The effect of debt market imperfection on capital structure and investment: Evidence from the 2008 global financial crisis in Japan

Accepted Manuscript Title: The effect of debt market imperfection on capital structure and investment: Evidence from the 2008 global financial crisis in Japan Author: Hiromichi Iwaki PII: DOI: Reference:

S1062-9769(18)30047-4 https://doi.org/doi:10.1016/j.qref.2019.01.008 QUAECO 1220

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Please cite this article as: Hiromichi Iwaki, The effect of debt market imperfection on capital structure and investment: Evidence from the 2008 global financial crisis in Japan, (2019), https://doi.org/10.1016/j.qref.2019.01.008 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Title: The effect of debt market imperfection on capital structure and investment: Evidence from the 2008 global financial crisis in Japan Author’s name: Hiromichi Iwaki, Ph.D

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Affiliations:

Faculty of Economics, Department of Modern Economics, Daito Bunka University, Japan

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Declaration of Interest:

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none Acknowledgements:

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I am especially grateful to my Advisory Professor Takashi Misumi for guidance and support. We thank Hikaru Fukanuma, ZhaoZhao He, Kotaro Inoue, Hiroshi Kamae, Masaru Konishi, Junichi Nakamura, Tadanobu Nemoto, Yoshiaki Ogura, Jungwook Shim, Kenichi Tatsumi, Serita Toshio, Konari Uchida, Taisuke Uchino, and Iichiro Uesugi for helpful comments and encouragements. We also thank participants at the 26th Asustralasian Finance and Banking Conference 2013, the 9th International Conference on Asian Financial Markets and Economic Development, the 13th Workshop at Hitotsubashi University in 2013, the Kanto Area Study Group of Japan Society of Monetary Economics (JMSE) in 2014, the 2014 Spring Annual Meeting of JMSE, the 1th Finance Camp of Japan Finance Association, the 15th RIETI-HIT-REFIND Joint Workshop, and the Kanto Area Study Group of the Japan Society of Household Economics 2014 for helpful comments. This paper was previously titled as “What do we know from empirical analysis of the credit crunch of 2008? Evidence from Japan”. All remaining errors are mine. Address:

1-9-1 Takashimadaira, Itabashi-ku, Tokyo, 175-8571 Japan. E-mail: [email protected]. Tel: +81-3-5399-7300 (ext.3517)

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The effect of debt market imperfection on capital structure and investment: Evidence from the 2008 global financial crisis in Japan

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Abstract

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December 8, 2018

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This paper investigates how debt market frictions affect real firm behaviors such as capital structures and investments differently based on whether a firm has access to the public debt market, taking debt structure differences into account. To this aim, using the natural experimental approach to examine the 2008 credit supply shock in Japan, we show that firms without access to the public debt market face decreased leverage and investment, accompanied by decreased bank debt, compared to firms with access. Considering that firms without access to the public debt market are more dependent on banks for their debt and are likely to have closer relationships with banks than those with access, it is intriguing that bank-dependent firms face reduced debt supplies from banks compared to other firms. Moreover, through investigation of the regression of investments where the interaction term with different debt structures is introduced, it is suggested that differences in debt structure or debt maturity between firms with access to public debt and those without access also play an important role in determining debt and investment and that bank-dependent firms faced more underinvestment or uncertainty after the financial crisis of 2008 than firms with access to the public debt market.

Keywords: Credit crisis; Bank loans; Public debt market; Debt structures; Investment; Financing constraints JEL classification: E22, G01, G21, G32

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1

Introduction

Firm capital structures involve different types of debt, such as bank loans or public bonds, though all are recognized as debt in the same way. Moreover, among publiclytraded firms, some have access to the public debt market in which commercial paper and

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public bonds are issued, whereas other firms lack access and are therefore dependent on bank loans for debt-financing. Although the firms with access may use both bank loans

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and public debt, the other group is confined to using bank loans.

When there is friction in the debt capital market supply and some firms are restricted

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from substitute debt-financing, that friction might have significant influence on firm behaviors such as leverage and investment decisions. The extant literature, as detailed in

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the next section, offers two different views. For example, on the one hand, Holmstrom and Tirole (1997) show that firms benefit from having access to both bank lending and

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the public bond market. On the other hand, from the bank–firm relationship perspective Haubrich (1989) predicted that bank-dependent firms benefit more from debt-financing

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because the exclusive lending relationships between firms and banks mitigate the informational asymmetries, which benefits both. In empirical studies, the question still remains

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unanswered as to whether firms benefit from bank–firm lending relationships. It is ambiguous whether bank-dependent borrowers are different from firms with access to the

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public bond market in light of financing constraints because both types of firms are exposed to strict regulatory disclosure rules that should mitigate asymmetric informational problems.

To tackle the research question, we run empirical investigations on the effect of bank– firm relationship on debt usage and investment using a natural experimental approach, taking into account the differences in detailed debt structure1 . Specifically we focus on the credit supply contraction in Japan that occurred after the Lehman Brothers collapse on September 15, 2008 using a quasi-natural experiment with the crash an exogenous shock. 1

Raugh and Sufi (2010, p.4278) suggest that “recognition of debt heterogeneity might prove useful in examining the effect of financing on investment”.

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— FIGURE 1 ABOUT HERE — Figure 1 describes the Japanese firms’ experiences. Panel A shows the banks’ lending attitude index toward large firms (D.I.: Diffusion Index of large enterprise) and Panel B shows the public bond issuances for a period that includes the two recent influential crises

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that occurred in Japan: the financial crisis in Japan 1997–1998 and the global financial crisis in 2008. The possible forms of debt substitution during the credit crunches of 2008

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and 1997–1998 can be seen in Figure 12 . While banks’ lending attitude toward firms drops sharply as described in Panel A, there is an upsurge in public bond issuances, as

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shown in Panel B. The capital market shrank after the Lehman Brothers bankruptcy because few institutional investors were taking investment risks, even in higher graded

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public bonds, as represented by the sudden increase in bond yields shown in Figure 2. The capital market soon regained its role because of the large demand from individual

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investors searching for attractive investment opportunities, constituting about 20% of the total public bond issuances in the fiscal year 2008.

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— FIGURE 2 ABOUT HERE —

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Institutions in Japan are generally viewed as being less influenced by the depressed subprime mortgage problems that increased distrust among financial institutions and led

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to the bankruptcy of Lehman Brothers. However, a credit crunch and accompanying credit contraction did occur in Japan and had considerable effect on the real business sector, as seen in the number of bankruptcies in publicly-traded firms. In Figure 3, the number of bankruptcies in 2008 shows an abrupt surge, reaching the highest number ever. Furthermore, as shown in Figures 4 and 5, the majority of public firms facing bankruptcy in 2008 belonged to industries highly dependent on debt, especially bank loans, or industries with higher leverage, such as real estate development, construction, finance (nonbank), and real estate fund management. Taken together, it could be said that firms in Japan also faced severely tightened credit due to the credit crunch during that period. 2

Some recent studies point out firms’ substitution between loans and bonds (Becker and Ivashina, 2014; Adrian et al. 2012).

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— FIGURE 3 ABOUT HERE — — FIGURE 4 ABOUT HERE — — FIGURE 5 ABOUT HERE —

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Holmstrom and Tirole (1997) made the theoretical prediction that firms that have no access to the public debt market and are therefore dependent on banks are likely to face

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a contraction of the bank loan supply even if their financial health remains stable. This prediction implies that it is possible for a credit crunch to have asymmetric effects for

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firms depending on whether they have access to the public debt market.

Consistent with Holmstrom and Tirole (1997), Chava and Purnanandam (2011) con-

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firm, through an investigation of the Russian crisis of 1998, that firms dependent on banks face a higher decline in firm performance indicators such as stock return, investment, and

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profitability than firms with access to the public debt market3 . In that investigation, the researchers removed firms issuing “junk bonds” (below BBB grade) from their sample

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because their research interest was an unambiguous comparison between firms with access to the public debt market and firms without. This removal is valid because a junk

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bond issuance market exists in the U.S. debt capital market, and due to the limited number of investors investing in junk bonds, it is not clear whether firms issuing such bonds

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maintain access to the public debt markets. On the other hand, this elimination could potentially, and essentially, increase the differences between firms with investment-grade ratings (and therefore, access to the public debt market) and firms without access. In this sense, the Japanese debt capital market might be suitable for an investigation of the effect of different debt sources on firm behaviors because of its clearly separated debt capital market, as shown in Figure 6. Firms with credit ratings above BBB can issue public bonds, and there is constant demand for those issuances. On the other hand, 3

There are some other studies on the impact of bank lending change on firm borrowers. Solvin, Suishka, and Polonchek (1993) show that firms dependent on bank are affected directly by their lending banks and moreover such firms are regarded as stakeholders of those banks. Campello, Graham, and Harvey (2010) point out that, faced with the fears of future uncertainties on funding from banks, firms tend to cancel or delay the investment decisions. This evidence suggests that upon a financial crisis, bank-dependent firms are potentially restricted from ideal investment decisions as compared with firms with alternative debt sources other than bank debt.

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firms below BBB can never issue public bonds. There is no junk bond issuance market. This obvious segmentation makes comparison among firms with similar characteristics more efficient for a natural experiment. — FIGURE 6 ABOUT HERE —

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When firms do not have access to the public debt market, they have little choice other than to be dependent on banks for debt-financing, and thus their relationship with banks

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is more crucial than the relationships of firms that have access to alternative debt sources

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such as public bonds or commercial paper. Diamond (1984) and Haubrich (1989) point out the benefits of bank–firm relationships for both parties: mitigating informational

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symmetries between firms and banks allows both to reap benefits from the reduced costs of both borrowing and lending. In this view, firms rated below BBB are likely to have less negative effect shock from bank lending compared to firms with access to public debt

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markets, which are less dependent on banks and less in need of deep relationships with banks. It is also interesting to examine the consistency of this view in the context of a

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credit crunch, when bank loan negative supply shock occurs. In light of this, we run a regression of bank loans outstanding through the credit crunch of 2008, with the result

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that firms that are more likely to be dependent on banks suffer more from decreased bank

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loan supply than firms with access to the public debt market. For the purpose of assessing how access to the public debt market affects firm behaviors such as leverage and investment, in this paper, we use the difference-in-differences approach and the propensity score-matching diagnosis as robustness checks of the main results. These approaches are seen in the recent studies of the effect of the credit crunch on firm behaviors (for example, Chava and Purnanandam, 2011; Lemmon and Roberts,2010). Using these methods, we show evidence that access to the public debt market has significant influence on debt structure, especially debt maturity, even if it is measured by the ratio of short-term bank debt to total bank debt. In line with this view of debt maturity, Duchin, Ozbas, and Sensoy (2010) suggest that firms with a high short-termdebt-to-total-assets ratio face financial constraints and suffer more from credit supply

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shock. Almeida et al. (2012), who examined the causal effect of debt maturity on investment through the credit crunch of 2007, present findings that the more in need of long-term debt refinancing firms are, the more investment reductions those firms face. In this paper, we present additional evidence that debt maturity has a more significant effect on investment for firms without access to the public debt market than firms with

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access, as shown by the difference-in-differences method, in which an interaction term with debt maturity is additionally introduced.

