GCOE Discussion Paper Series
Global COE Program

Human Behavior and Socioeconomic Dynamics
Discussion Paper No.11

Reluctance to Lend and Its Spatial Differences inJapan’s Lost Decade: What Can We Learn from It?

Daisuke Ishikawa and Yoshiro Tsutsui

October 2008; revised February 2011

GCOE Secretariat
Graduate School of Economics

OSAKA UNIVERSITY

1-7 Machikaneyama, Toyonaka, Osaka, 560-0043, JapanReluctance to Lend and Its Spatial Differences inJapan’s Lost Decade: What Can We Learn from It?∗
Daisuke Ishikawa (Ministry of Finance, Japan)
Yoshiro Tsutsui (Osaka University)

Abstract
This paper aims to elucidate whether supply side factors played a crucial role in causing the“reluctance to lend” in Japan in the 1990s. We estimate loan supply and demand functions using
prefectural panel data and calculate their shifts. Our analysis reveals that the demand sideplayed an equal or greater role, even in the context of the historical financial crisis in Japan
during the period 1997–2000, and that the reluctance to lend was severer in the urban
prefectures. These findings suggest that countermeasures in the banking sector that uniformly
influence all regions may not achieve the expected recovery.

JEL classification numbers: G21, E51, R51
Keywords: credit crunch, prefectural panel data, shift of functions, Japanese loan market

Please address all correspondence to:
Yoshiro Tsutsui
Graduate School of Economics, Osaka University
1-7 Machikaneyama, Toyonaka, Osaka 560-0043, Japan
Phone: 06-6850-5223, Fax: 06-6850-5274
E-mail: tsutsui@econ.osaka-u.ac.jp∗

We are grateful to Masaru Yoshitomi, Katsumi Matsuura, and to the participants of the RIETI
seminar and the Annual Meeting of Japan Society of Monetary Economics for their comments.
This study is financially supported by the Zengin Foundation for Studies on Economics andFinance. An earlier version of this paper was presented at the RIETI seminar and at the Annual
Meeting of Japan Society of Monetary Economics. The views expressed in this paper are thoseof the authors and do not necessarily reflect the views of the Ministry of Finance, Japan.

1. Introduction
Since 2007, subprime loan problems have hit financial institutions worldwide. Manyof these institutions have reported huge losses and financial troubles. Among them, in the
United States, Lehman Brothers filed for bankruptcy in September 2008, Bear Sterns was soldto JP Morgan Chase in March 2008 owing to pressure from the Federal Reserve Bank, and there
was a run on Indy Mac in July 2008. The possibility of a credit crunch has been discussed since
the summer of 2007 (e.g., The Japan Times, Oct. 13, 2007). Indeed, bank credit and loans andleases in bank credit in the US, which had been increasing almost monotonically for years,
started decreasing in April 2008 (see Figure 1). The purpose of this paper is to identify what canbe learned from the Japanese banking crisis of the 1990s.
The Japanese loan market was in a severe slump from 1990 to 2003, which isconsidered a significant historical event. This period of financial crisis might gain new
significance, however, if lessons can be gleaned from it. Figure 2 shows the level of bank credit
in Japan and the US. The solid line in the figure represents the amount of loans advanced bydomestically licensed banks in Japan since 1944. The path of this line indicates that the 1990s
was the first decade in postwar Japan to experience a significant decline in the amount of loans
being advanced. A closer inspection of Figure 2 reveals that lending growth actually becamesluggish around 1993 but still continued to increase until around 1998. Then the amount ofloans began to decrease until 2004; however, the graph shows a recovery after 2005. The dottedline in Figure 2 represents the bank credit of commercial banks in the US since 1973, whichshows a monotonic upward trend. However, as shown in Figure 1, this trend became stagnant
after April 2008, suggesting that the United States has been facing a sharp decline in outstandingloans since July 2008. Thus, it is important for us to ask what we can learn from the nightmarish
events of financial distress in Japan in the 1990s, and how can this knowledge benefit thecurrent lending issues in the US.
When Japan faced this remarkable stagnancy of loans in the 1990s, it was said that1

firms, especially small and medium sized enterprises (SMEs) were in trouble because theirapplications for loans had been refused by banks. The general opinion, including that of the Diet,
the government, and the media, was that the banks were to blame and that the problem stemmed
from the banks’ reluctance to advance loans.
Some economists attribute the Japanese depression of the 1990s to the fall in stock
prices, which decreased bank assets and, in turn, impaired their capital. Therefore, banks werecompelled to compress loans in order to clear regulations by the Bank for International
Settlements (BIS). A fall in land prices may have been another cause: it decreased the values ofcollateral and banks suffered losses due to the bankruptcy of their borrowers. Thus, Japan fell
into a vicious circle: an increase in the bankruptcy of borrowers together with a fall in land
prices led to a rise in nonperforming loans, which caused the banks to reduce lending to firms(i.e., it led to a credit crunch). Conversely, the credit crunch reduced borrower options and thus
contributed toward an increase in the number of bankruptcies, thereby worsening the Japanese
economy. In order to end this vicious circle, the Japanese government injected a huge amount ofpublic funds into banks in 1998 and in 1999.
However, it is important to recognize that the problem is not that simple. Because theJapanese economy was in a serious slump and the loan interest rate was falling throughout the
1990s, the decrease in the amount of loans may have come from the demand side, and not fromthe supply side. 1 If this was the case, it would have been necessary to increase the demand forloans by raising firms’ effective demand to revitalize the slumped Japanese economy. A measure
to support banks, such as injection of public funds, could only have a limited effect on raising
the loan amount. 2
This paper aims to elucidate which side, supply or demand, caused a decrease in loans
in Japan in the 1990s, with a special focus on its spatial differences. As explained in the next
1

Declining loan interest rate is derived only from a left shift in the demand function or a right shift
in the supply function.
2
Still, it helps banks to amortize their bad loans and to escape from their failures.
2

section, many of the previous studies on the credit crunch in Japan have focused on the effectsof banks’ own capital ratios and nonperforming loans on the supply of loans. The analysis inthese studies identifies how the supply side is affected by these factors, but they exclusively
consider the supply side; it may still be true that raising the demand for loans is the key torecovery. This paper is unique in that it estimates both the loan supply and demand functions
and calculates their shifts to reveal which side and which factors primarily caused the decreasein loans. In addition, using a prefectural panel data set, this paper tries to clarify whether thecredit crunch—or “reluctance to lend”—hit the Japanese economy uniformly or whether it
affected only specific regions.
The rest of the paper is organized as follows. In the next section, we survey theliterature. In section 3, we explain the analytical framework, specification of our model,
estimation method, and data. In section 4, we present and discuss the results. Section 5
concludes the article.

2. An overview of the literature
There have been many studies on the credit crunch or reluctance to lend in Japan.
Most of them focus on bank behavior (i.e., the supply side of loans). These studies have tried to
explain the stagnant loan market of the 1990s by focusing mostly on the fall in land prices, therise in bad loans, and the regulation of banks’ own capital ratios.
Ogawa and Kitasaka (2000) found that nonperforming loans had a negative effect onloan supply in the 1990s and banks’ own capital ratios had a positive effect. In other words, theirstudy supports the existence of the credit crunch. Conversely, Yoshikawa et al. (1994) found that
there was no significant negative correlation between nonperforming loan ratios and loan supply
in the early 1990s; therefore, they denied the occurrence of the credit crunch during that period.
In many other studies, the results are mixed. For example, Woo (1999) conducted across-sectional analysis using bank financial data from 1991 to 1997 in order to clarify the3effect of bank’s own capital ratios on loans, and reported that they had significant effects in
1997, but not in the other periods. Motonishi and Yoshikawa (1999) analyzed the fall in
corporate investment in the 1990s and found that it was largely explained by real factors in
1992–1994, but it was triggered by financial factors in 1997. Horie (2001), Ishikawa (2005),
and Watanabe (2007) obtained similar results. These studies suggest that the credit crunch wassevere in the late 1990s, but not in the early 1990s. Ito and Sasaki (2002) tested whether
increase in bank capital, which was financed by subordinate debentures, affected the loan supply.
They found that banks’ own capital ratios had positive effects on loan supply in larger banks but
not in smaller banks. Honda et al. (1995) obtained similar results. These studies also suggestthat the credit crunch was severer in larger banks than in smaller banks.
Some studies focused on banks’ excessive forbearance: banks advanced additionalloans to sluggish industries, such as construction and real estate, in order to postpone theirfailures. Peek and Rosengren (2005) confirmed empirically that the banks practiced forbearance
lending, particularly if a bank had a reported capital ratio close to its required capital ratio andparticularly to affiliated borrowers. Caballero et al. (2008) identified zombie firms—insolvent
borrowers surviving on forbearance lending by banks—and found that zombie-dominatedindustries exhibited more depressed job creation and lower productivity. The works of Sasaki(1998) and Tsuru (2001) are in line with this study.

