covariates (34%). The strongest impact on leverage comes from firm- and industry-specific factors, such as industry leverage, liquidity, profitability, tangibility, and
size, which explain up to 63% of the total variation in leverage; the least important impact comes from depreciation, taxes,
research-and-development (R&D) expenses, and growth opportunities, which overall explains only an additional 3% of the
total variation in leverage. Frank and Goyal (2009) and Lemmon et al. (2008) document similar findings for corporate capital
structures in the United States. The most influential country-specific determinants of leverage are the costs of the bankruptcy outcome, creditor rights and their enforcement, corporate transparency, ownership concentration, and contract enforcement. The
total direct impact of the institutional characteristics on firms' leverage is 22%. An indirect impact of the country-specific factors
also occurs through their influence on the roles of the firm-level, industry-level, and macroeconomic factors in determining capital structure. The effects of the firm-specific factors are either strengthened or moderated by the country-specific factors, and
these indirect effects of institutional factors explain 12% of the total variation in leverage.
Variance decomposition analysis conducted in the first part of the study emphasizes the general importance of the leverage
determinants in explaining firms' financing decisions without any assumptions on causality. This is a static analysis conducted
merely to understand the relative contribution of each determinant to the variation in leverage. Next, we relax this restriction
to examine the stylized relationships between leverage and its determinants. We go beyond a static model and account for the
potentially dynamic nature of the firm's capital structure and its unobserved heterogeneity to examine the relationships between
firm- and country-level determinants and leverage in the sample countries. We do this for two reasons: (1) the pattern of firm
financing decisions may not be stable over time, and (2) firms may allow their leverage to drift when exposed to shocks if it is
optimal to do so as a result of binding refinancing costs. We explore whether major differences exist in the capital structure of
firms across different institutional settings, how institutional arrangements affect corporate financial leverage, and how the
firm-level covariates relate to capital structure in different institutional environments. To our knowledge, this is the first paper
to relate country-level determinants to the traditional capital structure theories that explain a firm's leverage. Thus, our second
contribution is establishing a new relationship between country variables and the capital structure theories traditionally used
to explain firms' capital structure based only on firm-level variables.
Third, our study is the first to examine the influences of the firm- and country-specific determinants on capital structure decisions in
such a comprehensive manner with respect to the number of countries covered (cf. Antoniou et al., 2008; Booth et al., 2001; Giannetti,
2003; Rajan and Zingales, 1995), proxies employed (cf. Bae and Goyal, 2004; Giannetti, 2003; González and González, 2008; Qian and
Strahan, 2007), and model specification. Our dynamic panel data set spans 37 countries and 16 years and provides insight into a greater
range of institutional differences. Similar to González and González (2008), we analyze the influence of country characteristics not only
on firm leverage but also on the firm-level determinants of leverage. Our interactive modeling of international capital structure explicitly accounts for the multi-level structure of the data by using both firm- and country-specific factors and their interaction terms.
We find that the observed relationships between the country-level determinants and leverage provide strong support to the predictions of both the trade-off and the pecking-order theories. Consistent with the trade-off theory, leverage is higher in institutional settings that impose lower bankruptcy and agency costs of debt, higher taxes, and higher agency costs of equity. Consistent with the
pecking-order theory, leverage is higher in institutional environments that impose higher information asymmetry costs on firms. Moreover, we document that the country-level factors serve as substitute mechanisms for the firm-level, industry-level, and macroeconomic
factors in managing bankruptcy costs, agency costs, and information asymmetry costs and moderate their marginal impact on leverage.
2. An international theory of the determinants of capital structure
The purpose of this paper is to explore the relative importance of country characteristics and the effect of the different types of
institutional settings in explaining a firm's determination of capital structure. The major theories of capital structure make different
predictions about how leverage relates to observable firm- and country-level determinants. The trade-off and the pecking-order theories make specific and universal predictions about the influence of factors such as bankruptcy costs, agency costs, and information
asymmetry costs on firms' capital structures. Several firm-level proxies have been proposed to account for the relationship between
these factors and leverage. We add country-level proxies to the traditional list of determinants of capital structure and evaluate their
impact on the choice of firms' capital structures around the world. To test the relative importance of the country-level factors and their
impact on capital structure, we concentrate on three main categories of institutional determinants of capital structure: bankruptcy
costs and taxes, agency costs and information asymmetry costs.
2.1. Country-level determinants of corporate leverage
2.1.1. Bankruptcy costs and taxes
We use the design of the bankruptcy codes and debt contracts, including the attached creditor rights (“creditor”) and associated enforcement (“formalism”) mechanisms governing default on debt contracts, as determinants of the financial distress costs.
A. Gungoraydinoglu, Ö. Öztekin / Journal of Corporate Finance 17 (2011) 1457–1474
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In countries in which lenders can easily force repayment, repossess collateral, gain control of the firm, and enforce debt contracts,
the ex ante distress costs should be less prominent. Furthermore, firms from countries that administer the bankruptcy process in
court in a manner that is less time consuming (“time”), less costly (“cost”), and more efficient (“eff”) should have lower deadweight costs associated with the insolvency process, implying lower ex post distress costs.
Debt tax shields play an important role in determining a firm's capital structure (Graham, 1996). We use the effective corporate tax rates (“tax”) to evaluate the effect of the value of tax shields on leverage determination.
2.1.2. Agency costs
Though alleviating shareholder–manager conflicts, debt may lead to a moral hazard problem arising from the divergence of
interests between the shareholders and the debtholders. The agency costs of debt can be mitigated by adjusting the properties
of the debt contracts. The creditors would be able to modify and enforce debt contracts if they are granted the legal rights. We
use creditor rights (“creditor”) attached to debt contracts and the quality of their enforcement (“formalism”) as proxies for the
degree of agency costs of debt, because well-protected creditors would have more power against shareholder expropriation. As
an additional remedial measure of creditor protection, we use the existence of a legal reserve requirement (“reserve”), a requirement that forces firms to maintain a certain level of capital to avoid automatic liquidation to protect creditors before all the capital
is wasted by the insiders.
A particular type of agency problem in the capital structure literature is the conflict of interest between the manager and the
outside shareholders. This moral hazard problem arises from the separation of ownership and control, leading managers to seek
private benefits rather than maximize firm value. The degree of these agency costs should greatly depend on the rights attached
to equity securities and their enforcement. We use measures of shareholder rights (“antidir”) and the quality of private enforcement (“prenf”) to evaluate the impact of agency costs of equity on leverage determination. Dividends are a principal financing
constraint that restricts free cash flow available to managers, and thereby mitigates managerial discretion (Fama and French,
2002; Jensen, 1986; Myers, 1984). We also use the existence of mandatory dividend (“mdiv”) payments to evaluate the effect
of the agency costs of equity on capital structure. Ownership concentration (“owner”) in a firm's shares may be an efficient
way to provide managers with incentives aligned with outside shareholders, thus reducing agency costs of equity. Large investors
may also have more incentives to monitor the managers (Jensen and Meckling, 1976; Shleifer and Vishny, 1986). However, high
ownership concentration may constitute the agency problem itself because the controlling shareholders can implement policies
that benefit themselves at the expense of minority shareholders. Moreover, concentrated ownership may exacerbate the agency
problems of equity arising from overdiversifying. Therefore, the effect of ownership concentration on leverage determination is
ambiguous. The perseverance of agency costs also depends on the existence of internal and external disciplinary and monitoring
mechanisms, such as the legal rules, the effectiveness of the jurisdictions, and the quality of government, that limit managerial
discretion. We use executive quality (“exec”), the strength of law and order (“law”), the quality of government (“gov”), and
the quality of contract enforcement (“enforce”) as proxies for internal and external pressures to correct any conflict between
the managers and the shareholders.
2.1.3. Information asymmetry costs
We use a country's quality of accounting standards (“trans”) to proxy for asymmetric information arising from a lack of corporate
transparency. The quality of accounting standards and the quality of disclosure in general moderate the degree of information asymmetry among financial agents (Lambert et al., 2007; Verrecchia, 2001). In addition to accounting standards, we focus on information
asymmetry proxies for equity and debt markets separately. For the equity markets, we use the regulation of security laws governing
initial public offerings, with a focus on mandatory disclosure (“disclose”), liability standards (“liability”), and public enforcement
(“penf”). The law in some countries may require the disclosure of particular information in the prospectus to facilitate investors' evaluation of companies. The law may also specify liability standards for issuers and intermediaries who fail to reveal material information
mandated to be disclosed. Finally, an independent public enforcer, such as the Securities and Exchange Commission, may secure information from issuers and investors and impose sanctions. We also use a measure of the quality of capital market governance (“insider”), with a focus on the insider trading laws in individual exchanges around the world. For the debt markets, we use the presence
of public credit registries (“cinfo”) that collect information on credit histories and current indebtedness of various borrowers and
share it with lenders.
2.2. Predictions of the capital structure theories
The institutional setting may influence the capital structure of a firm in two ways: by affecting its long-term leverage, a “direct
effect,” or by attenuating or intensifying the relationship between firm-level, industry-level, and macroeconomic determinants
and capital structure, an “indirect effect.” One possibility is that firm- and country-specific characteristics are substitutes, in
which case the marginal impact of the firm-level, industry-level, and macroeconomic determinants on leverage would decrease
with the impact of the country determinants. They may also be complements, in which case the marginal influence of firm-level,
industry-level, and macroeconomic features on leverage would be strengthened through the higher influence of country
attributes.
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2.2.1. Trade-off theory
The trade-off theory maintains that the capital structure of a firm is the outcome of the trade-off between the benefits and the
costs of debt. Classic arguments for this trade-off are based on bankruptcy costs, tax benefits, and agency costs related to asset
substitution (Myers, 1977), and overinvestment (Jensen, 1986; Stulz, 1990). Each firm has a value-maximizing target leverage
ratio that it strives to reach.
According to the trade-off theory, for the direct impact of the country-level determinants, a lower leverage in institutional settings, with lower tax shields, higher bankruptcy and agency costs of debt, and lower agency costs of equity, would be expected.
Accordingly, lower leverage should incur lower effective tax rates, longer time to resolve the bankruptcy process, costlier and less
efficient bankruptcy outcomes, weaker creditor rights and poorer quality of their enforcement, lower reserve requirements,
stronger shareholder rights and higher quality of their enforcement, higher mandatory dividends, higher executive quality, stronger law and order, and better government and contract enforcement. If higher ownership concentration leads to higher (lower)
agency costs of equity, it should increase (decrease) a firm's leverage.
For the indirect impact of the country-level determinants, if firm and country attributes that serve as proxies for these costs and
benefits are substitute (complementary) mechanisms for each other, a moderated (strengthened) impact of the firm-level attribute
on leverage through the country-level attribute should result. Therefore, to analyze whether the firm and country attributes are
substitutes (complements), we test whether the interaction term between the firm and country attributes is significantly negative
(positive) for the firm attributes that increase the benefits of operating at higher leverage and is significantly positive (negative)
for the firm attributes that increase the benefits of operating at lower leverage. To illustrate the intuition, we evaluate the
interaction of a particular firm-level determinant and country-level determinant: firm size and creditor rights. According to the
trade-off theory, the firm-level determinant increases the benefits of operating at higher leverage by reducing the bankruptcy and/
or agency costs of debt. If firm size and creditor rights are substitutes (complements) – by definition, an increase in creditor rights –
a country-level decrease in bankruptcy and/or agency costs of debt should lead to lower (higher) reduction in bankruptcy and/or
agency costs of debt through the firm-level attribute. Consequently, firms in countries with stronger creditor rights would experience
a moderated (strengthened) amount of increase in their leverage compared with countries with weaker creditor rights, indicating
that the interaction term between the firm and country attributes would be negative (positive).