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As a result, we find that in the course of the credit crunch of 2008, firms without access

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to the public debt market faced decreased leverage defined as the ratio of debt to total assets, increased short-term debt, and decreased long-term debt measured in terms of

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amount compared with firms with access4 . Moreover, it is intriguing that bank-dependent firms faced a reduction in the ratio of short-term bank debt to total debt. Based on the result of the study on investment, consistent with previous research, firms without

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access to the public debt market invest less than firms with access. The investigation of investment taking debt structures into account shows that firms without access to

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the public debt market respond to an increase of both short-term debt and bank debt with less marginal investment than firms with access. This finding suggests that bank-

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dependent firms face more underinvestment or uncertainty after a credit crunch than other

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firms. The evidence in this paper suggests that both access to the public debt market and differences in debt structure or debt maturity play important roles in determining investment.

The extant literature has growing evidence on the issue of the effect of the global financial crisis on bank-dependent firms versus firms with access to public debt, including studies such as Becker and Ivashina (2014) for the United States (though their estimation period is relatively longer), Cotugno et al. (2013) and Chingano et al. (2016) for Italy, and Carvelho et al. (2016) and Fernandez et al. (2018) for cross-country analyses. There are very few of studies on the Japanese experience, however. In this sense, to 4

Uchino (2013) stresses that even firms with large public debt did not decrease investment relative to bank-dependent firms during the financial crisis period of 2008 in Japan and the banking sector works efficiently.

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our knowledge we are the first to show that even though Japanese banks were relatively immune to direct effect from the global financial crisis because they were less exposed to fraud securities products, bank-dependent firms were faced with drops in debt usage and investment more sharply than firms with access to public debt market, even though both types of firms are publicly-traded companies. We find some studies of financing

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constraints among bank-dependent firms compared with firms with access to the public debt market (for example, Whited, 1992; Kashyap, Stein, and Wilcox, 1993; and Almeida,

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Campello, and Wesbach, 2004). Most similar to our results, Becker and Ivashina (2014)

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show the debt substitution of firms with access to the bond market between bank loans and bond. But our paper contribute to the literature by suggesting the possibility that

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the exclusive bank–firm relationships do not necessarily function to a satisfying degree even under the circumstances in which banks’ balance sheet conditions are relatively normal but the capital market is, if anything, in turmoil. In this sense, our paper also

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shows evidence that suggests banks show opportunistic behaviors toward bank-dependent firms. Moreover, our results add another insight to the literature in that it is important

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to consider not only debt sources but also the combined effects of maturity differences when considering firms leverage and investment behaviors.

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The remainder of this paper is structured as follows. In Section 2, we describe the

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related literature. In Section 3, we explain the prediction using a simple adjustment model. In Section 4, the empirical method and approach using robustness tests are presented. In Section 5, the sample, data, and data-generation methods are described. Section 6 shows the results from regression and robustness tests and finally, Section 7 concludes..

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Related literature

Faulkender and Petersen (2006) focus on debt source differences and show that firms dependent on banks use less debt, with an economically significant magnitude, than firms with access to the public debt market, suggesting that firms could suffer from bank

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lending squeezes in different ways, depending on whether they have alternative debt sources. This prediction is confirmed by Leary (2009). The global financial crisis offers researchers a special opportunity to investigate how firms responded to it according to their debt-financing surroundings. There are mixed results among the related literature of the effect of bank dependence on firm behavior.

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On the issue on the effect of bank-lending standards toward borrowing firms, Ivashina and Scharfstein (2010) show that firms who hold relationships with banks who raise funds

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less from depositors suffer more from restrictive borrowing during a crisis period. This

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evidence suggests the importance of bank-lending channels and the imperfect capital market. In line with the theoretical prediction by Holmstrom and Tirole (1997) who

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stress the borrowing firms’ benefit of having access both to banks and the public bond market because of substitution between bank debt and public debt, Becker and Ivashina (2014) show evidence using loan level data in the U.S during the crisis that firms who

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use both bank loans and public bonds suffer less than bank-dependent firms,. Focusing on firms’ investment activity, Chingano et al. (2016) find that the bank-lending channel

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worked negatively in that due to the interbank market crush in Italy and the rise of external financing costs, bank-dependent firms experienced damage from significantly

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lowered investment expenditures.

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On the other hand, there is also evidence that cast doubts on the negative effects of the global financial crisis on bank-dependent borrowers. For example, using U.S. firm-level data, Kahle and Stulz (2013) argue that bank-dependent borrowers were not faced with a hardship environment in financing and investment activities. They point out overall drops in firms’ debt issuances and investment expenditures and say that the drops were driven by the demand shock. Using multi-national data during the crisis period, Carvelho et al. (2016) also do not find the relative benefit of having access to the public bond market on the side of firms. Those studies are in line with the benefit views of the bank–firm relationships suggested by Haubrich (1989). Related to issues on Japanese firms, there are a few studies with varying results. Uchino (2013) does not find any significant evidence of investment expenditure differences

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between bank-dependent firms and firms with access to the public bond market during the crisis period. Using Japanese listed firms (as we do), Amiti and Weinstein (2018) show that an idiosyncratic granular bank supply shock significantly lowered firms’ investments, using firm-bank loan level data . Their focus is related to our study but different in that

with access to public debt, the issue we are considering.

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they do not explicitly consider the differences between bank-dependent firms and firms

As described above, there still remains an unsolved issue as to whether bank-dependent

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borrowers reap benefit from their relatively stronger bank–firm relationships than firms

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with access to the public debt market. Especially in Japan, as compared to other developed countries, the debt market has a unique characteristic that separates bank-

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dependent firms (lower than BBB rating) and firms with access to the public bond market (BBB or higher rating). This clear cut allows us to rule out the opaqueness of identification of the two types of firms based on the debt market they face. Taking into account

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that bank-dependent borrowers exclusively rely on banks for debt-financing and cultivate relationships with banks, those firms draw necessary funds from their relationship banks

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more easily than other types of firms. Moreover, the extant literature does not necessarily take into account debt maturity differences, even though the differences are important

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to firms’ decision making. In this respect this paper investigates the relative influence of

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bank dependence in debt-financing on firms’ important financial issues such as debt usage (or leverage) and investment using the global financial crisis period as a quasi-natural experiment.

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Prediction implied by a simple adjustment model

We assume that firms have their optimal leverage and firms adjust their leverage at time t to make it close to the optimal at the time t + 1. So, we can simply write the relationship between the optimal leverage and the adjustment as follows: d t+1 = Levt + Adt , Lev

(1)

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d t+1 represents the optimal leverage at the time t + 1, Levt is the actual leverage where Lev level at the time t, and Adt denotes the leverage adjustment. In a frictionless capital market, firms can adjust their leverage instantaneously even if the actual leverage level is not at its optimal. However, if firms are in an imperfect capital market, then we need to consider those factors in the leverage adjustment model5 . Now, we assume firms face

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two different environments. One is a market where firms finance themselves mainly by bond issuances and also use bank loans for supplementary purposes. The other one is a

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market where firms exclusively use bank loans and cannot use the bond market. In these

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setting, we may have a quasi-optimal leverage at the time t + 1, considering the friction in the two different environments as follows:

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d t+1 = Levt + Ad(market type)t , quasi Lev

(2)

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d t+1 represents the “sub-optimal” leverage based on market frictions. where quasi Lev Specifically, under the condition of an imperfect capital market, we may define the lever-

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age adjustment Ad(market type)t depending on the capital market types as follows: if firms can access the bond market:

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Ad(bond)t = Adt − α · Adt = (1 − α)Adt ,

(3)

and if firms are dependent on banks for financing:

Ad(bank)t = Adt − β · Adt = (1 − β)Adt .

(4)

Here, we assume 0 ≤ α ≤ 1, and 0 ≤ β ≤ 1. We interpret that if α (or β) takes 0, then there is no friction or cost in adjusting leverage. But if α (or β) takes 1, then the capital markets are in the sticky condition and firms cannot adjust their leverage to an optimal

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Our simple idea is very close to partial adjustment model such as in Flannery and Rangan (2006).

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level. Then, we reasonably have the following expressions:

Ad(bond)t ≤ Adt ,

(5)

Ad(bank)t ≤ Adt .

(6)

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In other words, firms in the capital market with non-zero-friction adjust their leverage to a lower level than the adjustment level under a perfect capital market. Under the

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assumption described above, we do not set any restrictions on the relative relationship between α and β. We allow for the following three cases regarding that relationship.

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The first case is α < β, which leads to Adt ≥ Ad(bond)t > Ad(bank)t . This means that if firms use the bond markets, then they adjust their leverage more smoothly than bank-

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dependent firms do. The second case is α = β. In this condition, there is no difference in adjustment velocity between firms with access to the bond market and bank-dependent

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firms. The third case is α > β, where bank-dependent firms are in a frictionless financing environment compared to firms with access to the bond market. Theoretically, if firms

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have unwavering relationships with banks, firms can reap benefit from, for example, lower interest rates because bank process information about their client firms in the course of

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lending activities (Rajan, 1992; Haubrich, 1989). Therefore, in such a circumstances,

firms.

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a bank-dependent firm would adjust its leverage more smoothly than the other type of

As described above, there seems no clear and decisive prediction as to which case fits best to listed firms where some firms are able to access the public bond markets and other firms are solely dependent on banks for debt-financing. In this context, this paper tackles this ambiguousness and tries to solve which predictions are plausible and fit well in employing the quasi-natural experimental approach using Japanese listed firms during the global financial crisis period. Note that although the above arguments discuss leverage adjustment, the basic model analysis applies in the same way to investment adjustments under the condition of optimal investment. Both firms with access to the public bond market and bank-dependent firms

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need to raise funds in order to make investments. For this reason, we omit the detailed theoretical model description.

