Discussion Paper No. 617

PRUDENTIAL REGULATION,
THE “CREDIT CRUNCH” AND
THE INEFFECTIVENESS OF MONETARY POLICY:
EVIDENCE FROM JAPANWako Watanabe

September 2004

The Institute of Social and Economic Research
Osaka University
6-1 Mihogaoka, Ibaraki, Osaka 567-0047, Japan

Prudential Regulation, the “Credit Crunch” and the Ineffectiveness of Monetary Policy:
Evidence from Japan

Wako Watanabe*Institute of Social and Economic Research
Osaka University

January 2004

Abstract
The underlying causes of sharp declines in bank lending during recessions in large developed economies, as
exemplified by the U.S. in the early 1990s and Japan in the late 1990s, are still being debated due to a lack of any
convincing identification strategy of the supply side capital-lending relationship with lending demand. This paper is a
first attempt to construct a strong instrument for bank capital from empirical observation of the banks’ behavioral
changes in the past and to estimate the impact of capital adequacy on the lending supply. The implications ofprudential regulation and the ineffectiveness of a loose monetary policy are discussed based on the micro evidence
presented.

Keywords: Credit crunch, prudential regulation, bank lending channel, instrumental variable
JEL classification: C21, E50, G21*

I owe to Ben Bernanke for his continuous advice, support, and extensive comments and discussions. I also thankHan Hong, Charles Yuji Horioka, Takeo Hoshi, Jean-Phillip Laforte, Guido Lorenzoni, Anil K. Kashyap, Daiji
Kawaguchi, Yoichi Nemoto, Kazuo Ogawa, Gary Saxonhouse, and Michael Woodford. Thanks also to seminar
participants at the Princeton Macro/International Student Workshop, Osaka University, and Bank of Japan for many
useful comments and suggestions. Financial support from the Economics Department of Princeton University isgratefully acknowledged. Any possible errors in the pages that follow are my own.
†
Address: Institute of Social and Economic Research, Osaka University, 6-1 Mihogaoka, Ibaraki, Osaka, 567-0047
Japan
Phone: 81-6-6879-8566; Fax: 81- 6-6878-2766
E-mail: watanabe@iser.osaka-u.ac.jp

1. Introduction
During the last decade, the Japanese easing monetary policy has not seemed to have had any effectin revitalizing the stagnant Japanese economy. The Bank of Japan’s (BOJ) “zero interest rate policy”
that has been in effect since February 1999, has caused the overnight call rate to hover around zero
percent, but still has not made the economic recovery occur. The BOJ, in principle, gave up theshort-term interest rate as its policy goal, and instead has targeted the outstanding balance of its current
account, which is now called the “quantitative easing” policy. Furthermore, it is committed topurchasing more long-term government bonds, hoping that there is still room to ease.
Some economists argue that the “credit crunch” is behind the ineffectiveness of the monetary policy.
The balance sheets of Japanese banks are damaged by the huge amount of non-performing loans thathave accumulated over the last decade through their write-offs against equity capital. In order to satisfy
their capital adequacy requirement, banks may have increased their holdings of risk-free assets, and cut
back on their lending so that regulators (the government regulatory oversight agency) perceive less risk
embedded in their balance sheets. Bayoumi (1999) and Morsink and Bayoumi (1999) find strong
macroeconomic ripple-effects resulting from the negative shocks to bank balance sheets using Vector
Auto-regression (VAR). Ogawa and Kitasaka (2000) estimate that the conventional marginal q
investment rate function is augmented by the growth rate of bank loans, and find a strong correlation
between business investment and bank loans. If the bank lending channel of monetary policy
transmission has been severely damaged by the so-called “credit crunch” phenomenon, due to thegrowing non performing loans problem of the banks, monetary policy may lose its ability to influence
aggregate spending through corporate business investment.
According to Hoshi and Kashyap (2000), since large firms became almost independent of banks
following the capital market liberalization of the late 1980s, and banks themselves were still confinedto their traditional lending business, banks replaced their traditional keiretsu lending (relationshiplending) with lending to opaque small and medium firms, taking land as collateral, while also
expanding through even riskier real estate lending. Such a shift in the lending portfolio exposed banks
to asset price risks, which did not become apparent until the bubble burst in the 1990s. In fact,
examination of the “structural non-performing loans” serves as the instrument for identifying the
supply side capital-bank lending relationship from the observed equilibrium lending data.
One caveat is that the capital crunch is, by definition, regulatory driven. Furthermore, it isimportant to note that even the reported bank balance sheets themselves are the reflection of regulatory
toughness or the banks’ concessions to the regulator. For instance, the sudden increase in
non-performing loans (NPLs) may not show the actual increase in the same accounting period. It may
simply be a consequence of the regulator’s more rigorous assessment of the banks’ balance sheets. As
such, the econometric analysis of bank balance sheets needs the careful control of the regulatory andinstitutional oversight bodies. Any valid interpretation of results inevitably requires the regulatory andinstitutional history.
To best of our knowledge, this paper is the first attempt at identifying the supply side phenomenonof a credit crunch from the equilibrium data by a strong instrument and to estimate the resulting1

reduction in the lending supply. We examine the impact of capital “surplus” defined as the gap between
the actual and the estimated bank specific target capital asset ratios on lending supply by running year
by year cross section regressions. We find that banks cut back on their lending supply in the fiscal year
1997 in response to a large negative capital shock mainly due to the rigorous self-assessment of assetsthat accompanied the Prompt Corrective Action (PCA) regulation framework. A positive capital shock
mainly due to an injection of public capital in FY 1998, in turn, is likely to have induced constrainedbanks to raise lending supply. This positive shock, however, barely offsets the plunge in the lending
supply the year before. The cross section empirical approach that validates the bank specific target
inevitably has an unobservable and possibly endogenous fixed effect. Results of the cross section andthe auxiliary fixed effect regressions consistently imply that the bias, if any, would be caused by thebanks’ evergreening behavior that ties them to underperforming borrowers. It turns out that such a bias
would not undermine our conclusions.
The remainder of the paper is arranged as follows. In section 2, some aggregate evidence onmonetary policy and bank lending is shown. In section 3, the credit crunch is introduced and theliterature is reviewed. In section 4, the relevant regulatory and institutional background is discussed.
In section 5, data and econometric issues are set out. In section 6, results are reported and some policy
implications are derived. Section 7 concludes the paper.
2. Aggregate evidence: monetary policy and bank lending
Monetary policy
Repeated moves to ease the monetary policy since July 1991 resulted in unusually loose money
markets, which has not only virtually blocked the BOJ from taking further expansionary actions, but
has also prevented the standard interest rate channel from functioning effectively. The monetary policy
meeting of the BOJ held in February 1999 voted to try for an un-collateralized overnight call rate
fluctuating around zero percent. In March 2001, switching its policy measure (policy target
instrument) from the short rate to reserve targeting, in which the outstanding balance of the current
account at the BOJ is targeted, the BOJ altered its framework of executing market operations fromnominal short rate targeting to a “quantitative easing” policy. In fact, the targeted outstanding balancehas been raised several times to reach 27 to 32 billion yen as of November 2003, which is roughly 7 to
8 times as much as the balance before the operational policy framework change. As depicted in Figure
1, despite a series of unusual policy measures, satisfactory upward price movements have not seemed
to materialize.1
Bank lending
From the banks’ point of view, zero-cost external funds would always be available to themregardless of their internal liquidity position. This could also imply that banks do not necessarily lend
1

The price measure is the consumer price index (excluding perishables, in a nationwide statistic) since this is themeasure watched by the BOJ in its policy decisions. The BOJ in its official announcement on March 19, 2001, states,“The new procedures for money market operations continue to be in place until the consumer price index (excluding
perishables) on nationwide statistics, registers stably at zero percent or shows an increase year after year.”2more in response to additional liquidity if there is already an unusually loose inter-bank market
condition. The BOJ's official announcement at the monetary policy meeting on March 19, 2001, refers
explicitly to the reforms of the banking sector as a necessary condition for the effectiveness ofmonetary easing policy.
“In order to make this monetary easing fully effective in restoring Japan's economy to asustainable growth path, progress in structural reforms with respect to the financial systemis essential -- e.g., resolution of the non-performing asset problem, as well as in the area ofthe economy and industry.”
Figure 2 shows the time-series movements of year by year growth rates (the ratio of the amount of
total lending to the amount of the same series 12 months before) of domestic lending by all
domestically licensed banks and the time-series of the ratio of book equity capital to assets in thebalance sheet since July 1990 (The ordering of figures is the same for figures concerning city banks thatwill appear.). 2 The higher capital asset ratio and decrease in lending growth through September 1993
(the end of the 1993 interim accounting period) may especially indicate that the undercapitalized banks’rush toward complying with a higher risk based capital (RBC) standard which came fully into effect inMarch 1993 (the end of the fiscal year for 1992).3 The capital asset ratio falls abruptly at the end of the
fiscal year for 1997 by roughly 0.5 percent, and recovers its pre-shock level at the end of the fiscal year
for 1998. Meanwhile, the lending growth rate continues to fall into the middle of FY 1999. It bounces
back afterwards even though the rate is still negative and the level of lending itself does not grow.
Indeed, the lending growth and the one-year or half a year lagging capital asset ratio have been showing
the high correlation since April 1999, the beginning of FY 1999.4 This finding appears to be strong
supporting evidence that the “capital crunch” occurred.
Two factors, which cannot be controlled effectively in the aggregate balance sheet data, however,exaggerate the speed in decline of bank lending. The first is the write-offs of NPLs. Removing bad
assets from the balance sheets is a result of the banks' unconstrained rational behavior and should be
distinguished from the "credit crunch" that deprives viable bank-dependent borrowers of credit access.
The second is declining lending demand due to the contracting economy. The aggregate lending figure
reveals the reduced form equilibrium relationship between bank capital and lending and is not so
informative as to identify the banks’ behavioral responses. To this end, we need the micro-level data,
and this is what we plan to explore. Specifically, our research goal is to verify the time-series reduced
form equilibrium evidence by the micro-level behavioral evidence.
Direct testing method of the bank lending channel
The naïve way to test effectiveness of the bank lending channel is to directly relate bank lending
2

The monthly aggregate balance sheet statistics of domestically licensed banks has been published by the BOJ only
since July 1989. Thus the growth rate can only date back to July 1990. Similarly, the monthly data have not been
available since October 2000.
3
Berger and Udell (1994) survey the alternative explanation to the phenomenon of higher capital and lower lending,which they call the “voluntary risk-retrenchment hypothesis”. According to the hypothesis, such an observation is theconsequence of the banks' risk averseness.
4
The capital asset ratio is based on the balance sheets of domestically licensed banks including their foreign business3

growth to the monetary policy measure. Lending growth disaggregated by industry is regressed on
lagged and present values of the BOJ’s reserves and the corresponding index of industrial production.
The reserves are used as the policy measure since targeted interest rates (call rate/official discount rate)
barely varies in the second half of the 1990s in Japan and the reserves are chosen to be a targeting
instrument under the “quantitative easing policy”. 5 , 6 The equation over two different periods isestimated. One is the longest period available to us: the second quarter of 1994 through the second
quarter of 2001. The other is the period since the “ultra low” interest rate policy was implemented: thefirst quarter of 1996 through the second quarter of 2001. Results are shown in table 1. For the entire
period, no coefficients for the monetary measure are statistically significant at the 10 percent level. For
the date under the “ultra low” interest rate policy, no coefficients on reserve growth (except for thecurrent reserve growth) are statistically significant at the 10 percent level. The F test on the sum of
current and lagged growth rates of reserves shows the statistical significance over the latter period atonly the 10 percent level. These seem to be suggestive of the ineffectiveness of monetary policy.
The problem of this approach, however, results from the very nature of unusually loose policy.
Under such an exceptional circumstance, lack of sensitivity to the liquidity of their own balance sheets
may not imply that the banks are unresponsive to monetary policy. Instead, it may simply reflect thefact that excess liquidity available to banks in the inter-bank markets is already extremely abundant andthat the incremental increase in liquidity by further easing market operations has no economic meaning
to banks needing to finance their lending. The methodology, which is most productive under normal
market conditions as found in Kashyap and Stein ([1995], [2000]), exhibits serious limitations.
3. Credit crunch
Capital crunch
The observed decline in bank lending over the period of 1990 and 1991 in the U.S. became wellknown as the “credit crunch”. It attracted the attention of politicians and the media alike since it
occurred amid an ongoing deep recession. Bernanke and Lown (1991) report that both total lending
and commercial and industrial lending fell by more than 10 percent over one year from the second
quarter of 1990 through the first quarter of the following year there in New England, the area wherelending saw the sharpest decline. Peek and Rosengren (1995 a) discovered a large drop in bank capitalduring the same period in New England. The phenomenon became known as the “capital crunch”.
Some 8 years later in Japan the word “kashishiburi”, which literally means “unwillingness to lend” andthe popular Japanese translation of the “credit crunch”, showed up in newspapers and other media so
frequently that it was awarded the “Top Ten Award” of the 1998 annual “Japan New Words and Popular
Words Grand Prize”.

units.
5
Industries included are manufacturing, construction, utility, transportation and communications, commerce,
non-bank finance, real estate, services and local government. In addition, lending to individuals and internationalloans are included. The real Japanese GDP and the U.S. GDP are used to “proxy” economic activities of individual
lending and international lending respectively.
6
We chose lending by city banks (large commercial banks) as they traditionally specialize in short-term lending andare more likely to respond quickly to open market conditions.
4