2.2.2. Pecking-order theory
According to the pecking-order theory, the adverse selection costs of issuing risky securities, because of either asymmetric information (Myers, 1984; Myers and Majluf, 1984) or managerial optimism (Heaton, 2002), lead to a preference ranking over financing sources by creating a wedge between internal and external financing costs and by increasing the difficulty of issuing
securities. To minimize adverse selection costs, firms first issue internal funds, debt, and then equity.
For the direct impact of the country-level determinants, the pecking-order theory maintains that more internal funds and fewer
investment opportunities lead to less debt. Moreover, if equity is increased through equity issues and not by retaining earnings, the
theory implies that higher adverse selection costs would result in more debt. Accordingly, lower corporate transparency, weaker disclosure, liability and public enforcement standards, higher perseverance of insider trading in equity markets, and lower information
sharing in debt markets should result in higher leverage.
For the indirect impact of the country-level determinants, if the firm and country attributes that serve as proxies for the adverse selection costs are substitute (complementary) mechanisms for each other, a moderated (strengthened) impact of the
firm-level attribute on leverage through the country-level attribute should result. Therefore, to analyze whether the firm and
country attributes are substitutes (complements), we test whether their interaction term is significantly negative (positive) in
cases in which the firm attribute increases the benefits of operating at higher leverage and is significantly positive (negative)
in cases in which the firm attribute decreases the benefits of operating at higher leverage.
3. Data and empirical methodology
3.1. Data
For the tests of the hypotheses described in the previous section, we acquire both firm-level data and data on countries' institutional characteristics. 2 We construct our firm-level sample from all non-financial firms included in the Compustat Global Vantage database between 1991 and 2006. To minimize the impact of outliers, we winsorize the firm-level variables at the 1st and
99th percentiles. The sample consists of 15,177 firms from 37 countries, totaling 105,568 firm-years. A detailed description of
the country-level and the firm-level, industry-level, and macroeconomic variables and their sources appears in Appendices A
and B, respectively.
2
We are grateful to Andrei Shleifer for making several of the proxies freely available on his web page (http://www.economics.harvard.edu/faculty/shleifer/
dataset).
A. Gungoraydinoglu, Ö. Öztekin / Journal of Corporate Finance 17 (2011) 1457–1474
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3.2. Empirical methodology
3.2.1. Variance decomposition analysis
We examine how well several firm and institutional factors explain firms' leverage ratios to establish the relative importance
of the various determinants in providing information about firms' capital structure decisions. We estimate the following reducedform model of leverage:
LEVij;t ¼ α þ βf Xij;t−1 þ βc Yij þ βfc γij;t−1 þ εij;t ;
ð1Þ
where α is the average response across all firms, i indexes firms, j indexes countries, t indexes years, X is a set of firm-specific control variables, 3 Y is a set of country-level time-invariant institutional factors, γ is the institution–firm interaction effect, and ε is a
random error term.
The variance decomposition analysis reveals the relative influence of institutional effects compared with firm effects. It is a
static analysis conducted to understand the relative importance of various determinants of leverage without any assumptions
on causality. We relax this restriction subsequently and allow for dynamism and heterogeneity in capital structure decisions to
examine the stylized relationships between leverage and its determinants and to evaluate the empirical support for the capital
structure theories. Furthermore, country-level determinants can affect firm-level determinants, and the results of the variance
analysis may underestimate the relative importance of the country-level variables. 4 We also treat this potential endogeneity in
the dynamic analysis using the adequate instruments.
3.2.2. Dynamic capital structure estimations
We test the impact of the institutional factors on leverage and characterize the relationship of the country-specific determinants with the firm-specific determinants by using a partial adjustment model that incorporates rebalancing costs that
may slow down the firm's adjustment to its long-term leverage.
LEVij;t −LEVij;t−1 ¼ λ LEVij;t −LEVij;t−1 þ εij;t ;
ð2Þ
where LEVij, t is firm i's debt ratio in year t and country j, LEVij,* t is firm i's desired long-term debt ratio in country j and year t, and λ
is the adjustment parameter.
Prior research has examined the factors that determine leverage (Flannery and Rangan, 2006; Frank and Goyal, 2005, 2009;
Hovakimian et al., 2001). We follow the existing literature on the selection of the firm-specific factors affecting leverage but
also incorporate country-specific macroeconomic factors that are theoretically important in a firm's determination of leverage
(Cook and Tang, 2010; Frank and Goyal, 2009; Korajczyk and Levy, 2003). Furthermore, we include firm dummies for an unbiased
estimation of the determinants of capital structure (Flannery and Rangan, 2006; Huang and Ritter, 2009; Lemmon et al., 2008;
Öztekin and Flannery, 2011). Accordingly, we model each firm's desired leverage as a function of the observed firm-level,
industry-level, and macroeconomics characteristics, X; the observed country characteristics, Y; and the unobserved firm heterogeneity, μ. To evaluate whether firm and country attributes are complements or substitutes, we include their interactions in our
model as well. Therefore, we model leverage, allowing for the possibility that it might differ across firms and countries and over
time by specifying a desired long-term leverage ratio of the following form:
LEVij;t ¼ βf Xij;t−1 þ βc Yij þ βfc Xij;t−1 Yij þ μ i ;
ð3Þ
where βs and μ are coefficient vectors to be estimated, Xij, t − 1 and Yij are vectors of firm-level (industry-level, macroeconomic)
and country characteristics related to the costs and benefits of operating with various leverage ratios, and Xij, t − 1Yij is the interaction between the firm-level (industry-level, macroeconomic) and country-level determinants of capital structure.
Substituting Eq. (3) into the partial adjustment specification in Eq. (2) and re-arranging yields:
LEVij;t ¼ λβ f Xij;t−1 þ ð1−λÞLEVij;t−1 þ λβ c Yij þ λβ fc Xij;t−1 Yij þ λμ i þ εij;t :
ð4Þ
Eq. (4) constitutes a typical partial adjustment model of capital structure. We also include dummy country and time variables
to the specification of Eq. (4).
βf denotes the direct effect of the firm-level (industry-level or macroeconomic) features on firms' capital structure, which we simply
refer to as the “firm (industry or macroeconomic) effect.” To quantify the impact of the institutions in explaining international differences
in the capital structure choices, we can focus on two components. The first is the direct impact of the institutional determinants on
3
Controlling for the macroeconomic variables adds little to the observed variation in leverage and does not alter our conclusions. To save space, we do not report them in the variance decomposition results. For simplicity, we also refer to industry leverage as a firm-specific variable.
4
We thank an anonymous referee for pointing out this possibility.
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A. Gungoraydinoglu, Ö. Öztekin / Journal of Corporate Finance 17 (2011) 1457–1474
leverage. The term βc denotes the effect of country-level institutional characteristics on firms' capital structure, which we refer to as the
“direct institutional effect.” If the institutional feature in question is relevant for the capital structure choice, βc ≠0 should hold. Furthermore, the direction of the impact of the institutional variables should be consistent with the predictions of the capital structure theories.
For example, if the institution considered is creditor protection, we would expect βc N 0 under the trade-off theory, because
higher creditor protection would decrease bankruptcy and agency costs of debt, resulting in higher leverage. The second component is the indirect impact captured by the interaction term between the firm-level (industry-level, macroeconomic) and
country factors. Accordingly, βfc denotes the effect of the country-level institutional attributes on the impact of the firm-level
(industry-level, macroeconomic) attributes, which we call “indirect institutional effect.” To test whether the influence of the
firm-level, industry-level, and macroeconomic determinants of leverage depends on the institutional features of the country
in which the firm operates, we can test whether the indirect institutional effect is non-trivial (i.e., βfc = 0). If βfc N 0 (βfc b 0)
and if the firm attribute positively (negatively) affects leverage, the institutional characteristic can be thought to strengthen
the effect of the firm feature on leverage, in which case the firm and country features are “complements.” Conversely, if βfc b 0
(βfc N 0) and if the firm attribute positively (negatively) affects leverage, the institutional characteristic can be thought to
moderate the effect of the firm feature on leverage, in which case the firm and country features are “substitutes.” For
example, if tangibility has a positive impact on leverage, which is moderated in countries with stronger creditor rights, we
would expect to find βfc b 0, indicating that tangibility and creditor rights are substitute mechanisms to control bankruptcy
costs of debt.
The dynamic panel model in Eq. (4) requires instruments for the endogenous transformed lagged dependent variable (Baltagi,
2001) and other potentially endogenous explanatory variables. We use Blundell and Bond's (1998) generalized method of moments (GMM) methodology to estimate Eq. (4). We control for the potential endogeneity of the firm-specific variables and
their interactions with the country-level variables in the GMM estimations by using two- to four-period lags of the same variables as instruments. We confirm the validity of the instruments using the Hansen test. Because GMM is run on first differences,
we expect first-order serial correlation (AR(1)) but verify that there is no second-order serial correlation in the first-difference
residuals (AR(2)). We initially treat the country variables and the country and time dummy variables as exogenous. In the robustness section, we verify that our main conclusions remain unaltered when we account for the potential endogeneity of the
country variables.
4. Analysis and results
4.1. Variance decomposition of leverage
In this section, we examine how well the institutional- and firm-level determinants capture the international variation in capital structure by making use of the information at both levels. Tables 1–3 employ an interactive modeling of international capital
structure that explicitly accounts for the multi-level structure of the data by using firm- and country-specific factors and their interaction terms.
Each column of the tables provides a different country effect. We investigate three categories of institutions: bankruptcy costs
and taxes, agency costs, and information asymmetry costs. In addition, the tables provide the means of each institutional category
(Column 7 in Table 1, Column 12 in Table 2, Column 7 in Table 3) and the overall mean across all institutions (Column 8 in
Table 3).
4.1.1. Firm-level determinants
For the relative influence of the firm-specific attributes on leverage, the industry leverage seems to have the most significant
impact on international capital structure, followed by liquidity, profitability, tangibility, and firm size. The ordering of the importance of the firm-level covariates is robust to various model specifications in which different aspects of institutions are controlled.
For example, across all institutions, while these variables explain up to 96% of the total firm-level variation in leverage, taxes, R&D
expenses, depreciation expenses, and growth opportunities account for only the remaining 4%. This translates into 63% of the total
variation in leverage captured by liquidity, profitability, tangibility, and firm size compared with 3% of the total variation in leverage captured by taxes, R&D expenses, depreciation expenses, and growth opportunities. 5
4.1.2. Country-level determinants
To quantify the impact of the institutions in explaining international differences in the capital structure choices, we focus on
two components. The first is the direct impact of the institutional determinants on leverage, and the second is the indirect impact
captured by the interaction term between the firm and country factors.
5
The calculation is as follows: 27.03 for industry (Panel A, Row 7, Column 8 of Table 3) plus 17.96 for liquidity (Panel A, Row 9, Column 8 of Table 3) plus 12.07
for profit (Panel A, Row 1, Column 8 of Table 3) plus 3.64 for tangibility (Panel A, Row 5, Column 8 of Table 3) plus 2.57 for size (Panel A, Row 4, Column 8 of
Table 3) divided by 66.20 for firm effect (Panel C, Row 1, Column 8 of Table 3). Ignoring the scaling by the firm effect, this translates into 63% of the total variation
in leverage captured by liquidity, profitability, tangibility, and firm size compared with 3% of the total variation in leverage captured by taxes, R&D expenses, depreciation expenses, and growth opportunities across all institutions.
