DPRIETI Discussion Paper Series 11-E-073

Bank Dependence and Financial Constraints on Investment:
Evidence from the corporate bond market paralysis in JapanUCHINO Taisuke
RIETI

The Research Institute of Economy, Trade and Industry

http://www.rieti.go.jp/en/

RIETI Discussion Paper Series 11-E-073
November 2011

Bank Dependence and Financial Constraints on
Investment: Evidence from the corporate bond market
paralysis in Japan1
UCHINO Taisuke2(Research Institute of Economy, Trade and Industry)
Abstract
This paper investigates the causal relationship between firms' bank dependence
and financial constraints by utilizing the 2008 financial crisis and its impact on theJapanese economy as a natural experiment. Since the Japanese banking sector
remained healthy while the corporate bond markets were paralyzed, firms that had
reduced bank dependence were hit heavily by the shock. I examined whether
firms with large holdings of corporate bonds maturing in 2008 were financiallyconstrained, by comparing the changes in their investment expenditures andborrowing conditions with those of bank-dependent firms. The main empiricalresults show that (1) firms with large holdings of corporate bonds maturing in2008 did not cut investment expenditures; (2) instead, they observed higher
increments in bank loans; and (3) firms that maintained relatively close bank-firm
relationships had more access to bank loans with low borrowing costs, but
significant differences in investment expenditures were not found. These findings
imply that although there is a cost to reducing bank dependence, it is not very highfor Japanese listed firms.
Key words: Global financial crisis, Capital investment, Bank-firm relationships.
JEL Classifications: E22, E32, G21, G31
RIETI Discussion Papers Series aims at widely disseminating research results in the form ofprofessional papers, thereby stimulating lively discussion. The views expressed in the papers aresolely those of the author(s), and do not represent those of the Research Institute of Economy,
Trade and Industry.
1

I would like to express my gratitude to Etsuro Shioji for his supervision and encouragement. I also
thank Yukinobu Kitamura, Kaoru Hosono, Yosuke Takeda, Naohisa Hirakata, Daisuke Miyakawa,and Mitsuhiro Osada for their helpful comments and suggestions on my research. This research ispartly supported by the Hitotsubashi University Global COE Grant “Research unit for statistical andempirical analysis in social sciences.” Of course, all errors are my own.
2 Research Institute of Economy, Trade and Industry. Email: uchino-taisuke@rieti.go.jp
11 Introduction
The recent global financial crisis did severe damage to almost every economy. It is anurgent task for economists to shed light on the causes and remedies for the crisis. The
crisis also provides us the chance to investigate the causal links between financial
shocks and the real economy, which are usually difficult to identify. This paper utilizes
the crisis as a natural experiment to examine if a close bank-firm relationship mitigatesfirms’ financial constraints. Since the Japanese banking system remained robust even as
the commercial paper and corporate bond markets (hereafter referred to as capital
markets) experienced a functional decline after the collapse of Lehman Brothers, datafrom Japanese markets demonstrate how banks reacted to increases in the loan demand
from less bank-dependent firms, separate from the banks’ health problem.3
Before the 1980s, Japanese firms were highly dependent on bank loans and thebank-centered financial system (called the main bank system) was considered tomitigate the problem of asymmetric information between firms and banks. However,firms have incentives to reduce their dependency on their banks because closebank-firm relationships can cause hold-ups through an information monopoly (Sharpe,
1990; Rajan, 1992; Houston and James, 1996; Weinstein and Yafeh, 1998; Pinkowitz
and Williamson, 2000). Certainly, after the financial liberalization in the 1980s, thestructure of corporate finance in Japan changed dramatically and a number of firms
began issuing bonds (Shirasu and Xu, 2007).
Reducing bank dependency is expected to be effective in mitigating hold-ups but it
also involves costs. Hoshi, Kashyap, and Scharfstein (1990, hereafter HKS)
demonstrate that firms that tried to become less dependent on banks during the 1980s
were financially constrained compared to firms that kept close relationships with thebanks. That is, if firms reduce bank dependency, their access to bank loans will belimited, making information asymmetries between firms and banks more serious.
It is important for both economists and policy-makers to understand how much it
costs the firms to reduce their bank dependency. Following the discussions of3

Throughout this paper, the extent of bank dependency is defined by the ratio of bank loan balances
to total liabilities with interest. Therefore, less bank-dependent firms are more dependent on bondmarkets. Similar measures have been used in existing literature such as Hoshi, Kashyap, andScharfstein (1990), Pinkowitz and Williamson (2000) and Houston and James (2000).
2

Holmstrom and Tirole (1998), who analyze the role of indirect finance, the main cost of
the disintermediation is that firms lose their insurance against liquidity shocks. Certainly,
in Japan, it has been suggested that one of the advantages of the main bank system isthat firms are supplied with implicit insurance so that they can be rescued by their mainbanks in the face of liquidity shocks (Osano and Tsutsui, 1985; Sheard, 1989).
Therefore, losing such functions in the course of financial liberalization can result incapital market shocks being more easily propagated into the real economy.
Despite its importance, HKS’s methodology that measures the extent of financial
constraints by the sensitivities of cash flows in reduced form investment functions has
been criticized (Kaplan and Zingales, 1997; Hovakimian and Titman, 2006).4 From anempirical point of view, the simultaneity of the firms’ investments and financing
decisions makes it difficult for researchers to identify the causal relationship. As
Hovakimian and Titman (2006) suggest, the most serious criticism is the possibility thatcash flows also reflect investment opportunities that cannot be controlled by Tobin’s q.
In other words, the reason firms can earn cash flows may be related to the fact that they
have profitable investment projects. Since Tobin’s average q is measured by the market
value, which reflects the investors’ evaluations, the sensitivities of cash flows could belarger for firms suffering from severe asymmetric information problems between
themselves and their investors.
In order to overcome this limitation, a number of researches have tried to determine
the exogenous events wherein firms’ cash flows were altered independent of investment
opportunities. For example, Blanchard, Lopez-de-Silanes, and Shleifer (1994) focus on
the investment activities of firms that experienced cash windfalls in the U.S. Likewise,
Lamont (1997) compares the investment expenditures of non-oil subsidiaries of oilcompanies with those of non-oil companies during the 1986 oil price decline. Thesestudies reject the complete capital market hypothesis, that is, firms are financially
4

HKS (1990, 1991) follow the method proposed by Fazzari, Hubbard, and Petersen (1988). They
examine the differences in the cash flow sensibilities between firms that are a priori considered to besuffering from severe asymmetric information (between firms and investors) and firms that not. The
basic idea is that if firms are required to set high lemon premiums as a result of asymmetric
information, their investment expenditures are restricted to the amount of cash on hand. If closebank-firm relationships reduce the asymmetric information between firms and banks (andconsequently lemon premiums), the firms’ investment expenditures are expected to be less sensitive
to their cash flows. HKS (1990) observed higher cash flow sensitivities among keiretsu firms that
had increased their dependency on the bond markets in the 1980s.
3constrained when they make capital investments. However, they failed to obtainadequate sample sizes.5
Recent empirical literature focuses on large-scale exogenous financial shocks to firms.
Chava and Purnanandam (2010) utilize the Russian financial crisis in 1998 as an
exogenous shock to the U.S. banking sector in order to show that bank-dependent firms
experienced a larger decline in their investment expenditures as compared to those that
had access to bond markets during the period. Almeida et al. (2009) try to specify moreclearly the firms that may have experienced financial constraints during the recent
global financial crisis. They focus on the firm’s debt maturity structure and demonstratethat the U.S. firms that had large amounts of maturing long-term debts (more than 20%
of existing long-term debts) reduced their investment expenditures significantly.
Following the recent empirical literature, this paper utilizes the 2008 financial crisis
as a natural experiment to examine the question of whether it is costly for firms to
reduce their bank dependency and, if so, what is the cost. In Japan, it was reported thatthe corporate bond markets were paralyzed during the crisis, while the banking sector
stayed relatively healthy (Bank of Japan, 2009a, 2009b). Firms that had reduced theirbank dependency were therefore hit most heavily by the financial shock. The
identification strategy of this paper makes use of this fact and focuses on the firms’
maturity compositions of liabilities. That is, for “unlucky” firms that had issued large
amounts of corporate bonds that matured during the crisis, refinance by issuing new
bonds became difficult. As a result of the exogenous shock, their demand for bank loans
shifted outward. As discussed in HKS (1990), if the problem of asymmetric information
between firms and banks worsens by reducing bank dependency, these unlucky firms
would face financial constraints because their banks require high lemon premiums or
may possibly reject their loan applications (rationing). As a result, their investment
expenditures are expected to decline, other things being equal.
This paper classifies firms that had maturing bonds as “treated groups” and examines
whether they were financially constrained. This is done by comparing their investment
expenditures and borrowing conditions during the crisis with those of bank-dependent

5

Blanchard, Lopez-de-Silanes, and Shleifer (1994) and Lamont (1997) obtained only 11 and 39
observations, respectively.
4

firms, whose economic attributes were considered to be ex-ante identical.6
The empirical results of this paper are summarized as follows. First, the firms withlarge amounts of corporate bonds maturing in 2008 did not cut their investment
expenditure, as compared with bank-dependent firms. Second, bank loan balances
increased sharply for those firms with less bank dependency. Finally, the treated firmsthat kept relatively close bank-firm relationships had more access to bank loans withlow borrowing costs, but significant differences between their investment expenditureand that of bank-dependent firms were not observed. These findings suggest that it is
not that costly for the recent Japanese listed firms to reduce their bank dependency. Thatis, the problem of asymmetric information between firms and banks is not that serious.
This study contributes to the existing empirical literature on corporate finance andbanking with regards to bank-firm relationships and the role of the banking sectors byproviding causal evidence. In addition, it adds to the literature on credit channels ofmonetary policy, such as Gertler and Gilchrist (1994) and Kashyap and Stein (2000), by
examining the heterogeneous effects of financial shocks. Finally, this paper is also
related to the studies of the Japanese economy. Although Japan was not the epicenter ofthe 2008 financial crisis, its real economy was the most severely damaged among
advanced economies. This paper attempts to examine whether the huge and rapid
economic shrinkage that Japan experienced in the last two quarters of FY2008 wasrelated to the shocks in the capital markets.
The rest of the paper is organized as follows. In the second section, I describe thechanges in the Japanese capital market during the 2008 financial crisis to explain myidentification strategy. Section 3 describes the data set, which is followed by theempirical results described in Section 4. Section 5 discusses the results and theirimplications, and Section 6 concludes the paper.

