Bank loan supply shocks and leverage adjustment

Journal Pre-proof Bank loan supply shocks and leverage adjustment Masayo Shikimi PII:

S0264-9993(19)30379-7

DOI:

https://doi.org/10.1016/j.econmod.2019.11.020

Reference:

ECMODE 5078

To appear in:

Economic Modelling

Received Date: 14 March 2019 Revised Date:

11 November 2019

Accepted Date: 19 November 2019

Please cite this article as: Shikimi, M., Bank loan supply shocks and leverage adjustment, Economic Modelling (2020), doi: https://doi.org/10.1016/j.econmod.2019.11.020. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. 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. © 2019 Published by Elsevier B.V.

Bank Loan Supply Shocks and Leverage Adjustment∗ Masayo Shikimi+ Nagasaki University November 1, 2019

Abstract We investigate the effect of bank loan supply shocks on firms’ leverage adjustment. We show that the impact of bank shocks is larger for firms with greater dependence on financially troubled banks. We measure firms’ pre-crisis loan dependence on troubled banks by using matched firm–bank loan data. Using the boom-bust cycle from 1988 to 2014 in Japan as a quasi-experiment, we find that financially constrained firms adjust their leverage slower during credit-crunch periods than during other periods. During credit-crunch periods following banking crisis, firms associated with failing banks or with banks that have a limited capacity to supply loans show a slower adjustment than other firms. Bank shocks have significant effects on small firms’ adjustment but not on that of large firms. These results are robust when we consider demand-side effects and perform other robustness tests. Our results imply that bank shocks have a persistent effect on borrowers’ leverage.

Keywords: banking crisis, bank financial weakness, financial constraints, leverage adjustment, supply shocks JEL classification: G10, G20, G32

∗

I thank Kaoro Hosono, Kazumi Asako, Masaharu Hanasaki, and participants for IFABS conference, annual conference of Japan Economic Society, the 14th ICAFM, and DBJ seminar for their valuable comments, three anonymous referees and the editor. I gratefully acknowledge the financial support of Faculty of Economics, Nagasaki University. + Faculty of Economics, Nagasaki University, 4-2-1, Katafuchi, Nagasaki 850-8506, Japan. Phone: +81-95-820-6323; Fax: +81-95-820-6323; e-mail: [email protected]

Bank Loan Supply Shocks and Leverage Adjustment Abstract

We investigate the effect of bank loan supply shocks on firms’ leverage adjustment. We show that the impact of bank shocks is larger for firms with greater dependence on financially troubled banks. We measure firms’ pre-crisis loan dependence on troubled banks by using matched firm–bank loan data. Using the boom-bust cycle from 1988 to 2014 in Japan as a quasi-experiment, we find that financially constrained firms adjust their leverage slower during credit-crunch periods than during other periods. During credit-crunch periods following banking crisis, firms associated with failing banks or with banks that have a limited capacity to supply loans show a slower adjustment than other firms. Bank shocks have significant effects on small firms’ adjustment but not on that of large firms. These results are robust when we consider demand-side effects and perform other robustness tests. Our results imply that bank shocks have a persistent effect on borrowers’ leverage.

Keywords: banking crisis, bank financial weakness, financial constraints, leverage adjustment, supply shocks JEL classification: G10, G20, G32

1

1. Introduction Recent financial crises reveal that supply-side shocks significantly affect corporate financing behavior. Firms experience a decrease in credit financing and investment (Campello et al., 2010; Duchin et al., 2010; Ivashina and Scharfstein, 2010),1 resulting in low economic growth. Previous studies have found that the adverse effect of a financial crisis is particularly large for bank-dependent firms (e.g., Carvalho et al., 2016). However, few studies have investigated the lasting effect of bank shocks to the borrowers’ financing policy. How persistent are the effects of bank-specific shocks to the borrowers’ leverage? How quickly do borrowers adjust to bank loan supply shocks? Does their adjustment behavior vary, depending on their financial constraints and the degree of deterioration of their banks’ financial health? In this paper, we aim to answer these questions. According to the traditional capital structure theory, firms determine their target leverage by balancing their debt costs and benefits. In the absence of capital market friction and adjustment costs, firms revert their capital structure to the target level immediately when the leverage deviates from the target. However, adjustments take time in the presence of adjustment costs, which include transaction costs. Transaction costs decrease during an economic boom because financing costs decrease and firms’ ability to access capital markets increases. Thus, firms adjust their leverage faster in an economic boom than in a recession because of lower adjustment costs (Cook and Tang, 2010). During an economic downturn, bank credit is scarce, and the effects of credit market tightness are more evident for small firms (Faulkender et al., 2012; Drobetz et al., 2015). However, few studies have directly investigated the effect of bank credit fluctuations, particularly idiosyncratic bank shocks, on firms’ capital structure adjustment, with the exception of the study of Dang et al. (2014), which examines the effect of the 2008 global financial crisis on US firms’ speed of adjustment (SOA). Moreover, the negative effects of credit contraction following a bank

1

Chava and Purnanandam (2011), Garcia-Appendini and Montoriol Garriga (2013), and Becker and Ivashina (2014) provide the evidence on substitution of bank loans with other types of credit, such as non-bank debt and trade credit. Other studies on the effect of credit supply conditions on firm leverage and financing decisions include those of Bhamra et al. (2010), Lemmon and Roberts (2010), and Becker and Ivashina (2014).

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crisis are expected to be greater for firms with higher dependence on troubled banks. However, to the best of our knowledge, no study has investigated the heterogeneous propagation of a bank’s financial condition to the leverage adjustment of its borrowers although a growing number of studies have investigated the effects of bank health on borrower stock price and investment (Ongena et al., 2003; Khawaja and Mian, 2008; Chava and Purnanandam, 2011). Examining the effect of bank shocks on the SOA is important because the delay in growing firms’ adjustments possibly leads to slower economic recovery. In this study, we investigate the effects of bank credit expansion and contraction on firms’ capital structure adjustment. By using the firm–bank loan matched data, we measure the firms’ exposure to bank-specific shocks and investigate whether bank loan supply shocks have differential effects on the firms’ rebalancing behavior, depending on a firm’s financial constraints and its relational bank’s ability to extend credit. Prior studies have found that financially unconstrained firms adjust their capital structure faster than financially constrained ones because of the relatively lower transaction costs incurred by the former.2 Extending these studies, we hypothesize that the adjustment speed differs according to a firm’s financial constraints during a credit contraction period. This is because financially constrained firms face more difficulty in obtaining credit during credit contraction periods due to the increase in transaction costs, or they face credit rationing, whereas financially unconstrained firms alter their financing sources (Leary, 2009; Becker and Ivashina, 2014). Furthermore, we test whether the relational banks’ ability to supply loans affects the firms’ rebalancing behavior during credit contraction periods. Firms face difficulty in obtaining credit from non-relational banks because such banks consider them lemons (adverse selection problems). Thus, financially constrained (or bank-dependent) firms are more vulnerable to their banks’ ability to supply loans (Khawaja and Mian, 2008). We use the Japanese data for three reasons. First, Japanese firms are more dependent on bank

2

Faulkender et al. (2012) find that financially unconstrained firms adjust faster than financially constrained ones when they are underleveraged. 3

loans compared with those in the US and the United Kingdom; therefore, Japanese firms are more likely to be affected by the aggregate loan supply in the macro economy (Allen and Gale, 2000). Second, Japan has experienced sharp fluctuations in credit supply for the past three decades. Figure 1 depicts the growth rate of aggregate lending by Japanese banks. The growth rate of bank loans peaked in the fiscal year 1989 and declined sharply after the collapse of the bubble economy in 1991. After the banking crisis in 1998, bank lending showed a negative growth rate until 2004. This boom–bust cycle provides an ideal setting for a quasi-experiment to identify the effect of fluctuations in bank loan supply on a firm’s capital structure adjustment. Third, Japanese firms disclose their bank relationships and loans from each bank, enabling us to construct firm–bank matched data and measure the firms’ exposure to bank-specific shocks. As Hoshi and Kashyap (2010, 2015) state, there are many similarities between Japan’s banking crisis in the 1990s and the 2008 global financial crisis. 3 Furthermore, the post-banking crisis development of the macro economy is also similar in Japan and Europe; both have experienced sluggish economic growth (Hoshi and Kashyap, 2015). Japan’s experience of the boom–bust cycle and its effect on a firm’s dynamic capital structure adjustment provide important lessons for the worldwide experience of the financial crisis. Examining the data from Japanese-listed firms from 1988 to 2014, we find that firms adjust their capital structure slower during credit contraction periods than during other periods. Moreover, the effects of credit market tightness are more evident among small firms. Examining the firm–bank matched data, we also find that credit supply shocks have different effects on the rebalancing behavior of firms. Firms that are associated with failing banks or with banks that have a limited capacity to supply loans show a slower adjustment, suggesting that a bank’s financial weakness has a serious effect on the capital structure adjustment of the firms with a relationship to it. Our estimate shows that small firms

associated with failing banks take 8.9 years to revert to their target level during credit

crunch periods following a banking crisis. Our results are robust when we consider demand-side factors and alter adjustment cost functions. These results imply that supply-side shocks cause the

3

Both crises stemmed from the burst of the real-estate bubble. After the banking crisis, both Japan and the US experienced credit tightness. 4

leverage of firms, particularly the bank-dependent ones, to drop to a suboptimal level for a long period of time (Leary, 2009; Voutsinas and Werner, 2011). Our findings have several implications regarding capital structure adjustment. First, credit fluctuation adversely affects the adjustment of bank-dependent firms. Second, bank shocks have an adverse effect on borrowers’ capital structure adjustment during credit crunch periods following a banking crisis. The borrowing firms’ SOA becomes slower as their banks’ financial variables deteriorate. Our paper contributes to the literature on the effects of supply-side shocks on banking, corporate finance, and monetary policy in the following manner. Our research adds to the few studies on the effect of bank shocks on the capital structure adjustment of borrowers. Our contribution to the literature on capital structure adjustment lies in exploring the relation between firms’ exposure to bank shocks and the firms’ leverage adjustment policy by using the firm–bank matched data. Other studies have found that global financial crises adversely affect firms’ bank borrowing (Ivashina and Scharfstein, 2010) and their investment (Campello et al., 2010; Duchin et al., 2010).4 In their surveys involving corporate managers, Campello et al. (2010) find that firms forego profitable investments because of the tightened financial constraints after a crisis. Similarly, we find that bank-dependent firms with financial deficits show a slower adjustment during credit contraction periods, suggesting those firms’ possibility to forego profitable investment opportunities. Our study also relates to several works on the transmission of a bank’s financial conditions to its borrowers (Ongena et al., 2003; Khawaja and Mian, 2008; Chava and Purnanandam, 2011). Some researchers (Ongena et al., 2003; Chava and Purnanandam, 2011) have investigated the borrowers’ stock-price reaction to negative bank shocks. Using the firm–bank matched data, Khawaja and Mian (2008) find that small firms with no ability to hedge bank-liquidity shocks suffer large drops in overall loans, but large firms compensate for the loss. We find similar effects of bank-specific shocks on the capital structure adjustment of small borrowers. Our study also broadly relates to the monetary

4

Gan (2007) also finds that the banking crisis in Japan affects firm investment and market value through a lending channel. 5

economics literature on monetary policy transmission channels. Garcia-Posada and Marchetti (2016) investigate the effect of European Central Bank’s monetary policy (after the European sovereign debt crisis) on bank lending and find that very long-term refinancing operations have a positive effect on the credit supply to small and medium-sized firms.5 Using bank loan application data, Jiménez et al. (2012) examine whether the effects of monetary policies and macroeconomic conditions on banks’ granting of loans vary, depending on each bank’s balance-sheet strength. They find that banks with a low-capital ratio approve fewer loan applications during periods of tighter monetary conditions. We find that the banks’ limited capacity to supply loans has an adverse effect on the leverage adjustment of smaller firms during lower policy-rate periods, suggesting that a bank’s financial condition and the regulations on the capital-adequate ratio alter the effectiveness of monetary policies. We have organized the remainder of this paper in the following manner. In Section 2, we review the empirical literature on capital structure adjustment and present the testing hypotheses. In Section 3, we describe credit expansion and contraction in Japan. In Section 4, we present our sample data and our basic estimation strategy. In Section 5, we discuss the estimation results. Finally, we present the conclusions in Section 6.

