Discussion Paper No. 505

BANK LENDING IN JAPAN:ITS DETERMINANTS AND
MACROECONOMIC IMPLICATIONS

Kazuo Ogawa
andShin-ichi Kitasaka

April 2000

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

March 1998
Revised August 1999
Final Revision February 2000

Bank Lending in Japan:
Its Determinants and Macroeconomic Implications *Kazuo Ogawa
Institute of Social and Economic Research, Osaka University
6-1 Mihogaoka, Ibaraki,Osaka 567 JAPANShin-ichi Kitasaka

Graduate School of International Cooperation Studies, Kobe University
2-1 Rokkodai-cho, Nadaku, Kobe 657 JAPAN* This is an extended version of the paper with the same title to be published in Hoshi, T. and H.
Patrick, Crisis and Change in the Japanese Financial System, Kluwer Academic Publishers, 2000.
This paper was presented at the conference, “ The Japanese Financial System: Restructuring for the
Future,” at Columbia University on October 1 and 2, 1998. Early version of the paper was presented
at the Finance Forum, Kobe University, Nagoya City University and Osaka University. We thankAnil Kashyap, and two editors of this volume, Takeo Hoshi and Hugh Patrick for numerous
suggestion and comments in improving the quality of the paper. We are also grateful to Charles
Calomiris, Kenya Fujiwara, Atsuo Fukuda, Yuzo Honda, Charles Yuji Horioka, Kiyoshi Kuga,
Katsumi Matsuura, Eiji Nezu, Yoshiyasu Ono, Fumio Ohtake, Makoto Saito, Masaya Sakuragawa,Toshiaki Tachibanaki, Yasuhiko Tanigawa, Hideki Toya, Yoshiro Tsutsui, Yasuharu Ukai and theconference participants for helpful comments and suggestions. This research was partially supported
by grants-in-aid of Asset Management Service Industry in Osaka School of International Public
Policy of Osaka University. Any remaining errors are the sole responsibility of the authors.

Summary
We examine the role of bank loans in the Japanese economy by analyzing the lending behaviorof banking firms and the investment behavior of non-financial firms. As for the banks’ behavior, weconstruct a dynamic model of lending and test a variety of behavioral hypotheses on the bank’s loan
supply based on the panel data set over 1976 to 1995. Our main finding is that the lending behavior
is quite different by types of banks and of borrowers. For regional banks the bank loans are sensitive
to deposits, indicating that they face imperfections in capital market. For major banks such as city
banks, real estate plays a vital role as collateral in loan contracts. We also find that loans to small
and/or non-manufacturing firms are more dependent on real estate as collateral and sensitive to thedeposit growth. It is inferred that real estate functions as a device to reduce the agency cost
stemming from the asymmetric information between borrowers and banks.
We also find that expenditure on fixed investment is much more sensitive to bank loans forsmall firms than for large firms. Our simulation exercises demonstrate that cut of loan supply islargely responsible for the stagnancy of investment in the downturns after the burst of the bubble andthe financial turmoil in 1997. To sum up, lending channel is indeed important in Japan inpropagating the shocks in the asset markets to the real economy.

JEL Classification Number: G21
Key Words: Lending channel, Bank loans, Liquidity constraint, Collateral, Euler equation,
Generalized method of moments estimator, Investment function, Credit Crunch
Correspondence to : Kazuo Ogawa,
Institute of Social and Economic Research, Osaka University,
6-1 Mihogaoka, Ibaraki, Osaka, 567-0047 JAPAN
Tel: +81- 6- 6879-8570

Fax: +81- 6-6878-2766

E-mail: ogawa@iser.osaka-u.ac.jp

1.Introduction
The year of 1997 was full of failures of financial institutions for the Japanese economy. In
November large financial corporations such as The Hokkaido Takushoku Bank and Yamaichi
Securities Co. Ltd., went into bankruptcy. It prompted the risk-avert households to shift deposits
from the banks perceived to be in trouble to safe banks. The increase rate of deposits in
December from previous year was 6.2 % and 4.7 % for postal savings and city banks, while it
was only 1.6 % and–2.1 % for regional banks and the second regional banks, respectively.

Many argue that quick shift of deposits, together with fall of share price, makes banks very
cautious in making loans or even worse withdraw loans to maintain the requirement on bankcapital. It is further asserted that a contraction of bank credit in turn will choke off the real
activity of bank-dependent firms and thus aggravate the current stagnancy of the Japanese
economy.
The assertion above presumes that bank credit does play an important role in supporting
the real activity in the Japanese economy. It should be noted, however, that this assertion
crucially hinges upon the substitutability among the balance sheet items of banking firms as well
as borrowers. Suppose that a negative shock hits the deposit of one bank. When thesubstitutability of reservable deposits and the other items of balance sheet of bank is perfect, thenthe bank can offset the deposit shock completely and its lending behavior will not be affected at
all by the shock. Even if the substitutability is imperfect for the bank, the borrowers can offset
the cut of bank loans by raising the funds from another source when the substitutability betweenbank loans and other ways of financing is perfect for the borrowers. Thus it is only when the
degree of substitutability is imperfect for both banks and borrowers that the shocks to the banks’balance sheet are propagated into the real economy by way of bank loans.. This propagation
channel is well-known lending channel and bank credit does play a vital role in transmitting achange in monetary policy.1
Our goal in this study is to examine the role of bank credit in the Japanese economy. To
achieve this goal, we proceed in the following two steps. First of all, we analyze the lending
behavior of the Japanese banking firms based on the panel data set of commercial banks in the
period of 1976 to 1995. Then we turn to an analysis of borrowers. Specifically, we focus on theeffect of bank loans on fixed investment of non-financial firms, which constitutes a key

1

component of business fluctuations.
There are four features in our study. Three features are associated with the analysis ofbanking firms and the other is associated with that of non-financial firms. First of all, we estimate
the Euler equation of bank loans derived from the intertemporal model of banking firms. There
are very few studies that examine the lending behavior of Japanese banking firms from theviewpoint of intertemporal maximization principle. 2
Second, since our data set contains four different types of banks: city banks, long-term
credit banks, trust banks and regional banks, the following analysis will be conducted bycategorizing our sample banks broadly into two types of banks: regional banks and major banks(city banks, long-term credit banks and trust banks). 3 There are noticeable differences between
the two types of banks in terms of size, position in inter-bank market, composition of liabilities
and so forth. It is interesting to see how the lending behavior differs between the two types ofbanks.
Third, the sample period of our panel data set is from the fiscal year of 1976 to 1995,
which includes numerous interesting events in the financial scenes. To mention a few, financial
liberalization and internationalization had been under progress and large firms became lessdependent on bank loans and raised the fund directly from capital market. In the midst of the
bubble period from the late 1980’s to the early 1990’s banks increased bank credit tied with realestate enormously. Soaring asset prices were followed by sharp fall of them, which plunged anumber of banks into financial troubles, which some argue is mainly responsible for lingering
recession of the Japanese. The Basle Accord, which was reached in June 1988, put anotherconstraint on banks engaging in international business in such a manner that the ratio of bankcapital to bank assets should be kept above the minimum requirement ratio, 8%. It presumably
affects the bank’s portfolio balance, especially lending activity.
The virtue of our dynamic modeling of banking firm is its ability to examine the effectsof such episodes on the bank’s lending behavior empirically. Specifically we focus on three
episodes and examine how each of them affected bank’s lending behavior. One is the role of realestate as collateral in loan contract in the bubble period. We examine for what type of banks andborrowers real estate is important as collateral. 4 The second episode we are interested in is theintroduction of regulation on bank capital (Basle Accord) that took full effect in 1993. New2