A. Gungoraydinoglu, Ö. Öztekin / Journal of Corporate Finance 17 (2011) 1457–1474
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Table 1
Explanatory power of the firm- and country-level determinants of bankruptcy costs and taxes.
Panel A. Firm-specific factors
(1)
Profit
(2)
Growth
(3)
Depreciation
(4)
Size
(5)
Tangibility
(6)
R&D
(7)
Industry
(8)
Tax
(9)
Liquidity
Time
Cost
Eff
Tax
Creditor
Formalism
Bankruptcy
(1)
(2)
(3)
(4)
(5)
(6)
(7)
14.65
0.77
0.05
1.84
4.66
0.93
42.08
2.74
17.77
0.06
0.38
0.32
7.83
0.21
0.00
4.72
3.97
1.68
6.62
0.09
0.00
3.40
5.42
0.91
29.21
3.34
26.76
6.03
0.38
0.00
1.21
6.22
0.27
37.01
3.16
23.99
8.72
0.00
1.00
0.08
0.45
0.67
27.15
0.00
10.33
7.03
0.01
0.00
1.04
0.42
0.07
21.70
0.84
3.14
7.19
0.27
0.23
2.57
2.90
0.48
26.98
2.34
13.95
Panel B. Institutional factors
(1)
Direct institutional effect
(2)
Indirect institutional effect
(3)
Profit
(4)
Growth
(5)
Depreciation
(6)
Size
(7)
Tangibility
(8)
R&D
(9)
Industry
(10)
Tax
(11)
Liquidity
(12)
Adjusted R2
9.49
61.04
19.97
17.22
43.74
56.85
34.72
0.91
0.29
0.11
0.14
0.03
0.17
0.10
0.06
3.23
26.73
4.94
0.23
0.83
3.35
3.00
0.31
2.11
1.72
3.29
27.77
3.10
0.03
0.40
0.01
0.15
0.00
0.43
0.06
0.09
27.14
2.57
0.22
0.46
0.19
0.33
0.01
0.56
0.11
0.07
26.81
1.21
0.01
0.55
1.76
0.15
0.22
2.70
0.76
0.49
27.26
0.06
0.01
0.12
0.01
4.38
0.61
0.80
0.05
2.85
27.55
2.13
0.13
0.41
0.91
1.34
0.22
1.12
0.46
1.67
27.21
Panel C. Summary
(1)
Firm effect
(2)
Direct institutional effect
(3)
Indirect institutional effect
85.48
9.49
5.03
19.17
61.04
19.78
75.75
19.97
4.28
78.27
17.22
4.51
48.41
43.74
7.85
34.25
56.85
8.89
56.89
34.72
8.39
The definitions of the variables are provided in the Appendices. All numbers are reported in percentages. We estimate the following reduced-form model of
leverage, where α is the constant term, X and Y are a set of firm- and country-level control variables, γ is the interaction term between the firm and country
covariates, and ε is a random error term:
LEVij;t ¼ α þ βf Xij;t−1 þ βc Yij þ β fc γij;t−1 þ εij;t :
The estimates for firm-specific factors are reported in Panel A, Rows 1 -9, the direct institutional effect is reported in Panel B, Row 1, and the indirect institutional
effects are reported in Panel B, Rows 3–11 separately for each country-level determinant of the bankruptcy costs and tax benefits in Columns 1–6. Panel B, Row
12, reports the adjusted R-square. Panel C, Row 1 reports the sum of the effects reported in Panel A, Rows 1–9. Panel C, Row 2 simply repeats Panel B, Row 1. Panel
C, Row 3 reports the sum of the effects reported in Panel B, Rows 3–11. Column 7 reports the overall mean of the estimates across all country-level determinants
of the bankruptcy costs and taxes (i.e., across Columns 1–6).
In Table 1 (Panel B, Row 1), the most influential direct impact of the institutional factors related to the bankruptcy and tax
trade-off arises from the cost of the bankruptcy outcome (61%), creditor rights (44%) and their enforcement (57%), efficiency of
the bankruptcy process (20%), and taxes (17%). The time to resolve the bankruptcy process has the least significant (9%) direct
impact on leverage. In Table 2, the conflicts of interest among the shareholders and bondholders also constitute a significant
proportion of the variation in international capital structure if creditor rights (44%) and their enforcement (57%) are interpreted as proxies for the agency costs of debt. The most prominent institutions that possibly determine the manager–shareholder conflicts are ownership concentration, contract enforcement, and shareholder rights enforcement with an equally
strong direct impact (33%) on the variation in leverage. In Table 3, the outstanding institutions that capture the information
asymmetry costs are corporate transparency, with the highest explanatory power (42%), perseverance of insider trading
(28%), disclosure (24%), liability standards (16%), and public enforcement (12%) in equity markets.
Overall, the bankruptcy costs and tax benefit trade-offs seem to better explain the variation in capital structure (35% in Column 7
of Table 1), followed by agency costs (21% in Column 12 of Table 2) and information asymmetry (20% in Column 7 of Table 3). Across
all institutions, the explanatory power of the direct impact of institutions is 22% (Column 8 of Table 3).
The results on the indirect impact of the country-level covariates on international capital structure reinforce their importance because they consistently affect the roles of the firm-specific determinants beyond their direct impact on leverage. The
firm-specific determinants most influenced by the institutional differences across countries are the factors that have the
most explanatory power on their own as well: profitability, liquidity, size, industry, and tangibility. Overall, the most powerful
indirect impact on firm-specific variables originates from the institutions that affect information asymmetry costs (15% in Table 3,
Panel C, Row 3, Column 7), followed by agency costs (11% in Table 2, Panel C, Row 3, Column 12) and bankruptcy costs and taxes
(8% in Table 1, Panel C, Row 3, Column 7). The total average indirect impact of the institutional characteristics on firms' leverage across
all institutions is 12% (Table 3, Panel C, Row 3, Column 8) of the total variation in leverage across all institutions.
1464
A. Gungoraydinoglu, Ö. Öztekin / Journal of Corporate Finance 17 (2011) 1457–1474
Table 2
Explanatory power of the firm- and country-level determinants of agency costs.
Panel A. Firm-specific factors
(1)
Profit
(2)
Growth
(3)
Depreciation
(4)
Size
(5)
Tangibility
(6)
R&D
(7)
Industry
(8)
Tax
(9)
Liquidity
Creditor
Formalism
Reserve
Antidir
Prenf
Mdiv
Owner
Exec
Law
Gov
Enforce
Agency
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
(10)
(11)
(12)
8.72
0.00
1.00
0.08
0.45
0.67
27.15
0.00
10.33
7.03
0.01
0.00
1.04
0.42
0.07
21.70
0.84
3.14
16.72
0.07
0.02
5.36
2.13
0.86
43.54
2.11
22.16
7.71
0.15
2.17
0.64
5.24
1.37
37.02
0.00
24.61
8.10
0.01
0.95
1.45
10.01
0.53
21.79
0.11
12.52
16.00
0.28
0.16
1.43
4.75
0.47
47.39
2.71
26.51
10.61
0.48
0.10
8.25
5.78
1.25
6.95
5.48
15.05
25.28
0.54
1.60
2.90
0.27
0.09
20.26
0.04
17.52
21.90
0.00
0.27
0.12
0.31
2.37
23.98
0.17
22.60
19.90
0.03
0.29
3.18
4.27
0.58
31.04
2.62
27.23
16.59
0.46
0.68
0.60
0.33
0.25
16.66
0.00
15.22
14.41
0.18
0.66
2.28
3.09
0.77
27.04
1.28
17.90
Panel B. Institutional factors
(1)
Direct institutional effect
(2)
Indirect institutional effect
(3)
Profit
(4)
Growth
(5)
Depreciation
(6)
Size
(7)
Tangibility
(8)
R&D
(9)
Industry
(10) Tax
(11) Liquidity
(12) Adjusted R2
43.74
56.85
1.06
8.43
32.54
0.06
33.34
8.90
7.11
8.71
33.20
21.27
1.21
0.01
0.55
1.76
0.15
0.22
2.70
0.76
0.49
27.26
0.06
0.01
0.12
0.01
4.38
0.61
0.80
0.05
2.85
27.55
0.94
0.03
0.1
1.23
0.87
0.01
0
0.04
2.76
27.03
0.07
0.04
1.53
4.13
1.08
0.38
1.17
1.24
3.01
26.53
0.09
0.02
0.34
6.22
3.85
0.01
0.64
0.60
0.22
27.57
0.00
0.04
0.00
0.02
0.05
0.03
0.08
0.01
0.01
25.95
0.26
0.31
0.77
2.39
0.31
0.01
7.31
0.84
0.51
27.53
12.83
0.96
1.28
1.65
0.26
0.04
0.48
0.77
4.34
26.67
10.37
0.07
0.20
0.25
0.13
1.98
1.48
0.06
6.60
26.95
1.43
0.06
0.09
0.08
0.14
0.01
0.25
0.01
0.11
27.64
7.74
0.52
0.45
0.00
0.04
0.08
2.31
0.27
4.61
27.84
3.18
0.19
0.49
1.61
1.02
0.31
1.57
0.42
2.32
27.14
Panel C. Summary
(1)
Firm effect
(2)
Direct institutional effect
(3)
Indirect institutional effect
48.41
43.74
7.85
34.25
56.85
8.89
92.96
1.06
5.97
78.92
8.43
12.65
55.47
32.54
11.99
99.69
0.06
0.25
53.95
33.34
12.71
68.49
8.90
22.61
71.74
7.11
21.15
89.13
8.71
2.16
50.78
33.20
16.03
67.62
21.27
11.11
The definitions of the variables are provided in the Appendices. All numbers are reported in percentages. We estimate the following reduced-form model of leverage, where α is the constant term, X and Y are a set of firm and country-level control variables, γ is the interaction term between the firm and country covariates, and ε is a random error term:
LEVij;t ¼ α þ β f Xij;t−1 þ βc Yij þ β fc γij;t−1 þ εij;t :
The estimates for firm-specific factors are reported in Panel A, Rows 1 -9, the direct institutional effect is reported in Panel B, Row 1, and the indirect institutional
effects are reported in Panel B, Rows 3–11 separately for each country-level determinant of the agency costs in columns 1–11. Panel B, Row 12 reports the adjusted
R-square. Panel C, Row 1 reports the sum of the effects reported in Panel A, Rows 1–9. Panel C, Row 2 simply repeats Panel B, Row 1. Panel C, Row 3 reports the sum
of the effects reported in Panel B, Rows 3–11. Column 12 reports the overall mean of the estimates across all country-level determinants of the agency costs (i.e.,
across Columns 1–11).
The comparative analysis of the relative importance of the firm- and country-specific factors in the capital structure choices
around the world reveals that institutional arrangements matter for capital structure decisions; however, the firm-level covariates
drive most of the variation (66% in Table 3, Panel C, Row 1, Column 8) in capital structure across countries, on average, across all
institutions, not the country-level covariates (34% in Table 3, Panel C, sum of Rows 2 and 3, Column 8). Fig. 1 visually depicts the
relative contributions of the firm- and country-specific determinants in explaining the variation in the international capital structure across all institutions and the three separate categories of bankruptcy costs and taxes, agency costs, and information asymmetry costs.