3. Analytical Framework
3.1

Basic idea of the analysis
As evident in section 2, most of the previous studies on the Japanese loan market

focused on the effects of BIS regulations and nonperforming loans on the supply of loans.
However, this single estimation of the supply function may suffer from simultaneous biases. In
order to identify the supply side function, it is necessary to take into account the demand side,which is carefully considered in this paper.
4

Of course, some studies have tried to control the demand side of loans in a variety ofways. For example, Berger and Udell (1994) adopted a reduced-form approach by adding
demand-side variables, such as proxies for economic activities, into the reduced form equation.
Peek and Rosengren (1995) controlled for the demand side in a study on credit supply in New
England. However, it is still the case that such controls are not perfect and usually do notadequately isolate the supply shocks from the demand shocks. Therefore, our approach of
estimating both the loan supply and the demand functions is unique and may be superior toother widespread approaches.
Another problem that has seldom been addressed in previous studies is how to define
credit crunch. Bernanke and Lown (1991) defined credit crunch as a shift in the supply function
due to some exogenous shock, and most of the previous studies adhere to this definition. 3
However, this definition does not seem perfect—as argued below, it sometimes misclassifies
counterintuitive cases as cases of credit crunch. One of the aims of this paper is to propose a
new measure for credit crunch, or “reluctance to lend” (“Kashishibui” in Japanese), to address
this issue.
In most cases, the cause of a reduction in the size of credit may be shrinkage in the
demand for credit as well as shrinkage in the supply. In that regard, it is important to understandthe extent to which the supply side is responsible. Even if previous studies found a negative
effect of BIS regulations on loans, they at best confirmed a necessary condition for the situation
where the supply side is the main cause. It appeals to our intuition to call “credit crunch” thecase where supply side factors are the main cause for shrinkage in loans. Thus, we intend toinvestigate whether the supply side is the dominant cause for a decrease in loans or whether it is3

An alternative definition of credit crunch is a phenomenon that results from banks refusing loan
applications by firms, that is, credit rationing. As for the equilibrium credit rationing, although muchhas been written in the field of banking theory—for example, Jaffee and Russell (1976) and Stiglitz
and Weiss (1981)—a method to estimate its magnitude has not been adequately developed. In
addition, a change in magnitude of the equilibrium credit rationing should be associated with a shift
in supply and demand loan functions. Moreover, according to Fair and Jaffee (1972), the magnitudeof the disequilibrium credit rationing is relatively small after the 1990s, as noted in footnote 5.
5the demand side. This can be quantified by the difference between shifts in the supply anddemand functions.
It is possible to interpret our measure based on the attitude of banks towards advancing
loans. When the loan supply function shifts to the left more than the loan demand function,
borrowers face banks that have a stricter attitude towards advancing loans, and vice versa. To
understand this, suppose that a bank’s supply schedule shrinks slightly and the demand for loans
declines simultaneously, largely owing to an exogenous shock. In this case, a large excess
supply emerges until the interest rate is adjusted, which implies that the lender cannot find aborrower unless they soften their lending attitude. At the equilibrium, the interest rate falls (i.e.,banks offer more generous conditions to find a borrower). In this case, according to theconventional definition, a credit crunch is said to have occurred. In contrast, by our measure,there has been no occurrence of credit crunch.
Despite the merit of our measure, we do not use the term “credit crunch” from thispoint onward to avoid unnecessary confusion with the conventional definition of credit crunch.
Instead, we use the term “reluctance to lend” for our concept, to indicate that we measure adifferent concept. 4
In this paper, we estimate the loan supply and demand functions assuming that theloan market is in equilibrium. The assumption of equilibrium seems reasonable owing to thefact that several studies suggest that the Japanese loan market became increasingly competitive
after 1973. 5

3.2

Hypothesis of the segmented loan markets by prefecture
In this paper, we utilize prefectural panel data from 1990 to 2001. We use a prefectural

4

The English term “credit crunch” is indicated by the word Kashisiburi in Japanese, which literallymeans “reluctance to lend.”
5
Tsutsui (1982) reports that the excess demand for loans became smaller after 1973. Takenaka(1983) reports that the speed of adjustment became increasingly faster. Tsutsui (2005) reports thatthe largest excess demand (supply) was about 5% (2%) of outstanding loans in the 1990s, while it
was about 15% (5%) before 1977.
6

data set because we understand the loan markets in Japan to be segmented by prefecture: theamount of loans and the loan interest rate within a prefecture are determined by the demand andthe supply of loans in the prefecture.
This assumption may be supported because regional banks, shinkin banks (cooperativebanks), and credit cooperatives operate in restricted areas. Of course, this segmentation, if itexists, is not perfect. First, city banks have nationwide branch networks, so they engage inarbitrage between the prefectural loan markets. Second, regional banks have 19% of theirbranches outside the home prefecture, so “prefectural loan markets” actually overlap. Using
cross-sectional data from 1996, Kano and Tsutsui (2003) demonstrate that the loan markets ofshinkin banks were segmented by prefectures, while those of regional banks were not. However,this time, we may obtain different results because we use the panel data set from 1990 to 2001.
In this paper, we estimate the loan supply and demand functions assuming that the loan markets
are segmented by prefecture. If this assumption is not valid, the estimates of these functions
would be unreasonable. However, if the estimated coefficients of loan interest rate and otherprefecture-specific variables show the expected signs, the assumption of market segmentation isvalidated.

3.3

Specification of the loan supply function
We derive a loan supply function from an inter-temporal profit maximization problem

of a bank (see Ishikawa (2005), Ogawa and Kitasaka (2000), and Elyasiani et al. (1995)). First,
we define profit π tk of the k -th bank operating in prefecture i in year t as

π tk = rit ( Lit )ltk + rtc cltk − rt d d tk − C (ltk , fltk ) ,(1)

where, rit ( Lit ) , ltk , rt c , cltk , rt d , d tk , fltk are the loan interest rate, amount of loan

7

outstanding, call rate, amount of the call loan, deposit interest rate, amount of the deposit, andnet increase in the loan ( fltk ≡ ltk − ltk−1 ), respectively. Because a few banks compete within aprefecture, we assume an imperfect loan market so that the loan interest rate rit ( Lit ) depends
on the aggregate of loans in prefecture i, Lit ≡∑ l . We also assume that the loan andktk∈ prefecture i

deposit interest rates are common for banks in the same prefecture. 6 The call rate is the sameacross all prefectures. The operational cost function of a bank is C (ltk , fltk ) , where we assume

Cl > 0 , Cll ≥ 0 , C fl > 0 , and C fl , fl ≥ 0 . Note that C fl > 0 reflects a positive search cost
to obtain new customers and a positive monitoring cost to mitigate asymmetric information
between new customers and the bank.
The k -th bank aims to maximize its firm value Vt k under the balance sheet constraint

ltk + cltk = d tk + captk .