regulation on bank capital can be incorporated formally into our dynamic model and its effect onlending behavior is examined empirically. The third episode we are interested in is the extent towhich a shift of deposits from one bank to another, which occurred soon after the collapse oflarge financial corporations in November in 1997, affects the lending behavior of banks. This isnothing but to investigate into the substitutability of reservable deposits and the other items ofbalance sheet of banks. As was discussed earlier, imperfection of substitutability is necessary forthe shocks to the banks’ balance sheet to be propagated into the real economy by way of bankloans.
Fourth, we estimate the investment function of non-financial firms by industry and firmsize. Large firms have much more variety of ways of financing than small firms. Therefore it isexpected that bank loan is much more important for small firms. Estimation of investment
function by firm size enables us to examine this conjecture. Furthermore, based on the estimatesof investment function, we simulate the extent to which a change in loan supply affects theinvestment activities with different firm size to obtain the impact on the aggregate investment.
Now we preview our main findings. First, we find that real estate played a vital role as
collateral for loans to small and/or non-manufacturing firms by major banks such as city banks,
long-term credit banks and trust banks. Progress of financial liberalization enabled large nonfinancial corporations, especially in manufacturing industries, affiliated with these banks to raise
funds directly from capital market, which made the banks lend to small and/or nonmanufacturing firms with which they had not established close ties. The problem of asymmetric
information is severe in this situation, and it is the real estate as collateral that has a role tomitigate this problem by decreasing the default risk. Note that real estate had been correctly
judged to be the most safe form of collateral for the entire postwar period until 1990 since realestate price had not ever declined significantly and loans were only for a certain proportion (60 –80%) of the appraised market value.
Second, the loans by regional banks are quite sensitive to deposits and this is especially sofor loans to small and/or non-manufacturing firms. Third, as for the introduction of BIS capital
adequacy requirements on bank capital, we detect significant effect on lending behavior of majorbanks. Fourth, expenditure on fixed investment of small firms are much more sensitive to bankloans than that of large firms. Our simulation results indicate that the contraction of loan supply3

in the early 1990’s as well as the collapse of large financial institutions in fall of 1997 hadtremendous negative effects on fixed investment of small and non-manufacturing firms.
Taken together, our evidence implies that the shocks in the asset markets are propagatedinto the real activity of bank-dependent firms via bank loan and thus the lending channel is
especially important in the Japanese economy.
The paper is organized as follows. The next section will describe the major characteristicsof lending behavior of the Japanese banking firms in the late 1970’s to the middle of 1990’s,
while Section 3 describes the major characteristics of investment activities of the Japanese nonfinancial firms in the 1970’s to the 1990’s. Section 4 develops a dynamic model of banking firmand derives an Euler equation of loans. Section 5 describes the data set employed for estimationand discusses econometric issues in estimating the Euler equation and testing hypotheses on the
bank’s lending behavior. Section 6 shows the estimation results and interprets them. Section 7
estimates the investment function by firm size with special focus on the role of bank loan andthen evaluates the effects of change in loan supply on investment quantitatively by simulation
technique. Section 8 concludes this study.

2. Characteristics of Lending Behavior by Japanese Banks in the late 1970’s to the 1990’s
It is our task in this section to describe the characteristics of bank lending on the basis of
our panel data set, which is collected from the Nikkei NEEDS Company Data Base. Our data set
consists of 139 banks for the period of 1976 to 1995. The sample banks are categorized into fourtypes of banks: 7 city banks, 3 long-term credit banks, 7 trust banks and 122 regional banks.
They are chosen on the criterion that there are no large merges or acquisitions during the sample
period. 5
Let us first look at the growth rate of bank loans for the sample period. Figure 1 shows thegrowth rate of bank loans separately for the four types of banks. Table 1 gives a summary tableof the average of growth rate of bank loans for the whole sample period (1977-1995) as well as
three subperiods (1977-1986, 1987-1990, and 1991-1995). The pattern of growth rate is quite
similar across the four types of banks. There are spikes in the growth rate for the period of 1982,
1985, and 1990. The growth rate also shares a common increasing trend from 1986 to 1990 socalled “ bubble period “ except for trust banks. The growth rate fell sharply in 1991 with the4declining trend prevalent for the rest of our sample period. In particular the growth rate plunged
into negative in 1993 for long-term credit banks and in 1994 for city banks and trust banks.
Contrasted with two-digit growth rate during the bubble period, the average growth rate of bankloans was only 1.07 %, -0.02 %, 0.06 % and 3.81 % , respectively, for city banks, long-term
credit banks, trust banks and regional banks for the period of 1991 to 1995. Note that a regulation
on loans to real estate industry took effect in April in 1990, which might have had a direct effecton bank lending.
Three characteristics of bank lending in the late 1970’s to the 1990’s deserve pointing out.
First of all, bank lending shifted from large firms to small firms, irrespective of the type ofbanks.6 Figure 2 shows the proportion of bank loans to small firms over 1977 to 1995. Table 2
summarizes the sample mean of the proportion of small firms in loan stock for the whole sample
period (1977-1995) and three subperiods( 1977-1986, 1987-1990, and 1991-1995). The largest
shift is observed for trust banks. The share of small firms was merely 17.8 % in 1977 and it
jumped up to 44.1 % in 1988. The share of small banks in bank loans also increased by more than
20 percentage points for city banks and long-term credit banks. Even the regional banks, whose
proportion of bank loans to small firms is by far larger than any other type of banks, increases theshare of small banks in bank loans gradually in the 1970’s to the 1980’s.
Secondly, we observe an interesting pattern of bank loans secured by real estate. Figure 3
shows the proportion of bank loans secured by real estate and Table 3 gives the sample mean of
the proportion of bank loans secured by real estate. For city banks the proportion rose noticeably
during the middle of 1980’s to the early 1990’s, reversing a declining trend from 1977 (18.5 %)to 1985 (15.1 %). It started to rise in 1986 and reached its peak in 1991 (23.0%). Although theproportion exhibits a sharp declining trend for long-term credit banks and trust banks from thelate 1970’s to the late 1980’s, it leveled off in the 1990’s. For regional banks the proportion israther stable in the range of 36.5 % (1977) to 39.9 % (1993). Casual observation hints that sharp
hike of land price in the bubble period might be responsible for a rise in loan shares secured byreal estate for city banks or more dependence of loans on real estate for long-term credit banks
and trust banks.
The third characteristic, which is related to the second, is a tilt of bank loans toward nonmanufacturing industries especially tertiary industries. Figure 4 shows the proportion of bank5

loans to non-manufacturing industries for the four types of banks. What is common to all types ofbanks is an increasing trend of bank loans to non-manufacturing industries until 1990. The trend
is especially noticeable for long-term banks and trust banks. The share of loans to nonmanufacturing industries is 56.4% and 62.0%, respectively, for long-term banks and trust banks
in 1977, but it rises to as high as 90.5% and 93.1% in 1990. Table 4 gives the average growth rateof bank loans for major industries. The growth rate of bank loans is highest for financial
institutions for the period of 1977-1986 and 1987-1990, followed by personal loans excluding
housing loans, real estate, and service industries. The growth rate of bank loans to financial
institutions amounts to 30 % and 25 % per annum in the period of 1977-1986 and 1987-1990,
respectively. Since financial institutions include non-deposit money corporations engaged in theprovision of finance, credit and investment, so called non-banks, and they loaned out what theyborrowed from banks to real estate and construction industries in the late 1980’s to the early
1990’s, the average growth rate of loans to real estate industry in Table 4 will probably
underestimate the actual figures.
By combining the three characteristics stated above, it is conjectured that real estate played
a collateral role in loan contracts between banks and new customers in the bubble period. When
there exists asymmetry in information between lenders and borrowers, real estate, expected torise in value or at least not to decline in value by any serious amount or for any sustained periodof time, will be quite potent to lessen the default risk. Major banks increased their search forlending to small and/or non-manufacturing firms who were new clients. Similarly regional banks
also sought new clients (as some of their established clients switched to some of the major banks)
in the process of taking them away from credit associations and credit cooperatives. Thus as bothmajor and regional banks tilt their loans toward new clients, it is likely that asymmetry of
information becomes more severe.

We will examine this conjecture empirically, based on anintertemporal model of banking firms developed in Section 4.