2. Financial constraints, fluctuations in credit supply, and capital structure adjustment: The hypotheses Numerous empirical studies have examined the rebalancing of the capital structure and tested the trade-off theory. Firms adjust their capital structure by balancing the costs and the benefits of adjustment. The costs of adjustment include transaction costs, such as information asymmetry. Transaction costs are higher for informationally opaque firms than for transparent firms; this difference creates debt market segmentation, with the former subject to private bank monitoring. The latter can raise funds in the public bond markets. Previous studies have found that unrated (financially unconstrained) firms adjust slower than rated ones (Korajczyk and Levy, 2003; Elsas and Florysiak,

5

Other empirical studies on the bank-lending channel of unconventional monetary policy include those of Rodnyansky and Darmouni (2017) for the US and Bowman et al. (2015) for Japan. D’Avino (2018) investigates the international spillover effects of US monetary policy by US global banks. Morais et al. (2019) examine the spillover effects of US and European monetary policies on the Mexican economy. 6

2011; Faulkender et al., 2012) because of the relatively higher transaction costs. Transaction costs are also affected by macroeconomic conditions. Transaction costs are lower during an economic boom (a credit expansion period) than during a recession (a credit contraction period). During an economic expansion (recession), firm profits increase (decrease), and the tax-benefit of debt increases (decreases). Moreover, expected bankruptcy costs decrease (increase), which raises (lowers) a firm’s target leverage. Furthermore, collateral value increases (decreases) during an economic expansion (recession), resulting in lower (higher) transaction costs of bank loans (Gertler and Gilchrist, 1993). Thus, firms can borrow more (less) during a credit expansion (contraction). The increased transaction costs of bank loans affect informationally opaque firms disproportionally because they cannot substitute bank loans with public debts. Using Japanese firm data, Gan (2007) finds that bank loan-dependent firms became financially constrained after the collapse of the bubble economy because their collateral value severely decreased. In contrast, transparent firms can replace bank loans with public debts. Therefore, the capital structure adjustment of transparent firms is less affected by fluctuations in the bank loan supply. Another scenario is that creditors become more risk-averse during an economic recession (a credit contraction period) because of the increased economic uncertainty and shift their capital to firms with higher creditworthiness (fight for quality) (Caballero and Krishnamurthy, 2008). Thus, credit tightness affects bank-dependent firms more than less risky firms with alternative sources of credit. In this context, we propose the following hypotheses: H1. Financially constrained (bank-dependent) firms adjust their capital structure slower during credit contraction periods than during credit expansion periods. H2. During credit contraction periods, financially constrained firms adjust slower than financially unconstrained firms. Furthermore, the negative effect of credit contraction is more severe for firms whose banks have a limited capacity to supply loans.6 In this context, we propose the following hypothesis:

6

Jiménez et al. (2012) argue that the negative effect of macroeconomic conditions on credit availability depends on the capital asset ratio of banks. Other studies on the effect of banks’ financial conditions on 7

H3. Firms whose banks have a limited capacity to supply loans adjust slower during credit contraction periods.

3. Credit expansion and contraction in Japan Japan’s economy experienced a land bubble during the late 1980s and subsequently, a prolonged recession between the 1990s and the 2000s, which is also known as the Lost Decade. The average GDP growth rate was 4.4% during the 1980s but declined to 1.5% in the 1990s and to 0.53% in the 2000s. Japan has suffered from a chronic deflation since the late 1990s (Nishizaki et al., 2014). During the land bubble, land and stock prices skyrocketed. The Nikkei Stock Average increased to its highest-ever level of 38,915 yen in 1989 but sharply declined to 23,848 yen in 1990. The CPI inflation rate also hit its peak of 3.3% in 1991 but turned negative in 1998. The asset bubble ended in 1991, causing land prices to decline sharply. Japanese banks were burdened with non-performing loans because of the increased loan supply to risky sectors during the bubble economy. Furthermore, the late 1990s witnessed a financial crisis, in which major securities companies (e.g.,Yamaichi Shoken and Sanyo Shoken), city banks (e.g., Hokkaido Takushoku Bank), and long-term trust banks (e.g., Japan Long-term Trust Bank and Japan Credit Trust Bank) became bankrupt (in 1997 and 1998). Furthermore, 13 regional and second-tier regional banks became bankrupt over the 1995–2003 period. In October 1998, the Financial Revitalization Law was enforced, and in 1999, the Japanese government rescued a few Japanese commercial banks. The capital injection into those troubled banks amounted to 13.3 trillion yen (2.5% of the nominal GDP) in 1998 and 7.5 trillion yen (1.4% of the nominal GDP) in 1999 (Hoshi and Kashyap, 2010). In 1997, the Ministry of Finance required banks to carry out more rigorous self-assessment of their assets and loan-loss write-offs and provisions to raise the capital ratio. In response to these regulations, banks cut their loan supplies sharply in 1997, causing the regulatory-driven credit crunch (Watanabe, 2007). As for the monetary policy, the Bank of Japan (BOJ) introduced unprecedented measures in the

borrowers’ performance include those of Amiti and Weinstein (2011) and Chava and Purnanandam (2011).

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face of financial instability, the prolonged economic downturn, and deflation. First, the BOJ cut the policy rate in 1991 just after the bubble burst, introduced a zero-interest rate policy in 1999, and attempted to stimulate the economy. The zero-interest rate policy was discontinued in 2000 but resumed in 2001, lasting until 2006. Second, the BOJ introduced the qualitative easing policy and committed to provide massive liquidity in the markets. It purchased asset-backed securities, asset-backed commercial papers, and stocks held by banks to reduce market risks (Ueda, 2012). Despite the massive monetary easing and the zero-interest policy, bank lending showed a negative growth during and after the banking crisis, resulting in a severe economic downturn. Figure 2 illustrates the lending attitudes of financial institutions (diffusion index [DI] of “accommodative” minus “severe”). We obtained the data from Tankan, a short-term economic survey of enterprises in Japan, which was conducted by the BOJ. The data reveals the rather severe lending attitude of banks immediately after the collapse of the bubble economy, during and after the banking crisis from 1997 to 2004, and during the 2008 global financial crisis. These changes in the DI indicate fluctuations in the bank loan supply over the previous three decades. Moreover, the banks’ more severe lending attitude toward small enterprises7 indicates the possibility that firms with lower creditworthiness would remain in a credit crunch. Figure 3 depicts the trend of the banks’ non-performing loan ratio. It increased from 1997 onward, hit its peak in 2001, and gradually decreased until 2005, indicating that the banks’ financial health was damaged due to the accumulated non-performing loans until the middle of 2000.

4. Data and methodology Our sample comprises non-financial firms listed in the first or the second section of Japan’s equity markets for at least five years between 1983 and 2014 because our estimation methods require at least five consecutive observations for each firm.8 We obtained the firms’ accounting and equity

7

In Tankan, small enterprises are those whose capital ranges from 20 million yen to less than 100 million yen. 8 We estimate the dynamic panel models by employing the generalized method of moments (GMM) estimator, which uses past variables as instruments (Arellano and Bond, 1991). The estimation bias in small 9

price data from the Financial Quest database of Nikkei Media Marketing. We excluded the following firms/industries: electricity, gas, and water supply industries because they are heavily regulated; firms with a book debt-to-asset ratio that exceeds one or is negative to minimize the effect of outliers;9 and firms with missing equity price information. Following previous studies,10 we also excluded firms whose growth rate of total assets exceeded 100% or was below -50% because they might have been involved in a merger, an acquisition, or a large asset sale. The variables are deflated by the consumer price index and then winsorized at 1% or 99% of the distribution. Table 1 presents the summary statistics. The variables are defined in Appendix Table 1. It is evident that book leverage ratio decreases over the three periods. The table reveals that the majority of the firms rely on debt financing rather than on equity.

4.1 Estimation model According to the trade-off theory, firms decide their target capital structure by balancing the costs and the benefits of their debt ratio. Without friction, the firms’ leverage is always at the target level. However, because of the information asymmetry and other transaction costs (e.g., Hovakimian et al., 2001), firms cannot rebalance their capital structure immediately. Previous studies (e.g., Flannery and Rangan, 2006) have estimated the following partial adjustment model:11
 −
 = 
∗ −
  +  +  ,

1

where
 and
∗ denote the actual and the target leverage ratios of firm , respectively,  represents the firm’s unobservable effect,  is an error term, and  signifies the SOA, which ranges between 0 and 1. Then, the target leverage ratio is unobservable and is estimated using the following equation:
∗ =  , 2

panel data is mentioned by Flannery and Hankins (2013). 9 We follow the method of Hovakimian and Li (2011). 10 Chang and Dasgupta (2009) and Frank and Goyal (2009) exclude firms involved in major mergers. 11 The partial adjustment model assumes that firms adjust their capital structure toward the target at the same rate of speed. A convex adjustment cost function is assumed. 10

where  denotes the vector of determinants of the target leverage ratio. Model (1) assumes the continuous and time-invariant or constant SOA. In other words, the speed or the manner in which firms adjust their capital structure is assumed to be identical, whether they are in a credit expansion or a credit contraction period or whether or not they have financial constraints. As discussed in Section 2, the costs of adjustments differ, depending on the credit fluctuation in a macroeconomy, firms’ financial constraints (or bank dependence), and their banks’ financial health. Therefore, we explore the possibility of heterogeneous adjustment speeds as follows:  =  +   +   ,

(3)

where  represents the credit fluctuation in a macroeconomy, and  denotes the firms’ financial constraints that affect the adjustment speed.  takes a value of one during a credit contraction period; otherwise, it takes zero value. By replacing  with  in equation (1) and plugging equation (3) into equation (1), we obtain the following equation: ∆
 =  +   +   
∗ −
  +  +  . 4 We expect  to be negative for financially constrained firms if H1 is supported and  to be negative during a credit contraction period if H2 is supported. We also test whether SOA  differs, depending on the banks’ capacity to supply loans or the firms’ exposure to idiosyncratic bank shocks (H3). **************** and plugging it into equation (1), we obtain the By assuming that  = " + # $%&_(ℎ& following equation: ∆
 = +" + # **************** $%&_(ℎ& ,
∗ −
  +  +  ,

5

**************** denotes the proxy of the weighted average of a firm’s bank shock. Equation where $%&_(ℎ& (5) tests H3. We use a bank’s lending share to a firm as a weight and calculate the weighted average of a firm’s bank shock variables, as follows: **************** $%&_(ℎ&, = . /,0, ∗ $%&_(ℎ&0, , 0

11

34567,8,9:;

where bank 1’s weight in firm  is w,0, = ∑

8 34567,8,9:;

.

We estimate equations (4) and (5) using the generalized method of moments (GMM) estimator suggested by Arellano and Bond (1991). The reason why we employ the GMM is that the firm-fixed effects estimator is biased even though it eliminates a firm’s unobservable effect,  (Flannery and Hankins, 2013).

4.2. Credit fluctuation in a macroeconomy To investigate whether the credit supply fluctuation in a macroeconomy has a differential effect, we use the DI (the lending attitude of banks) as a proxy for credit supply fluctuation. We also split the sample into pre-credit, credit, and post-credit-crunch periods. We conduct the sample split in the following procedure and test whether there is a structural break in the firms’ adjustment of their capital structure. As explained in Section 3, the Japanese economy experienced a banking crisis in 1997. The bank loan growth dropped substantially in 1997 (Figure 1), and the banks’ lending attitude also became severe (Figure 2). After the banking crisis, the Japanese economy experienced a credit crunch until the middle of 2000.12 To determine the range of the credit crunch period, we first perform the structural break test on the macro data, as proposed by Clemente et al. (1998), the banks’ lending attitude (Figure 2), and the trend of the banks’ non-performing ratio (Figure 3). After obtaining the date of the estimated break point, we split the sample based on the break point and conduct the Chow test for the capital structure adjustment model (firm-level data). The break-point test results are presented in Table 2. The structural break-point test on the macrodata shows the year 2003 as the break point. In Section 5, we test directly whether the macroeconomic variables affect the speed of the capital structure adjustment and conduct the Chow test to determine whether there is a structural break among the pre-crisis, crisis, and post-crisis periods.