In general, the “credit crunch” refers to the reduction in credit supply available to borrowers,
particularly bank lending supply, for some lender specific reasons.7 The major explanation for thecredit crunch phenomenon is the “regulatory driven capital crunch hypothesis”. The internationallyrecognized bank capital regulation, known as the risk based (adjusted) capital (RBC) standard, is at thecenter of the banking regulatory framework. The principle of the regulation is that banks exposed tohigher risks should hold more equity capital as a buffer against any realization of risk.8 In practice it
requires that the ratio of capital to risk weighted assets (riskier assets are assigned to a higher weightand vice versa) not be below the specified minimum threshold. Lending has been assigned to 100
percent irrespective of the credit risks of each contract (credit worthiness of each borrower). It hadgone partially into effect by the time of the U.S. credit crunch.
Theoretical works have shown that asymmetric information -- involving investors, a bank, andborrowers -- makes issuing the new equity costly.9 Therefore, undercapitalized banks failing to satisfy
the regulatory minimum may raise the (risk based) capital asset ratio by cutting back on lending (anumerator of the ratio) rather than raising equity capital (a denominator of the ratio) in order toimmediately clear the regulatory hurdle. The easiest way to raise the risk-based capital asset ratio is to
shift the asset portfolio away from lending that is assigned the highest risk weight of all asset classes(100 percent risk weight) to assets with less weight, such as the government bonds of OECD countries(0 percent risk weight). It is frequently argued that the introduction of the RBC requirements may haveinduced the substitution of the lending portfolio away from risky lending to safer lending, and thus
could have prevented the credit crunch from occurring, if the variation of credit risks within lending
were considered. 10
In modeling a bank's profit maximization, the RBC capital adequacy requirements is expressed as
the following inequality and usually constitutes one of a set of constraints with typically the reserve
requirement representing another.
Ki≥ γ (1)Li
Ki is the equity capital of a bank i, Li is a bank i’s holding of lending, and γ is the minimum requirement
7

Banks may charge higher lending rates in a crisis period rather than reducing lending supply directly. Such aphenomenon is not observed in the data. This could be because the banks' selection of better borrowers with lower
interest rates in the crisis period offsets higher lending rates for less qualitative borrowers as discussed by Bernanke(1983). Furthermore, it has not been usual for Japanese banks to differentiate interest rates according to the borrowers'
credit worthiness.
8
A few works have theoretically justified the banks’ capital constraint from the viewpoint of the informational friction
between banks and borrower firms rather than assuming the regulation. Holmstrom and Tirole (1997) discuss thatbank capital is used for monitoring of borrower firms. Bond (2003) shows that the joint liability arrangement among
borrowers that reduces the expected costs of information disclosure of a bank leads to capital crunch in that a failure ofsome of borrower firms reduce bank capital and subsequently bank lending supply to viable borrowers.
9
Stein (1998) states that the informational asymmetry between investors and a bank leads to the adverse selection
problems in that the equity issuing banks are considered to be under-performing. Diamond and Rajan (2000) argue
that equity finance generates inefficient rent when a bank is a relationship lender.
10
The Basel Committee on Banking Supervision that coordinates the international agreement on the RBC regulatory
framework has proposed the amendment to take into account credit risks within bank lending in computing therisk-weighted asset of an individual bank. Their working paper surveys the empirical literature on the impact of the
RBC framework including the capital crunch. (Furfine e al. [1999]) The recognition that the old design of theregulatory framework may have resulted in the capital crunch motivated the proposed amendment.
5

imposed by a regulator.11 Then a bank with binding capital constraint lends out the multiple of its own
capital. On the other hand, a bank with an unbinding RBC constraint determines the level of lending
for the optimal interior solution of its unconstrained profit maximization problem in the static model
setting.12,13
In reality, it may be the case that banks around the certain lower threshold of the risk-based capital
asset ratio are capital constrained, whereas banks whose capital asset ratio is far above the threshold areunconstrained. Alternatively, most banks may be pushed into the lower region of capital asset ratio andbecome capital constrained when the entire banking industry is faced with negative aggregate capitalshocks. These shocks may be either regulatory -- a requirement of the more stringent assessment ofassets (lending) and the widened definition of non-performing loans resulting in charge-offs that werepreviously considered unnecessary -- or macro economic; a fall in land prices that turn viable loans into
non-performing ones through the devaluation of collateral. If this is the case, one may observe few
unconstrained banks in the positive industry wide capital-lending relationship.
In practice, variations of the following partial adjustment specification of the lending growth havebeen tested with the data of a set of banks at certain time periods in the literature.
K∆ ln Lit = α 0 + α 1 ∆ ln Lit −1 + β it + λX it + ε it (2)Ait
The dependent variable is the lending growth of an individual bank at date t whereas explanatory
variables are the lagged dependent variable, one of the capital asset ratio measures and other control
variables. Many works in the literature take a lag of the capital asset ratio measure K/A, in order toavoid the simultaneity of the lending growth and the capital ratio.
Empirical literature
Bernanke and Lown (1991) examine a book capital as K for the U.S. state-by-state cross section
data and the New Jersey bank-by-bank cross section data in a one-year period from the second quarterof 1990 to the first quarter of 1991, and find a statistically strong coefficient on the lagged K/A. Berger
and Udell (1994) run panel regressions with various loan classes as a dependent variable using thequarterly data of all U.S. banks from the late 1970s to the early 1990s. They augment (2) by including
the interaction terms of the time dummy indicating the credit crunch period (1990-1992) and various
capital asset ratios including the RBC ratio itself, but find no capital effect on lending during the period.
With the quarterly panel data in and around the credit crunch period (1989:Q2-92:Q4) Peek andRosengren (1995 a) find a significant capital effect only for banks with low regulatory ratings which
they claim are likely to be with binding constraints.14, 15
11 It is assumed for simplicity’s sake that only lending is assigned a 100 percent risk weight.
12

If we assume the dynamic optimization model of a banking firm, an unconstrained bank holds capital stock as abuffer against future uncertainty.
13
Alternatively, one can interpret that lower capital incurs cost and that the higher capital benefits a bank. Such cost
includes facing a tougher regulator, a higher risk of regulatory intervention, and the market's underrating based on thelower regulatory rating.
14
In the related study with the panel through the second quarter of 1992 of the same data set, Peek and Rosengren(1995 b) find that the coefficient on the interaction term of the capital asset ratio and the variable indicating a bankunder regulatory formal action is significant but that the coefficient on the similar interaction term with the dummy
6Using the panel data of Japanese banks of the early 1990s Ogawa and Kitasaka (2000) and Ito andSasaki (2002) estimate variants of (2) with the RBC ratio as the capital asset ratio and find that onlyinternationally operating banks operating within the Basel RBC regulatory framework have astatistically significant coefficient of the ratio.16 More recently, Montgomery (2001) uses the longer
panel from FY 1982 to 1999 and applies the period dummy methodology of Berger and Udell (1994) tothe post Basel (after FY 1988) years and finds that coefficients on the book based capital asset ratio are
significant during the post Basel years (FY 1988 to 1999) and insignificant during the pre- Basel years(FY 1982 to 1987) for all international, domestic and "switcher" banks.17
There are few works focusing on Japan in the late 1990s, though the "credit crunch" over that
period may have had serious macroeconomic impact and have caused the recession in the fiscal yearsof 1998 and 1999. To this end, Woo (1999) turns to the year-by-year cross section regressions ofequation (3). Constructing the total new loan data from FY 1991 to 1997 by adding charge offs of
NPLs to net yearly increase in lending on balance sheets, he finds that it is only in FY 1997 thatcoefficients on such capital asset ratios as the book ratio and the regulatory RBC ratio are significant
and positive.18 Surprisingly he finds strong negative coefficients in some of the introductory years of
the Basel RBC framework.
As we discuss later in greater details, we suspect that the incomplete identification of the lending
supply with its demand is behind the mixed micro results in examining the three events above of falling
economy wide bank lending both in the U.S. and Japan.19
Credit crunch and its impact on real economic activities
Economists have examined the aggregate and semi aggregate data to assess the impact of the banklending supply on real economic activities. If borrowers could manage to find substitutes for bankloans the (bank) credit crunch would not have a real negative effect. With the semi-aggregate state bystate cross section data Bernanke and Lown (1991) find that the impact of the loan growth predicted by
the capital asset ratio on the employment growth was negligible during the 1990-91 period. Hancock
and Wilcox (1998), however, find that both loans by and bank capital of small banks have a strong
effect on real economic activity through their influence on bank dependent smaller firms.20 Hayashi

variable indicating a bank not under action is insignificant.
15
Hall (1993), Hancock, Laing, and Wilcox (1995), Hancock and Wilcox (1998) also find statistically significant
coefficients on capital asset ratios during the period.
16
Ito and Sasaki (2002) and Ogawa and Kitasaka (1999) examine two-year periods before and after the full
implementation of the regulation at the fiscal year end of 1992.
17“Switcher” banks are banks that abandoned their privilege to conduct international business so that the minimum
RBC requirement is loosened.
18
He tests three capital asset ratio measures, the book based ratio, the Basel RBC ratio, and the market based capital
asset ratio. The book ratio in FY 1991 and the RBC ratio in both fiscal years of 1991 and 1993 are negative andsignificant whereas coefficients on market based ratios are not significant.
19
Regarding international lending, Peek and Rosengren (1999) tests the impact of bank capital shock to Japaneseparent banks on lending by their subsidiaries in the US.
20
In their time series data analysis Ogawa and Kitasaka (1999) estimate the standard investment function with Tobin’s
marginal q and the growth of bank loans as independent variables and find that smaller firms are more constrained tobank loans than larger firms are. There are a large number of empirical works on the advantage of banks as
relationship lenders in small business finance. For example see Berger and Udell (1995), Cole (1998), Degryse and
7and Prescott (2002) run similar cross section regressions of GDP growth on loan growth acrossprefectures in Japan during three long lasting recessions since 1975 and find that it was only in the
recent recession from FY 1996 to FY 1998 that bank lending made a difference.
However, one should be careful in interpreting such results since it is even harder to isolate
lending supply with aggregate or semi aggregate data than it is with data from individual banks.21
Motonishi and Yoshikawa (1999), though working on the aggregate data, effectively avoid thedemand-supply simultaneity problem by using the tankan diffusion index measuring the banks’
willingness to lend which is constructed from the direct qualitative questions asked borrower firms
regarding their perceptions of the attitude of lenders (banks), and find that the negative effect of thebank lending supply on business investment emerges only in FY 1997.
4. The regulatory background of Japanese banks
In what follows, we review the regulatory history and its influence on bank capital. Amounts
shown on the balance sheets of banks depend on the regulator's judgement. In particular, any
managerial decisions effecting their capital position inevitably involve their consultation with theregulator and virtually needs its agreement, although the degree of closeness of the regulator and banks
has been somewhat eased since the market oriented FSA took over the role of the Ministry of Financeand led the way to fairer and more transparent rule-based regulatory actions.22
Basel Capital Accord: FY 1992
The first generation of the RBC requirements was agreed to by the G-10 countries, representing
the Basel Accord of 1988. 23 The Accord allowed a transitional period so that banks with capital
shortages could take measures to meet the minimum standard. In Japan, the current minimum ratio of 8
percent has been effective since the end of the fiscal year for 1992 (March 1993), after the two-year
transition period with a temporal target of 7.25 percent.
Several points are worth noting. First, the BIS Capital Accord framework classifies elements ofcapital in two tiers: core capital called Tier 1 and the element supplementing it called Tier 2. Tier 1
includes equity capital and published reserves from post-tax retained earnings. "Equity capital" in abank balance sheet matches approximately Tier 1. Elements that can be included are undisclosed
reserves, (asset) revaluation reserves, general provisions/general loan-loss reserves, hybrid debt capital
instruments, and subordinated term debts. The Accord mandates banks to satisfy half of the minimum
standard (4 percent in the current regime) by Tier 1 elements.

Cayseele (2000) and Berger, Klapper, and Udell (2001).
21
Bayoumi (2001) conducts the VAR analysis and finds that innovation in bank loans has a large impact on aggregate
demand. Using the similar VAR approach Morsink and Bayoumi (1999) find that the aggregate influence of banklending is predominantly attributable to the banks’ financial health measured by the stock price index of the banking
industry. Ogawa and Kitasaka (1999) estimate the standard classical investment function augmented by bank lending
and find its significant impact on investment, and find22
For detailed discussions on the Japanese prudential policy, see Hoshi and Kashyap (2000), chapter 8 of Hoshi and
Kashyap (2001), Ueda (2000), and Fukao (2001)
23
Belgium, Canada, France, Germany, Italy, Japan, the Netherlands, Sweden, Switzerland, the United Kingdom, andthe United States
8

In Japan, up to 45 percent of unrealized (latent) capital gains were allowed to be included into Tier
2. Thus, in contrast to those of the U.S. banks, the RBC ratios of Japanese banks are not resistant toswings in stock prices. Since subordinate debts are counted as Tier 2 capital, banks can manipulate the
RBC ratio relatively easily through their accounting policies. Issuing subordinate debts in response tonegative capital shocks such as asset price falls and disposal of non-performing loans masks a shortageof core Tier 1 elements.24
Liquidation of jusen: FY 1995
Jusen companies are housing loan companies founded by mainly large banks and financed bybanks and other lending institutions. After the bubble burst, most of their loans, which had been shiftedto the real estate businesses, were deemed non-performing and their liquidation was just a matter oftime. Toward the end of the fiscal year for 1995, that is, early 1996, the government implemented its
liquidation plan. 6.41 trillion yen of unrecoverable assets of the jusen companies were written off.
Founder institutions, which are mainly large banks, contributed 3.5 trillion.
Prompt corrective action: FY 1997
The failure of the Hokkaido Takushoku Bank in November 1997, which marked the first failure ofa large bank, along with the bankruptcy of two securities companies, spawned a financial crisis. In
response, the MOF implemented the prudential policy guidelines for prompt corrective action (PCA).
The PCA allows the regulator (then the MOF, currently the FSA) to intervene in banks with a Basel
RBC capital asset ratio below the regulatory threshold. The regulator intervenes when the RBC ratio
falls below the BIS minimum standard of 8 percent. Several intermediate action thresholds were set up.
As a bank enters the lower interval, crossing a threshold, government intervention enters the more
rigorous action stage. It was applied to "international" banks in April 1998, and to "domestic banks" ayear later. The minimum requirement was set higher for "international" banks (8 percent) than for"domestic" banks (4 percent). At the time when the PCA was introduced, banks were faced with formal
government actions based on the Basel RBC standard. The PCA marks the first occurrence ofinstitutionalizing the RBC standard to the domestic formal action framework.
Prior to the PCA taking effect in April 1998 (the beginning of the FY for 1998), the MOF requiredbanks to carry out a more rigorous self-assessment of their assets and the adequate loan loss write offs
and the provisions based on them. Loan loss write offs and provisions amounted to 13.3 trillion yen in
FY 1997.25 Toward the closing of the fiscal year 1997, the government decided to inject public capital
into the banks (18 large banks and 3 regional banks) for the first time, but it was not adequate to offset
losses caused by the large write offs and loan loss provisions.26
Public capital injection: FY 199824