3. Characteristics of Investment Behavior by Japanese Non-financial Firms in the late 1970’s to
the 1990’s
In this section we describe the characteristics of fixed investment of the Japanese non-6

financial firms in the late 1970’s to the 1990’s. In particular we place emphasis on thecomparison of the investment behavior across industry and firm size since the effect of banklending on investment is expected to vary, depending on the firm size and the industry.
We use the quarterly data in the Quarterly Report of Financial Statements of Incorporated
Business or Hojin Kigyo Tokei Kiho (abbreviated as QRFS) of the Ministry of Finance. The
QRFS reports quarterly the major items of balance sheet and profit and loss statement
disaggregated by firm size for the manufacturing industries as well as for non-manufacturingindustries. The measure of firm size is capital. In original format there are five size classes offirms, but for the purpose of simplifying the analysis we aggregate the size categories into threegroups, each of which corresponds to small, medium, and large firm group. Small firm group
includes the firms under ¥ 100 million yen in capital. Medium firm group includes the firms
between ¥ 100 and ¥ 1000 million yen in capital. Large firm group includes the firms over ¥ 1000
million yen in capital.
.

There is one problem in making use of the time series data in the QRFS. That is discontinuityof the data series. This arises from a complete renewal of the corporations in the sample every
April. After renewing the sample in April, they are fixed for one year. It is necessary to adjust thediscontinuous data series in a consistent manner. Fortunately the survey contains for the main
items in the balance sheet the values of the beginning and end of each period covered for thesame sample. This implies that we can compute the time series of flow variables consistently. As
for a series of investment, we use the new increment of construction in progress and assets ofproperty, plant, and equipment. The loan data includes that in liquid liabilities as well as in fixedliabilities. Once the flow series become available, perpetual inventory method can be used toconstruct the series of capital stock and loan outstandings.7
The shares of gross fixed investment in 1997 nominal private aggregate fixed investment
are 3.5%, 3%, 13.9%, 11%, 7.1%, and 24.8% for small, medium, and large firm groups inmanufacturing industries and for small, medium, and large firm groups in non-manufacturingindustries, respectively. The rest of fixed investment (36.7%) comes from financial institutions
and small non-incorporated businesses, the latter of which occupies most of the share. Figure 5
depicts the gross fixed investment rate from the second quarter of 1975 to the first quarter of
1998. The investment rate of manufacturing industries is shown in Figure 5-1. We observe three7

peaks in investment rate common to three firm groups. They are centered around 1980, 1984 to
1985, and 1989 to 1991. Each corresponds to boom periods in the Japanese economy. However,the peaks are more pronounced for small firm group and for the long-lived boom called Heisei
Keiki starting from the fourth quarter of 1986. We also observe precipitous fall of investment
rates from 1991 to 1994 for all firm groups during severe downturns after the bubble burst(Heisei Fukyo). Figure 5-2 shows the investment rate of non-manufacturing industries. Here thepeaks of investment rate are less conspicuous except for small firm group. For small firm groupthe peak reaches as high as 6.6% in the fourth quarter of 1989. Common to all the firm groups isthe sharp fall of investment rate from 1991 to 1994.
Table 5 shows the sample average of the gross fixed investment rate over the whole sample
period as well as three subperiods. They are the second quarter of 1975 to the fourth quarter of
1986, the first quarter of 1987 to the first quarter of 1991(Heisei Keiki), and the second quarter of1991 to the first quarter of 1998 (Heisei Fukyo). The average gross fixed investment rate ishighest in the second subperiod and lowest in the third subperiod for manufacturing as well as
non-manufacturing industries. This indicates that the fixed investment was the driving force ofthe booms in the late 80’s, although it remained stagnant in the 90’s. Note that the volatility of
fixed investment in terms of standard deviation is highest in small firm group for bothmanufacturing and non-manufacturing industries.

4. Intertemporal Model of Banking Firms
We construct an intertemporal model of banking firms and derive an Euler equation to be
examined empirically. Our model is general enough to test a variety of hypotheses on the lending
behavior of banking firms such as the role of collateral, effect of regulation on bank capitalrequirement, excess sensitivity of bank loans. The banking firm chooses the optimal level of bankloans, call money borrowing and security holdings given the level of deposit and bank capital sothat the value of firm can be maximized.8 9 The banking firm is subject to two constraints:
reserve requirement on deposits and minimum requirement on the ratio of bank capital to bankassets. The balance sheet of the banking firm is shown in Figure 6.10
There are two types of cost associated with making loans. One is the agency cost arisingfrom the asymmetry between lenders and borrowers, which depends on how long the bank8

customers have been affiliated with the banks. The longer the bank-customer relationship is, theless severe the asymmetric information is. The agency cost is highest for the loans to new clients.
Since larger change in loan stock usually accompanies larger volume of loans to new customers,
especially during our sample period, we assume that the agency cost is an increasing function of
net change in bank loans.11
The other loan-related cost is the expected cost of default. The larger the loan stock is, thelarger is the probability that part of the loan goes into default. Hence it is assumed that thedefault cost is an increasing function of loan stock. However, the default cost may depend onwhether the loan is secured and if so on the value of collateral. As was shown in the previous
section, land has played a vital role of collateral in Japan. We examine the role of land as
collateral formally by including the change in land price as a shift variable in the default costfunction.
Lastly, note that the loan-related cost depends on the size of bank. The same change ofloan stock will cost less for larger banks. Larger bank has the capacity to diversify loan risks. We
measure the size of bank by the sum of deposit and bank capital.
To summarize, the loan-related cost is an increasing function of net flow of bank loans andloan stock and a decreasing function of the sum of deposit and bank capital.
In what follows we assume that the banking firm will hold minimum balance of required
reserves since it bears no interest. Maximization of the value of banking firm subject to the twoconstraints and the balance sheet identity yields the first order condition for bank loans.
According to the first order condition, the optimal level of bank loans is determined in such amanner that the marginal cost of loans may be equal to the discounted marginal revenue of loans
in the next period including the savings of the cost associated with adjusting the loan in the next
period and the expected default cost arising from the change in the current loan stock. Note thatthe marginal cost will be higher for banks with the capital requirement constraint binding.
For the purpose of estimation, we assume the following type of loan-related cost function:

9

2
 
 α1  FLtFLt
C ( FLt , Lt −1 , Dt −1 + CAPt −1 ) = α 0 
− µ  + 
− µ   ( Dt −1 + CAPt −1 )  Dt −1 + CAPt −1 2  Dt −1 + CAPt −1
 


Lt −1
+ α 2
 ( Dt −1 + CAPt −1 )
 Dt −1 + CAPt −1 

(1)
where

Lt −1 : loan stock at the end of period t-1
Dt −1 : deposit stock at the end of period t-1
FLt : net flow of bank loans in period t
CAPt −1 : bank capital at the end of period t-1

α 1 > 0, α 2 > 0
Substituting the loan-related cost function above into the first order condition and arranging
terms under the assumption that the banking firm forms expectations rationally, we obtain thefollowing optimal loan supply equation.

 FLt +1 FLt = β 0 + β1 (1 + rt +1 ) −1
− (1 + rt +1 ) −1 
Dt −1 + CAPt −1
 Dt + CAPt q+ β 2 (1 + rt +1 ) (rL,t +1 − rC ,t +1 ) − λt + ν t +1
α1

(2)

−1

where β0 = µ −

α0
α1

β1 =

α0 −α2
−µ
α1

β2 =

1
α1

rt +1 : one-period required rate of return in period t
rL ,t +1 : interest rate on bank loans in period t+1
rC ,t +1 : call rate in period t+1

λt : non-negative Lagrangean multiplier associated with the requirement onbank capital10q : minimum required ratio of bank capital to loans

ν t +1 : forecast errorEq.(2) is the basic equation to be estimated in the subsequent sections. The ratio of net
lending to the bank size, measured by the sum of deposit and capital, is a function of interest
differentials between the loan interest rate and the call rate, required rate of return, non-negative
Lagrangean multiplier, and the future lead variables of the ratio of net lending to the bank size.
It should be noted that the forecast error v t +1 is uncorrelated with any variables containedin the bank’s information set in period t under rational expectation assumptions. This property
proves to be very useful in conducting a test of some hypotheses on the banking firm’s behavior.

5.Procedures of Estimation and Hypotheses Testing of the Bank’s Lending Behavior
To begin with, we modify eq.(2) so that we may test several hypotheses on the bank’s
lending behavior. Then we will discuss econometric issues in estimating the Euler equation,
followed by the description of the data set.