12

Watanabe (2007) observes that the credit crunch occurred from 1997 to 1999, whereas Ishikawa and Tsutui (2013) estimate the loan supply and demand functions by using the prefecture data and find that the credit crunch occurred from 1996 to 2000. 12

4.3. Financial constraints (bank dependence) Following Gertler and Gilchrist (1993) and Leary (2009), we define firms as bank dependent when their total assets are lower than the 30th percentile of all firms. Other studies use the bond rating as a measure of no financial constraint. However, we lack the bond rating information for the whole sample period because public bond issuances in domestic markets were regulated until the middle of the 1990s in Japan.13 Although firms faced no restriction on bond issuances after 1996, only a small number of firms had a bond rating because only those seeking bond issuances had a rating.14 We use low accessibility to bond markets as an alternative measure of bank dependence. Following Faulkender and Petersen’s (2006) methodology, which was also used by Leary (2009), we estimate the probability of the firms’ access to the bond market. First, we estimate the probit model to determine whether or not the firms have positive bond amounts. The explanatory variables are ROA, tangible fixed assets to total assets, ln (total assets), ln (1 + firm age), market-to-book ratio, industry dummies, and year dummies. We use the one-year lagged value for all financial independent variables. Next, we use the predicted probability and define the firms as having access to the bond market if the predicted probability is above the 70th percentile of the annual sample distribution. The other firms whose predicted probability falls below the 30th percentile are defined as those without access. We estimate the regression model using the data after 1997.

4.4. A bank’s capacity to supply loans: bank shocks Following Amiti and Weinstein’s (2018) method, we construct a set of four measures as a proxy for bank supply shocks. The first is a bank’s capital adequacy ratio, as bank lending is constrained by capital requirements (Minetti, 2007). According to Peek and Rosengren (2005), the capital adequacy ratio of banks in Japan in the late 1990s was overstated. We use the second measure, the real-estate

13

The deregulation process took several stages. The restrictions on unsecured bonds and other types of bonds were gradually relaxed, starting in 1987. The issue standard criteria were also removed in 1993, and all restrictions were removed in 1996. 14 Only 8% of the firms had bond rating information from 1996 to 2015. 13

lending share in the late 1980s, as an alternative, as most of these loans to the real estate industry became non-performing loans in subsequent periods (Watanabe, 2007). Moreover, since Japanese banks resolved the non-performing loan problems before the problems that arose from the 2008 global financial crisis, we use this measure only for the credit crunch period (1997–2003). The third measure is the bank failure dummy, which takes the value of one if a firm’s main bank becomes bankrupt; otherwise, it takes zero value. The fourth measure is the growth of the banks’ market-to-book ratio. Poorly performing banks are reluctant to extend loans; therefore, firms that are associated with such banks face difficulty in obtaining credit. Because most firms borrow from several banks, we construct these variables by following the methodology of Amiti and Weinstein (2018) and Nakashima and Takahashi (2018) and take the weighted average.

4.5 Target capital structure We use the estimated fitted value from the regression analysis as a proxy for the target capital structure. Following the method of previous studies, we use the pooled ordinary least squares (OLS) estimator. Following the methodology of Hovakimian et al. (2001), Flannery and Rangan (2006), and Frank and Goyal (2009), we regress the book debt ratios on firm size, growth, profitability, collateral, and industry median leverage. The book debt ratio is defined as the sum of short-term and long-term debts divided by the sum of short-term and long-term debts and capital.15 Furthermore, the proxy for firm size is ln (sales) and that for firm growth is the market-to-book ratio. EBITDA/total assets and tangible fixed assets/total assets are used as proxies for profitability and collateral, respectively. Industry median debt ratios are included to control for variations across industries, and year dummies are included to control for macroeconomic conditions.16 For the estimation, we use the entire sample to account for the possibility that the debt level of small firms is lower owing to financial constraints. Appendix Table 2 presents the estimation results. Model (1) shows the estimation results for the

15

The estimation results remain unchanged when debt is defined as the sum of short-term and long-term debts minus convertible bonds. 16 We also estimate a model that includes the R&D-to-sales ratio in the regression and find that the coefficient is not significant. 14

entire sample period, while Models (2) to (4) present those for the subsample periods (pre-credit, credit, and post-credit-crunch periods). Most of the results are consistent with previous studies’ findings (e.g., Flannery and Rangan, 2006).

5. Empirical results 5.1. Base estimation results First, we examine whether the credit supply fluctuations in a macroeconomy affect the speed of the capital structure adjustment. Table 3 presents the estimation results of equation (4). We winsorize the deviation from the target at 0.1% of both tails of the distribution. We find that firms adjust their capital structure more slowly in credit contraction periods than in credit expansion periods. The interaction term of the DI and the deviation is positively significant (at the 1% level in Model (1)), meaning that firms adjust slowly when the banks’ lending attitude is severe. The SOA is 0.198 when the DI is at the 25th percentile, while it is 0.229 when the DI is at the 75th percentile. In Model (2), the negative DI dummy is included. The SOA is lower in the negative DI periods (p < 0.01), suggesting that firms face difficulty in bank-loan financing and adjust their capital structure slowly when banks’ lending attitude is severe. These results suggest that the SOA differs, depending on the credit supply fluctuation. In Models (3) and (4), we control for the demand effect in the economy by including the GDP growth rate (Korajczyk and Levy, 2003). The results of the DI (negative DI dummy) are unchanged. In Model (5), we include the interaction terms of the credit crunch (post-credit crunch) dummy and the deviation. The results show that the SOA is significantly slower in credit crunch periods (p < 0.01). The Chow test results show a structural change over the credit-crunch and the post-credit-crunch periods.

5.2. Pre-credit, credit, and post-credit-crunch periods As indicated in Table 2, the DI becomes negative in 1997, and the structural break-point date is 2003. We split the sample into three subsample periods and test whether there is a change in the SOA among them. Section 5.1 assumes that firms have a long-term target. However, firms’ financial 15

policies may change over an almost 30-year period. On one hand, during an economic boom, corporate profits increase and default risks decrease, which increase the target leverage. On the other hand, during recession periods, the situation is reversed, and the target leverage decreases. In this section, we relax this assumption and allow the target change, depending on the credit fluctuation. We estimate the leverage equation for each sample period and obtain the predicted value of each target. After obtaining the target value, we estimate equation (4) for each sample period. We divide the sample into three subsample periods: 1987–1996 (pre-credit crunch), 1997–2003 (credit crunch), and 2004–2013 (post-credit crunch). The last sample period ends in 2013 to have the same number of years as that of the pre-credit-crunch period.17 Table 4 presents the results of equation (4) for the subsample periods. Panel A shows the results by firm financial constraints. We find that during the credit crunch period, small firms show slower adjustment than larger ones (p < 0.01) but exhibit no difference in the credit expansion period (Models (1) and (2)). Additionally, the SOA of smaller firms is slowest in the credit crunch period. These results indicate that credit contraction has a negative effect on adjustment, particularly on that of smaller firms, supporting H1 and H2. In Models (4) and (5), we alternate the small firm dummy with the without-bond-market-access dummy. We obtain mostly similar results in the credit crunch period (Model (4)). Our results confirm H1 and H2. A few studies argue that small firms are not necessarily financially constrained, and firms with financial deficits are more likely to be affected by credit contraction. We include the interaction terms of the financial deficit dummy and the deviation from the target. Following Chang and Dasgupta (2009), we define the financial deficit dummy as taking a value of one if the change in total assets is greater than the change in retained earnings. As presented in Table 4, Panel B, the results show that firms adjust slowly when they experience a financial deficit in every period, suggesting that adjustment costs are higher when firms have financial deficits than when they have financial surpluses.

17

The estimation results for the last period are almost unchanged when we define the post-credit-crunch period as occurring from 2004 to 2014. 16

Furthermore, we explore whether the effect of the financial deficit varies between financially constrained and unconstrained firms. Table 5 presents the results. Table 5, Panel A shows that small firms with financial deficits show a slower adjustment in all three periods, suggesting that transaction costs for financially constrained firms are higher when they issue debts than when they retire debts (Byoun, 2008). In contrast, the SOA of large firms does not differ when they have financial deficits or financial surpluses during credit expansion and credit crunch periods (Models (4) and (5)), suggesting that they can substitute bank loans with other debts. When we split the sample based on firms’ access to bond markets (Table 5, Panel B), we find that both types of firms with financial deficits show a slower SOA than firms with financial surpluses. Firms without access to bond markets show a slower SOA during the credit crunch period than during other periods (p < 0.01, Model (7)).

5.3. Speed of adjustment based on banks’ ability to supply loans In the previous section, we have found that the SOA of firms, particularly that of small firms, is slower during the credit crunch period than during other periods. In this section, we explore whether a bank’s ability to supply loans affects the rebalancing behavior of firms. As mentioned in Section 3, Japanese banks accumulated non-performing loans after the collapse of the bubble economy and faced difficulty in supplying loans to firms. The banks’ capital-asset ratio that was prevalent prior to the banking crisis is used as a proxy for their ability to supply loans. The estimation results are presented in Table 6. The interaction term of the deviation and the banks’ capital-asset ratio is expectedly positive but insignificant (Model (1)). Since banks forged their financial statements in the crisis periods (Peek and Rosengren, 2005), we replace the capital-asset ratio with the banks’ real-estate lending share at the end of the 1980s because these loans subsequently turned out to be non-performing loans. Banks with high real-estate lending loan amounts suffer from non-performing loan problems and are unable to extend loans. We obtain the expected result (Model (2)). This finding shows that firms associated with greatly troubled banks adjust more slowly than firms associated with less troubled banks (p < 0.05). Furthermore, we test whether bank failure has a negative effect on firms’ adjustment (Model (3)). The results suggest that firms associated with failing banks show a 17

slower adjustment than those associated with less troubled banks (p < 0.05). The adjustment speed of firms associated with failing banks is 18.8%, meaning that it takes 5.3 (= 100/18.8) years for those firms to close the leverage gap. All these results imply that a bank’s crisis has an adverse effect on the targeting behavior of its client firms. Furthermore, we include the growth of banks’ market-to-book ratio as an alternative measure of bank shocks (Model (4)). The interaction term is positive and highly significant (p < 0.01), suggesting that firms associated with better performing banks adjust faster than those associated with poorly performing banks.

5.4. Robustness 5.4.1. Deviation from the target It may be argued that financially constrained firms and firms associated with troubled banks adjust slower during credit crunch periods than other firms because of their small deviation from the target. To check for this possibility, we measure the absolute value of the deviation of these firms. The results shown in Table 7 indicate that smaller firms, firms without access to bond markets, and firms associated with banks that heavily lend to real-estate industries in the late 1990s have a larger deviation from the target than that of other firms. Thus, we can eliminate this possibility.