Max V

k

t

{ltk , cltk } ∞= Et ∑ γ pπ tk+ p  .
 p =0s.t.

ltk + cltk = d tk + captk(2)

To simplify the model, we assume that d tk and captk are exogenous. 7 Further, Et is
the operator of conditional expectation based on the information available in year t , γ(0<γ<1)
is the time discount rate. The first order condition becomesγEt [∂Ct +1∂L∂C ∂C
1
S kt kit ) − rtc = kt + kt ,] + rit (1 −kη it∂flt +1
∂lt
∂flt
∂lt

6

(3)

Although inspection of the data of each bank reveals this assumption does not rigorously hold, theframework of prefectural segmented markets implies the existence of a market interest rate for eachprefecture.
7
In Japan, since the regulation on deposit interest rates was liberalized in 1993, the amount ofdeposits may be treated as a decision variable. Even in this case, however, the results of thefollowing analysis are in essence not altered.
8r dLitis the loan supply elasticity to the loan interest rate in prefecture i, and
Lit drit

where η it ≡ − itltk
is bank k’s market share in prefecture i.
S kt ≡
Lit
Taking a linear approximation of the cost function and solving equation (3) for ltk yields∂Lrltk =α 0 + α1 Et [ltk+1 ] + α 2ltk−1 + α 3 (rir − rtr ) − α 3 it S kt kit + α 4 LPit
ηit
∂lt

,

(4)

+ α 5badtk + α 6 captk + α 7 SPt + α 8CITYBANKSit + α 9 DENSITYit

where we add land price ( LPit ), nonperforming loans ( bad tk ), bank’s own capital ( captk ),stock market price index ( SPt ), the dominance ratio of the number of branches of city banks tothat of all types of banks (city, regional, and second regional banks) in prefecture i( CITYBANKSit ), and the population density ( DENSITYit ) to the explanatory variables. We
assumethat

α 1 ≡ γ (C fl , fl + C fl ,l ) / δ > 0,

α 2 ≡ (C fl , fl + C fl ,l ) / δ > 0,

α 3 ≡ 1/ δ > 0,

8,9
α 4 > 0, α 5 < 0, α 6 > 0, α 9 > 0, and δ ≡ C fl , fl (1 + γ ) + 2C fl ,l + Cll > 0 .

Here, α1 > 0 captures a smoothing effect: When loans are expected to increase tomorrow, abank will increase loans today to save operational costs. This type of formulation of
forward-looking behavior is typical of Japanese banks, which has been empirically supported by
Ogawa and Kitasaka (2000). In addition, α 2 > 0 captures an inertia effect: if loans increased
yesterday, which can lower the monitoring costs today, a bank will increase loans today because
8

The population density is included in the operational cost function and, in turn, in the loan supply
function, because it is less costly for bank staffs in higher density areas to visit their customers.
9
If C fl ,l was a large negative value, δ became negative, thus yielding a negative α 3 . However,a negative sign on α 3 is not likely because it implies that a bank will increase its lending as theloan interest rate rises. Therefore, we postulate C fl ,l does not take a large negative value.
9

it can take advantage of the lowered costs. 10 We expect α 4 > 0 because a decrease in land
price lowers the value of land held by a bank, leading to the impairment of capital and therefore
a decrease in loans. A decrease in land price also lowers the collateral value of land, which may
increase the screening costs of loan provisions, leading to a decrease in loans. We expect α 5 < 0and α 6 > 0 because an increase in nonperforming loans and a decrease in own capital will
result in a rise in the probability of a bank’s default, leading to a reduction in the advancementof risky loans. However, α 5 > 0 is possible, which represents the case of forbearance lending(i.e., the advance of more loans by banks to troubled firms in order to postpone theirbankruptcy) (Berglof and Roland (1995, 1997)).
The sign of α7 is not known a priori because a bank, faced with a blip in the stock
market price index, may increase investment in stocks, consequently leading to a reduction ofloans. Conversely, a rise in the index makes the value of owned stocks higher, consequently
leading to an increase in capital, and therefore an increase in loans. The sign of α 8 is also not
known: if bank k is as competitive as the city banks, it can increase its loans, but if it is not ascompetitive, it will be overwhelmed by the competition and decrease its loans. We expect
α 9 > 0 because a bank faced with high population density can expand business easily and will

increase its loans.
Loan supply function for prefecture i in year t is derived by aggregating equation(4). By denoting the number of banks in prefecture i as I , we obtain

LSit =α 0 + α1 Et [ Lit +1 ] + α 2 Lit −1 + α 3 ( rit − rtc ) + α 4 LPit + α 5 BADit + α 6CAPit+ α 7 SPt + α 8CITYBANKSit + α 9 DENSITYit + α10 HI it + ν iS + ε itS

10

,

(5)

Behavioral equations of forward-looking economic agents, which include both the lead and lag ofthe dependent variable, are often seen in recent macroeconomic literature, such as the hybrid New
Keynesian Phillips curve (Gali, Gertler, and Lopez (2005)).
10

where Lit ≡∑lktk∈ prefecture i, BAD ≡
it∑ badk∈ prefecture ikt, CAPit ≡ ∑ captk . We redefine α k Iask

α k , k = 3, 4, 7, 8, 9 . Moreover, ν iS is an individual effect of prefecture i and ε itS
represents an i.i.d. disturbance term with E[ε itS ] = 0 . Following Kano and Tsutsui (2003), we
assume that

rit
∂L
2
( S kt kit ) is associated with the Herfindahl index, HI it ≡ ∑ S kt .
∑
ηit k∈prefecture i∂ltk∈ prefecture i

If the market structure-performance hypothesis is valid, then α10 < 0 .

3.4

Specification of the loan demand function
We assume that loan demand increases as loan interest rates decline, sales increase,retained earnings decrease, population density increases, and the costs of alternative funds (e.g.,bonds and equities) increase (Me’litz and Pardue (1973), Fase (1995), Ogawa and Suzuki(2000)). We also assume that land prices and the stock market price index can affect loan
demand. An increase in land prices boosts the value of real estate owned by firms, lowers theprobability that firms will default, and helps to increase investments, which may augment loan
demand. There are two ways to look at the effects of the stock market price index on loan
demand. Firms faced with a climb of the index may increase funds by issuing stocks, which
would lead to a reduction of loan demand. On the other hand, a rise of the index can be viewed
as better profitability in the future, which may increase loan demand. The dominance ratio of
SMEs in prefecture i, (defined as the number of employees of firms that have 4 to 99 staff
members in prefecture i / the number of employees of firms that have more than 4 staff
members in prefecture i), can be an explanatory variable in the loan demand equation. Since theSMEs have difficulty in raising funds directly in the financial markets, they are morebank-dependent: the higher the dominance ratio of the SMEs, the stronger the bank loan
demand. In addition, the number of housing starts may be an important explanatory variable in
the loan demand equation, because people may borrow funds in order to buy houses.

11When a firm or a household borrows funds from a new bank, they may incur
additional search costs, as is the case with the loan supply. Thus, the loan demand also depends
on its past and expected values. As the data of retained earnings by prefectures are not available,
we specify the loan demand function as follows.

LDit =β 0 + β1 Et [ Lit +1 ] + β 2 Lit −1 + β3 rit + β 4 SALEit + β5 HOUSEit+ β 6 LPit + β 7 SPt + β8 rtb + β9 SMEit + β10 DENSITYit + ν iD + ε itD,(6)where SALEit is shipment of products, HOUSEit is the number of housing starts, rtb is theinterest rate of government bonds, SMEit is the dominance ratio of SMEs, ν iD is anindividual effect of prefecture i , and ε itD represents an i.i.d. disturbance term withE[ε itD ] = 0 . We expect thatβ! > 0, β 2 > 0, β 3 < 0, β 4 > 0, β 5 > 0, β 6 > 0, β8 > 0, β 9 > 0,and

β10 > 0 . The sign of β 7 is not known a priori.