A. Formulation of Hypotheses on the Lending Behavior of Banking Firm
Let us first state the stripped-down version of the loan supply equation where theregulation of bank capital requirement is not binding. It is written as FLt +1 FLt = β 0 + β1 (1 + rt +1 ) −1
− (1 + rt +1 ) −1 
Dt −1 + CAPt −1
D
CAP
+
t 
 t

(3)

+ β 2 (1 + rt +1 ) −1 (rL,t +1 − rC ,t +1 ) + ν t +1

As was seen in the previous section, the proportion of loans secured by real estate rose in the
bubble period for city banks. For the other types of banks, its declining trend ceased in the same
period. These observation hints that land price might affect the loan supply schedule of thebanking firms.
Formally this idea can be incorporated into the model by formulating the rate of change in land
price as a shift variable of the default cost function.12

11

Specifically, the parameter of α 2 in theloan-related cost function can be expressed as a linear function of the rate of change in land price.
In other words,

α 2 = α 2′ − α 3′

ptL−1
ptL− 2(4)

where α 3′ > 0

ptL−1 : land price in period t-1
An increase of land price leads to a rise in collateral value and hence reduces the expected cost ofdefault.
It is a well-known procedure to add extra terms such as cash flow or some kind proxy ofinternal funds in investment function to test the existence of liquidity constraints of non-financial
firms. The same procedure is applicable in our case to test whether the banking firm isconstrained in the capital markets. Suppose that a negative shock hits the bank’s deposits and alarge amount of deposits is withdrawn unexpectedly from the bank. When the banking firm is notconstrained in the capital market, it can offset the shocks to the bank’s deposits completely byborrowing in the call market, or issuing CD’s even if the bank does not hold securities to cushion
the negative shock. Thus its lending behavior will be unaffected by shocks. On the other hand,when the banking firm is constrained in the capital market and do not hold enough liquid assets,then the lending volume will be sensitive to the shocks and the original shocks on the bank’sbalance sheet will be transmitted to the real economy unless the borrowers have perfect
substitutes for bank loans. This implies that the lending channel is indeed working in transmitting
the policy shocks into the real economy. Thus whether the banking firm is constrained in thecapital market or not has important implications on the transmission mechanism of monetary
policy.
In the framework of intertemporal model developed above, it is easy to test rigorously
whether the banking firm is constrained in the capital market. Since the forecast error in eq.(2) is
uncorrelated with any elements in the bank’s information set in period t, the lending behavior byunconstrained banks will not be affected at all by them even if they are added to the explanatory
variables. One natural candidate will be the growth rate of deposits.13 If the growth rate ofdeposits does matter for the bank’s lending, it is an indication that the bank is constrained in the12capital market and does not have enough liquid assets. The testing procedure is simply to add thegrowth rate of deposits to the explanatory variables and test whether its coefficient is significant
or not.
When we incorporate the two hypotheses on lending behavior: collateral role of land inloan contracts and imperfection of capital markets facing the banks, the basic loan supply
equation (2) is augmented as follows.

 FLt +1 FLt = β 0 + β1 (1 + rt +1 ) −1
− (1 + rt +1 ) −1 
Dt −1 + CAPt −1
 Dt + CAPt pL∆Dt+ β 2 (1 + rt +1 ) −1 (rL,t +1 − rC ,t +1 ) + β 3 (1 + rt +1 ) −1 Lt + β 4+ ν t +1
pt −1
Dt −1

where

β1 =

α 0 − α 2′
−µ
α1

β3 =

α 3′
α1

(5)

Now we briefly discuss the identification problem. We term eq.(5) as loan supply equation, butsome may argue that the land price is a proxy of future economic activity and appears in the
demand equation of loans. Suppose that the loan demand equation is specified as a function ofloan rate, rate of change in land price and activity level such as GDP or sales variable. Then it iseasy to verify that both the loan supply equation (5) and the loan demand equation are
overidentified. One of the key conditions for identification is the accessibility of call market only
by financial institutions, which implies that the call rate does not appear in the loan demand
equation.
Before proceeding to the formal analysis, let us have a brief look at the growth rate ofdeposits. Figure 7 shows the growth rate of deposits over 1977 to 1995 for each type of banks
and Table 6 summarizes the average growth rate of deposits for the whole sample period as wellas three subsample periods (1977-1986, 1987-1990 and 1991-1995). We observe spikes in thegrowth rate of deposits for 1982, 1985 and 1990, which are also found in the growth rate of loans

13

in Figure 1. The growth rate of deposits fell sharply in 1991 and in actuality the averaged growthrate of deposits during 1991 to 1995 is negative for all types of banks but regional banks. A
similar trend is also observed for the growth rate of loans. Since the level of deposits is mainly
determined by the non-financial sectors and the banks take it as given, it seems that the
movement of bank loans is quite affected by that of deposits. In fact the simple correlation
coefficient between the growth rate of loans and that of deposits over the whole sample period is0.8336, 0.6907, 0.5921, 0.6836 for city banks, long-term credit banks, trust banks and regionalbanks, respectively. Further investigation into the relationship between bank loans and deposits
will be made in the next section.
Lastly, the efficacy of regulation on bank capital is formally tested by adding the
Lagrangean multiplier in either eq.(3) or (5). One caveat is that in general the Lagrangean
multiplier per se is not observable, so that some proxy of the extent to which the regulation isbinding should be employed instead. It is quite difficult to choose the relevant measure for thispurpose. Therefore we decide to use the realized ratio of bank capital to total asset as a proxy.
Whether the regulation is binding or not can be seen simply by testing the hypothesis that thecoefficient of bank capital/total asset ratio is zero.14

B. Estimation Method
The loan supply equations are estimated by the generalized method of moments (GMM)
technique originally developed by Hansen(1982). We get rid of firm-specific effects by firstdifferencing equations, as is suggested by Anderson and Hsiao(1981). Under the assumption ofrational expectations, candidate for the instruments for the first-differenced form should be thevariables included in the information set in period t-1. Our basic list of instruments in estimation
includesconstant

term,

twice

laggedfirst-differenced

LHS

variable,∆(1 + rt +1 ) −1 ,∆(1 + rt +1 ) −1 ( rL,t +1 − rC ,t +1 ) and thrice lagged ∆(1 + rt +1 ) −1 ( rL,t +1 − rC ,t +1 ) . The twice lagged
first- differenced GDP growth rate is also included as an instrument to account for thedeterminant of loan demand. The twice and thrice lagged variables of ptL / p tL−1 are added as
instruments to estimate the loan supply equation with the land price as explanatory variable. On
the other hand, the twice and thrice lagged variables of ∆Dt Dt are added as instruments toestimate the loan supply equation with the growth rate of deposit as explanatory variable.

14

C. Data Set Description
The loan supply equations are estimated on the basis of panel data set of Japanesecommercial banks, which is taken from the Nikkei NEEDS Company Data Base. Our panel data
set consists of 139 banks for the period of 1976 to 1995. There are 7 city banks, 3 long-term
credit banks, 7 trust banks and 122 regional banks. It should be noted that the regional banks arequite different from major banks in terms of size, loan customers, position in the inter-bankmarkets and so forth. Table 7 compares the selective figures from the banks’ balance sheets
between major banks and regional banks based on our panel data set. They are the sample mean
for the period of fiscal year 1976 to 1995.

