Institutional determinants of capital structure adjustment speeds

Journal of Financial Economics 103 (2012) 88–112

Contents lists available at SciVerse ScienceDirect

Journal of Financial Economics journal homepage: www.elsevier.com/locate/jfec

Institutional determinants of capital structure adjustment speeds$ ¨ zde O ¨ ztekin a,n, Mark J. Flannery b O a b

School of Business, University of Kansas, Lawrence, KS 66045, USA Warrington College of Business Administration, University of Florida, Gainesville, FL 32611, USA

a r t i c l e in f o

abstract

Article history: Received 12 November 2009 Received in revised form 21 September 2010 Accepted 11 March 2011 Available online 5 September 2011

Many authors relate a firm’s performance to legal and political features and the regulatory environment in which it operates. This article compares firms’ capital structure adjustments across countries and investigates whether institutional differences help explain the variance in estimated adjustment speeds. We find that legal and financial traditions significantly correlate with firm adjustment speeds. More narrowly, institutional features also relate to adjustment speeds, consistent with the hypothesis that better institutions lower the transaction costs associated with adjusting a firm’s leverage. Such associations between institutional arrangements and leverage adjustment speeds are consistent with the dynamic trade-off theory of capital structure choice. & 2011 Elsevier B.V. All rights reserved.

JEL classification: F33 G15 G32 Keywords: Dynamic capital structure Trade-off theory International Partial adjustment Institutions

1. Introduction Prior research has tried to assess the determinants of a firm’s capital structure. Most of these studies have examined firms in a single country, usually the United States. The trade-off theory of capital structure intrinsically involves legal and contracting issues in two broad ways. First, a firm’s institutional environment could affect its optimal (target) capital structure. Second, the environment could influence the speed with which a firm

$ We would like to thank a very constructive anonymous referee, M. Nimalendran, Jay Ritter, and seminar participants at Koc- University, the University of Florida, the University of Kansas, the University of Memphis, the University of Mississippi, and Villanova University for ¨ ztekin gratefully acknowledges helpful comments and suggestions. O research support from the Faculty of Business at the University of Kansas. n Corresponding author. Tel.: þ 1 785 864 7545; fax: þ1 785 864 5328. ¨. O ¨ ztekin). E-mail address: [email protected] (O

0304-405X/$ - see front matter & 2011 Elsevier B.V. All rights reserved. doi:10.1016/j.jfineco.2011.08.014

converges to its target. In this article, we control for institutional effects on target leverage but emphasize the second issue by investigating how cross-country variations in institutional variables affect the speed with which firms converge on their target capital ratios. Survey evidence indicates that some firms have a target debt ratio and some firms issue debt or equity with a target in mind (Graham and Harvey, 2001). Yet research also suggests that firms confront nontrivial costs of adjusting to their target leverage (Fischer, Heinkel, and Zechner, 1989; Leary and Roberts, 2005). A partial adjustment model of capital structure thus has attracted considerable attention (e.g., Hovakimian, Opler, and Titman, 2001; Leary and Roberts, 2005; Flannery and Rangan, 2006; Strebulaev, 2007). An advantage of examining a target adjustment model in an international context is that the costs and benefits of adjusting to targets should depend on each firm’s institutional, legal, and financial environment. For example, better accounting standards should reduce information asymmetries, making it easier

¨. O ¨ ztekin, M.J. Flannery / Journal of Financial Economics 103 (2012) 88–112 O

for firms to issue new securities or to retire outstanding ones. Thus, we posit that better accounting should correspond to more rapid leverage adjustment speeds. If so, finding that adjustment speeds vary plausibly with institutional variables would support the hypotheses that firms have target capital ratios and encounter transaction costs when adjusting to those targets. This article evaluates the institutional determinants of measured adjustment speeds in different countries, conditional on the partial adjustment model of capital structure. Our dynamic panel data set spans 37 countries and 16 years. We contribute to the capital structure literature in three ways. First, we estimate similar regression models for firms in many countries, providing comparable evidence on leverage adjustment speeds outside the United States. Second, we link institutional features to an international measure of securities trading costs. Third, we relate international variation in estimated adjustment speeds to differences in the financial and institutional systems in which firms operate. Different environments impose different adjustment costs and benefits on firms, and these are reflected in our estimated adjustment speeds. In countries with weaker institutions (impeded access to capital markets, higher information asymmetry and distress costs, limited financial flexibility), issuing either debt or equity is more difficult, and adjustment speeds are correspondingly lower. At a relatively broad level, we find that firms from countries with strong legal institutions, a financial structure based on the effectiveness of capital markets instead of intermediaries, and better functioning financial systems adjust to their targets as much as 50% more rapidly. For example, firms with below-median adjustment costs exhibit estimated adjustment speeds 11–12% faster than firms with higher adjustment costs. We find that many specific institutional features have economically and statistically significant effects. That is, adjustment costs and benefits play an important role in the speed of convergence to optimal capital structure around the globe. This paper is organized as follows. Section 2 estimates a partial adjustment model of leverage across a sample of 37 countries. Section 3 relates the (large) discrepancies in estimated adjustment speeds to a measure of securities trading costs. Our main hypothesis is that a country’s institutional and legal arrangements affect the costs and benefits of moving toward a firm’s optimal leverage ratio, and this effect should be reflected in international differences in estimated speeds of adjustment. Section 4 provides the theoretical basis for this hypothesis, and Section 5 presents empirical results showing that better institutions correspond to more rapid adjustments to target leverage. Firms confronting lower adjustment costs or higher adjustment benefits or both choose to attain their target leverage more rapidly. In addition to affecting adjustment speeds, however, the institutional environment could affect firm characteristics that indirectly influence adjustment speed. For example, higher capital market transaction costs are associated with larger firms, lower earnings, and lower market-to-book ratios. Section 6 decomposes the total impact of transaction costs into their direct and indirect components. We again find that higher

89

costs tend to reduce adjustment speeds, but endogenous firm characteristics also adjust to offset approximately 30% of the direct effect. The final section summarizes and concludes. 2. International results: a first look Prior research indicates that the institutional environment ¨ - -Kunt influences firms’ financing policies (e.g., Demirguc and Maksimovic, 1999; Bancel and Mittoo, 2004; Rajan ¨ - -Kunt, and and Zingales, 1995; Booth, Aivazian, Demirguc Maksimovic, 2001). However, most international empirical work on capital structure imposes the implicit but unrealistic assumption that firms are always at their equilibrium leverage. This assumption is incompatible with a trade-off theory in which firms find it costly to alter their capital structures (Strebulaev, 2007). If firms confront a set of costs and benefits when moving toward target leverage, their adjustment speed is endogenous. To the degree that institutional features affect adjustment costs and benefits, variations in these factors should influence the leverage adjustments. 2.1. Basic regression specification To determine whether the institutional environment significantly affects firms’ adjustment speeds, we estimate the same partial adjustment model of leverage in each of 37 countries. We can write a general partial adjustment model as n LEVij,t LEVij,t1 ¼ lj ðLEVij,t LEVij,t1 Þ þ dij,t

ð1Þ

where LEVij,t is firm i’s debt ratio at the end of year t and j denotes a country or a group of countries with similar n institutional features; LEVij,t is firm i’s desired debt ratio in country or institutional setting j in year t; and lj measures the proportional adjustment during one year for firms in group j.1 This specification permits each firm’s optimal n leverage ðLEVij,t Þ to vary over time and according to its characteristics. The adjustment speed (lj) permits the typical firm to move only part way to its target leverage within any given year. This specification assumes that all sample firms in one country (or across countries with similar institutional settings) j adjust uniformly at the constant rate lj. We relax this assumption and allow different ls across sample firms in Section 6. What determines target leverage (LEV*)? Existing literature models each firm’s optimal leverage in a particular country or institutional setting as a function of firm and macroeconomic characteristics (Frank and Goyal, 2009). Following Flannery and Rangan (2006), Lemmon, 1 Prior research has emphasized that investment opportunities could affect capital structure revisions (Brennan and Schwartz, 1984; Berk, Green, and Naik, 1999; Titman and Tsyplakov, 2007; Hennessy and Whited, 2005; Strebulaev, 2007; DeAngelo, DeAngelo, and Stulz, 2010), but the more limited partial adjustment specification Eq. (1) is common in the literature. We utilize the specification Eq. (1) because it permits ready comparison with a plethora of US studies. A natural extension of our work would be to explore a calibrated dynamic trade-off model and simulate firms’ capital structure and investment paths simultaneously.

¨. O ¨ ztekin, M.J. Flannery / Journal of Financial Economics 103 (2012) 88–112 O

90

Roberts, and Zender (2008), and Huang and Ritter (2009), we also include a set of firm fixed effects to control for unobserved firm heterogeneity: n LEVij,t ¼ bj Xij,t1 þ Fij ,

ð2Þ

where bj and Fij are coefficient vectors to be estimated and Xij,t1 is a vector of firm and macroeconomic characteristics related to the costs and benefits of operating with various leverage ratios. According to the trade-off theory, ba0, and the varian tion in BLEVi,j,t , is nontrivial. Substituting Eq. (2) into the partial adjustment specification Eq. (1) and rearranging yields the following estimable specification: LEVij,t ¼ ðlj bj ÞXij,t1 þ ð1lj ÞLEVij,t1 þ lj Fij þ dij,t :

ð3Þ

This dynamic panel model requires special treatment in short panels to avoid a biased adjustment speed estimate (Baltagi, 2001). We find similar results using two alternative estimation methods: a two-step system generalized method of moments (GMM) (Blundell and Bond, 1998) and the bias-corrected least squares dummy variable approach (LSDVC) (Bruno and Giovanni, 2005). The model in Eq. (3) is more general than many prior international comparisons because it accounts for the potentially dynamic nature of the firm’s capital structure and its unobserved heterogeneity. At the same time, Eq. (3) is only a basic model. That is, it includes no information on firms’ institutional environments, which in turn could affect the estimated coefficients because they (1) directly affect adjustment speed lj; (2) directly affect target capital ratios bj; (3) influence firm characteristics, which indirectly affect the adjustment speed; and (4) influence firm characteristics, which indirectly affect the target capital ratios.2 The basic model in Eq. (3) controls for the first two of these effects. Our tests based on Eq. (3) also account for the last two effects by allowing different sensitivities for the firm-specific determinants of the optimal leverage (bs) and the adjustment speed (ls) and by instrumenting for the firm-specific characteristics. Nevertheless, Section 6 presents a more complex empirical model that separates the impact of the institutional environment into its direct [effects (1) and (2)] and indirect [effects (3) and (4)] components. 2.2. Firm-specific data In general, the results for book and market leverage in the US context are comparable, but no previous study has examined adjustment speeds for such a large panel of countries. Therefore, we report initial estimates with both book and market leverage measures for comparative purposes. We define book and market leverage, respectively, as BLEV ¼

Long-Term Debt þ Short-Term Debt Total Assets

ð4Þ

2 For example, if a country’s institutional environment discourages the emergence of relatively large firms, the estimated lj might reflect equilibrium firm characteristics rather than a direct effect of the institutional environment. We thank an anonymous referee for pointing out this possibility.

and MLEV ¼

Long-Term Debt þ Short-Term Debt : Total AssetsBook Equityþ Market Equity

ð5Þ

The firm-level data required to test our hypotheses derive from all nonfinancial firms in the Compustat Global Vantage database during the 1991–2006 period. We collect data only for firms in one of the 37 countries for which we could locate information about country-level institutional features (see Section 3). We also collect data on each country’s rate of gross domestic product (GDP) growth and inflation from the World Development Indicators Database of the World Bank. Table 1 defines and summarizes the firm-level variables. The final sample is an unbalanced panel with 15,177 firms from 37 countries, for a total of 105,568 firm-years. To minimize the potential impact of outliers, we winsorize all the firm-level ratios at the 1st and 99th percentiles. 2.3. International adjustment speeds Our first contribution comes from estimating the partial adjustment model (Eq. (3)) separately for each of the 37 countries in our sample. Table 2 presents four different estimates of each country’s adjustment speed. The estimated speeds are all significantly positive and lie within the zero– one interval, consistent with a typical firm’s capital structure converging to its optimal level over time. In line with prior literature, the results are similar for market value leverage (MLEV) and book value leverage (BLEV) for both estimation methods. Therefore, we limit our subsequent discussion to BLEV results based on the Blundell and Bond (1998) GMM estimates (BB). For US firms, we find an estimated adjustment speed of 24.10% per year, consistent with Lemmon, Roberts, and Zender (2008). Across our sample of 37 countries, the sample mean (median) estimated adjustment speed for BLEV is 21.11% (21.34%). Equivalently, the average firm takes approximately three years to close half the gap between actual and optimal capital structure.3 The estimated adjustment speeds in the BLEV BB column of Table 2 vary reliably across countries, from 4% to 41%. When we estimate all 37 countries’ models [Eq. (3)] as seemingly unrelated regressions, we reject the hypothesis that the estimated lj values are equal across all countries. (For this test, we permit the determinants of capital structure, bj, to vary between countries.) Conditional on our regression model, these results are consistent with the view that the net costs or benefits of deviating from target leverage vary across countries. 2.4. Observed balance sheet adjustments A firm’s estimated adjustment speed should be reflected in its observed capital market transactions. That is, if countries have high adjustment speeds, more firms should access external capital markets or make larger leverage changes or both. Table 3 and Fig. 1 present the capital market access by firms in countries with above- versus 3 The calculation is LN(0.5)/LN(1–0.21), where 0.21 is the sample average of the adjustment speeds using BLEV and the system GMM (BB) estimation method.