Ito and Sasaki (2002) give empirical evidence of such behavior by Japanese banks.
These are the aggregate figures reported by the BOJ and include figures in trust accounts.
26
It was in FY 1998 that the PCA framework, based on the RBC capital asset ratio, was extended to include domestic
banks, of which most were regional, and regional 2 banks.
925

The nationalization of the Long-Term Credit Bank and the Nippon Credit Bank, both of which are
long-term credit banks, happened almost simultaneously; the former in October 1998 and the latter inthe following December. During the crisis, the government rescued other banks facing capital
shortages due to the large write offs throughout two consecutive fiscal years. Loan-loss write offs andprovisions recorded 13.5 trillion yen in FY 1998, slightly surpassing the previous fiscal year's mark. A
total public capital injection of 7.5 trillion yen (6.2 trillion yen of preferred stocks and 1.3 trillion yen ofsubordinated debts were underwritten by the government) was given to 16 mostly large banks in March
1999 in order to enhance their capital and help them satisfy the RBC requirement in the closing days of
FY 1998.27
In order to mitigate the credit crunch, the FSA approved public capital infusions to banks only on
the condition that there would be "measures for smoothing lending funds and other credit supplies" inthe "management improvement plans" that banks with public capital injection were required to submitand .28 The FSA also had meetings with the top management of different banks to insist on a soundlending supply.
5. Econometrics and data
We run versions of equation (2) using the micro individual bank level data to see if the “regulatory
driven capital crunch” occurred in Japanese banks in the late 1990s. In the following, we discuss issues
that arose in conducting the empirical analysis.29
Data
The main data source of bank level data is the Nikkei NEEDS bank financials data bank. It is thestandard data source for any research on Japanese banks. The data represents a 27 year-long period
from FY 1974 to FY 2000. It contains not only balance sheets and income statements of all
domestically licensed banks, but also details of lending classified by industry, by types of collateral, byuse (equipment funds/working capital), as well as the amount of lending to small and medium sized
firms. The Nikkei data has become fairly standard for the analysis of Japanese banks recently.30 The
Basel RBC ratios and unrealized gains on assets are taken from the Japanese Bankers Association's
Analysis of Financial Statements of All Banks.31

27

See Nakaso (1999) for further details on the public capital injection in FY 1998. Another contributing factor to theincreased equity capital was the accrued deferred tax asset due to the new accounting standard harmonized with theInternational Accounting Standard, which amounted to 8.9 trillion yen.
28
The FSA was founded as the Financial Supervisory Agency in June 1998. It was reorganized as the Financial
Services Agency and undertook policy planning functions with regard to the financial industry from the MOF in July
2000. I designate both with the same abbreviation, FSA, to avoid inconvenience.
29
We also examined the specification similar to Ogawa and Kitasaka’s in which the interest rate differential between
the lending rate and the inter-bank call rate. The estimation results were virtually unchanged. The variable, however,is inevitably endogenous and can be a serious source of bias. Besides, differences in the interest rate variable
constructed this way may reflect differences in the default risk they face, the rate of arrears, and other factors unrelated
to the true return on lending.
30 Ogawa and Kitasaka (2000), Hoshi and Kashyap (2000), Ueda (2000), and Hoshi (2001)
31
Missing items on recent balance sheets of a few banks are supplemented by their annual reports.
10Sample selection
While a “financial crisis” refers to instability of the financial system due to management crises andeventual bank failures, the “credit crunch” is a phenomenon hurting the lending supply functions ofviable banks due to capital shortage. It is efficient that banks defeated in the competition of the lending
markets exit the markets, as Hoshi and Kashyap (2000) discuss. The “credit crunch” is far more serious
since it damages the lending supply function of viable banks.32 To this end, we drop banks affected bybank failures, failed (liquidated or nationalized) banks, as well as banks having experienced rescue
mergers or acquisitions.33 A total of 126 banks remain in the sample.
Dropping "troubled" industries
Similarly, from the borrower side, the “credit crunch” hurts healthy borrowers because of thebanks’ unwillingness to lend. However, a bank's decision to cut back on lending to firms that will
default on loans, and shifting its lending portfolio to healthier firms, is desirable. In the same context, abank's decision to dispose of existing NPLs, that is, loans that borrowers have already defaulted on, isdesirable too. To this end, we construct the lending data for non-troubled industries. Following the
BOJ's aggregate survey, we designate an industry whose share of NPLs to the industry exceeds theshare of total lending a “troubled” industry. 34 Such industries include the construction, wholesale andretail, service, and real estate industries.35
Capital asset ratio measures
Three different measures of capital asset ratio: the book based capital asset ratio (CARP), theBasel RBC ratio (BIS), and the market based capital asset ratio (CAPRM) are examined. The bookcapital is a proxy for the core Tier 1 capital.36 The book capital to asset ratio, CAPR is constructed bytaking the ratio of equity capital minus land price re-evaluation to the total asset. CAPRM isconstructed by taking the ratio of book capital, the denominator of CAPR, plus unrealized gains on

32

Recall Diamond and Rajan (2000). Lower capital does not necessarily mean that banks are not viable. Unlike
non-financial firms whose higher net worth (economic capital) lowers the cost of external funding through lowering
the external finance premium, banks need to finance lending by demand deposits in order to provide liquidity toborrowers. Net worth for banks is the buffer against unexpected large negative shocks such as a fall in the collateralvalue through a fall in land prices. The necessity of higher book capital required by the Basel Accord is even moredifficult to justify.
33
Banks having experienced non-rescue mergers are treated as single banks in pre-merger dates by adding values ofvariables for banks involved in the deals. One long-term credit bank was dropped since detailed lending data for the
1980s are missing. One regional 2 bank founded in the 1990s is also dropped.
34
The BOJ (2001)
35
Ideally, one needs to construct the new loan data as Woo (1999) does. This is not possible, since the industry level
micro data on write offs of NPLs are not publicly available. We believe that exclusion of troubled industries defined
above is the best way possible. The resulting bias should not be significant since it is mostly lending to “troubled”
industries that is disposed of by the banks.
36
The BIS capital asset ratio until FY 1996 is subtracted the regulatory minimum for each bank that is 8 percent or 4
percent depending on whether a bank is “international” or “domestic” when the level itself of the ratio is included in aregression. This procedure smoothes the discontinuity in the level of the ratio due to the certain accounting policy thatallows a bank to clear the regulatory minimum. Until FY the BIS ratios of many “domestic” banks are around 4
percent. Since FY 1997 when many “international” banks switched their regulatory status to “domestic”, distinctionof “domestic” and “international” banks disappears.
11

holding assets to the total asset. As such, both CAPR and CAPRM are non-risk based. This is not justbecause the individual bank level data of the risk-adjusted asset, the numerator of the RBC ratio, ispublicly unavailable. It is even advantageous in the sense that "capital constrained" banks should
respond to negative capital shocks, that is, negative shocks to the denominator of the risk-adjusted
capital asset ratio. The RBC ratio by definition may behave like the weighted average of CAPR andCAPRM. On the other hand, the denominator of the RBC may not uncover the capital shocks masked
by the offsetting increase of subordinate debts that are also elements included in the denominator.37
Controlling lending demand
As is evident, the equilibrium quantity of bank lending not only decreases in response to theshortage of the lending supply, but also decreases accordingly with a leftward shift of its demand curve.
If the aggregate (regional) economic environment worsens, product sales fall with weak demand, andfirms adjust their investment outlay on plant and equipment downwards in response, which in turnresults in a fall in their demand for new bank financing. Such a fall in bank loans, however, is not thecause of the recession. but the simple reflection of the weak economy.
One may argue for the demand side by including explanatory variables that are the proxy tolending demand. Such variables could be aggregate or regional economic indicators (Berger and Udell[1994]). The reduced form approach, however, mixes the demand side with the supply side, and is notdesigned for extracting the structural lending behavior of banks. One may, rather, use the micro levelbank characteristic (institutional) variables to control the demand side indirectly. Banks from one
region behave differently from those from other regions. Yet, they also operate in different markets andface different lending demands. Along with this approach, Peek and Rosengren (1995a, 1995b) focus
only on banks in New England. In addition, Peek and Rosengren (1995 b) include the ratio of offbalance sheet income to the total income since the larger off balance activities indicate the banks’relatively smaller exposure to changes in the lending demand. One, however, needs to be cautious inselecting such supply variables since items on the banks’ balance sheets themselves are likely to beendogenously determined and do not particularly represent the bank supply behavior per se.
The institutional classification of banks rarely changes over time and can be a good candidate forthis goal. Banks are conventionally classified into five classes: city banks, long-term credit banks, trustbanks, regional banks, and regional 2 banks, and the first three classes form the large banks operating
nationwide. Regulatory actions as well as their customer base differ across bank classes. All the banks
base their legal foundations on the banking act, though trust banks and long-term credit banks are
regulated by additional respective special laws. Regional 2 banks have been classified separately fromregional banks since they had been formerly administered under a special law and were converted tostandard banks in the deregulation process. Dummy variables indicating the institutional class, CITY
for city banks, TRUST for trust banks, and REGIONAL for regional banks, are included, while
regional 2 banks are considered as the base class. 38, 39
37

See Ito and Sasaki (2002) for the banks’ control of the RBC ratio.
9 city banks, 6 trust banks, 63 regional banks, and 48 regional 2 banks remain in the sample. Long-term credit
banks disappear from the sample.
1238

Simultaneity of capital and lending
The similar storyline holds for the OLS estimator of the coefficient on capital asset ratio, β. It is
likely to be biased because bank capital and loan growth are very likely endogenously determinedthrough the performance of borrower firms (demand side). If the aggregate (regional) economic
environment worsens, the firms’ demand for new bank loans falls. Under such circumstances, thefirms’ sluggish sales performances in their product markets may prevent them from gaining returns
high enough to service the repayments to their lender banks on time. Thus, their existing loans become
non-performing, which hurts the lender bank's capital position through the provisions taken for loan
losses and/or charge offs against their equity capital. Similarly, under continuing deflation, if the largepart of the existing borrowing contracts is not indexed, their real burden of existing debt increases. The
same simultaneity mechanism between bank capital and bank lending occurs in reverse. In an
economic upturn, lending demand soars, while the higher profits of the banks increase their equity
capital. The resulting OLS estimate, therefore, may be biased upward.
In overcoming the identification problem, one needs a valid instrument. Such instruments should
be independent of the supply shock, εit, and strongly and consistently correlated to the capital asset ratio,
Kit/Ait in equation (2). Almost all contemporaneous variables are endogenous and are not very effective
to this end. The commonly used approach is employment of lagged “predetermined” variables as
instruments (Peek and Rosengren [1995 a], Ogawa and Kitasaka [2000]).40 The drawback to thisapproach is that such predetermined variables lack an economic account of the bank capital and that thestrong correlation with capital asset ratios is not guaranteed.
Instrument: structural hypothesis
Ueda (2000) and Hoshi (2001) discuss that the regulatory driven “structural” change of thefinancial markets in the 1980s forced banks to reorganize their business. The deregulation of corporatebond markets that followed the liberalization of the secondary markets of government bonds madelarge keiretsu firms less dependent on bank lending. While large firms benefited from raising funds inthe credit markets, regulations governing the banks' activities confined them to their traditional lending
business.41 In response to the loss of long-standing core borrowers, the banks needed to drum up somenew customers. As the asset price bubble developed, banks rapidly increased lending to the real estate
39