The sample mean of total assets is 1,285 billion yen

for regional banks and 16,091 billion yen for major banks, which is 12.5 times as large as that ofregional banks. The stock of call loan is larger than that of call money borrowing for regionalbanks, but the opposite is true for major banks. It implies that in general the regional banks
supply the surplus fund to the call market, while major banks raise the fund in need there. Morethan half of the total liabilities is time deposits for regional banks, but it is only 30 % for majorbanks. However the proportion of certificate of deposits (CD) in total liabilities is higher for
major banks (2.88%) than that of regional banks (0.83%). To sum up, comparison between the
two types of banks suggests that it will be desirable to conduct the empirical analysis separately
for major banks and regional banks.
We also conduct the subsequent analysis by disaggregating loans in two manners. First,
loans are divided into those to small firms and to large firms. As was seen in the previous section,
the banks have increasingly put more weight on small firms in making loans that are relatively
new customers to the banks. Therefore we conjecture that the degree of asymmetry in
information between lenders and borrowers will be more severe for small firms, which might bereflected on the lending behavior of banking firms.
We also disaggregate loans into those to manufacturing industries and non-manufacturingindustries. As was seen in the previous section, the proportion of loans to non-manufacturingindustries exhibited an increasing trend over the 1980’s, especially in the bubble period. The
growth pattern of non-manufacturing industries is quite different from that of manufacturingindustries. Hence the tilt of bank loans toward non-manufacturing industries might reflect the15changing industry structure in the Japanese economy. Estimating loan supply equations
separately for manufacturing industries and non-manufacturing industries has the merit to control
these demand-side conditions of loans.
Description of the variables used for estimation is now in order. The loans and deposits
variables are taken from the balance sheet of banks.15 The size of bank is defined as the sum ofdeposits, bank debentures, and bank capital. The flow of loans and deposits are simply calculated
as the current end-of-fiscal-year stock minus the end-of-fiscal-year stock in the previous year.
The interest rate on loans is computed as the interest receipts on loans and discounts divided by
the end-of-fiscal-year loan stock in the previous year. The required rate of return and the call rate
are the average of the government bond yields listed on Tokyo Stock Exchange(10 year) andcollateralized overnight call rate, respectively, over the fiscal year. We assume that all the banks
face the same required rate of return and the call rate. As for the land price, it is assumed that thebanks face different land prices, depending on the location of their head offices. When they arelocated in the prefectures of Tokyo, Kanagawa, Saitama, Chiba, Aichi, Osaka, Kyoto, Hyogo,
Nara, and Wakayama, we use the land price index of six largest cities.16 For the other locations,
we use the land price index of the other cities except six largest cities. They are taken from Japan
Research Institute of Real Estate.

6.Estimation Results and Interpretations
Total Loans
The basic loan supply equation (3) is estimated separately for major banks and regionalbanks with the ratio of total loans to the bank size as dependent variable. The estimation isconducted for the whole sample period, 1981-1994 as well as three subsample periods: 19811986, 1987-1990 and 1991-1994.17 The 1987-90 period corresponds to the bubble period whenasset prices soared, while the 1991-94 period corresponds to the period when the bubble burst.
By dividing the whole sample period into three, it is expected that we may capture the different
responses of bank lending to the change in economic conditions.
The estimation results of the basic loan supply equation are not satisfactory on the whole.
For major banks the interest rate differential exerts a significantly positive effect on the loan
supply irrespective of the sample period chosen. For regional banks the coefficients of the16interest rate differential are also significantly positive except for the period of 1987-1990 and1991-1995. However the J-statistics indicates that overidentifying restrictions are rejected at thestandard level for all the cases but the 1991-1995 regression for major banks. This evidence hints
that the interest rate differential alone cannot adequately explain the growth rate of loans and thatthe basic model is misspecified.
Table 8 shows the estimation results of eq.(4), extended version of eq.(3). We firstexamine the estimation results for major banks, which are shown in Table 8-1. When the loan
supply equation is estimated over the whole sample period, the interest rate differential as well as
the growth rate of deposits has positive and significant coefficients. The overidentifying
restrictions are satisfied for the case where both the rate of change in land price and the growthrater of deposit are included as explanatory variables. Surprisingly the rate of change in land
price picked up the wrong sign and that significant. However, once the sample period is divided
into three, a different story shows up. For the period of 1981-1986 all the explanatory variables
including the rate of change in land price are significant and that the overidentifying restrictions
are not rejected. For the period of 1987-1990 both the rate of change in land price and the growthrate of deposits have positive effects on loan supply and they are significant. For the period of
1991-1994 the rate of change in land price loses its significance, but the interest differential aswell as the growth rate of deposits keeps their significance. 18
To sum up the results, the rate of change in land price is a significant variable for theperiod of 1981-1986 and 1987-1990. Moreover the coefficient of land price change in the periodof 1987-1990 is larger than that in the period of 1981-1986 by 32% for the case where all theregressors are employed. The coefficient of the growth rate of deposits is positive and significant
irrespective of the sample period. It indicates that the major banks face imperfect capital market,
although the degree of imperfection was somewhat alleviated in the period of 1991-1994. Theinterest rate differential is significant except for the bubble period. Since the interest differential
is the fundamental variable to measures the profitability of lending activities, we can infer thatthe lending behavior of city banks in the bubble period was somewhat different from that in theother periods judging from the negative sign of the interest differential.
Now we discuss the estimation results for regional banks, which are shown in Table 8-2.
When the loan supply equation is estimated over the whole sample period, either the growth rate17

of deposits or the interest differential is significant, but the overidentifying restrictions aredecisively rejected. Turning to the estimation results for the three subsample periods, the growthrate of deposits exerts positive and significant effect on loan supply for the period of 1976-86.
Moreover, the overidentifying restrictions are not rejected when all the regressors are taken
together. For the period of 1987-1990 as well as 1991-1994 all the explanatory variables gain
statistical significance and the overidentifying restrictions are not rejected. Note that the growthrate of deposits remains significant irrespective of the sample period chosen. Furthermore thecoefficient estimates of the growth rate of deposits for regional banks are larger in size than thosefor major banks for most of the cases. This evidence suggests that regional banks are more
vulnerable to the shocks to deposits and hence their lending behavior is affected much moresensitively by the deposit shocks. In other words, regional banks are constrained in the capital
market much more severely than major banks.
Contrasted with the response of bank loans to deposits, the coefficient estimates of the rateof change in land price is larger for major banks. As was seen in the previous section, the shift ofbank loans to small and/or non-manufacturing firms is more noticeable for major banks than forregional banks. It implies that major banks are likely to make loan contract with relatively new
customers the information of which has not been accumulated within the banks, so that collateralwill play a more important role. This can explain why the loans by major banks are more
responsive to land price. We scrutinize this conjecture by estimating the Euler equation
separately for loans by firm size and by industries.

Loans by Firm SizeFirst we estimate the basic loan supply equation (3) separately for loans to small firms andlarge firms. Two loan supply equations are jointly estimated. The coefficient estimates of theinterest differential are shown in Table 9. For major banks the coefficient of interest rate
differential is significantly positive for all the subsample periods irrespective of firm size. The
overidentifying restrictions are not rejected at the 5% significance level for the period of 19871990 and 1991-94. For regional banks some of the interest differential coefficients are
significantly positive, but the overidentifying restrictions are decisively rejected for all the cases.
Table 10-1 shows the estimation results of the extended version, eq. (4) for major banks.

18

Although the rate of change in land price pick up the wrong sign and the overidentifying
restrictions are rejected for all the cases over the whole sample period, we can detect aninteresting pattern of lending behavior once the sample period is split into three. This suggeststhat the pattern of lending behavior for major banks is changing over time and that estimation of
the Euler equation without proper consideration for the sample period might lead tomisunderstanding of the bank’s behavior. For the period of 1981-1986 the interest differential
has significantly positive effects on both small firm and large firm loans. Besides the interest
differential, the rate of change in land price as well as the growth rate of deposits are positive andsignificant for loans to large firms. Moreover the overidentifying restrictions are not rejected at
the 5 % significance level when all the regressors are taken into consideration. For the period of
1987-1990 the growth rate of deposits are positive and significant for both types of loans, but therate of change in land price remains significant only for the loans to small firms when all theregressors are included in regression. For the period of 1991-1994 both the interest differential
and the growth rate of deposits have significantly positive effect on the loans to large firms, but
we cannot detect any significant explanatory variables for the loans to small firms. Note that in
the bubble period the rate of change in land price has significantly positive effect on loans tosmall firms, but not to large firms. This evidence seems to support our conjecture that real estateis more potent as collateral in lowering the cost associated with asymmetric information for loans
to borrowers with shorter customer relationship.19
Table 10-2 shows the estimation results of loan supply equations by firm size for regionalbanks. Over the whole sample period the growth rate of deposits is the only significant variablein the loan supply equation to small firms and none of the explanatory variables can explain theloans to large firms satisfactorily. Moreover, the overidentifying restrictions are decisively
rejected. By dividing the sample periods into three, we find that the growth rate of deposits has
always positive and significant effects on loans to small firms. To the contrary, the growth rate ofdeposits is not significant in the loan supply equation to large firms. In addition to the growthrate of deposits, both the rate of change in land price and the interest differential exert
significantly positive effects on loans to small firms in the period of 1987-90. To sum up, we seethat it is the growth rate of deposits that matters for loans to small firms by regional banks.
Moreover, in the bubble period land also played a collateral role in lending to small firms and19

mitigated the problem of asymmetric information between lenders and borrowers.