2 Identification Strategies
6

The identification strategy of this paper follows that of Almeida et al. (2009). However, since theJapanese banking sector, unlike its U.S. counterpart, was not so damaged by the global financial
shock, it enables an examination of the role of the banking sector separately from bank healthproblems. In addition, I categorize firms that may have been financial constrained into more than
two groups according to their volumes of maturing bonds by extending the methodology to multiple
treatment models. This allows greater quantitative results to be obtained.
5This paper examines whether the problem of asymmetric information between firms andbanks becomes serious if firms reduce their bank dependency. To test the hypothesis, I
focus on the changes in the Japanese capital market during the 2008 financial crisis,
which had unique characteristics as compared to those of other advanced economies.
Here, while the capital markets were paralyzed, the banking sector remained relatively
healthy.
Bank of Japan (2009a, 2009b) reports that the capital markets were paralyzed in thelast half of FY2008 (2008Q4-2009Q1, see Figure 1). For example, firms with ratings
below “A” could not raise bonds at all during the period and even the public sector, likethe issuers of government guaranteed bonds and the local governments, decided topostpone their scheduled issuances. The Bank of Japan also reports that this market
turmoil was related to the decline in the investors’ risk appetites. The most recent
empirical analysis on the Japanese capital markets certainly supports this hypothesis(Oyama and Hongo, 2010).7
Other countries experienced similar capital market paralysis after the collapse of theLehman brothers. The banking system in Japan remained healthy unlike other advanced
economies. Therefore, in Japan, firms that had reduced bank dependency were hit mostheavily by the crisis in terms of financing activities. According to Bank of Japan (2009a,
2009b), the main reason behind Japanese banks escaping the fate of their global
counterparts was that their exposure to securitized products was very limited. In thissense, the fact that Japanese banks could not succeed in their investment bankbusinesses turned out to be a key factor in the preservation of their functions ascommercial banks.
In order to examine the above hypothesis, this paper focuses on the maturity
compositions of liabilities held by the firms. That is, for firms that had large amounts ofcorporate bonds maturing in 2008, it became difficult to refinance by issuing new bonds
and their demand for bank loans shifted outwards. If the problem of asymmetric
information was serious for these less bank-dependent firms, their banks did not extend
sufficient loans (or required higher “lemon premiums”) and these firms would have
7

Although the causes of a functional decline in the capital markets and the international propagationof financial market shocks themselves are urgent subjects of empirical analysis, I leave them for the
future research.
6

become financially constrained. Besides, the more the volume of maturing bonds firms
had, the more financial constraints they faced, because more finance was needed to
repay their debts. Accordingly, they observed both, a decline in their investment
expenditures and an increase in the borrowing costs, and the extent of these would belarger in keeping with the greater volume of maturing bonds. Conversely, if the problem
of information asymmetries did not become serious, no decline in investment
expenditures but an increase in bank loan balances would be observed. Since it is easy
for firms to borrow from banks, they use bank loan extensions to cover the cash flow
lost through bond repayments. Therefore, I set firms with issued bonds maturing in2008 as treated groups and estimate the treatment effects on investment expendituresand borrowing conditions, allowing the effects to differ according to the amount of
maturing bonds.
This paper employs propensity score matching methods, which are often applied in
the analysis of medical science (Rosenbaum and Rubin, 1983; Heckman, Ichimura, andTodd, 1997). In doing so, I take the effect of cigarette smoking on a person’s health as
an example to understand the basic idea behind the method. Whether a person smokes iscalled “treatment” and the group composed of smokers is called the “treatment group.”
Likewise, the group of non-smokers is called the “non-treated group.” However, it is
impossible to measure the effect of smoking on health by just comparing the average
health status between the two groups. This is because people who are stressed are more
likely to smoke even as the stress also damages their health.
Now, each smoker is matched with a non-smoker who has the same attributes. This
non-smoker is called a “control sample.” After conditioning on the factors that affect thedecision to smoke (called “covariates”), whether a person smokes is determined
randomly. Factors such as damage to health through stress are expected to be the same
between the two people. Therefore, the observed average difference in health status,
called the average treatment effect on the treated (ATT), has causal effects. However, it
is difficult to find control samples as the dimension of covariates becomes larger.
Rosenbaum and Rubin’s (1983) method proposes to use the estimated probabilities(called “propensity scores”) for matching those that are obtained from probit regressionsof the treatment status on covariates. Samples with the same propensity scores basically7

have similar covariates, given the parameter estimates.
This paper assumes that the crisis was highly exogenous to Japanese individual firms,
because it is apparent that the investment or financing activities of individual Japanesefirms was not the cause of the crisis.8 However, the assumption does limit the treatedfirms (or less bank-dependent firms) to those that had ever issued corporate bonds. Thismeans that the distribution of the firms’ attributes (such as firm size, investment
opportunities, and leverages) might be different between treated and non-treated firms(or bank-dependent firms). Therefore, I need make adjustments to randomize thetreatment. For this purpose, I employ the method of matching estimators to calculate thetreatment effects (Rosenbaum and Rubin, 1983; Heckman, Ichimura, and Todd, 1997).
In the next subsection, I explain the econometric methodologies in detail.

2.1 Matching Estimators
In order to estimate the treatment effects allowing for heterogeneity in the volume of
maturing bonds, this paper utilizes the multiple propensity score matching techniqueproposed by Lechner (2002). Lechner’s method is a natural extension of the binomial
treatment model introduced by Rosenbaum and Rubin (1983) to multiple treatments.
The applications of Rosenbaum and Rubin’s method are basically limited to the casewhere the treatment status is binomial. Therefore, it is highly restrictive when the actual
treatment is continuous or multiple. In the example of cigarette smoking, by defining
the treatment status as only whether the person smokes or not, the treatment effects on
the “light” smokers are assumed to be the same as on “heavy” smokers.
Lechner’s method is aimed at bridging this discrepancy. Although I refer the reader toLechner (2002) for a detailed discussion on his matching estimator, this paper considers
the validity of the so-called strongly ignorable treatment assignment assumption. To dothis, the set of covariates, treatment statuses that happen to agents and the corresponding
counterfactual outcomes are defined as X, D={1,2,…,M}, and {Y(1), Y(2),…, Y(M)}8

As Peek and Rosengren (1997) emphasize, it is difficult to isolate demand and supply shocks in
the usual reduced form analysis. They use the Japanese banking crisis in the 1990s as an exogenous
negative loan supply shock to the Japanese banks in the U.S. and show the negative real effect on
construction activities in the U.S. Their empirical findings also provide evidence of the international
propagation of financial shocks.
8respectively. We can once again return to the cigarette smoking allegory. Covariates are
personal attributes that affect smoking decisions (such as stress and lifestyle) andtreatment statuses mean the number of cigarettes that a person smokes per day.
Counterfactual outcomes are health statuses that might have been realized according to
the treatment status. Then the condition can be expressed as follows.
{Y (1), Y ( 2 ),..., Y ( M )}  D | X .