4.2. Dynamic capital structure estimations
The leading capital structure theories have different predictions about the impact of bankruptcy costs, agency costs, tax benefits, and information asymmetry costs on corporate leverage. In this section, we characterize the relationships between firmlevel, industry-level, macroeconomic, and country-level characteristics and leverage in different institutional environments to
evaluate the empirical relevance of the capital structure theories. We also consider the interactive effects because firm- and
country-level determinants of the benefits and costs of operating at different debt levels may be either complements or
substitutes.
Panel A and Panel B in Table 4 document the impact of several firm and country attributes that determine bankruptcy costs and
taxes and agency costs, respectively, on leverage to evaluate their implications for the trade-off theory. Panel C in Table 4 illustrates
the relationships of several firm and country attributes that determine information asymmetry costs with leverage and evaluate the
implications of the pecking-order theory. In each panel, Rows 2–14 illustrate the effect of the firm-level (industry-level and
A. Gungoraydinoglu, Ö. Öztekin / Journal of Corporate Finance 17 (2011) 1457–1474
1465
Table 3
Explanatory power of the firm- and country-level determinants of information asymmetry costs.
Panel A. Firm-specific factors
(1)
Profit
(2)
Growth
(3)
Depreciation
(4)
Size
(5)
Tangibility
(6)
R&D
(7)
Industry
(8)
Tax
(9)
Liquidity
Trans
Disclose
Liability
Penf
Insider
Cinfo
Info Asymmetry
All institutions
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
8.63
0.06
1.28
6.20
3.45
0.77
9.13
0.37
6.57
0.47
0.06
0.54
2.25
3.39
0.34
37.64
0.01
15.38
5.90
0.00
0.74
0.03
4.23
0.79
38.96
0.06
24.98
11.92
0.00
0.72
0.18
7.82
0.76
19.11
1.75
34.05
19.37
0.06
1.71
1.16
1.68
0.69
13.96
0.05
11.42
21.16
0.00
1.44
4.72
5.40
0.20
38.23
0.24
17.67
11.24
0.03
1.07
2.42
4.33
0.59
26.17
0.41
18.35
12.07
0.18
0.67
2.57
3.64
0.67
27.03
1.42
17.96
Panel B. Institutional factors
(1)
Direct institutional effect
(2)
Indirect institutional effect
(3)
Profit
(4)
Growth
(5)
Depreciation
(6)
Size
(7)
Tangibility
(8)
R&D
(9)
Industry
(10)
Tax
(11)
Liquidity
(12)
Adjusted R2
41.88
23.78
16.39
11.65
28.20
0.28
20.36
22.09
4.46
0.01
1.08
8.06
1.95
0.46
2.37
0.91
2.35
26.73
2.29
0.04
0.25
5.56
0.64
0.00
4.96
0.95
1.42
27.12
0.32
0.07
0.21
1.69
0.46
0.00
2.59
0.91
1.66
27.22
1.21
0.01
0.28
1.08
2.52
0.08
0.10
0.09
6.67
27.75
10.96
0.12
1.45
2.25
0.39
0.43
2.02
0.39
3.67
27.53
5.13
0.09
0.97
1.98
0.74
0.03
0.00
0.21
1.52
26.26
4.06
0.06
0.71
3.44
1.12
0.17
2.01
0.58
2.88
27.10
3.38
0.15
0.55
2.00
1.02
0.23
1.55
0.48
2.36
27.12
Panel C. Summary
(1)
Firm effect
(2)
Direct institutional effect
(3)
Indirect institutional effect
36.46
41.88
21.66
60.09
23.78
16.13
75.69
16.39
7.92
76.31
11.65
12.04
50.10
28.20
21.69
89.05
0.28
10.67
64.62
20.36
15.02
66.20
22.09
11.71
The definitions of the variables are provided in the Appendices. All numbers are reported in percentages. We estimate the following reduced-form model of leverage where α is the constant term, X and Y are a set of firm and country-level control variables, γ is the interaction term between the firm and country covariates, and ε is a random error term:
LEVij;t ¼ α þ βf Xij;t−1 þ βc Yij þ β fc γij;t−1 þ εij;t :
The estimates for firm-specific factors are reported in Panel A, Rows 1 -9, the direct institutional effect is reported in Panel B, Row 1, and the indirect institutional
effects are reported in Panel B, Rows 3–11 separately for each country-level determinant of the information asymmetry costs in Columns 1–6. Panel B, Row 12
reports the adjusted R-square. Panel C, Row 1 reports the sum of the effects reported in Panel A, Rows 1–9. Panel C, Row 2 repeats Panel B, Row 1. Panel C,
Row 3 reports the sum of the effects reported in Panel B, Rows 3–11. Column 7 reports the overall mean of the estimates across all country-level determinants
of the information asymmetry costs (i.e., across Columns 1–6). Column 8 reports the overall mean of the estimates across all country-level determinants (i.e.,
across Columns 1–6 of Table 1, Columns 3–11 of Table 2, Columns 1–6 of Table 3).
macroeconomic) determinants on leverage; Row 15 documents the direct impact of the country-level factors on leverage; and
Rows 16–27 illustrate the indirect impact of the country-level factors on leverage as captured by their interaction terms.
4.2.1. Direct impact of the country-level determinants
In Panel A, higher tax benefits and lower bankruptcy costs are associated with higher leverage, consistent with the predictions of
trade-off theory. Lower ex post financial distress costs positively affect leverage through the efficiency of the bankruptcy outcome
(Column 3), but the timeliness and costliness of the bankruptcy procedures (Columns 1 and 2) do not have a statistically significant
impact on corporate leverage around the world. Higher effective tax rates (Column 4) lead to higher leverage. In countries in which
the ex ante distress costs are lower, as implied by stronger creditor rights (Column 5) and better enforcement (lower formalism) of
these rights (Column 6), leverage is also higher. Overall, four of the six proxies we employ to account for the country-level bankruptcy
costs and taxes provide strong empirical support to the predictions of the trade-off theory.
In Panel B, lower agency costs of debt are associated with higher leverage, consistent with the agency view of the trade-off theory.
In countries in which debtholder rights are stronger (Column 1) and more efficiently enforced (Column 2), firm leverage is higher.
Legal reserve requirements, a measure for remedial creditor rights (Column 3), also have a significant, positive impact on corporate
leverage around the world. In general, lower agency costs of equity are associated with lower leverage, consistent with the agency
view of the trade-off theory. For example, stronger protection of shareholder rights (Column 4) and higher quality of private enforcement of these rights (Column 5) lead to lower leverage. Mandatory dividends, a measure for remedial shareholder rights (Column 6),
and ownership concentration (Column 7) do not have a significant impact on leverage. Finally, higher quality of internal and external
monitoring, as measured by more constraints on executive power (Column 8), stronger law and order (Column 9), higher quality of
1466
Cost
Creditor
Eff
Formalism Creditor
Bankruptcy
8.39
Exec
Gov
Mdiv
Enforce
Law
Reserve
Agency
11.11
Tax
Time
21.27
67.62
56.89
Owner
Cinfo
Disclose
Insider
Prenf
Antidir
All Institutions
Info Asymmetry
11.71
22.09
15.02
66.2
Liability
PEnf
Trans
20.36
64.62
Firm Effect
Direct Institutional Effect
Indirect Institutional Effect
Fig. 1. The figure is a visual representation of the relative contribution of the firm- and country-level covariates to the variation in leverage. The pies refer to the numbers reported in Rows 1, 2, and 3 in Panel C (“Summary”),
respectively, for the firm effect, direct institutional effect, and indirect institutional effect in Columns 1–7 of Table 1 for the country-level determinants affecting bankruptcy costs and taxes, in Columns 1–12 of Table 2 for
the country-level determinants affecting agency costs, in Columns 1–7 of Table 3 for the country-level determinants affecting information asymmetry costs, and in Column 8 of Table 3 for all institutions.
A. Gungoraydinoglu, Ö. Öztekin / Journal of Corporate Finance 17 (2011) 1457–1474
34.72
Formalism
A. Gungoraydinoglu, Ö. Öztekin / Journal of Corporate Finance 17 (2011) 1457–1474
1467
government (Column 10), and contract enforcement (Column 11), leads to lower agency costs of equity and is associated with lower
leverage. Overall, nine of the 11 proxies we employ to account for the country-level agency costs of debt and equity provide strong
support to the predictions of the agency view of the trade-off theory.
In Panel C, lower adverse selection costs, as indicated by higher corporate transparency (Column 1), stronger disclosure (Column 2),
liability (Column 3), public enforcement standards (Column 4), and lower perseverance of insider trading (Column 5), are associated
with lower debt, consistent with the pecking-order theory. Except for information sharing in debt markets (Column 6), which does
not have a statistically significant impact on corporate leverage, the remaining five of the six proxies we employ to account for
the country-level information asymmetry costs provide strong support to the predictions of the pecking-order theory.
Overall, the results reveal strong empirical support for both the trade-off and the pecking-order theories around the world;
bankruptcy costs, taxes, agency costs, and information asymmetry costs all significantly matter for leveraging choices in the
direction the theories hypothesize.
4.2.2. Indirect impact of the country-level determinants
In the discussion that follows, we interpret the impact of the country-level factors on the effect of the firm-level determinants to
evaluate whether they are substitutes or complements when both firm and indirect institutional effect are statistically significant and
when the firm effect carries a sign that is consistent with the predictions of the capital structure theories.
First, bigger firm size, as well as higher profitability, industry leverage, tangibility and liquidity imply lower bankruptcy costs of
debt and, thus, more debt, indicating a positive relationship between these determinants and leverage, according to the trade-off theory. On the other hand, higher inflation decreases the benefits of leverage because of higher bankruptcy costs of debt imposed on
firms. Panel A shows that in countries in which bankruptcy procedures are more costly (Column 2) and the enforcement of the creditor rights is less stringent (Column 6), the positive relationship between leverage and profitability (Row 2) is weaker (Row 16). This
result indicates that minimizing the costliness of the bankruptcy process and strengthening strict enforcement of creditor rights are
substitute mechanisms to increasing profitability for controlling financial distress costs. Similarly, in countries in which the bankruptcy procedures are more costly and the creditors are granted stronger rights (Column 5), the positive relationship between leverage
and industry leverage (Row 9) is weaker (Row 22). In other words, minimizing the costliness of the bankruptcy process and strengthening creditor rights are substitute mechanisms to maintaining higher industry leverage for managing financial distress costs. The observed positive relationship between leverage and firm size (Row 5) is moderated in countries subject to lower bankruptcy costs. In
countries in which the bankruptcy procedures are more timely (Column 1), less costly (Column 2), and more efficient (Column 3) and
the enforcement of the creditor rights is more stringent (Column 6), the relationship between leverage and firm size is weaker (Row
19). Therefore, the timeliness, costliness, and efficiency of the bankruptcy process, as well as the quality of enforcement of creditor
rights, act as substitute mechanisms to firm size for controlling financial distress costs. The observed positive relationship between
leverage and tangibility or GDP growth (Rows 6 and 14) is moderated in countries subject to lower bankruptcy costs. In countries in which the bankruptcy procedures are more efficient (Column 3), the relationship between leverage and tangibility or
GDP growth is weaker (Rows 20 and 27). Similarly, in countries with stronger creditor rights (Column 5), the relationship between leverage and GDP growth is moderated (Row 27). This suggests that these two country-level determinants act as substitute mechanisms to tangibility and economic growth for controlling financial distress costs. In other words, the need for
collateral and higher economic growth is more pronounced in countries with lower-quality institutions. The positive relationship between liquidity and leverage (Row 11) is moderated (Row 24) in weaker institutional environments subject to higher
bankruptcy costs as measured by the quality of enforcement of creditor rights (Column 6), which acts as a substitute mechanism
to liquidity for controlling financial distress costs. The negative relationship between leverage and inflation (Row 13) is weaker
(Row 26) in countries in which the bankruptcy procedures are more costly (Column 2) and more efficient (Column 3). Thus,
minimizing the costliness and maximizing the efficiency of the bankruptcy process are substitute mechanisms to reducing inflation for controlling financial distress costs. Overall, the results indicate that firm- and country-level determinants of bankruptcy costs are substitute mechanisms for each other. The impact of the firm-level tax benefits on leverage is independent of
the institutional setting because the interaction terms between the firm- and country-level determinants of taxes (Rows 18
and 23, Column 4) are insignificant.