Loans by Industries
Here we estimate the loan supply equation by industries. In particular we divide the totalloans to manufacturing and non-manufacturing industries. The virtue of estimating the loan
supply equation separately for different industries is to control the demand-side conditions ofloans. It is also expected that we may see how the banks supplied loans in response to thechanging industrial structure. First, the basic loan supply equation (3) is estimated jointly forloans to manufacturing and non-manufacturing industries. For major banks we obtain positiveand significant coefficients of the interest differential for loans to non-manufacturing industries,
but we fail to obtain significantly positive coefficients for loans to manufacturing industries.
Moreover, the overidentifying restrictions are rejected for all the subperiods but the period of
1987-90. For regional banks the interest differentials affected the loan supply to manufacturingindustries as well as non-manufacturing industries in a significantly positive manner for most of
the subperiods, but the overidentifying restrictions are rejected for all the cases.
Next, we estimate the extended version of the loan supply function. The estimation
results are shown in Table 11. Table 11-1 shows the results for major banks. When the loan
supply equation is estimated over the whole sample period, we find that some coefficients of theinterest differential and the growth rate of deposits are significantly positive, although theoveridentifying restrictions are rejected in all the cases. This again hints the need to estimate theloan supply equation by dividing the sample period into the subperiods. For loans to nonmanufacturing industries, it turns out that the growth rate of deposits has always positive andsignificant effects on loan supply. Furthermore, the rate of change in land price gains its
significance in the period of 1981-86 and 1987-1990. Note that the coefficient of land pricechange in the period of 1987-1990 is 2.6 times as large as that in the period of 1981-86 for thecase where all the explanatory variables are taken into consideration. The interest differential isalso positive and significant for the period of 1981-86 and 1991-94. To the contrary, we cannot
detect meaningful explanatory variables for loan supply to manufacturing industries except for
the land price change in the period of 1991-94.
Table 11-2 shows the estimation results of loan supply equation by industry for regional

20

banks. When it is estimated over the whole sample period, some of the coefficients satisfy thesign conditions the theory requires, but the rejection of overidentifying restrictions indicate thatthe model is misspecified. When we estimate the loan supply equation for the subsample periods,
we obtain much more reasonable coefficient estimates. For loans to non-manufacturing industries,
we obtain positive and significant coefficients on both the growth rate of deposits and the interest
differential irrespective of the sample period. In addition to these explanatory variables, the rateof change in land price has positive and significant effect on loans in the period of 1987-90. For
loans to manufacturing industries, the coefficients of the growth rate of deposits is positive andsignificant for the period of 1981-86, and the interest differential as well as the land price change
has positive and significant effects on loan supply for the period of 1991-94. However, none of
the explanatory variables are economically meaningful in the period of 1987-90.
What are common to lending to non-manufacturing industries by major banks andregional banks is the sensitivity of loans to deposits irrespective of the subsample periods and thepositive response of loans to the land price appreciation in the bubble period. Note that the
response loans to land price change by major banks is much larger than that by regional banks.
This is consistent with our early observation that the shift of loans toward non-manufacturingindustries is much more rapid for major banks than for regional banks and hence major banks are
more liable to the problem of asymmetric information.

Effect of Basle Accord on Lending Behavior
Lastly we examine the introduction of regulation on bank capital that took full effect inMarch in 1993 (The Basle Accord). According to the Accord, the banks should keep the ratio ofbank capital to risk-weighted assets above 8 % for banks dealing with international business.
When this condition is binding, the positive Lagrangean multiplier does appear as an additional
factor to affect the growth rate of loans (Recall eq.(2)).
To test whether the Basle Accord affects the lending behavior of banks, we reestimate theEuler equation by adding the variable closely correlated with the level of Lagrangean multipliersince the Lagrangean multiplier per se is not observable. Our candidate for the proxy of themultiplier is an observed ratio of bank capital to bank assets. The ratio of bank capital to riskadjusted bank assets on the basis of BIS standard is available for the banks that have international

21

operations since 1989, when the capital requirements became effective. Therefore, we reestimate
the loan supply equation with the bank capital ratio as an additional explanatory variable for theperiod of 1992-1994. Now the sample banks are reduced from 139 to 82.20

The estimation ofloan supply equation is conducted not only for total loans but also for loans by firm size andindustries. The estimation results are shown in Table 12.
The ratio of bank capital to bank assets has positive and significant effects on the lending
behavior of major banks, but not that of regional banks. That is, when the ratio of bank capital tobank assets is lowered for some reasons, the major banks will decrease lending. Our results thatthe capital requirement was binding in terms of lending behavior only for major banks withactive international business are consistent with the findings of Ito and Sasaki(1998).
When the loans are further broken down into those to small firms and large firms, it is seenthat the bank capital ratio exerts significantly positive effects on loans to large firms, but not tosmall firms. We also find that the bank capital ratio has larger positive effects on loans to nonmanufacturing industries. When the bank capital ratio falls, we also observe the reallocation ofloans by regional banks from manufacturing firms to non-manufacturing firms even though thetotal loan volume is unaffected.

7.Bank Lending and Its Macroeconomic Consequences: Evidence from the Estimation of
Investment Function
In the previous sections we obtained the evidence that loans to small and/or nonmanufacturing firms were quite sensitive to the shocks in asset markets. In the bubble period theloans to small and/or non-manufacturing firms by major banks were very much affected by achange of land price that affected the collateral value. The loans to small and non-manufacturingfirms by regional banks were quite sensitive to the change in deposits irrespective of the period
chosen.
Note that our evidence has very important implications as to the propagation mechanism ofshocks in the financial sector into the real economy. Suppose that a negative shock hits thebanking sector. Then major banks as well as regional banks will cut loans to small and/or nonmanufacturing firms more severely than to large firms and/or manufacturing firms. If the firms
hit by the loan cut do not have substitutes for bank loans, it will directly affect the investment

22

activity of those firms. This is the point to be examined in the subsequent sections.
Our empirical strategy is to measure the sensitivity of fixed investment to bank loans byindustry and firm size. The sensitivity of investment to bank loans can measure the degree ofsubstitutability among alternative financing method. It is conjectured that investment by smallerfirms is more sensitive to bank loans since they have much fewer alternatives to bank loans inraising the funds. Therefore we estimate the investment function by industry and firm size.
First, let us state the basic framework of investment function. We assume that there are
two types of firms in the economy. One type is the firm without any constraints in the capital
market. For this type of firm, investment is determined solely by the fundamental profitability ofinvestment, which is measured by Tobin’s marginal q. It is defined as the expected present valueof the future marginal products of capital divided by capital stock. The other type of firm faces
borrowing constraints. The level of investment for this type of firm is determined by theavailability of bank loans, which is measured by the growth rate of loans. For this type of firms,
investment will not increase even if the future profitability of investment improves, since thefirms cannot finance the investment expenditure. It is only when the loan supply to these firms
increases that they increase investment spending. Note that increase of loan supply will not affectthe investment activities of unconstrained firms as long as the future profitability of investment isunchanged.
The aggregate investment is thus determined by two factors: the marginal q and the growthrate of loans. To account for the delayed response of investment to these two factors, we
introduce the distributed lags in specifying the effects of the two factors on investment. We also
add the lagged investment rate to cope with the nonstationarity of investment rate. The
investment function to be estimated is written as:

N
N
It
I
∆Lt −i −1γγMq= 0 + ∑ 1,i t −i + ∑ γ 2,i+ γ 3 t −1 + ut
K t −1
Lt −i − 2
K t −2i =0
i =0where I t : gross fixed investment in period t