Methodology

4.1

Difference-in-differences estimation

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In order to measure the net effect of the difference in firm behavior responses such as leverage and investment through the credit crunch of 2008 in the light of a natural

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experiment, we employ the difference-in-differences approach. We take the following

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basic specification:

yit = α0 + α1 Tt + α2 Di + α3 Tt Di + Xit β + ϵit ,

(7)

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where yit represents real outcomes such as leverage and investment; Tt is an indicator variable equal to one in the specific year: 2008, 2009, and years after 2010, respectively;

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Di denotes an indicator variable equal to one if the firm does not have access to the public debt market and thus is dependent on banks for its debt and restricted from

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issuing public debt, whether in the form of public bonds or commercial paper; and Xit

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is a vector representing control variables. Following the specification of Leary (2009), we divide the period after the year of the Lehman shock into three subsequent terms: 2008, 2009, and the remainder of the years. Doing so, we grasp the different time-varying responses for the two types of firms.

4.2

Propensity score-matching diagnosis

There are many differences in firm characteristics between firms with access to the public debt market and those without, as shown in Table 1. There is also a concern that those differences have some influence on firm leverage or investment through unobservable variables determining such corporate outcomes. To control for this potential endogeneity, Faulkender and Petersen (2006) introduced a method similar to the instrumental variables 12 Page 13 of 46

approach, which we use for robustness in this study as described in Section 5.3. — TABLE 1 ABOUT HERE — As another method to tackle the possible endogeneity problems, we conduct a propensity score-matching diagnosis6 of the main result using difference-in-differences regression

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of leverage and investment. This method is employed by recent related studies7 . According to the suggestions by Roberts and Whited (2013), we employ this matching method

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as a robustness test8 .

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In the first stage, using probit estimation, we regress the indicator variable of whether firms have access to the public debt market on firm characteristics and the outcome before

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the credit crunch. In the second stage, we match9 probabilities as propensity scores, which are calculated from the first-stage probit estimation, between firms with credit ratings as a treatment group and firms without as a control group. For the purpose of

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controlling for the distance of propensity scores between each matched pair, we use the nearest neighborhood caliper-matching approach, in which we limit the distance to within

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one standard deviation. As Chava and Purnanandam (2011) noted, there is a trade-off between bias and efficiency: if we require nearer matching so that the more similar firms

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are selected from both groups, it reduces the size of the matching sample.

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A similar trade-off lies in the number of matches. Moreover, an appropriate and outright criterion based on which the number of matches is set appears to be lacking. This is why we show the results of several matching diagnoses, varying the number of matches for robustness.

Theoretically, after matching based on the near propensity scores of firms with access to public debt and firms dependent on banks, the potential endogeneity is controlled and removed; however there still remains a concern that unobservable factors affect both 6

Roberts and Whited (2013) present a detailed discussion of the propensity score-matching method. For example, Chava and Purnanandam (2011); Lemmon and Roberts (2010). Almeida et al. (2012) match not the form of propensity score but firm characteristics as covariates. 8 Roberts and Whited (2013, p.546) point out as follows “Matching does alleviate some of the concerns associated with linear regression · · · However matching by itself is unlikely to solve an endogeneity problem since it relies crucially on the ability of the econometrician to observe all out come relevant determinants.” 9 We follow the method suggested by Abadie and Imbens (2006) for the choice of distance metric. 7

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groups of firms and lead to differences in outcomes, such as leverage and investment. Thus, we report not only the average treatment effect for the treated (hereafter, ATT) but also the average treatment effect (hereafter, ATE), which is the same as the net effect calculated by the difference-in-differences method between the two samples. ATT and

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ATE are defined as follows:

1 ∑ af ter (y − yjaf ter ), n 1 i n

(8)

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ATT =

1 ∑ af ter ATE = [(yi − yibef ore ) − (yjaf ter − yjbef ore )], n 1

(9)

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n

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where n is the number of matched sample observations; i and j represent firms among the treatment group with access to the public debt market and firms among the control

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group, respectively; y is outcomes such as leverage or investment; and bef ore and af ter indicate whether the outcome is from the year before or the year after the collapse of

Data Data source

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5.1

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5

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Lehman Brothers, taking 2006 as bef ore and 2009 as af ter.

I use data from Nikkei Needs Financial Quest for the financial information of each firm and from Rating and Investment Information, Inc. (R&I) for the information on a credit rating for each firm.

We restrict the sample observations to publicly-traded firms with information about stock prices. Publicly-traded firms usually provide relatively adequate information to outside investors compared to not-publicly-traded firms. We remove financial industry firms such as banks, securities, insurance companies, and leasing firms because they are placed under strict regulations and behave differently (due to requirements by regulation agencies). For the same reason, we also remove the electric power industry10 . 10

Exceptional public bond issuances by electric power companies have been made since long before

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The estimated period for the regression in the form of difference-in-differences is set to be balanced between the pre-credit crunch period (the fiscal years11 of 2004–2007) and the post-credit crunch period (the fiscal years of 2008–2011). During the pre-credit crunch period, the macro economy in Japan and other countries was stable and business

circumstances faced severe turmoil, as shown in Figure 1.

Recognition of having access to the public debt market

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5.2

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conditions were moderate. Conversely, during the post-credit crunch period, business

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In this paper, public debt is defined as publicly-traded bonds or commercial paper, which has high liquidity. Because it is indispensable when issuing public debt to have a credit

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rating from a rating agency such as R&I or Standard and Poors (S&P), public debt issuance is generally accompanied by a credit rating. Cantillo and Wright (2000) pointed

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out the coincidence that firms with a credit rating have public debt outstanding.; we therefore use the information on the existence of a credit rating and the experience of public debt issuance to classify whether each firm-year observation has access to the

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public debt market12 . For example, because R&I’s information on credit ratings before 2007 is not available publicly, we recognize each firm-year observation for that period as

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having access to the public debt market if that firm had issued public debt since 1993.

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For consistency and to be conservative, in deciding whether a firm-year observation is recognized as having access to the public debt market for the period from 2004 to 2006, we require a firm recognized as having access to have both public debt issuance since 1993 and credit ratings since 2007. In addition, we restrict the sample to firms with (without) consistent access to the public debt market; that is, any sample firms that do not have consistent experiences throughout the estimation period are not included. Moreover, we require whole sample observations to have successive information on financial data for the estimation period, to meet the criteria noted above, and to have debt outstanding. For the same reason as previous studies (Faulkender and Petersen, 2006; Chava and 1996, when the deregulation of overall public bond issuance came into effect. 11 In Japan, a fiscal year is from April to March. 12 As for the recognition of having access to public debt, we follow the previous studies such as Faulkender and Petersen (2006) and Chava and Purnanandam (2011).

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Purnanandam,2011), we exclude firms without debt because it is not certain whether they have access to the public debt market; they may have decided not to enter the public debt market even if they could.

5.3

Alternative measure of access to the public debt market

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As suggested by Faulkender and Petersen (2006), there is a possible endogeneity problem in using presence of a credit rating or past public debt issuance as an indicator variable

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in regressing the effect of leverage or investment on firm characteristics if there some

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unobservable variables related to some explanatory variable affect those outcomes. To circumvent this problem, as an alternative variable, we also use the predicted

an

probability13 of having a credit rating, as introduced by Faulkender and Petersen (2006). This methodology can be regarded as similar to the two-stage instrumental variable

M

approach, as follows. In the first stage, we estimated the probit model using the sample14 from 2007 to 2011, the period for which the credit rating information is available, of the

ed

existence of a credit rating on the explanatory variables as follows: tangibility; log of firm age; market-to-book ratio; an indicator as an instrument of whether the firm is listed on the Tokyo Stock Exchange’s first section at each fiscal year; and another indicator as

pt

an instrument of whether the firm meets a modified criterion implied by the criterion

Ac ce

in use as of the year 1985 for firms to be entitled to issue convertible bonds. We chose the latter indicator variable15 as an instrument in the first-stage probit model estimation because firms meeting the modified criterion are likely to have easy access to the public debt market; for the same reason, following Faulkender and Petersen (2006), we chose the former instrumental variable because of the higher reputation accrued from being listed on the Tokyo Stock Exchange’s first section. Applying the coefficients from estimation of the probit model to the full sample generates the predicted probability of access to the public debt market for the estimated years. In the second stage, we use the predicted 13

Leary (2009) uses this methodology. Here, the whole sample size used for the probit estimation is 13,518, which contains those observations excluded because of lacking the needed requirements for the difference-in-differences estimation done in this paper. 15 The correlation coefficient between the firms with credit ratings and the modified criterion for the period since 2007 is 0.52, a high enough value. 14

16 Page 17 of 46

probability as an instrumental variable or alternative measure indicating the degree of access to the public debt market.

5.4

Sample description

As a result of the screening noted above, the entire sample consists of 13,855 firm-year

ip t

observations including 2,472 firm-year observations recognized as having access to the public debt market and 11,383 others. Table 1 shows summary statistics for the sample.

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Differences in the averages of firm characteristics between two subsamples based on

us

access to the public market are clearly observed for capital structures, debt structures, and investment level. The most remarkable differences in firm characteristics appear in

an

firm size, measured by assets or sales, as suggested by Faulkender and Petersen (2006). In particular, firms that are dependent on banks for debt have, on average, about a 48%

M

higher ratio of short-term debt to total debt than firms with access to the public debt market, which is in line with previous studies such as Leary (2009) and Barclays and

ed

Smith (1995). For almost all measures, firms with access to the public debt market are different from those without. This fact indicates that it is crucial to control for those differences when extracting the net effect of the credit crunch on firms without access

pt

to the public debt market. In order to control for the fundamental differences, we run a

Ac ce

regression in the form of difference-in-differences. In addition, as noted before, we test the main result through a propensity score-matching diagnosis as an alternative approach to circumvent the potential endogeneity.