Under the current FSA regime, banks are classified into “international” banks and “domestic” banks.
‘International” banks are required to satisfy a higher minimum RBC requirement than “domestic” banks. One may
think of including the dummy variable indicating whether a bank is registered as “international” or “domestic.” We
do not do so because many banks have switched their BIS regulatory status from “international” to “domestic”
throughout the period of interest. A bank’s switch of a regulatory status reflects its capital position. If its capitaldeclines significantly, a bank gives up its international operation, which allows it for the lower minimum standard of
the BIS capital ratio. Thus, such a dummy variable is likely to be endogeneous. We find that including it into theregressions does not change the results significantly.
40
Peek and Rosengren (1995 a) adds the current change in equity capital as one of instruments to lagged variables.
The point estimate of the coefficient on the capital asset ratio in the instrumental variable regression (2SLS) and thatin the OLS surprisingly coincide. One may wonder whether such instrumental variables are not exogenous to thecontemporaneous supply-demand system.
41
For more on the Japanese financial deregulation process since the 1970s, see Hoshi and Kashyap (2000)
13

industry with the strong and illusory expectations that land prices would never fall. The cross-sectional
data of individual banks in the late 1990s show that the banks’ portfolios tilt toward real estate lending
most strongly explains the accumulation of the NPLs more than a decade later. “Riskier” banks piled
up more NPLs while less “riskier” banks avoided the deterioration of their balance sheets as the land
price bubble busted. Such empirical evidence provides us with the ideal instrument. Such behavioral
responses in the mid-1980s are an exogenous factor to the demand-supply system of bank lending in
the 1990s, yet, they best explain the development of NPLs in the late 1990s. Consequently, banks withhigher NPLs write off more assets against their equity capital, and incur severer capital shortages thanbanks with lower NPLs. Hence there should be a negative correlation between the banks' portfoliochanges toward real estate lending and their capital asset ratio.42, 43
The intuition behind the instrumental variable regression is the following two-step estimation.
The first step runs the regression of the capital asset ratio on the banks’ lending portfolio shift toward
real estate lending. The fitted value of the capital asset ratio represents the structural component of thecapital asset ratio that is independent of current borrowers, whereas the demand side influenced by thebusiness cycle fluctuations is absorbed in the residual. Then, the fitted value is used as an explanatory
variable in running the capital-lending regression. This way, one is able to estimate the response ofbank lending to the structural component of capital asset ratio attributable to the banks' structural
behavioral change in the 1980s.
In practice, we construct both the level of and the change in real estate lending over the 1980s anduse them as instruments for the capital asset ratio. For the “level” instrument we use REAL89: eachbank's share of lending to the real estate industry in FY 1989, when land prices recorded a historicalpeak. For the "change" instrument we use PORT: each bank's 10-year growth of lending share to thereal estate industry since FY 1980.44
Target behavior
What is uncertain from the level regression (2) is how a bank changes its lending in response to42

One may argue that the share of real estate lending in the late 1980s and the lending supply in the late 1990s could
be endogenously determined. If banks ex-ante had known that real estate lending was a very bad investment andforesaw that they would lose money, the ex-ante correlation of two variables would occur. However, this argument
may arise from confusion between ex-ante and ex-post banking behavior. It is true that banks ended up with huge
losses from real estate lending due to the burst bubble in land prices. We, however, need to keep in mind that land
prices had never significantly fallen before the bubble burst and that the public, including the banks’ managements,
believed in the “myth of land speculation”. (They're not making any more of it.) Banks must have regarded real estate
lending as a lucrative, low risk, high return alternative to keiretsu lending.
43
Suppose, rather, that banks anticipated ex-ante that real estate lending was very risky. Such banks' prescience does
not lead to an ex-ante correlation between the real estate lending share in the late 1980s and the lending supply shockin the late 1990s. The expectation of a lending supply shock conditional on the real estate lending share is still zero
because riskier investment does not mean a negative expected return but merely a positive variance.

In addition, constant, predetermined variables including lagged and twice lagged loan growths,
lagged and twice lagged interest rate differentials, and other lagged variables including twice, threetimes, and four times lagged deposit growth rates, and lagged and twice lagged land price growths, areincluded as a set of instrument variables. The (one period) lagged deposit growth is excluded from instruments
44

due to a concern about the possible behavioral endogeneity between lending and deposits as described by Diamondand Rajan (2000).
14

changes in its own capital position. Consider equation (3).
t arg et
 K
 Ki 
it∆ ln Lit = α 0 + α 1 ∆ ln Lit −1 + β t −  
 + λX it + ε it (3) Ait  Ai 

The only modification from equation (2) is the replacement of the level of capital asset ratio, K/A, withthe difference between actual and desired levels of the ratio, K/A-(K/A)target. 45 This specification
assumes that each bank has its own target. It cuts back on its lending only when the actual ratio falls
below the target ratio. This allows us to compute the portion of (negative) aggregate lending growth
due to capital constraint. More concretely, one computes the average of a product of a point estimate ofβ and capital shortage (surplus) measured by each bank's K/A-(K/A)target weighted by asset A. By doing
so one can extract the component of the aggregate lending growth accounted for by capital constraint.
One needs to estimate the target capital asset ratio, K/A target. As we saw in Figure 3, the aggregate
capital asset ratio of domestically licensed banks steadily soars at the beginning of the 1990s up until
around the end of FY 1992, then, stays at a high plateau of around 5 percent until FY 1994. Ourinterpretation that banks set their capital target to move toward the full implementation of the BIS risk
based capital regulation framework, leads to the idea of estimating the target from this early period
rather than estimating it from the entire sample period. We estimate the target by the data from FY
1992 through FY 1994. This is not only the period when the capital asset ratio is stably high but also
the post Basel pre- “credit crunch” period. We should not include the “credit crunch” period because it
is quite likely that banks were running shy of the target at that time. We should not include the preBasel period because banks may have been short of capital and in the process of achieving their goals as
the end of the fiscal year for 1992 approached. Of course, we should not include the data before FY
1988 when the Basel capital regulatory framework had not manifested itself and capital ratios to banks
were insignificant.
In estimating, we apply a relatively simple method: we compute the time-series average of each
capital asset ratio measure for each bank over the fiscal years of 1992-1994 and use it as a target. Thus
the target variable constructed this way varies across banks but is time invariant. A bank's target should
be bank specific. It may vary depending on a bank's characteristics such as risk averseness, size andinstitutional and legal status. The actual internal capital target may vary across fiscal years. For
example, banks facing the tougher regulator in certain fiscal years may harden their targets as Hancock
and Wilcox (1994) discuss. The FSA/MOF does not explicitly change actual regulatory minimum
requirements over time, although it began to base its formal regulatory actions on the BIS capital ratio
as the intervention criteria in FY 1997, and we can say that the regulator got tougher that year.
Unfortunately it is impossible to measure to what extent banks responded to the regulatory action ofraising their capital targets, since it is solely an internal response and is not reported anywhere.
Furthermore, such changes in regulatory toughness are likely to accompany changes in the banks'
45

In fact we subtract the regulatory minimum from the RBC ratio in the level specification until FY 1996, since most“international” banks stay above the 8 percent regulatory minimum whereas “domestic” banks stay around their 4
percent regulatory minimum. The distinction between both regulatory types became less obvious after many“international” banks switched their regulatory status to “domestic”.
15

balance sheets reflecting increases or decreases in NPLs, and therefore should be treated as secularchanges of the banks' capital positions rather than changes in targets. On the other hand, in an
economic downturn when it is harder to reach the initial capital targets, banks may lower their targets.
Under such a circumstance, at the micro level, each bank may reach its internal goal, but from themacroeconomic point of view, we should assume that negative aggregate shocks lowered their capitalrather than that the banks lowered their targets in response to negative shocks. In other words, therelative capital position of an individual bank among other banks may not change, but its absolute
capital position does. Thus, as a conclusion, it is safer to extract the information regarding the banks’capital targets from publicly observed data in the pre- “credit crunch” years than to make targets varyacross time using both past and contemporaneous data. 46 Such a way of constructing the target
implicitly assumes the backward looking behavior of banks. This is more appropriate since the banks’constrained behavior has resulted from the legacy of the past and the banks’ relatively short-term
objective itself is to overcome this legacy, which is regressed by nature.
Possible non-linearity in banks' adjustments and the cross section
We do not assume that banks are always capital constrained in making decisions on supplying
loans. When a bank is adequately capitalized, and its management thinks that the capital position is far
from the position that incurs the regulatory intervention or any other adverse effects on the
management decisions, their decision is free from the constraint. A bank chooses the optimal size
(growth rate) of lending supply derived from its profit maximization criteria. Its lending supply
decisions are constrained only when the actual capital asset ratio is approaching the target ratio. This
assumption implies that banks adjust lending supply either upwardly or downwardly in response tochanges in capital only when the absolute level of capital is sufficiently low.
Such a behavioral non-linearity of banks' lending supply advocates use of econometric techniquesthat allow for the time variation in the coefficient on the capital asset ratio, in particular, use of
year-by-year cross section regressions. It also allows us to keep the bank specific target unwashedunlike standard fixed effect estimation techniques. If a negative capital shock is aggregate, all banks
move in and out of the constraint region year by year simultaneously.47 The earlier discussion on thehistory of regulatory regimes and their impacts on aggregate bank capital justifies the aggregate natureof capital shocks to banks. The regulatory regime switches, such as the introduction of Prompt
Corrective Action (PCA) and urging more stringent assessment of assets are aimed at the entirebanking industry rather than particular individual banks. The ordering of banks' capitalization does not
dramatically change after the regulatory shock. In equations (2) and (3), the coefficient, β, varies
46

Alternatively one may estimate the relationship between the banks' capital ratio and their characteristics (size,
regulatory and institutional dummy variables from pre- crisis and post Basel years (1992-1994) and then compute
fitted values for out of sample crisis years (1995-2000). This would accommodate the banks' switch in regulatory
status from higher to lower minimum capital requirement if they actually do so over FY 1995- FY 2000. In principle,
the target would not vary much over the time horizon unless their size or regulatory status changes dramatically. Yet,each individual bank has its own target each year according to its size and institutional characteristics. I estimated thetarget this way as an experiment. The relationship over 1992-1994 is very inaccurately estimated, and quite a numberof banks have negative values for their targets during FY 1997- FY 1999.
47
If the shock were idiosyncratic, the non-linear specification could be used.
16

across years. Importantly, in years when banks are making a capital free optimization, the estimate of βshould not be statistically significant.
6. Results
6.1. Preliminary results: Conventional level specification
Strength of the instrument
As Table 2 shows, REAL89 is negatively correlated to CAPR since FY 1997 and seems to be areasonably valid instrument over the “credit crunch” years. The strong correlation in FY 1997 suggeststhat the serious writing off of the NPLs to the real estate industry against equity capital did not begin
until the start of a more rigorous self-assessment of bank assets in that year.48 As with CAPR, CAPRM
has been negatively correlated to REAL89 since FY 1997. BIS is not sufficiently negatively correlated
to REAL89 in any year except for a modest correlation in the fiscal years 1999 and 2000. It is striking
that REAL89 has virtually no correlation to BIS in FY 1997, when a large amount of NPLs wererevealed and the strong negative correlation between book capital and lending took place. Large losses
in the book capital of real estate concentrated banks may have been wiped off the books by creative
accounting techniques.
Regression results
Tables 3 shows coefficients on CAPR in year-by-year regressions from FY 1995 to FY 2000. The
first two columns correspond to total domestic non-troubled lending (NOTRB) consisting of consumer,
manufacturing, and non-troubled non-manufacturing lending, which is explained below. The third andthe fourth columns correspond to lending to the manufacturing industry (MANUF). Finally, the fifth
and the sixth correspond to lending to the non-troubled non-manufacturing industries (NOTNM).
Industries classified in NOTNM include agriculture, mining, finance and insurance, transportation andtelecommunications, and utilities. CAPR takes a one-year lag (Lagged) in the first column of the twocolumns for each lending category, and is contemporaneous (Cont.) in the second. The first column ofeach class of lending serves as a testing measure on the persistence of capital shocks to lending as wellas the conventional way of avoiding simultaneity bias.
Only two cells report a positive and significant coefficient in the top OLS table up until FY 1996.
Then coefficients in all cells turn positive and significant in FY 1997. In FY 1998, only a coefficient of
the contemporaneous capital ratio for manufacturing lending is positive and significant. In FY 1999
coefficients, except for manufacturing lending, turn positive and significant again.
The bottom table on 2SLS (two stage least square) regressions implies the demand side
simultaneity bias in the OLS estimates. In contrast to the OLS regression results, all coefficients before
FY 1997 are statistically insignificant. In FY 1997, none of coefficients on lagged capital is significant.
The coefficient on the contemporaneous capital ratio for non-manufacturing lending in FY 1999 is not
48

An puzzling absence of negative correlation in FY 1996 that marks the liquidation of jusen housing loan companies
and resulting accounting loses of banks reflects that jusen themselves, which specialized in real estate lending, areclassified as non-bank financial companies. If the lending to jusen companies were classified as real estate loans
instead, a negative correlation would appear.
17

statistically significant. On the other hand, the coefficient on the lagged CAPR for total lending isfound to be weakly positive at the 10 percent significance level. The capital shock in FY 1997 isshort-lived, whereas the shock in FY 1998 could be persistent enough to effect lending in the following
year. 49, 50
6.1. Target behavior specification
Capital “shortage” and “surplus”
Figure 3 plots the estimated target CAPR and the actual CAPR of every individual bank over thethree year “credit crunch” period starting in FY 1997. The horizontal axis represents the target CAPR
whereas the vertical axis does the actual CAPR. Thus, the actual CAPR of a bank plotted above the
45-degree line is higher than its own target, and therefore such a bank shows a “surplus” of capital. The
actual CAPR of a bank below the 45-degree line, on the other hand, falls short of the target, and such abank shows a “shortage” of capital. As we described earlier, a bank specific target is indeed timeinvariant so that values along the horizontal axis are common to all dates. In FY 1997 all large banks
are plotted far below the 45-degree line, meaning that they are all showing a severe capital shortage.
Relatively, a fewer regional and regional 2 banks are below the 45-degree line. A majority of regionalbanks concentrate slightly above the 45-degree line, and many regional 2 banks are clustered aroundthe line. The highly concentrated structure of the Japanese bank lending market suggests that Japanesebanks are experiencing a severe shortage of capital in the aggregate sense. In FY 1998, ten large banks
cross above the 45-degree line, and only five such banks remain in a capital shortage position. This is
the direct positive influence of capitalization by using public funds. Positions of regional and regional
2 banks largely remain the same. In FY 1999, large banks still mostly maintain a capital surplus. In
addition, most regional and regional 2 banks are plotted higher than in FY 1998. This may imply that
because they recently came under RBC based PCA regulatory oversight, domestic banks raised thelevel of capital to minimize the likelihood of falling below the regulatory minimum threshold due toany unpredictable negative shocks.
Strength of the instrument
Table 4 shows the correlation coefficients of REAL 89 and the distance measure of each capital
asset ratio since FY 1994. All three kinds of distance measures are quite negatively correlated to
REAL89 for almost the entire sample period. The correlation coefficient between REAL89 and CAPR
is strongly negative from FY 1995, and the value of the coefficient varies from -0.27 to -0.54. Similarly
49