3.5

Data
We use annual data from 1990 to 2001 for each prefecture. 11 However, only the data

for the following items are available for each prefecture: the amount of loans outstanding,dominance ratio of city banks, population density, shipment of products, number of housing
starts, and dominance ratio of SMEs. Therefore, as for the data of loan interest rates, the amountof nonperforming loans, and the amount of bank capitals, we construct these data for eachprefecture by aggregating the corresponding data of individual banks. Specifically, denoting thefinancial data of the k -th bank as x kt —(1) for regional and second regional banks whose head
offices are located in prefecture i (referred to as group A), we just add up x kt and (2) for city
11

Because the Japanese economy began to recover in January 2002, we chose the period up to 2001
to analyze times when the reluctance to lend was supposed to be most severe, if it existed.
12

banks, trust banks, and long-term credit banks (referred to as group B), we multiply xkt withwkit , a weight for prefecture i , and then add up the products. 12
Therefore, the data for prefecture i in year t , X i ,t , is

X it =∑k ∈ A group andhead office locatesin prefecture i

xkt +

∑w x

k ∈B group

kit

kt

,

(7)

where wkit is defined as the number of employees of the k -th bank in year t who work at
branches located in prefecture i / the total number of employees of k -th bank in year t . 13
Data description is given in Table 1. It is interesting to note that in Table 1 the gaps
between the minimums and the maximums are quite large except for the loan interest rate andthe dominance ratio of SMEs. Indeed, the ratio maximum/minimum is 248 for loans, 39 for land
price, 126,650 for nonperforming loans, 663 for own capital, 87 for population density, 74 forsales, and 39 for the number of housing starts. The necessity of utilizing the prefectural datastems from the fact that the economic variables are largely diversified among prefectures. The
precise definitions of the data are given in Appendix.

3.6 Estimation method
Assuming that the loan markets are in equilibrium, we estimate equations (5) and (6)simultaneously by the Generalized Method of Moments (GMM). 14 The endogenous variables
are the loan interest rate and the amount of outstanding loans, and the instrumental variables are12

We omitted the Bank of Tokyo because it specialized primarily in international transactions. We
also omitted the Shinsei and Aozora Banks because of data restrictions.
13
Since the information on the number of employees for each branch was made public only up until
1998, the data for 1999–2001 were replaced by the values for 1998.
14
We also estimated equations (5) and (6) by the two-stage least squares (2SLS). The results arealmost the same as those by GMM; therefore, we skip reporting them.
13all the variables in equations (5) and (6) except for the endogenous variables. 15 As for the
expected value of the loans, we assume perfect foresight, Et [ Lit +1 ] = Lit +1 . 16
We control the fixed effects by subtracting the individual mean from the equations. We
do not employ the random effects model because the distribution of the associated Hausman test
statistics in this estimation is not yet well known to us.

4. Results
4.1

Estimates of the supply and demand functions
We estimated equations (5) and (6) using the data of BAD and CAP in the current

and former periods. Estimation with the data in the current period is problematic because

SALE is not significant and J-statistic is significant at the 10% level, which suggests
misspecification of the model. Banks probably refer to BAD and CAP in relation to theprevious period because they cannot obtain their current data by the time they make decisions
on advancing loans.
The results using the data of BAD and CAP in the previous period are presented
in the left column of Table 2. The results of the supply function are as expected: the lead and lagof the loan are significantly positive. Spread between the loan interest rate and the call rate,( SPR ) and land price (LP) are significantly positive, which was also to be expected. Likewise,bank’s own capital is significantly positive. However, the coefficient of nonperforming loans is

15

If we estimate a dynamic equation with panel data, it will also suffer from the endogeneity
problem that lags of the dependent variable are not orthogonal to the error terms. In order to copewith this problem, one may take a difference of equations (5) and (6), and estimate them with
instrumental variables chosen to be orthogonal to the error term (Arrelano and Bond (1991),
Anderson and Hsiao (1981)). However, we have no other choice than to sacrifice econometriclegitimacy because what we need to know is the magnitudes of the shifts of demand and supply
curves and not the shifts of “differenced” demand and supply curves, which are hard to interpret.
16
Some may argue that concomitant forecast errors will contaminate the consistency of theestimates. However, as is shown in the next subsection, the J-statistic is not significant at the 10%
level, implying that the estimation may not be blurred by the endogeneity problem.
14

insignificant, 17 which suggests that some banks must have advanced forbearance lending tosome firms. The Herfindahl index is insignificant, indicating the market structure-performance
hypothesis is not valid. The stock market price index is significantly negative, implying thatbanks in the bull equity market may reduce the amount of loans and may increase investments
in stocks instead. The dominance ratio of city banks is insignificant, but the population density
is significantly positive as was expected.
Looking at the results of the demand function, the lead and lag of the loan are
significantly positive, loan interest rate is significantly negative, land price is significantly
positive, sales are significantly positive, bond interest rate is significantly positive, thedominance ratio of SMEs is significantly positive, and population density is significantly
positive; all of these take the expected signs. The number of housing starts is insignificant,suggesting that households borrowing to buy houses may not be a substantial part of the loans
advanced by private banks. The stock market price index is significantly negative, implying thatfirms in the bearish equity market may decrease funds raised from stocks and may increase
borrowing from banks instead. The J-statistic is not significant at the 10% level, supporting thisspecification.
We estimate the functions deleting the insignificant variables, such as nonperforming
loans, the Herfindahl index, the ratio of city banks, and the number of housing starts. The results
are shown in the right column of Table 2. They are almost the same as those in the left column.
In sum, these results are satisfactory, and the assumption of segmented prefectural loan markets
fits the reality fairly well.
In the late 1990s, large banks went into bankruptcy. It is an interesting question
whether the bank supply shrank owing to the bank failures. Thus, we ran regressions to measure
the possible effects of the bank failures. Specifically, we constructed the bank failure dummies
and estimated the same equation system shown in Table 2, incorporating the dummies to the17

When we drop own capital, nonperforming loans becomes negative but remains insignificant at
the 10% level.
15

supply function. Bank failure dummies are Hyogo in 1995, Hokkaido in 1997, Osaka in 1999,
and Tokyo in 1999. We also checked the effect of the dummy variable defined as the sum ofthese dummies, which represents the average bank failures. However, in any case, thesedummies do not show significant negative impact on the supply of loans (the results are notshown to save space). One possible reason for these results is that the other banks took over theloans of the failed banks.

4.2

Has a reluctance to lend occurred?
Based on the estimates shown in the right column of Table 2, we calculate the shift ofthe supply and demand functions for each year from 1991 to 2001. First, the average of eachexplanatory variable across all prefectures in 1991 is substituted into the supply and demand
functions, and these fitted values (fitA_supply, fitA_demand) are calculated. Then, the averageof loan interest rates across all prefectures in 1991 and the averages of the other variables acrossall prefectures in 1992 are substituted into the supply and demand functions, and these fitted
values (fitB_supply, fitB_demand) are calculated. The differences between these fitted values(fitB_supply－fitA_supply, fitB_demand－fitA_demand) are the magnitude of the supply anddemand shifts from 1991 to 1992.
Figure 3, which presents the results of such calculations, reveals two facts. First, theactual value, the equilibrium value that equates loan demand and supply, the supply function,and the demand function all shifted toward the right (i.e., increasing direction) before 1996,
while they shifted toward the left (decreasing direction) thereafter. Second, before 1996 the right(i.e., increasing) shifts of the supply function always exceeded those of the demand function,
while after 1996 both shifts were roughly of the similar magnitude.
Let us first consider the period prior to 1996. The positive shifts of the actual andequilibrium values imply that the amount of outstanding loans increased every year. Figure 3
indicates, however, that the increase in loans during this period was induced by an increase in16

supply rather than an increase in demand, implying that the stagnant loan market was caused by
the stagnant loan demand. Therefore, we conclude that the reluctance to lend did not occur inthe earlier period.
In the period after 1996, the actual amount of loans decreased and the left shifts of the
supply and demand functions are roughly of the same magnitude—implying that the decrease inloans depended on both supply and demand. When the results from the period after 1996 arecompared with the results before 1996, it is clear that the reluctance to lend became moreprevalent during the latter period.
In order to measure the magnitude of the reluctance to lend directly, let us calculate
demand shift − supply shift, which we denote as RLi ,t . The demand and supply functions aredenoted as =LDi ,t β3 rit + β X iD,t and =LSi ,t α 3 rit + α X iS,t , the corresponding demand and(supply “shifts” can be written as ∆LDi ,t ≡ β X iD,t − X iD,t −1) and ∆L ≡ α ( X − X )
S
i ,t

S
i ,t

S
i ,t −1respectively, where X iD,t and X iS,t stand for the exogenous explanatory variables in the
demand and supply functions. Then, the reluctance to lend measure RLi ,t is defined as∆LDi ,t −=∆LSi ,t β ( X iD,t − X iD,t −1 ) − α ( X iS,t − X iS,t −1 ).