6 6.1

Results

Impact on leverage and various types of debt

In the estimation of the credit crunch’s impact on debt, following the previous literature (Faulkender and Petersen, 2006; Rajan and Zingales, 1995), we select the control variables as follows: growth opportunity defined as market-to-book ratio (MB ), profitability (PROFITABILITY ), tangibility (TANGIBILITY ), log of book assets (LOG(ASSETS)), 17 Page 18 of 46

and log of firm age (LOG(1+AGE)). In addition, in Japan, some typical situations close to credit rationing might affect the bank lending market so that a firm’s decisions about debt could be distorted; we thus include another two control variables in the estimation: REDOPE2, an indicator that takes a value of one if the firm has two consecutive fiscal year records of operating deficit because Japanese banks almost automatically regard

ip t

these firms negatively for lending; and UNDER MED ERATIO, another indicator that takes a value of one if the ratio of equity to assets is under the median value of the firm’s

cr

industry. This is based on the idea that the lower this ratio is, the less likely the firm is

us

to borrow from banks compared to other similar firms. For the purpose of controlling for the macroeconomic factor, the coincidence index of business conditions (MACRO CI ),

an

which is released monthly by the Cabinet Office, a government agency, is introduced as an explanatory variable.

Table 2 shows the estimation of difference-in-differences regression results of leverage,

M

defined as the ratio of debt to total assets (in Columns 1 and 2), and debt growth (in Columns 3 and 4). In Columns 1 and 3, the results are presented when using an

ed

indicator variable of having access to the public debt market. In Columns 2 and 4, instead of the indicator variable, a probability variable estimated from the probit model

pt

explained in Section 5.3 is used. It should be noted that in order to focus on how firms

Ac ce

without access to the public debt market responded to the credit crunch, the variable NO ACCESS, referring to having no access to the public debt market, is interacted with dummy variables indicating the specific periods. — TABLE 2 ABOUT HERE —

Regardless of whether the variable measuring access to the public debt market is defined as an indicator or as a probability variable, the result in Table 2 suggests that firms dependent on banks suffer a net decreasing effect of leverage compared with firms with access at 1.5% and 2.6%, respectively, in 2008, coefficients that are economically and statistically significant. For example, considering the average leverage ratio of firms without access to the public debt market, the magnitude of that net downward effect constitutes 6% or 11% of the average leverage of those firms. Similar effects are seen in 18 Page 19 of 46

the other post-Lehman shock period. The negative effect on debt usage from the credit crunch for firms dependent on banks is also confirmed by the result of regression of debt growth defined as the ratio of the annual change in debt to debt in the previous year. — TABLE 3 ABOUT HERE —

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In Table 3, the dependent variables are defined as the ratios of different types of debt (long-term debt, short-term debt, and bank debt, as well as total debt for comparison

cr

purpose) to total assets. This investigation allows us to observe the different impacts of the credit crunch on the amounts of different types of debt. Interestingly, although

us

firms dependent on banks suffer more in terms of the amount of total debt, long-term debt, and bank debt, they increase the amount of short-term debt. Even though firms

an

without access to the public debt market are generally dependent on banks and have closer relationships with lending bank than firms with access, it is the counterintuitive

M

evidence that the latter firms faced the reduction of bank debt. Taken together with the fact that the bank-dependent firms suffer a decreasing effect of other form of debt

ed

such as total debt and long-term debt, it is suggested that they have no other choice for debt-financing than short-term debt, which is plausibly supplied by banks. In fact, as

pt

shown in Column 5 in Table 4, bank-dependent firms increase their short-term bank debt ratio to total bank debt more by 4.1% in 2008 than firms with access to public debt. In

Ac ce

other words, this finding suggests that banks offer those bank-dependent firms relatively more short-term than long-term bank loans compared to firms with access to the public debt market.

— TABLE 4 ABOUT HERE —

In Table 4, we further extend the investigation to a detailed analysis of bank debt, taking maturity differences into account. As Barclays and Smith (1995) suggest, in examining debt maturity structures, the manner in which a specific debt is divided makes a difference. From this point of view, the dependent variables are defined as the ratio of long-term bank debt or short-term bank debt to total assets, total debt, and bank debt. The striking result is that when short-term bank debt is measured by the ratio to total 19 Page 20 of 46

assets, Column 2 shows that firms dependent on banks increased the ratio by 0.5% more than firms with access to public debt in 2008, but when measured by the ratio to total debt, Column 4 shows that those firms decreased the ratio by 1% less than firms with access to public debt in 2008. In view of the debt structure, this evidence suggests that after the credit crunch, firms with access to the public debt market turned to banks, and

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banks offered them relatively more long-term loans and short-term loans than they did to bank-dependent firms; this is consistent with the steep increase in outstanding bank

cr

loans to large enterprises around the credit crunch shown in Figure 7. During the credit

us

crunch, there was turmoil in the debt capital market because of the upsurge in the yields on public bonds, as shown in Figure 2. In other words, during the credit crunch, even

an

some firms with access to the public debt market, though perhaps briefly, faced difficulty in financing debt so they had no other choice than to borrow from banks, even with

M

short-term contracts.

— FIGURE 7 ABOUT HERE —

ed

Through the detailed investigation of debt usage responses, it is implied that while firms with ex ante access to the public debt market benefit from bank-lending relation-

pt

ships even if the debt capital market is in turmoil, mitigating the adverse effects of the

Ac ce

credit crunch, ironically, firms dependent on banks face decreases of both absolute debt level and bank debt16 . Moreover, for the latter firms, the fraction of short-term debt in the total debt increases relatively as shown in Column 5 of Table 4; this is consistent with the argument that banks try to mitigate risk in lending to such firms by acquiring firms’ control rights, as Rajan (1992) suggests.

16

The results in this paper have similarities in those in Becker and Ivashina (2014) who investigate for U.S. firms. Their investigation period includes the global financial crisis, but is relatively longer (1990-2010)) and does not explicitly focus on the crisis period as a natural experiment as we did. For the case of Italian firms, Cotugno et al. (2013) show the result opposite to ours in that exclusive firm-bank relationship can mitigate the firms credit rationing. Using cross-country analyses, Carvelho et al. (2016) also suggest that firms with access to the public debt market do not mitigate the negative effect from the global financial crisis. In this regard our result for the Japanese experience during the crisis period has the contrast with other similar studies.

20 Page 21 of 46

6.2

Impact on investment

In this section, we investigate how access to the public debt market affects firms’ investment behavior. In addition to this, as Almeida et al. (2012) show that firms in greater need of refinancing debt before the credit crisis faced relative decreases in investment; the

ip t

debt maturity difference might play a critical role in investment decisions. In light of this, we also investigate, in the next section, how the credit crunch affected bank-dependent firms’ investment decisions, considering the effect of the debt structure difference.

cr

Following Kashyap et al. (1994), because of their suggestion that sales information

us

have a close relation to investment, we select explanatory variables for the regression of investment as follows: market-to-book ratio (MB ), log of sales (LOG(SALES)), and log

an

of lagged sales. In addition, we add log of firm age (LOG(1+AGE)) to the regression, based on the idea that a firm’s maturity is likely to affect investment decisions in a way

M

that growth opportunity and sales information alone cannot capture. Table 5 shows the regression result of investment in response to the credit crunch. The

ed

first two columns present the result using the ratio of investment expenditure to lagged total assets as a dependent variable, and the last two columns present the result using the ratio of investment expenditure to lagged tangible assets. In Columns 1 and 3, the dummy

pt

variable taking the value of one if the specific firm does not have access to the public debt

Ac ce

market, is used as an independent variable representing which condition of debt-financing the specific firm faces. In Columns 2 and 4, instead of a dummy variable, the probability of access to the public debt market calculated from probit model estimation is introduced for robustness.

— TABLE 5 ABOUT HERE —

In every measure of access to the public debt market as an independent variable and of the definition of investment as a dependent variable, the result in Table 5 confirms that bank-dependent firms suffered more of a decrease in investment in 2008 and 2009 than did firms with access to the public debt market. Given the average ratio (0.04) of investment to lagged assets for those firms dependent on banks, Column 1 suggests that 21 Page 22 of 46

they faced a net decreasing effect on investment level of 12.5% upon the credit crunch in 2008 compared to firms with access to the public debt market. This is a finding with economic and statistical significance. Similar effects are observed in the other columns. The result shown in Table 5 is consistent with previous studies on the impact of the credit

6.3

ip t

crunch on investments, such as the work of Chava and Purnanandam (2011).

Response of investment through debt

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This section examines how the bank-dependent firms’ investments responded to the credit

us

crunch through the interaction with debt structure differences.

Table 6 shows the striking finding that, even after controlling for the effect of the

an

change in debt structure, firms dependent on banks faced a relatively larger reduction in investments, as each type of their short-term debt ratio, as shown in Columns 1, 2, and 4,

M

increased by the same magnitude as firms with access to the public debt market during the credit crunch of 2008 and 2009. In addition, the more remarkable finding is shown

ed

in Column 3: (where the variable DEBTTYPE is defined as the ratio of bank debt to assets) even if their bank debt increases in amount, firms dependent on banks face a net reduction of investment in the course of the same increase of bank debt as firms with by

pt

10%, then bank-dependent firm faced a decrease in investment 0.24% higher than firms

Ac ce

with access to the public debt market after the credit crunch. This is equivalent to a downward effect on the investment ratio of 16.7% for the average bank-dependent firm. — TABLE 6 ABOUT HERE —

Evidence shown in table 6 suggests that firms without access to the public debt market respond with less marginal investment to an increase of both short-term debt and bank debt than firms with access, a finding that implies the former firms are put into situations of greater underinvestment or uncertainty than the latter firms. In other words, upon the credit crunch, firms dependent on banks suffer more in terms of investment than firms with access to the public debt market; a non-negligible segmentation or friction in the debt market does exist, as suggested by Leary (2009). This is why the capital 22 Page 23 of 46

market condition of firms is a decisive factor in determining investment: firms dependent on banks plausibly face financing constraints. These findings are in line with previous studies on financing constraints (Almeida and Campello, 2007; Almeida et al., 2004; Fazzari et al., 1988; Gilchrist and Himmelberg, 1995; Kashyap et al., 1993; Kashyap et al., 1994; Whited, 1992). Firms in need of refinancing before the credit crunch are more

ip t

likely to suffer more from the decrease in investment; the result in this paper supports

Matching test

us

6.4

cr

the work of Almeida et al. (2012) and Duchin, Ozbas, and Sensoy (2010).

As Table 1 shows, there are large differences for various firm characteristics. In this

an

sense, the method of propensity score matching can be regarded as an alternative measure because of its underlying idea of comparing observations with similar characteristics.