(1)

It means that treatment statuses are independent of outcomes after controlling theattributes. In the smoking example, this assumption implies that smoking decisions are
only dependent on personal attributes and never on future health statuses. That is,people whose health is more affected by smoking are not allowed to stop smoking or
reduce the number of cigarette. Beyond this example, in many applications of matching
estimators, it is doubtful if this condition holds because the treatments themselves areusually dependent on the agents’ decisions (agents who expect higher outcomes are
more likely to apply for the programs). For the purpose of this paper however,treatments or whether firms had issued corporate bonds that matured during the crisis
are predetermined and they could not avoid the shock unless they had alreadyanticipated it.9 Therefore, it is reasonable to employ matching estimators the factorsthat affect firms’ motives to issue bonds can be controlled. With regard to the treatment
status D, four categories are defined according to the number of bonds that matured in
2008, and the treatment effects are expected to be larger in accordance with the number.
According to Lechner’s method, the concrete estimation steps are as follows,
contextualized for the purpose of this study. First, four categories (B0, B1, B2, B3, B4)
are set according to the volume of bonds matured in 2008, as shown in Table 1. In thesecond step, each firm’s probabilities of falling into B0, B1, B2, B3, or B4 are estimated,conditional on the firms’ attributes (Tobin’s q, cash-flows, firm-sizes, default-risks,
cash-holdings, leverages, bank-firm-relationships, and industry-dummies) by themultinomial probit model:

9

Firms could have avoided to be “treated” if they anticipated the crisis in advance, for example, by
buying the maturing bonds back and issuing new bonds while the market was normal. It is known
although that the strongly ignorable treatment assignment assumption cannot be tested statistically(see Cameron and Trivedi, 2005), but I consider this predictability roughly in the later section.
9

Pk ( x)  Pr(S  k | X  x) , k  B0, B1, B2, B3, B4.

(2)

The selection of covariates X follows the method proposed by Almeida et al. (2009).
However, Altman’s Z score (Altman, 2002) is used as a measure of default risks insteadof ratings, because only a select number of firms managed to acquire them frominstitutes such as Standard and Poor’s and Moody’s.10
In the third step, I make four subsamples, (B1, B0), (B2, B0), (B3, B0), and (B4, B0),and calculate the conditional probability that firms fall into B1, B2, B3 and B4 in eachpair using the estimated multinomial probit probabilities. Considering any two different
categories {l,m} in D, they will be

P l|ml ( x)  Pr(S  l | S  l , or, S  m, X  x)P l ( x).
P l ( x)  P m ( x )(3)In this paper, I investigate the effects of the financial crisis on investment expendituresand borrowing conditions of firms with large volumes of maturing bonds by estimating
the ATT. This is defined as an average deviation between the actual outcomes of treatedfirms and their counterfactual outcomes namely, the outcomes that might have beenrealized if the firms were not treated. In this study, counterfactual outcomes refer to how
the treated firms’ investment expenditures and borrowing conditions would have
evolved after the financial crisis if they had not issued corporate bonds at all. When land m are set to be a treated group and a non-treated group respectively, the ATT can be
expressed as follows.
m,l
 EP
 ATT

l | ml( X )|S l[ E[Y (l ) | P l|lm ( X ), S  l ]  E[Y (m) | P l|lm ( X ), S  m]]

 E[Y (l ) | S  l ]  EPl|lm ( X )|S l [ E[Y (m) | P l|lm ( X ), S  m]].

(4)

The second term of the right hand side calculates the counterfactual outcome. ATTs as
defined above are allowed to be different depending on the selected l and m. Intuitively,
Altman’s revisited Z score is defined as Z
0.717T1
0.847T2
3.107T3
0.420T4
0.998T5 .
Hereis the ratio of quick asset minus current liability to total assets.
is the ratio of earned
reserve to total assets,
is the ratio of pretax net profit plus interest paid to total assets,
is theratio of net book value to total liabilities, andis the ratio of sales to total assets. The probabilityof a firm’s bankruptcy is considered to get lower as the Z value becomes larger.
1010

ATTs can purely extract the effects of the maturing bonds because treated firms werematched with control firms, which are ex-ante identical except in the extent of theirbank dependence. Therefore, the ATT estimators suggest a “causal effect.”
Practically, ATTs are estimated in the following way.
m ,l
ˆATT




1
Yi (l )   w(i, j )Y j ( m ) .

N l i{ S l } j{ S  m}
(5)

Firms in categories B1, B2, B3, and B4 are defined as treatment groups and firms in B0,
whose conditional propensity scores are closest to each treated firm, are defined ascontrol firms. For each treated firm, the control firms are matched and differences in theoutcome between the two groups are calculated.
There is another important assumption called the balancing condition, which states
that, for those firms sharing the same conditional propensity scores, treatment statuses
should be independent of covariates. If we suppose the function 1() to be an indicator,
then the condition can be written as1(S  l | S  l, or, S  m)  X | Pl|lm ( x) .

(6)

It means that there are no differences in the distributions of the covariates between
treated and control firms, that is, the treatments are randomized. Unlike a strongly
ignorable treatment assignment assumption, these conditions can be tested statistically.
This paper therefore checks them to consider the validity of selected covariates.
With respect to the matching methods, caliper matching and 10 nearest neighbors
matching is undertaken. In both calculations, the control firms are matched with theirreplacements and imposed a maximal tolerance level on the maximal propensity scoredistance (radius matching). The tolerance level is set to the absolute value of 0.01 (or1%) and the treated samples that cannot find control firms within the radius are
discarded. The weights w (i , j ) in the above equation are therefore set to 1 or 0.1 foreach control firms depending on the matching method.11
In order to eliminate the firms’ unobservable factors that affect outcome variables (orfixed effects), differenced outcome variables are used to estimate ATTs. That is to say,11

For details of the implementations of propensity score matching estimators, see Caliendo andKopeinig (2008) for example.
11

they are propensity score difference-in-differences matching estimators (Hereafterreferred to as PSM-DIDs) and they enable us to control both the fixed effects and theaggregate shocks.

3 DataAs mentioned in an earlier section, this paper uses Japanese listed firms’ financial
statements and corporate information such as market capitalizations and thecompositions of major shareholders and the stock market where firms are listed. The
former was extracted from the “Nikkei NEEDS CD-ROM” (hereafter, Nikkei-NEEDS)
published by Nikkei Inc. and the latter from the “Japanese Company Handbook
Quarterly” published by Toyo Keizai Inc. My data set is composed of non-financial andnon-agricultural firms listed on the Sapporo, Tokyo, Nagoya, Osaka, and Fukuoka Stock
Exchanges and stock markets for emerging firms such as Jasdaq, Mothers, and Hercules,
which settle accounts in March.
The treatment statuses (B0, B1, B2, B3, and B4) are classified according to thevariable MATURITY, which is defined as the ratio of the amount of corporate bondbalances that had matured in FY2008 (2008Q2-2009Q1) to the sum of liabilities withinterest (short-term bank loans (Nikkei-NEEDS’ FB075), long-term bank loans(FB076+FB101), commercial papers (FB075), and corporate bonds (FB078+FB099)).12
Firms that had no outstanding balances of bond or commercial paper at the end of
FY2007 (therefore, they did not have any maturing bond either) are classified asbank-dependent firms (B0). This definition is similar to those of previous literature suchas HKS (1990) and Pinkowitz and Williamson (2000), which examines the effect ofbank dependency. The “lucky” firms that had positive outstanding balances of bonds but
none of which matured in FY2008 are classified as B1. Firms in category B2 had issued
a small number of bonds (less than 5% of total liabilities with interest) that matured in
FY2008. Similarly, firms in B3 or B4 had issued a medium number (5–10% of total
liabilities with interest) or a large number (more than 10% of total liabilities withinterest) of bonds matured in FY2008, respectively. I therefore expected that the latter
12

In order to specify the amount, I use the reported balances of corporate bond that were scheduled
to mature within 1 year (FB078) at the end of FY2007 (March 2008).
12

categories would have larger treatment effects for firms.
As discussed earlier, in order to estimate PSM-DIDs, this paper controls the Tobin’s q,
cash flows, firm size, default risks, cash holdings, leverages, bank-firm relationships,
and industry-dummies as covariates. Constructions of these variables basically follow
Almeida et al. (2009). Tobin’s q (Q) is defined as the ratio of market capitalization plus
total assets (FB063) minus net assets (FB166) minus differed taxes (FB056) to total
assets. Cash flow (CF) is the sum of net income (FC058) plus depreciation (FC046)
divided by the lag of fixed tangible assets (FB032). Size (lnASSET) is defined as thenatural log of total assets. Default risk (RATING) is calculated according to Altman’s
definition.13 Cash holding (CASH) is defined as the ratio of quick assets (FB02) to total
assets. Leverage or capital ratio (CAP) is defined as the ratio of net assets to total assets.
On the bank-firm relationships (MAINBANK), a dummy variable is constructed, whichtakes on a value of 1 if there is more than one bank in the list of major shareholders.
Industry dummies are constructed by utilizing the 2-digit codes of “Japan Standardized
Industrial Classification (JSIC)” ver.12. I also limit the stock markets where the firms
are listed: dummy variables MARKET1-MARKET4 stand for Tokyo stock exchange
first section; Tokyo stock exchange second section; regional stock exchanges (Sapporo,
Nagoya, Osaka and Fukuoka stock exchanges); and stock markets for emerging firms(Jasdaq, Mothers, and Hercules), respectively. Detailed definitions of each variable areshown in Tables 1 and 2. Following Caliendo and Kopeinig (2008), I use lagged values(values at March 2007) of each covariate in the estimation.
When it comes to the outcome variables, this paper focuses not only on the changes
in investment expenditures (INVEST) but also on the changes in bank loan balances andborrowing costs. This includes log of total bank loan balances (lnLOAN); log of shortand long-term bank loan balances (lnSHORT and lnLONG); and bank loan interest rates(RATE)).14 Here the variable INVEST is defined as the ratio of capital expenditure(CAPEX, FP01143) to the lag of fixed tangible assets (FB032) and RATE is defined as
the ratio of interest paid on bank loans (FC016-FC017-FC018-FC124-FC125) to
13