A large and positive RLi ,t means that the reluctance to lend is severer in prefecture i
in

year

t.

In

addition,wecalculatethefactordecomposition

FDi ,tas

FDi ,t ≡ β ( X iD,t − X iD,t −1 ) for each explanatory variable in the demand function andFDi ,t ≡ −α ( X iS,t − X iS,t −1 ) for that in the supply function in order to evaluate the cause of thereluctance to lend. 18
Figure 4, which shows the degree of reluctance to lend (demand shift − supply shift)18

If an exogenous variable Z is included both in the demand and supply functions, then the factor
decomposition of this variable becomes FDi ,t ≡ ( β − α )( Z i ,t − Z i ,t −1 ) .
17and its factor decomposition, reveals three facts. First, the degree of reluctance to lend
consistently increased from 1992 to 2000, implying that the reluctance to lend became severerin the late 1990s. 19 Second, the reluctance to lend was greatly eased in 1995. Third, the call rate
factor alone is almost enough to understand the severity of the reluctance to lend before 1996.
However, other factors such as stock prices, bond rates, and bank capital became relatively
important in the latter period.
Let us take a closer look at the factor decomposition. In Table 3, we present the
average factor decomposition across all prefectures during the periods 1992–1996 and
1996–2000, in which the large and positive RLi ,t means that the reluctance to lend is severer
in prefecture i in year t. Among them, the call rate was the largest factor that alleviated thereluctance to lend. This result reflects the fact that the Bank of Japan lowered the call rate
consecutively to ease money throughout the period. The effect, however, became weaker in thelatter period because the call rate was lowered to near zero, so that there remained almost no
room to fall further. 20 The bond rate was the second largest factor that alleviated the reluctanceto lend by suppressing the loan demand, in which the easing of the monetary policy also playeda leading role. Another variable that had a large effect on easing the reluctance to lend was stock
prices, which were declining throughout the period. This affected the reluctance to lend because
the decrease in stock prices boosted both the supply of and the demand for loans; since the boost
to the supply was stronger, it helped to alleviate the reluctance to lend.
The other variables had a relatively small effect on the reluctance to lend. It isinteresting to note, however, the results for the capital factor: the negative value of the factor
decomposition for both periods indicates that bank capital was augmenting throughout theperiods and helped to ease the reluctance to lend, although the contrary is often suggested by

19

One may alternatively argue that the reluctance to lend was not the case at all before 1996, except
for 1994, but it might have been thereafter.
20
The Bank of Japan began to adopt a zero-interest-rate policy in February, 1999 and effectively
lifted it in July, 2006.
18

previous studies. The results indicate that the difference between the two periods is significantly
positive, which implies that the augmentation of bank capital contributed to easing thereluctance to lend more in the latter period than in the former.

4.3 The reluctance to lend was severer in populous and urban prefectures
In order to allow the comparisons of the degree of reluctance to lend among
prefectures, we calculate the rate of shift RRLi ,t , dividing RLi ,t by the equilibrium value ofthe loan (the demand-and-supply balancing loan amount) from the previous year. RRLit is thenaveraged over 1992 to 1996, and 1996 to 2000.
We present the rate of reluctance to lend, RRL, for each prefecture in Figure 5(1992–1996) and in Figure 6 (1996–2000), which reveal the following four facts. First, the RRL
is negative in every prefecture, suggesting that an expansion in supply (a reduction in demand)
outweighed an expansion in demand (a reduction in supply) in 1992–1996 (in 1996–2000) in
every prefecture. Second, when we compare the two periods, the RRL is larger (smaller innegative value) in 1996–2000 than in 1992–1996 for most of the prefectures, which is consistent
with our intuition that the reluctance to lend was severer in the latter period. Third, theprefectures in the top five RRLs are Tokyo, Kanagawa, Aichi, Osaka, and Fukuoka in either
period, all of which have megacities. Meanwhile, the prefectures in the bottom five RRLs are
Iwate, Tottori, Shimane, Kochi, and Miyazaki in the former period (Akita, Yamanashi, Tottori,
Shimane, and Kochi in the latter period), all of which do not have large cities and arecharacterized as rural regions. In a word, the reluctance to lend was severer in populated areas. 21
Fourth, most of the prefectures with larger (smaller in negative value) RRLs in the earlier periodalso have larger (smaller in negative value) RRLs in the latter period. Indeed, the correlation
between the RRLs in both periods is quite high with a correlation coefficient of 0.989.
In order to confirm the third fact, we selected the five largest and five smallest RRL
21

Actual outstanding loans also decreased in the populated prefectures like Tokyo, Aichi and Osaka.
19prefectures and examined their characteristics. In Table 4, we present the mean of the population,
population density, GDP, per capita GDP, sales, per capita sales, the dominance ratio of thenumber of branches of city banks to that of all types of banks, and the SME ratio for the top fiveand bottom five prefectures in 1992–1996 and in 1996–2000.
In 1992–1996 (the upper panel of Table 4), the mean of the population for the top fiveprefectures is 8.09×106 and for the bottom five prefectures it is 9.59×105. This is shown by themean difference test in the rightmost column; the former is significantly larger than the latter.
Similarly, the means of population density, GDP, per capita GDP, sales, per capita sales, and thedominance ratio of city banks for the top five prefectures are significantly larger than those forthe bottom five prefectures. The mean of the dominance ratio of SMEs, SME, is significantly
smaller for the top five prefectures than that for the bottom five prefectures. In sum, the top fiveprefectures are characterized by larger population, GDP, sales, and city bank branches; therefore,
we will refer to them as “populous prefectures” from this point on. 22

4.4

Why were populous prefectures hit by a reluctance to lend?
In the previous subsection, we found that the reluctance to lend was severer in theprefectures that have megacities than in others. In order to elucidate the possible causes, we
calculate the factor decompositions for the top five prefectures and for the bottom five, which
are presented in Table 5. 23 To highlight the characteristics of the two groups, we test thedifference of the mean of the factor decomposition, which are shown in the right hand columnsof the table.
Let us take a look at the results of the mean difference in the table. Changes in bankcapital and the dominance ratio of SMEs were the principal causes of the intensified reluctance22

We also examined whether populous and urban prefectures tend to decrease the actual amount ofloans. The results are just the same as those of RRLs.
23
Here, we present only the results of the factor decompositions for the prefecture-specific variables,
because those for the other variables, such as the call rate, are identical to every prefecture, so that
their factor decompositions will also be identical.
20

to lend in populous prefectures throughout both periods. Population density similarly affected
the intensified reluctance to lend, but its effect became much smaller. The value of capital
factors is significantly positive in the earlier period, which indicates that bank capital was animportant element that intensified the reluctance to lend in populous prefectures in comparison
with the bottom five prefectures. In the later period, the mean difference is still positive andlarge, though not significant. 24
In the earlier period, SME factors, although not significant, were also important.
However, in the later period, SME factors did become significant and were, in fact, the largest
contributing element that intensified the reluctance to lend in populous prefectures incomparison with the bottom five prefectures, while capital factors were the second largest. 25
In sum, variations in the intensity of the reluctance to lend among prefectures camefrom discrepancies in the health of the local economies, which is represented by the capital ofbanks as well as by the SME ratio, which are indicators of profitability among firms in theprefecture.