M

The main findings of the test of leverage and investment responses after the credit crunch, as shown in Tables 2 and 6, suggest that bank-dependent firms are more likely to

ed

suffer from credit supply shock than firms with access to the public debt market in Japan. To ensure the robustness of these results, we run a propensity matching diagnosis on the response of leverage and investment level before and after the credit crunch of 2008.

pt

Panel A of Table 7 shows the different leverage responses for firms with access to the

Ac ce

public debt market (treatment group) and those without, both before the credit crunch (in 2006) and after (in 2009). As noted in Section 4.2, we report ATE (ATE (DID)) as well as ATT. Sample firms used for matching diagnosis are those with full sets of data in 2006 and 2009. Explanatory variables (covariates) used for generating propensity scores through the probit model estimation of access to the public debt market are as follows: log of firm age; market-to-book ratio; profitability; tangibility; log of assets; and leverage defined as the ratio of debt to total assets as of 2006, the year before the credit crunch. We include leverage as an explanatory variable to control for the ex ante leverage level. It is noticeable that the choice of the number of matched control firms for each treatment firm seems to lack a strict standard; thus, for robustness reasons, we report the results with one to 10 matches. 23 Page 24 of 46

Throughout the reported results in Panel A, both measures of average difference in leverage response between treatment firms with access to the public debt market and control firms without confirm the main finding that leverage was affected by the debt supply shock that occurred after the credit crunch: bank-dependent firms suffered greater debt decreases than firms with access to the public debt market having characteristics

ip t

similar to bank-dependent firms. In the economic sense, the magnitude of the ATE ranging from 0.011 to 0.024 is near the result from the regression of leverage shown in

cr

Table 2.

us

For the purpose of comparison with other cases of the credit crunch and for relative investigation, we also report in Panel B the result from the matching test on leverage

an

throughout the credit crunch of 1997-1998 in Japan, which had complex causes such as regulatory pressure on banks to meet capital requirements and to apply stricter standards in lending to firms as well as credit crises in Asia and Russia. Although there seems to be

M

similarity in the phenomenon during the two credit crunch periods, as shown in Figure 1, the older credit crunch appears to have much less of a differential impact between

ed

bank-dependent firms and those with alternative debt sources, such as public debt, than the latest credit crunch. This comparison sheds light on how large the impact from the

pt

credit crunch following the collapse of the subprime mortgage market and the Lehman

Ac ce

shock was, as implied in Figure 3 by the difference in the number of bankruptcies between the two crises.

In Table 8, using the matching method, we show the result of investigation of the different impacts on investment. In the same manner used in the matching diagnosis for leverage difference in table 7, matching pairs are constructed between treatment firms with access to the public debt market and control firms without as of 2006, and then the average differences in investment level within the treatment group and control group before the credit crunch (2006) and after (2009) are calculated. The explanatory variables (covariates) used to generate a propensity score through probit model estimation of access to the public debt market are as follows: log of firm age; market-to-book ratio; profitability; tangibility; log of assets; and investment, defined as the ratio of investment

24 Page 25 of 46

expenditure to lagged total assets in 2006, the year before the credit crunch, to control for the ex ante investment level. For both ATT and ATE (DID), the net advantage in investment level for the treatment firms over the control firms is evident as a whole, except for the case of allowing for two matches, a result that is consistent with Chava and Purnanandam (2011). The magnitude of ATE (DID) is consistent with the result in

Caveats

cr

6.5

ip t

Table 5.

us

There might be some caveats that a demand shock could affect our regression results. For example, Kahle and Stulz (2013) stress the significant role of the impact of a de-

an

mand shock, rather than a bank-lending supply shock, on firm behaviors such as capital expenditures and net debt issuances during the recent financial crisis17 .

M

To overcome this concern, we introduce industry dummy variables for all regressions of the impacts of the credit crisis on debt, and individual firm dummy variables for all

ed

regressions of the impacts of the credit crisis on investment18 . As a result of controlling for these factors, our regression results remain unchanged. As an another robustness test for possible non-linearity in the effect of rating on debt

pt

financing, we run the regression introducing the squared term of NO ACCESS, as the

Ac ce

predicted value of NO ACCESS instead of as a dummy variable19 . This time, the results still remain unchanged.

Another concern is related to the sample used in this paper. In order to meet the ideal requirement of difference-in-differences regression in light of a natural experiment, as mentioned in Section 5.2, we use firm-year observations with successive financial information and consistent status of access or no access to the public debt market over the estimation period. This methodology might potentially cause sample selection bias, so we 17

On the other hand, in the study of the factors that drive the credit cycle, Mian and Sufi (2010) show that through the investigation of the subprime mortgage crisis, not the demand side but “an outward shift in the supply of credit from 2002 to 2006 was a primary driver of the macroeconomic cycle of 2002 to 2009”. 18 Notice that the demand shocks common among all firm are controlled in the difference-in-differences specification used in this paper. 19 We appreciate the anonymous referees for pointing out this possibility.

25 Page 26 of 46

reran the regression using the alternative sample that includes firms that were dropped from the sample because of a lack of successive financial information20 . As a result the sample selection bias problem seems not to be plausible and the robustness of the major results in this paper hold.

Conclusions

ip t

7

cr

The Lehman shock was called a “one in 100 years event.” During the credit crunch, the magnitude of the effect of the shock to the Japanese financial system was often portrayed

us

as being relatively smaller than that in other developed countries such as the United States and European countries. The evidence in this paper, however, shows that the

an

shock was extraordinary enough that even publicly-traded firms in Japan faced negative impact from the shock. Moreover, there were asymmetric effects on capital structures,

M

debt structures, and investments, depending on the debt source or whether firms had access to the public debt market. This point supports Faulkender and Petersen (2006)

ed

and Leary (2009). Our investigation sheds light on the fact that the marginal change in debt structures

pt

affected investment in different ways after the credit crunch, depending on whether firms had access to the public debt market and the firms’ debt structures. The finding that

Ac ce

debt structure may influence investment is consistent with the suggestion in Raugh and Sufi (2010). Taken together with these findings, it is suggested that the differences in debt structures as well as accessibility to the public debt market, through their interactive effects, play a significant role in investment and that bank-dependent firms faced more underinvestment or uncertainty after the financial crisis of 2008 than firms with access to the public debt market. Our results suggest that the segmented debt capital market imposes some relative disadvantage in debt-financing and investment for firms without access to the public debt market. There remains room to expand the public debt market to at least slightly below the BBB rating in light of the alternative debt choice and its benefits. At the 20

The regressions are taken place for the estimations on leverage, debt growth, and investment.

26 Page 27 of 46

same time, the results also imply that bank-dependent firms might face somewhat more uncertainty in debt-financing after a banking shock than other firms; this might be related to capital structure, debt structure, and investment decisions—all topics that remain for future research.

ip t

Appendix: Definitions

cr

AGE The period since the firm was founded.

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ASSETS ; assets The book value of assets.

term bank loans matured over one year.

an

BANK DEBT ; bank debt Short-term bank loans matured within one year plus long-

M

DEBT ; debt Long-term debt plus short-term debt.

INVEST ; Invest Capital expenditure reported on the annual financial statements.

ed

SALES Gross sales of the firm.

pt

Lt Debt matured over one year.

Ac ce

St Debt matured within one year.

MACRO CI Coincidence index of business conditions released monthly by the Cabinet Office, a government agency. MB The ratio of the market value to the book assets, where the market value of assets is defined as book assets minus book equity plus the market value of equity. LEH08 An indicator equal to one in 2008, and zero otherwise. LEH09 An indicator equal to one in 2009, and zero otherwise. LEHAFTER An indicator equal to one after the credit crunch except for 2008 and 2009, and zero otherwise.

27 Page 28 of 46

PROFITABILITY Operating profit divided by sales. TANGIBILITY Net property plant and equipment scaled by the book value of assets. REDOPE2 An indicator equal to one if the firm records an operational deficit for two prior years, and zero otherwise.

ip t

ST BANK Short-term bank debt matured within one year.

cr

LT BANK Long-term bank debt matured over one year.

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BOND Public bonds plus private bonds.

BANK DEBT Short-term bank debt plus long-term bank debt.

an

ST DEBT Short-term debt matured within one year.

M

LT DEBT Long-term debt matured over one year.

TANGIBLE ASSETS ; tangible assets Net property plant and equipment.

ed

UNDER MED ERATIO An indicator equal to one if the firm has a ratio of equity capital to total capital below the median value in the industry in which the firm

Ac ce

pt

operates, and zero otherwise.

References

[1] Abadie, A., and Imbens, G., 2006. Large sample properties of matching estimators for average treatment effects. Econometrica 74, 235-267. [2] Adrian, T., Colla, P., Shin, H.S., 2012. Which financial frictions? Parsing evidence from the financial crisis of 2007-2009. NBER Working Paper No. 18335. [3] Almeida, H., Campello, M., 2007. Financial Constraints, asset tangibility, and corporate investment. Review of Financial Studies 20, 1429-1460.

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[20] Flannery, M.J., Rangan, K.P., 2006. Partial adjustment toward target capital structures. Journal of Financial Economics 79, 469-506.

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Studies 23, 4242-4280.

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ip t cr us an M ed pt Ac ce Figure 1: Panel A and B represent Diffusion Index (D.I.) of large enterprise in terms of banks’ lending attitudes and public bond issuances from 1993 to 2012, respectively. Data for public bond issuances are from the Japan Securities Dealers Association, and data for D.I. are from the Short-Term Economic Survey of Enterprises (Tankan) released every three months by the Bank of Japan. The Tankan is a statistical survey based on Statistics Law; it includes approximately 210,000 private enterprises (excluding financial institutions) and is constructed as follows. First, the responding firms respond to specific questions, indicating whether a situation is (1) favorable, (2) not so favorable, or (3) unfavorable. The percentage of enterprises responding (3) is then 32 subtracted from the percentage of enterprises responding (1). The right vertical line represents Page 33 of 46 the level of D.I., and the left vertical line represents the amount of public bonds issued per month.

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Figure 2: Public bond yields for three different terms from 2006 to 2012: long-term, mid-term,

Ac ce

pt

ed

and short-term. Data are from the Nikkei Bond Index.

Figure 3: Annual trends in the number of bankruptcies of publicly traded firms. Data are from Teikoku Databank from 1992 to 2011.

33 Page 34 of 46

ip t cr us an M ed pt

Ac ce

Figure 4: The number of bankruptcies among public firms based on industries to which firms belong. Data are from Teikoku Databank from 2007 to 2009. The figure shows the following four classifications of bankrupted firms for each year: (1) all industries; (2) real estate development, construction, real estate fund management, and finance (nonbank) industries; (3) real estate development, construction, and real estate fund management industries; and (4) real estate fund management industries.