Second, large firms face lower agency costs of debt and therefore prefer more debt in their capital structure according to the
agency view of the trade-off theory. In Panel B, the observed positive relationship between leverage and firm size (Row 5) is
moderated (Row 19) in countries subject to lower agency costs, as indicated by higher legal reserve requirements (Column 3).
This suggests that strengthening the remedial creditor rights serves as a substitute mechanism to increasing firm size for controlling agency costs of debt. Furthermore, higher liquidity reduces agency costs of debt but increases agency costs of equity,
leading to higher leverage according to the agency view of the trade-off theory. The positive relationship between leverage
and firm size or liquidity (Row 11) is moderated (Rows 19 and 24) in weaker institutional environments subject to higher
agency costs of debt, as indicated by lower quality of creditor rights enforcement (Column 2). Therefore, strengthening the
quality of enforcement of creditor rights acts as a substitute mechanism to increasing firm size and liquidity for controlling
agency costs of debt. Higher growth opportunities indicate higher agency costs of debt but lower agency cost of equity, leading
to lower leverage according to the agency view of the trade-off theory. This negative relationship (Row 3) is moderated (Row
17) in institutional settings subject to lower agency costs of equity, as measured by the quality of shareholder rights (Column
4) and their enforcement (Column 5), higher mandatory dividends (Column 6), and ownership concentration (Column 7), indicating that growth opportunities and these country-level determinants of agency costs are substitute mechanisms for each
other. Higher profitability and economic growth increase agency costs of equity, leading to higher leverage according to the
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A. Gungoraydinoglu, Ö. Öztekin / Journal of Corporate Finance 17 (2011) 1457–1474
Table 4
The impact of firm- and country-level determinants on capital structure.
Panel A. The impact of bankruptcy costs and taxes on capital structure
(1)
Leverage
(2)
Profit
(3)
Growth
(4)
Depreciation
(5)
Size
(6)
Tangibility
(7)
R&D missing
(8)
R&D
(9)
Industry
(10)
Tax
(11)
Liquidity
(12)
Regulated
(13)
Inflation
(14)
GDPG
(15)
Institution (I)
(16)
Profit ⁎ I
(17)
Growth ⁎ I
(18)
Depreciation ⁎ I
(19)
Size ⁎ I
(20)
Tangibility ⁎ I
(21)
R&D ⁎ I
(22)
Industry ⁎ I
(23)
Tax ⁎ I
(24)
Liquidity ⁎ I
(25)
Regulated ⁎ I
(26)
Inflation ⁎ I
(27)
GDPG ⁎ I
Observations
Firms/countries
AR(1)/AR(2)
Hansen
Time
Cost
Eff
Tax
Creditor
Formalism
(1)
(2)
(3)
(4)
(5)
(6)
0.7533⁎⁎⁎
0.8236⁎⁎⁎
0.8437⁎⁎⁎
0.8357⁎⁎⁎
0.8439⁎⁎⁎
(0.0195)
− 0.0166
(0.0265)
0.0094⁎⁎
(0.0071)
0.0250⁎⁎⁎
(0.0092)
0.0000
(0.0005)
− 0.2328⁎⁎⁎
(0.0092)
− 0.0525
(0.0399)
0.0007⁎
(0.0072)
0.0205
(0.0192)
0.0013⁎
(0.0004)
0.1132
(0.1614)
0.0078⁎⁎⁎
(0.0020)
0.0855⁎⁎⁎
(0.0007)
− 0.0899
(0.0948)
0.0028⁎⁎⁎
(0.0009)
0.0151
(0.0158)
0.0042
(0.0075)
0.0328
(0.0204)
0.1570⁎⁎⁎
(0.0370)
− 0.0007
(0.0034)
0.0030⁎⁎⁎
(0.0089)
− 0.0455⁎⁎
(0.0188)
0.0002
(0.0004)
− 0.3364⁎⁎⁎
0.8190⁎⁎⁎
(0.0072)
0.0414⁎
(0.0227)
− 0.0001
(0.0007)
− 0.3209⁎⁎⁎
(0.0038)
− 0.0682
(0.1111)
0.0284⁎⁎
(0.0121)
0.1459⁎⁎
(0.0580)
− 0.0328
(0.0230)
− 0.2875⁎⁎⁎
(0.0975)
0.2072⁎⁎
(0.0935)
0.0018
(0.0022)
0.0013
(0.0020)
− 0.7384⁎
(0.4439)
0.1760
(0.2618)
− 0.5814⁎⁎⁎
(0.1845)
0.2025
(0.1299)
− 0.0096
(0.0171)
− 0.0036⁎⁎
(0.0016)
− 0.1706⁎⁎⁎
(0.0480)
0.0079⁎⁎⁎
(0.0014)
0.0035
(0.0105)
0.0513⁎⁎⁎
(0.0056)
0.1017⁎⁎⁎
(0.0222)
0.1114⁎⁎⁎
(0.0204)
0.0037⁎⁎
(0.0016)
0.0001
(0.0006)
− 0.1503⁎⁎⁎
(0.0342)
− 0.1668⁎⁎⁎
(0.0458)
− 0.0195
(0.0293)
0.1616
(0.2144)
− 0.5928⁎⁎⁎
(0.0576)
0.0014
(0.0029)
− 0.6309⁎
(0.0569)
− 0.0115⁎⁎
(0.0053)
− 0.0363
(0.0281)
0.0123
(0.0110)
− 0.0282
(0.0278)
− 0.0030⁎
(0.0016)
− 0.0023⁎⁎
(0.3597)
− 0.0245⁎⁎⁎
(0.0090)
0.3353⁎⁎⁎
(0.0671)
− 0.4139⁎⁎⁎
(0.0564)
− 0.2058⁎⁎
(0.0981)
− 0.0418⁎⁎
(0.0172)
− 0.0073⁎⁎
(0.0009)
0.2050
(0.2387)
− 0.0225
(0.0718)
0.1905⁎⁎⁎
(0.0718)
103,609
14,870/35
0.00/0.34
1.00
(0.0035)
0.6220⁎⁎⁎
(0.2344)
1.0895⁎⁎⁎
(0.2602)
1.5248⁎⁎⁎
(0.1918)
103,609
14,870/35
0.00/0.51
0.79
(0.0266)
0.0233
(0.0193)
− 0.0288
(0.0341)
− 0.1495⁎⁎⁎
(0.0453)
− 0.0472⁎⁎⁎
(0.0165)
− 0.0012
(0.0021)
− 0.0293
(0.0430)
− 0.2248⁎⁎⁎
(0.0749)
0.4885⁎⁎⁎
(0.0730)
0.0010⁎⁎⁎
(0.0003)
0.0007
(0.0005)
− 0.0000⁎
(0.0000)
− 0.0045⁎⁎
(0.0022)
− 0.0001⁎⁎⁎
(0.0000)
− 0.0011⁎⁎⁎
(0.0003)
− 0.0003
(0.0002)
0.0019⁎⁎⁎
(0.0006)
0.0006⁎⁎⁎
(0.0002)
− 0.0000
(0.0000)
0.0006
(0.0006)
0.0051⁎⁎⁎
(0.0013)
− 0.0069⁎⁎⁎
(0.0010)
103,609
14,870/35
0.00/0.42
0.19
(0.0011)
− 0.1413⁎⁎⁎
(0.0494)
− 0.0636
(0.0772)
0.3189⁎⁎⁎
(0.0946)
0.0016
(0.0013)
− 0.0433⁎⁎
(0.0176)
0.0611⁎⁎⁎
(0.1080)
0.0038⁎⁎
(0.0019)
0.0568⁎⁎
(0.0087)
0.1675⁎⁎⁎
(0.0335)
0.1662⁎⁎⁎
(0.0383)
0.0582⁎⁎⁎
(0.0234)
− 0.0835⁎⁎⁎
(0.0219)
0.0336
(0.0230)
0.1257⁎⁎
(0.0512)
− 0.0109⁎⁎
(0.0126)
0.0017
(0.0014)
− 0.0799⁎⁎
(0.0326)
0.3608⁎⁎⁎
(0.0651)
0.2332⁎⁎⁎
(0.0052)
0.0027⁎⁎
(0.0013)
− 0.2400⁎⁎⁎
(0.0606)
− 0.0600
(0.0916)
0.1497⁎⁎
(0.0423)
0.0032⁎⁎⁎
(0.0008)
− 0.0022⁎⁎
(0.0009)
− 0.0001
(0.0000)
− 0.0066
(0.0048)
− 0.0002⁎⁎⁎
(0.0000)
0.0003
(0.0008)
− 0.0000
(0.0003)
− 0.0034⁎⁎
(0.0017)
− 0.0000
(0.0001)
− 0.0002⁎⁎⁎
(0.0510)
0.0225⁎⁎⁎
(0.0057)
0.0228⁎⁎⁎
(0.0068)
− 0.0002
(0.0002)
0.0431
(0.0305)
− 0.0005
(0.0006)
0.0199⁎⁎⁎
(0.0067)
− 0.0149⁎⁎⁎
(0.0752)
− 0.0215⁎⁎⁎
(0.0079)
− 0.0222⁎⁎⁎
(0.0082)
− 0.0001
(0.0002)
0.0035
(0.0364)
− 0.0011⁎
(0.0006)
0.0004
(0.0082)
0.0326⁎⁎⁎
(0.0032)
− 0.0367⁎⁎
(0.0176)
− 0.0127⁎⁎⁎
(0.0045)
− 0.0017⁎⁎⁎
(0.0001)
0.0079⁎⁎⁎
(0.0026)
0.0039
(0.0036)
− 0.0207⁎⁎⁎
(0.0022)
105,560
15,177/37
0.00/0.48
0.32
(0.0005)
0.0364⁎⁎
(0.0148)
− 0.1429⁎⁎⁎
(0.0246)
− 0.0608⁎⁎⁎
(0.0205)
105,560
15,177/37
0.00/0.32
0.90
(0.0072)
− 0.0055
(0.0161)
0.0042⁎⁎
(0.0017)
− 0.0013⁎⁎⁎
(0.0005)
0.0657⁎⁎⁎
(0.0187)
0.0248
(0.0275)
− 0.0163
(0.0252)
105,560
15,177/37
0.00/0.42
0.11
The definitions of the variables are provided in the Appendices. The estimates are obtained from Blundell and Bond's (1998) two-step system GMM. Regressions
include unreported year and country dummies. The robust standard errors are reported below the coefficient estimates. ***, **, and * indicate significance at the
1%, 5%, and 10% levels respectively. AR(1) and AR(2) denote the p-values for the first- and second-order autocorrelation in the residuals. Hansen reports the pvalue under the null hypothesis of joint validity of the instrument set.
A. Gungoraydinoglu, Ö. Öztekin / Journal of Corporate Finance 17 (2011) 1457–1474
1469
Table 4
The impact of firm- and country-level determinants on capital structure.