¨. O ¨ ztekin, M.J. Flannery / Journal of Financial Economics 103 (2012) 88–112 O

91

Table 1 Firm and macroeconomic characteristics. Panel A describes the variables. In Panel B, Rows (1)–(37) provide information on variables’ values within each country, specifically the mean of the sample years’ median variable values. Rows (38) and (39) provide mean and median statistics for each variable, across all firms and countries. Panel A: Descriptions and sources of firm and macroeconomic variables Variable Description

Source

BLEV MLEV

Compustat Global Vantage Compustat Global Vantage

Book value leverage. (Long-term debt[106] þShort-term debt[94])/Total assets[89] Market value leverage. (Long-term debt[106] þ Short-term debt[94])/(Total assets[89]  Book equity[135] þMarket Value of Equity[PRCCI  SHOI]). EBIT Profit. A firm with higher earnings per asset dollar could prefer to operate with either lower or higher leverage. (Operating Income[14] þ Interest and related expense[15] þCurrent Income Taxes[24])/Total assets[89] MB Market-to-book. A higher market-to-book tends to be a sign of more attractive future growth options, which a firm tends to protect by limiting its leverage. (Long-term debt[106] þ Short-term debt[94] þ Preferred capital[119]þ Market Value of Equity[PRCCI  SHOI])/Total assets[89] DEP Depreciation. Firms with more depreciation expenses have less need for the interest deductions provided by debt financing. Total Depreciation and Amortization[11]/Total assets[89] Ln(Size) Larger firms tend to operate with more leverage, perhaps because they are more transparent, have lower asset volatility, or have better access to public debt markets. Log[89] FA Tangibility. Firms operating with greater tangible assets have a higher debt capacity. Fixed assets[76]/ Total assets[89] R&D Research and development (R&D) expense. Firms with more intangible assets in the form of R&D expenses prefer to have more equity. Research and Development Expense[52]/Total assets[89] RD_DUM R&D dummy. A dummy variable equal to one if R&D expenditures are not reported and zero otherwise. Approximately 65% of the sample firm-years do not report R&D expenses. For these firms, we set R&D expense to zero and set R&D_DUM equal to one IND Industry median. The prior year’s median leverage ratio for the firm’s industry, based on the 48 MED industry categories in Fama and French (1997) TAXES If the firm’s earnings are not already oversheltered by nondebt tax shield mechanisms (DeAngelo and Masulis, 1980), firms have incentive to increase debt to benefit from the tax shield due to interest deductibility. Current income taxes[24]/Income before income taxes[21] LIQUID Liquidity. Firms with more liquid assets can use them as another internal source of funds instead of debt, leading to lower optimal debt equity ratio. Total Current Assets[75]/Total current liabilities[104] REG Regulated industry. A dummy variable equal to one for firms operating in regulated industries. Dnum [4900–4999] INF Annual Inflation rate. Growth in consumer price index GDPG

Annual growth in nominal gross domestic product (GDP). The rebalancing costs should be lower in good states than in bad states (e.g., Frank and Goyal, 2009; Korajczyk and Levy, 2003)

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 World Development Indicators (WDI), World Bank WDI, World Bank

Panel B: Descriptive statistics of firm and macroeconomic variables Row

Country or statistic

BLEV

EBIT

MB

DEP

Ln(Size)

FA

R&D

TAXES

LIQUID

INF

GDPG

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) (15) (16) (17) (18) (19) (20) (21) (22) (23) (24) (25) (26) (27) (28) (29)

Argentina Australia Austria Belgium Brazil Canada Chile Columbia Denmark Finland France Germany Greece Hong Kong India Indonesia Ireland Israel Italy Japan Malaysia Mexico New Zealand Norway Pakistan Peru Philippines Portugal South Korea

0.35 0.14 0.25 0.25 0.27 0.18 0.22 0.12 0.27 0.26 0.23 0.16 0.30 0.17 0.28 0.39 0.26 0.25 0.25 0.23 0.22 0.26 0.27 0.28 0.24 0.23 0.25 0.34 0.27

0.06 0.04 0.05 0.06 0.08 0.00 0.07 0.07 0.08 0.08 0.07 0.06 0.08 0.04 0.11 0.07 0.09 0.05 0.06 0.05 0.05 0.09 0.10 0.06 0.12 0.11 0.04 0.05 0.07

0.52 0.49 0.85 0.80 12.11 0.43 0.24 0.08 0.26 0.43 0.37 0.34 0.44 0.28 0.32 0.22 0.30 0.30 0.47 0.76 0.38 3.30 0.41 0.30 0.27 0.64 0.18 10.09 0.19

0.05 0.04 0.05 0.05 0.04 0.06 0.03 0.03 0.05 0.05 0.04 0.06 0.04 0.02 0.03 0.04 0.04 0.03 0.04 0.03 0.03 0.03 0.04 0.05 0.04 0.04 0.03 0.05 0.04

7.28 4.11 6.92 7.32 7.96 4.91 11.51 13.18 7.11 6.34 6.35 5.64 7.78 7.64 9.19 13.23 5.32 6.72 9.25 10.49 5.63 9.37 5.51 7.25 8.85 6.67 8.60 8.61 13.86

0.60 0.28 0.35 0.28 0.49 0.54 0.55 0.47 0.34 0.30 0.18 0.24 0.34 0.30 0.39 0.41 0.34 0.22 0.24 0.30 0.38 0.56 0.41 0.30 0.48 0.47 0.44 0.40 0.40

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

0.03 0.04 0.13 0.19 0.11 0.00 0.06 0.09 0.22 0.21 0.24 0.19 0.20 0.08 0.13 0.06 0.15 0.11 0.20 0.43 0.15 0.12 0.12 0.05 0.19 0.11 0.05 0.15 0.06

0.97 1.55 1.48 1.35 1.22 1.55 1.54 1.49 1.61 1.51 1.35 1.80 1.45 1.52 1.50 1.34 1.50 1.56 1.37 1.34 1.45 1.42 1.42 1.59 1.17 1.37 1.14 1.12 1.18

0.05 0.03 0.02 0.02 0.09 0.02 0.04 0.10 0.02 0.02 0.02 0.02 0.04 0.02 0.05 0.10 0.03 0.04 0.03 0.00 0.02 0.08 0.02 0.02 0.05 0.03 0.06 0.03 0.03

0.04 0.03 0.02 0.02 0.03 0.03 0.03 0.03 0.02 0.03 0.02 0.01 0.04 0.05 0.06 0.04 0.06 0.03 0.01 0.01 0.06 0.03 0.03 0.03 0.04 0.04 0.04 0.02 0.05

¨. O ¨ ztekin, M.J. Flannery / Journal of Financial Economics 103 (2012) 88–112 O

92

Table 1 (continued ) Panel B: Descriptive statistics of firm and macroeconomic variables Row

Country or statistic

BLEV

EBIT

MB

DEP

Ln(Size)

FA

R&D

TAXES

LIQUID

INF

GDPG

(30) (31) (32) (33) (34) (35) (36) (37) (38) (39)

Singapore South Africa Spain Switzerland Thailand Turkey United Kingdom United States Sample mean Sample median

0.19 0.13 0.24 0.24 0.33 0.17 0.17 0.23 0.24 0.25

0.04 0.13 0.07 0.07 0.06 0.12 0.10 0.09 0.07 0.07

0.48 0.34 1.99 0.20 0.51 6.28 0.45 0.39 1.25 0.41

0.03 0.04 0.04 0.04 0.04 0.06 0.04 0.04 0.04 0.04

5.06 7.63 8.98 6.48 7.73 12.89 4.48 6.00 7.89 7.32

0.32 0.27 0.38 0.33 0.44 0.36 0.28 0.26 0.37 0.35

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

0.16 0.10 0.19 0.18 0.04 0.07 0.20 0.25 0.14 0.13

1.47 1.42 1.22 1.76 1.22 1.64 1.37 1.86 1.43 1.45

0.01 0.06 0.03 0.01 0.03 0.13 0.03 0.03 0.04 0.03

0.06 0.04 0.03 0.01 0.05 0.05 0.03 0.03 0.03 0.03

below-median estimated adjustment speeds shown in Table 2’s BLEV BB column. Following previous research, we define a firm as accessing external financial markets if its debt or equity issuance exceeds 5% of the firm’s prior year-end total assets (Hovakimian, Opler, and Titman, 2001; Korajczyk and Levy, 2003; Leary and Roberts, 2005). Market access occurs with a debt retirement exceeding 5% as well, but we use a lower cutoff (1.25%) to identify access through equity repurchases (Leary and Roberts, 2005). Panel A of Table 3 shows the proportion of firm-years with access to external capital markets and how that access breaks down between debt and equity and between issuances and redemptions. Of the firms in slow-adjusting countries, 72.32% accessed the external capital market during any given year; of the firms in fast-adjusting countries, 80.15% did so. In other words, a firm from a slow-adjusting country is 7.83% less likely to access external capital markets in a typical year. This difference is slightly greater for access to equity markets (8.57%) than for debt markets (6.54%). Note also that issuances differ slightly more between the two groups than retirements. The results illustrate that firms from slow-adjusting countries make significantly fewer adjustments, consistent with the presence of higher adjustment costs (or lower adjustment benefits or both) in these countries. Of the firms that do access external markets, slow adjusters make relatively smaller adjustments to their capital structures (see Panel B of Table 3). Thus, observed balance sheet transactions are consistent with estimated adjustment speeds. Firms in slow-adjusting countries are less likely to access capital markets and, even given access, make smaller adjustments. Slower adjusters also have larger mean absolute deviations from their optimal leverage. Thus, they close a smaller proportion of their leverage gaps each period, consistent with transaction costs limiting their ability to attain target capital ratios promptly. We also confirm that firms accessing external capital markets adjust faster to their target leverage ratios. We estimate the partial adjustment model of Eq. (3) separately for accessors and nonaccessors across all countries. In unreported results, we find that firms that raise significant external capital adjust to their optimal capital structure 16% faster than those that do not. The difference (25% versus 9%) is statistically and economically significant. Prior research suggests that estimating Eq. (3) could yield reasonable-looking parameter estimates even with random data (Shyam-Sunder and Myers, 1999; Chen and

Zhao, 2007; Chang and Dasgupta, 2009; Hovakimian and Li, 2011). The multicountry data evaluated here provide a means of refuting questions about the power of a partial adjustment regression model. Estimating the same model across many countries enables us to examine whether the estimated adjustment speeds reflect institutional arrangements. If adjustment speeds plausibly reflect the quality of national institutions, the results support a partial adjustment specification of leverage, such as Eq. (3). This is a major advantage of examining capital structure adjustments in a multicountry context. 3. Adjustment speeds and trading costs What factors cause cross-country differences in the adjustment speeds? By definition, these must relate to some variation in firms’ costs or benefits of reaching their optimal leverage. However, in general, direct measures of transaction costs are not available. We therefore begin our exploration of international variations in leverage adjustment speeds by tying them to one specific measure of trading costs in various countries. Elkins McSherry provides an international comparison of securities trading costs. The firm tracks one thousand investment managers, 17 hundred global brokers, and 208 exchanges to determine the direct and indirect costs of engaging in equity and debt transactions in 42 countries. The trading cost data reflect fees and commissions as well as market impact. The market impact captures the degree to which an individual trade affects the market price by measuring the percentage movement of the buy or sell price from a daily benchmark average of prices.4 The Elkins McSherry data for debt transactions are first available for the third quarter of 2005. We use the 2005-IV data to conduct our tests, which implicitly assume that cross-country differences in trading costs were stable over our 1991–2006 period. (Using 2006 data yields similar conclusions.) Elkins McSherry’s 42 countries overlap with the 34 countries in our sample. Data are missing for India, Israel, and Pakistan. The mean and median debt (equity) transaction costs in other countries are 12.08 (14.62) and 7.66 (14.07) basis points, respectively. 4 See www.elkinsmcsherry.com for discussions of the Elkins McSherry data.

¨. O ¨ ztekin, M.J. Flannery / Journal of Financial Economics 103 (2012) 88–112 O

93

Table 2 Dynamic panel structure and adjustment speed estimates. The table summarizes the dynamic panel structure and the adjustment speed estimates for book and market leverage using Blundell and Bond (1998) and Bruno and Giovanni (2005), for the sample period 1991–2006. The definitions and the sources of the variables employed in the regressions are provided in Table 1. Rows (1)–(37) provide information on each country included in the sample. Rows (38) and (39) provide sample mean and median statistics, respectively. The dynamic panel structure columns report the number of firms and the mean number of observations per firm. The adjustment speed estimates columns report annual adjustment speeds (percent) obtained from the Blundell and Bond (1998) two-step system generalized method of moments (BB) and the Bruno and Giovanni (2005) corrected least squares dummy variable (LSDVC) through the estimation of the following model, run separately for each country, using book and market leverage: LEVij,t ¼ ðlj bj ÞXij,t1 þ ð1lj ÞLEVij,t1 þ lj Fij þ dij,t :

Row

Country or statistic

Dynamic panel structure Number of firms

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) (15) (16) (17) (18) (19) (20) (21) (22) (23) (24) (25) (26) (27) (28) (29) (30) (31) (32) (33) (34) (35) (36) (37) (38) (39)

Argentina Australia Austria Belgium Brazil Canada Chile Columbia Denmark Finland France Germany Greece Hong Kong India Indonesia Ireland Israel Italy Japan Malaysia Mexico New Zealand Norway Pakistan Peru Philippines Portugal Singapore South Africa South Korea Spain Switzerland Thailand Turkey United Kingdom United States Sample mean Sample median

ð3Þ

Adjustment speed estimates

Average number of observations per firm

27 939 92 116 140 165 91 21 152 119 661 692 80 131 220 262 61 41 244 3,063 679 81 95 137 45 21 93 42 400 193 239 147 207 300 43 1,568 3,571 410 140

The standard deviation of the debt (equity) transaction costs is 8.02 (4.72), with a range from a minimum of 3.54 (6.02) to a maximum of 31.95 (23.03) basis points. To determine whether these trading costs correlate with firms’ leverage adjustment speeds, we separate sample countries into two portfolios based on the median value of Elkins McSherry’s trading costs. This comparison between two groups of countries is complicated by the possibility that institutional features could affect both leverage targets (the bjs in Eq. (3)) and adjustment speeds (ljs). Inappropriate constraints on the target coefficients could bias

6 5 7 7 6 4 5 5 8 8 7 7 6 8 7 7 8 5 6 6 7 7 6 7 5 6 5 8 7 5 5 8 9 8 6 8 8 7 7

BLEV

MLEV

BB

LSDVC

BB

LSDVC

12.81 38.29 19.10 15.08 13.29 29.31 21.34 4.03 26.01 23.43 27.66 25.87 11.40 35.82 23.63 21.90 15.65 32.86 9.46 15.07 12.96 20.43 40.61 26.04 13.37 9.42 16.68 6.40 25.64 27.07 32.79 20.11 15.47 24.20 12.27 31.45 24.10 21.11 21.34

14.62 43.25 20.43 25.43 25.90 29.56 23.74 3.65 28.87 24.22 28.74 22.83 32.58 33.82 23.43 27.39 21.76 27.40 10.39 22.38 16.81 32.85 36.23 32.07 11.09 17.98 15.36 4.88 27.20 28.65 34.21 17.55 10.69 25.90 5.95 32.57 27.22 23.45 25.43

15.38 40.08 19.91 18.48 22.70 52.86 26.90 12.26 28.11 23.57 29.38 27.55 17.36 32.48 29.90 22.12 17.05 38.39 27.36 23.06 22.10 23.36 47.46 28.13 13.80 10.87 23.12 18.33 36.75 30.65 39.73 23.57 24.53 28.59 12.74 38.71 25.32 26.29 24.53