See Altman (2002) for details.
In terms of the calculations of lnSHORT and lnLONG, it turns out that some firms had nooutstanding balance of short or long-term bank loans. If they are eliminated, quite a few samples will
be discarded. To deal with this problem, I add 1 to short and long bank loan balances before taking
the logs for firms with positive outstanding balances of total bank loans.
1413

two-year averaged total bank loan balances (FB075+FB076+FB101).15

3.1 Descriptive Statistics
Descriptive statistics are shown in Table 3. It indicates that there is a sufficient numberof treated and control firms to obtain trustworthy results. Each treated category (B1-B4)
has no less than 100 samples and about 60% of total observations are classified as a
non-treated group (B0). Table 4.1 and Table 4.2 show the subsample means of eachcategory and indicate that there are differences in covariates between the treated andcontrol firms. For example, Tobin's q is slightly higher for treated firms on average andcontrol firms are, on the whole, smaller in terms of asset size. Against intuition, theaverage RATING is higher for non-treated firms. As for outcome variables, somevariables also show differences among categories. Especially, the average growth ofbank loan balances (lnLOAN) for firms in B3 and B4 are about 14% and 23%
respectively, while those for non-treated firms are only about 4%. On the other hand,contrary to prior expectations, INVEST and RATE show small differences.
In this paper, observations that are larger or smaller than 99.5 or 0.5 percentile pointsof each variable are eliminated as outliers. Nevertheless, outcome variables show large
heterogeneity. Since the calculations of the ATT estimators are based on averages, they
are weak in dealing with outliers. I examine whether the outliers affect the results byadopting alternative econometric methods in the next section.

4 Estimation Results
Table 5.1, Table 5.2 and Table 6 show the main estimation results. First, the marginal
effects of MNP (Table 5.2) indicate that firms are more likely to issue bonds as theirasset sizes become larger and the levels of cash holdings, capital ratios, and ratings
become lower. Although significant differences in investment opportunities (Tobin’s q)
and cash flows are not observed, their estimated coefficients differ slightly among the15

For example, RATE in FY2008 is calculated as the bank loan interests paid in FY2008, divided
by the average bank loan balances from FY2007 to FY2008. This definition allows us to take into
account the changes in the bank loan balances for the same period compared to the usual calculationthat divides interest expenditures by the lag of loan balances (Caballero, Hoshi, and Kashyap, 2008).
14

categories (Table 5.1). Therefore, in principle, it enables us to match the control firms
whose Tobin’s q and cash flows are also close to each treated firm.
Some of above results might be related to reverse causalities. The firms’ past bondissuances make recent leverages higher and, consequently, default risks are alsoheightened by definition. However, the main purpose of MNP estimation is to control
the firms’ attributes by constructing propensity scores and not to estimate the structuralparameters. These problems, therefore, are not fundamentally serious.
The kernel density functions of the conditional propensity scores for the treated andnon-treated groups are shown in Figure 2. Here, the conditional propensity scores arecalculated as the probabilities of the treatment statuses (B1, B2, B3 or B4) occurring ineach subsample, under the condition that two events might occur, (B0, B1), (B0, B2),
(B0, B3) and (B0, B4). In each case, firms classified as non-treated group (B0) havelower probabilities, while treated firms (B1, B2, B3, and B4) have relatively higher
probabilities. Besides, the distributions of conditional propensity scores for treated andnon-treated groups are sufficiently overlapped in all cases. Therefore, it can beinterpreted that all the matched non-treated samples have similar propensity scores. The
results of PSM-DID are as shown in Table 6. The estimation results from caliper
matching are shown in the upper table and those from the nearest neighbor matching areshown in the bottom table in the same way.
The results demonstrate that the treatment effects of INVEST are not so small. The
estimated values from caliper matching show that firms in categories B2 and B3
decreased investment rates by 3.1% and 4.1% more than the control firms did. Firms in
B4, on the other hand, increased their investment rate by 3.8%. Nevertheless, theyremain insignificant and these tendencies were not altered by an alternate matching
method.16 Likewise, the changes in RATE were not different for the treated and control
firms. However, the estimation results for bank loan balances (lnLOAN, lnLONG, andlnSHORT) are noteworthy. First, the estimated ATTs of lnLOAN show apparent andsignificant increments for firms in categories B3 and B4. Specifically, the total bankloan balances rose, on average, by 11% for the former and 18% for the latter. The ATTsof the compositions of bank loans (lnLONG and lnSHORT), on the other hand, are16

Since no large differences are observed between the two methods, the results from caliper
matching are explained hereafter.
15

somewhat different between categories. For firms in B3, the short-term bank loanbalances increased sharply (44%) while large increments in the long-term bank loanbalance (37%) were observed for firms in B4.
The above empirical findings demonstrate that there is no strong evidence that treatedfirms become financially constrained during the crisis and the more the number ofmaturing bonds they had, the more bank loans were supplied without a corresponding
rise in borrowing costs. This paper interprets these facts as follows: (1) firms that had
reduced bank-dependency were also supplied with bank loans without the imposition ofhigh lemon premiums; (2) the bank loan supply was plentiful enough to have prevented
firms from being financial constrained; and (3) the problem of asymmetric information
between banks and firms is not serious for recent Japanese listed firms.
In the following subsections, I reinforce the empirical evidence. Specifically, I checkif the treatment statuses are randomized, whether the findings are unique to the crisis
period, and review the predictability of the crisis.

4.1 Balancing Tests
The trustworthiness of above empirical results depends on whether the treatment
statuses are randomized or not. In other words, they need to satisfy the conditions
shown in (6). To check them statistically, I employ two tests proposed in Caliendo andKopeinig (2008): the standardized bias (SB) test and the pseudo R-squared test. The
former checks whether the differences in each covariate are small on average by
calculating the mean and median absolute values of SB indicators defined as follows.
SB k ,l ,m 

x k ,l  x k , m
0.5 * (var( x k ,l )  var( x k ,m )).

(7)

Here l and m indicate the treated and control firms respectively and k stands for the k-th
covariates. Their medians or means are required to be less than 5% after matching. The
pseudo R-squared test proposed by Sianesi (2004) is similar to the former in its basic
ideas. For each l and m, we regress 1(S=l | S=l, or, S=m) on covariates X by probit model
after matching to test the null hypothesis that all the coefficients on X are zero. If thebalancing condition holds, then the null hypothesis will be rejected and pseudo16

R-squared will be adequately small because the covariates no longer have the ability to
predict treatment statuses under the condition.
The results of these tests are shown in Table 7. Both tests suggest that the balancing
conditions are satisfied after matching; that is, the medians and means of SBs are allless than 5% in absolute value and the null hypothesis is rejected with large p-values foreach subsample. These results imply that the selection of covariates is appropriate andthe treatments are successfully randomized.

4.2 Treatment Effects in the Normal Time
Another interpretation of the empirical findings of this paper is that the phenomena are
not special to the crisis period. That is, firms that have maturing bonds might alsoborrow from their banks to repay in the “normal time.” If this were true, theinterpretations of this paper would be misleading. In order to eliminate this possibility, I
estimate the same models for FY2005. This period is chosen not only was the Japanese
economy enjoying an economic expansion at the time, but it had also recovered fromthe bad loan problem.
Estimated PSM-DIDs shown in Table 8 are all negligible at the significance level of
5%. This demonstrates that no strong differences in INVEST and the borrowing
conditions (RATE, lnLOAN, lnLONG and lnSHORT) between the treated and control
firms are observed in FY2005. Therefore, it would suffice to say that the main findingsof this paper are unique to the crisis period.

4.3 Could Firms Have Anticipated the 2008 Financial Crisis?
As discussed in section 2, if the firms had anticipated the financial crisis, they could
have avoided being “treated,” for example, by buying their maturing bonds back while
the markets were normal. Then, in such a situation, the strongly ignorable treatment
assignment assumption would be invalid. In this subsection, I examine this point byusing the firms’ maturity schedules reported in the Nikkei-NEEDS database. Here I
focus on two items: corporate bonds scheduled to mature in more than one year lessthan two years (FF044) and corporate bonds scheduled to mature within one year17(FF043). If the global financial crisis were anticipated in March 2008, then the amountof FF043 could be expected to be smaller than of FF044, as reported at the end of
FY2007. This paper, therefore, generates variable CHANGE defined as FF043 minus
lagged FF044 and compares its descriptive statistics to earlier years.
The results are shown in Table 9. The top section of the table shows that the averagechanges in maturing bonds were 0.9 billion Yen and its median was zero in FY2008.
Besides, no large differences are observed comparing it to earlier years. In the bottom
section of the table, the descriptive statistics for the subsamples of nonzero values areshown. This also demonstrates that the average changes were non-negative and its
distributions and sample sizes were not so different from those of earlier years.
Therefore, no strong evidence that firms could anticipate the crisis beforehand isobserved.