5. Conclusions
In this paper, we analyzed whether the supply side played a crucial role in causing
stagnant and declining outstanding loans in Japan in the 1990s, with a special focus on its
spatial differences. In other words, this paper attempted to clarify whether a reluctance to lend
24

The bank capital factor for the top five prefectures had a positive sign in the earlier period,
implying that loans had decreased in populous prefectures (the first column in the upper panel). In
the latter period, however, the bank capital factor of populous prefectures became negative, whichmeans that loans had increased. This may be because bank capital in the populous prefectures wasaugmented possibly due to the injection of public funds. The same is true for the bottom fiveprefectures, and the mean difference was positive, which implies that bank capital in the bottom fiveprefectures experienced greater augmentation than the populous prefectures during the latter period.
25
A larger SME ratio expanded loan demand (see Table 2), leading to a deterioration of thereluctance to lend. The bottom panel of Table 5 reveals that in the latter period, SME factors took ona large negative value for the bottom five prefectures. Comparison with the corresponding value inthe earlier period (upper panel) indicates that loans for SMEs had sharply increased for the bottom
five prefectures during the latter period. This is the reason why the SME ratio became an importantelement in the latter period.
21

actually occurred in the Japanese loan market. To this end, we calculate the magnitude of theshift of the supply and demand functions, provided that the loan market was in equilibrium. If
the supply function shifted towards the left more than the demand function did, borrowers
would have faced a strict attitude from the banks regarding the advancement of loans, which isin effect a reluctance to lend. Under the assumption that the Japanese loan markets aresegmented by prefecture, we estimated the supply and demand functions using prefectural panel
data from 1990 to 2001. This paper is unique in that it estimates both equations simultaneously
and evaluates the severity of the reluctance to lend by comparing the magnitudes of the supply
and demand shifts.
The estimation results reveal that until 1996, the supply function shifted toward theright more than the demand function did, indicating that stagnant loans in this period cannot be
explained by the supply side. However, after 1996, when the loan amount decreased, the loan
supply shifted leftward as much as the loan demand, implying that the contraction of supply
contributed partly to the decrease in loans.
Closer examination revealed the salient characteristics of those prefectures that
suffered from a severer reluctance to lend. First, the degree of the reluctance to lend was severerin the urban and populous prefectures. Second, the call rate and bond rate, which were declining
because of a monetary easing policy, played a leading role in alleviating the reluctance to lendthroughout the period.
These results suggest the following lessons. First, it is important to evaluate how muchbanks contribute to the reduction of loans—the role of the supply side is often overemphasized,
as was the case in Japan. Our analysis reveals that the demand side played an equal or greaterrole, even in the context of the historical financial crisis in Japan during the period 1997–2000.
If the reduction is mainly due to the lack of demand, countermeasures in the banking sector may
not achieve the expected recovery. For example, according to an exercise conducted in 1998 and1999 to evaluate the effect of the injection of public funds on bank lending, the injection

22

increased outstanding loans by only a couple of percentage points among the actual decrease of−142.31 and −173.53 billion yen respectively. 26 Second, we demonstrate that the degree of thereluctance to lend was not uniform in Japan, but differed markedly among regions. Thissuggests that measures such as traditional monetary policy that influenced all regions uniformly
were inadequate. Region-specific policies are therefore called for.
As shown in Figure 1, bank loans in the US are in sharp decline and have been sinceApril 2008. Whether this decline is brought about from the supply or demand side, however, is
not known a priori. Although the supply side did not play an important role in the Heisei
Depression in Japan, as discussed in this paper, mechanisms of the crises differ between Japanand the US. In the case of Japan, the stock bubble burst first in 1990, the real estate bubble burst
in 1991, then the GDP growth rate started to decline in 1992, and finally, financial crisis(bankruptcy of financial institutions) started in 1997-98. On the other hand, the order ofoccurrence differed for the US: the real estate bubble burst, the default of financial derivatives
occurred, financial crisis (bankruptcy of financial institutions) started, and then the GDP growthrate declined. This fact suggests that the collapse of the financial system in the US preceded thedepression, which is contrary to what happened in Japan. Therefore, the supply side of loans
should play a more important role in the US than in Japan; however, since the corporate profits
started to decline already in 2006, it might be that the opposite is true. In order to obtain adefinitive answer, we will need to estimate the magnitudes of the shifts of demand and supply
functions in the case of the US.

26

The effect of the injection should be smaller than these estimates, if we consider the effect offinancing the injection fund from a tax increase. Ishikawa (2004) simulated his general equilibrium
model and found that the injection resulted in a decrease rather than an increase in the short run.
23

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26

Appendix: Precise definitions of the data
Bank Loans Outstanding: “Deposits, Loans and Discounts Outstanding of Domestically
Licensed Banks by Prefecture (at the end of the fiscal year),” Source: Economic Statistics
by Prefecture, Bank of Japan.
Land Prices: “Land Price of Residential District (average, unit is 100 yen/m2),” Source: Land
Price Survey by Prefecture, Ministry of Land, Infrastructure and Transport.
Call Rates: “Unsecured Overnight Call Rate (monthly average balance),” Source: Financial andEconomic Statistics Monthly, Bank of Japan.
Shipment of Products: “Statistics by Prefecture, Shipment of Products (employees over 4 people,
all manufacturing industries, and calendar year),” Source: Industrial Statistics(Manufacturing Industry), Ministry of Economic, Trade and Industry.
Housing Starts: “The Number of Housing Starts, Classified by Funds and Purposes (houses
funded by private sector, owned house),” Source: Statistical Report on Execution ofConstruction Work, Ministry of Land, Infrastructure and Transport.
Bond Rates: “Subscriber’s Yield of 10-year Government Bond (annual average),” Source: Home
Page of Bank of Japan.
Stock market price index: “Nikkei Average of 225 Selected Issues in the Tokyo Stock Exchange(at the end of the fiscal year, monthly average), Source: Home Page of Tokyo Stock
Exchange.
The number of branches of city banks, regional banks, second regional banks: “The number ofbranches of city banks, regional banks, second regional banks,” Source: Financial JournalMonthly, Extra Edition (in Japanese, Gekkan Kin-yu Journal, Zoukan Gou), The Japan
Financial News Co., Ltd.
Dominance ratio of city banks of prefecture i at t-th year is calculated as “(the number ofbranches of city banks of prefecture i at t-th year) / (the number of branches of city banks,27

regional banks, and second regional banks of prefecture i at t-th year).”Population: “Population by Prefectures, with intercensal adjustment (as of October 1 of eachyear),” Source: Population Census, Ministry of Internal Affairs and Communications.
Area of prefecture: “Area by Prefectures (as of October 1 of each year, in square kilometers),”
Source: Area Survey, Geographical Survey Institute, Ministry of Land, Infrastructure andTransport.
Population Density of prefecture i at t-th year is calculated as “(population of prefecture i at t-thyear) / (area of prefecture i at t-th year in square kilometers).”
The number of employees of small and medium sized firms: “Number of employees, Statistics
Tables by Prefecture (establishments with 4 or more employees),” Source: Industrial
Statistics (Manufacturing Industry), Ministry of Economic, Trade and Industry.
Dominance ratio of small and medium sized firms of prefecture i at t-th year is calculated as“(the total number of employees of the firms of 4 to 99 staffs in the prefecture i at t-thyear) / (the total number of employees of the firms of more than 4 staffs in the prefecture iat t-th year).”
Nonperforming loans: “Allowances for Loan Losses,” Source: Nikkei Needs Bank data.
Own capital: “the sum of capital stock, payment for new shares, capital reserve, earned surplus
reserve, voluntary reserve, and unappropriated profit,” Source: Nikkei Needs Bank data.
Loan interest rate of prefecture i at t-th year is calculated as “(loan interest revenue ofprefecture i at t-th year)/ (amount of outstanding loan of prefecture i at the end of t-1-thyear),” Source: Nikkei Needs Bank data.