34 Page 35 of 46

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Ac ce

Figure 5: The total amount of liabilities of bankrupted public firms based on the industries to which firms belong. Data are from Teikoku Databank from 2007 to 2009. The figure shows the following four classifications of bankrupted firms for each year: (1) all industries; (2) real estate development, construction, real estate fund management, and finance (nonbank) industries; (3) real estate development, construction, and real estate fund management industries; and (4) real estate fund management industries.

35 Page 36 of 46

ip t cr us an

Ac ce

pt

ed

rating agency founded in Japan.

M

Figure 6: Distribution of credit ratings as of August 31, 2012. Data are from R&I, a credit

Figure 7: The total amount of outstanding bank loans to large enterprises and medium and small enterprises. Data are from the Financial and Economic Statistics Monthly by the Bank of Japan. Data described are adjusted so that both datasets as of the fourth quarter 1996 have the same value, 100, so that both datasets show the relative amount to those as of the fourth quarter 1996.

36 Page 37 of 46

ip t

Table 1: Summary statistics

an

ed

M

Capital structures and debt structures 0.24 (0.17) 0.28 (0.16) 0.21 (0.16) 0.21 (0.14) 0.11 (0.11) 0.17 (0.12) 0.13 (0.11) 0.11 (0.08) 0.59 (0.26) 0.42 (0.22) 0.09 (0.10) 0.12 (0.10) 0.12 (0.11) 0.09 (0.08) 0.62 (0.26) 0.49 (0.24) 0.33 (0.24) 0.35 (0.21) 0.56 (0.25) 0.35 (0.22) 0.90 (0.19) 0.73 (0.23) 0.08 (0.17) 0.21 (0.21)

pt

DEBT/ASSETS BANK DEBT/ASSETS LT DEBT/ASSETS ST DEBT/ASSETS ST DEBT/DEBT LT BANK/ASSETS ST BANK/ ASSETS ST BANK/BANK DEBT LT BANK/DEBT ST BANK/ DEBT ST BANK/DEBT BOND/DEBT

No access 1.04 (0.54) 3.91 (0.51) 0.05 (0.12) 0.32 (0.18) 10.32 (1.17) 10.39 (1.25)

us

MB LOG(1+AGE) PROFITABILITY TANGIBILITY LOG(ASSETS) LOG(SALES)

cr

Firm characteristics All Access 1.05 (0.51) 1.15 (0.33) 3.96 (0.20) 4.19 (0.39) 0.05 (0.12) 0.06 (0.06) 0.33 (0.18) 0.37 (0.19) 10.79 (1.56) 12.97 (1.28) 10.83 (1.57) 12.90 (1.28)

Ac ce

INVEST /lagged ASSETS INVEST /lagged TANGIBLE ASSETS

0.05 0.20

Investment (0.05) 0.05 (1.07) 0.15

(0.03) (0.12)

Difference 0.11∗∗∗ 0.28∗∗∗ 0.01∗∗∗ 0.05∗∗∗ 2.66∗∗∗ 2.51∗∗∗

0.23 0.21 0.10 0.13 0.63 0.08 0.19 0.65 0.33 0.61 0.93 0.06

(0.17) (0.16) (0.10) (0.11) (0.26) (0.10) (0.11) (0.25) (0.25) (0.27) (0.16) (0.14)

0.05∗∗∗ 0.00 0.08∗∗∗ -0.02∗∗∗ -0.2∗∗∗ 0.03∗∗∗ -0.1∗∗∗ -0.16∗∗∗ 0.02∗∗∗ -0.25∗∗∗ -0.2∗∗∗ 0.16∗∗∗

0.04 0.21

(0.05) (1.18)

0.01∗∗∗ -0.06∗∗∗

This table shows summary statistics for firm characteristics, capital structures, debt structures, and investments for all firms as well as subsample firm year observations divided based on whether a sample firm has access to the public debt market. The variables are generated based on data from Nikkei Needs Financial Quest. Sample firms are restricted to those that have the same status of access to the public debt market for the whole estimated period from 2004 to 2011. For detailed definitions of each variable, please see the Appendix. White’s heteroskedasticity-consistent standard errors, corrected for correlation across observations of a given firm, are reported in parentheses. The asterisk symbols *, **, and *** denote significance at the 10%, 5%, and 1% levels, respectively.

37 Page 38 of 46

ip t

Table 2: Impact of the credit crunch on debt level and growth

13,834 0.411

13,780 0.414

ed

N R2

us

cr

Debt growth Dummy Probability −0.428∗∗∗ (0.108) −0.526∗∗∗ (0.115) 0.048∗∗∗ (0.017) 0.102∗∗∗ (0.034) 0.106∗∗∗ (0.014) 0.194∗∗∗ (0.023) 0.035 (0.042) 0.062 (0.046) 0.023 (0.031) 0.05 (0.031) −0.031∗∗ (0.014) −0.138∗∗∗ (0.024) −0.016 (0.014) −0.046∗∗ (0.023) −0.034∗ (0.018) −0.065∗∗∗ (0.023) −0.01 (0.010) −0.009 (0.010) −0.18∗∗ (0.083) −0.178∗∗ (0.084) 0.035∗∗ (0.017) 0.034∗∗ (0.017) 0.023∗∗∗ (0.005) 0.027∗∗∗ (0.007) −0.033∗∗ (0.016) −0.034∗∗ (0.017) −1.535∗∗∗ (0.549) −1.518∗∗∗ (0.546) 0.024∗∗∗ (0.008) 0.023∗∗ (0.009) 0.002∗∗ (0.001) 0.003∗∗ (0.001)

an

Constant NO ACCESS LEH08 LEH09 LEHAFTER NO ACCESS*LEH08 NO ACCESS*LEH09 NO ACCESS*LEHAFTER MB PROFITABILITY TANGIBILITY LOG(ASSET BV) LOG(1+AGE) REDOPE2 UNDER MED ERATIO MACRO CI

Leverage Dummy Probability 0.48∗∗∗ (0.088) 0.686∗∗∗ (0.076) −0.041∗∗∗ (0.002) −0.109∗∗∗ (0.007) 0.034∗∗∗ (0.002) 0.042∗∗∗ (0.003) −0.042 (0.026) −0.035 (0.025) −0.014 (0.012) −0.003 (0.012) −0.015∗∗∗ (0.002) −0.026∗∗∗ (0.003) −0.013∗∗∗ (0.001) −0.029∗∗∗ (0.003) −0.016∗∗∗ (0.002) −0.034∗∗∗ (0.004) 0.004 (0.007) 0.002 (0.007) −0.037 (0.023) −0.042∗ (0.023) 0.306∗∗∗ (0.005) 0.3∗∗∗ (0.005) −0.002∗∗∗ (0.000) −0.014∗∗∗ (0.001) −0.042∗∗∗ (0.002) −0.043∗∗∗ (0.003) 0.043 (0.125) −0.003 (0.131) 0.169∗∗∗ (0.003) 0.172∗∗∗ (0.003) −0.002∗∗ (0.001) −0.002∗∗∗ (0.001)

M

NO ACCESS:

13,834 0.017

13,780 0.017

Ac ce

pt

This table shows the regression results for how the credit crunch affected leverage and debt growth in firms with access to the public debt market as compared with those without access throughout the period before the credit crunch (2004–2007) and after (2008–2011). The dependent variables are the ratio of total debt to total assets for Columns 1 and 2, and the ratio of the difference for one year to debt one year prior for Columns 3 and 4. Data are from Nikkei Needs Financial Quest. Sample firms are required to meet the condition of no missing data and constant recognition of access to the public debt market for the whole estimated period. NO ACCESS represents either a dummy variable (Columns 1 and 3) that takes one if the firm has no access to the public debt market, and zero otherwise, or a probability variable (Columns 2 and 4). The probability of access to the public debt market is calculated by applying the coefficients, which are generated by estimating the probit model of whether a firm has a credit rating with R&I from 2007 to 2011, to all sample observations. NO ACCESS, using the probability of access, is then constructed as one minus the generated probability of access. For detailed definitions of the other variables, please see the Appendix. White’s heteroskedasticity-consistent standard errors, corrected for correlation across observations of a given firm, are reported in parentheses. The asterisk symbols *, **, and *** denote significance at the 10%, 5%, and 1% levels, respectively.

38 Page 39 of 46

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Table 3: Impact of the credit crunch on the amount of each type of debt: long-term, short-term,

N R2

an

us

Share of assets Lt debt St debt 0.169∗∗∗ (0.038) 0.311∗∗∗ (0.060) −0.037∗∗∗ (0.003) −0.004 (0.002) 0.022∗∗∗ (0.001) 0.012∗∗∗ (0.001) −0.008 (0.008) −0.034∗ (0.020) 0.004 (0.004) −0.018∗∗ (0.009) ∗∗∗ −0.019 (0.001) 0.004∗∗ (0.002) ∗∗∗ −0.024 (0.001) 0.011∗∗∗ (0.002) −0.02∗∗∗ (0.001) 0.004∗ (0.002) 0.003 (0.003) 0.001 (0.005) 0.025∗∗ (0.012) −0.062∗∗∗ (0.023) 0.229∗∗∗ (0.003) 0.077∗∗∗ (0.002) 0.008∗∗∗ (0.001) −0.01∗∗∗ (0.001) −0.03∗∗∗ (0.002) −0.013∗∗∗ (0.001) 0.048∗ (0.029) −0.005 (0.127) 0.076∗∗∗ (0.001) 0.093∗∗∗ (0.002) −0.001∗∗∗ (0.000) −0.001 (0.001)

M

Constant NO ACCESS LEH08 LEH09 LEHAFTER NO ACCESS *LEH08 NO ACCESS *LEH09 NO ACCESS *LEHAFTER MB PROFITABILITY TANGIBILITY LOG(ASSETS) LOG(1+AGE) REDOPE2 UNDER MED ERATIO MACRO CI