Caution should be used in interpreting the regression results with the contemporaneous capital asset ratio. Usually,
the banks’ or regulatory actions negatively affecting bank capital occur at the end of the fiscal year toward the closingof the accounting period. Nevertheless, banks must internally be aware of their accounting actions such as disposal of
NPLs in the course of the fiscal year. Banks receive acknowledgement from the regulator regarding their regulatory
actions such as the regulator's consultation, research, and ultimately its negotiations with bank management. For
example, periodical research meetings, which MOF officials, bank officers, and certified public accountants
participated, were held to discuss details of regulatory measures relevant to the PCA until the early FY 1997.
50
We also estimated equation (2) with BIS and CAPR (Results are not reported). The results with BIS are hard tointerpret since REAL89 is not negatively correlated to BIS in most specification-year pairs. The results with themarket based CAPRM resemble the results with the book based CAPR.
18

the coefficient between REAL89 and CAPRM since FY 1994 stays in the large negative range from
-0.39 to -0.54. Unlike the "level" of BIS, the distance measure for BIS is also negatively correlated to
REAL89 from the beginning of the sample period, though the correlation coefficient is somewhat moremodest than that with other measures. Thus we can expect somewhat reliable results for the BIS ratio
in the "change" regressions. REAL89 appears to serve as a much better instrument for the distance
between actual and target capital asset ratios than the ratios themselves. The strong negative
correlation of REAL89 to distance measures, with the use of targets constructed above, supports thelegitimacy of such estimated targets. 51 We can interpret that the computed capital shortage
appropriately reflects loss of capital due to the disposal of structural NPLs attributable to real estate
lending of earlier years.
Regression results
Table 5-1 reports the regression results of the target behavior specification, (3), and shows how
much banks have changed the lending supply of three classes of lending: non-troubled total lending,manufacturing lending, and non-troubled non-manufacturing lending, in response to the capital surplus
or shortage measured by the target - actual distance of a book based capital ratio, CAPR. The top table
and the bottom table represent the OLS results and the 2SLS instrumental variable regression results
respectively.
A glance at the OLS results reveals a slightly different picture from the results of the level
regressions. In FY 1997, coefficients on both lagged and contemporaneous CAPR are strongly positiveand significant for all classes of lending. In FY 1998, unlike the level regression results, the coefficienton the contemporaneous CAPR is positive and significant for all classes of lending. In FY 1999,
coefficients on both lagged and contemporaneous CAPR are positive and significant for non-troubled
total lending and manufacturing lending.
The 2SLS regression results are the most essential ones, upon which the aggregate implications
are drawn. In FY 1997, the coefficient of CAPR is found to be strongly and consistently positive.
Coefficients of both lagged and contemporaneous CAPR are positive and significant in all cells except
for the cell corresponding to the lagged capital ratio for non-manufacturing lending. Manufacturinglending was more sensitive to the capital shock the year before, whereas non-manufacturing lending
was more so to the contemporaneous shock. The point estimate of a larger one is more than twice aslarge as that of a smaller one for both types of lending. On the other hand, the responsiveness of total
lending to capital shock is equalized between lagged and contemporaneous ones, reflecting an
averaging out of the differences in the subclasses of lending. The negative capital shock in FY 1997 isfound to be temporary. Coefficients of the lagged CAPR in FY 1998 are not significant. The capitalshock in FY 1998 is persistent only for total lending. The structural capital - lending relationship does
not appear for subclasses of lending in FY 1999. The total non-troubled lending does not respond to the51

The negative correlation between capital asset ratios and REAL89 remains unchanged after the injection of large
public funds into 13 mostly large banks (12 large and 1 regional banks). Indeed, the MOF decided to purchasepreferred stocks of these banks almost proportionally to their pre-injection capitalization. The correlation coefficient
between size of preferred stocks purchased by the government and core capital less preferred stocks purchased as of19

contemporaneous CAPR unlike in the level regression results.52 As in the level regression results,
point estimates of the statistically significant coefficients are the largest in FY 1997 in most cases.
Against our anticipation, all point estimates of coefficients remaining to be statistically significant
with a correct positive sign in 2SLS regressions are much larger than OLS point estimates. One
possible explanation for this puzzle is that the lending demand and the lending supply moved in
opposite directions during these years. In FY 1997, “capital constrained” banks with a “shortage” ofcapital cut back on their lending while the “strong” aggregate demand (economic recovery) shifted thelending demand function rightward, rather than the “weak” aggregate demand reducing the lending
demand as the standard literature on the credit crunch claims. As a consequence, the observed decreasein the equilibrium quantity of lending was smaller than the leftward shift of the lending supply function.
Conversely, in the fiscal years 1998 and 1999, the same banks, still constrained by their capital
positions in making their lending supply decisions, were driven by the “surplus” of capital and“increased” their lending supply, while the “weak” aggregate demand in the ongoing recession shifted
the lending demand function to the left. Therefore, the observed equilibrium increase in lending wasagain smaller than the rightward shift of the supply function
Table 5-2 presents the complete results of the 2SLS regressions over the "credit crunch" period.
The lagged dependent variable is positive and significant in FY 1997 (NOTRB) and FY 1999 (NOTRB
and MANUF) and their point estimates range from 0.28 to 0.45. The large bank dummy variable ispositive and significant in fiscal years 1998 and 1999. One caveat in interpreting the target behavior
specification is that the bank specific and time invariant target may capture the institutional effect, andabsorb the negative trust bank effect observed in level regressions.
Further discussion on the instrument
Table 5-3 shows the "partial squared correlation coefficient" proposed by Shea (1997) to serve asa goodness of fit test for a capital asset ratio with a set of instruments employed. (Only results with thebook ratios are shown.). This testing is, roughly speaking, an R-square taken into account thecollinearity among the instrumental variables. The results are reasonably good for the Japanese banks'cross-sectional data. 20 out of 36 partial squared correlation coefficients are greater than 0.1.53
Yet another potential problem is a possible correlation between REAL89 and lending supply
shock, which causes the biased estimator of the coefficient on CAPR. A bank that shifted its lending
portfolio more aggressively toward real estate lending may have also supplied more loans to ex-postrisky firms in the “non-troubled” industries than other banks. If so, attempting to make its loan
portfolio less risky, such a bank may have written off more NPLs, terminated more existing lending
contracts to poor performers in the “non-troubled” and “troubled” industries alike, and thereby,

FY 1998 is 0.92.
52
One possible explanation follows. Banks changed their lending supply in response to a large change in their own
capital positions that occurred in the previous year, FY 1998. Bank capital does not change much in the following year,
FY 1999. Since the level of the banks' capitalization is almost identical in two consecutive years, in such a case banklending responds to the level in both years, but does so to the change only in the shock year.
53
Shea gives an example where the normal R-square is 0.1 and this statistic is 0.05, and concludes that the goodness of
fit is not as good as the standard R-square test implies.
20

supplied less loans in the “non-troubled” industries than banks that had not been much dependent on
the real estate lending. The resulting correlation is negative. Such a bank may have acted in the
opposite manner, and launched more rescue lending programs to poor performing borrowers. This caseresults in a positive correlation.
This type of endogeneity can be described as the correlation of an unobservable bank specificfixed effect to REAL89. Our empirical strategy is much less prone to this type of problem. Rememberthat our main explanatory variable, CAPR, is constructed as the actual capital asset less the bankspecific target. The target and included dummy variables on the bank’s institutional characteristics
could absorb the fixed effect. The data are not suggestive of this possibility, either. Standard statisticaltests do not imply endogeneity over the period from FY 1997 to FY 1999. Correlation coefficients of
REAL89 and the estimated residual are found to be small in absolute values. Hansen’s (1982)
overidentification tests do not reject the null hypothesis that instruments are uncorrelated with a set of54
explanatory variables at least at 10 %. More importantly, the point estimate of the coefficient on CAPR
changes inconsistently with the negative correlation, and correcting the bias caused by the positive
correlation, if any, would not undermine but rather strengthen our interpretation of the larger reactions
by banks in FY 1997. Since CAPR on average is smaller in FY 1997 than in FY 1996 for most of thebanks, as Figure 10 shows, a simple analysis of the 2SLS formula demonstrates that the point estimateof the coefficient on CAPR in FY 1997 must be smaller than that in FY 1996 if the bias caused by thenegative correlation were large enough to reverse the order of the coefficients’ magnitudes over the twoyears. The result of point estimates is opposite to what the negative bias would lead to, and hence, itwould not cause serious problems in interpreting the results. Similarly, since CAPR is greater in FY
1998 than in FY 1997, the point estimate in FY 1998 must exceed the corresponding estimate in thepreceding year.55 This, in turn, suggests that it is harder to rule out the opposite scenario of a positive
correlation. Should this be true, the symmetric discussion suggests that the coefficient would beunderestimated in FY 1997 and overestimated in fiscal years 1998 and 1999.56
Fixed effect estimation
Use of such standard techniques as first (time) difference and time demeaning (subtracting thetime series average for each bank) allows one to remove the fixed effect. Such an approach, however,would undermine advantages of our empirical strategy. Time demeaning endogenizes lagged
explanatory variables and shrinks the number of available instrumental variables, whereas twice or
54

There are some exceptions. Correlation coefficients are large and negative in FY 1997 and FY 1998 when a capital
asset ratio is taken a one-year lag. However, such a negative correlation may be a byproduct of misspecification.
Though bank lending supply only responds to a contemporaneous capital shock, a regression of lending on the lagged
capital ratio reveals a positive and significant coefficient since the lagged and the contemporaneous capital asset ratios
are highly positively correlated. Since REAL89 is negatively correlated with the lagged ratio as well, omitting thecontemporaneous ratio leads to a negative correlation of REAL89 and the estimated residual. Indeed regressions withboth lagged and contemporaneous ratios erase both a statistical significance of coefficients on the lagged ratio and acorrelation of REAL89 and the residual. Overidentification tests reject the null hypothesis at 10 % level for the lag
specification for non-troubled lending in fiscal years 1997 and 1998 and both lag and contemporaneous specifications
for non-troubled lending in FY 1999.
55
See Appendix for further discussion with a simple model.
21

more lagged explanatory variables are predetermined and orthogonal to the first differenced lending
supply shock in the absence of any serial correlation. However removing the fixed effect, no matter
what technique is used, makes it impossible to identify the level regression model (2), with the target
regression model (3) since any transformation aimed at removing the fixed effect washes away the timeinvariant target capital asset ratio. Indeed any arbitrarily chosen time invariant target would result inthe same estimator and the model (2) can be viewed as the special case in which the target is constantover both time and the cross section at zero. Therefore one would not be able to estimate the impact of
capital shock on the lending supply with the fixed effect estimator of coefficients.
Having said that, the fixed effect estimation of the lending supply function provides persuasiverobust side evidence on how the endogenous fixed effect would bias the cross section estimator of thecoefficient on capital adequacy in the general lending supply function nesting the model of our interest.
We consider the following model of the lending supply function that factors in the time variation in thecoefficient on capital “surplus”.
t arg et
 K
 Ki 
it∆ ln Lit = α 0 + α 1 ∆ ln Lit −1 + β t −  
 + λX i + η i + µ t + u it , t=1,…,T Ait  Ai 

(4)This is essentially the same as equation (5) with a major modification in the time subscript attached toβ. The time subscript disappears from a set of control variables X as we use the same time invariant
dummy variables to indicate the bank’s institutional characteristics as in estimating the cross sectional
regressions. The residual is further decomposed into the bank fixed effect ηi, the time effect μi, andthe random error uit. First differencing (4), we obtain,KK∆ ln Lit − ∆ ln Lit −1 = α 1 (∆ ln Lit −1 − ∆ ln Lit − 2 ) + β t it − β t −1 it −1 + ∆µ t + ∆u it , t=2,…,T(4)’
Ait
Ait −1The simple matrix algebra shows that estimating (4)’ is equivalent to estimating the following equationthat involves interaction terms of time dummy variables Dt’s with the history of capital asset ratios
stretching over the entire panel.
T
 K
K 
∆ ln Lit − ∆ ln Lit −1 = α 1 (∆ ln Lit −1 − ∆ ln Lit − 2 ) + ∑ β τ*  Dt iτ − Dt −1 iτ −1  + ∆µ t + ∆u it , t=2,…,T (5)AiτAiτ −1 τ =1In practice, besides the interaction terms we use time dummy variables as explanatory variables torepresent the time effect ∆µ t . As the fixed effect estimation is meant to supplement our cross section
regressions, the almost identical set of instruments is employed to run the 2SLS regressions with thepanel data.57
Table 5-4 reports results of the fixed effect estimation for the three year period from FY 1997 toFY 1999.58 It turns out that the coefficient of the contemporaneous CAPR in FY 1997 is large andstatistically significant at least at the 5 percent significance level for all definitions of lending. On theother hand, the coefficients of CAPR are not statistically significant for all but only one model. The
57