5.4.2. Adjustment cost function and debt issuances, equity issuances, and debt retirements The rebalancing behavior of firms is discrete when adjustment cost functions are not strictly convex (Leary and Roberts, 2005). If fixed costs are large, smaller firms adjust less frequently. To investigate this possibility, we follow Leary and Roberts’ (2005) approach and examine the firms’ rebalancing behavior by focusing on their financing decisions, such as issuance and repurchase of debts (equity). We do not investigate the repurchase of equity because it was regulated until 2001. Following Chang and Dasgupta (2009), we define net equity issuance as the change in book capital minus that in retained earnings, whereas net debt issuance is the change in total assets minus that in retained earnings and net equity issuance. Net debt (equity) issuance is divided by the total assets in the 18

beginning of the year. Following Hovakimian et al. (2001) and Leary and Roberts (2005), we define a firm as issuing debt (equity) if the net issuance exceeds 5% of the total assets in the beginning of the year and as retiring debt if the net retirement exceeds 5% of the total assets in the beginning of the year. Debt issuance (retirement) includes public debts and bank loans. The estimation results are presented in Table 8. We estimate each equation using the linear probability model because the dependent variable is binary. The expected sign of the deviation from the target is positive for debt issuances and negative for stock issuances and debt retirements. The deviation from the target has the expected sign and is highly significant in all models. Small firms are less sensitive to the deviation from the target than other firms. During the credit crunch period, small firms show lower sensitivity to debt issuance but higher sensitivity to debt retirement (Models (4) and (6)) than during the other periods. These results suggest that small firms are negatively affected by supply shocks and show a slower adjustment compared with other firms, confirming the previous section’s results. When we alter the definition of financial constraint in terms of access to bond markets, we obtain similar results. Firms without access to bond markets show lower sensitivity to deviations when making financial decisions compared with firms with access to bond markets for every financial decision.18

5.4.3. Investment opportunities It may be argued that the credit demand of smaller firms shrinks during credit crunch periods, which drives our results. Although we control for the demand of firms by including EBITDA and q in the target debt estimation, to further isolate the effect of the remaining demand for credit, we divide the sample into two groups, according to their investment opportunities in future periods, following the methodology of Fernández et al. (2018). Firms whose q is above (below) the annual sample median are categorized as high-q (low-q) firms. We expect small high-q firms to be vulnerable to credit tightness during a credit crunch if the supply-side scenario applies. The estimation results are 18

The results are available from the authors upon request. 19

presented in Table 9. Table 9, Panel A reveals that smaller firms exhibit a slower adjustment than other firms, regardless of investment opportunities during the credit crunch period (p<0.01, in Models (3) and (4)). Furthermore, small high-q firms exhibit a slower adjustment than small low-q firms during the credit crunch period. Table 9, Panel B splits the sample according to the firms’ access or lack of access to bond markets. Our obtained results are qualitatively similar to those shown in Table 9, Panel A during the credit crunch period. In Table 10, Panel A, we compare the effects of bank shocks between high-q firms and low-q firms. The results confirm the troubled banks’ adverse effect on the rebalancing behavior of the firms belonging to the high-q group (Models (1), (3), and (5)), suggesting that our findings are not driven by the firms’ low demand for bank loans. Although we have controlled for the demand effects explained above, some concerns about the possible endogeneity remain because we split the sample based on firm-level variables. For an alternative way to disentangle the demand-side effect from the supply-side one, we use the industry’s set of future investment rates as an instrument for the firms’ investment opportunities.19 We divide the sample into high-investment and low-investment industries. High- (low-) investment industries are those whose future investment rates are higher (lower) than the annual median. Table 10, Panel B compares the effects of bank shocks between industries with high and low investment rates. The estimation results shown in Panel B are similar to those presented in Panel A (Table 10). Firms associated with banks that heavily lent to real estate industries in the late 1990s and firms that belong to high-growth industries show a slower adjustment (in Model (7), at the 1% significance level), whereas firms in low-growth industries demonstrate a faster adjustment (in Model (8), at the 10% significance level). Bank failure has a negative impact on the adjustment behavior of firms in both high-growth and low-growth industries (Models (9) and (10)); however, firms in high-growth industries show a slower adjustment than those in low-growth industries. In contrast, firms associated with financially healthy banks exhibit a faster adjustment when they have high-growth opportunities (in Model (11), at the 1% significance level), supporting the argument that the supply-side shocks 19

Industry-level measures are considered more exogeneous. Faulkender et al. (2012) use similar measures. 20

affect firms’ capital structure adjustment during credit crunch periods.

5.4.4. Effects of bank shocks and firms’ financial constraints In this section, we investigate whether the effects of bank shocks differ depending on firm financial constraints, and whether such effects are more profound in financially constrained firms. The sample is divided into two groups according to firm size. Table 11 presents the results. Small (large) firms have total assets below (above) the annual 30th (70th) percentile of the value. The results reveal that bank shocks have statistically significant effects on small firms’ adjustment but not on that of large firms. Small firms associated with banks that lent heavily to real estate industries in the late 1980s and small firms associated with failing banks show a slower adjustment compared with firms associated with less troubled banks (p<0.05, Models (1) and (2)). Small firms associated with failing banks take 8.9 years to revert to their target level. Bank market performance variables are also only significant for small firms (p<0.01, Model (3)). These results suggest that small firms are more affected by bank shocks. When we change the definition of financial constraint in terms of access to bond markets, we obtain similar results. We find that firms without access to bond markets and that are associated with banks that lent heavily to real estate industries in the late 1980s show a slower adjustment than firms associated with less troubled banks (p<0.01), confirming the results shown Table 11. The bank market performance variables are significantly positive for both types of firms, suggesting that banks with improved performance extend loans to both types of firms.20

5.4.5. Potential survivorship bias of long-lived firms We limit our sample to firms with at least five consecutive-year observations. This limitation avoids the estimation bias due to the small T but leads us to face the potential survivorship bias (Elsas and Florysiak, 2011). Bank-dependent firms would more likely be affected by the banking crisis and failed in their capital structure adjustment and would thus be excluded from the sample due to bankruptcy or

20

The results are available from the authors upon request.

21

delisting during crisis periods. Unfortunately, we cannot access their data after they are delisted. There were 105 firms listed in 1997 but delisted from fiscal year 1998 to 2003, comprising 3.2% of the total sample firms. We cannot rule out the possibility of survivorship bias due to the omission of those bankrupt or delisted firms; however, the effect is not so large, considering the sample size.

5.4.6. Merger and acquisition and capital structure adjustment Based on previous studies,21 we exclude the firms that may be involved in a merger and acquisition (M&A). Another potential bias may stem from the exclusion of these firms. It is possible that a banking crisis would pressure firms to reorganize their corporate structure.22 However, it is challenging to distinguish the direct effect of credit fluctuation on capital structure adjustment from the indirect effect through M&A. Previous studies have found that acquired firms lower their capital structure to have a debt capacity for a future M&A (e.g., Harford et al., 2009). We cannot rule out the possibility that firms planning the M&A delay it because they cannot finance the M&A with debt issuances.

6. Conclusions In this study, we have examined whether bank credit fluctuations affect firms’ capital structure adjustment. Examining the data from Japanese-listed firms from 1984 to 2014, we find that a credit crunch has a negative effect on the targeting behavior of firms. In particular, smaller firms with financial deficits and firms associated with troubled banks show a slower adjustment than other firms. We also find that the sensitivity of debt issuances to deviation is lower for smaller firms during the credit crunch period than during other periods, suggesting that smaller firms experience difficulty in adjustments when there is credit tightness in the economy. Moreover, this result that is related to a credit crunch remains unchanged when we control for the demand-side effects and consider an alternative adjustment cost function, supporting our hypothesis that supply-side factors have a significant effect on the capital structure adjustment of firms. These results imply that supply-side 21

See Chang and Dasgupta (2009) and Frank and Goyal (2009). Campello et al. (2010) find that financially constrained firms sold more assets during the global financial crisis periods than before.

22

22

shocks cause the leverage of firms, particularly the bank-dependent ones, to drop to a suboptimal level for a long period of time (Leary, 2009; Voutsinas and Werner, 2011). Our findings also suggest that bank-dependent firms without alternative sources of credit face difficulty in obtaining credit when their banks have financial troubles, leading to the possibility of foregoing investment opportunities with a positive net present value (Campello et al., 2010). Considering that small firms are engines of economic growth, these results suggest that impairing banks’ financial health is detrimental in terms of delaying the recovery process of the economy. During credit crunch periods, small firms with growth opportunities and high exposure to bank shocks face difficulty in raising funds even though the monetary policy rate is nearly zero. This finding suggests the possibility that financially troubled banks are discouraged from extending loans to smaller firms because the profit margin is reduced, resulting in a weakened effect of the monetary easing policy. These banks with high non-performing loans possibly cut lending to smaller firms to meet capital requirements, which further delays the rejuvenation of the economy. Considering the similarity of Japan’s banking crisis to the global banking crisis, our results help stakeholders understand why it takes so long to revive the economy after a banking crisis.

23

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Figure 1. Growth of bank loans

This figure depicts the growth of aggregate bank loans. We obtained each bank’s outstanding loans from the Nikkei Financial Quest database and aggregate them here by fiscal year. We include domestically licensed banks, such as long-term, city, regional, second regional, and trust banks.

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Figure 2. Banks’ lending attitudes

Lending attitudes (diffusion index of accommodative minus severe)

40 30 20 10 0 -10 -20 -30 All

Small

This figure illustrates the lending attitudes of financial institutions (diffusion index of accommodative easy minus severe). We obtained the data from Tankan (a short-term economic survey of enterprises in Japan), Bank of Japan. In Tankan, enterprises are considered small if their capital ranges from 20 million yen to less than 100 million yen. “All” represents all enterprises.

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Figure 3. Non-performing loans of banks 0.1 0.09 0.08 0.07 0.06 0.05 0.04 0.03 0.02 0.01 0

Average non-performing loan ratio Aggregate non-performing loan/aggregate bank loan lending This figure depicts the banks’ non-performing loan ratios. The average non-performing loan ratio is the average of each bank’s non-performing loans-to-outstanding loans ratio. The aggregate non-performing loan/aggregate bank loan denotes the aggregate non-performing loans in the year divided by the aggregate bank loan. We have obtained each bank’s non-performing loans and outstanding loans from the Nikkei Financial Quest database. We include domestically licensed banks, such as long-term, city, regional, second regional, and trust banks.

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Table 1. Summary statistics 1987–1996

1997–2003

2004–2013

Credit expansion

Credit crunch

Post-credit crunch

Variables

Mean

Mean

Mean

Debt-to-total assets

(Std. Dev.) (Std. Dev.) (Std. Dev.) 0.607 0.542 0.478 (0.196)

(0.216)

(0.212)

Deviation from the target, absolute value

0.137 (0.102)

0.148 (0.109)

0.155 (0.108)

Deviation from the target

-0.007 (0.170)

-0.001 (0.184)

-0.003 (0.189)

Proportion of firms that issue debt Proportion of firms that issue equity

0.271 0.033

0.141 0.016

0.200 0.022

Proportion of firms that retire debt q (t-1)

0.152 1.665

0.240 1.139

0.158 1.164

EBITDA/total assets (t-1)

(0.716) 0.080

(0.649) 0.072

(0.679) 0.075

Ln (sales (t-1))

(0.043) 10.798

(0.052) 10.379

(0.060) 10.065

Tangible fixed assets/total assets (t-1)

(1.413) 0.263

(1.430) 0.290

(1.642) 0.250

(0.154)

(0.173)

(0.185)

Industry median leverage (t-1)

0.617 (0.061)

0.659 (0.182)

0.531 (0.162)

Loan to interest-bearing debt (t-1)

0.599 (0.362)

0.716 (0.319)

0.867 (0.257)

Bank shock variables Bank capital 95-96

0.024

Real estate lending 89

(0.010) 0.074

Bank failure

(0.032) 0.034

Growth of banks’ market-to-book ratio

(0.182) -0.003 (0.009)

Macro variables DI GDP growth Number of observations

14.383

-3.383

7.188

(12.075) 2.957

(6.940) 0.657

(7.224) 0.789

(2.080) 19329

(1.185) 18788

(2.475) 29242

This table presents the summary statistics. The sample comprises non-financial listed firms in Japanese stock markets in the fiscal years 1984–2014. Except for debt-to-total assets, deviation from the target, and the proportion of firms that issue debt (equity) and those that retire debt, all the financial variables of firms are values in the year t-1. DI is diffusion index.

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Table 2. Structural break test

Banks’ lending attitude (all firms) Banks’ lending attitude (small firms) Non-performing loans of banks

Supremum Wald test of Estimated break date no structural break point 12.1248** 2003 12.7295** 2003 133.4007 *** 2003

This table presents the results of the structural break test. The banks’ lending attitude is the diffusion index. The non-performing loans of banks is the aggregate non-performing loans divided by the aggregate bank loans. The null hypothesis is that there is no structural break point. **p < 0.05, ***p < 0.01.