Total debt 0.48∗∗∗ (0.088) −0.041∗∗∗ (0.002) 0.034∗∗∗ (0.002) −0.042 (0.026) −0.014 (0.012) −0.015∗∗∗ (0.002) −0.013∗∗∗ (0.001) −0.016∗∗∗ (0.002) 0.004 (0.007) −0.037 (0.023) 0.306∗∗∗ (0.005) −0.002∗∗∗ (0.000) −0.042∗∗∗ (0.002) 0.043 (0.125) 0.169∗∗∗ (0.003) −0.002∗∗ (0.001)

ed

Debt type:

cr

and bank debt

13,834 0.411

13,834 0.361

13,834 0.253

Bank debt 0.355∗∗∗ (0.074) 0.008∗∗∗ (0.003) 0.037∗∗∗ (0.002) −0.012 (0.022) 0.004 (0.010) −0.015∗∗∗ (0.002) −0.017∗∗∗ (0.002) −0.019∗∗∗ (0.003) 0.004 (0.007) −0.074∗∗∗ (0.025) 0.29∗∗∗ (0.005) −0.005∗∗∗ (0.000) −0.034∗∗∗ (0.002) −0.053 (0.128) 0.157∗∗∗ (0.003) −0.001∗ (0.001) 13,834 0.391

Ac ce

pt

This table shows the regression results for how the credit crunch affected specific debt levels in firms with access to the public debt market compared to those without access throughout the period before the credit crunch (2004–2007) and after it (2008–2011). The dependent variables are the ratios of total debt, long-term debt, short-term debt, and bank debt to total assets in Columns 1 (reproduced from Table 2 for comparison purposes), 2, 3, and 4, respectively. Data are from Nikkei Needs Financial Quest. Sample firms are required to meet the condition of no missing data and constant recognition of access to the public debt market for the whole estimated period. NO ACCESS represents a dummy variable that takes one if the firm has no access to the public debt market, and zero otherwise. For detailed definitions of the other variables, please see the Appendix. White’s heteroskedasticity-consistent standard errors, corrected for correlation across observations of a given firm, are reported in parentheses. The asterisk symbols *, **, and *** denote significance at the 10%, 5%, and 1% levels, respectively.

39 Page 40 of 46

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N R2

13,834 0.321

Lt bank 0.057 0.015∗ 0.043∗∗∗ 0.093∗∗∗ 0.073∗∗∗ −0.042∗∗∗ −0.04∗∗∗ −0.033∗∗∗ 0.006∗∗ 0.008 0.387∗∗∗ 0.013∗∗∗ −0.036∗∗∗ −0.103 0.044∗∗∗ 0.001∗∗∗

13,834 0.253

Share debt (0.037) (0.009) (0.005) (0.007) (0.006) (0.007) (0.007) (0.007) (0.003) (0.017) (0.011) (0.002) (0.003) (0.068) (0.003) (0.000)

of debt St bank 0.483∗∗∗ 0.186∗∗∗ 0.019∗∗∗ 0.048∗∗∗ 0.023∗∗∗ −0.01∗∗∗ −0.016∗∗∗ −0.013∗ −0.015∗∗∗ −0.12∗∗∗ −0.338∗∗∗ −0.026∗∗∗ 0.055∗∗∗ −0.25∗ −0.043∗∗∗ 0.001∗∗∗

13,834 0.110

debt (0.091) (0.009) (0.002) (0.017) (0.008) (0.003) (0.003) (0.007) (0.004) (0.030) (0.004) (0.003) (0.003) (0.138) (0.004) (0.001)

us

debt (0.054) (0.003) (0.001) (0.018) (0.008) (0.002) (0.002) (0.002) (0.005) (0.024) (0.002) (0.001) (0.001) (0.124) (0.002) (0.001)

an

Share of assets debt St bank (0.026) 0.293∗∗∗ (0.003) 0.012∗∗∗ (0.001) 0.012∗∗∗ (0.004) −0.031∗ (0.002) −0.014∗ (0.002) 0.005∗∗ (0.002) 0.007∗∗∗ (0.002) 0 (0.003) 0.001 (0.009) −0.067∗∗∗ (0.004) 0.076∗∗∗ (0.001) −0.01∗∗∗ (0.002) −0.011∗∗∗ (0.024) −0.034 (0.001) 0.089∗∗∗ (0.000) −0.001∗

M

Constant NO ACCESS LEH08 LEH09 LEHAFTER NO ACCESS*LEH08 NO ACCESS*LEH09 NO ACCESS*LEHAFTER MB PROFITABILITY TANGIBILITY LOG(ASSET BV) LOG(1+AGE) REDOPE2 UNDER MED ERATIO MACRO CI

Lt bank 0.062∗∗ −0.004 0.025∗∗∗ 0.019∗∗∗ 0.018∗∗∗ −0.02∗∗∗ −0.024∗∗∗ −0.019∗∗∗ 0.003 −0.008 0.213∗∗∗ 0.004∗∗∗ −0.023∗∗∗ −0.02 0.067∗∗∗ 0∗∗

ed

Debt type:

cr

Table 4: Impact of the credit crunch on long-term and short-term bank debt

13,834 0.196

Share of bank debt St bank debt 0.785∗∗∗ (0.049) 0.073∗∗∗ (0.008) −0.036∗∗∗ (0.004) −0.053∗∗∗ (0.007) −0.049∗∗∗ (0.005) 0.041∗∗∗ (0.006) 0.037∗∗∗ (0.005) 0.027∗∗∗ (0.005) −0.012∗∗∗ (0.003) −0.056∗∗∗ (0.018) −0.406∗∗∗ (0.011) −0.023∗∗∗ (0.003) 0.05∗∗∗ (0.003) −0.007 (0.090) −0.047∗∗∗ (0.003) 0 (0.000) 13,784 0.161

Ac ce

pt

This table shows the regression results for how the credit crunch affected long-term and short-term bank debt in firms with access to the public debt market compared to those without access throughout the period before the credit crunch (2004–2007) and after it (2008–2011). The dependent variables are the ratio of long-term and short-term bank debt to total assets (Columns 1 and 2), total debt (Columns 3 and 4), and bank debt (Column 5). Data are from Nikkei Needs Financial Quest. Sample firms are required to meet the condition of no missing data and constant recognition of access to the public debt market for the whole estimated period. NO ACCESS represents a dummy variable that takes one if the firm has no access to the public debt market, and zero otherwise. For detailed definitions of the other variables, please see the Appendix. White’s heteroskedasticity-consistent standard errors, corrected for correlation across observations of a given firm, are reported in parentheses. The asterisk symbols *, **, and *** denote significance at the 10%, 5%, and 1% levels, respectively.

40 Page 41 of 46

N R2

12,848 0.073

12,800 0.073

ip t

us

Invest/lagged Dummy 1.086∗∗∗ (0.258) −0.013 (0.030) 0.036 (0.026) −0.027 (0.032) 0.009 (0.015) −0.055∗∗∗ (0.011) −0.052∗∗∗ (0.011) −0.056∗∗∗ (0.016) 0.14∗∗∗ (0.017) −0.064 (0.140) 0.039 (0.139) −0.187∗∗∗ (0.033)

an

C NO ACCESS LEH08 LEH09 LEHAFTER NO ACCESS*LEH08 NO ACCESS*LEH09 NO ACCESS*LEHAFTER MB LOG(SALES) LOG( lagged SALES) LOG(1+AGE)

Invest/lagged assets Dummy Probability 0.079∗∗∗ (0.013) 0.123∗∗∗ (0.016) −0.007∗∗∗ (0.002) −0.024∗∗∗ (0.004) 0.01∗∗∗ (0.002) 0.015∗∗∗ (0.003) −0.003 (0.003) 0.001 (0.004) −0.005∗ (0.003) −0.004 (0.004) −0.005∗∗∗ (0.001) −0.01∗∗∗ (0.003) −0.002∗ (0.001) −0.007∗∗∗ (0.003) −0.001 (0.001) −0.003 (0.003) 0.014∗∗∗ (0.001) 0.013∗∗∗ (0.001) 0.042∗∗∗ (0.009) 0.041∗∗∗ (0.009) −0.042∗∗∗ (0.010) −0.043∗∗∗ (0.009) −0.011∗∗∗ (0.002) −0.011∗∗∗ (0.001)

M

NO ACCESS:

cr

Table 5: Impact of the credit crunch on investment

12,848 0.016

tangible assets Probability 1.251∗∗∗ (0.404) −0.07 (0.088) 0.084∗∗∗ (0.029) 0.013 (0.034) 0.042∗∗ (0.021) −0.113∗∗∗ (0.028) −0.099∗∗∗ (0.028) −0.097∗∗∗ (0.036) 0.133∗∗∗ (0.019) −0.072 (0.140) 0.038 (0.137) −0.187∗∗∗ (0.035) 12,800 0.015

Ac ce

pt

ed

This table shows the regression results for how the credit crunch affected investment by firms with access to the public debt market compared to those without access throughout the period before the credit crunch (2004–2007) and after it (2008–2011). The dependent variables are the ratio of investment expenditure to lagged total assets for Columns 1 and 2, and to lagged tangible assets for Columns 3 and 4. Data are from Nikkei Needs Financial Quest. Sample firms are required to meet the condition of no missing data and constant recognition of access to the public debt market for the whole estimated period. NO ACCESS represents either a dummy variable (Columns 1 and 3) that takes one if the firm has no access to the public debt market, and zero otherwise, or a probability variable (Columns 2 and 4). The probability of access to the public debt market is calculated by applying the coefficients, which are generated by estimating the probit model of whether a firm has a credit rating with R&I from 2007 to 2011 to all sample observations. NO ACCESS using the probability of access is then constructed as one minus the generated probability of access. For detailed definitions of the variables, please see the Appendix. White’s heteroskedasticity-consistent standard errors, corrected for correlation across observations of a given firm, are reported in parentheses. The asterisk symbols *, **, and *** denote significance at the 10%, 5%, and 1% levels, respectively.