The third lagged dependent variable and time dummy variables are included to make the number of instruments
exceed that of explanatory variables.
58
Longer panels result in imprecise estimates of coefficients.
22

surviving coefficient of the contemporaneous CAPR for manufacturing lending is significant only at
the 10 percent level. Such results are consistent with our conjecture on the possible bias, if any, in the
cross section estimation: underestimation of the coefficient in FY 1997 and overestimation in later
years.
Alternative capital asset ratio measures
The regression results with alternative capital asset ratio measures support findings in the resultswith the book based CAPR. Table 6-1 reports results with BIS as a capital ratio measure. Unlike thelevel regressions, validity of REAL89 as an instrument for BIS helps uncover the way bank lending
supply is constrained to BIS in a qualitatively similar fashion to the way it is to CAPR. Coefficients on
the lagged BIS are positive and significant for all classes of lending in FY 1997. Coefficients on thecontemporaneous BIS are positive and significant for all classes of lending in FY 1998. The capitalshock in FY 1998 is somewhat persistent, and effects the total non-troubled lending in the following
year, but does not effect the lending subclasses. Table 6-2 reports results with CAPM. The results arelargely consistent with the CAPR results. Lending in FY 1997 responds positively to both lagged andcontemporaneous CAPRM regardless of the class of lending. Lending in FY 1998 responds positively
to only the contemporaneous CAPRM for every class of lending. Only the total lending is constrained
to the lagged CAPRM in FY 1999.
Regional and regional 2 banks
In order to check on the robustness of our position, we apply the same methodology to subgroupsof banks. Groups investigated are regional banks registered as "domestic" as of the end of the fiscal
year for 2000, and regional 2 banks. Regional banks are limited to "domestic" banks since it isimportant to control the banks’ regulatory status. Banks maintaining an “international” status arewilling to accept the higher minimum RBC standard, which suggests that they are less capitalconstrained than the rest. There are 49 such “domestic” regional banks in the sample. All 48 regional 2banks surviving the sample selection process mentioned above are registered as “domestic”. Thus any
further breakdown of regional 2 banks into subgroups is not beneficial and is quite unnecessary.59
Table 7-1 reports the 2SLS regression results of equation (3) with CAPR for domesticallyoperating regional banks. All coefficients on CAPR are positive and significant in FY 1997. The
capital - lending relationship within regional banks, however, is not robust in the later years of the"credit crunch" period. In FY 1998, non-manufacturing lending supply seems strongly constrained,
whereas manufacturing lending is free from the constraint. The coefficient on the contemporaneous
capital ratio for total lending supply is significant only at the 10 percent level. In FY 1999, only
coefficients for manufacturing lending are positive and significant. Table 7-2 reports the 2SLS
regression results of equation (3) for regional 2 banks. Both total and manufacturing lending respondto the contemporaneous book based capital asset ratios in FY 1997. 60
59

Owing to the small number of observations (15 banks) and even the group being divided into two smaller
institutional sub groups, large banks are not analyzed as a single group.
60
We also estimated equation (3) with alternative capital asset ratio measures for these sub samples. (Results are not23

6.3. Macroeconomic implications
Aggregate lending growth
Table 8 reports the aggregate lending growth rates over the six-year period from FY 1995 to FY
2000 of all the selected 126 banks. Lending to "troubled" industries (TROUB) and real estate lending(REALE) are added to the three "non-troubled" classes of lending analyzed above in order to grasp how
the presence of NPLs and their disposal effected the aggregate lending.
Non-troubled total lending in the first column is steady over time with its growth rate ranging from
-1.2 percent to 1.8 percent. It grew by a modest 2 percent for the six-year period. It experienced amodest decline for two years in a row since FY 1996, but recovered in FY 1998. Manufacturinglending had declined until FY 1997. After two years of recovery from FY 1998, it plunged again in FY
2000. Non-manufacturing lending seems to be the most declining class of all. In all years except for
FY 1999, it experienced a negative growth. As a consequence, non-manufacturing lending dropped bya little over 15 percent for the six years, whereas manufacturing lending dropped by just 8 percent.
Lending to industries fell in the fiscal years 1995, 1996, and 2000 as well as in the "credit crunch" years.
Our finding of an absence of any structural capital - lending relationship in these years implies that thefall in lending is mostly due to a decline in lending demand.
Aggregate growth rates of lending to troubled industries and the real estate industry surprise us
since they stay almost always higher than those to relatively sound industries. Compared to thesounder counterpart of non-manufacturing industries, though lending to troubled industries had nevergrown positively, the growth rates of such lending classes were higher for all years except for FY 1999,
the only year in which non-troubled and non-manufacturing lending grew. Lending to troubled
industries had declined by a little less than 8 percent over the six-year period, about half of thecorresponding figure to the sounder counterpart. More surprisingly, real estate lending grew until FY
1997, when all other classes of lending had declined. Afterward, the rate of decline of real estate
lending was relatively modest compared to sound non-manufacturing lending. Real estate lending had
grown by 3 percent over the six-year period. Such data may indicate that firms with high lending
demands in the weak macroeconomic environment were highly leveraged firms groaning under theburden of NPLs and in desperate need for infusions of more cash for debt repayment. Japanese banks
were said to be engaged in a lending practice called “evergreening”, which allows economicallybankrupt firms to keep operating. Taking into account the large write offs of NPLs in troubled
industries, the difference in new lending between that given to sound industries and that to troubled
industries may be even much starker. 61
Aggregate impact of capital constraint
Table 9 reports what we consider to be the most important results of this paper, how much either

reported.) The results with CAPRM are generally consistent with those with CAPR. The results with BIS are hard tointerpret because of the weakness of REAL89 as an instrument for BIS in FY 1997.
61
The empirical literature on the Japanese banks’ evergreening has been growing recently. See Kobayashi, Saita andSekine (2002), Peek and Rosengren (2003), Caballero, Hoshi and Kashyap (2003).
24

capital shortage or capital surplus of banks, measured by the three definitions of capital, contribute to
the aggregate lending supply by all 126 banks in its percentage growth.
The contemporaneous effect of the negative book capital shock in FY 1997 is large for all classesof lending. It cuts total manufacturing, and non-manufacturing lending by 3.72 percent, 5.70 percent,and 8.54 percent respectively. A positive capital shock in FY 1998 shows a modest recovery in lending
in the same year. It raises the total manufacturing, and non-manufacturing lending supply by 1.07
percent, 1.43 percent, and 3.82 percent respectively. It makes the total non-troubled lending supply
grow in FY 1999 by a modest 1.05 percent, but failure of the overidentification test casts doubt on sucha finding. Besides neither manufacturing nor non-manufacturing lending is effected in the year.
Recovery of total non-troubled lending, made possible by the positive shock in FY 1998, in the same
year and the following year represents 2.13 percent in percentage growth but does not make up for thelending cut caused by the negative capital shock in FY 1997. However, it may have been a positive
shock large enough to help the Japanese banks escape from the capital constraints. The net effect of
capital shocks in the fiscal years 1997 and 1998 on total, manufacturing, and non-manufacturinglending is -1.67 percent, -4.35 percent, and -5.04 percent respectively in contributing to the percentage
growth of the lending supply. Had the Japanese banks not been constrained by the contemporaneousbook capital shock of FY 1997, the non-troubled total lending, manufacturing lending, andnon-troubled non-manufacturing lending supply would have grown by 3.02 percent, 3.78 percent, and4.62 percent respectively. On the other hand, had Japanese banks not responded to the positive shock in
FY 1998, each lending category would have shown a decline of 0.51 percent, 0.25 percent, and 9.23
percent respectively.
The negative risk based BIS capital shock in FY 1996 reduced the non-troubled total,
manufacturing, and non-troubled non-manufacturing lending supply by 1.42 percent, 1.22 percent, and3.77 percent respectively. However, the positive capital shock in FY 1998 increases the non-troubled
total lending supply by 2.76 percent and the same class of lending supply by 4.12 percent in FY 1999.
The same positive shock increases manufacturing and non-manufacturing lending by 4.85 percent and7.64 percent. Results with the risk based BIS capital asset ratio cannot be overly exaggerated. Thoughbanks were suffering a severe shortage of core (book) capital, none of the banks failed to satisfy the BIS
minimum requirement in FY 1997. The positive shock in FY 1998 was added to the base RBC ratiothat was rigged to appear higher by the banks' accounting manipulations. At most, the BIS results givethe most optimistic view of the behavior of the bank lending supply during the "credit crunch" period.
The negative market based capital shock in FY 1997 reduces these three classes of lending supply evenmore astonishingly by 7.15 percent, 10.88 percent, and 15.94 percent. Not only the market based
capital shock in FY 1997 but also the shock in FY 1998 is negative, and contributes negatively to thelending supply growth in both FY 1998 and FY 1999. This may be an overestimation. It is hard tojustify the banks' behavior of targeting the uncontrollable and unrealized gains of their securities.
Security prices, mainly stock prices, though post bubble era in the period from FY 1992 to FY 1994,
had stayed much higher then than in the "credit crunch" period. At most, the market based capital ratio
results give the most pessimistic view of the banks' lending supply behavior during the "credit crunch"period.
25

Interpretation and policy implications
These findings lead to the following interpretation of the banks’ lending behavior during the late
1990s. A rigorous self-assessment of bank assets revealed large non-performing loans that had been
covered up years before. Accounting losses, arising mainly from the liquidation of jusen companies in
FY 1996, caused a large negative capital shock, which resulted in considerable losses in the banks'
equity capital. Large, newly discovered NPLs coupled with tougher regulations pressuring the banks
to dispose of them under the watchful eye of the recently introduced PCA, caused huge accounting
losses, hurting further the banks' book capital in FY 1997. The accumulative effect of these two
negative shocks over a two years period was enough to push the banks' capital positions downward to
the neighborhood of their own targets. As a result the banks became capital constrained in making
decisions on supplying loans. Many of them, in turn, failed to satisfy their individual targets and cut
back on their lending irrespective of the borrowers' credit worthiness. Such a negative lending supply
shock narrowed channels of credit supply to bank dependent borrowers who needed more funding tofinance their real and immediate needs, at a time when the economic outlook looked sunnier andaggregate demand for lending was in an upturn during a period of a fragile economic recovery. Thus,
the observed amount of loans supplied in equilibrium ran short of the amount the unconstrained
equilibrium would have brought about.
The same mechanism may have worked in reverse in FY 1998 and eased borrowers' borrowing
conditions more than the unconstrained equilibrium could have. The positive capital shock represented
by the infusion of public capital encouraged the already capital constrained banks. In fact, the amountof injected capital in the form of preferred stocks into the sample of 126 banks totals 58,090 million yen
and is equivalent to 0.7627 percent of their assets.62 Knowing that the industry wide capital asset ratio
exceeds the target ratio by just 0.68 percent, injected public funds must have been the positive shock
large enough to raise bank capital a little beyond the desired level, which resulted in the banks' positive
response to the lending supply. It is certain that without public funds, the banks would have been stillseverely short of capital into FY 1998. In response to such a positive shock banks may have shifted
their lending supply upward. 63 However, the positive impact was too small to offset the negative
lending supply shock in the year before if it existed at all. It is not readily clear from combined findingsof the main cross section regressions and the auxiliary fixed effect estimation whether the positive
impact boosted lending supply in fiscal years 1998 and 1999 or it simply helped banks to escape fromtheir capital constraints. After all the net impact of capital on bank lending supply appeared to be
62

Nakaso (1999) classifies the public funds injected into those raised in the form of preferred stocks and subordinateddebts by individual bank. As a whole, 61,590 million out of 74,590 million yen was issued in the form of preferredstocks. The Industrial Bank of Japan, which is omitted from the sample, had 3,500 million yen of preferred stocks
underwritten by the government. Note that Yokohama Bank, the largest regional bank, is the only local bank injectedwith public funds in FY 1999.
63
The same regression as equation (3) for FY 1998 and FY 1999 that replaces the gap of actual and target capital asset
ratios with the same gap less the public fund to asset ratio and the public fund to asset ratio partially supports this view.
The latter variable captures the impact of the public funds. The coefficient on the public fund to asset ratio in FY 1998
is found to be statistically significant for non troubled total lending in FY 1999 at the 10 percent level but it is not so
for lending in FY 1998.
26

substantially negative by all accounts. Particularly, industry lending was hit hard. Banks returned tocapital free decision making by FY 2000.
As our findings from both main cross section regressions and the auxiliary fixed effect estimationsuggest, the bias due to the possible endogeneity of REAL 89, if any, would simply underestimate thenegative impact of capital shortage in FY 1997 and overestimate the positive impact of capital surplus
in subsequent years. As we discussed earlier, these findings can be interpreted as circumstantial
evidences that banks that had been engaged in aggressive real estate lending in the 1980s may havebeen indulged in evergreening of underperforming firms across the board.
Assuming the importance of bank lending supply on aggregate demand, particularly on business
investment, the "credit crunch" in FY 1997 contributed negatively to the aggregate demand and led toan end of the short-lived economic recovery and ultimately to the recession in the following years.
Conversely, a rise in the lending supply in the subsequent year, brought about mainly by an infusion of
public funds into the severely capital constrained large banks, may have contributed positively to theaggregate demand and prevented the declining aggregate demand from getting worse.
As far as monetary policy is concerned, the BOJ's claim that the banking sector hurts theeffectiveness of policy implementation, is not fully convincing. Constrained by capital shortages,
banks certainly reduced the lending supply in FY 1997. Funds raised by banks in the extremely loose
short term markets under the ultra easing monetary policy were mostly invested in risk free assets suchas government bonds rather than financing private, in particular, corporate lending. The expansionary
monetary policy, if it was relying heavily for its success on the role of the bank lending channel underthe bank centered corporate finance structure, must have been neutralized. On the other hand, in FY
1998 and FY 1999, the capital shock accelerated bank lending growth. The declining quantity oflending simply reflects the weak lending demand due to the worsening macroeconomic performanceand disposal of structural NPLs. In this sense, the bank lending channel may have helped inpropagating easing monetary policy but did not hurt monetary policy. Some findings could beinterpreted as the circumstantial evidence that evergreening of underperforming firms prevent funds
from channeling into productive firms and that easing monetary policy had little effect throughout theperiod.
The interesting policy question to ask is what would have happened if the amount of funds
injected were much larger than they actually were. Would banks have raised their lending supply evenmore? Our answer drawn from our micro evidence is "perhaps no". Further public capital would havechanged the structure of the lending supply function sooner and made banks unconstrained as actually
happened late. Thus banks would not have responded positively to any marginal increase in theirequity capital, and the quantity of lending would have been simply governed by contracting lending
demand under a circumstance of extremely low lending rates. 64
64

There is one caveat to interpreting the empirical findings for FY 1998. What seems to be a negative contribution ofcapital shortage to bank lending may not be a causal relationship but just be simultaneously occurring phenomena. On
the one hand, banks raise their lending supply in response to the regulatory measures outside the regulatory
framework based solely on the numerical RBC standard aimed exclusively at preventing the credit crunch fromoccurring. On the other, public capital injection raises the banks' equity capital. Strictly speaking, the fact that thecapital asset ratios of several large banks receiving public funds far exceed their targets is contrary to the idea that27

7. Conclusion
It has been already eight years since the ultra loose policy was launched in September 1995. Any
positive effect, however, has yet to be seen. Indeed, corporate finance in Japan is still bank centeredand the so-called "bank-lending channel" plays an important role in instigating monetary policy shocks.
Underperformance of banks damages the bank-lending channel and greatly reduces the effectiveness ofmonetary policy. A direct testing procedure to assess the banks’ response to the monetary policy
measure may not be an effective strategy since they must have already been provided with abundantliquidity and may not feel compelled to lend out more in response to further easing policy. It ispopularly rumored that the "capital crunch" of banks caused the negative bank lending supply shocks
under the RBC regulation framework. Following the conventional wisdom of abundant credit crunch
literature using micro data, the lending growth is regressed on measures of capital asset ratio.
The first methodological contribution of this paper is its use of a unique and strong instrument forcapital asset ratio. The literature's testing results are mixed, largely because of lack of a convincing
identification strategy of the supply side capital - lending causal relationship with the demand side.