28.07 39.01 22.84 30.33 32.41 51.64 42.23 4.56 29.96 20.60 33.31 30.49 31.91 29.36 31.38 27.77 29.08 37.88 36.87 38.43 29.43 34.24 43.49 31.36 16.72 13.72 33.15 37.20 32.65 33.34 38.86 28.69 22.95 24.76 10.76 36.81 28.07 30.39 31.36

estimated adjustment speeds. We therefore undertake two tests that differ in their treatment of potential cross-country differences in the formation of leverage targets. First, the SEPARATE test methodology estimates Eq. (3) separately for each country, and we use a t-test to determine whether the two groups’ average adjustment speeds differ significantly. The SEPARATE methodology permits each country to have its own coefficients in the leverage target Eq. (2). Second, we estimate Eq. (3) across the firms residing in higher-thanmedian trading cost countries and then across all firms in lower trading cost countries. This POOLED procedure

¨. O ¨ ztekin, M.J. Flannery / Journal of Financial Economics 103 (2012) 88–112 O

94

Table 3 The frequency and size of the capital structure adjustments: slow versus fast adjusters. The table presents information on capital market accessors among firms in slow- and fast-adjusting countries. Panel A shows the incidence of access by reporting the proportion of firms that accessed external capital markets and how that access breaks down between debt (D.) and equity (E.) and between issuances (Issue) and retirements (Retire). Panel B shows the mean size of capital market access by reporting the magnitude of adjustments conditional on access for debt and equity in the form of either issuances or retirements. Access is defined as a change in absolute value of outstanding equity or debt. Debt issue, debt retirement, and equity issue are defined as a security issuance or repurchase of at least 5% of book assets. Equity retirement is defined as a security issuance or repurchase of at least 1.25% of book assets. Each year, the median value of the frequency and size of capital structure adjustment is calculated for each country. The reported statistic is the mean of these time series medians. Slow (fast) adjusters are the group of countries with adjustment speed smaller (larger) than the sample median of estimates reported in the BB column of BLEV in Table 2. n, nn, and nnn indicate significant difference between the groups at the 10%, 5%, and 1% significance level, respectively. Panel A: Incidence of capital market access Slow versus fast adjusters Frequency of adjustments (percent)

Slow adjusters Fast adjusters Difference Significance

Access

Debt

D. Issue

D. Retire

Equity

E. Issue

72.32 80.15 7.83

62.73 69.27 6.54

38.39 42.1 3.71

26.69 28.24 1.55

44.16 52.73 8.57

28.31 32.36 4.05

nnn

nnn

nnn

nnn

nnn

nnn

E. Retire 31.44 34.49 3.05 nnn

Panel B: Mean size of capital market access Slow versus fast adjusters Size of adjustments (percent)

Slow adjusters Fast adjusters Difference Significance

Debt

D. Issue

D. Retire

Equity

E. Issue

E. Retire

14.68 17.99 3.31

14.65 16.42 1.77

14.83 20.37 5.54

12.05 14.65 2.6

14.11 15.89 1.78

8.74 12.99 4.25

nnn

nnn

nnn

nnn

nnn

nnn

80

20 Slow adjusters Fast adjusters

15 Size of adjustments

Frequency of adjustments

60

40

20

10

5

0

0 Debt D. Issue D. Retire Equity E. Issue E. Retire

Debt D. Issue D. Retire Equity E. Issue E. Retire

Fig. 1. Capital structure adjustments. The figure plots the frequency and magnitude of the capital structure adjustments as explained and presented in Table 3.

b) and adjustment speeds (l b) on imposes common slopes (b all firms in similar countries. We then test whether the adjustment speeds differ between the two regressions.

Each methodology has its own merits. The SEPARATE method allows for full heterogeneity in parameter estimates and error variances but forfeits any efficiency gains

¨. O ¨ ztekin, M.J. Flannery / Journal of Financial Economics 103 (2012) 88–112 O

95

Table 4 Trading costs as determinants of the adjustment speeds. The table illustrates the impact of the trading costs on the adjustment speeds. Countries are allocated into a portfolio according to equity, debt, and total trading costs. Pairwise comparisons of the adjustment speeds of the selected portfolios are conducted using the SEPARATE and POOLED methods. For both methods, we estimate the following model: LEVij,t ¼ ðlj bj ÞXij,t1 þ ð1lj ÞLEVij,t1 þ lj Fij þ dij,t :

ð3Þ

The SEPARATE methodology estimates Eq. (3) separately for each country, averages the estimated l values for countries that are in the same portfolio, and tests whether the adjustment speeds differ between the groups using t-tests. The POOLED methodology estimates Eq. (3) across all firms in countries sharing the same institutional feature imposing common slopes (b) within each portfolio. A single adjustment speed is estimated for each portfolio, and tests are conducted for the significance of the differences between the two portfolios. n, nn, and nnn indicate significant difference between groups at the 10%, 5%, and 1% significance level, respectively. The definitions and the sources of the variables are provided in Appendix A. Portfolio

Group

Number of countries

SEPARATE Tests Mean

POOLED Tests

Difference

Significance

Mean

Difference

Significance

16.51 7.06

9.45

nnn

Equity trading costs

Low High

18 17

25.42 21.42

4.00

nnn

Debt trading costs

Low High

18 17

25.18 22.50

2.68

nnn

16.09 8.07

8.02

nnn

Total trading costs

Low High

18 17

25.27 22.36

2.91

nnn

16.31 7.39

8.92

nnn

from pooling countries with similar institutional characteristics. Yet estimating Eq. (3) for countries with relatively few firms might yield noisy or biased parameter estimates. Sources of bias include the ‘‘weak instruments problem’’ (Nelson and Startz, 1990) and the ‘‘many instruments problem’’ (Tauchen, 1986).5 The POOLED method imposes slope and error variance homogeneity across countries, but truly heterogeneous slopes could negate any gains from pooling. Because neither method is obviously superior, we show our results using both. For both methods, the GMM estimator instruments for the (potentially endogenous) firm characteristics. Table 4 presents evidence linking adjustment speed variations to a measure of security trading costs, which indicates that higher trading costs are associated with significantly lower estimated adjustment speeds. Higher trading costs in debt or equity markets reduce the adjustment speed by 3% to 9%. That is, international differences in adjustment speeds correlate with differences in the cost of transacting in bond and equity markets, at least as measured by these trading costs. 4. Institutional effects on cross-country variations in adjustment speed: theory Both intuition and theory suggest that institutional factors should affect a firm’s adjustment to optimal capital structure. Our main hypothesis is that firms’ legal, financial, political, and regulatory constraints affect their cost and benefits of adjusting to an optimal (target) capital structure. A rich literature connects firm performance to the legal, financial, and regulatory environment in which 5 We obtain the same conclusions if we either specify the instrument set without employing all available instruments (to minimize potential bias) or we weigh the adjustment speed estimates by the number of observations in each country (to reduce the small sample bias associated with some countries having fewer firms in their panel).

¨ - -Kunt and Maksimovic, those firms operate (Demirguc 1999; Bancel and Mittoo, 2004; Rajan and Zingales, 1995; ¨ - -Kunt, and Maksimovic, 2001). Booth, Aivazian, Demirguc Motivated by this literature, we interpret various institutional arrangements as affecting the costs and benefits of adjusting leverage. If a country’s institutional characteristics make it more expensive to issue debt and equity, firms in that country are likely to exhibit slower adjustment speeds. Likewise, institutional features that raise the benefit of being closer to target leverage should make the affected firms adjust more rapidly. More generally, firms in countries with similar institutional arrangements should confront similar adjustment costs and benefits and thus exhibit similar adjustment speeds in Eq. (3). The literature provides many indicators of national institutions’ strength or weakness (e.g., La Porta, Lopez de Silanes, and Shleifer, 2006; La Porta, Lopez de Silanes, Shleifer, and Vishny, 1997, 1998, 1999, 2000a, 2000b, 2002; La Porta, Lopez de Silanes, Pop-Eleches, and Shleifer, 2004; Djankov, La Porta, Lopez de Silanes, and Shleifer, 2002, 2003, 2008). We collect data on a variety of countrylevel structural and institutional features, similar to their categorization in the law and finance literature. Some variables are binary (e.g., the presence or absence of a national credit registry), and others represent more nuanced assessments, such as the Quality of Contract Enforcement measure that varies between zero and ten. Table 5 provides detailed information and summary statistics on indices of country-level institutional features, for 37 countries. Appendix A describes these indices and their sources.6 The empirical challenge is to summarize the available information in the institutional indices parsimoniously. Although evaluating all the available indices concurrently

6 We are grateful to Andrei Shleifer for making several of our proxies freely available on his web page (http://www.economics.harvard.edu/ faculty/shleifer/dataset). Appendix A provides the original data sources.

96 Table 5 Institutional characteristics. The definitions and the sources of the variables are provided in Appendix A. Rows (1)–(37) provide information on each country included in the sample. Rows (38) and (39) provide sample mean and median statistics, respectively. Legal origin

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) (15) (16) (17) (18) (19) (20) (21) (22) (23) (24) (25) (26) (27) (28) (29) (30) (31) (32) (33) (34) (35) (36) (37) (38) (39)

French English German French French English French French Scandinavian Scandinavian French German French English English French English English French German English French English Scandinavian English French French French English English German French German English French English English – –

Argentina Australia Austria Belgium Brazil Canada Chile Columbia Denmark Finland France Germany Greece Hong Kong India Indonesia Ireland Israel Italy Japan Malaysia Mexico New Zealand Norway Pakistan Peru Philippines Portugal Singapore South Africa South Korea Spain Switzerland Thailand Turkey United Kingdom United States Sample mean Sample median

Financial emphasis 0 1 0 0 1 1 1 0 0 0 0 0 0 1 0 0 0 0 0 0 1 1 0 0 0 1 1 0 1 1 0 0 0 1 1 1 1 0 0

Size Efficiency

2.99 4.82 4.54 4.14 3.6 4.81 4.54 3.51 4.16 4.45 4.71 4.71 3.88 – 3.69 – 4.49 4.37 4.13 5.49 5.23 3.47 4.55 4.64 3.47 2.76 3.91 4.26 – 5.35 – 4.5 5.51 4.55 2.99 5.02 5.54 4.33 4.49

 1.91 1.71 0.48 0.19  0.62 1.84 0.2  2.51 0.58 0.98 0.64 1.91  0.92 – 0.52 – 4.14 1.43 0.13 3.32 3.27 0.23 1.07 0.91  0.45  2.02 0.03  0.19 – 0.75 – 0.57 2.98 2.33  0.03 2.72 2.24 0.80 0.58

Aggregate Quality

Shareholder rights

Shareholder right enforcement

Creditor rights

Creditor right enforcement

Corporate transparency

 1.39 0.84 0.26  0.16  0.53 0.86 0.1  1.31 0.05 0.28 0.5 0.89  0.62 –  0.3 – 1.11 0.51  0.09 1.76 1.52  0.49 0.42 0.47  0.78  1.62  0.26  0.17 – 0.79 – 0.3 1.88 0.86  0.81 1.27 1.37 0.23 0.28

4 4 2 0 3 5 5 3 2 3 3 1 2 5 5 2 4 3 1 4 4 1 4 4 5 3 3 3 4 5 2 4 2 2 2 5 5 3 3

0.34 0.76 0.21 0.54 0.27 0.64 0.63 0.57 0.46 0.46 0.38 0.28 0.22 0.96 0.58 0.65 0.79 0.73 0.42 0.50 0.95 0.17 0.95 0.42 0.41 0.45 0.22 0.44 1.00 0.81 0.47 0.37 0.27 0.81 0.43 0.95 0.65 0.54 0.47

1 1 3 2 1 1 2 0 3 1 0 3 1 4 4 4 1 4 2 2 4 0 3 2 4 0 0 1 4 3 3 2 1 3 2 4 1 2 2

5.40 1.80 3.52 2.73 3.06 2.09 4.57 4.11 2.55 3.14 3.23 3.51 3.99 0.73 3.34 3.90 2.63 3.30 4.04 2.98 2.34 4.71 1.58 2.95 3.76 5.60 5.00 3.93 2.50 1.68 3.37 5.25 3.13 3.14 2.53 2.58 2.62 3.28 3.14

45 75 54 61 54 74 52 50 62 77 69 62 55 69 57 – – 64 62 65 76 60 70 74 – 38 65 36 78 70 62 64 68 64 51 78 71 63 64

¨. O ¨ ztekin, M.J. Flannery / Journal of Financial Economics 103 (2012) 88–112 O

Row Country or statistic

Table 5 (continued ) Debt information sharing

Dividends Reserve Time requirements to repay

Bankruptcy costs

Bankruptcy efficiency

Tax rate

Executive quality

Contract Law enforcement and order

Corruption Expropriation Repudiation

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) (15) (16) (17) (18) (19) (20) (21) (22) (23) (24) (25) (26) (27) (28) (29) (30) (31) (32) (33) (34) (35) (36) (37) (38) (39)

1 1 1 1 1 1 1 1 1 1 0 1 1 1 0 0 1 1 1 1 1 1 1 1 0 1 1 1 0 1 1 1 1 0 0 1 1 1 1

0.00 0.00 0.00 0.00 0.50 0.00 0.30 0.50 0.00 0.00 0.00 0.00 0.40 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.04 0.00

0.12 0.08 0.18 0.04 0.12 0.04 0.15 0.01 0.09 0.04 0.09 0.08 0.09 0.09 – 0.18 0.09 0.23 0.22 0.04 0.15 0.18 0.04 0.01 – 0.07 0.38 0.09 0.01 0.18 0.04 0.15 0.04 0.36 0.07 0.06 0.07 0.11 0.09

35.80 87.80 78.00 90.80 13.40 93.20 40.90 64.80 76.70 92.40 54.10 57.00 53.80 88.30 – 25.10 89.90 66.20 45.30 95.50 48.40 72.60 90.70 91.80 – 41.80 17.50 82.30 96.10 39.80 88.10 82.00 60.40 54.90 6.60 92.30 85.80 65.72 72.60

23.54 21.96 20.86 16.71 15.49 21.78 15.09 24.28 21.94 16.30 14.06 23.50 19.78 0.00 20.28 20.84 9.62 25.72 23.82 28.66 10.50 22.21 26.44 18.50 31.28 22.03 22.08 16.03 10.25 18.10 14.94 18.52 13.74 22.04 16.92 18.61 18.19 19.04 19.78

3.76 7.00 7.00 7.00 3.37 7.00 4.02 6.10 7.00 7.00 4.95 7.00 5.44 – 6.95 2.48 7.00 7.00 7.00 7.00 5.17 3.52 7.00 7.00 4.24 3.87 4.10 4.75 3.30 7.00 – 4.69 7.00 3.46 6.07 7.00 7.00 5.69 6.95