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Table 3. Effects of credit supply fluctuations on firms’ leverage adjustment Panel A: Regression results Model Deviation DI * Deviation DI Negative DI dummy * Deviation Negative DI dummy GDP growth* Deviation GDP growth

(1) (2) (3) 0.198*** 0.193*** 0.198*** (0.0108) (0.00987) (0.0111) 0.00178*** 0.00177*** (0.000196) (0.000229) -0.0000407* -0.0000903*** (0.0000215) (0.0000257) -0.0203*** (0.00252) -0.00196*** (0.000448) 0.0000465 (0.000683) 0.000341*** (0.000116)

(4) 0.186*** (0.0106)

(5) 0.209*** (0.0109)

-0.0149*** (0.00323) -0.00190*** (0.000541) 0.00190*** (0.000653) 0.0000161 (0.000121) -0.0380*** (0.00754) -0.0212** (0.00992) -0.0000473 (0.00106) 0.000758 (0.00144)

Credit-crunch dummy * Deviation Post-credit-crunch dummy * Deviation Credit-crunch dummy Post-credit-crunch dummy

Number of observations 68650 68650 Chi-squared 439.0 433.6 1.037 0.956 AR (2) test 5.91e-27 23.50 Hansen test Chow F test Panel B: Speed of adjustment (SOA) DI is at the 25th percentile 0.198 DI is at the 75th percentile 0.229 DI is negative 0.173 Credit-crunch period Post-credit-crunch period This table presents the estimation results of equation (4).

68650 472.3

68650 467.1

68650 381.2

1.031 7.84e-27

1.014 34.76

1.044 9.90e-27 28.96 ***

0.198 0.229 0.171 0.171 0.188

∆
 =  +   
∗ −
  +  +  . 4 The dependent variable is a change in the leverage ratio. The deviation is the target leverage (t) minus the leverage (t-1). The negative diffusion index (DI) dummy takes a value of one when the DI is negative; otherwise, it takes zero value. The credit-crunch dummy takes a value of one during the period from 1997 to 2003. The post-credit-crunch dummy takes a value of one after 2004. The AR(2) test is a test for the second-order serial correlation. The Chow F test is a test that a coefficient on credit-crunch dummies * deviation equals that on post-credit-crunch dummies * deviation. The standard errors are enclosed in parentheses. *p < 0.1, **p < 0.05, ***p < 0.01

34

Table 4. Speed of adjustment and financial constraints (subsample analysis) Panel A. Estimation results by financial constraints Credit expansion

Deviation Small * Deviation

(1) 0.317*** (0.0342) -0.0146 (0.0306)

Credit crunch (2) 0.160*** (0.0357) -0.0905*** (0.0292)

Post-credit crunch (3) 0.162*** (0.0225) 0.0682*** (0.0263)

Without access to bond markets * Deviation Year dummies Yes Number of observations 16851 Chi-squared 356.8 AR (2) test 1.394 Hansen test 4.590 SOA of financially constrained firms 0.30 Panel B: Estimation results by financial deficits Credit expansion (6) Deviation 0.301*** (0.0209) Financial deficits * Deviation -0.0207*** (0.00640) Year dummies Yes Number of observations 16851 Chi-squared 394.8 AR (2) test 0.983 Hansen test 98.85 SOA of firms with financial deficits 0.28

Yes 20173 195.4 -1.615 28.75 0.07 Credit crunch (7) 0.176*** (0.0212) -0.0334*** (0.00528) Yes 20169 286.9 -1.783 135.9 0.14

Yes 28305 302.6 1.150 9.494 0.23

Credit crunch

Post-credit crunch

(4) 0.235*** (0.0438)

(5) 0.187*** (0.0240)

-0.131***

0.0278

(0.0442) Yes 12515 186.7 -2.646 82.55 0.10

(0.0327) Yes 24354 302.7 0.474 115.4 0.21

Post-credit crunch (8) 0.234*** (0.0159) -0.0439*** (0.00456) Yes 28291 565.6 1.536 140.2 0.19

This table presents the estimation results of equation (4): ∆
 =  +   +   
∗ −
  +  +  , 4 where  denotes the firms’ financial constraints. Panel A presents the results by firm financial constraints. Small firms are firms with total assets below the annual 30th percentile of value. The dependent variable is a change in the leverage ratio. The deviation is the target leverage (t) minus the leverage (t-1). Firms without access to bond markets are defined in the following way. We estimate the probability that firms have access to bond markets by estimating the probit model of whether or not the firms have positive bond amounts. The independent variables in the probit model are ROA, tangible fixed assets to total assets, ln (total assets), ln (1 + firm age), market-to-book assets ratio, industry dummies, and year dummies. Next, we use the predicted probability and define firms as having no access to bond markets if the predicted probability of issuing bonds is below the 30th percentile. Panel B presents the results by financial deficits. Financial deficits take a value of one if the change in total assets is greater than the change in retained earnings. We include year dummies. SOA means the speed of adjustment. The AR(2) test is a test for the second-order serial correlation. The standard errors are enclosed in parentheses. *p < 0.1, **p < 0.05, ***p < 0.01

35

Table 5. Speed of adjustment and financial deficits by financial constraints Panel A. Effects of credit supply conditions and financial deficits by firm size Small firms Large firms Credit Post-credit Credit Post-credit Credit crunch Credit crunch expansion crunch expansion crunch Model (1) (2) (3) (4) (5) (6) Deviation 0.235*** 0.135*** 0.255*** 0.497*** 0.389*** 0.238*** (0.0362) (0.0368) (0.0300) (0.0539) (0.0495) (0.0321) Deviation * Financial deficits -0.0205* -0.0462*** -0.0595*** -0.0133 -0.0138 -0.0322*** (0.0117) (0.0116) (0.0100) (0.0125) (0.00952) (0.00829) Year dummies Yes Yes Yes Yes Yes Yes Number of observations 4531 5361 7548 5508 6552 8991 Chi-squared 161.9 93.45 218.4 214.3 222.3 238.0 SOA of firms with financial deficits 0.21 0.09 0.20 0.48 0.38 0.21 Panel B. Effects of credit supply conditions and financial deficits by firms’ access to bond markets Firms without access to bond markets Firms with access to bond markets Post-credit Post-credit Credit crunch Credit crunch crunch crunch Model (7) (8) (9) (10) Deviation 0.143*** 0.213*** 0.287*** 0.176*** (0.0406) (0.0283) (0.0415) (0.0316) Deviation * Financial deficits -0.0679*** -0.0653*** -0.0177* -0.0406*** (0.0119) (0.00918) (0.0101) (0.00826) Year dummies Yes Yes Yes Yes Number of observations 4031 7081 4338 7415 Chi-squared 108.2 245.0 146.1 202.9 SOA of firms with financial deficits 0.08 0.15 0.27 0.14

This table presents the estimation results of equation (4). ∆
 =  +   +   
∗ −
  +  +  , 4 where  denotes the firms’ financial constraints. Panels A and B present the results by firm size and firms’ access to bond markets, respectively. The dependent variable is a change in the leverage ratio. Deviation is the predicted value of the target leverage minus the leverage in year t-1. Financial deficits equal one if the change in total assets is greater than the change in retained earnings. We include year dummies. The standard errors are enclosed in parentheses. * p < 0.1, ** p < 0.05, *** p < 0.01

36

Table 6. Speed of adjustment and the effect of bank shocks: 1997–2003 Panel A. Effects of bank shocks Bank shock variables

Deviation Deviation * Bank shock Bank shock Year dummies Number of observations Chi-squared

Bank capital95-96

Real estate lending 89

Bank failure

(1) 0.207*** (0.0767) 2.977 (1.896) 10.16*** (2.896) Yes 13329 178.0

(2) 0.399*** (0.0834) -1.345** (0.640) -0.913 (0.880) Yes 13329 173.6

(3) 0.250*** (0.0365) -0.0618** (0.0287) -0.0179 (0.0138) Yes 13554 190.8

Growth of banks’ market-tobook ratio (4) 0.273*** (0.0367) 0.684*** (0.259) 0.229 (0.377) Yes 13329 225.9

Panel B. SOA of firms associated with troubled banks The bank shock variables are at the 0.290 0.280 0.271 median. A main bank has failed. 0.188 This table presents the estimation results of equation (5). 5 ∆
 = +# + = **************** $%&_>ℎ&, ,
∗ −
  +  +  , ****************, denotes the proxy of the weighted average of a firm’s bank shock. We use the where $%&_>ℎ&

bank’s lending share to the firm as the weight and calculate the weighted average of a firm’s bank shock variables, as follows: **************** $%&_>ℎ&, = . /,0, ∗ $%&_>ℎ&0, , 0 34567,8,9:;

where bank 1’s weight in firm  is w,0, = ∑

8 34567,8,9:;

.

The dependent variable is a change in the leverage ratio. Deviation from the target is the predicted value of the target leverage minus the leverage in year t-1. The target is obtained using the pooled ordinary least squares (OLS) in Appendix Table 2. The bank capital in Model (1) is the weighted average of a firm’s relational banks’ capital ratio in the years 1995 and 1996. We use the bank’s lending share to the firm as the weight. Real-estate lending 89 in Model (2) is the weighted average of a firm’s relational banks’ real-estate lending ratio in the year 1989. Bank failure takes a value of one if a firm’s main bank fails. The growth of banks’ market-to-book ratio is the weighted average of a firm’s relational banks’ growth of market-to-book ratio. Panel B shows the speed of adjustment (SOA) of firms associated with troubled banks. All models include year dummies. The standard errors are enclosed in parentheses. * p < 0.1, ** p < 0.05, *** p < 0.01

37

Table 7. Absolute value of deviation from the target (1997–2003) Test of equal means

Measure of classification Firm size

Small 0.162

Others 0.143

-12.149 ***

Without 0.156

With 0.136

-10.426 ***

Above median 0.145

Below median 0.131

-8.593 ***

Above median 0.143

Below median 0.134

-5.306 ***

Main bank has failed 0.130

Main bank has not failed 0.137

Above median 0.138

Below median 0.139

Access to bond markets

Bank real-estate lending 89

Bank capital 95-96

Bank failure

Growth of banks’ market-to-book ratio

1.892 *

0.696

This table presents the absolute value of the deviation from the target leverage. Deviation from the target is the predicted value of the target leverage minus the leverage in year t-1. Target is the target capital structure, and we use the estimated fitted values from the pooled ordinary least squares (OLS) regression analysis in Appendix Table 2. We divide the sample into smaller firms (firms with access to bond markets) and others in row 1 (2). Small firms are those with total assets below the annual 30th percentile of value. Firms categorized as ‘others’ are those with total assets above the annual 30th percentile of value. In rows 3 and 4, the samples are divided by the median value of the banks’ real-estate lending ratio in 1989 and the banks’ capital ratio in the years 1995 to 1996, respectively. In row 5, the samples are divided according to whether or not a firm’s main bank has failed. In row 6, the samples are divided according to the median value of the growth of the banks’ market-to-book ratio. The last column shows the t-statistics of the equal mean of the two samples. * p < 0.1, ** p < 0.05, *** p < 0.01

38

Table 8. Decisions on debt issue, equity issue, and debt retirement 1987–1996 Credit expansion Debt Stock issuances issuances (1) (2) 1.436*** -0.435***

Debt retirements (3) -0.819***

1997–2003 Credit crunch Debt Stock issuances issuances (4) (5) 1.025*** -0.146***

Debt retirements (6) -1.340***

2004–2013 Post-credit crunch Debt Stock issuances issuances (7) (8) 1.112*** -0.112***

(0.0603) 1.031*** (0.0686)

(0.0250) -0.221*** (0.0284)

(0.0506) -0.618*** (0.0576)

(0.0442) 0.781*** (0.0529)

(0.0167) -0.121*** (0.0200)

(0.0514) -1.147*** (0.0616)

(0.0417) 0.849*** (0.0495)

(0.0160) (0.0375) -0.0638*** -0.912*** (0.0190) (0.0446)

Small firms

0.144*** (0.0217)

0.00903 (0.00899)

-0.108*** (0.0182)

0.0834*** (0.0156)

0.0247*** (0.00591)

-0.129*** (0.0181)

0.0589*** (0.0151)

0.00271 (0.00579)