5 Robustness Checks and Extensions
The estimation results in Section 2.4 demonstrate that changes in investment
expenditures were not different between treated and control firms, while a sharp rise in
the bank loan balances was observed for the former. This section goes into the details tocheck the robustness. First, I check whether the main results are robust to the selectionof control groups. Second, I examine whether the outliers of outcome variables affectthe estimated treatment effects through matching estimators. At the same time, I also
investigate the heterogeneity of the treatment effects.

5.1 Changing Control Groups
In PSM-DID estimations, the treatment sample or the firms that had maturing corporate
bonds are compared with the firms that had not issued corporate bonds at all. However,if there exist some unobservable attributes that affect both the decision on bond issue
and outcome variables, the estimated treatment effects might be biased. For example,
firms might refrain from issuing corporate bonds partly because they are afraid of thefreeze in capital markets that might occur in the future. Such firms may have made theprudent decision to reduce their investment expenditures during the crisis period. If this18

situation were true, the estimated treatment effects in the above section would beerroneous.
To consider this possibility, I re-estimate the PSM-DIDs, limiting the treated andnon-treated groups to firms whose outstanding balances of corporate bonds are non-zero.
If firms that had issued corporate bonds faced more severe information asymmetries, theinvestment expenditure for firms that had a large amount of maturing corporate bonds
would have declined. Conversely, if the problem of asymmetric information were notsevere, increments in bank loan balances were expected to be larger as the magnitude of
the shock–namely, the amount of maturing bound–becomes larger. Since both treatedfirms and non-treated firms are limited to bond issuers in this case, their treatment
statuses reflect the firms’ “luckiness” more precisely.
In this section, I classified firms in B3 and B4 as treated groups (firms that had a
middle or large amount of maturing bonds) while firms in B1 and B2 are combined into
one non-treated group (firms that had a small amount of maturing bonds). Therefore, thenumber of treatment statuses is reduced to 4 in this case. I applied Lechner’s (2002)
method to this 4-status model following the procedure explained in the previous section.
The estimation results are shown in Table 10. Again, the treatment effects of investment
expenditures were insignificant for both B3 and B4. However, significant increments inthe bank loan balances were observed. Besides, the magnitudes of treatment effects
were not so different from previous results. This implies that the problem, which mightarise from choosing control firms from B0, is negligible.

5.2 Median Treatment Effects
As discussed in Section 4.1, the first differenced outcome variables show large
heterogeneity. Since matching estimators are notorious for their weakness in dealing
with outliers, I must check if they did not alter the main results of this paper. For thispurpose, I also estimate the median treatment effects, which are known to be robust foroutliers. These can be estimated by utilizing the usual median regression model when
the treatment is exogenous but after controlling for covariates. However, this methodneeds functional-form assumptions for the relationship among outcome variables,
treatment variables, and covariates. This paper assumes that they are linear and utilize a19

“dose-response” model (Koenker, 2005) by setting the variable MATURITY as acontinuous treatment variable. Thus, the marginal median treatment effect (  ) is thesolution of the following M-estimation:1(ˆ, ˆ )  arg min  | Yi  MATURITYi  X i  | .
 , Ni(8)

Another point is on the heterogeneity of treatment effects where the matching
estimators indicate the average treatment effect, but treatment effects are probably
dependent on the firms’ attributes. Especially, those for firms that kept relatively closebank-firm relationships among treated firms might be different, because the problem ofasymmetric information between banks and firms are more or less mitigated for them.
The literature on the Japanese main bank system also uses capital ties to measure thestrength of the bank-firm relationships. Therefore, treated firms, whose banks are one of
the major shareholders, are considered to have relatively close bank-firm relationships.
Since the median treatment-effect models are more parametric than matching estimators,
they enable an examination of this possibility at the same time.17 To be clearer, I add across term (MATURITY*MAINBANK) to the above model and get
1(ˆ,ˆ , ˆ )  arg min  | Yi  MATURITYi  MATURITYi * MAINBANK i  X i  | .
 , ,  N
i(9)where  indicates the marginal treatment effect for firms whose bank is one of the
major shareholders (treated-A) and that for firms whose bank is not one of the major
shareholders (treated-B). This is measured by    . This paper reports the result fromthis augmented model.
The estimation results of the median treatment effects are shown in Table 11. To
express the results visually, I also present the dose-response functions (DRFs) of eachoutcome variable with 95% confidence intervals in Figures 3 where the lines withdiamonds are the estimated DRFs for treated-A and lines with circles are those fortreated-B. The DRFs verify that the results obtained by matching estimators are
qualitatively unchanged and that statistical significance is maintained. It demonstrates
17

This flexibility is also one of the drawbacks of these parametric models because they are notrobust to misspecifications (see Drake, 1993). Therefore, results from parametric models and thosefrom matching estimators substitute each other.
20

that the outliers do not drive the main results of this paper. However, on the issue ofheterogeneity of the treatment effects, it shows large differences between the treated-A
and treated-B. This indicates that the changes in investment expenditures were notsignificantly different, while those in total bank loan balances (lnLOAN) and long-termloan balances (lnLONG) were significantly higher for firms with relatively closer
bank-firm relationships. If we suppose the value of MATURITY to be 0.2 for example,
then the DRFs show that the lnLOAN (lnLONG) rose by 20% (23%) for firms intreated-A, while increments of only 10% (2%) were observed for treated-B. Besides, the
DRF of lnLONG for treated-B is insignificant. The changes in borrowing costs, or
RATE, also showed interesting results. For firms in treated-A, the DRF is not significant
while, on the other hand, they were for treated-B, showing an increment of 18bps when
MATURITY was 0.2. In addition, the null hypothesis that the difference between the
two DRFs is equal is rejected at a small significance level.
These results show that firms that kept relatively close bank-firm relationships had
more access to bank loans; especially long-term bank loans with relatively low
borrowing costs. For firms without such close relationships, relatively higher borrowing
costs were charged and their access to long-term loans was limited. The changes in
INVEST show that these differences in borrowing conditions were not serious enoughto cause financial constraints.

6 Conclusions
This paper investigates the causal relationship between a firm’s bank dependency and its
financial constraints by examining whether the Japanese listed firms with not-so-closebank-firm relationships were financially constrained during the 2008 financial crisis.
The empirical results from matching estimators show that firms with large amounts ofbonds maturing in 2008 did not cut their investment expenditure compared tobank-dependent firms. In contrast, their bank loan balances rose sharply without
simultaneous increments in borrowing costs. However, when the heterogeneity of thetreatment effects is taken into account, it comes to light that firms with relatively closebank-firm relationships among treated firms had more access to bank loans, especiallyto long-term loans with low borrowing costs. Although firms without such close21

relationship did experience a rise in their borrowing costs, the changes in the investment
rates were not statistically different from control firms.
These empirical results imply that there does exist a cost to reducing bankdependency, but it is not so high for recent Japanese listed firms. Existing literature,such as Chava and Purnanandam (2010), shows that the shocks in the banking sector
affect bank-dependent firms’ performance negatively. Nevertheless, the results of thisstudy demonstrate that the shocks in the capital markets in 2008 were offset by thebanking sector and this prevented propagation to the real economy. In terms of policy
implications, this research also demonstrates that a robust banking system can mitigate
the effect of shocks in the capital markets on the real economy.
The results of this paper also raise additional questions for the future research. First,
in order to learn a lesson from the financial crisis, it is an urgent subject for empiricalanalysis to understand the cause of a capital market paralysis and the mechanism behind
an international propagation of shocks. The second point would be to investigate howmuch it would benefit a firm to reduce its bank dependency. Since one of the main
reasons why firms issue bonds is to reduce the banks’ monopoly powers, which areunobservable and reflected in the firms’ interest payments, we need more elaborate
empirical strategies to deal with the possible endogeneity. The last point would be toassess the role of the credit lines. Firms with lines of credit must have relied on themduring the crisis but lack of data on this prevented this paper from pursuing this topic.
Nonetheless, it can be an important research subject to understand how the lines ofcredit functioned during the crisis period and what kinds of bank-firm relationships
were behind them.

22References[1] Almeida, H., M. Campello, B. Laranjeira, and S. Weisbenner, (2009), “Corporate
Debt Maturity and the Real Effects of the 2007 Credit Crisis,” NBER Working Paper,
No.14990.

[2] Altman, E.I. (2002), “Revisiting Credit Scoring Models in a Basel 2 Environment,”
Stern School of Business New York University Working Paper.

[3] Bank of Japan (2009a), Financial Markets Report, (March).

[4] Bank of Japan (2009b), Financial System Report, (September).

[5] Blanchard, O. J., F. Lopez-de-Silanes and A. Shleifer, (1994), “What Do Firms Do
with Cash Windfalls?,” Journal of Financial Economics, 36, pp.337-360.
[6] Caballero, R.J., T. Hoshi and A. K. Kashyap, (2008), “Zombie Lending andDepressed Restructuring in Japan,” American Economic Review, 98, pp. 1943-1977.

[7] Caliendo, M., and S. Kopeinig, (2008), “Some Practical Guidance for the
Implementation of Propensity Score Matching,” Journal of Economic Surveys, 22,
pp.31-72[8] Cameron, C. A. and P. K. Trivedi, (2005), Microeconometrics: Methods andApplications, Cambridge University Press.

[9] Chava, S., and A. Purnanandam, (2010), “The Effects of Banking Crisis on
Bank-Dependent Borrowers,” forthcoming in Journal of Financial Economics.