Panel B. The impact of agency costs on capital structure
Creditor
Formalism
Reserve
Antidir
Prenf
Mdiv
Owner
Exec
Law
Gov
Enforce
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
(10)
(11)
0.8439⁎⁎⁎
(0.0089)
− 0.0455⁎⁎
(0.0188)
0.0002
(0.0004)
− 0.3364⁎⁎⁎
0.8190⁎⁎⁎
(0.0072)
0.0414⁎
(0.0227)
− 0.0001
(0.0007)
− 0.3209⁎⁎⁎
0.8139⁎⁎⁎
(0.0075)
− 0.0271⁎⁎⁎
(0.0078)
0.0015⁎⁎⁎
0.8199⁎⁎⁎
(0.0077)
− 0.1010⁎⁎⁎
(0.0180)
− 0.0011⁎⁎⁎
0.8241⁎⁎⁎
(0.0074)
− 0.0510⁎⁎⁎
(0.0190)
− 0.0012⁎⁎
0.7757⁎⁎⁎
(0.0044)
− 0.0198⁎⁎⁎
(0.0076)
− 0.0008⁎⁎⁎
0.8254⁎⁎⁎
(0.0070)
0.0229⁎
(0.0135)
− 0.0031⁎⁎⁎
0.8226⁎⁎⁎
(0.0072)
− 0.1738⁎⁎⁎
(0.0309)
0.0012⁎⁎
0.8200⁎⁎⁎
(0.0075)
− 0.0252⁎⁎⁎
(0.0067)
0.0006⁎⁎
0.8184⁎⁎⁎
(0.0070)
− 0.2756⁎⁎⁎
(0.0338)
0.0016⁎⁎
(0.0946)
0.0016
(0.0013)
− 0.0433⁎⁎
(0.0176)
0.0611⁎⁎⁎
(0.1080)
0.0038⁎⁎
(0.0019)
0.0568⁎⁎
(0.0005)
− 0.2537⁎⁎⁎
(0.0369)
0.0070⁎⁎⁎
(0.0013)
0.0620⁎⁎⁎
(0.0004)
− 0.2616⁎⁎⁎
(0.0637)
− 0.0058⁎⁎⁎
(0.0015)
0.0989⁎⁎⁎
(0.0005)
− 0.3023⁎⁎⁎
(0.0737)
− 0.0054⁎⁎⁎
(0.0017)
0.1027⁎⁎⁎
(0.0002)
− 0.2798⁎⁎⁎
(0.0367)
0.0087⁎⁎⁎
(0.0009)
0.0473⁎⁎⁎
(0.0008)
− 0.2024
(0.1452)
− 0.0030⁎⁎
(0.0012)
− 0.0824⁎⁎⁎
(0.0220)
0.0795⁎⁎⁎
(0.0164)
0.0386
(0.0261)
0.0271
(0.0437)
0.0028
(0.0030)
− 0.0032⁎⁎⁎
(0.0209)
0.0387⁎⁎⁎
(0.0147)
− 0.0088
(0.0238)
0.1452⁎⁎⁎
(0.0527)
0.0012
(0.0028)
− 0.0034⁎⁎⁎
(0.0089)
0.0320⁎⁎⁎
(0.0047)
0.1402⁎⁎⁎
(0.0299)
0.1341⁎⁎⁎
(0.0136)
0.0006
(0.0010)
− 0.0009⁎⁎
(0.0005)
− 0.2723⁎⁎
(0.1335)
0.0012
(0.0011)
0.0215
(0.0229)
− 0.1431⁎⁎⁎
(0.0003)
− 0.2907⁎⁎⁎
(0.0310)
0.0050⁎⁎⁎
(0.0009)
0.0493⁎⁎⁎
(0.0099)
0.0120
(0.0094)
0.0173
(0.0321)
0.0129
(0.0273)
− 0.0035⁎⁎
(0.0052)
(0.0014)
0.0027⁎⁎
− 0.0003
(0.0013)
(0.0005)
− 0.2400⁎⁎⁎ − 0.0927⁎⁎⁎
(0.0606)
(0.0262)
− 0.0600
0.0414
(0.0916)
(0.0752)
0.1497⁎⁎
0.2685⁎⁎⁎
(0.0752)
(0.0481)
− 0.0215⁎⁎⁎ 0.3722⁎⁎⁎
(0.0079)
(0.0832)
− 0.0222⁎⁎⁎ − 0.1905⁎⁎⁎
(0.0082)
(0.0503)
− 0.0001
− 0.0066⁎⁎⁎
(0.0008)
− 0.4397⁎⁎⁎
(0.0726)
− 0.0001
(0.0007)
0.0247
(0.0151)
0.0120⁎
0.8177⁎⁎⁎
(0.0076)
− 0.1892⁎⁎⁎
(0.0293)
0.0001
(0.0006)
− 0.2378⁎
(0.0266)
0.0301⁎⁎⁎
(0.0105)
0.0478⁎⁎
(0.0201)
0.2046⁎⁎⁎
(0.0384)
− 0.0012
(0.0065)
− 0.0083⁎⁎⁎
(0.0002)
0.0035
(0.0364)
− 0.0011⁎
(0.0006)
0.0004
(0.0082)
0.0326⁎⁎⁎
(0.0001)
− 0.0076
(0.0173)
0.0031⁎⁎⁎
(0.0005)
− 0.0097⁎
(0.0056)
− 0.0176⁎⁎⁎
(0.0081)
0.0076
(0.0048)
0.0269
(0.0227)
0.0090
(0.0206)
− 0.0009
(0.0010)
− 0.0010⁎⁎⁎
(0.0004)
− 0.0754⁎⁎⁎
(0.0225)
0.1418⁎⁎
(0.0555)
− 0.0427
(0.0373)
− 0.0225⁎⁎
(0.0098)
0.0223⁎⁎⁎
(0.0054)
0.0000
(0.0002)
0.0514⁎⁎
(0.0087)
0.1675⁎⁎⁎
(0.0335)
0.1662⁎⁎⁎
(0.0383)
0.0582⁎⁎⁎
(0.0126)
0.0017
(0.0014)
− 0.0799⁎⁎
(0.0326)
0.3608⁎⁎⁎
(0.0651)
0.2332⁎⁎⁎
(0.0510)
0.0225⁎⁎⁎
(0.0057)
0.0228⁎⁎⁎
(0.0068)
− 0.0002
(0.0002)
0.0431
(0.0305)
− 0.0005
(0.0006)
0.0199⁎⁎⁎
(0.0067)
− 0.0149⁎⁎⁎
(0.0234)
− 0.0835⁎⁎⁎
(0.0219)
0.0336
(0.0230)
0.1257⁎⁎
(0.0512)
− 0.0109⁎⁎
(0.0024)
− 0.3262⁎
(0.1753)
−0.0290⁎⁎⁎
(0.0066)
0.0542
(0.0496)
− 0.0255
(0.0032)
(0.0072)
(0.0340)
− 0.0367⁎⁎ − 0.0055
0.0333
(0.0176)
(0.0161)
(0.0857)
− 0.0127⁎⁎⁎ 0.0042⁎⁎
0.0249⁎⁎⁎
(0.0045)
(0.0017)
(0.0071)
− 0.0017⁎⁎⁎ − 0.0013⁎⁎⁎ − 0.0104⁎⁎⁎
(0.0005)
(0.0005)
(0.0026)
0.0364⁎⁎
0.0657⁎⁎⁎
0.3210⁎⁎⁎
(0.0148)
− 0.1429⁎⁎⁎
(0.0246)
− 0.0608⁎⁎⁎
(0.0205)
105,560
15,177/37
0.00/0.32
0.90
(0.0187)
0.0248
(0.0275)
− 0.0163
(0.0252)
105,560
15,177/37
0.00/0.42
0.11
(0.0009)
− 0.0217
(0.0383)
0.0548
(0.0834)
0.1692⁎⁎
(0.0713)
− 0.0285⁎⁎
(0.0137)
0.0164⁎⁎⁎
(0.0043)
0.0006⁎⁎⁎
(0.0040)
− 0.0047
(0.0122)
− 0.0007
(0.0008)
0.0006⁎⁎
(0.0002)
− 0.0067
(0.0938)
(0.0106)
0.3571
− 0.0299
(0.2874)
(0.0249)
− 1.5021⁎⁎⁎ − 0.0463⁎⁎
(0.1720)
(0.0187)
105,560
105,560
15,177/37
15,177/37
0.00/0.47
0.00/0.49
0.87
0.25
(0.0067)
0.0395⁎⁎
(0.0200)
0.0112
(0.0304)
− 0.0010
(0.0020)
0.0008
(0.0010)
(0.0005)
(0.0008)
− 0.1254⁎⁎⁎ − 0.1680⁎⁎⁎ 0.0930⁎⁎⁎
(0.0466)
(0.0260)
(0.0324)
0.0572
0.0297
0.0885
(0.0811)
(0.0332)
(0.0720)
− 0.0877
0.0934⁎⁎⁎
− 0.2030⁎⁎⁎
(0.0701)
(0.0225)
(0.0571)
− 0.0794⁎
− 0.2314
− 0.0213
(0.0424)
(1.0209)
(0.0224)
0.0242
− 0.1491⁎⁎⁎ − 0.1814⁎⁎⁎
(0.0270)
(0.0184)
(0.0355)
0.0039⁎⁎⁎
0.0014⁎⁎⁎
0.0061⁎⁎⁎
(0.0012)
(0.0005)
(0.0015)
0.0289
0.1507
0.6431⁎⁎⁎
(0.1103)
0.0164⁎⁎⁎
(0.0029)
− 0.0478⁎
(0.0288)
− 0.0543⁎⁎
(0.0940)
− 0.0365⁎⁎⁎
(0.0019)
− 0.0095
(0.0217)
− 0.0583⁎⁎⁎
(0.1755)
0.0008
(0.0015)
− 0.0355
(0.0369)
− 0.0243
(0.0218)
(0.0104)
(0.0166)
− 0.2603⁎⁎⁎ − 0.3811⁎⁎⁎ 0.1644⁎⁎⁎
(0.0795)
(0.0269)
(0.0633)
− 0.0034
−0.0092⁎⁎⁎ 0.0028
(0.0047)
(0.0027)
(0.0065)
0.0045⁎⁎⁎
− 0.0021
− 0.0044⁎⁎
(0.0014)
(0.0016)
(0.0022)
0.1290
0.3877⁎⁎⁎
− 0.1922⁎⁎⁎
(0.0786)
(0.0510)
(0.0717)
0.0159
− 0.6484⁎⁎⁎ − 0.1642
(0.1182)
(0.0657)
(0.1365)
0.3203⁎⁎⁎
0.0630
0.5286⁎⁎⁎
(0.0872)
(0.0512)
(0.1191)
105,560
105,560
105,560
15,177/37
15,177/37
15,177/37
0.00/0.45
0.00/0.40
0.00/0.54
0.38
0..99
0.49
(0.1299)
− 0.0045⁎
(0.0027)
0.0305
(0.0268)
− 0.0339
(0.0287)
(0.0269)
0.0678⁎⁎⁎
0.0180
(0.0223)
(0.0273)
0.1261⁎⁎⁎
0.1296⁎⁎⁎
(0.0317)
(0.0463)
0.0072
0.0019
(0.0050)
(0.0050)
− 0.0038⁎⁎⁎ − 0.0035⁎⁎
(0.0014)
− 0.0168
(0.0358)
0.0590
(0.0747)
0.5131⁎⁎⁎
(0.0014)
− 0.0122
(0.0526)
− 0.1088
(0.0894)
0.1979⁎⁎
(0.0699)
− 0.0152⁎⁎⁎
(0.0038)
0.0229⁎⁎⁎
(0.0047)
− 0.0002⁎⁎
(0.0043)
− 0.0040
(0.0052)
− 0.0012
(0.0008)
0.0005⁎⁎
(0.0868)
− 0.0082⁎
(0.0046)
0.0172⁎⁎⁎
(0.0032)
0.0001
(0.0001)
− 0.0035
(0.0147)
0.0010⁎⁎⁎
(0.0003)
0.0021
(0.0032)
0.0042
(0.0031)
− 0.0116⁎
(0.0063)
− 0.0003
(0.0006)
0.0003⁎
(0.0002)
0.0065
(0.0064)
− 0.0051
(0.0125)
− 0.0808⁎⁎⁎
(0.0123)
103,302
14,807/35
0.00/0.37
0.33
(0.0002)
− 0.0036
(0.0065)
0.0252
(0.0156)
− 0.0170
(0.0115)
104,382
14,938/36
0.00/0.50
0.30
(0.0001)
0.0061
(0.0209)
− 0.0003
(0.0002)
− 0.0016
(0.0039)
0.0224⁎⁎⁎
(0.0251)
0.0024⁎⁎⁎
(0.0007)
− 0.0062
(0.0057)
− 0.0002
(0.0019)
− 0.0091
(0.0094)
0.0013
(0.0010)
0.0003
(0.0003)
− 0.0085
(0.0141)
0.0617⁎⁎⁎
(0.0236)
− 0.1368⁎⁎⁎
(0.0193)
105,560
15,177/37
0.00/0.53
0.59
(0.0019)
0.0057
(0.0449)
− 0.2187⁎⁎
(0.0963)
0.3557⁎⁎⁎
(0.1039)
− 0.0057⁎⁎
(0.0024)
0.0319⁎⁎⁎
(0.0044)
− 0.0003⁎
(0.0001)
0.0040
(0.0189)
0.0005⁎⁎⁎
(0.0002)
0.0124⁎⁎⁎
(0.0037)
− 0.0029⁎⁎
(0.0015)
− 0.0164⁎⁎⁎
(0.0055)
0.0001
(0.0008)
0.0010⁎⁎⁎
(0.0002)
− 0.0003
(0.0065)
0.0539⁎⁎⁎
(0.0161)
− 0.0376⁎⁎
(0.0147)
103,938
14,951/35
0.00/0.27
0.86
(continued on next page)
1470
A. Gungoraydinoglu, Ö. Öztekin / Journal of Corporate Finance 17 (2011) 1457–1474
Table 4
The
impact
of firm- and country-level determinants on capital structure.