K t −1 : capital stock at the end of period t-1
Mqt : marginal q in period t
Lt : loan outstanding at the end of period t
23(6)

u t : disturbance term

The coefficients of the growth rate of loans measure the extent to which bank loans can besubstituted for another financing method. We exclude the current growth rate of loans from theexplanatory variables to avoid the simultaneity problem of loan demand and investment.
Therefore it is expected that the growth rate of loans in eq.(6) can measure the effects of loan
supply on investment.21
The series of marginal q is constructed in the following manner. First, the stochastic
properties of the two components of marginal q, the profit rate and the discount factor, areexamined. The profit rate is constructed as the ratio of operation surplus to the beginning-ofperiod capital stock. The discount factor is computed as (1 – depreciation rate)/ (1 + borrowinginterest rate) where the depreciation rate is assumed to be constant, 1.98% per quarter, acrossindustries.22 The unit root test is conducted for these two series and we detect unit roots for all theseries in manufacturing and non-manufacturing industries. Next we test the cointegration
relationship between the profit rate and the discount factor and find no cointegration relationship.
Therefore it is assumed that these two series follow the VAR model in first-difference form.
Finally, based on the VAR estimates, we construct the marginal q series.23
Figure 8 depicts the constructed series of marginal q from the fourth quarter of 1975 to thefirst quarter of 1998. Figure 8-1 shows the marginal q of manufacturing industries by firm size.
The level of marginal q is higher for small firms than for medium and large firms for most of theperiod. We observe two peaks in marginal q for small firm group in 1979 and 1989, each ofwhich corresponds to the peaks in investment rate. The marginal q falls sharply from 1991 to
1994 after the burst of the bubble. The marginal q series of medium and large firm groups arevery much alike and much smoother than that of small firm group. Figure 8-2 shows the marginal
q of non-manufacturing industries. Here we also observe two peaks in 1979 and 1989 for small
firm group. The marginal q series of medium and large firm group move very closely each otherand much smoother than that of small firm group.
Table 13 shows the sample average of the marginal q over the whole sample period as wellas three subperiods: the fourth quarter of 1975 to the fourth quarter of 1986, the first quarter of1987 to the first quarter of 1991, and the second quarter of 1991 to the first quarter of 1998. The

24

marginal q is highest in the second subperiod for small firm group of manufacturing as well as
non-manufacturing industries. On the other hand, the marginal q series of medium and large
firms in the bubble period are not discernibly difference from those before the bubble period.
However, the marginal q is lowest in the third subperiod irrespective of firm size and industry,
hinting how severe the downturns are after the burst of the bubble. Note that the volatility of
marginal q in terms of standard deviation is highest in small firm group of both manufacturing
and non-manufacturing industries.
Eq.(6) is estimated separately for each firm size group and industry by applying the Almonlag procedure. The lag length is taken to be 16 quarters .The estimation results are shown in
Table 14. The coefficient estimates in table show the sum of lagged coefficients of marginal q
and loan growth rate. The coefficients of marginal q are positive and significant for all the cases
but large firm group in non-manufacturing industries. The coefficients of the growth rate of loan
are significantly positive for small firm group in both manufacturing and non-manufacturingindustries, medium firm group in manufacturing industries, and large firm group in nonmanufacturing industries.24 It should be noted that the non-negligible proportion of small firms in
manufacturing as well as non-manufacturing industries are constrained in the capital market andthat their investment is bound by the availability of bank loans. 25
Our evidence confirms our conjecture that smaller firms do not have substitutes for bankloans, so that their investment activities are much more affected by a shock in the financial sector.
To assess the importance of bank loans to firms’ investment behavior quantitatively, we evaluate
the extent to which the shocks of bank loan supply affect firms’ investment activities bysimulation technique. Before proceeding to the formal analysis, we first show descriptively how
two financial events leading to the change in lending attitude of banks affected the actual loan
amount. One event is the introduction of restrictions on bank lending to real estate industry thattook effect in April in 1990 and lasted until December in 1991. The other is the failure of large
financial corporations in November in 1997. The empirical strategy is very simple. We computethe average growth rate of loans over the three years prior to the event and then subtract the
averaged growth thus computed from the actual growth rate after the event. What is computing isthe deviation of the growth rate of loans from the recent trend and it shows the effects of theevent on actual loan growth.

25

Figure 9 shows the effects of bank loan restriction on real estate industry on actual loan
amount from the second quarter of 1990 to the second quarter of 1993. The introduction ofregulations had large negative effects on the growth rates of loans to non-manufacturing firms,
irrespective of firm size. The growth rate of loans was lowered from 1991 on. The growth rate ofloans of small firms in manufacturing industries was also reduced from 1992 on. It should be
noted that the growth rate of loans increased considerably for large firms in manufacturingindustries. This indicates that banks shifted loans from non-manufacturing industries to large
firms in manufacturing industries.
Table 15 shows the effects of bank failures in November in 1997 on the growth rate ofloans. It is also seen that the actual growth rate of loans to small firms was hit severely by thisincidence. For small firms in non-manufacturing industries, the loan growth rate was decreasedby 1.2% and 2.1% per quarter in the last quarter of 1997 and the first quarter of 1998, while it
was reduced by 1% per quarter in the first quarter of 1998 for small firms in manufacturingindustries.
Now we simulate the effects of bank lending on firms’ investment expenditure. Twosimulation exercises are conducted. One is to measure the effects of loan supply contraction in
the early 90’s on firms’ investment. The discount rate was raised as many as five times from May
in 1989 to September in 1990. Furthermore, as was mentioned above, restrictions of bank loans
to real estate industry took effect in April in 1990 and lasted until December in 1991. This tightmonetary policy reduced loan supply to a large extent. The other is the failure of big financial
institutions in autumn in 1997. As was seen above, banks became very cautious in lending,especially to small firms, after this financial turmoil.
The effects of loan supply cut on investment expenditure in these two cases are examined
as follows: First, we simulate the model consisting of the investment function and the capitalstock identity over the period we are interested in. Then we simulate under the alternative
lending scenario. Specifically, we compute the average growth rate of loans over the several
quarters prior to the simulation period and use it as the alternative. We can measure the effects ofloan supply cut on investment by subtracting the benchmark solution of investment from thesolution under the alternative scenario.
In the first exercise, the simulation period covers from the third quarter of 1991 to the26second quarter of 1993. The simulation results are shown in Table 16. It shows in billion yen andpercentage terms how much investment spending would have been changed if the lending
behavior had not been changed. To obtain the aggregate change in investment, we assume thatthe rest of investment spending not covered by our sample changes as the same proportion as thesmall firm group in non-manufacturing industries. This assumption will be justified since the restof investment mainly comes from small businesses that are not incorporated. The aggregatedinvestment would have been increased by about 3 trillion yen or by 15% per quarter in the firstand second quarter in 1993. When the effects are disaggregated, we see that the effects are
centered on non-manufacturing industries, especially on small firm group. The investment
increase amounts to 650 to 700 billion yen in the first and the second quarter of 1993 for small
and large firm groups of non-manufacturing industries. In percentage terms the investment would
have been increased by 25 to 30% in the same quarters in 1993 for small and medium firmgroups of non-manufacturing industries. Our results are quite contrasted with the findings ofMotonishi and Yoshikawa(1998). They report that a significant fall of investment during 1992-94
is basically caused by worsening real factors rather than financial factors. Their results are basedon the investment function using the Bank of Japan diffusion indices of ‘real profitability’ and‘banks’ willingness to lend.’ To evaluate their assertions in our model, we conduct anothersimulation exercise where the averaged marginal q over the two years prior to the simulation
period is used instead of the actual figures. The difference in investment between the alternative
and the benchmark is shown in Table 17. The aggregate investment would have been increased
by 4.5 to 5.1 trillion yen or by 23-27% in the first and second quarters in 1993. These figures arelarger than those in the previous bank lending simulation, but not so overwhelming. The figuresin 1991 to 1992 are more or less the same as those in the bank lending simulation. Furthermore,
the effects of bank lending on investment in non-manufacturing industries are at least as large as
those of real factor on investment. The difference between their results and ours appears to bemainly due to the choice of explanatory variables in investment function. The diffusion indices,
their explanatory variables, give some distributional information across firms on profitability or
evaluation of the lending attitude of financial institutions. However, it is not so clear how they
are associated with the traditional explanatory variables such as marginal q, sales or interest rate.
Lastly, we examine the effects of successive failures of financial institutions or credit27

crunch in November in 1997 on investment activities. The simulation period covers from thesecond quarter of 1996 to the first quarter of 1998. The alternative scenario is based on thepremise that the average growth rate of loans over the first quarter of 1995 to the first quarter of1996 continues over the simulation period. This period corresponds to the short-lived recovery of
the economy. The difference in investment between the alternative and the benchmark is shownin Table 18. The aggregate investment would have been expanded by 1.6 and 1.9 trillion-yen orby 7.4% and 9.3% in the last quarter of 1997 and the first quarter of 1998, respectively. It should
be noted that the effects of credit crunch fall mostly on small firms in non-manufacturingindustries. Their investment would have been increased by around 300 billion-yen or 15-20% inthat period. 26
To sum up, lending channel does exist in Japan and in particular small firms are quite
vulnerable to the shocks in asset markets.