[10] Drake, C. (1993), “Effect of Misspecification of the Propensity Score on
Estimators of Treatment Effect,” Biometrica, 49, pp.1231-1236.

[11] Fazzari, S. M., R. G. Hubbard and B.C. Petersen, (1988), “Financing Constraints
23and Corporate Investment,” Brookings Papers on Economic Activity, 1, pp.141-195.
[12] Gertler, M., and S. Gilchrist, (1994). “Monetary Policy, Business Cycles, and the
Behavior of Small Manufacturing Firms.” Quarterly Journal of Economics, 109, pp.
309-340.
[13] Heckman, J., H. Ichimura and P. Todd, (1997), “Matching as an Econometric
Evaluation Estimator: Evidence from Evaluating a Job Training Programme,” Review of
Economic Studies, 64, pp. 605-654.
[14] Holmström, B., and J. Tirole, (1998), “Private and Public Supply of Liquidity,”
Journal of Political Economy, 106, pp.1-40.

[15] Hongo, Y. and S. Oyama, (2010), “Nihon no Shasai Hakko Spread no Hendo Yoin
(The Determinants of Fluctuations of Spreads on Issued Corporate Bonds in Japan),”
Bank of Japan Working Paper Series, No.10-J-10. (in Japanese).

[16] Hoshi, T., A. Kashyap, and D. Scharfstein, (1990), “Bank Monitoring andInvestment: Evidence from the Changing Structure of Japanese Corporate banking
Relationship, in R.G. Hubbard, ed., Asymmetric Information, Corporate Finance andInvestment, University of Chicago Press.

[17] Hoshi, T., A. Kashyap, and D. Sharfstein, (1991), “Corporate Structure, Liquidity,
Investment, Evidence from Japanese Industrial Groups,” Quarterly Journal of
Economics, 106, pp. 33-60.
[18] Houston, J., and C.M. James, (1996), “Bank Information Monopolies and the Mixof Private and Public debt choices,” Journal of Finance, 51, pp.1863-1889.
[19] Houston, J., and C.M. James, (2001), “Do Relationships Have Limits? Banking
Relationships, Financial Constraints, and Investment,” Journal of Business, 74,
pp.347-374.

24

[20] Hovakimian, G. and T. Sheridan, (2006), “Corporate Investment with Financial
Constraints: Sensitivity of Investment to Funds from Voluntary Asset Sales,” Journal of
Money, Credit and Banking, 38, pp.357-374.

[21] Kaplan, S. N., and L. Zingales, (1997), “Do Investment-Cash Flow Sensitivities
Provide Useful Measures of Financing Constraints?,” Quarterly Journal of Economics,
112, pp.169-215.
[22] Kashyap A.K., and J. C. Stein, (2000), “What Do a Million Observations on Banks
Say about the Transmission of Monetary Policy?,” American Economic Review, 90,
pp.407-428.
[23] Koenker, R. (2005), Quantile Regression, Cambridge University Press.

[24] Lamont, O. A. (1997), “Cash Flow and Investment: Evidence from Internal CapitalMarkets,” Journal of Finance, 52, pp. 83-109.

[25] Lechner, M. (2002), “Program Heterogeneity and Propensity Score Matching: An
Application to the Evaluation of Active Labor Market Policies,” Review of Economics
and Statistics, 84, pp.205-220.

[26] Osano, H. and Y. Tsutsui, (1985), “Implicit Contracts in the Japanese Bank Loan
Market,” Journal of Financial and Quantitative Analysis, 20, pp.211-229.

[27] Peek, J., and E. Rosengren, (1997), “The International Transmission of Financial
Shocks: The Case of Japan,” American Economic Review, 87, pp. 495-505.

[28] Pinkowitz, L., and R. Williamson, (2001), “Bank Power and Cash Holdings:
Evidence from Japan,” Review of Financial Studies, 14, pp.1059-1082.

[29] Rajan, R.G. (1992), “Insiders and Outsiders: The Choice between Informed and25

Arm's-Length Debt,” Journal of Finance, 47, pp.1367-1400.

[30] Rosenbaum, P., and D. Rubin, (1983), “The Central Role of the Propensity Score
in Observational Studies for Causal Effects,” Biometrika, 70, pp.41-55.

[31] Sharpe, S.A. (1990), “Asymmetric Information, Bank Lending, and Implicit
Contracts: A Stylized Model of Customer Relationships,” Journal of Finance, 45, pp.
1069-87.

[32] Sheard, P. (1989), “The Main Bank System and Corporate Monitoring and Control
in Japan,” Journal of Economic Behavior and Organization, 11, pp.399-422[33] Shirasu, Y. and P. Xu, (2007), “The Choice of Financing with Public Debt Versus
Private Debt: New Evidence from Japan after Critical Binding Regulations were
Removed,” Japan and the World Economy, 19, pp.393-424.

[34] Sianesi, B. (2004), “An Evaluation of the Active Labour Market Programmes in
Sweden,” Review of Economics and Statistics, 86, pp.133-155.

[35] Weinstein, D.E. and Y. Yafeh, (1998), “On the Costs of a Bank-Centered Financial
System: Evidence from the Changing Main Bank Relations in Japan,” Journal of
Finance, 53, pp. 635-672.

26

Tables and Figures
Table 1: Definitions of treated and non-treated categories
Categories
B0

Definitions
The firms whose balances of corporate bonds were 0 in FY2008.

B1

The firms that had issued corporate bonds but none of them matured in FY2008.

B2

The firms that had issued a small amount (less than 5% of liabilities with interest) of corporate bondsthat matured in FY2008.

B3

The firms that had issued a medium amount (5‐10% of liabilities with interest) of corporate bondsthat matured in FY2008.

B4

The firms that had issued a large amount (more than 10% of liabilities with interest) of corporate
bonds that matured in FY2008.

27

Table 2: Definitions of variables
Variables

Definitions

MATURITY

Maturing‐bond‐balances: the ratio of the amount of outstanding balances of corporate bond that matured in FY2008 to the sum of liabilities
with interest (short‐term bank loans (Nikkei‐NEEDS’ FB075), long‐term bank loans (FB076+FB101), commercial papers (FB075) and corporate
bonds (FB078+FB099)) reported at the end of FY2007.

Q(t‐1)

Tobin's q: the ratio of market capitalization plus total assets (FB063) minus net assets (FB166) minus differed taxes (FB056) to total assets.

CF(t‐1)

Cash‐flows: the sum of net income (FC058) and depreciation (FC046) divided by the lag of fixed tangible assets (FB032).

lnASSET(t‐1)

Firm‐sizes: the natural log of total assets.

CAP(t‐1)

Leverages: the ratio of net assets to total assets.

CASH(t‐1)

Cash‐holdings: the ratio of quick assets (FB02) to total assets.

RATING(t‐1)

Default‐risks: Altman's (2002) revised Z‐scores.

MAINBANK(t‐1)

Bank‐firm‐relationships: the dummy variable that takes 1 if there is more than one bank in the list of major shareholders, otherwise 0.

MARKET1‐4

Stock‐market‐dummies: 1:The firms that are listed on Tokyo stock exchange first section; 2:The firms that are listed on Tokyo stock
exchange second section; 3:The firms that are listed on regional stock exchanges (Sapporo, Nagoya, Osaka, and Fukuoka stock exchange);
4:The firms that are listed on stock markets for emerging markets (Jasdaq, mothers and Hercules).

INDUSTRY1‐15

Industry‐dummies: 1: Mining; 2: Construction; 3: Manufacture of food; 4: Manufacture of textile; 5: Manufacture of chemical (including
petrochemical); 6: Manufacture of primary metal and metal products; 7: Manufacture of machinery; 8: Manufacture of electricalappliances; 9: Manufacture of transportation equipment; 10: Other manufacturing; 11: Electricity, gas and heat supply; 12:
Telecommunication; 13:Transportation; 14: Wholesale and Retail trade; 15: others.

Outcome variables
INVEST

Investment‐rates: the ratio of capital expenditures (CAPEX, FP01143) to the lag of fixed tangible assets (FB032).

RATE

Bank‐loan‐interest‐rates: the ratio of interest paid on bank loans (FC016‐FC017‐FC018‐FC124‐FC125) to two‐year averaged total bank loanbalances (FB075+FB076+FB101).

lnLOAN

Bank‐loan‐balances: the natural log of total bank loan balanceslnLONG

Long‐term‐bank‐loan‐balances: the natural log of long‐term bank loan balances.

lnSHORT

Short‐term‐bank‐loan‐balances: the natural log of short‐term bank loan balances.

Notes: Codes in parentheses indicate the items of Nikkei-NEEDs.