-0.0961*** (0.0136)

Constant

0.129*** (0.0102)

0.0233*** (0.00422)

0.194*** (0.00856)

0.164*** (0.00745)

0.0227*** (0.00282)

0.180*** (0.00866)

0.238*** (0.00808)

0.0330*** (0.00310)

0.106*** (0.00727)

F-test: Deviation of non-small firms = Deviation of small firms

216.86***

156.72***

94.15***

194.21***

24.70***

209.57***

253.04***

23.12***

300.63***

Firm-fixed effects and year dummies

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Number of observations R-squared

17806 0.124

17806 0.0522

17806 0.0902

21141 0.0556

21141 0.0120

21141 0.0886

29242 0.0487

29242 0.0133

29242 0.102

Model Deviation of non-small firms Deviation of small firms

Debt retirements (9) -1.131***

This table presents the estimation results of financial decisions. The dependent variables are financial decisions, such as debt issuances, equity issuances, and debt retirements. Following Hovakimian et al. (2001) and Leary and Roberts (2005), we define a firm as issuing debt (equity) if the net issuance exceeds 5% of the total assets in the beginning of the year and as retiring debt if the net retirement exceeds 5% of the total assets in the beginning of the year. Small equals one if the total assets of firms are below the annual 30th percentile of value. All models are estimated using linear probability models. We include firm-fixed effects and year dummies. The standard errors are enclosed in parentheses. * p < 0.1, ** p < 0.05, *** p < 0.01

39

Table 9. Speed of adjustment (SOA), financial constraints, and investment opportunities Panel A. SOA by firm size and investment opportunities 1987–1996 Credit expansion

Deviation Small * Deviation Small Year dummies Number of observations Chi-squared AR (2) test SOA of small firms

1997–2003 Credit crunch

2004–2014 Post-credit crunch

q is above the median

q is below the median

q is above the median

q is below the median

q is above the median

q is below the median

(1) 0.434*** (0.0852) -0.190 (0.116) -0.346 (0.228) Yes 7626 64.25 1.440 0.244

(2) 0.584*** (0.0720) -0.108* (0.0628) 0.0153 (0.0514) Yes 7705 183.1 0.706 0.476

(3) 0.524*** (0.0875) -0.483*** (0.117) -0.528*** (0.113) Yes 9842 148.0 -1.023 0.041

(4) 0.554*** (0.0618) -0.219*** (0.0789) 0.101 (0.0731) Yes 10319 308.0 0.987 0.335

(5) 0.415*** (0.0773) -0.273** (0.114) -0.423*** (0.115) Yes 10160 186.3 1.356 0.142

(6) 0.363*** (0.0570) 0.00120 (0.0636) -0.00340 (0.0879) Yes 11404 242.4 1.353 0.3642

Panel B. SOA by access to bond markets and investment opportunities 1997–2003 Credit crunch

Deviation Without access to bond markets * Deviation Without access to bond markets Year dummies Number of observations Chi-squared SOA of firms without access to bond markets

2004–2014 Post-credit crunch

q is above the median

q is below the median

q is above the median

q is below the median

(7) 0.276*** (0.0334) -0.187*** (0.0488) -0.0440 (0.0273) Yes 6092 228.7 0.089

(8) 0.476*** (0.0452) -0.108** (0.0444) 0.0126 (0.0292) Yes 6423 369.9 0.368

(9) 0.241*** (0.0222) -0.00162 (0.0251) 0.0155 (0.0130) Yes 11825 488.0 0.239

(10) 0.349*** (0.0238) 0.00695 (0.0213) -0.000510 (0.0107) Yes 12529 541.8 0.356

This table presents the results of equation (4). ∆
 =  +   +   
∗ −
  +  +  , 4 where  denotes firms’ financial constraints. The sample is divided into two groups according to their investment opportunities in future periods. Firms whose q is above (below) the annual sample median are categorized into high-q firms (low-q firms). The dependent variable is a change in the leverage ratio. Deviation from the target is the predicted value of the target leverage minus the leverage in year t-1. Models are estimated using the generalized method of moments (GMM) estimator. We include year dummies. The standard errors are enclosed in parentheses. * p < 0.1, ** p < 0.05, *** p < 0.01

40

Table 10. Effects of bank shocks by investment opportunities (1997–2003) Panel A. Effects of bank crisis by firm investment opportunities Bank shock variables

Real estate lending 89

Bank failure

q is above q is below the median the median (1) (2) Deviation 0.421*** 0.485*** (0.0384) (0.0480) Deviation * Bank shock -0.685** -0.0140 (0.281) (0.359) Year dummies Yes Yes Number of observations 6435 6894 Chi-squared 389.8 415.2 SOA of firms associated with troubled banks The bank shock variables are at the median.

0.36

q is above the median (3) 0.276*** (0.0289) -0.0449* (0.0272) Yes 6563 254.3

0.48

0.34

A main bank has failed. Panel B. Effects of bank crisis by industry investment rates High-growth Low-growth industry industry (7) (8) 0.477*** 0.218** (0.0932) (0.101) Deviation * Bank shock -1.953*** 1.154* (0.600) (0.698) Year dummies Yes Yes Number of observations 7898 5438 Chi-squared 116.7 134.6 SOA of firms associated with troubled banks

Deviation

The bank shock variables are at the median.

0.30

q is below the median (4) 0.475*** (0.0355) -0.0109 (0.0144) Yes 6991 338.7

Growth of banks’ marketto-book ratio q is above q is below the median the median (5) (6) 0.339*** 0.462*** (0.0280) (0.0289) 0.892*** 0.149 (0.257) (0.199) Yes Yes 6435 6894 333.3 402.7

0.23

0.46

High-growth Low-growth industry industry

High-growth Low-growth industry industry

(9) 0.139** (0.0545) -0.0393*** (0.0126) Yes 8038 82.14

(11) 0.250*** (0.0554) 1.027*** (0.310) Yes 7898 110.0

(12) 0.356*** (0.0599) 0.202 (0.336) Yes 5438 137.2

0.25

0.36

(10) 0.386*** (0.0687) -0.0426** (0.0198) Yes 5522 128.9

0.32

A main bank has failed.

0.46

0.10

0.34

This table presents the results of equation (5). ****************, ,
∗ −
  +  +  , 5 ∆
 = +# + = $%&_>ℎ& **************** where $%&_>ℎ&, denotes the proxy of the weighted average of a firm’s bank shock. We use the bank’s lending share to the firm as the weight and calculate the weighted average of a firm’s bank shock variables, as follows: ****************, = . /,0, ∗ $%&_>ℎ&0,, $%&_>ℎ& 0 34567,8,9:;

where bank 1’s weight in firm  is w,0, = ∑

8 34567,8,9:;

.

In Panels A and B, the sample is divided into two groups, according to investment opportunities in future periods and according to industry investment rates in future periods, respectively. In Panel A, firms whose q is above (below) the annual sample median are categorized into high-q firms (low-q firms). In Panel B, industries with investment rates above (below) the annual sample median are categorized into high- (low-) growth industries. The dependent variable is a change in the leverage ratio. The bank real-estate lending, 89, is the weighted average of a firm’s relational bank’s real-estate lending ratio in the year 1989. The bank failure takes a value of one if a firm’s main bank fails. The growth of banks’ market-to-book ratio is the weighted average of a firm’s associated bank’s growth of market-to-book ratio. All models include year dummies. The standard errors are enclosed in parentheses. *p < 0.1, **p < 0.05, ***p < 0.01 41

Table 11. SOA and the effect of bank shocks by firm size (1997–2003) Panel A. The effect of bank shock by firm size Small firms

Large firms

Growth of Real estate Bank shock variables Bank failure banks' lending 89 market-toModel (1) (2) (3) Deviation 0.349*** 0.157*** 0.195*** (0.0904) (0.0326) (0.0490) Deviation * Bank shock -1.430** -0.0450** 1.125*** (0.723) (0.0220) (0.395) Year dummies Yes Yes Yes Number of observations 3634 3565 3634 Chi-squared 99.94 106.2 98.48 Panel B. SOA of small firms associated with troubled banks The bank shock variables 0.218 0.192 are at the median. A main bank has failed. 0.112

Real estate lending 89

Bank failure

(4) 0.351*** (0.0923) -0.297 (0.564) Yes 4169 133.4

(5) 0.389*** (0.0751) -0.00351 (0.0133) Yes 4368 3121.4

Growth of banks' market-to(6) 0.326*** (0.0654) 0.301 (0.375) Yes 4169 117.5

This table presents the second-stage results of equation (5). 5 ∆
 = +# + = **************** $%&_>ℎ&, ,
∗ −
  +  +  , ****************, denotes the proxy of the weighted average of a firm’s bank shock. We use the where $%&_>ℎ& bank’s lending share to the firm as the weight and calculate the weighted average of a firm’s bank shock variables, as follows: **************** $%&_>ℎ&, = . /,0, ∗ $%&_>ℎ&0, , 0 34567,8,9:;

where bank 1’s weight in firm  is w,0, = ∑

8 34567,8,9:;

.

Panels A shows the results by firm size. Small (large) firms are those with total assets below (above) the annual 30th (70th) percentile of value. The dependent variable is a change in leverage ratio. Deviation is the predicted value of the target leverage minus the leverage in year t-1. Real-estate lending 89 is the weighted average of a firm’s relational banks’ real-estate lending ratio in the year 1989. We use the bank’s lending share to the firm as the weight. Bank failure takes a value of one if a firm’s main bank fails. The growth of banks’ market-to-book ratio is the weighted average of a firm’s relational banks’ growth of market-to-book ratio. All models include year dummies. Panels B shows the SOA of small firms associated with troubled banks. The standard errors are enclosed in parentheses. * p < 0.1, ** p < 0.05, *** p < 0.01

42

Appendix Table 1. Definitions of variables Variables

Definitions

Debt-to-total assets

Short-term plus long-term liabilities/total assets

Deviation

Predicted target estimated by pooled OLS – Debt-to-total assets (t-1)

Proportion of firms that issue debt

A firm is defined as issuing debt if the net debt issuance exceeds 5% of the total assets in the beginning of the year (Hovakimian 2004; Leary and Roberts, 2005).

Proportion of firms that issue equity

A firm is defined as issuing equity if the net equity issuance exceeds 5% of the total assets in the beginning of the year (Hovakimian 2004; Leary and Roberts, 2005).

Proportion of firms that retire debt

A firm is defined as retiring debt if the net debt retirement exceeds 5% of the total assets in the beginning of the year (Hovakimian 2004; Leary and Roberts, 2005).

Small

A firm is defiend as small if the total assets is below the annual 30th percentile of value.

Without access to bond markets

A firm is defiend as without access to bond markets if predicted probability of the firms’ access to the bond market falls below the 30th percentile.

q (t-1)

The ratio of the market value to the book value of total assets

EBITDA/total assets (t-1)

EBITDA divided by total assets

Ln (sales (t-1))

Logarithm of sales

Tangible fixed assets/total assets (t-1)

Tangible fixed assets divided by total assets

Industry median leverage (t-1)

Industry median of leverage ratio

Loan to debt (t-1)

Short-term plus long-term bank loans divided by debt

Bank shock variables Real estate lending 89

Bank capital 95-96

The weighted average of a firm’s associated bank’s real-estate industry lending share in fiscal year 1989. We use the bank’s lending share to the firm as the weight. The weighted average of a firm’s associated bank’s capital ratio in the years 1995 and 1996. We use the bank’s lending share to the firm as the weight.

Bank failure

Bank failure takes a value of one if a firm’s main bank fails.

Growth of banks’ market-to-book ratio

The weighted average of the growth of the market-to-book ratio of a firm’s associated bank. We use the bank’s lending share to the firm as the weight.