Making use of the empirical finding that the structural component of non-performing loans are best
explained by the portfolio reorganization toward real estate lending over the 1980s, we employ thewithin bank share of real estate lending in the late 1980s as an instrument. It turns out that theconstructed variable is very negatively correlated to the capital asset ratio and can be an effective
instrument.
The second methodological contribution is the measurement of the aggregate impact of capital
shortage or surplus on lending growth. The bank specific target capital asset ratio is estimated as atime-series average in the three year period from FY 1992 to FY 1994, based on the assumption that thebanking behavior of the banks was to meet their targets right after the full implementation of the BIS
capital regulation framework. Lending growth is regressed on the gap between the actual and theestimated target capital asset ratios. Averaging the individual impacts of the banks gives the aggregate
lending supply shock caused by capital constraint.
Regression results suggest that the severest "capital crunch" occurred in FY 1997. Banks were not
only capital constrained but the aggregate actual capital asset ratio ran short of the aggregate target ratio.
We interpret that this is largely attributable to the large accounting losses caused by the rigorous asset
assessment that accompanied the new formal action based PCA regulatory framework. The reducedbank capital made banks constrain capital in their decision making on lending policy, which eventuallylead to the "credit crunch". Constrained banks are also found to respond to their concurrent capital
position in FY 1998. This suggests that the bank lending channel of monetary policy transmission was
severely damaged in FY 1997 by the credit crunch, but not in later years. Though the positive capitalshock in FY 1998 was large enough to allow banks to avoid any capital constraint, the net impact of"capital constrained" banks adjust lending negatively in response to capital shortage. The distinction between the two
hypotheses is not possible in the current analytical framework. The alternative hypothesis, however, does not change
the aggregate implication. The positive lending shock remains to be the supply shock even if this alternative is true.
28

capital shocks during the fiscal years of 1997, 1998 and 1999 on lending is unambiguously negative,
suggesting that the "credit crunch" was a major factor behind the recession. Indeed the cross section
empirical approach, which validates the bank specific target, inevitably has an unobservable andpossibly endogenous fixed effect. Results of the cross section and the auxiliary fixed effect regressions
consistently imply that the bias, if any, would be caused by the banks’ evergreening behavior that ties
them to underperforming borrowers. It turns out that such a bias would not undermine our conclusions.
There are possible extensions to this paper and relevant future research areas. There are twointeresting extensions from the methodological point of view. First, by applying the same instrumental
variable strategy to financially unhealthier industries and comparing the results with our results for thehealthy industries, one can show whether banks reorganized their lending portfolio to the specificdirection. The direction of the bank finance in response to negative capital shocks suggests how
efficient and effective use of financial resources is. Second, if we find that some structural behavioral
change years before explains capital shortages in the 1990 - 1991 period of the U.S. credit crunch, it ispossible to settle the debate over whether the “credit crunch” is merely the reflection of the recession oris a supply side phenomenon. This paper mainly focuses on the “unwillingness to lend” of healthybanks to healthier lenders. As Bernanke (1983) and Calomiris and Mason (2002) explore in the context
of the U.S. Great Depression, financial distress itself could be the negative lending supply shock.
Though the “capital crunch” may not have occurred in the fiscal years of 1998 and 1999, the financial
distress represented by increasing bank failures may have been the negative financial shock to bankdependent borrowers and blocked the monetary policy transmission. This would be an interesting
research project from the macroeconomic point of view.

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33

Appendix: The Fixed EffectLet us consider the simple panel regression of one time variant explanatory variable, xt (capital
asset ratio in our empirical setup), and one time invariant instrumental variable, zi (REAL89 in ourempirical setup). We estimate the following regression by instrumental variable regression period by
period.
y it = xit β t + η i + u it where ηi is the fixed effect.
We assume that uit is independent of zi.
Since
−1
−1
−1
p lim βˆt = β t + Et [zi xit ] E [ziηi ], p lim( βˆ t − βˆ t −1 ) = ( β t − β t −1 ) + E t [z i x it ] − E t −1 [z i xit −1 ] E [z iη i ]{}The data show E 97 [z i xi ,97 ] < E 96 [z i xi ,96 ] . If E [z iη i ] < 0 , the second term in the right hand side at t=97^^

is negative. Therefore if this source of bias is large enough to reverse the order of the estimatedcoefficients’ magnitudes, βˆ 97 < βˆ 96 must result. The argument for t=98 is symmetrical.

34

Figure 1 Inflation rate

(%)
3.5
3
2.5
2
1.5
1
0.5
0
-0.51990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

-1
-1.5
The price measure is the consumer price index (excluding perishables) on nationwide statistics.

Figure 2 Domestic loan growth and capital asset ratio of domestically licensed
(%)

12

5.5

10
5

8
6

4.5

4
2

4

0
1990.07 1991.07 1992.07 1993.07 1994.07 1995.07 1996.07 1997.07 1998.07 1999.07 2000.073.5
-2
-4

3

-6
-8

2.5
Lending growth

Capital asset ratio

35

Figure 3 Target and actual CAPRFY 1997

FY 1998

FY 1999

36

Table 1 The influence of reserves on lending, regression results with the industry level data

Variable
y
y(-1)
y(-2)
y(-3)
y(-4)
m
m(-1)
m(-2)
m(-3)
m(-4)
F test (p value)

1994Q2 - 2001Q2
Randomeffect Fixed effect(MLE)-0.11751-0.17515(-0.71)(-0.96)0.17635
0.07958(1.41)(0.48)-0.33700**-0.24158**(-1.96)(-2.09)0.15284
0.05969(1.24)(0.37)
0.082750.04086(0.49)(0.23)
0.101180.10133(1.61)(1.57)-0.05060-0.04086(-0.82)(-0.78)0.10168
0.10454(1.37)(1.37)
0.006700.00816(0.11)(0.13)
0.082890.08671(1.28)(1.30)
0.16750.1654
4

The null hypothesis of F test is

∑m
j =0

t− j

1996Q1 - 2001Q2
Randomeffect Fixed effect(MLE)-0.21764-0.26415(-1.04)(-1.16)0.152280.07778(0.95)(0.39)-0.36063 **-0.43128**(-2.19)(-2.27)
0.195210.12410(1.24)(0.63)
0.06689
0.03635(0.32)(0.16)
0.13592*
0.135378*(1.87)(1.80)-0.05223-0.05144(-0.74)(-0.70)0.126940.12925(1.49)(1.45)
0.00558
0.00678(0.08)(0.09)
0.122510.12600(1.64)(1.61)
0.09150.0999=0

Table 2 Correlation coefficients of REAL89 and capital asset ratios (level variables)

CAPR
BIS
CAPRM

1994
0.24470.0186
0.03751995-0.0511-0.0816-0.0247

1996
0.0281-0.0434-0.0980

1997-0.25150.0371-0.347537

1998-0.1230-0.0326-0.2867

1999-0.0951-0.1230-0.16142000-0.2485-0.0868-0.3236

Table 3 Year by year coefficients on CAPR since the FY 1995
OLS

1995
1996
1997
1998
1999
2000

NOTRB
Lag
Cont.
0.389305941*(1.0341)-0.2313

MANUF
Lag
Cont.
-0.1951
0.1435(1.6789)(-0.2956)(0.2304)(-0.3907)**

0.01440.0426

1.7939(-0.7457)(-0.6206)(0.0281)(0.1043)(2.0859)

***

***

***

***

0.9555(2.6852)-0.1897-0.1532

NOTNM
Lag
Cont.
-0.3124
0.27651.70492.3082(5.4598)(4.0023)0.30770.39522.77822.5863(5.2731)*(2.4724)

1.1540***-1.3191(0.3629)0.7447(1.0707)4.5159***(4.8534)-0.1037(-0.4758)(0.8738)(0.7722)(2.7028)(-1.2806)(-0.1142)

0.6771*0.9870*0.48680.69092.8447***3.2006***(1.7099)(2.3161)(1.0816)(1.4113)(2.7903)(2.8519)-0.2630

0.10720.00950.57450.29770.3301(-0.6102)(0.2824)(0.0210)(1.4252)(0.2192)(0.2814)

2SLS

1995
1996
1997
1998
1999
2000

NOTRB
Lag
Cont.

MANUF
Lag
Cont.

NOTNM
Lag
Cont.

0.7001

0.48222.03980.9884-0.2592

2.2223(0.3769)(0.2269)(0.6426)(0.2589)(-0.0676)(0.4579)

1.61750.0678(0.2351)(0.0168)-0.18360.7890-2.9265(-0.0885)(0.5754)(-0.6928)

0.28422.7925(0.2550)(2.9852)

1.76873.5250****4.8777**(2.5641)6.6540-0.2012(-0.1080)

5.5042***(3.3538)

4.7152**-0.8185(-0.2344)-4.8418

6.3679**(2.4373)

6.7106(0.8320)(1.9824)(1.5832)(2.6393)(-0.9486)(1.3928)3.6444**4.6037*0.92671.53975.7412*2.2697(2.4546)(1.9803)(0.7089)

0.34140.91190.4476(0.2667)(0.9136)(0.3394)(0.7805)

0.9016(0.7887)(1.8948)2.4911(0.6569)(0.5252)

6.1826*(1.8882)

Cells at the bottom table with italic letters indicate that CAPR is negatively and significantly correlated with REAL89.
Cells at the bottom table with italic letters indicate that CAPR is negatively and significantly correlated with REAL89.
*** shows 1%, **, 5%, and *, 10%, respectively.

38

Table 4 Correlation coefficients of REAL89 and the distance between actual and desired capital ratios

CAPR
BIS
CAPRM

1994
-0.0096-0.2551-0.39401995-0.4607-0.2321-0.3255

1996-0.2767-0.2139-0.3740

1997-0.5345-0.1055-0.53921998-0.3443-0.1568-0.51051999-0.3214-0.2340-0.3149

2000-0.4358-0.1933-0.5102

Table 5-1 Year by year coefficients on the distance between actual and target CAPR ratios
OLS
NOTRB
Lag
Cont.
1995
1996
1997
1998
1999
2000

MANUF
Lag
Cont.

3.11171.8926**

5.9431(1.4425)(2.2517)(1.6053)

NOTNM
Lag
Cont.

2.7331*(1.8689)

6.38774.3631**(1.3920)(2.4024)0.16300.02991.53390.5644(0.2164)(0.0720)(1.2443)(0.8242)(1.8615)(0.0534)

2.5916***2.7034***4.6387***3.5159***5.0866***

6.1439***(4.5467)(7.3160)(4.9095)(5.2271)(2.9315)(5.2850)**

0.6116

0.9486(1.1238)(2.3796)******

1.2463(2.7560)-0.0807(-0.1668)

1.5436(3.3693)0.2924(0.7292)

0.8160(1.2681)0.8764*(1.7262)*0.8442(1.7278)

1.50173.9105****(3.2350)

1.0820**1.23950.06291.8368*(0.9289)(1.8395)

1.86881.7384(2.0559)(1.5700)(1.3953)1.2409(3.0320)0.9464(0.6653)

0.7719(0.6493)

2SLS
Lag
1995
1996
1997
1998
1999
2000

NOTRB
Cont.