28

Table 3: Descriptive statistics
MATURITY
B0
B1
B2
B3
B4
Q(t‐1)
CF(t‐1)lnASSET(t‐1)
CAP(t‐1)
CASH(t‐1)
RATING(t‐1)
MAINBANK(t‐1)
MARKET1
MARKET2
MARKET3
MARKET4
Outcome variables
INVESTRATE
lnLOANlnLONG
lnSHORT

N. of Obs.
1880
1880
1880
1880
1880
1880
1880
1880
1880
1880
1880
1880
1880
1880
1880
1880
1880

Mean
0.02260.60430.07980.17710.07290.06601.17950.188010.83980.44050.36912.04410.75900.52770.13510.10850.2287

Std.dev.
0.06470.48910.27100.38190.26000.24830.49151.01151.59640.17980.16040.83620.42780.49940.34190.31110.4201min
0.00000.00000.00000.00000.00000.00000.4685‐9.8500
6.40520.02110.00780.10090.00000.00000.00000.00000.0000max
0.88241.00001.00001.00001.00001.00008.106818.602215.7568
0.89600.89346.45091.00001.00001.00001.00001.0000

1880
1880
1880
1880
1880

‐0.02820.00030.06890.03410.08100.32360.01950.48601.15401.4879‐5.6283‐0.2844‐2.0867‐6.3986‐9.48732.01070.30252.84637.5501
10.1581

Subsample
B2=1
0.0202
1.16520.1222447.940.32860.30101.63980.75080.59760.11110.08710.2042333

B3=10.0712
1.23690.0540560.970.35660.3242
1.69830.76640.52550.12410.10220.2482
137

Table 4.1: Subsample descriptive statistics (covariates)
All
MATURITY
Q(t‐1)
CF(t‐1)ASSET(t‐1)
CAP(t‐1)
CASH(t‐1)
SCORE(t‐1)
MAINBANK(t‐1)
MARKET1
MARKET2
MARKET3
MARKET4
N. of Obs.

0.0226
1.17950.1880227.930.44050.36912.04410.75900.52770.13510.10850.22871880

B0=10.00001.1696
0.2449100.41
0.48140.39932.23930.75440.48770.15490.11620.24121136

B1=10.00001.19510.0735360.980.43980.32301.82680.80000.68670.07330.10000.1400150

Notes: The unit of ASSET is billion Yen.

29

B4=1
0.20941.22550.1300276.530.45880.38091.98640.76610.51610.10480.11290.2661124

Table 4.2: Subsample descriptive statistics (outcome variables)All
INVESTRATE
lnLOANlnLONG
lnSHORT
N. of Obs.

B0=1
‐0.0272
0.00020.0449‐0.0092
0.05841136‐0.02820.00030.06890.03410.08101880

B1=1‐0.03210.00110.12500.12460.2561150

Subsample
B2=1‐0.0357
0.00030.0373‐0.0041‐0.0808333

B3=1‐0.0435‐0.00070.13850.10270.3163137

B4=10.00460.00120.22830.34870.2515124

Table 5.1: Multinomial probit estimation (parameter estimates)
Multinomial probit model
Dependent variable: 0,1,2,3,4
coef.
p>|z|
B1
Q
0.0959CF‐0.1297
lnASSET
0.3305 ***CAP‐0.4032
CASH‐0.9180 *RATING‐0.3760 ***MAINBANK
0.0222const.
‐4.2253 ***Market_dummies
Yes
Industry_dummies
Yes
N. of Obs.
1896
Wald chi2(96)511.41p‐value(0.000)Log‐likelihood‐1956.04

std.err.

coef.

p>|z|

std.err.

B2
0.13380.14940.06090.49050.55780.14660.16030.7782

coef.

p>|z|

std.err.

B3‐0.07340.02030.3147 ***‐3.6601 ***‐1.8435 ***‐0.2647 **‐0.2534 **‐2.1283 ***Yes
Yes

0.1377
0.09180.04830.42970.4056
0.10960.1256
0.6142

coef.

p>|z|

std.err.

B40.2013‐0.1038
0.4052 ***‐2.4441 ***‐0.9797 **‐0.2689 *‐0.1113‐4.2584 ***Yes
Yes

0.16300.08320.05780.51820.47790.14140.16540.76780.0874‐0.05440.2591 ***‐0.1349
0.0415‐0.3684 ***0.0616‐4.2103 ***Yes
Yes

0.15920.06280.05910.59000.61770.13050.17680.8449Notes: *,**,*** indicate significance levels of 10%, 5% and 1%, respectively. Standard errors are estimated by BHHH method.

Table 5.2: Multinomial probit estimation (marginal effects)
Multinomial probit model
Dependent variable: 0,1,2,3,4
m.e. p>|z| std.err.
B0Q‐0.0152
0.0271
CF
0.01590.0183lnASSET‐0.0982 ***0.0121CAP
0.5984 ***0.0952CASH
0.3292 ***0.0960RATING
0.0943 ***0.0251
MAINBANK
0.03230.0309Market_dummies
Yes
Industry_dummies
Yes
N. of Obs.
1896
Wald chi2(96)511.41p‐value(0.000)Log‐likelihood‐1956.04

m.e. p>|z| std.err.
B1
0.00940.0154‐0.0138
0.01730.0231 ***0.00670.07230.0550‐0.0533
0.0634‐0.0299 *0.01640.00940.0169Yes
Yes

m.e. p>|z| std.err.
B2‐0.0215
0.02370.01000.01650.0351 ***0.0078‐0.5973 ***0.0713‐0.2913 ***0.0686‐0.0255
0.0184‐0.0475
0.0229Yes
Yes

m.e. p>|z| std.err.
B30.02000.0166‐0.00880.00840.0280 ***0.0056‐0.1705 ***0.0495‐0.05210.0469‐0.0132
0.0139‐0.0071
0.0167
Yes
Yes

m.e. p>|z| std.err.
B4
0.00730.0170‐0.0033
0.00650.0120 **0.00580.09720.06070.06740.0640‐0.0258 *0.01340.01280.0167
Yes
Yes

Notes: *,**,*** indicate significance levels of 10%, 5% and 1%, respectively. The marginal effects are evaluated at mean values of
explanatory variables. Standard errors are estimated by the delta method.

30

Table 6: Estimation results of PSM-DIDs (Year 2008)
Method: Caliper Matching
Treated=B2
Treated=B3
Control=B0
Control=B0
DID
p>|z| std.err.
DID
p>|z| std.err.
‐0.0310.030‐0.041
0.035

INVEST

Treated=B1
Control=B0
DID
p>|z| std.err.
t+1‐0.009
0.026

RATE

t+1

0.001

0.002

0.000

0.002

‐0.001

0.002

0.002

0.002

lnLOAN

t+1

0.027

0.047

‐0.0140.038

0.113 **

0.052

0.180 ***

0.060

lnLONG

t+1

0.096

0.105

0.042

0.089

0.152 *

0.092

0.367 **

0.143

lnSHORT

t+1

0.048

0.128

‐0.1770.109

0.436 ***

0.158

0.164

0.150

INVEST

Treated=B1
Control=B0
DID
p>|z| std.err.
t+1‐0.016
0.023

RATE

t+1

0.000

0.002

0.000

0.002

‐0.001

0.002

0.002

0.002

lnLOAN

t+1

0.024

0.046

‐0.011

0.036

0.115 **

0.052

0.194 ***

0.062

lnLONG

t+1

0.085

0.102

0.050

0.083

0.122

0.084

0.395 ***

0.148

lnSHORT

t+1

0.023

0.130

‐0.1500.108

0.471 ***

0.161

0.206

0.155

Method: 10 Nearest Neighbor Matching
Treated=B2
Treated=B3
Control=B0
Control=B0
DID
p>|z| std.err.
DID
p>|z| std.err.
‐0.0350.025‐0.036
0.035

Treated=B4
Control=B0
DID
p>|z| std.err.
0.038
0.027

Treated=B4
Control=B0
DID
p>|z| std.err.
0.031
0.028

Notes: *,**,*** indicate significance levels of 10%, 5% and 1% respectively. Standard errors are calculated following the method
proposed by Lechner (2002). Estimation results from caliper matching are shown in the upper table. Results from
10-nearest-neighbor matching are shown in the bottom table.

31

Table 7: Balancing tests(a) Standardized bias test

Unmatched
MatchedTreated=B1
Control=B0
mean
median18.22012.560
4.724
2.810

Treated=B2
Control=B0
mean
median18.974
9.201
3.983
3.105

Treated=B3
Control=B0
mean
median15.806
8.613
4.920
3.917

Treated=B4
Control=B0
mean
median10.204
8.533
2.597
2.100

(b) Sianesi’s pseudo R squared test

Unmatched
MatchedTreated=B1
Control=B0
R_squared LR chi2
0.136127.990.019
7.14

p‐value
0.000
1.000

Treated=B2
Control=B0
R_squared LR chi2
0.228
360.41
0.007
6.34

p‐value
0.000
1.000

Treated=B3
Control=B0
R_squared LR chi2
0.174
151.800.014
4.92

p‐value
0.000
1.000

Treated=B4
Control=B0
R_squared LR chi2
0.065
53.19
0.004
1.23

p‐value
0.001
1.000

Notes: In the upper table, mean and median absolute values of Standardized Bias (SB) are shown. In the bottom table, the values of
Pseudo R squared and the Likelihood Ratio tests for the null hypothesis that all the coefficients from the probit regression of
1(S=l|S=l,or, S=m) on X are zero.