43

Appendix Table 2. Estimation results of target leverage 1987–2014

q Ln (sales) Tangible fixed assets/total assets Industry median leverage EBITDA/total assets

(1) 0.0192*** (0.00115) 0.0323*** (0.000454) 0.189*** (0.00400) 0.565*** (0.00999) -1.380*** (0.0145)

GDP growth Constant

-0.0139

1987–1996

1997–2003

2004–2013

Credit expansion

Credit crunch

Post-credit crunch

(2) -0.00316* (0.00186) 0.0269*** (0.000875) 0.132*** (0.00824) 0.766*** (0.0203) -1.758*** (0.0315) 0.0125*** (0.000624) -0.0845***

(3) 0.0256*** (0.00225) 0.0354*** (0.000898) 0.204*** (0.00763) 0.609*** (0.0199) -1.827*** (0.0281) 0.00170** (0.000849) -0.140***

(4) 0.0334*** (0.00190) 0.0321*** (0.000700) 0.231*** (0.00615) 0.490*** (0.0138) -1.097*** (0.0212) 0.000837* (0.000448) -0.110***

(0.00937) (0.0154) (0.0145) (0.00969) Year dummies Yes No No No Number of observations 75829 20424 22190 30048 R-squared 0.273 0.254 0.265 0.210 Mean of estimated target 0.526 0.603 0.546 0.481 This table presents the estimation results of the target leverage. The results were obtained using the pooled ordinary least squares (OLS) estimator. The dependent variable is the total liabilities/total assets. All the independent variables are lagged values. The standard errors are enclosed in parentheses. * p < 0.1, ** p < 0.05, *** p < 0.01

44

Figure 1. Growth of bank loans

This figure depicts the growth of aggregate bank loans. We obtained each bank’s outstanding loans from the Nikkei Financial Quest database and aggregate them here by fiscal year. We include domestically licensed banks, such as long-term, city, regional, second regional, and trust banks.

Lending attitudes (Diffusion index of accommodative minus severe)

Figure 2. Banks’ lending attitudes

40 30 20 10 0 -10 -20 -30 All

Small

This figure illustrates the lending attitudes of financial institutions (diffusion index of accommodative easy minus severe). We obtained the data from Tankan (a short-term economic survey of enterprises in Japan), Bank of Japan. In Tankan, enterprises are considered small if their capital ranges from 20 million yen to less than 100 million yen. “All” represents all enterprises.

Figure 3. Non-performing loans of banks 0.1 0.08 0.06 0.04 0.02 2016

2015

2014

2013

2012

2011

2010

2009

2008

2007

2006

2005

2004

2003

2002

2001

2000

1999

1998

1997

0

Average non-performing loan ratio Aggregate non-performing loan/aggregate bank loan

This figure depicts the banks’ non-performing loan ratios. The average non-performing loan ratio is the average of each bank’s non-performing loans-to-outstanding loans ratio. The aggregate nonperforming loan/aggregate bank loan denotes the aggregate non-performing loans in the year divided by the aggregate bank loan. We have obtained each bank’s non-performing loans and outstanding loans from the Nikkei Financial Quest database. We include domestically licensed banks, such as longterm, city, regional, second regional, and trust banks.

Table 1. Summary statistics

Variables Debt-to-total assets Deviation from the target, absolute value Deviation from the target Proportion of firms that issue debt Proportion of firms that issue equity Proportion of firms that retire debt q (t-1) EBITDA/total assets (t-1) Ln (sales (t-1)) Tangible fixed assets/total assets (t-1) Industry median leverage (t-1) Loan to interest-bearing debt (t-1)

1987–1996 Credit expansion Mean (Std. Dev.) 0.607 (0.196) 0.137 (0.102) -0.007 (0.170) 0.271 0.033 0.152 1.665 (0.716) 0.080 (0.043) 10.798 (1.413) 0.263 (0.154) 0.617 (0.061) 0.599 (0.362)

1997–2003 Credit crunch Mean (Std. Dev.) 0.542 (0.216) 0.148 (0.109) -0.001 (0.184) 0.141 0.016 0.240 1.139 (0.649) 0.072 (0.052) 10.379 (1.430) 0.290 (0.173) 0.659 (0.182) 0.716 (0.319)

2004–2013 Post-credit crunch Mean (Std. Dev.) 0.478 (0.212) 0.155 (0.108) -0.003 (0.189) 0.200 0.022 0.158 1.164 (0.679) 0.075 (0.060) 10.065 (1.642) 0.250 (0.185) 0.531 (0.162) 0.867 (0.257)

Bank shock variables Bank capital 95-96

0.024 (0.010) 0.074 (0.032) 0.034 (0.182) -0.003 (0.009)

Real estate lending 89

Bank failure Growth of banks’ market-to-book ratio Macro variables DI GDP growth Number of observations

14.383 (12.075) 2.957 (2.080) 19329

-3.383 (6.940) 0.657 (1.185) 18788

7.188 (7.224) 0.789 (2.475) 29242

This table presents the summary statistics. The sample comprises non-financial listed firms in Japanese stock markets in the fiscal years 1984–2014. Except for debt-to-total assets, deviation from the target, and the proportion of firms that issue debt (equity)

assets, deviation from the target, and the proportion of firms that issue debt (equity) and those that retire debt, all the financial variables of firms are values in the year t-1. DI is diffusion index.

Table 2. Structural break test

Banks’ lending attitude (all firms) Banks’ lending attitude (small firms) Non-performing loans of banks

Supremum Wald test of no structural break Estimated break date point 12.1248** 2003 12.7295** 2003 133.4007 *** 2003

This table presents the results of the structural break test. The banks’ lending attitude is the diffusion index. The non-performing loans of banks is the aggregate non-performing loans divided by the aggregate bank loans. The null hypothesis is that there is no structural break point. **p < 0.05, ***p < 0.01.

Panel A: Regression results Model Deviation DI * Deviation

DI Negative DI dummy * Deviation Negative DI dummy

(1) (2) 0.198*** 0.193*** (0.0108) (0.00987) 0.00178** *

(3) 0.198*** (0.0111)

(0.000196)

(0.000229)

(4) 0.186*** (0.0106)

0.00177***

-0.0000407* -0.0000903*** (0.0000215) (0.0000257) -0.0203*** (0.00252) -0.00196*** (0.000448)

-0.0149*** (0.00323) -0.00190*** (0.000541)

GDP growth* Deviation

0.0000465

0.00190***

GDP growth

(0.000683) 0.000341*** (0.000116)

(0.000653) 0.0000161 (0.000121)

Credit-crunch dummy * Deviation Post-credit-crunch dummy * Deviation Credit-crunch dummy

-0.0380*** (0.00754) -0.0212** (0.00992) -0.0000473 (0.00106) 0.000758 (0.00144)

Post-credit-crunch dummy

Number of observations 68650 Chi-squared 439.0 1.037 AR (2) test 5.91e-27 Hansen test Chow F test Panel B: Speed of adjustment (SOA) DI is at the 25th percentile 0.198 DI is at the 75th percentile 0.229 DI is negative Credit-crunch period Post-credit-crunch period

(5) 0.209*** (0.0109)

68650 433.6

68650 472.3

68650 467.1

68650 381.2

0.956 23.50

1.031 7.84e-27

1.014 34.76

1.044 9.90e-27 28.96 ***

0.198 0.229 0.173

0.171 0.171 0.188

Table 4. Speed of adjustment and financial constraints (subsample analysis) Panel A. Estimation results by financial constraints Post-credit Post-credit Credit Credit crunch Credit crunch expansion crunch crunch

Deviation Small * Deviation

(1) 0.317*** (0.0342) -0.0146 (0.0306)

(2) 0.160*** (0.0357) -0.0905*** (0.0292)

(3) 0.162*** (0.0225) 0.0682*** (0.0263)

Without access to bond markets * Deviation Year dummies Yes Number of observations 16851 Chi-squared 356.8 AR (2) test 1.394 Hansen test 4.590 SOA of financially constrained firms 0.30 Panel B: Estimation results by financial deficits Credit expansion (6) Deviation 0.301*** (0.0209) Financial deficits * Deviation -0.0207*** (0.00640) Year dummies Yes Number of observations 16851 Chi-squared 394.8 AR (2) test 0.983 Hansen test 98.85 SOA of firms with financial deficits 0.28 (Please use the notes in word file.)

Yes 20173 195.4 -1.615 28.75 0.07 Credit crunch (7) 0.176*** (0.0212) -0.0334*** (0.00528) Yes 20169 286.9 -1.783 135.9 0.14

Yes 28305 302.6 1.150 9.494 0.23 Post-credit crunch (8) 0.234*** (0.0159) -0.0439*** (0.00456) Yes 28291 565.6 1.536 140.2 0.19

(4) 0.235*** (0.0438)

(5) 0.187*** (0.0240)

-0.131***

0.0278

(0.0442) Yes 12515 186.7 -2.646 82.55 0.10

(0.0327) Yes 24354 302.7 0.474 115.4 0.21

Table 5. Speed of adjustment and financial deficits by financial constraints Panel A. Effects of credit supply conditions and financial deficits by firm size Small firms Large firms Credit Credit Post-credit Credit Credit Post-credit expansion crunch crunch expansion crunch crunch Model (1) (2) (3) (4) (5) (6) Deviation 0.235*** 0.135*** 0.255*** 0.497*** 0.389*** 0.238*** (0.0362) (0.0368) (0.0300) (0.0539) (0.0495) (0.0321) Deviation * Financial deficits -0.0205* -0.0462*** -0.0595*** -0.0133 -0.0138 -0.0322*** (0.0117) (0.0116) (0.0100) (0.0125) (0.00952) (0.00829) Year dummies Yes Yes Yes Yes Yes Yes Number of observations 4531 5361 7548 5508 6552 8991 Chi-squared 161.9 93.45 218.4 214.3 222.3 238.0 SOA of firms with financial deficits 0.21 0.09 0.20 0.48 0.38 0.21 Panel B. Effects of credit supply conditions and financial deficits by firms’ access to bond markets Firms without access to bond markets Firms with access to bond markets Credit Post-credit Credit Post-credit crunch crunch crunch crunch Model (7) (8) (9) (10) Deviation 0.143*** 0.213*** 0.287*** 0.176*** (0.0406) (0.0283) (0.0415) (0.0316) Deviation * Financial deficits -0.0679*** -0.0653*** -0.0177* -0.0406*** (0.0119) (0.00918) (0.0101) (0.00826) Year dummies Yes Yes Yes Yes Number of observations 4031 7081 4338 7415 Chi-squared 108.2 245.0 146.1 202.9 SOA of firms with financial deficits 0.08 0.15 0.27 0.14 (Please use the notes in word file.)

Panel A. Effects of bank shocks Bank shock variables

Deviation Deviation * Bank shock Bank shock Year dummies Number of observations Chi-squared

Bank capital95-96

Real estate lending 89

(1) 0.207*** (0.0767) 2.977 (1.896) 10.16*** (2.896) Yes 13329 178.0

(2) 0.399*** (0.0834) -1.345** (0.640) -0.913 (0.880) Yes 13329 173.6

Panel B. SOA of firms associated with troubled banks The bank shock variables are at the 0.290 median. A main bank has failed.