5.04 7.71 8.25 8.16 5.30 8.38 5.20 4.76 8.22 7.50 6.36 8.40 5.81 – 4.53 4.29 7.78 7.30 5.18 7.57 5.71 4.92 – 8.48 3.85 4.29 4.84 4.54 7.64 6.87 5.52 6.23 8.94 5.61 4.79 8.50 8.73 6.43 6.23

6.01 8.51 8.57 8.81 6.31 10.00 5.30 5.00 10.00 10.00 9.05 8.93 7.26 8.52 4.58 2.14 8.51 8.33 6.13 8.51 7.38 4.76 10.00 10.00 2.98 4.70 2.92 7.38 8.21 8.91 5.30 7.38 10.00 5.18 5.18 9.11 8.63 7.26 8.21

0.50 0.75 0.25 0.42 0.25 0.92 0.58 0.42 0.58 0.50 0.75 0.42 0.33 0.92 0.92 0.50 0.67 0.67 0.67 0.75 0.92 0.58 0.67 0.58 0.58 0.33 0.83 0.42 1.00 0.83 0.75 0.50 0.67 0.92 0.50 0.83 1.00 0.64 0.67

0.22 0.66 0.11 0.44 0.33 1.00 0.33 0.11 0.55 0.66 0.22 0.00 0.50 0.66 0.66 0.66 0.44 0.66 0.22 0.66 0.66 0.11 0.44 0.39 0.39 0.66 1.00 0.66 0.66 0.66 0.66 0.66 0.44 0.22 0.22 0.66 1.00 0.50 0.55

0.58 0.90 0.17 0.15 0.58 0.80 0.60 0.58 0.37 0.32 0.77 0.22 0.32 0.87 0.67 0.62 0.37 0.63 0.48 0.00 0.77 0.35 0.33 0.32 0.58 0.78 0.83 0.58 0.87 0.25 0.25 0.33 0.33 0.72 0.63 0.68 0.90 0.53 0.58

3.5 5.7 5.5 5.1 4.0 5.2 4.3 4.0 5.5 5.5 5.1 4.9 3.2 4.4 3.5 2.8 5.4 4.9 4.2 5.1 4.4 3.8 5.6 4.1 – 3.5 2.9 4.9 5.5 4.3 4.4 4.1 5.3 3.3 3.8 6.2 5.5 4.54 4.40

0.20 0.00 0.10 0.10 0.20 0.00 0.20 0.50 0.25 0.00 0.10 0.10 0.33 0.00 0.00 0.00 0.00 0.00 0.20 0.25 0.00 0.20 0.00 0.20 0.00 0.20 0.00 0.20 0.00 0.00 0.50 0.20 0.50 0.10 0.20 0.00 0.00 0.13 0.10

2.75 0.58 0.92 0.92 3.67 0.75 5.08 3.00 2.50 0.92 1.69 0.92 1.92 0.63 – 5.50 0.42 1.50 1.17 0.58 2.25 1.83 0.67 0.92 – 3.08 5.67 2.00 0.58 1.92 1.50 1.00 3.00 2.67 5.88 0.50 2.00 2.03 1.69

8.33 10.00 10.00 8.33 3.33 10.00 8.33 1.67 10.00 10.00 8.33 8.33 5.00 8.33 6.67 3.33 10.00 8.33 10.00 8.33 5.00 3.33 10.00 10.00 5.00 5.00 3.33 8.33 10.00 3.33 – 6.67 10.00 8.33 6.67 10.00 10.00 7.55 8.33

5.91 9.27 9.69 9.63 7.62 9.67 7.50 6.95 9.67 9.67 9.65 9.90 7.12 8.29 7.75 7.16 9.67 8.25 9.35 9.67 7.95 7.29 9.69 9.88 5.62 5.54 5.22 8.90 9.30 6.88 8.31 9.52 9.98 7.42 7.00 9.71 9.98 8.39 8.90

4.91 8.71 9.60 9.48 6.30 8.96 6.80 7.02 9.31 9.15 9.19 9.77 6.62 8.82 6.11 6.09 8.96 7.54 9.17 9.69 7.43 6.55 9.29 9.71 4.87 4.68 4.80 8.57 8.86 7.27 8.59 8.40 9.98 7.57 5.95 9.63 9.00 7.93 8.59

¨. O ¨ ztekin, M.J. Flannery / Journal of Financial Economics 103 (2012) 88–112 O

Row Equity Equity Equity Equity disclosure liability public insider enforcement trading

97

98

¨. O ¨ ztekin, M.J. Flannery / Journal of Financial Economics 103 (2012) 88–112 O

is possible, such an approach could obscure valuable information because these indices are likely to be correlated. Consider two complementary indices, such as debtholders’ legal protections and the vigor with which those protections are enforced. Simply entering these two indices additively into a model would omit their important interactive effects. Similarly, if two (other) institutional features are substitutes, countries could obtain the same economic outcome using various combinations of the two features. We use the first principal component of related subindices and dummy variables to represent a few, broad indices of each country’s institutional environment: legal tradition (common versus civil); financial system organization (market- versus bank-oriented); and financial system aggregate quality (high versus low); ease of access to capital markets; asymmetric information; financial constraints; distress costs; tax shields; and deviation penalties. Appendix A provides details on the procedure for combining institutional indices. For example, consider the ease of access to capital markets. We collect four measures related to this concept: shareholder rights, shareholder right enforcement, creditor rights, and creditor right enforcement. The Ease of Access measure for equity is the first principal component of the first two indices. The corresponding debt measure is the first principal component of the latter two indices, and an overall Ease of Access measure is the first principal component of all four subindices. To assess the impact of these institutional features on estimated adjustment speeds, we separate sample countries into two portfolios according to the median value of one selected feature. (In general, these two portfolios can be characterized as providing strong or weak investor protections.) If the institutional attribute consists of an indicator variable, we group the sample countries according to the presence or absence of that attribute, that is, according to whether the indicator variable equals zero or one. We expect to find higher estimated adjustment speeds in the portfolios with lower cost (or higher benefit) institutional features. 4.1. Adjustment cost components As Appendix A shows, we divide adjustment (transaction) costs into three components: the direct costs of accessing debt or equity markets, the extent of information asymmetry between inside and outside investors, and the extent to which firms’ financial flexibility is constrained by law or regulation. 4.1.1. Ease of access Firms more readily complete capital market transactions in a dependable legal environment, which ensures that investors receive promised cash flows. Claessens, Djankov, and Lang (2000) and La Porta, Lopez de Silanes, Shleifer, and Vishny (2002) argue that investors pay more for equity when legal institutions effectively protect their rights. Rajan and Zingales (1995) suggest that strong creditor rights enhance ex ante contractibility. Furthermore, several studies emphasize the enforcement of investor rights as an effective

mechanism that reduces external financing costs (La Porta, Lopez de Silanes, Shleifer, and Vishny, 1997, 1998; Levine, 1999) and argue that the effective enforcement of laws matters more than the quality of laws itself (Berkowitz, Pistor, and Richard, 2003). Easier capital market access should lower a firm’s rebalancing costs, leading to faster adjustment to optimal capital structure. We construct Ease of Access measures for the equity market alone, the debt market alone, and a combination of both markets.

4.1.2. Asymmetric information Information asymmetry increases the difficulty of issuing (or retiring) securities and creates a wedge between internal and external financing costs (Myers, 1984; Myers and Majluf, 1984). If firms depend on external financing to correct their deviation from optimal leverage, costlier financing could impede leverage adjustments. For example, better accounting information could help investors distinguish between good and bad investments, lowering the adverse selection costs and consequently decreasing the cost of external financing (Verrecchia, 2001; Amihud and Mendelson, 1986; Merton, 1987; Lombardo and Pagano, 2002; Lambert Leuz and Verrecchia, 2007). We recognize information sharing in the equity and debt markets using the security laws governing initial public offerings (which focus on mandatory disclosure, liability standards, and public enforcement). Daouk, Lee, and Ng (2006) and Bhattacharya and Daouk (2002) show that transaction costs are higher in stock markets in which insiders trade with impunity, and so we also use the strength of insider trading laws as a proxy for the quality of capital market governance. For the debt markets, we use the presence of public credit registries that collect and disseminate information on credit histories and current indebtedness. We calculate the Asymmetric Information index as the first principal component of corporate transparency and information sharing in equity, debt, and all markets. We anticipate more rapid leverage adjustments with lower information asymmetry.

4.1.3. Financial constraints Some countries require that firms distribute a minimum percentage of net income as dividends among ordinary shareholders. Some countries also require that firms liquidate if they cannot maintain a minimum equity level, as a means of protecting creditors from shareholder expropriation. We use these two institutional features to measure impediments to firms’ financial flexibility: dividend payments and reserve requirements. We hypothesize that Financial Constraints should reduce a firm’s speed of convergence to its optimal leverage.7 7 Dividends and reserve requirements can also be considered remedial investor rights. If so, their existence could lower external financing costs and result in faster adjustment. The effectiveness of mandatory dividend rule would also depend on the strength of the accounting standards, because earnings could be misrepresented to avoid the distribution of dividends. In countries with no mandatory dividend rule, the firms could also voluntarily distribute dividends to signal quality or to reduce agency costs associated with free cash flow.

¨. O ¨ ztekin, M.J. Flannery / Journal of Financial Economics 103 (2012) 88–112 O

99

Table 6 Frequency and size of the capital structure adjustments: high versus low Aggregate Adjustment Costs. The table presents information on capital market accessors among firms from countries with high and low Aggregate Adjustment Costs. Panel A shows the incidence of access by reporting the proportion of firms that accessed external capital markets and how that access breaks down between debt (D.) and equity (E.) and between issuances (Issue) and retirements (Retire). Panel B shows the mean size of capital market access by reporting the magnitude of adjustments conditional on access for debt and equity in the form of either issuances or retirements. Access is defined as a change in absolute value of outstanding equity or debt. Debt issue, debt retirement, and equity issue are defined as a security issuance or repurchase of at least 5% of book assets. Equity retirement is defined as a security issuance or repurchase of at least 1.25% of book assets. Each year, the median value of the frequency and size of capital structure adjustment is calculated for each country. The reported statistic is the mean of these time series medians. High (low) costs are the group of countries with scores on the Aggregate Adjustment Costs proxy for all markets smaller (larger) than the sample median. n, nn, and nnn indicate significant difference between the groups at the 10%, 5%, and 1% significance level, respectively. Panel A: Incidence of capital market access Frequency of adjustments (percent) High versus low costs Low costs High costs Difference Significance

Access

Debt

D. Issue

D. Retire

Equity

E. Issue

E. Retire

82.20 74.22 7.98

69.66 64.49 5.17

40.24 39.72 0.52

30.85 26.62 4.23

57.74 45.58 12.16

31.17 29.90 1.27

40.06 30.77 9.29

nnn

nnn

nnn

nnn

nnn

nnn

nnn

Panel B: Mean size of capital market access Size of adjustments (percent) High versus low costs Low costs High costs Difference Significance

Debt

D. Issue

D. Retire

Equity

E. Issue

E. Retire

19.67 15.26 4.41

15.44 15.26 0.18

25.11 15.33 9.78

15.86 12.59 3.27

15.04 15.01 0.03

17.89 8.79 9.10

nnn

nnn

nnn

nnn

nnn

nnn

4.1.4. Aggregate adjustment costs Finally, we take the first principal component of the indices measuring Ease of Access, Information Asymmetry, and Financial Constraints to form a separate Aggregate Adjustment Costs (AAC) variable for equity, debt, and all markets. Table 6 shows that firms in high Aggregate Adjustment Costs countries access external capital markets significantly less frequently. Conditional on access, firms confronting higher costs also make smaller leverage adjustments. Fig. 2 shows that our measures of adjustment costs correspond to estimated adjustment speeds. Firms in countries with easier access to external markets and fewer statutory financial constraints and firms facing lower information asymmetries adjust faster. Firms with lower Aggregate Adjustment Costs also adjust faster. 4.2. Adjustment benefit components The speed of rebalancing also depends on the benefits of adjustment to optimal leverage. Convergence to optimal leverage is most valuable in institutional settings in which financial distress is more costly or leverage provides more valuable tax shelters. Debt covenants can also encourage rapid reductions when a firm becomes overleveraged. 4.2.1. Distress costs Insolvency codes and the related court mechanisms should affect the resolution of financial distress. First, we use the design of the bankruptcy codes and debt contracts, including the attached creditor rights and the associated enforcement mechanisms governing default on debt

contracts as determinants of the ex ante financial distress costs. In countries in which lenders can easily force repayment, repossess collateral, gain control of the firm, or enforce debt contracts, the value of quickly reversing leverage increases is likely higher. We present ex ante distress costs as the first principal component of two measures: creditor rights and creditor rights enforcement. We hypothesize that higher ex ante distress costs lead to faster speed of adjustment. Second, the success of managers in maintaining and reaching their target leverage should be higher in countries with more efficient bankruptcy resolution processes because the deadweight costs are less significant. We present ex post distress costs as the first principal component of three measures: time to repay, bankruptcy costs, and bankruptcy efficiency. We expect that firms from countries that administer the bankruptcy process in a less time consuming, less costly, and more efficient manner adjust more rapidly to their target because of lower deadweight costs associated with the insolvency process. Therefore, we hypothesize that higher ex post distress costs lead to slower speed of adjustment.

4.2.2. Tax rate Debt tax shields play an important role in capital structure decisions (Graham, 1996). The tax benefits of leverage should increase the value of reaching and maintaining the leverage target for underleveraged firms. We use country-level effective corporate tax rate to evaluate the effect of the value of tax shields on the adjustment decision. We hypothesize that a higher tax rate leads to faster adjustment to optimal leverage, at least for underleveraged firms.

¨. O ¨ ztekin, M.J. Flannery / Journal of Financial Economics 103 (2012) 88–112 O

100

.4 Adjustment speed

Adjustment speed

.4

.3

.2

.1

.3

.2

.1

0

0 Ease of Access

Information Asymmetry .4 Adjustment speed

Adjustment speed

.4

.3

.2

.1

.3

.2

.1

0

0 Financial Constraints

Adjustment speed

Fitted

Aggregate Adjustment Costs

Fig. 2. Adjustment speeds and adjustment cost factors. The figure ranks countries according to various aggregate indices of adjustment costs and plots their adjustment speed estimates obtained from the Blundell and Bond (1998) two-step system generalized method of moments (BB) that are reported in Table 2. Countries are ranked from low to high Ease of Access, Asymmetric Information, Financial Constraints, and Aggregate Adjustment Costs. The solid line illustrates the fitted values of the adjustment speed estimates. Appendix A provides detailed information on the formulation of the aggregate indices of adjustment costs.