-4.6343-0.7305

MANUF
Lag
Cont.
16.3497-1.6173(-0.3864)(-0.3051)(0.9723)(-0.3794)-1.61590.34485.58863.0451(-0.7352)(0.2271)

4.5115**(2.3562)

2.1365(1.2144)2.5871

**

NOTNM
Lag
Cont.
-4.0994(-0.2480)-3.55913.5128(0.6069)-2.0894(1.4239)(1.2147)(-0.6131)(-0.5302)

3.9885*** 12.3775***

6.1146***4.30039.1686***(4.4082)(3.5254)(3.5618)(0.7476)(3.2906)

2.7277**3.6816

3.6378**5.74999.6862**(2.2862)(1.4249)(2.5574)(1.0688)(2.3266)

1.86000.76080.8394(2.3899)(1.5108)(0.6374)(0.5741)0.41650.85540.62581.0091(0.4386)(1.0096)(0.6680)(1.1012)2.3607(0.8584)

2.4694(0.9846)-2.7237(-0.7951)

4.4350*(1.9501)

Cells at the bottom table with italic letters indicate that CAPR is negatively and significantly correlated with REAL89.
*** shows 1%, **, 5%, and *, 10%, respectively.

39

Table 5-2 Regression results of equation (3) with CAPRFY 1997

Constant
Lagged dependent
variable

NOTRB
Lag
Cont.

MANUF
Lag
Cont.

NOTNM
Lag
Cont.

0.0130**-0.0238*0.0304(2.0194)

0.4196***(3.3086)

0.0257***(3.8910)0.4440***(3.8621)(-1.9634)-0.2420-0.0068(-0.5798)-0.2101(-1.5482)(-1.5989)

CAPR(2.3562)(4.4082)(3.5254)(3.5618)

Large bank dummy

0.02310.03420.07230.0609Trust bank dummy(0.7120)(1.3259)-0.0006(-0.5802)

Regional bank dummy

0.0018

J statistics

12.9991(-0.0218)-0.0028(0.1846)(0.0431)(1.2353)

0.1096*(1.6762)-0.1234

4.5115**-0.0202

3.9885*** 12.3775***

0.0611***

6.1146***(3.1000)-0.0432(-0.9206)

4.3003(0.7476)-0.0962(-0.3325)

9.1686***(3.2906)

0.0151(1.2083)(-0.9348)(0.1736)0.0585-0.11130.0051(1.7326)(1.2141)0.00820.0138(-1.1242)-0.0122(0.0619)-0.0356(-0.3238)(0.4225)(0.8429)(-0.4062)(-1.2931)

3.21903.04718.59532.39663.4398(0.7809)(0.8029)(0.1977)(0.8799)(0.7520)

FY 1998
NOTRB
Lag
Cont.

MANUF
Lag
Cont.

NOTNM
Lag
Cont.

Constant

0.01970.0182**(1.5729)(2.0735)

Lagged dependent
variable

0.15940.1138(0.9242)(0.8049)(-0.9724)(-0.9710)

CAPR

2.13652.72773.6816

3.63785.74999.6862*:(1.2144)(2.2862)(2.3266)

Large bank dummy
Trust bank dummy

0.0493-0.0607***(0.0637)-0.0930(-1.0113)-0.12990.0015(1.6558)-0.0136***

0.0567**(-1.9903)(1.7041)(2.2252)-0.0540-0.1346(-0.3448)(-0.9512)**(1.4249)(2.5574)(1.0688)0.12940.0582-0.0202-0.1457**(-2.0903)****(2.6618)(2.0610)(-0.2053)

0.0766

0.0260-0.0800-0.1567**(1.3073)(0.8505)(-0.7709)(-2.2588)(-3.5970)

Regional bank dummy-0.0225-0.0251(-1.6171)(-1.9535)(-0.5038)

J statistics

11.7720

7.11449.8956(0.3104)(0.1291)(0.0673)

0.0566*-0.0886(-1.9643)*-0.0203**-0.0094**-0.1077**(-0.5726)(-2.0862)(-2.6449)

10.11715.4541

5.5215(0.4870)(0.4789)-0.0093(0.1198)-0.0884

FY 1999
Lag

NOTRB
Cont.

Constant

0.0028Lagged dependent
variable

0.2765**(0.2957)(2.0841)-0.0114(-1.2344)

0.3669**

MANUF
Lag
Cont.

Lag

NOTNM
Cont.

-0.0255**-0.0299**-0.0098(-2.4243)(-2.3611)

0.3427***(-0.3873)(-0.2993)-0.1316-0.0783

0.3332**-0.0081(3.1410)(2.5925)(2.7769)(-1.0556)(-0.6259)

1.86000.76080.83942.3607-2.7237

CAPR

2.5871**(2.3899)(1.5108)

Large bank dummy

0.0808**(-0.7951)

0.02530.0815**(0.8584)

0.02330.10600.1196(0.7728)(0.8908)(2.3191)(2.3140)(1.2323)(1.3415)

Trust bank dummy-0.04190.00380.0062-0.0109-0.0289

Regional bank dummy-0.0195(-1.3366)

J statistics

11.0862(0.0857)(0.0104)(-1.3789)-0.0315(-1.0903)(0.6374)-0.0364**-0.0338**(-0.1475)(-0.3698)-0.0401-0.0194(-0.5851)(-2.3747)(-2.3563)(-1.0130)(-0.5066)

16.72036.73706.75508.588310.8697(0.3459)(0.3441)(0.1981)-0.0073(0.1280)(0.5741)(0.1950)(0.0925)

Cells at the bottom table with italic letters indicate that CAPR is negatively and significantly correlated with REAL89.
*** shows 1%, **, 5%, and *, 10%, respectively, and numbers shown in parentheses below J statistics are p-values.
40Table 5-3 Partial squared correlation coefficients

1995
1996
1997
1998
1999
2000

NOTRB
Lag
Cont.

MANUF
Lag
Cont.

NOTNM
Lag
Cont.

0.05050.11270.10870.0776
0.09890.26480.03680.1292
0.09530.10370.11590.26110.07810.15140.08870.06490.10910.32170.12230.08640.16830.13490.04340.19380.13590.07740.18120.1346
0.05690.2276

0.0961
0.09860.17630.0871
0.05890.2899

Table 5-4 Results of the fixed effect estimation of equation (7)NOTRB
Lagged dependent variable
D97CAPR
D98CAPR
D99CAPR
D98
Observations
J statistics

MANUF

NOTNM

Lag

Cont.

Lag

Cont.

Lag

Cont.

-0.1086

0.13350.0156-0.1468-0.0950-0.0278(-0.4843)(0.5532)*****

6.5261(2.1715)

1.2446(0.4626)

0.8230(0.3466)

0.1946**(2.0493)

5.6670(2.7599)-0.1310(-0.0563)-0.3415(0.0945)

10.4791*(1.7283)-4.5492(-0.8789)-4.8133(-0.1629)(-1.0344)

0.1906*0.5471**(1.8159)(2.1780)(-1.5675)***

10.4689(3.6998)

5.8631*(1.7362)4.6860(1.5133)

0.1318(0.8041)(-0.7085)

4.9835(0.5393)-3.9959(-0.6675)-3.8177(-0.7148)0.3052(0.9081)(-0.1583)

16.6790**(2.4302)-0.9960(-0.1173)-1.1888(-0.1537)

0.5956(1.4031)252
6.0354252
1.8178252
5.8431252
7.4343252
2.6548252
3.17130.30280.87370.32180.19030.75300.673641

Table 6-1 Year by year coefficients on the distance between actual and desired BIS ratios
NOTRB
Lag
Cont.
1995
1996
1997
1998
1999
2000

-1.2880(-0.1159)-0.9350(-0.7037)

6.4798*(1.9544)2.0713(0.9602)2.2806***(2.6681)0.2275(0.4057)

MANUF
Lag
Cont.

-0.861123.7172(-0.6101)-0.8996(-0.8908)(-0.7430)(-0.9051)

1.1874-5.2090(0.8153)(0.8757)(-1.3123)**

5.5378(2.0093)7.6596(2.2372)(1.4958)

1.34851.3096(1.5545)0.6818(0.8314)-5.0820(1.3874)(1.6077)

1.5393-23.2494

1.75971.8096**-1.8901

NOTNM
Lag
Cont.

(1.3157)0.3312(0.6369)

1.5959(0.6043)2.7031***(3.0631)0.7898(0.7867)

0.6767(0.8727)17.1556(-1.5475)-2.8174*(-1.0099)

9.8893(1.7993)(1.5864)0.2987

4.2528**(0.0501)1.1858(1.9868)-1.8280(0.6348)(-0.8435)

2.10144.0075*(1.4518)(1.9191)

Table 6-2 Year by year coefficients on the distance between actual and desired CAPRM ratios

Lag
1995
1996
1997
1998
1999
2000

NOTRB
Cont.

-5.1154(-1.2992)-0.2959-2.3156*(-1.7200)-4.0616(-0.7338)-0.8450-10.1156-5.9153**(-0.4627)(-1.3233)(-2.0892)

1.6414-3.2084-3.0940(-0.2581)(-0.2144)(1.18169)(-1.3994)(-1.2890)

***

***

***

***

*

5.9576***(3.3564)

1.05512.6700-0.2571

NOTNM
Lag
Cont.

(-0.4065)

4.1685-0.2054

MANUF
Lag
Cont.

7.23094.0634

7.0660(4.5396)(3.5457)(3.6669)(1.9178)(3.1200)

1.4821**0.9333

1.6617**2.3332

5.1633**(1.0516)(2.0453)(0.7674)

1.5086**

0.97990.3140-0.0859(2.0213)0.3895(0.9766)(2.5024)(2.1586)(1.4708)(0.4133)(-0.1261)(0.2166)(-0.4268)

0.27420.27810.43750.39042.60392.1791(0.4015)(0.4863)(0.6195)(0.6233)(1.2131)(1.2319)-0.7325

Cells with italic letters indicate that differences between capital asset ratio and its desired levels are negatively andsignificantly correlated with REAL89.
*** shows 1%, **, 5%, and *, 10%, respectively.

42

Table 7-1 Year by year coefficients on the book based capital surplus measure, regional banks
NOTRB
Lag
Cont.
1995
1996
1997
1998
1999
2000

4.5859(0.6001)-1.1297

3.0071*24.0536*(1.8341)-0.5654(-0.7345)(-0.6241)

4.5970***3.3836***(6.0502)

2.1982(1.2290)

1.1228(1.6798)-0.9152(-0.9170)

MANUF
Lag
Cont.

(7.2131)2.8101*(1.7629)0.8013(1.0729)-0.4589(-0.4144)

NOTNM
Lag
Cont.

1.37942.26627.4764**(1.8561)(0.4697)(0.1647)(2.1618)

0.23440.63035.4119(0.1068)(0.4490)(1.4886)(0.3823)5.8640*** 4.0976***

7.7449**6.4116***(3.8921)-0.5405(-0.3952)

1.7190*(1.8978)-0.2561(-0.2994)

0.9829(3.9628)(2.4217)(3.1778)-0.0835

11.3092***

8.4511***(-0.0756)(3.0981)(3.3579)

2.5032**

3.5083

2.1911(2.5473)(1.4816)(0.8130)

0.68601.82273.7935(0.6919)(0.4674)(0.8397)

Table 7-2 Year by year coefficients on the book based capital surplus measures, regional 2 banks
NOTRB
Lag
Cont.
1995
1996
1997
1998
1999
2000

MANUF
Lag
Cont.
-2.9487

NOTNM
Lag
Cont.

-1.5858-0.1850-3.9529

24.6967-0.4540(-0.1518)(-0.0509)(-0.2302)(-0.4718)(1.0757)(-0.0519)

0.31690.54894.0102

1.00681.62161.4291(0.1253)(0.5132)(0.7660)(0.4421)(0.1749)(0.3664)2.66222.1666**2.22994.3635***-3.3035

1.8580(0.9549)(2.4839)(0.5693)(2.9290)(-0.4264)(0.5451)

0.33880.08680.06300.0485-1.1813-0.5111(0.3326)(0.0924)(0.0549)(0.0381)(-0.4533)(-0.1994)-0.96110.31290.14211.1614-4.5208-7.1490(-0.9471)(0.2427)(0.1118)(0.7213)(-1.5053)(-1.5584)-0.1649

0.14581.52811.2129-0.6743-0.1052(-0.1475)(0.2190)(0.9814)(1.2431)(-0.2400)(-0.0633)

Cells with italic letters indicate that differences between capital asset ratio and its desired levels are negatively andsignificantly correlated with REAL89. *** shows 1%, **, 5%, and *, 10%, respectively.

43

Table 8 Aggregate lending growth, all banks

1995
1996
1997
1998
1999
2000

NOTRB

MANUF

NOTNM

TROUB

REALE

1.75
-1.19
-0.70
0.56
1.29
0.04

-2.41
-4.69
-1.92
1.18
1.27
-1.64

-1.10
-4.65
-3.92
-5.41
4.08
-5.00

-0.15
-0.32
-0.66
-1.97
-2.18
-2.57

1.30
2.32
3.12
-1.44
-0.25
-1.71

Table 9 Aggregate capital shocks to bank lending supply (all 126 banks)
CAPR

1997
1998
1999

NOTRB
Lag
Cont.
**
-1.87
-3.72***-1.94
1.07**
**
1.05
1.27*

MANUF
Lag
Cont.
***
-5.13
-5.70***-3.34
1.43***
0.31
0.57

NOTNM
Lag
Cont.
-1.78
-8.54***-5.22
3.82**
0.96
-1.85

1997
1998
1999

NOTRB
Lag
Cont.
*
-1.42
0.37
0.45
2.76**
4.12***
3.04

MANUF
Lag
Cont.
**
-1.22
0.33
1.67
4.85***
2.37
1.78

NOTNM
Lag
Cont.
*
-3.77
2.02
0.07
7.64**
2.14
-4.12

NOTRB
Lag
Cont.
***
-5.18
-7.15***-2.79-2.08**
**
-2.10
-0.30

MANUF
Lag
Cont.
***
-8.98
-10.88***-2.46-2.33**
-0.44
0.03

NOTNM
Lag
Cont.
*
-8.77
-15.94***-6.16-7.23**
-0.54
0.23

BIS

CAPRM

1997
1998
1999

44