Table 8: Estimation results of PSM-DIDs (Year 2005)
Method: Caliper Matching
Treated=B2
Treated=B3
Control=B0
Control=B0
DID
p>|z| std.err.
DID
p>|z| std.err.
‐0.0120.018‐0.100 *0.055

INVEST

Treated=B1
Control=B0
DID
p>|z| std.err.
t+1‐0.002
0.017

RATE

t+1

‐0.0010.001‐0.0010.001‐0.001

0.001

0.004

0.005

lnLOAN

t+1

0.062 *

0.034

0.055 *

0.033

0.071

0.055

0.054

0.054

lnLONG

t+1

0.049

0.068

0.034

0.065

0.057

0.101

0.018

0.093

lnSHORT

t+1‐0.0580.090‐0.0700.108‐0.139

0.142

0.082

0.110

INVEST

Treated=B1
Control=B0
DID
p>|z| std.err.
t+1‐0.002
0.015

RATE

t+1

‐0.0010.001‐0.0010.001‐0.001

0.001

0.004

0.005

lnLOAN

t+1

0.049

0.034

0.057 *

0.031

0.062

0.053

0.055

0.055

lnLONG

t+1

0.045

0.068

0.029

0.063

0.042

0.093

0.033

0.096

lnSHORT

t+1‐0.0730.092‐0.0780.107‐0.143

0.140

0.092

0.115

Method: 10 Nearest Neighbor Matching
Treated=B2
Treated=B3
Control=B0
Control=B0
DID
p>|z| std.err.
DID
p>|z| std.err.
‐0.0190.018‐0.097 *0.054

Treated=B4
Control=B0
DID
p>|z| std.err.
‐0.022
0.015

Treated=B4
Control=B0
DID
p>|z| std.err.
‐0.0140.018

Notes: *, **, *** indicate significance levels of 10%, 5% and 1% respectively. Standard errors are calculated following the method
proposed by Lechner (2002). Estimation results from caliper matching are shown in the upper table. Results from
10-nearest-neighbor matching are shown in the bottom table.

32

Table 9: Changes in the maturity schedules
CHANGE (Overall)Mean
25 percentile
Median75 percentile
0.927
0.000
0.000
0.000
1.138
0.000
0.000
0.020
1.136
0.000
0.000
0.060
0.846
0.000
0.000
0.054
CHANGE (Subsample: CHANGE≠0)Mean
25 percentile
Median75 percentile
3.021‐0.060
0.048
0.200
3.239
0.010
0.068
0.200
2.879
0.020
0.100
0.240
2.064
0.020
0.081
0.206

FY2008FY2007
FY2006
FY2005

FY2008FY2007
FY2006
FY2005

Std.dev.
15.21520.83819.32614.981

N. of Obs.
688
669
646
595

Std.dev.
27.40535.11130.71523.369N. of Obs.
211
235
255
244

Notes: The unit is 1 billion Yen. The variable CHANGE is defined as “corporate bonds scheduled to mature within one year” minus
lagged “corporate bonds scheduled to mature in more than one year less than two years.”

Table 10: Estimation results from PSM-DIDs after changing control groups
Treated=B3
Control=B1, B2
Treated Control‐0.0435 ‐0.0365

Method: Caliper Matching
Treated=B4
Control=B1,B2
DID
p>|z| std.err.
Treated Control‐0.00700.03100.0046 ‐0.0420

DID
p>|z| std.err.
0.04660.0297INVEST

t+1

RATE

t+1

‐0.00070.0006‐0.00130.00080.00120.00090.00030.0021lnLOAN

t+1

0.13850.05570.0828 **0.04120.22830.06650.1618 ***0.0584lnLONG

t+1

0.10270.03100.0716

0.05240.34870.05790.2908 **0.1325lnSHORT

t+1

0.31630.05960.2567 *0.15520.25150.00770.24380.1835

Notes: *,**,*** indicate significance levels of 10%, 5% and 1% respectively. Standard errors are calculated following the method
proposed by Lechner (2002).

33

Table 11: Median treatment effects
INVESTRATE
lnLOANlnLONG
lnSHORT
coef. p>|z| std.err.
coef. p>|z| std.err.
coef. p>|z| std.err.
coef. p>|z| std.err.
coef. p>|z| std.err.
MATURITY‐0.01640.03680.0091 ***0.00210.5125 ***0.16890.10720.1532
0.3369 **0.1552
MATURITY*MAINBANK(t‐1)
0.02000.0458‐0.0066 **0.0026
0.5488 ***0.20891.0493 ***0.19040.3633 *0.1923Q(t‐1)‐0.0131 ***0.0032‐0.00020.00020.0291 **0.01460.01830.01320.01070.0124CF(t‐1)‐0.00130.0014‐0.0001
0.0001‐0.0147 **0.00680.00160.0057‐0.0051
0.0059lnASSET(t‐1)‐0.00030.00130.00000.00010.00830.00590.0149 ***0.00530.00160.0054
CAP(t‐1)‐0.0400 ***0.0109‐0.0001
0.0007‐0.0847 *0.0496
0.03450.0449‐0.1642 ***0.0450CASH(t‐1)‐0.00200.0112‐0.00020.0007‐0.06630.0508‐0.0127
0.0460‐0.0666
0.0464
RATING(t‐1)
0.00160.0026‐0.0001
0.00020.00820.01190.00050.01080.01500.0108
MAINBANK(t‐1)
0.00420.00370.0005 **0.00020.01890.01700.01770.01530.0192 **0.0153
const.
0.02100.0170‐0.0004
0.0011‐0.11630.0783‐0.2926 ***0.07060.06020.0711Market_dummies
Yes
Yes
Yes
Yes
Yes
Industry_dummies
Yes
Yes
Yes
Yes
Yes
N. of Obs.
1880
1880
1880
1880
1880
Pseudo R‐squared
0.00860.0116
0.04110.01210.0075
F‐Test(1)F(26,1853)=2.6
F(26,1853)=4.01
F(26,1853)=7.64
F(26,1853)=7.76
F(26,1853)=5.29(P‐Value)(0.000)(0.000)(0.000)(0.000)(0.000)F‐Test(2)F(1,1853)=0.02
F(1,1853)=2.32
F(1,1853)=72.47
F(1,1853)=102.06
F(1,1853)=37.07(P‐Value)(0.897)(0.128)(0.000)(0.000)(0.000)Notes: *,**,*** indicate significance levels of 10%, 5% and 1%, respectively. F-Test (1) tests the null hypothesis that all the coefficients are zero and F-Test (2) tests respectively the null hypothesis that thesum of coefficients on MATURITY and MATURITY*MAINBANK are zero.

34

Figures 1: The volume of bonds issued(a) The amounts of Yen-denominated corporate straight bonds issued
2500

Unit: billion yen

2000
1500
1000
500

Jun‐07

Aug‐07

Oct‐07

Dec‐07

Feb‐08Apr‐08Jun‐08Aug‐08Oct‐08Dec‐08Feb‐09Jun‐07

Aug‐07

Oct‐07

Dec‐07

Feb‐08Apr‐08Jun‐08Aug‐08Oct‐08Dec‐08Feb‐09Apr‐07

Feb‐07

Dec‐06

Oct‐06

Aug‐06

Jun‐06

Apr‐060(b) Changes from year-earlier month
800
600
400

Apr‐07

Feb‐07

Dec‐06

Oct‐06‐400

Aug‐06‐200

Jun‐060
Apr‐06

Unit: billion yen

200

‐600
‐800
‐1000
‐1200
Notes: The data sources are the Bank of Japan and the Japan Security Dealers Association (JSDA). The bonds that were issued inoverseas markets and private placement bonds are also included.

35

Figures 2: Kernel density functions of conditional propensity scores
Treated: B2, Control: B0

0

0

1

2

Kernel Density
2
3

Kernel Density
4
6

4

8

Treated: B1, Control: B0

0

.2

.4
x

.6

.8

0

.2

.4

.6

.8

1

x

Control (B0)Treated (B1)

Control (B0)Treated: B4, Control: B0

0

0

2

2

Kernel Density
4
6

Kernel Density
4
6

8

8

Treated: B3, Control: B0

Treated (B2)

0

.2

.4

.6

.8

1

0

.1

.2

x
Control (B0)

.3

.4

.5

x
Treated (B3)

Control (B0)Treated (B4)

Notes: The dotted lines and solid lines respectively indicate the estimated distribution functions for treated samples and control
samples in each subsample.

36

Figures 3: Dose-response functions(Lines with diamonds: treated-A; Lines with circles: treated-B)
14

x 10

-3

0.06

12
0.04

10

0.02

8

RATE

INVEST

0
-0.02

6
4

-0.04
-0.06

2

-0.08

0

-0.1

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

-2

1

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0.6

0.7

0.8

0.9

1

MATURITY

MATURITY
1.4
1.4

1.2

1.2
1

1

0.8

lnLONG

LOAN

0.8

0.6

0.6
0.4

0.4
0.2

0.2

0

0

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

-0.2

1

MATURITY

0

0.1

0.2

0.3

0.4

0.5

MATURITY

0.9
0.8
0.7

lnSHORT

0.6
0.5
0.4
0.3
0.2
0.1
0

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

MATURITY

Notes: The dashed lines indicate 95% confidence intervals for treated-A and the dotted lines indicate those for treated-B. Standard
errors for treated-B are calculated by the delta method.

37