4.2.3. Deviation penalties Loan and debt covenants should increase the benefits of adjustment to the target for overleveraged firms. Although we cannot observe firms’ covenant restrictions, we can proxy for some of the things that influence their effectiveness. We use executive quality as a proxy for internal pressure and use the quality of contract enforcement, the strength of law and order, and the quality of government as proxies for external pressure to correct any suboptimal leverage situation. We hypothesize that the rebalancing benefits are greater in countries with more constraints on executive power, better quality of contract enforcement, better legal systems, and stronger governance. Thus, higher deviation penalties should result in faster adjustment.

4.2.4. Aggregate adjustment benefits Finally, we take the first principal component of the indices measuring distress costs, tax rate, and deviation penalties to form an aggregate adjustment benefits variable. Fig. 3 offers a visual representation of the relation between the adjustment speed estimates and the adjustment benefit factors. Firms in countries with higher (lower) ex ante (ex post) distress costs and deviation penalties adjust faster. Firms with higher aggregate adjustment benefits also adjust faster.

5. Institutional effects on cross-country variations in adjustment speed: results First, we establish a relation between a country’s institutional features and the Elkins McSherry measures of security trading costs. Second, we examine adjustment speeds across the relatively broad characteristics of legal tradition, financial structure, and financial development. Finally, we examine the impact of more specific adjustment cost and benefit measures on speeds of adjustment. 5.1. Trading costs If our cost and benefit indices are related to firms’ transaction costs, we expect them to be similarly related to the Elkins McSherry index of trading costs. To test these hypotheses, we form portfolios of firms by dividing their countries into above- and below-median values of each cost-benefit index. Table 7 reports the results of comparing securities trading costs between weak and strong institutional settings. The results indicate that institutional characteristics determine the country-level trading costs. Consistent with our hypotheses, higher trading costs are almost always associated with lower quality institutions. In countries with weaker institutions (impediments to capital market access, higher information asymmetry, distress, deviation penalties, and limits to financial flexibility), the

.4

.4

.3

.3

Adjustment speed

Adjustment speed

¨. O ¨ ztekin, M.J. Flannery / Journal of Financial Economics 103 (2012) 88–112 O

.2

.1

0

.2

.1

0 Ex post distress costs

Ex ante distress costs .4

.4

.3

.3

Adjustment speed

Adjustment speed

101

.2

.1

0

.2

.1

0 Tax rate

Deviation penalties

Adjustment speed

.4 .3

Adjustment speed Fitted

.2 .1 0 Aggregate adjustment benefits

Fig. 3. Adjustment speeds and adjustment benefit factors. The figure ranks countries according to various aggregate indices of adjustment benefits and plots their adjustment speed estimates obtained from the Blundell and Bond (1998) two-step system generalized method of moments (BB) that are reported in Table 2. Countries are ranked from low to high ex ante and ex post distress costs, tax rate, deviation penalties, and aggregate adjustment benefits. The solid line illustrates the fitted values of the adjustment speed estimates. Appendix A provides detailed information on the formulation of the aggregate indices of adjustment benefits.

trading costs of both equity and debt are significantly higher. For example, in countries with higher ex post distress costs, total trading costs are 10 basis points higher. This relation between trading costs and adjustment speeds suggests that the institutional environment, which we hypothesize affects transaction costs and benefits, should also affect adjustment speeds. 5.2. Legal and financial traditions In the law and finance literature, most general distinctions are measured by legal origins, the distinction between bank- and market-oriented financial systems, and the extent of financial sector development. We form

portfolios reflecting each of these institutional dimensions and present the results in Table 8. Panel A of Table 8 compares firms across basic legal traditions. La Porta, Lopez de Silanes, Shleifer, and Vishny (1997, 1998) conclude that English-origin (common) countries provide stronger institutions and legal protections to both shareholders and creditors than civil law provides. Accordingly, adjustment costs should be lower or adjustment benefits higher or both in common law– originating countries, leading to faster adjustment. Consistent with this proposition, we find that firms in the common law countries adjust to optimal capital structure significantly faster than firms operating under civil law. The estimated adjustment speed difference is 8.9% (14.1%)

¨. O ¨ ztekin, M.J. Flannery / Journal of Financial Economics 103 (2012) 88–112 O

102

Table 7 Institutional determinants of the trading costs. Countries are allocated into portfolios according to the sample median of the aggregate institutional indices. Pairwise comparisons of the mean Elkins McSherry estimated trading costs of the two portfolios are then conducted using t-tests. In Panel A, equity, debt, and total trading costs are employed for equity, debt, and all markets, respectively. In Panel B, total trading costs are used. n, nn, and nnn indicate significant difference between groups at the 10%, 5%, and 1% significance level, respectively. The definitions and the sources of the variables are provided in Appendix A. Institutional feature

Panel A: Adjustment costs Ease of access (1) All markets (2) Equity markets (3) Debt markets Asymmetric information (4) All markets (5) Equity markets (6) Debt markets Financial constraints (7) Financial constraints Aggregate adjustment costs (8) All markets (9) Equity markets (10) Debt markets Panel B: Adjustment benefits (1) Ex ante distress costs (2) Ex post distress costs (3) Tax rate (4) Deviation penalties (5) Aggregate adjustment benefits

Group

Number of countries

Trading costs Mean

Difference

Significance

2.70

nnn

1.56

nnn

Low High Low High Low High

19 15 19 15 19 15

18.36 15.67 15.33 13.78 11.28 13.02

High Low High Low High Low

16 16 17 15 18 14

18.44 14.44 18.65 13.93 19.11 13.00

High Low

21 13

High Low High Low High Low Low High Low High Low High Low High Low High

in our SEPARATE (POOLED) test. In the category of civil legal traditions, La Porta, Lopez de Silanes, Shleifer, and Vishny (1997, 1998, 2002) rank the Scandinavian system highest, followed by the German and French systems. We compare these traditions pairwise in Rows 2–7 of Panel A and find support for these rankings. Using the SEPARATE test method, we find fastest adjustment for book leverage in the English legal tradition (27%), followed by Scandinavian (25%), German (22%), and French (15%). Qualitatively similar results derive from the POOLED test method. The finance literature has evaluated differences between bank-based and market-based financial systems. The market-based view highlights the role of well-functioning markets in increasing liquidity (Holmstrom and Tirole, 1993), enhancing corporate governance (Jensen and Murphy, 1990), and facilitating risk management (Levine, 1991; Obstfeld,

1.74

4.00

nnn

4.71

nnn

6.11

nnn

18.23 14.07

1.71

nnn

17 15 17 15 17 15

18.53 14.07 17.71 15.00 18.12 14.53

4.46

nnn

2.71

nnn

3.58

nnn

19 15 17 16 18 15 15 15 15 15

11.28 13.02 12.39 22.56 15.16 19.73 24.20 10.25 24.73 9.75

1.74 10.17

nnn

4.57 13.95

nnn

14.98

nnn

1994). The bank-based view stresses the positive role of banks in collecting information about firms and managers and thereby improving capital allocation and corporate governance (Diamond, 1984; Ramakrishnan and Thakor, 1984), managing cross-sectional and liquidity risk (Allen and Gale, 1999; Bencivenga and Smith, 1991), mobilizing capital to take advantage of economies of scale (Sirri and Tufano, 1995), and ameliorating moral hazard through effective monitoring (Boot and Thakor, 1997). Antoniou, Guney, and Paudyal (2008) provide an empirical analysis. The market-based view further maintains that banks can hinder innovation by protecting established firms that constitute their source of informational rents (Hellwig, 1991; Rajan, 1992) and that markets act to offset the inefficiencies associated with banks. Conversely, the bank-based view maintains that greater market development can hamper corporate control (Rajan and Zingales, 1998) and that banks

¨. O ¨ ztekin, M.J. Flannery / Journal of Financial Economics 103 (2012) 88–112 O

103

Table 8 The impact of the legal and financial traditions on the adjustment speeds. The table illustrates the impact of the legal and financial traditions on the adjustment speeds. Countries are allocated into an institution portfolio according to legal origin (common, civil, English, French, German, Scandinavian) in Panel A, financial system structure (bank-based, market-based) in Panel B, and financial system development (low, high) if the countries are below (above) the sample median of the indices representing size, efficiency, and overall quality of the financial system, respectively, in Panel C. Pairwise comparisons of the adjustment speeds of the selected portfolios are conducted using the SEPARATE and POOLED methods. For both methods, the following model is estimated: LEVij,t ¼ ðlj bj ÞXij,t1 þ ð1lj ÞLEVij,t1 þ lj Fij þ dij,t :

ð3Þ

The SEPARATE methodology estimates Eq. (3) separately for each country, averages the estimated l values for countries that are in the same portfolio, and tests whether the adjustment speeds differ between the groups using t-tests. The POOLED methodology estimates Eq. (3) across all firms in countries sharing the same institutional feature imposing common slopes (b) within each portfolio. A single adjustment speed is estimated for each portfolio, and tests are conducted for the significance of the differences between the two portfolios. n, nn, and nnn indicate significant difference between groups at the 10%, 5%, and 1% significance level, respectively. The definitions and the sources of the variables are provided in Appendix A. Institutional feature

Number of countries

SEPARATE tests

POOLED tests

Mean (percent)

Difference (percent)

Significance

Mean (percent)

Difference (percent)

Significance

8.93

nnn

14.12

nnn

11.73

nnn

12.96

nnn

1.50

nnn

4.13

nnn

5.00

nnn

12.54

nnn

10.23

nnn

8.83

nnn

6.73

nnn

0.43

nnn

3.50

nnn

18.91 4.79 18.91 5.95 18.91 14.78 18.91 6.37 5.95 14.78 5.95 6.37 14.78 6.37

8.40

nnn

Panel A: Legal traditions (1) Common Civil (2) English French (3) English Scandinavian (4) English German (5) French Scandinavian (6) French German (7) Scandinavian German

14 23 14 15 14 3 14 5 15 3 15 5 3 5

26.66 17.73 26.66 14.94 26.66 25.16 26.66 21.66 14.94 25.16 14.94 21.66 25.16 21.66

Panel B: Financial system structure Bank-based Market-based

22 15

19.94 22.82

2.87

nnn

2.74 18.93

16.19

nnn

15.56 24.92 14.89 25.63 15.08

9.36

nnn

15.63

nnn

10.74

nnn

12.83

nnn

10.34

nnn

0.66 16.30 3.58 16.41 2.10

14.09

nnn

Panel C: Financial system development (1) Small 16 Large 17 (2) Low efficiency 16 High efficiency 17 (3) Low aggregate 16 quality High aggregate 17 quality

25.42

reduce the inherent inefficiencies associated with financial markets (Petersen and Rajan, 1995). The test results appearing in Panel B of Table 8 suggest that a market-based structure imposes lower costs of adjusting or higher benefits of converging to a firm’s optimal capital ratio, or both. Specifically, using the SEPARATE (POOLED) method, we find that firms in market-based financial systems adjust at an average annual rate of 23% (19%) while firms in bank-based financial systems adjust at an average rate of 20% (3%). The differences are highly significant using both methodologies. Levine (2002) argues that more sophisticated financial systems reduce market imperfections by providing better financial services. He offers three measures of financial sector development. (1) Size: The size of stock markets and intermediaries is measured as the logarithm of the market capitalization ratio times the private credit ratio. The market capitalization ratio captures the size of the domestic

16.19

stock market and the private credit ratio captures the size of intermediaries. (2) Efficiency: The efficiency of the financial sector is measured by total value-traded ratio times overhead costs. The total value-traded ratio captures the efficiency of stock markets and the overhead costs of the banking system relative to banking assets capture the efficiency of the banking sector. (3) Aggregate quality: This measure is the first principal component of the size and efficiency of the financial sector. For each measure, we form two portfolios based on its median value and report the test results in Panel C of Table 8. The financial market’s size, efficiency, and aggregate quality matter for the adjustment speeds. The estimated effect of aggregate quality exceeds the effects of the financial sector’s structure (bank versus market) and the legal system.

¨. O ¨ ztekin, M.J. Flannery / Journal of Financial Economics 103 (2012) 88–112 O

104

Table 9 Institutional determinants of the adjustment speeds. Aggregate indices are formed by calculating the principal components of the related individual indices. Countries are allocated into portfolios according to the sample median of the aggregate institutional indices. Pairwise comparisons of the adjustment speeds of the two portfolios are then conducted using the SEPARATE and POOLED methods. For both methods, the following model is estimated: LEVij,t ¼ ðlj bj ÞXij,t1 þ ð1lj ÞLEVij,t1 þ lj Fij þ dij,t :

ð3Þ

The SEPARATE methodology estimates Eq. (3) separately for each country, averages the estimated l values for countries that are in the same portfolio, and tests whether the adjustment speeds differ between the groups using t-tests. The POOLED methodology estimates Eq. (3) across all firms in countries sharing the same institutional feature imposing common slopes (b) within each portfolio. A single adjustment speed is estimated for each portfolio, and tests are conducted for the significance of the differences between the two portfolios. n, nn, and nnn indicate significant difference between groups at the 10%, 5%, and 1% significance level, respectively. The definitions and the sources of the variables are provided in Appendix A. Institutional feature

Panel A: Adjustment costs Ease of access (1) All markets (2) Equity markets (3) Debt markets Asymmetric information (4) All markets (5) Equity markets (6) Debt markets Financial constraints (7) Financial Constraints Aggregate adjustment costs (8) All markets (9) Equity markets (10) Debt markets Panel B: Adjustment benefits (1) Ex ante distress costs (2) Ex post distress costs (3) Tax rate (4) Deviation penalties (5) Aggregate adjustment benefits