41 Page 42 of 46

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us

Invest/lagged assets St bank debt/bank debt Bank debt/assets 0.091∗∗∗ (0.010) 0.065∗∗∗ (0.013) 0.006∗∗∗ (0.002) −0.01∗∗∗ (0.003) 0.003 (0.002) 0.007∗∗∗ (0.001) −0.013∗∗∗ (0.003) 0.001 (0.002) −0.011∗∗∗ (0.002) −0.002∗ (0.001) −0.027∗∗∗ (0.004) 0.02∗∗ (0.008) −0.015∗∗∗ (0.004) 0.02∗∗ (0.008) 0.012∗∗∗ (0.002) 0.012∗ (0.007) ∗∗∗ 0.013 (0.002) −0.009 (0.007) 0.008∗∗∗ (0.003) −0.003 (0.009) −0.008∗∗∗ (0.001) −0.024∗∗∗ (0.006) −0.002 (0.001) −0.019∗∗∗ (0.005) 0 (0.003) −0.018∗∗ (0.008) 0.014∗∗∗ (0.001) 0.014∗∗∗ (0.001) 0.04∗∗∗ (0.009) 0.044∗∗∗ (0.009) −0.04∗∗∗ (0.009) −0.043∗∗∗ (0.010) −0.01∗∗∗ (0.002) −0.01∗∗∗ (0.002)

an

St debt/debt Constant 0.091∗∗∗ (0.011) NO ACCESS 0.003 (0.002) LEH08 0.004 (0.002) LEH09 −0.011∗∗∗ (0.003) LEHAFTER −0.01∗∗∗ (0.002) DEBTTYPE −0.032∗∗∗ (0.003) NO ACCESS *DEBTTYPE −0.007∗∗ (0.003) LEH08 *DEBTTYPE 0.014∗∗∗ (0.002) LEH09 *DEBTTYPE 0.014∗∗∗ (0.002) LEHAFTER *DEBTTYPE 0.01∗∗∗ (0.002) NO ACCESS *LEH08 *DEBTTYPE −0.012∗∗∗ (0.002) NO ACCESS *LEH09 *DEBTTYPE −0.004∗∗∗ (0.002) NO ACCESS *LEHAFTER *DEBTTYPE −0.003 (0.002) MB 0.014∗∗∗ (0.001) LOG(SALES) 0.04∗∗∗ (0.009) LOG( lagged SALES) −0.04∗∗∗ (0.010) LOG(1+AGE) −0.01∗∗∗ (0.002) N R2

ed

M

DEBTTYPE:

cr

Table 6: Impact of credit crunch on investment through debt structures

12,848 0.102

12,816 0.105

12,848 0.082

St debt/assets −0.031∗∗∗ (0.010) −0.01∗∗∗ (0.003) 0.007∗∗∗ (0.001) 0.001 (0.002) −0.002∗ (0.001) 0.02∗∗ (0.008) 0.02∗∗ (0.008) 0.012∗ (0.007) −0.009 (0.007) −0.003 (0.009) −0.024∗∗∗ (0.006) −0.019∗∗∗ (0.005) −0.018∗∗ (0.008) 0.014∗∗∗ (0.001) 0.044∗∗∗ (0.009) −0.043∗∗∗ (0.010) −0.01∗∗∗ (0.002) 12,848 0.075

Ac ce

pt

This table shows the regression results for how firms dependent on banks are affected by the credit crunch through the effect interacted with the specific debt structures as compared with firms with access to public debt. The estimated period spans the period before the credit crunch (2004–2007) and the period after (2008–2011). The dependent variable is the ratio of investment expenditure to lagged total assets. Data are from Nikkei Needs Financial Quest. Sample firms are required to meet the condition of no missing data and constant recognition of access to the public debt market for the whole estimated period. NO ACCESS represents a dummy variable that takes one if the firm has no access to the public debt market, and zero otherwise. For detailed definitions of the other variables, please see the Appendix. White’s heteroskedasticity-consistent standard errors, corrected for correlation across observations of a given firm, are reported in parentheses. The asterisk symbols *, **, and *** denote significance at the 10%, 5%, and 1% levels, respectively.

42 Page 43 of 46

Table 7: Propensity score-matching diagnosis on the effect of credit crunch on leverage Panel A

Credit crunch effect on leverage: (2006 vs. 2009) ATE (DID) Treatment.obs Control.obs (sd.error) (sd.error) (droped.obs) (unique.obs) Total 0.022∗∗∗ (0.007) 0.014∗∗∗ (0.003) 303 (0) 303 (121) 1,732 0.007 (0.007) 0.011∗∗∗ (0.004) 303 (0) 606 (192) 1,732 0.032∗∗∗ (0.008) 0.021∗∗∗ (0.004) 243 (60) 972 (295) 1,732 0.04∗∗∗ (0.008) 0.024∗∗∗ (0.004) 237 (66) 1,422 (359) 1,732 0.036∗∗∗ (0.008) 0.02∗∗∗ (0.004) 228 (75) 1,824 (394) 1,732 0.032∗∗∗ (0.007) 0.011∗∗∗ (0.004) 207 (96) 2,070 (430) 1,732

ATT

Credit crunch effect on leverage: (1996 vs 1998) ATE (DID) Treatment.obs Control.obs (sd.error) (sd.error) (droped.obs) (unique.obs) Total −0.089∗∗∗ (0.009) −0.026∗∗∗ (0.003) 145 (0) 145 (74) 1,405 0.004 (0.010) 290 (117) −0.014∗∗∗ (0.003) 145 (0) 1,405 0.073∗∗∗ (0.010) −0.003 (0.003) 145 (0) 580 (179) 1,405 0.038∗∗∗ (0.010) 0.001 (0.002) 117 (28) 702 (223) 1,405 0.018∗ (0.009) 0.001 (0.003) 111 (34) 888 (266) 1,405 0.017∗ (0.009) 0.001 (0.003) 106 (39) 1,060 (304) 1,405

us

Panel B

cr

ip t

M 1 2 4 6 8 10

M

M 1 2 4 6 8 10

an

ATT

Ac ce

pt

ed

This table shows the results from propensity score-matching diagnosis of the difference in the effect of the credit crunch on leverage between the differences in leverage of treatment firms with access to the public debt market and the differences of control firms without. ATT denotes the average treatment effect for the treated and is computed as the average leverage of treatment firms less the average of control matched firms after the credit crunch, where the matching pairs are constructed using propensity scores generated from firm characteristics before the credit crunch. ATE (DID) denotes the average treatment effect and is computed as the average difference in leverage of treatment firms between after and before the credit crunch less the average difference in leverage of control matched firms between after and before the credit crunch, where the matching pairs are constructed using propensity scores generated from characteristics before the credit crunch. Data are from Nikkei Needs Financial Quest. Sample firms are required to meet the condition of no missing data and constant recognition of access to the public debt market for the whole period from 2004 to 2009 for the matching diagnosis of the credit crunch of 2008 (panel A), and from 1995 to 2000 for the Asian and Russian crises (Panel B). In Panel A (B), the diagnosis is made by comparing leverages of sample firms as of 2006 (1996), one fiscal year before the credit crisis event, with those of corresponding firms in 2009 (1998), one fiscal year after the credit crisis. The propensity score is equal to the probability calculated by the estimation of the probit model where the dependent variable is an indicator of whether the firm has access to the public debt market and the independent variables are growth opportunity, profitability, firm size, firm age, and leverage, defined as the ratio of debt to total assets, all before the event. The nearest neighborhood caliper-matching approach is used in generating matched pairs, where we ensure that the treatment (access to the public debt market) firm’s propensity score is within one standard deviation of the control (dependent on banks) firm’s score, so that samples are dropped by that measure. M denotes the number by which a treatment firm is matched to control firms. Matching is done with replacement. Treatment.obs (Control.obs) is the number of treatment (control) group observations used in matching. The standard error (denoted as sd.error) is reported using the bootstrap method with 1,000 replications. The asterisk symbols *, **, and *** denote significance at the 10%, 5%, and 1% levels, respectively.

43 Page 44 of 46

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Table 8: Propensity score-matching diagnosis on the effect of credit crunch on investment

Ac ce

pt

ed

M

an

us

cr

Credit crunch effect on investment: (2006 vs. 2009) ATT ATE (DID) Treatment.obs Control.obs M (sd.error) (sd.error) (droped.obs) (unique.obs) Total 1 0.008∗∗∗ (0.002) 0.003∗∗∗ (0.002) 280 (0) 280 (120) 1,566 2 0.01∗∗∗ (0.002) 0.003 (0.002) 280 (0) 560 (187) 1,566 4 0.009∗∗∗ (0.002) 0.006∗∗∗ (0.002) 238 (42) 952 (284) 1,566 6 0.006∗∗∗ (0.002) 0.007∗∗∗ (0.002) 228 (52) 1,368 (341) 1,566 8 0.003∗∗∗ (0.002) 0.008∗∗∗ (0.002) 197 (83) 1,576 (397) 1,566 ∗ ∗∗∗ 10 0.004 (0.002) 0.008 (0.002) 170 (110) 1,700 (439) 1,566 This table shows the results from propensity score-matching diagnosis of the difference in the effect of the credit crunch on investments between the differences in investments of treatment firms with access to the public debt market and the differences of control firms without it. ATT denotes the average treatment effect for the treated firms and is computed as the average investment of the treatment firms less the average of the control matched firms after the credit crunch, where the matching pairs are constructed using propensity scores generated from firm characteristics before the credit crunch. ATE (DID) denotes the average treatment effect and is computed as the average difference in the investment of treatment firms between after and before the credit crunch less the average difference in investment of control matched firms between after and before the credit crunch, where the matching pairs are constructed using propensity scores generated from characteristics before the credit crunch. Data are from Nikkei Needs Financial Quest. Sample firms are required to meet the condition of no missing data and constant recognition of access to the public debt market for the whole period from 2004 to 2009 for the matching diagnosis of the credit crunch of 2008. The diagnosis is made by comparing investments of sample firms as of 2006, a fiscal year before the credit crisis event, with those of corresponding firms as of 2009, a fiscal year after the credit crisis. The propensity score is equal to the probability calculated by estimation of the probit model where the dependent variable is an indicator of whether the firm has access to the public debt market, and the independent variables are growth opportunity, profitability, firm size, firm age, and investment defined as the ratio of debt to lagged total assets, all before the event. The nearest neighborhood caliper-matching approach is used in generating matched pairs, where we ensure that the treatment (access to the public debt market) firm’s propensity score is within one standard deviation of the control (dependent on banks) firm’s score, so that samples are dropped by that measure. M denotes the number by which a treatment firm is matched to control firms. Matching is done with replacement. Treatment.obs (Control.obs) is the number of treatment (control) group observations used in matching. The standard error (denoted as sd.error) is reported using the bootstrap method with 1,000 replications. The asterisk symbols *, **, and *** denote significance at the 10%, 5%, and 1% levels, respectively.

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Highlights ⚫

We investigate whether and how the debt market friction works in corporate finance. We focus on the global financial crisis of 2008 in Japan as a natural experiment.

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Firms without access to public debt decrease their leverage and investment.

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Bank dependent firms decrease not just leverage but their bank loans relatively.

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Bank dependent firms face underinvestment after the crisis.

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