28

Table 1.

Descriptive Statistics(variables)
L
SPR
RL
RC
RB
LP
SP
CITYBANKSDENSITY
HI
BAD
CAP
SALE
HOUSE
SME

average standard deviation
3
4
9.91×102.90×10
2.09
0.920
4.02
1.89
1.93
2.53
3.24
1.80
8.68
9.42
4
3
1.80×10
3.79×100.09580.159
2
6.52×10
1.12×103
0.405
0.172
2
3.04×1078.6
2
6.45×101.70×1033
3
6.61×10
7.11×103
4.66×10
4.56×103
0.559
0.0789minimum maximum
25
8.38×10
2.09×10
−1.68
3.80
1.65
8.56
2.00×10-3
8.28
0.972
6.80
2.23
8.59
4
4
1.15×102.65×100.00435
0.736
5.90×10367.7
0.07470.802
34.09×100.323
1.31×10419.8
2
4
5.27×10
3.88×102
7.15×10
2.80×104
0.356
0.778

Notes：L is amount of outstanding loans (in billion yen). SPR is spread
between the loan interest rate and the call rate (in percent). RL is the loaninterest rate (in percent). RC is the call rate (in percent). RB is the bondinterest rate (subscriber’s yield of 10-year government bond, in percent).
LP is the land price (per unit square meter, in ten thousand yen). SP is thestock market price index (in yen). CITYBANKS is the dominance ratio ofcity banks. DENSITY is the population density (persons in square
kilometers). HI is the Herfindahl index. BAD is the amount of
nonperforming loans (in billion yen). CAP is the amount of own capital (inbillion yen). SALE is the amount of sales (in billion yen). HOUSE is thenumber of housing starts. SME is the dominance ratio of SMEs. The
sample period is from 1990 to 2001, and the number of prefectures is 47.
The number of observations is 564.

29

Table 2. Estimation Results of the loan supply and demand functions (GMM)+
0.488*** (15.1)
0.489*** (13.6)
L(+1)
***
+
0.476
(11.6)
0.481*** (11.1)L(−1)+
4.96×102** (2.52)
4.27×102** (2.36)
SPR
**
**
+
2.07
(2.33)
1.84(2.00)LP−
BAD(−1)0.0109 (0.871)+
0.0520*** (4.27)
0.0400*** (3.46)
CAP(−1)
2−
3.76×10 (0.771)
HI?
−0.102*** (−3.75)−0.0895*** (−3.10)SP
2?
9.72×10 (1.28)
CITY BANKS+
22.1** (2.01)
20.5* (1.73)
DENSITY+
0.486*** (14.6)
0.488*** (13.4)
L(+1)
***
+
0.475
(11.3)
0.480*** (10.9)L(−1)−−1.50×102*** (−2.72)–1.36×102** (−2.50)
RL
**
**
+
2.08
(2.30)
1.88 (2.02)
LP
-3**
-3**
+
5.60×10(2.24)
5.53×10(2.34)
SALE-3+−2.86×10 (−0.331)
HOUSE
***
**
?
−0.0683(−2.594)−0.0613(−2.19)
SP
**
**
+
57.0
(2.31)
56.0(2.30)
RB
3*
3+
2.95×10(1.72)
2.42×10(1.56)
SME+
22.2** (1.97)
21.1* (1.72)
DENSITY
J-Statistics

8.04[0.430]6.43[0.377]

Notes: See notes to Table 1 for definition of the variables. The dependent variable is the amount ofoutstanding loans. L(+1) and L(−1) denote a one-year lead and lag of the amount of outstandingloans. BAD(−1) and CAP(−1) denote a one-year lag of the amount of nonperforming loans and own
capital. The loan demand and supply equations are estimated simultaneously by the GMM, in which
we control fixed effects by subtracting individual mean from the equations. Endogenous variables
are the amount of outstanding loans and loan interest rate. Instrumental variables are all of thevariables in the loan demand and supply equations except for the endogenous variables. The
numbers in parentheses ( ) are t-ratios. The superscripts ***, **, and * mean that the variables are
significant at the 1% (***), 5% (**), and 10% (*) level. J-Statistics tests the null hypothesis thatover-identified restriction is satisfied in the GMM estimation. p-values are shown in brackets [ ]. The
sample period is from 1991 to 2000, and the number of prefectures is 47. The number ofobservations is 470.

30

Table 3.

Factor decompositions of the reluctance to lend measure (average of all prefectures)variables
reluctance to lendmeasure RLit
call rate factors
land price factors

average of all prefectures
1992–1996(b) means.d.

(a) - (b)T

d.f.

−1.537***1.910−0.255***0.480−1.282***−10.00264−1.372***1.790−0.081***0.241−1.292***−10.96

242

−1.18

300

−2.89

465

2.01

317

−1.64×10－3 ***−5.05×10－3 ***capital factors−2.33×10－3 ***stock price factors
SME factors
population density
factors

mean difference (Welch) tests.d.

sales factors

bond rate factors

1996–2000(a) mean

6.12×10－3−1.14×10－3 ***

0.026
0.015

−7.49×10－3 ***−0.039***−0.106***−0.012***
1.17×10－3 ***

2.32×10

2.16×10－3－3−5.00×10－4−7.21×10－3***

0.028
0.036

5.17×10－3**

0.294−0.122***0.344

0.083***

2.81

457

0.211

−0.039***0.120−0.067***

−4.26

370

0.066

−0.020***0.077

8.37×10

－3

1.27

457

－4***

4.57

466

1.93×10－3

3.80×10－4***

1.80×10－3

7.86×10Notes: The reluctance to lend measure RLit is defined as “demand shift－supply shift,” or RLit =β (XDit－XDit-1 )－ α (XSit－XSit-1), where XDit and XSit are the exogenous explanatory variables in the
demand and supply functions, α and β are the corresponding coefficients. Large and positive
RLit means that the reluctance to lend is severer in prefecture i in year t. The factor decompositionsof the reluctance to lend measure, for example, correspond to β (XDit－XDit-1 ) or α (XSit－XSit-1).
The null hypothesis of the mean difference (Welch) test is that the mean difference (a)-(b) is zero.
The statistics T is defined as ( µ A − µ B )sA

2nA + sB2nB , whereµ A and µ B are the sample

means of group A and B, sA and sB are the sample standard deviations, nA and nB are the number ofobservations, respectively. Under the null, the statistics T follows t-distribution with degrees of
freedom d.f. Also refer to the notes for Table 2.

31

Table 4. Characteristics of prefectures whose reluctance to lend measures are top five andbottom five
[1992–1996]
variables

top 5 prefectures

bottom 5 prefectures

mean difference (Welch) test

Reluctance to lendmeasures RRLit(a) means.d.

(b) means.d.

(a) － (b)T

d.f.

−0.164***

0.224

−3.41

3.13

3.25 ***

5.18

24

population

8.09×106***

9.59×105***

15.06

25

population density

1.36×102***

2.98×105
33.99

7.13×106 ***3.24×103***

2.35×106
1.87×103

3.10×103 ***

8.27

24

GDP

3.99×105***

2.20×105

2.88×104***

8.84×103

3.70×105 ***

8.42

24

per capita GDP

0.046***

0.012

0.030***

2.07×10-3

0.016 ***

6.67

25

Sales

2.27×105***

9.34×104

1.27×104***

5.27×103

2.14×105 ***

11.43

24

per capita sales

0.029***

0.014

0.013***

3.36×10-3

0.016 ***

5.52

27

city banks

0.100***

0.064

1.49×10-3***

4.59×10-4

0.098 ***

7.65

24

SME

0.519***

0.102

0.621***

0.075−0.102 ***

−4.03

44

[1996–2000]
top 5 prefectures

bottom 5 prefectures(a) means.d.

(b) means.d.

(a) – (b)T

d.f.