Group

Number of countries

SEPARATE tests

POOLED tests

Mean

Difference

Significance

Mean

Difference

Significance

7.86

nnn

11.75

nnn

6.46

nnn

12.22

nnn

9.67

nnn

5.62 17.37 5.42 17.64 13.24 15.67

2.43

nnn

9.49 16.76 12.61 16.43 8.78 16.89

7.28

nnn

3.82

nnn

8.11

nnn

Low High Low High Low High

19 18 19 18 19 18

17.29 25.15 17.97 24.43 16.41 26.07

High Low High Low High Low

17 17 17 17 17 17

16.07 26.78 16.71 26.14 17.85 25.95

10.71

nnn

9.43

nnn

8.10

nnn

High Low

21 16

17.54 25.80

8.26

nnn

4.22 17.95

13.74

nnn

High Low High Low High Low

17 17 17 17 17 17

15.65 27.20 16.19 26.65 15.64 27.20

11.56

nnn

10.56

nnn

10.46

nnn

11.52

nnn

11.56

nnn

6.60 17.16 5.78 17.29 3.65 18.48

14.83

nnn

Low High Low High Low High Low High Low High

19 18 17 18 19 18 17 17 16 16

16.41 26.07 23.82 18.65 20.95 21.28 15.96 23.56 16.14 23.64

9.67

nnn

2.43

nnn

5.18

nnn

11.35

nnn

0.33

nnn

7.60

nnn

7.50

nnn

5.3. Specific institutional features Having concluded that adjustment speeds differ across relatively broad institutional systems, we now investigate more narrowly defined features of each country’s legal and institutional framework. We focus on specific institutional factors that might affect the costs and benefits of adjusting to target leverage. We attempt to explain why legal and financial traditions matter and to determine which factors are most responsible for cross-country differences in adjustment behavior. Table 9 reports the results. Panel A reports the tests for the effect of

13.24 15.67 17.60 6.24 16.25 10.03 7.05 16.10 7.16 16.35

6.22 9.05

nnn

9.19

nnn

institutional features on adjustment costs, and Panel B provides adjustment benefits. 5.3.1. Adjustment cost effects As Appendix A reports, the Ease of Access variable reflects both stakeholder rights and the quality of enforcement of those rights. The first row in Panel A of Table 9 indicates that Ease of Access to capital markets relates positively to estimated adjustment speeds. Firms in countries with above-median values for this index adjust 7.9%–11.8% faster. The next two rows indicate that both equity and debt Access Costs affect the adjustment speed,

¨. O ¨ ztekin, M.J. Flannery / Journal of Financial Economics 103 (2012) 88–112 O

105

We undertake sensitivity analyses of the basic model. First, we experiment with an alternative method for constructing target leverage proxies. Strebulaev (2007) and Hovakimian and Li (2011) conclude that using ex post information to estimate leverage targets causes substantial bias to the adjustment speed. Following Hovakimian and Li (2011), we repeat our tests (unreported but available on request) using only the current and historical information in the target estimations. The results are similar. Furthermore, we address the concern that the target estimation could be more biased for some countries or during specific periods by including country and year fixed effects in our model. In addition, we rank countries according to each institutional index, sum their scores within each related category, and repeat our tests according to these new measures. Therefore, if there is an error component in the estimated targets and, consequently, estimated adjustment speeds, this procedure

prevents sorting the countries according to the error component. (Similar results we obtain are available on request.) Second, we omit firms with relatively few observations from our sample. Estimating a dynamic model with short panels presents challenging econometric problems. The average firm in our sample is observed for seven years. For some countries, the minimum number of years for a particular firm can be as low as four. This discrepancy makes it difficult to estimate an unbiased adjustment speed. Although the Blundell and Bond (1998) and Bruno and Giovanni (2005) estimation methods can correct short panel biases, we repeat our analysis using only firms with at least ten years of data and obtain similar conclusions. Third, we exclude firm-years with extreme leverage values. Shyam-Sunder and Myers (1999), Chen and Zhao (2007), Chang and Dasgupta (2009), and Hovakimian and Li (2011) argue that mechanical mean reversion can lead to an upward bias in adjustment speeds, which can prevent the model from rejecting the null hypothesis that the adjustment speed is zero. Strebulaev and Yang (2007) find that zero leverage is a persistent behavior, and Hovakimian and Li (2011) suggest that dropping extreme leverage observations greater than 90% and less than 10% avoids spurious results. We find even stronger results when we drop these extreme leverage observations from our sample. In addition, we get more support for our hypotheses than the reported results when the zero debt firm-years alone are dropped.9 We also test whether our estimated targets are meaningfully and significantly different across weak and strong institutional settings similar to our adjustment speed estimates. We find that they are. These results are consistent with the hypothesis that firms do not simply mean revert. Instead, firms have target capital structures to which they converge over time, at a rate that reflects the magnitude of the adjustment costs and benefits imposed by their institutional environment. Finally, we investigate the effect of permitting over- and underleveraged firms to adjust at different speeds. Not all our identified institutional features affect all firms to the same extent. We assess the importance of such asymmetries by splitting each of our high and low portfolios into further subsamples according to firm characteristics. For example, high distress costs might influence adjustment only for overleveraged firms. We define a firm as overleveraged (underleveraged) if its actual leverage is 10% above (below) the calculated target leverage. We also test for asymmetries with respect to additional firm characteristics, such as firm size and proportion of fixed assets to total assets, using the median firm in each institutional setting. The unreported test results indicate adjustment speed differentials qualitatively similar to those appearing in Table 9, regardless of which subset a firm falls into with respect to its relative leverage, size, and proportion of fixed assets to total assets. Therefore, allowing for asymmetric response to adjustment costs and benefits does not qualitatively change our

8 We replicate the tests in Table 9 for each of the individual institutional subindices appearing in Appendix A. Each individual costbenefit proxy significantly affects adjustment speed in the hypothesized direction.

9 In our sample, of 15,177 firms, 1,520 have zero leverage, amounting to 9,263 firm-year observations of 105,568 total firm-year observations, or approximately 9% of the sample.

but equity costs have a greater impact on adjustment speed (i.e., 6%–12% versus 2%–10%). Row 4 indicates that greater Asymmetric Information reduces the adjustment speed to optimal capital structure by a magnitude of 7.3%–10.7%. The separate effects for equity and debt markets (in Rows 5 and 6) indicate that, on average, debt market information asymmetries are slightly more important. Row 7 reveals that Financial Constraints affect adjustment speeds about as much as the Asymmetric Information and Ease of Access proxies do (8.3%–13.7%). Given the significance of the three adjustment cost components, it is not surprising that Aggregate Adjustment Costs also exhibit significant effects on adjustment speeds. Row 8 reveals that the adjustment speed is 10.6% to 11.6% higher in the presence of lower adjustment costs. Separately, higher equity (10%–12%) and debt (12%–15%) adjustment costs significantly affect adjustment speed, with a slightly stronger impact associated with the debt market costs. 5.3.2. Adjustment benefit effects Panel B of Table 9 reports the effects of adjustment benefit proxies on estimated adjustment speeds. Row 1 indicates that the adjustment speed is faster in countries with more binding ex ante distress costs. Row 2 of Panel B indicates that higher ability to prevent ex post distress costs leads to faster adjustment ranging from 5% to 11% on average. Row 3 indicates a weaker effect of tax rate, which increases adjustment speeds significantly only in the SEPARATE test. In Row 4, more binding deviation penalties lead to faster adjustment of 8% to 9% on average. Finally, Row 5 indicates significantly different adjustment speeds for the high versus low values of aggregate adjustment benefits: Larger benefits lead to an 8%–9% average increase in adjustment speed. The adjustment benefit proxies we chose yield results consistent with our hypotheses.8 5.4. Robustness

¨. O ¨ ztekin, M.J. Flannery / Journal of Financial Economics 103 (2012) 88–112 O

106

Table 10 Firm and industry-specific characteristics and institutional environment. The table presents information on firm and industry-specific features in countries with high and low Aggregate Adjustment Costs. The table shows the median values of each variable in countries with high and low Aggregate Adjustment Costs. n, nn, and nnn indicate significant difference between the groups at the 10%, 5%, and 1% significance level, respectively. The definitions and the sources of the variables are provided in Table 1. High versus low costs

EBIT

MB

DEP

Ln

FA

R&D

TAXES

IND MED

LIQUID

High costs Low costs Difference Significance

0.065 0.073 0.008

0.297 0.473 0.176

0.047 0.038 0.009

7.421 6.573 0.848

0.334 0.296 0.038

0.000 0.000 0.000

0.153 0.231 0.078

0.218 0.210 0.008

1.481 1.512 0.031

nnn

nnn

nnn

nnn

nnn

nnn

nnn

nnn

nnn

conclusion that adjustment costs and benefits significantly affect leverage adjustments speeds. 6. A more sophisticated leverage specification Thus far, we have shown that country-specific institutional characteristics affect the adjustment speed, and we have emphasized how institutions influence a firm’s costs and benefits of adjusting its leverage. In addition, the institutional environment could influence (endogenous) firm characteristics, which in turn affect adjustment speed. In other words, the institutional environment could have both direct and indirect effects on a country’s adjustment speed. For example, high information asymmetry could reduce optimal leverage directly, and it could discourage the formation of larger firms that would tend to adjust their leverage more quickly. So far, we have controlled for firm characteristics without explicitly providing for any indirect effects of the institutional environment on adjustment speeds. Table 10 indicates that some relevant firm characteristics [including those used to compute target leverage in Eq. (3)] vary between countries with high versus low Aggregate Adjustment Costs. In other words, it is plausible that firms’ characteristics partially reflect their institutional settings. Specifically, firms in countries with high Aggregate Adjustment Costs have lower profitability and liquidity but are larger and have more tangible assets. Do institutional variables affect adjustment speeds only through their effect on transaction costs in a country? Or do institutions also affect adjustment speeds indirectly by affecting firm size, volatility, and so on, which could affect an individual firm’s transaction costs? We investigate these questions using a second model specification, in which each firm’s adjustment speed directly reflects both its own characteristics and the institutional features under which it operates. This model provides information on the specific channels through which institutional arrangements affect adjustment speeds. Although a firm’s characteristics and environment could affect its target adjustment, the literature provides scarce evidence. Drobetz and Wanzenried (2006) show faster adjustment for Swiss firms that grow more rapidly or are further away from their optimal leverage. They also find that the adjustment speed varies with business cycles, as do Korajczyk and Levy (2003). Drobetz, Pensa, and Wanzenried (2007) show that financial constraints matter for the adjustment behavior of European firms. Dudley (2008) reports that faster-growing US firms that rely on external capital adjust faster because fixed costs of adjusting are no longer marginal costs if a firm raises

capital for other reasons, such as to finance growth. Hovakimian, Opler, and Titman (2001) show that overleveraged US firms make faster adjustments with debt reductions. Finally, Bhamra, Kuehn, and Strebulaev (2010) examine the effect of macroeconomic variables on optimal dynamic capital structure and the aggregate dynamics of firms in a cross section but do not include time-invariant country characteristics. We modify Eq. (2) to specify the optimal leverage ratio as n LEVij,t ¼ bF Xij,t1 þFij þ bC Qj ,

ð6Þ

where Qj denotes institutional features related to the costs and benefits of operating with various leverage ratios. Substituting Eq. (6) into the partial adjustment specification Eq. (1) and rearranging yields the following estimable specification: LEVij,t ¼ ðlbF ÞXij,t1 þ lFij þ ðlbC ÞQj þð1lj ÞLEVij,t1 þ dij,t : ð7Þ The estimated coefficients from Eq. (7) imply each firm’s deviation from its target debt ratio: d ij,t ¼ LEV dn DEV ij,t1 LEV ij,t1 :

ð8Þ

Substituting Eq. (8) into Eq. (7) gives a regression that can be estimated with ordinary least squares (OLS): d ij,t Þ þ dij,t : ðLEVij,t LEVij,t1 Þ ¼ lij,t ðDEV

ð9Þ

The simplification of Eq. (9) permits us to relax the assumption that all firms adjust at a constant rate in the country or institutional context. Given an estimated target, we can allow the adjustment speed to depend on the firm’s specific conditions:

lij,t ¼ Lij,t Zij,t1 ¼ LXij Xij,t1 þ LQj Qjt , X

ð10Þ

Q

where L and L are vector of coefficients. Substituting Eq. (10) into our partial adjustment model Eq. (9) and rearranging yields d ij,t Þ þ dij,t : LEVij,t LEVij,t1 ¼ Lij,t Zij,t1 ðDEV

ð11Þ

b from which we can calculate Estimating Eq. (11) yields L a unique, time-varying adjustment speed for each firm i in country j. The specification Eq. (11) enables us to distinguish between the direct and indirect effects of a firm’s institutional environment. First, the direct effects of institutional features on target leverage are given by the bC coefficients in Eq. (7). Second, the indirect effects on target leverage

¨. O ¨ ztekin, M.J. Flannery / Journal of Financial Economics 103 (2012) 88–112 O

are given by the effect of institutions on firm characteristics: bF ð@Xij,t =@Qjt Þ. Third, the direct effect of institutional features on adjustment speed are given by LQ in Eq. (10). Fourth, the indirect effect on adjustment speed is given by LX ð@Xij,t =@Qjt Þ. Although we could incorporate any set of countryspecific characteristics in Eq. (10), in the interest of space, we report results using only the Aggregate Adjustment Costs proxy. We solve our model sequentially. In the first step, we estimate our partial adjustment model Eq. (7) for all firms under the assumption that the adjustment speed is constant (Blundell and Bond, 1998). This provides an initial set of estimated bs and l, which we use to calculate an initial dn estimated target leverage ratio ðLEV Þ and the deviation ij,t1

d ij,t Þ for each firm-year from the target leverage ratio ðDEV through Eqs. (6) and (8). We then estimate Eq. (11) using OLS. We can compute the impact of Aggregate Adjustment Costs on target leverage from the left-hand side of Table 11. Compared with the sample mean (median) leverage of 21.1% (21.3%), the adjustment cost effect is small. A one standard deviation increase in AAC directly raises target leverage by 0.29% (p40.10). (AAC is the first principal component of several subindices, and we standardize its standard deviation to unity.) Computing the indirect effect of AAC on target leverage requires estimating the impact of adjustment costs on each firm characteristic, that is, ð@Xij,t =@Qjt Þ. We estimate these effects by regressing each firm characteristic in Xij,t on AAC and noting its slope coefficient.10 Combining these point estimates with the estimated bF coefficients from Eq. (7) indicates an indirect effect of –0.74% (po0.01). Thus, the direct and indirect dn effects of higher AAC on LEV ij,t1 sum to  0.45% (p¼ 0.069), which is statistically and economically small. The right-hand side of Table 11 indicates that the effect of AAC on adjustment speed is more substantial. A one standard deviation increase in AAC decreases the typical firm’s adjustment speed by 3.09% (p o0.01), compared with an average adjustment speed in Table 2 of 21.1%. The indirect effect of AAC on adjustment speeds, P ð LX ð@Xij,t =@Qjt Þ ¼ 0:96%, p o0.001), offsets 30% of the direct effect. The net impact of AAC on adjustment speed is 2.13% (p ¼0.022), or 24% of the cross-sectional standard deviation of adjustment speed estimates in Table 2. We also experiment with several different versions of our extended empirical specification to further evaluate alternative economic explanations underlying our results and conduct several robustness tests to eliminate any potential bias in estimating leverage targets and adjustment speeds. For example, we study the determinants of adjustment speeds using a model that simultaneously controls for various institutional features (individual indices or subindices of our aggregate indices); firm-specific, industry-

10 The estimated indirect effects are: profits ( 1.75%), market-tobook (  20.06%), depreciation ( 0.34%), size (  6.51%), tangibility (  1.16%), research and development (R&D) dummy (  4.62%), R&D expense (15.31%), industry median ( 16.29%), tax (  3.93%), liquidity (8.88%), regulated industry (0.92%), inflation (6.91%), and GDP growth (24.80%).