Table
4 (continued).
Panel C. The Impact of Information Asymmetry Costs on Capital Structure
Trans
Disclose
Liability
Penf
Insider
CInfo
(1)
(2)
(3)
(4)
(5)
(6)
0.8272⁎⁎⁎
(0.0077)
− 0.1667⁎⁎⁎
0.8136⁎⁎⁎
(0.0074)
− 0.0816⁎⁎⁎
0.8270⁎⁎⁎
(0.0071)
− 0.0955⁎⁎⁎
0.8201⁎⁎⁎
(0.0074)
− 0.3091⁎⁎⁎
0.8158⁎⁎⁎
(0.0084)
− 0.0955⁎⁎⁎
Size
(0.0624)
− 0.0058⁎⁎⁎
(0.0014)
− 0.4630⁎
(0.2581)
− 0.0397⁎⁎⁎
(0.0224)
− 0.0003
(0.0005)
− 0.2133⁎⁎
(0.0933)
− 0.0114⁎⁎⁎
(0.0141)
− 0.0002
(0.0004)
− 0.1697⁎⁎⁎
(0.0556)
− 0.0018⁎⁎⁎
(0.0433)
− 0.0031⁎⁎
(0.0013)
− 0.5515⁎⁎⁎
(0.1919)
− 0.0230⁎⁎⁎
(6)
Tangibility
(0.0055)
0.2189⁎⁎⁎
R&D missing
(8)
R&D
(9)
Industry
(0.0625)
0.0784
(0.0485)
− 0.0302
(0.0235)
0.2800⁎⁎
(0.0005)
0.0339⁎⁎⁎
(0.0131)
0.0059
(0.0044)
− 0.0020
(0.0210)
0.0815⁎⁎⁎
(0.0045)
0.1746⁎⁎⁎
(0.0420)
− 0.0360
(0.0408)
0.0071
(0.0248)
0.2088⁎⁎⁎
(0.0147)
0.0011⁎⁎
(0.0005)
− 0.4654⁎⁎⁎
(0.0579)
− 0.0002
(0.0015)
0.1102⁎⁎⁎
(7)
(10)
Tax
(0.1206)
0.0275⁎⁎⁎
(11)
Liquidity
(12)
Regulated
(13)
Inflation
(0.0377)
0.0059
(0.0106)
− 0.0056⁎⁎⁎
(0.0015)
− 0.0161
(0.0346)
− 0.1672⁎⁎
(0.0191)
0.0028⁎⁎
(0.0012)
− 0.0054⁎⁎⁎
(0.0008)
− 0.0425
(0.0270)
0.1260⁎⁎
(14)
GDPG
(15)
Institution (I)
(16)
Profit ⁎ I
(0.0101)
− 0.0095⁎⁎⁎
(0.0033)
− 0.1528
(0.1429)
− 0.1064
(0.2449)
− 0.5220⁎⁎
(0.2074)
− 0.0036⁎⁎⁎
(0.0013)
0.0018⁎⁎
(0.0019)
0.0853⁎⁎⁎
(0.0275)
0.1299⁎⁎⁎
(0.0217)
0.1049⁎⁎⁎
(0.0222)
− 0.0139
(0.0498)
0.0043
(0.0042)
− 0.0052⁎⁎⁎
(0.0014)
0.0338
(0.0438)
− 0.0444
(0.0940)
0.0789
(0.0906)
− 0.0877⁎
(0.0496)
0.0686⁎⁎
0.8445⁎⁎⁎
(0.0083)
− 0.0191
(0.0214)
− 0.0001
(0.0004)
− 0.0647
(0.0941)
− 0.0009
(0.0014)
0.0282
(0.0198)
0.0261⁎⁎
(0.0123)
− 0.0040
(0.0356)
− 0.0632⁎
(0.0518)
− 0.3150⁎⁎⁎
(0.0396)
− 0.0384⁎⁎⁎
(0.0128)
0.0944⁎⁎⁎
(0.0228)
0.0038
(0.0023)
− 0.0009
(0.0007)
− 0.0638
(0.0599)
0.0331
(0.0606)
0.3635⁎⁎⁎
(0.0452)
0.3911
(0.3151)
0.0651⁎⁎⁎
(17)
Growth ⁎ I
(0.0009)
0.0001⁎⁎⁎
(0.0795)
0.1350⁎⁎
(0.0632)
− 0.0992⁎⁎
(0.0416)
0.0043
(0.0294)
0.0018⁎
(0.0689)
− 0.0009
(0.0084)
− 0.0038⁎
(0.0022)
− 0.1460
(0.1382)
0.0743
(0.1655)
0.9071⁎⁎⁎
(0.1697)
− 0.0538⁎⁎
(0.0230)
0.0521⁎⁎⁎
(18)
Depreciation ⁎ I
(19)
Size ⁎ I
(0.0192)
0.0012
(0.0008)
− 0.0148
(0.0882)
0.0049⁎⁎⁎
(0.0081)
0.0008⁎⁎⁎
(0.0003)
0.0516
(0.0369)
0.0057⁎⁎⁎
(0.0166)
− 0.0003
(0.0007)
0.1850⁎⁎⁎
(0.0702)
0.0060⁎⁎⁎
(20)
Tangibility ⁎ I
(21)
R&D ⁎ I
(22)
Industry ⁎ I
(23)
Tax ⁎ I
(0.0011)
− 0.0062
(0.0193)
− 0.0119⁎
(0.0068)
− 0.0348
(0.0304)
− 0.0047⁎⁎
(0.0022)
− 0.0534⁎⁎⁎
(0.0185)
0.0128
(0.0128)
− 0.0699⁎⁎
(0.0336)
− 0.0055⁎⁎
(24)
Liquidity ⁎ I
(25)
Regulated ⁎ I
(26)
Inflation ⁎ I
(27)
GDPG ⁎ I
(0.0001)
0.0001⁎⁎⁎
(0.0000)
0.0013
(0.0022)
0.0028
(0.0039)
0.0088⁎⁎⁎
(0.0010)
− 0.0237⁎⁎⁎
(0.0082)
0.0077
(0.0079)
− 0.0453⁎⁎⁎
(0.0149)
0.0001
(0.0016)
0.0006
(0.0004)
0.0192
(0.0270)
− 0.0079
(0.0377)
− 0.1714⁎⁎⁎
Observations
Firms/countries
AR(1)/AR(2)
Hansen
(0.0030)
103,302
14,851/34
0.00/0.31
0.31
(1)
Leverage
(2)
Profit
(3)
Growth
(4)
Depreciation
(5)
(0.0000)
0.0028
(0.0037)
0.0007⁎⁎⁎
(0.0001)
− 0.0022⁎⁎
(0.0009)
− 0.0011
(0.0007)
− 0.0049⁎⁎
(0.0019)
− 0.0004⁎⁎⁎
(0.0276)
0.0007
(0.0009)
− 0.1024
(0.1232)
0.0247⁎⁎⁎
(0.0011)
− 0.1991
(0.1364)
0.0077⁎⁎⁎
(0.0029)
− 0.0451
(0.0345)
− 0.1491⁎⁎⁎
(0.0277)
0.1856⁎⁎⁎
(0.0646)
− 0.0036
(0.0053)
0.0056⁎⁎⁎
(0.0026)
− 0.0461
(0.0288)
− 0.0164
(0.0183)
0.0760
(0.0572)
0.0110
(0.0179)
0.0048⁎⁎
(0.0016)
− 0.1794⁎⁎⁎
(0.0588)
0.0485
(0.1283)
0.0074
(0.1100)
105,560
15,177/37
0.00/0.47
0.30
(0.0019)
0.0147
(0.0466)
0.1005
(0.1497)
− 0.1642⁎
(0.0935)
105,560
15,177/37
0.00/0.44
0.14
(0.0023)
0.0068⁎⁎⁎
(0.0011)
0.0334
(0.0418)
− 0.3482⁎⁎⁎
(0.0917)
0.5099⁎⁎⁎
(0.0527)
105,560
15,177/37
0.00/0.52
0.89
(0.0361)
105,330
15,132/36
0.00/0.49
0.43
(0.0145)
− 0.0103
(0.0125)
0.0107
(0.0300)
0.0715⁎⁎⁎
(0.0025)
− 0.0003
(0.0008)
0.0179
(0.0689)
− 0.1076
(0.0822)
− 0.4143⁎⁎⁎
(0.0553)
105,560
15,177/37
0.00/0.51
0.73
A. Gungoraydinoglu, Ö. Öztekin / Journal of Corporate Finance 17 (2011) 1457–1474
1471
agency view of the trade-off theory. The positive relationship between profitability and leverage (Row 2) is moderated (Row
16) in institutional environments subject to lower agency costs of equity, as indicated by higher ownership concentration
(Column 7), which acts as a substitute mechanism to profitability for controlling agency costs of equity. Similarly, the positive
relationship between economic growth and leverage (Row 14) is moderated (Row 27) in institutional settings subject to
lower agency costs of equity. In other words, shareholder rights (Column 4), executive quality (Column 8), and contract enforcement (Column 11) are substitute mechanisms to economic growth for controlling agency costs of equity. Thus, firmand country-level determinants of agency costs of debt and equity are substitute mechanisms for each other for controlling
these costs.