Growth of banks’ Bank failure market-tobook ratio (3) (4) 0.250*** 0.273*** (0.0365) (0.0367) -0.0618** 0.684*** (0.0287) (0.259) -0.0179 0.229 (0.0138) (0.377) Yes Yes 13554 13329 190.8 225.9

0.280

0.271 0.188

Table 7. Absolute value of deviation from the target (1997–2003)

Measure of classification Firm size

Access to bond markets

Bank real-estate lending 89

Bank capital 95-96

Bank failure

Growth of banks’ market-to-book ratio

Test of equal means Small 0.162

Others 0.143

-12.149 ***

Without 0.156

With 0.136

-10.426 ***

Above median 0.145

Below median 0.131

-8.593 ***

Above median 0.143

Below median 0.134

-5.306 ***

Main bank has failed Main bank has not failed 0.130 0.137 Above median 0.138

Below median 0.139

1.892 *

0.696

This table presents the absolute value of the deviation from the target leverage. Deviation from the target is the predicted value of the target leverage minus the leverage in year t-1. Target is the target capital structure, and we use the estimated fitted values from the pooled ordinary least squares (OLS) regression analysis in Appendix Table 2. We divide the sample into smaller firms (firms with access to bond markets) and others in row 1 (2). Small firms are those with total assets below the annual 30th percentile of value. Firms categorized as ‘others’ are those with total assets above the annual 30th percentile of value. In rows 3 and 4, the samples are divided by the median value of the banks’ realestate lending ratio in 1989 and the banks’ capital ratio in the years 1995 to 1996, respectively. In row 5, the samples are divided according to whether or not a firm’s main bank has failed. In row 6, the samples are divided according to the median value of the growth of the banks’ market-to-book ratio. The last column shows the t-statistics of the equal mean of the two samples. * p < 0.1, ** p < 0.05, *** p < 0.01

Table 8. Decisions on debt issue, equity issue, and debt retirement

Model Deviation of non-small firms Deviation of small firms Small firms Constant

1987–1996 Credit expansion Debt Stock issuances issuances (1) (2) 1.436*** -0.435*** (0.0603) (0.0250) 1.031*** -0.221*** (0.0686) (0.0284) 0.144*** 0.00903 (0.0217) (0.00899) 0.129*** 0.0233*** (0.0102) (0.00422)

Debt retirements (3) -0.819*** (0.0506) -0.618*** (0.0576) -0.108*** (0.0182) 0.194*** (0.00856)

1997–2003 Credit crunch Debt Stock issuances issuances (4) (5) 1.025*** -0.146*** (0.0442) (0.0167) 0.781*** -0.121*** (0.0529) (0.0200) 0.0834*** 0.0247*** (0.0156) (0.00591) 0.164*** 0.0227*** (0.00745) (0.00282)

Debt retirements (6) -1.340*** (0.0514) -1.147*** (0.0616) -0.129*** (0.0181) 0.180*** (0.00866)

2004–2013 Post-credit crunch Debt Stock issuances issuances (7) (8) 1.112*** -0.112*** (0.0417) (0.0160) 0.849*** -0.0638*** (0.0495) (0.0190) 0.0589*** 0.00271 (0.0151) (0.00579) 0.238*** 0.0330*** (0.00808) (0.00310)

Debt retirements (9) -1.131*** (0.0375) -0.912*** (0.0446) -0.0961*** (0.0136) 0.106*** (0.00727)

F-test: Deviation of non-small firms = Deviation of small firms

216.86*** 156.72*** 94.15***

194.21*** 24.70***

209.57***

253.04*** 23.12***

300.63***

Firm-fixed effects and year dummies Number of observations R-squared

Yes 17806 0.124

Yes 21141 0.0556

Yes 21141 0.0886

Yes 29242 0.0487

Yes 29242 0.102

Yes 17806 0.0522

Yes 17806 0.0902

Yes 21141 0.0120

Yes 29242 0.0133

This table presents the estimation results of financial decisions. The dependent variables are financial decisions, such as debt issuances, equity issuances, and debt retirements. Following Hovakimian et al. (2001) and Leary and Roberts (2005), we define a firm as issuing debt (equity) if the net issuance exceeds 5% of the total assets in the beginning of the year and as retiring debt if the net retirement exceeds 5% of the total assets in the beginning of the year. Small equals one if the total assets of firms are below the annual 30th percentile of value. All models are estimated using linear probability models. We include firm-fixed effects and year dummies. The standard errors are enclosed in parentheses. * p < 0.1, ** p < 0.05, *** p < 0.01

Panel A. SOA by firm size and investment opportunities 1987–1996 1997–2003 Credit expansion Credit crunch

Deviation Small * Deviation Small Year dummies Number of observations Chi-squared AR (2) test SOA of small firms

2004–2014 Post-credit crunch

q is above the median

q is below the median

q is above the median

q is below the median

q is above the median

q is below the median

(1) 0.434*** (0.0852) -0.190 (0.116) -0.346 (0.228) Yes 7626 64.25 1.440 0.244

(2) 0.584*** (0.0720) -0.108* (0.0628) 0.0153 (0.0514) Yes 7705 183.1 0.706 0.476

(3) 0.524*** (0.0875) -0.483*** (0.117) -0.528*** (0.113) Yes 9842 148.0 -1.023 0.041

(4) 0.554*** (0.0618) -0.219*** (0.0789) 0.101 (0.0731) Yes 10319 308.0 0.987 0.335

(5) 0.415*** (0.0773) -0.273** (0.114) -0.423*** (0.115) Yes 10160 186.3 1.356 0.142

(6) 0.363*** (0.0570) 0.00120 (0.0636) -0.00340 (0.0879) Yes 11404 242.4 1.353 0.3642

Panel B. SOA by access to bond markets and investment opportunities

Table 10. Effects of bank shocks by investment opportunities (1997–2003)

Panel A. Effects of bank crisis by firm investment opportunities Bank shock variables

Real estate lending 89

q is above q is below the median the median (1) (2) Deviation 0.421*** 0.485*** (0.0384) (0.0480) Deviation * Bank shock -0.685** -0.0140 (0.281) (0.359) Year dummies Yes Yes Number of observations 6435 6894 Chi-squared 389.8 415.2 SOA of firms associated with troubled banks The bank shock variables are at the median.

0.36

Bank failure q is above the median (3) 0.276*** (0.0289) -0.0449* (0.0272) Yes 6563 254.3

0.48

A main bank has failed. 0.23 Panel B. Effects of bank crisis by industry investment rates High-growth Low-growth High-growth industry industry industry (7) (8) (9) Deviation 0.477*** 0.218** 0.139** (0.0932) (0.101) (0.0545) Deviation * Bank shock -1.953*** 1.154* -0.0393*** (0.600) (0.698) (0.0126) Year dummies Yes Yes Yes Number of observations 7898 5438 8038 Chi-squared 116.7 134.6 82.14 SOA of firms associated with troubled banks The bank shock variables are at the median.

0.30

q is below the median (4) 0.475*** (0.0355) -0.0109 (0.0144) Yes 6991 338.7

0.34

0.46

Highgrowth industry (11) 0.250*** (0.0554) 1.027*** (0.310) Yes 7898 110.0

Low-growth industry (12) 0.356*** (0.0599) 0.202 (0.336) Yes 5438 137.2

0.25

0.36

0.115 (0.140)

-0.121 (0.124)

0.46 Low-growth industry (10) 0.386*** (0.0687) -0.0426** (0.0198) Yes 5522 128.9

0.32

A main bank has failed.

Growth of banks’ marketto-book ratio q is above q is below the median the median (5) (6) 0.339*** 0.462*** (0.0280) (0.0289) 0.892*** 0.149 (0.257) (0.199) Yes Yes 6435 6894 333.3 402.7

0.10

0.34

-0.0105 (0.00821)

-0.00584 (0.00893)

(Please use the notes in word file.)

Bank shock

-0.0300 (0.129)

0.505*** (0.153)

Table 11. SOA and the effect of bank shocks by firm size (1997–2003) Panel A. The effect of bank shock by firm size Small firms Bank shock variables Model Deviation

Real estate lending 89 (1) 0.349*** (0.0904)

Deviation * Bank shock -1.430**

Large firms

Growth of banks' Bank failure market-tobook ratio (2) (3) 0.157*** 0.195*** (0.0326) (0.0490) -0.0450**

1.125***

(0.723) (0.0220) (0.395) Year dummies Yes Yes Yes Number of observations 3634 3565 3634 Chi-squared 99.94 106.2 98.48 Panel B. SOA of small firms associated with troubled banks The bank shock 0.218 0.192 variables are at the A main bank has failed. 0.112 (Please use the notes in word file.)

(4) 0.351*** (0.0923)

Growth of banks' Bank failure market-tobook ratio (5) (6) 0.389*** 0.326*** (0.0751) (0.0654)

-0.297

-0.00351

0.301

(0.564) Yes 4169 133.4

(0.0133) Yes 4368 3121.4

(0.375) Yes 4169 117.5

Real estate lending 89

Panel B. SOA of firms with troubled banks SOA when bank shock variable 0.383 is at the median 0.262 SOA when a main bank is failed 0.194 SOA when a main bank receives government capital injection Panel B. SOA of firms with troubled banks SOA when bank shock variable 0.29247775 is at the median 0.2615158 SOA when a main bank is failed 0.1942 SOA when a main bank receives government capital injection 0 0 0

Appendix Table 1. Definitions of variables Variables Debt-to-total assets

Definitions Short-term plus long-term liabilities/total assets

Deviation

Predicted target estimated by pooled OLS – Debt-to-total assets (t-1) A firm is defined as issuing debt if the net debt issuance exceeds 5% of the total assets in the beginning of the year (Hovakimian 2004; Leary and Roberts, 2005). A firm is defined as issuing equity if the net equity issuance exceeds 5% of the total assets in the beginning of the year (Hovakimian 2004; Leary and Roberts, 2005). A firm is defined as retiring debt if the net debt retirement exceeds 5% of the total assets in the beginning of the year (Hovakimian 2004; Leary and Roberts, 2005).

Proportion of firms that issue debt

Proportion of firms that issue equity

Proportion of firms that retire debt

Small

A firm is defiend as small if the total assets is below the annual 30th percentile of value.

q (t-1)

A firm is defiend as without access to bond markets if predicted probability of the firms’ access to the bond market falls below the 30th percentile. The ratio of the market value to the book value of total assets

EBITDA/total assets (t-1)

EBITDA divided by total assets

Ln (sales (t-1))

Logarithm of sales

Without access to bond markets

Tangible fixed assets/total assets (t-1) Tangible fixed assets divided by total assets Industry median leverage (t-1)

Industry median of leverage ratio

Loan to debt (t-1)

Short-term plus long-term bank loans divided by debt

Bank shock variables The weighted average of a firm’s associated bank’s real-estate industry lending share in fiscal year 1989. We use the bank’s lending share to the firm as the weight. The weighted average of a firm’s associated bank’s capital ratio in the Bank capital 95-96 years 1995 and 1996. We use the bank’s lending share to the firm as the weight. Bank failure Bank failure takes a value of one if a firm’s main bank fails. The weighted average of the growth of the market-to-book ratio of a Growth of banks’ market-to-book ratio firm’s associated bank. We use the bank’s lending share to the firm as the weight. Real estate lending 89

Appendix Table 2. Estimation results of target leverage 1987–2014

q Ln (sales) Tangible fixed assets/total assets Industry median leverage EBITDA/total assets

1987–1996

1997–2003

2004–2013

Credit expansion

Credit crunch

Post-credit crunch

-0.0139 (0.00937) Yes 75829 0.273 0.526

(2) -0.00316* (0.00186) 0.0269*** (0.000875) 0.132*** (0.00824) 0.766*** (0.0203) -1.758*** (0.0315) 0.0125*** (0.000624) -0.0845*** (0.0154) No 20424 0.254 0.603

(3) 0.0256*** (0.00225) 0.0354*** (0.000898) 0.204*** (0.00763) 0.609*** (0.0199) -1.827*** (0.0281) 0.00170** (0.000849) -0.140*** (0.0145) No 22190 0.265 0.546

(4) 0.0334*** (0.00190) 0.0321*** (0.000700) 0.231*** (0.00615) 0.490*** (0.0138) -1.097*** (0.0212) 0.000837* (0.000448) -0.110*** (0.00969) No 30048 0.210 0.481

(0.113)

(0.100)

(0.110)

(0.097)

(1) 0.0192*** (0.00115) 0.0323*** (0.000454) 0.189*** (0.00400) 0.565*** (0.00999) -1.380*** (0.0145)

GDP growth Constant Year dummies Number of observations R-squared Mean of estimated target mean s.d.

This table presents the estimation results of the target leverage. The results were obtained using the pooled ordinary least squares (OLS) estimator. The dependent variable is the total liabilities/total assets. All the independent variables are lagged values. The standard errors are enclosed in parentheses. * p < 0.1, ** p < 0.05, *** p < 0.01

values. The standard errors are enclosed in parentheses. * p < 0.1, ** p < 0.05, *** p < 0.01

Highlights We show that bank loan supply shocks affect firms’ leverage adjustment. ・We use Japan’s experience of the boom-bust cycle as a quasi-experiment. Bank-dependent firms adjust slower than other firms during credit-crunch periods. ・Firms associated with failing banks show a slower adjustment than other firms. Our results imply that bank shocks have a persistent effect on borrowers’ leverage.