107

Table 11 A more sophisticated leverage specification. We estimate Eq. (7) to obtain coefficient estimates with which to dn generate leverage targets ðLEV Þ: ij,t1

LEVij,t ¼ ðlbF ÞXij,t1 þ lFij þ ðlbC ÞQj þ ð1lj ÞLEVij,t1 þ dij,t :

ð7Þ

We then substitute those estimated leverage targets into Eq. (11) to produce estimates of the determinants of a firm’s adjustment speed. d ij,t Þ þ dij,t , LEVij,t LEVij,t1 ¼ Lij,t Zij,t1 ðDEV

ð11Þ

n

X Q d d ij,t ¼ LEV where DEV ij,t1 LEVij,t1 ; lij,t ¼ Lij,t Zij,t1 ¼ Lij Xij,t1 þ Lj Qjt ; Xij,t1

are firm, industry, and macroeconomic characteristics; Qj,t is an index of national institutional costs or benefits; and LX and LQ are vector of coefficients. Reported results include only one index (Qj,t ), the Aggregate Adjustment Costs (AAC) proxy. In step 1, we estimate our partial adjustment model Eq. (7) for all firms (using Blundell and Bond, 1998) under the assumption that the adjustment speed is constant. This provides an initial set of estimated bs and l, which we use to calculate n d an initial estimated target leverage ratio (LEV ) and deviation from ij,t1

d ij,t Þ for each firm-year. Then, in step 2, we the target leverage ratio (DEV estimate Eq. (11) using ordinary least squares. Variable definitions are provided in the Table 1. n, nn, and nnn indicate significant difference between groups at the 10%, 5%, and 1% significance level, respectively. Variable

Step 1: Leverage targets, Eq. (7)

Step 2: Adjustment speeds, Eq. (11)

Leverage

0.7910nnn (0.0107)  0.0053nnn (0.0016) 0.0004 (0.0012)  0.0081nnn (0.0015) 0.0102nnn (0.0033) 0.0164nnn (0.0024) 0.0046nnn (0.0014) 0.0010 (0.0022) 0.0106nnn (0.0016)  0.0001 (0.0004)  0.0017 (0.0011)  0.0005

 0.0299nnn (  0.0854) 0.0210nnn (0.0379) 0.0172nnn (0.0408)  0.0386nnn (  0.0681)  0.0373nnn ( 0.0670)  0.0067nn ( 0.0109)  0.0207nnn (  0.0518)  0.0075nnn (  0.0134)  0.0197nnn ( 0.0302)  0.0417nnn ( 0.0740) 0.0025

(0.0023)  0.0029nnn (0.0009)  0.0023nnn (0.0005) 0.0029 (0.0026) 0.0498nnn (0.0026) 103,411

(0.0034)  0.0442nnn (  0.0642) 0.0117nnn (0.0201)  0.0309nnn (  0.0975) 0.4545nnn (0.7552) 103,411

Profit Market-to-book Depreciation Ln(Size) Tangibility R&D dummy R&D expense Industry median Taxes Liquidity Regulated industry Inflation GDP growth AAC Constant Number of observations

specific, and macroeconomic characteristics; and both. Consistent with our principal component analysis, we find that even when we jointly enter several individual country indices in the adjustment speed estimation, all but three individual indices (shareholder right enforcement, corporate transparency, and equity liability standards) have statistically and economically significant impacts on adjustment

108

¨. O ¨ ztekin, M.J. Flannery / Journal of Financial Economics 103 (2012) 88–112 O

speeds. The individual country-level adjustment cost and benefit indices continue to be important determinants of adjustment speeds beyond the effect of the firm-level, industry-level, and macroeconomic costs and benefits.

7. Conclusion An increasing amount of research estimates partial adjustment models for leverage, based on the hypothesis that costly transactions make the speed of adjustment to target leverage ratios endogenous. Yet some studies challenge the power of these tests. Although we find no direct evidence on the power of a partial adjustment specification, our international results are consistent with the model’s underlying logic. We find that international differences in firms’ adjustment behavior reflect the costs and benefits of transacting in local markets. A firm’s capital structure reflects not only its own characteristics but also the environment and traditions in which it operates. We begin by estimating a standard partial adjustment model of leverage for the firms in 37 countries during the 1991–2006 period. When we constrain all firms in the same country to have the same adjustment speed, those estimated speeds all lie in the zero–one interval, consistent with a dynamic trade-off model of leverage. The mean adjustment speed is approximately 21% per year, half-life of three and two years for book and market leverage, respectively, but the estimated adjustment speeds vary from 4% (in Columbia) to 41% (in New Zealand) per year. In terms of the adjustment’s half-life, the mean speed implies three years, and the range varies between one and a half and 17 years. However, the constraint that firms in all countries have the same adjustment speed is strongly rejected. By definition, variation in leverage adjustment speeds must reflect something about the costs and benefits of moving toward target leverage. We conjecture that the effectiveness of a country’s legal, financial, and political institutions is systematically related to cross-country differences in the adjustment speeds. We show a logical

relation between measured transaction costs and institutional features. Given the close association between transaction costs and institutional variables, we empirically test and quantify the impact of various institutional characteristics on the speed of adjustment to optimal capital structure. Our institutional proxies for countrylevel transaction costs are reliably associated with the country’s estimated adjustment speed. Firms from different countries differ in both the costs and the benefits of attaining target leverage. We form indices of adjustment costs and benefits from a variety of institutional subindices available in the literature. For example, firms confronting above-median Aggregate Adjustment Costs adjust approximately 12% per year slower than firms in the below-median group. Similarly, firms enjoying abovemedian benefits from adjusting do so approximately 8% per year faster. These differences are both economically and statistically meaningful. Our findings from a standard partial adjustment model suggest that a country’s legal and financial institutions significantly affect both the costs and the benefits of moving toward target leverage. A more general model of the partial adjustment process in Section 6 indicates that capital market transaction costs have a weaker effect on leverage targets. However, the institutional environment substantially affects adjustment speeds. On net, higher Aggregate Adjustment Costs reduce estimated adjustment speed by roughly 12% of the average country’s adjustment speed, even after we account for adaptations to firm characteristics that tend to raise adjustment speeds. Evidence that adjustment speeds vary plausibly with international differences in important financial system features provides support for the applicability of a partial adjustment model of leverage adjustment to private firms.

Appendix A See Table A1.

Table A1 Variables describing national institutions. Variable

Description and source

Legal and financial traditions Legal traditions Categorical variables (for common law, civil law, and the English, French, German, and Scandinavian traditions) equal to unity if the firm operates under the named legal origin, and to zero otherwise. Source: La Porta, Lopez de Silanes, Shleifer, and Vishny (1998) Financial emphasis Dummy variables equal unity if the financial system structure is bank-based or market-based and zero otherwise. Source: Levine (2002) Size Financial system’s size. Measure of the size of stock markets and intermediaries is captured by the logarithm of the market capitalization ratio [the value of domestic equities listed on domestic exchanges divided by gross domestic product (GDP)] times the private credit ratio (the value of financial intermediary credits to the private sector as a share of GDP). The market capitalization ratio captures the size of the domestic stock market and the private credit ratio captures the size of intermediaries. Source: Levine (2002) Efficiency Financial system’s efficiency. Measured by the logarithm of the total value-traded ratio divided by overhead costs. The total value-traded ratio (the value of domestic equities traded on domestic exchanges divided by GDP) captures the efficiency of stock markets and the overhead costs (overhead costs of the banking system relative to banking assets) capture the efficiency of the banking sector. Source: Levine (2002) Aggregate quality Financial system’s aggregate quality. Principal component of size and efficiency. Source: Levine (2002)

¨. O ¨ ztekin, M.J. Flannery / Journal of Financial Economics 103 (2012) 88–112 O

109

Table A1 (continued ) Variable Adjustment costs Ease of access Shareholder rights

Shareholder right enforcement Creditor rights

Creditor right enforcement

Description and source

This index of shareholder rights is formed by adding one when the country allows shareholders to mail their proxy vote; shareholders are not required to deposit their shares before the general shareholders meeting; cumulative voting or proportional representation of minorities on the board of directors is allowed; an oppressed minorities mechanism is in place; the minimum percentage of share capital that entitles a shareholder to call for an extraordinary shareholders meeting is less than or equal to 10%; or shareholders have preemptive rights that can be waived only at a shareholders meeting. The range for the index is from zero to five. Source: La Porta, Lopez de Silanes, Shleifer, and Vishny (1998) Quality of shareholder right enforcement. Average of ex ante and ex post private control of self-dealing. Source: Djankov, La Porta, Lopez de Silanes, and Shleifer (2008) A score of one is assigned when each of the following rights of secured lenders is defined in laws and regulations: First, there are restrictions, such as creditor consent or minimum dividends, for a debtor to file for reorganization. Second, secured creditors are able to seize their collateral after the reorganization petition is approved (i.e., there is no automatic stay or asset freeze). Third, secured creditors are paid first from the proceeds of liquidating a bankrupt firm, as opposed to other creditors such as government or workers. Finally, if management does not retain administration of its property pending the resolution of the reorganization. The index ranges from zero (weak creditor rights) to four (strong creditor rights). Source: La Porta, Lopez de Silanes, Shleifer, and Vishny (1998) Quality of creditor rights enforcement. The index measures substantive and procedural statutory intervention in judicial cases. The index ranges from zero (strong enforcement) to seven (weak enforcement). Source: Djankov, La Porta, Lopez de Silanes, and Shleifer (2003)

Aggregate ease of access: first principle component variables (factor loadings in parentheses) Equity markets Principal component of shareholder rights (0.89) and shareholder right enforcement indices (0.89) Debt markets Principal component of the creditor rights (0.74) and creditor right enforcement indices (  0.74) All markets Principal component of shareholder rights (0.74), creditor rights (0.42), shareholder right enforcement (0.93), and creditor right enforcement ( 0.81) Asymmetric information Corporate transparency

Equity disclosure

Equity liability

Equity public enforcement Equity insider trading Debt information sharing

Corporate transparency as indicated by the quality of accounting standards. 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). Source: La Porta, Lopez de Silanes, Shleifer, and Vishny (1998) Disclosure requirements in equity markets. The index of disclosure equals the arithmetic mean of prospectus, compensation, shareholders, inside ownership, contracts irregular, and transactions. Source: La Porta, Lopez de Silanes, and Shleifer (2006) Liability standards in equity markets. The index of liability standards equals the arithmetic mean of liability standard for the issuer and its directors, liability standard for distributors, and liability standard for accountants. Source: La Porta, Lopez de Silanes, and Shleifer (2006) Public enforcement in equity markets. The index of public enforcement equals the arithmetic mean of supervisor characteristics index, rule-making power index, investigative powers index, orders index, and criminal index. Source: La Porta, Lopez de Silanes, and Shleifer (2006) Insider trading in equity markets. Prevalence of insider trading (1¼pervasive; 7¼ extremely rare). Source: Porter and Schwab (1999) and La Porta, Lopez de Silanes, and Shleifer (2006) Information sharing in debt markets as indicated by the presence of public credit registries. The variable equals one if a public credit registry operates in the country and zero otherwise. A public registry is defined as a database owned by public authorities that collects information on the standing of borrowers in the financial system and makes it available to financial institutions. Source: Djankov, McLiesh, and Shleifer (2007)

Aggregate asymmetric information: first principle component variables (factor loadings in parentheses) Equity markets Principal component of corporate transparency (0.80), equity disclosure (0.87), equity liability (0.80), equity public enforcement (0.75), and equity insider trading indices (0.62) Debt markets Principal component of corporate transparency (0.70) and debt information sharing indices (0.52) All markets Principal component of corporate transparency (0.76), equity disclosure (0.85), equity liability (0.86), equity public enforcement (0.64), equity insider trading (0.65), and debt information sharing (0.21) Financial constraints Dividends

Reserve requirements

The percentage of net income that the company law or commercial code requires firms to distribute as dividends to ordinary stockholders. It takes a value of zero for countries without such restriction. Source: La Porta, Lopez de Silanes, Shleifer, and Vishny (1998) 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. Source: La Porta, Lopez de Silanes, Shleifer, and Vishny (1998)

Aggregate financial constraints: first principle component variables (factor loadings in parentheses) Financial constraints Principal component of dividends (  0.77) and reserve requirements ( 0.77) Aggregate adjustment costs: first principle component variables (factor loadings in parentheses) Equity markets Principal component of shareholder rights (0.85) and shareholder right enforcement indices (0.77), corporate transparency (0.81), equity disclosure (0.87), equity liability (0.79), equity public enforcement (0.69), equity insider trading indices (0.60), and dividends (  0.32)

110

¨. O ¨ ztekin, M.J. Flannery / Journal of Financial Economics 103 (2012) 88–112 O

Table A1 (continued ) Variable

Description and source

Debt markets

Principal component of creditor rights (0.31), creditor right enforcement (  0.82), corporate transparency (0.88), debt information sharing (0.10), and reserve requirements (  0.78) Principal component of shareholder rights (0.82), shareholder right enforcement (0.81), creditor rights (0.09), creditor right enforcement (  0.73), corporate transparency (0.82), equity disclosure (0.83), equity liability (0.74), equity public enforcement (0.71), equity insider trading (0.60), debt information sharing (0.11), dividends (  0.30), and reserve requirements ( 0.76)

All markets

Adjustment benefits Ex post distress costs Time to repay Bankruptcy costs Bankruptcy efficiency

Time to resolve the insolvency process. Source: Djankov, Hart, McLiesh, and Shleifer (2008) The costs to complete the insolvency proceeding, expressed as a percentage of the bankruptcy estate at the time of entry to the bankruptcy. Source: Djankov, Hart, McLiesh, and Shleifer (2008) The present value of the terminal value of the firm after bankruptcy costs. Source: Djankov, Hart, McLiesh, and Shleifer (2008)

Aggregate distress costs: first principle component variables (factor loadings in parentheses) Ex ante distress costs Principal component of the creditor rights (0.74) and creditor right indices (  0.74) Ex post distress costs Principal component of time to repay (  0.85), bankruptcy costs ( 0.83), and bankruptcy efficiency (0.91). Tax shields Tax rate Deviation penalties Executive quality