Third, according to the pecking-order theory, small firms should carry more debt in their capital structure if they are
prone to adverse selection costs, resulting in a negative relationship between leverage and firm size. In Panel C, the negative
relationship between leverage and firm size (Row 5) is moderated (Row 19) in institutional settings subject to lower information asymmetry, as indicated by higher accounting (Column 1) and disclosure standards (Column 2), better public enforcement (Column 4), and insider trading laws (Column 5). These country determinants act as substitute mechanisms to
firm size for controlling information asymmetry costs. The pecking-order theory further suggests that when adverse selection reflects assets in place, higher tangibility may increase adverse selection costs and result in higher debt. The observed
positive relationship between tangibility and leverage (Row 6) is moderated (Row 20) in institutional settings subject to
lower information asymmetry, as indicated by higher accounting standards (Column 1), lower perseverance of insider trading (Column 5), and higher information sharing in debt markets (Column 6). In other words, these country determinants
serve as substitute mechanisms to tangibility for controlling information asymmetry costs. Overall, firm- and countrylevel determinants of information asymmetry costs are substitute mechanisms for each other.
5. Robustness
In this section, we discuss additional sensitivity analyses. First, we control for the potential endogeneity of the country-level
variables and obtain similar conclusions. Instead of the observed values of each country variable, we use legal origin dummy
variables (English, French, German, and Scandinavian) as instruments for the country-level determinants (La Porta et al.,
2008). Second, we verify the robustness of our results to an alternative definition of leverage. We employ market leverage, defined as the sum of the long-term and short-term debt divided by total assets minus book value of equity plus market value of
equity. Third, we construct factor scores that load differently on the three categories of institutional determinants of capital
structure and find that the results are robust to using several alternative specifications of the composite (principal components) indices. Finally, Hovakimian and Li (2011) and Strebulaev (2007) conclude that using ex post information to estimate
leverage causes substantial bias to the parameter estimates. Hovakimian and Li (2011) also suggest that dropping extreme leverage observations greater than 90% and less than 10% avoids spurious results. We repeat our tests either using only the current and historical information in the estimations or dropping these extreme leverage observations from our sample. We find
similar results.
6. Conclusion
This paper examines the firm-level, industry-level, macroeconomic, and country-level determinants of capital structure across
37 countries during the 1991–2006 period. We conjecture that the effectiveness of a country's legal, financial, and political institutions is systematically related to cross-country differences in firms' choices of capital structure through the influence of bankruptcy costs, agency costs, and information asymmetry costs imposed on firms. Our intuition is that the capital structure
determination of a firm is not only the outcome of its own characteristics but also the result of its environment and traditions
in which it operates.
We first examine which factors, at the firm and country level, are reliably important for predicting the variation in leverage.
We find that institutional arrangements matter for capital structure decisions; however, the firm-level covariates drive twothirds of the variation in capital structure across countries, while the country-level covariates explain the remaining one-third.
In general, the institutional factors affecting bankruptcy costs and taxes drive most of the country heterogeneity in capital structure, followed by the agency costs and information asymmetry costs.
We also explore how institutional arrangements affect corporate leverage and how the firm-level covariates relate to capital
structure in different institutional environments. The observed relationships between the country-level determinants and leverage provide strong support to the predictions of both the trade-off and the pecking-order theories. Higher effective tax rates,
lower bankruptcy costs and taxes, lower agency costs of debt, higher agency costs of equity, and higher adverse selection costs
are all associated with higher leverage, consistent with the trade-off and pecking-order theories. This direct impact of the institutional characteristics on firms' leverage captures 22% of the explainable variation in leverage. An indirect impact of the
country-specific factors also occurs, through their influence on the roles of the firm-specific factors determining capital structure.
Country-specific factors moderate the effects of firm-specific factors, and these indirect effects of institutional factors explain 12%
of the total explainable variation in leverage.
1472
A. Gungoraydinoglu, Ö. Öztekin / Journal of Corporate Finance 17 (2011) 1457–1474
Appendix A. Description and sources of the country-level determinants
Variable name
Bankruptcy costs
Time
Cost
Eff
Tax
Creditor
Formalism
Agency costs
Creditor
Formalism
Reserve
Antidir
Prenf
Mdiv
Owner
Exec
Law
Gov
Enforce
Information asymmetry costs
Trans
Disclose
Liability
Variable description
Variable source
Time to resolve the insolvency process.
Cost of bankruptcy. The costs to complete the insolvency proceeding,
expressed as a percentage of the bankruptcy estate at the time of entry to the
bankruptcy.
Efficiency of bankruptcy. A dummy variable for whether the bankruptcy
outcome is efficient.
Effective tax rate. The tax rate obtained by dividing the total corporate tax
by pretax earnings.
An index aggregating creditor rights. The index ranges from 0
(weak creditor rights) to 4 (strong creditor rights).
Debt enforcement. The index measures substantive and procedural statutory
intervention in judicial cases at lower-level civil trial courts and ranges from
0 to 7, where 7 means a higher level of control or intervention in the judicial
process.
Djankov et al. (2008b).
Djankov et al. (2008b).
An index aggregating creditor rights. The index ranges from
0 (weak creditor rights) to 4 (strong creditor rights).
Debt enforcement. The index measures substantive and procedural statutory
intervention in judicial cases at lower-level civil trial courts and ranges from
0 to 7, where 7 means a higher level of control or intervention in the judicial
process.
Legal reserve requirements. The minimum percentage of total share capital
mandated by Corporate Law to avoid the dissolution of an existing firm.
It takes a value of zero for countries without such restriction.
Shareholder rights. This index of anti-director rights ranges from
0 (weak shareholder rights) to 5 (strong shareholder rights).
Equity enforcement. Average of ex ante and ex post private control of
self-dealing. Higher scores indicate better enforcement.
Mandatory dividend. The percentage of net income that the Company
Law or Commercial Code requires firms to distribute as dividends among
ordinary stockholders. It takes a value of zero for countries without such restriction.
Ownership concentration. The average percentage of common shares owned by
the three largest shareholders in the ten largest non-financial, privately owned
domestic firms.
Executive quality. An index of constraints on the executive power based on the
number of effective veto points in a country. Veto points include (1) an effective
legislature (represents two veto points in the case of bicameral systems), (2)
an independent judiciary, and (3) a strong federal system.
Law and order. Integrity of legal system with a scale from 0 to 10, higher
scores indicating stronger law and order.
Quality of government. Principal components of corruption in government,
risk of expropriation, and repudiation of contracts by government.
Higher scores indicate better government.
Corruption of the government has a scale from 0 to 10, lower values indicating
higher corruption.
Risk of expropriation is the ICR's assessment of the risk
of “outright confiscation” or “forced nationalization.” Scale from 0 to 10, with
lower scores for higher risks.
Repudiation of contracts by government is ICR's assessment of the “risk of a
modification in a contract taking the form of repudiation, postponement, or
scaling down.” Scale from 0 to 10, with lower scores for higher risks.
Enforceability of contracts. “The relative degree to which contractual agreements
are honored and complications presented by language and mentality differences.”
Scale for 0 to 10, with higher scores indicating higher enforceability.
Transparency. The index is created by examining and rating companies' 1990
annual reports on their inclusion or omission of 90 items. These items fall into
seven categories (general information, income statements, balance sheets, funds
flow statement, accounting standards, stock data, and special items). Higher
scores indicate higher transparency.
Disclosure requirements. The index equals the arithmetic mean of
(1) prospectus, (2) compensation, (3) shareholders, (4) inside ownership,
(5) contracts irregular, and (6) transactions. Higher scores indicate better disclosure.
Liability standards. The index equals the arithmetic mean of (1) liability
standard for the issuer and its directors, (2) liability standard for distributors,
Djankov et al. (2008b).
Djankov et al. (2008a).
La Porta et al. (1998).
Djankov et al. (2003).
La Porta et al. (1998).
Djankov et al. (2003).
La Porta et al. (1998).
La Porta et al. (1998).
Djankov et al. (2008c).
La Porta et al. (1998).
La Porta et al. (1998).
Djankov et al. (2002).
Djankov et al. (2003).
La Porta et al. (1998).
Djankov et al. (2003).
La Porta et al. (1998).
La Porta et al. (2006).
La Porta et al. (2006).
A. Gungoraydinoglu, Ö. Öztekin / Journal of Corporate Finance 17 (2011) 1457–1474
1473
Appendix
(continued)
A (continued)
Variable name
Penf
Insider
Cinfo
Variable description
Variable source
and (3) liability standard for accountants. Higher scores indicate stronger
liability standards.
Securities market enforcement. The index equals the arithmetic mean of
(1) supervisor characteristics index, (2) rule-making power index,
(3) investigative powers index, (4) orders index, and (5) criminal index.
Higher scores indicate better public enforcement.
Prevalence of insider trading (1 = pervasive; 7 = extremely rare).
Information sharing in debt markets. The variable equals 1 if a public
credit registry operates in the country and 0 if otherwise.
La Porta et al. (2006).
La Porta et al. (2006).
Djankov et al. (2007).
Appendix B. Description and sources of the firm-level, industry-level, and macroeconomic determinants
Variable name
Variable description
Variable source
Blev
Book leverage.
(Long-term debt[106] + short-term debt[94])/total assets[89]
Earnings before interest and taxes as a proportion of total assets.
(Operating income[14] + interest and related expense[15] + current
income taxes[24])/total assets[89].
The ratio of assets' market to book values.
(Long-term debt[106] + short-term debt[94] + preferred capital[119] +
market value of equity[PRCCI * SHOI])/total assets[89].
Depreciation expense as a proportion of total assets.
Total depreciation and amortization[11]/total assets[89].
Log of total book assets.
Log[89].
Fixed assets as a proportion of total assets.
Fixed assets[76]/total assets[89].
R&D expenses as a proportion of total assets.
R&D expense[52]/total assets[89].
A dummy variable equal to one if R&D expenditures are not reported
and zero if otherwise. Approximately 65% of the sample firm-years do
not report R&D expenses. For these firms, we set R&D to zero and set
R&D Missing equal to one.
The prior year's median leverage ratio for the firm's industry. Industry
classifications are based on the 48 industry categories in Fama and French (1997).
Ratio of total income taxes to pre-tax income.
Current income taxes[24]/income before income taxes[21].
Total current assets as a proportion of total current liabilities.
Total current assets[75]/total current liabilities[104].
A dummy variable equal to one for firms operating in regulated industries.
Dnum[4900–4999].
Annual inflation rate.
Growth in Consumer Price Index (CPI).
Economic growth.
Growth in nominal Gross Domestic Product (GDP).
Compustat Global Vantage
Profit
Growth
Depreciation
Size
Tangibility
R&D
R&D missing
Industry
Tax
Liquidity
Regulated
Inflation
GDPG
Compustat Global Vantage
Compustat Global Vantage
Compustat Global Vantage
Compustat Global Vantage
Compustat Global Vantage
Compustat Global Vantage
Compustat Global Vantage
Compustat Global Vantage
Compustat Global Vantage
Compustat Global Vantage
Compustat Global Vantage
WDI (World Development Indicators).
WDI (World Development Indicators).
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