−0.0200.059−0.5780.858

0.558 ***

3.24

24

population

8.17×106***

8.58×105***

15.83

24

population density

1.46×102***

1.99×105
41.34

3.71×106 ***3.26×103***2.30×106
1.86×103

3.12×103 ***

8.35

24

GDP

4.14×105***

2.30×105

2.84×104***

6.51×103

3.85×105 ***

8.38

24

per capita GDP

0.048***

0.013

0.033***

2.06×10-3

0.014 ***

5.49

25

sales

2.11×105***

8.82×104

1.41×104***

6.35×103

1.97×105 ***

11.15

24

per capita sales

0.027***

0.013

0.017***

6.80×10-3

0.010 ***

3.46

36

city banks

0.096***

0.061

2.27×10-3***

1.87×10-3

0.094 ***

7.71

24

SMEs

0.523***

0.100

0.622***

0.073−0.093 ***

−4.00

44

variables
Reluctance to lendmeasures RRLit

mean difference (Welch) test

Notes: The reluctance to lend measure RRLit is defined as RLit divided the equilibrium value of theloan (demand-and-supply balancing loan amount) in the previous year. Also refer to the notes for
Tables 2 and 3.

32

Table 5. Factor decompositions of the reluctance to lend measures(top five and bottom five prefectures)[1992–1996]
variables
reluctance to lendmeasures RRLit
land price factors
sales factors
Capital factors
SME factors
population density
factors

top 5 prefectures

bottom 5 prefectures

mean difference (Welch) test(a) means.d.

(b) means.d.

(a) – (b)T

d.f.

−0.164***0.224−3.41***

3.13

3.25 ***

5.18

24−1.97×10-3***

1.99×10-3

1.40×10-3***

2.55×10-3−3.37×10-3 ***0.014−9.71×10-3***

−5.20

45

−0.013 ***

−2.52

41

0.015 ***

3.07

48

0.021

3.05×10-3

6.05×10-3

0.017−8.52×10-3***

0.016−9.08×10-4

4.71×10-3−0.020

0.124

0.019

0.78

24

1.47×10-3***

1.58×10-3−1.83×10-4

9.41×10-4

1.65×10-3 ***

4.49

39

[1996–2000]
variables
reluctance to lendmeasures RRLit
land price factors
sales factors
Capital factors
SME factors
population density
factors

top 5 prefectures

bottom 5 prefectures

mean difference (Welch) test(a) means.d.

(b) means.d.

(a) – (b)T

d.f.

−0.0200.059−0.578***0.858

0.558 ***

3.24

24

−7.37×10-4***

5.84×10-4−6.35×10-4

3.02×10-3−1.02×10-4

−0.17

26

−3.00×10-3

0.022

5.81×10-3

0.027−8.81×10-3

−1.27

46

−4.04×10-3

0.044−1.47×10-2***

0.024

0.011

1.07

37

1.63×10-3

6.97×10-3−0.062*0.149

0.064 **

2.15

24

1.50×10-3***

1.51×10-3−4.09×10-4

1.33×10-3

1.91×10-3 ***

4.73

47

Notes: Refer to the notes for Tables 2, 3, and 4.

33

Figure 1. Recent decline in the amount of loans in the United States due to the subprime loan problem
billion dollars
10,000

9,000

8,000

7,000

6,000

5,000

20
05
20 01
05
20 03
05
20 05
05
20 07
05
20 09
05
20 11
06
20 01
06
20 03
06
20 05
06
20 07
06
20 09
06
20 11
07
20 01
07
20 03
07
20 05
07
20 07
07
20 09
07
20 11
08
20 01
08
20 03
08
20 05
08
20 07
08
20 09
08
20 11
09
20 01
09
20 03
09
20 05
09
20 07
09
20 09
09
-1
1

4,000

Loans and leases in bank credit; All Commercial Banks; NSA

Loans and leases in bank credit; All Commercial Banks; NSA

Notes: The data source is the website of The Federal Reserve Board.

34

Figure 2. Bank credit in Japan and the United Statestrillion yen

billion dollars

600

9000
8000

500
7000
400

6000
5000

300
4000
200

3000
2000

100
1000
0

1944
1946
1948
1950
1952
1954
1956
1958
1960
1962
1964
1966
1968
1970
1972
1974
1976
1978
1980
1982
1984
1986
1988
1990
1992
1994
1996
1998
2000
2002
2004
2006

0

loans in Japanloans in the USA

Notes: The data source of bank credit in Japan is from the Financial Statistics Annual, the Bank of Japan. The sample period is from 1944 to 2004. The data
source of bank credit in the US is from the Board of Governors of the Federal Reserve System. The sample period is from 1973 to 2003.

35

Figure 3. Shifts in the loan supply and demand functions
400
300

billion yen

200
100
0
-100
-200

shift due to supply side

shift due to demand side

equilibrium shift

00
20

99
19

98
19

97
19

96
19

95
19

94
19

93
19

19

92

-300
Fiscal Yearactual shift

Notes: The shift due to supply and demand side for year t is calculated as follows: first, we substitute the prefectural average of each explanatory variable inyear t-1 and calculate the fitted value of supply of and demand for loans in year t-1. Then, substituting the mean of loan interest rate in year t-1 and themean of the other variables in year t, we calculate the fitted value in year t. The difference between these fitted values is the magnitude of the shifts fromyear t-1 to t. The equilibrium shift for year t is the difference between the prefectural average of equilibrium loans (supply-demand balancing loans) in year
t and that in year t-1. The actual shift for year t is the difference between the prefectural average of outstanding loans in year t and that in year t-1.

36

Figure 4. The degree of reluctance to lend and its factor decompostion(Severe)20

0billion yen

-20
-40
-60
-80
-100

the degree of reluctance to lendsales
lag of lending
sme

call rate
capitalstock pricepopulation density

Notes: Refer to the notes for Table 3.

37

land pricelead of lending
bond rate

20
00

19
99

19
98

19
97

19
96

19
95

19
94

19
93

(easy)

19
92-120
Fiscal Year

Figure 5. The degree of reluctance to lend and its factor decompostion (by prefecture; 92–96)(severe)

0.5
0
-0.5
-1

%

-1.5
-2
-2.5
-3
-3.5

call rate
lead of lending
sme

land pricelag of lending
population density

Notes: Refer to the notes for Tables 3 and 4.

38

sales
stock price

Okinawa

MiyazakiKagoshima

Kumamoto
Oita

Saga

prefecture
the degree of reluctance to lend
capitalbond rate

NagasakiFukuoka

Ehime
Kochi

KagawaTokushima

Hiroshima
Yamaguchi

Shimane

Okayama

Nara

Wakayama
Tottori

Hyogo

Kyoto
Osaka

Mie

Shiga

Aichi

Gifu
Shizuoka

NaganoYamanashi

Ishikawa
Fukui

NiigataToyama

Chiba

Tokyo
Kanagawa

Saitama

IbarakiTochigi
Gunma

Yamagata

Iwate

Miyagi
Akita

Aomori

Hokkaido-4.5(easy)

Fukushima-4

Figure 6. The degree of credit cruch and its factor decompostion (by prefecture; 96–00)(severe)

0.1
0
-0.1
-0.2

%

-0.3
-0.4
-0.5
-0.6

call rate
lead of lending
sme

land pricelag of lending
population density

Notes: Refer to the notes for Tables 3 and 4.

39

sales
stock price

Okinawa

MiyazakiKagoshima

Kumamoto
Oita

Saga

prefecture
the degree of reluctance to lend
capitalbond rate

NagasakiFukuoka

Ehime
Kochi

KagawaTokushima

Hiroshima
Yamaguchi

Shimane

Okayama

Nara

Wakayama
Tottori

Hyogo

Kyoto
Osaka

Mie

Shiga

Aichi

Gifu
Shizuoka

NaganoYamanashi

Ishikawa
Fukui

NiigataToyama

Chiba

Tokyo
Kanagawa

Saitama

IbarakiTochigi
Gunma

Yamagata

Iwate

Miyagi
Akita

Aomori

Hokkaido-0.8(easy)

Fukushima-0.7