Contract enforcement

Law and order

Corruption

Expropriation

Repudiation

First-year effective tax rate (percent). The tax rate obtained by dividing the total corporate tax TaxpayerCo pays by its pretax earnings. Source: Djankov, Hart, McLiesh, and Shleifer (2008) Index of constraints on the executive power based on the number of effective veto points in a country. Veto points include an effective legislature (represents two veto points in the case of bicameral systems), an independent judiciary, and a strong federal system. Average of the years 1945 through 1998. Source: Djankov, La Porta, Lopez de Silanes, and Shleifer (2002) The relative degree to which contractual agreements are honored and complications presented by language and mentality differences. Scale from zero to ten, with higher scores indicating higher enforceability. Source: Djankov, La Porta, Lopez de Silanes, and Shleifer (2003) Integrity of legal system in 2000. This component is based on the Political Risk Component 1 (Law and Order) from the PRS Group’s International Country Risk Guide (various issues). Rankings are modified to a ten-point scale. Source: Djankov, La Porta, Lopez de Silanes, and Shleifer (2003) Scale from zero to ten. Low ratings indicate ‘‘bribes connected with import and export licenses, exchange controls, tax assessment, policy protection, or loans.’’ Average of the months of April and October in the monthly index between 1982 and 1995. Source: La Porta, Lopez de Silanes, Shleifer, and Vishny (1998) Integrated Corporate Relations (ICR) assessment of the risk of ‘‘outright confiscation’’ or ‘‘forced nationalization.’’ Average of the months of April and October of the monthly index between 1982 and 1995. Scale from zero to ten, with lower scores for higher risks. Source: La Porta, Lopez de Silanes, Shleifer, and Vishny (1998) ICR assessment of the ‘‘risk of a modification in a contract taking the form of a repudiation, postponement, or scaling down’’ due to ‘‘budget cutbacks, indigenization pressure, a change in government, or a change in government economic and social priorities.’’ Average of the months of April and October of the monthly index between 1982 and 1995. Scale from zero to ten, with lower scores for higher risks. Source: La Porta, Lopez de Silanes, Shleifer, and Vishny (1998)

Aggregate government quality: first principle component variables (factor loadings in parentheses) Government quality Principal component of corruption (0.92), expropriation (0.95), and repudiation (0.95) Aggregate deviation penalties: first principle component variables (factor loadings in parentheses) Deviation penalties Principal component of executive quality (0.78), contract enforcement (0.92), law and order (0.84), corruption (0.89), expropriation (0.95), and repudiation (0.90) Aggregate adjustment benefits: first principle component variables (factor loadings in parentheses) Aggregate adjustment Principal component of subindices forming ex ante and ex post distress costs, tax shields, and deviation penalties: creditor benefits rights (0.13), creditor right enforcement indices (  0.44), time to repay (  0.69), bankruptcy costs ( 0.77), bankruptcy efficiency (0.83), tax rate (0.24), executive quality (0.81), contract enforcement (0.87), law and order (0.80), and the subindices of the government quality: corruption (0.88), expropriation (0.91), and repudiation (0.90)

References Allen, F., Gale, D., 1999. Diversity of opinion and financing of new technologies. Journal of Financial Intermediation 8, 1–22. Amihud, Y., Mendelson, H., 1986. Asset pricing and bid-ask spread. Journal of Financial Economics 17, 223–249. Antoniou, A., Guney, Y., Paudyal, K., 2008. The determinants of capital structure: capital market–oriented versus bank-oriented institutions. Journal of Financial and Quantitative Analysis 43, 59–92. Baltagi, B.H., 2001. Econometric Analysis of Panel Data. John Wiley & Sons, West Sussex, England.

Bancel, F., Mittoo, U.R., 2004. Cross-country determinants of capital structure choice: a survey of European firms. Financial Management 33, 103–132. Bencivenga, V.R., Smith, B.D., 1991. Financial intermediation and endogenous growth. Review of Economics Studies 58, 195–209. Berk, J.B., Green, R.C., Naik, V., 1999. Optimal investment, growth options, and security returns. Journal of Finance 54, 1553–1607. Berkowitz, D., Pistor, K., Richard, J.-F., 2003. Economic development, legality, and the transplant effect. European Economic Review 47, 165–195. Bhamra, H.S., Kuehn, L.A., Strebulaev, I.A., 2010. The aggregate dynamics of capital structure and macroeconomic risk. Review of Financial Studies 23, 4187–4241.

¨. O ¨ ztekin, M.J. Flannery / Journal of Financial Economics 103 (2012) 88–112 O

Bhattacharya, U., Daouk, H., 2002. The world price of insider trading. Journal of Finance 57, 75–108. Blundell, R., Bond, S., 1998. Initial conditions and moment restrictions in dynamic panel data models. Journal of Econometrics 87, 115–143. Boot, A.W.A., Thakor, A.V., 1997. Financial system architecture. Review of Financial Studies 10, 693–733. ¨ --Kunt, A., Maksimovic, V., 2001. Capital Booth, L., Aivazian, V., Demirguc structure in developing countries. Journal of Finance 56, 87–130. Brennan, M.J., Schwartz, E.S., 1984. Optimal financial policy and firm valuation. Journal of Finance 39, 593–607. Bruno, S., Giovanni, F., 2005. Approximating the bias of the LSDV estimator for dynamic unbalanced panel data model. Economic Letters 87, 361–366. Chang, X., Dasgupta, S., 2009. Target behavior and financing: how conclusive is the evidence? Journal of Finance 64, 1767–1796. Chen, L., Zhao, X., 2007. Firm Financing Decisions. Unpublished Working Paper. Michigan State University, East Lansing, MI. Claessens, S., Djankov, S., Lang, L., 2000. The separation of ownership and control in East Asian corporations. Journal of Financial Economics 58, 81–112. Daouk, H., Lee, C.M.C., Ng, D., 2006. Capital market governance: how do security laws affect market performance? Journal of Corporate Finance 12, 560–593. DeAngelo, H., DeAngelo, L., Stulz, R., 2010. Seasoned equity offerings, market timing, and the corporate lifecycle. Journal of Financial Economics 95, 275–295. DeAngelo, H., Masulis, R.W., 1980. Optimal capital structure under corporate and personal taxation. Journal of Financial Economics 8, 3–29. ¨ --Kunt, A., Maksimovic, V., 1999. Institutions, financial markets, Demirguc and firm debt maturity. Journal of Financial Economics 54, 295–336. Diamond, D., 1984. Financial intermediation and delegated monitoring. Review of Economic Studies 51, 393–414. Djankov, S., Hart, O., McLiesh, C., Shleifer, A., 2008. Debt enforcement around the world. Journal of Political Economy 116, 1105–1149. Djankov, S., La Porta, R., Lopez de Silanes, F., Shleifer, A., 2002. The regulation of entry. Quarterly Journal of Economics 1, 1–37. Djankov, S., La Porta, R., Lopez de Silanes, F., Shleifer, A., 2003. Courts. Quarterly Journal of Economics 118, 453–517. Djankov, S., La Porta, R., Lopez de Silanes, F., Shleifer, A., 2008. The law and economics of self-dealing. Journal of Financial Economics 88, 430–465. Djankov, S., McLiesh, C., Shleifer, A., 2007. Private credit in 129 countries. Journal of Financial Economics 84, 299–329. Drobetz, W., Pensa, P., Wanzenried, G., 2007. Firm Characteristics, Economic Conditions and Capital Structure Adjustments? Unpublished Working Paper. University of Hamburg, Hamburg, Germany. Drobetz, W., Wanzenried, G., 2006. What determines the speed of adjustment to the target capital structure? Applied Financial Economics 16, 941–958. Dudley, E., 2008. Capital Structure and Large Investment Projects. Unpublished Working Paper. University of Florida, Gainesville, FL. Fama, F.E., French, K.R., 1997. Industry costs of equity. Journal of Financial Economics 43, 153–193. Fischer, E.O., Heinkel, R., Zechner, J., 1989. Dynamic capital structure choice: theory and tests. Journal of Finance 44, 19–40. Flannery, M.J., Rangan, K.P., 2006. Partial adjustment toward target capital structures. Journal of Financial Economics 79, 469–506. Frank, M.Z., Goyal, V.K., 2009. Capital structure decisions: which factors are reliably important? Financial Management 38, 1–37. Graham, J.R., 1996. Debt and the marginal tax rate. Journal of Financial Economics 41, 41–73. Graham, J.R., Harvey, C., 2001. The theory and practice of corporate finance: evidence from the field. Journal of Financial Economics 60, 187–243. Hellwig, M., 1991. Banking, financial intermediation, and corporate finance. In: Giovanni, A., Mayer, C. (Eds.), European Financial Intermediation. Cambridge University Press, Cambridge, England, pp. 35–63. Hennessy, C.A., Whited, T.M., 2005. Debt dynamics. Journal of Finance 60, 1129–1165. Holmstrom, B., Tirole, J., 1993. Market liquidity and performance monitoring. Journal of Political Economy 101, 678–709. Hovakimian, A., Li, G., 2011. In search of conclusive evidence: how to test for adjustment to target capital structure. Journal of Corporate Finance 17, 33–44. Hovakimian, A., Opler, T., Titman, S., 2001. The debt-equity choice. Journal of Financial and Quantitative Analysis 36, 1–24.

111

Huang, R., Ritter, J., 2009. Testing theories of capital structure and estimating the speed of adjustment. Journal of Financial and Quantitative Analysis 44, 237–271. Jensen, M.C., Murphy, K.J., 1990. Performance pay and top-management incentives. Journal of Political Economy 98, 225–264. Korajczyk, R.A., Levy, A., 2003. Capital structure choice: macroeconomic conditions and financial constraints. Journal of Financial Economics 68, 75–109. Lambert, R.A., Leuz, C., Verrecchia, R.E., 2007. Accounting information, disclosure, and the cost of capital. Journal of Accounting Research 45, 385–420. La Porta, R., Lopez de Silanes, F., Pop-Eleches, C., Shleifer, A., 2004. Judicial checks and balances. Journal of Political Economy 112, 445–470. La Porta, R., Lopez de Silanes, F., Shleifer, A., 2006. What works in securities laws? Journal of Finance 61, 1–32. La Porta, R., Lopez de Silanes, F., Shleifer, A., Vishny, R.W., 1997. Legal determinants of external finance. Journal of Finance 52, 1131–1150. La Porta, R., Lopez de Silanes, F., Shleifer, A., Vishny, R.W., 1998. Law and finance. Journal of Political Economy 106, 1113–1155. La Porta, R., Lopez de Silanes, F., Shleifer, A., Vishny, R.W., 1999. The quality of government. Journal of Law, Economics, and Organization 15, 222–279. La Porta, R., Lopez de Silanes, F., Shleifer, A., Vishny, R.W., 2000a. Agency problems and dividend policies around the world. Journal of Finance 55, 1–33. La Porta, R., Lopez de Silanes, F., Shleifer, A., Vishny, R.W., 2000b. Investor protection and corporate governance. Journal of Financial Economics 58, 3–27. La Porta, R., Lopez de Silanes, F., Shleifer, A., Vishny, R.W., 2002. Investor protection and corporate valuation. Journal of Finance 57, 1147–1170. Leary, M.T., Roberts, M.R., 2005. Do firms rebalance their capital structures? Journal of Finance 60, 2575–2619. Lemmon, M.L., Roberts, M.R., Zender, J.F., 2008. Back to the beginning: persistence and the cross section of corporate capital structure. Journal of Finance 63, 1575–1608. Levine, R., 1991. Stock markets, growth, and tax policy. Journal of Finance 46, 1445–1465. Levine, R., 1999. Law, finance, and economic growth? Journal of Financial Intermediation 8, 8–35. Levine, R., 2002. Bank-based or market-based financial systems: which is better? Journal of Financial Intermediation 11, 1–30. Lombardo, D., Pagano, M., 2002. Law and Equity Markets: A Simple Model. Corporate Governance Regimes: Convergence and Diversity. Oxford University Press, Oxford, England. Merton, R.C., 1987. A simple model of capital market equilibrium with incomplete information. Journal of Finance 42, 483–510. Myers, S.C., 1984. The capital structure puzzle. Journal of Finance 39, 575–592. Myers, S.C., Majluf, N.S., 1984. Corporate financing and investment decisions when firms have information that investors do not have. Journal of Financial Economics 13, 187–224. Nelson, C.R., Startz, R., 1990. Some further results on the exact small sample properties of the instrumental variable estimator. Econometrica 58, 967–976. Obstfeld, M., 1994. Risk-taking, global diversification, and growth. American Economic Review 84, 1310–1329. Petersen, M.A., Rajan, R.G., 1995. The effect of credit market competition on lending relationships. Quarterly Journal of Economics 110, 407–443. Porter, M.E., Schwab, K., 1999. The Global Competitiveness Report 1998–1999. World Economic Forum, Geneva, Switzerland. Rajan, R.G., 1992. Insiders and outsiders: the choice between informed and arm’s-length debt. Journal of Finance 47, 1367–1400. Rajan, R.G., Zingales, L., 1995. What do we know about capital structure? Some evidence from international data. Journal of Finance 50, 1421–1460. Rajan, R.G., Zingales, L., 1998. Financial dependence and growth. American Economic Review 88, 559–586. Ramakrishnan, R.T.S., Thakor, A.V., 1984. Information reliability and a theory of financial intermediation. Review of Financial Studies 51, 415–432. Shyam-Sunder, L., Myers, S.C., 1999. Testing static tradeoff against pecking order models of capital structure. Journal of Financial Economics 51, 219–244. Sirri, E.R.M., Tufano, P., 1995. The economics of pooling. In: Crane, D.B., Froot, K.A., Mason, S.P., Perold, A.F., Merton, R.C., Bodie, Z., Sirri, E.R.,

112

¨. O ¨ ztekin, M.J. Flannery / Journal of Financial Economics 103 (2012) 88–112 O

Tufano, S. (Eds.), The Global Financial System: A Functional ApproachHarvard Business Press, Cambridge, MA, pp. 81–128. Strebulaev, I.A., 2007. Do tests of capital structure theory mean what they say? Journal of Finance 62, 1747–1787. Strebulaev, I.A., Yang, B., 2007. The Mystery of Zero-Leverage Firms. Unpublished Working Paper. Stanford University, Stanford, CA. Tauchen, G., 1986. Statistical properties of generalized method of moments estimators of structural parameters obtained from

financial market data. Journal of Business and Economic Statistics 4, 397–416. Titman, S., Tsyplakov, S., 2007. A dynamic model of optimal capital structure. Review of Finance 11, 401–451. Verrecchia, R.E., 2001. Essays of disclosure. Journal of Accounting and Economics 32, 97–180.