8. Concluding Remarks
We examined the role of bank loans in the Japanese economy by analyzing the lending
behavior of banks and the investment behavior of borrowers.
We could explain successfully the lending behavior of Japanese banking firms by splitting
the sample period into three: the pre-bubble period(1976-1986), the bubble period(1987-1990),
and the post-bubble period(1991-1995). Our main findings are that the lending behavior is quite
different by types of banks and of borrowers. For regional banks the bank loans are moresensitive to deposits. It implies that the degree of capital market imperfections is more severe forregional banks and that the shocks to the balance sheet of regional banks cannot be completely
offset by the adjustment of portfolio. For major banks real estate plays a more vital role as
collateral in loan contracts. We also find that real estate is more important for lending to small
and/or non-manufacturing firms. The share of small and/or non-manufacturing firms in totalloans has increased constantly. Therefore real estate functioned as a device to reduce the agency
cost stemming from the asymmetric information between the new borrowers and banks. It is also
notable that loans to small and/or non-manufacturing firms are more sensitive to the shocks indeposits.
As for the findings on the real side of the economy, we find that expenditure on fixed28

investment is more sensitive to bank loans for smaller firms that do not have close substitute forbank loans. In particular our simulation exercises demonstrate that cut of loans supply is largely
responsible for the stagnancy of investment in the downturns after the burst of the bubble and thefinancial turmoil in 1997.
Our findings are quite useful in designing the stabilizing policy of the real economy. Due tothe inability to offset the shocks to the balance sheet of relatively small banks such as regionalbanks, the shocks in the financial sector are easily propagated into the real economy. Since thecustomers of small banks are also small in size, they do not have substitutes for bank loans andthus their activity is directly affected by bank lending.
Shocks to asset prices, notably land price, also affect the supply of loans to small firms bylarge banks by way of changing the collateral value. This dependence of bank loans on thecollateral value also makes the loans to small firms more sensitive to the shocks in the asset
markets. In particular small firms are more likely to be constrained in credit markets in recession.
To sum up, the effects of tight credit conditions or financial distress fall disproportionately
on small firms. This distributional aspect should be borne in mind in implementing thestabilization policy. Recognizing this problem, the government expanded the amount of credit
insurance to small firms by 20 trillion in October in 1998. It is expected that bank loans arechanneled to the small firms in need of funds in an efficient manner by this policy.

29

Mathematical Appendix- Derivation of Optimal Loan Supply Equation (2) -The banking firm chooses the optimal level of bank loans, call money and security holdings
given the level of deposit and bank capital so that the value of firm can be maximized. The
banking firm is subject to two constraints: reserve requirement on deposits and minimum
requirement on the ratio of bank capital to bank assets. The value of banking firm ( Vt ) is definedas the discounted sum of future net cash flow. In other words,∞
Vt = Et ∑ γ t + j Ψt + j 
 j =0

where γ t + j =j∏ (1 + r )
i =1

−1

t +i

(A-1)

( j = 1,2,) γ t ≡ 1

Ψt : net cash flow in period t, defined as
Ψt = rL,t Lt −1 + rS ,t St −1 − rC ,t CLt −1 − rD ,t Dt −1 − C (FLt , Lt −1 , Dt −1 + CAPt −1 )rt +1 : one-period required rate of return in period t+1

γ t +1 : discount factor in period t+1
rL,t : interest rate on bank loans in period t
rS ,t : interest rate on securities in period t
rC ,t : call rate in period t
rD ,t : interest rate on deposits in period t
Lt −1 : loan stock at the end of period t-1
S t −1 : securities stock at the end of period t-1
CLt −1 : stock of call money borrowing or loans at the end of period t-1
Dt −1 : deposit stock at the end of period t-1
FLt : net flow of bank loans in period t
C ( FLt , Lt −1 , Dt −1 + CAPt −1 ) : loan-related cost function
CAPt −1 : bank capital at the end of period t-1
30

Et [⋅] : mathematical expectation operator conditional on theinformation available in period tTheloan-related

∂C

> 0,

∂FLt

cost

∂C

∂Lt −1function,C ( FLt , Lt −1 , Dt −1 + CAPt −1 )> 0 , ∂C

< 0 and ∂ Chas2∂ ( Dt −1 + CAPt −1 )∂FL2t −1theproperty> 0.

The two constraints facing the banking firm is expressed as follows:

RRt ≥ kDt

(A-2)

CAPt ≥ qLt(A-3)

where RRt : reserves

k : minimum reserve requirement ratio
q : minimum required ratio of bank capital to loans

The balance sheet identity for the banking firm is written as

RRt + Lt + S t = Dt + CLt + CAPt

(A-4)

The relationship between stock and flow of the assets is expressed as

Lt = Lt −1 + FLt(A-5)

S t = S t −1 + FS t

(A-6)

CLt = CLt −1 + FCLt

(A-7)

where FS t : flow of securities holdings in period t

FCLt : flow of call money borrowing or loans in period t
31

that

In what follows we assume that the banking firm will hold minimum balance of required
reserves since it bears no interest. In other words,

RRt = kDt

(A-8)

Maximization of eq.(A-1) subject to eqs.(A-3) to (A-8) yields the following first ordercondition for bank loans:



∂C
∂C 
∂C
Et (1 + rt +1 ) −1 rL,t +1 − rC ,t +1 −+=0 − λt q −∂Lt ∂FLt +1 
∂FLt



(A-9)

where λt : non-negative Lagrangean multiplier associated with therequirement on bank capital

Substituting the loan-related cost function (eq.(1)) in the text into eq.(A-9) and arranging terms,
we obtain the following optimal loan supply equation.

[

]

[

]

FLt
= β 0 + β1 Et (1 + rt +1 ) −1 + β 2 Et (1 + rt +1 ) −1 (rL,t +1 − rC ,t +1 )Dt −1 + CAPt −1 FLt +1 q− λt + Et (1 + rt +1 ) −1 α1 Dt + CAPt where β0 = µ −(A-11)

α0
α1

β1 =

α0 −α2
−µ
α1

β2 =

1
α1

The final step to obtain the equation to be estimated is to decompose the conditional expectation32

terms in eq.(A-11) into the realized values and the forecast errors under the assumption that thebanking firm forms expectations rationally. Then eq.(A-11) is rewritten as FLt +1 FLt = β 0 + β1 (1 + rt +1 ) −1
− (1 + rt +1 ) −1 
Dt −1 + CAPt −1
D
CAP
+
t 
 t
q
+ β 2 (1 + rt +1 ) −1 (rL,t +1 − rC ,t +1 ) − λt + ν t +1
α1where v t +1 : forecast error
This is nothing but eq.(2) in the text.

33(A-12)

Footnotes
1

The discussion on lending channel in the past is summarized in Kashyap and Stein(1994).
Kashyap and Stein(1995, 1997) obtain the evidence supporting the lending channel for U.S.
using the panel data of commercial banks. For the test of lending channel in Japan based on thetime series data, see Ueda(1993), Miyagawa and Ishihara(1997), Hatakeda(1997),
Hiroshima(1997), and Tanaka(1997).

2

Sui(1995) is the only study in Japan to estimate the bank loan equations derived from the
dynamic model of banking firms as far as the authors know, although he does not use panel databut quarterly aggregated data. As for the case of U.S., Elyasian et al. (1995) construct a dynamic
model of bank behavior and estimate the optimal conditions for loans and deposits jointly basedon the panel data of U.S. commercial banks.

3

The member banks of the Second Association of Regional Banks are included in our definitionof regional banks.

4

Based on the time series data, Shimizu(1995), Yamazaki and Takeda(1996) and Ogawa andKitasaka(1998) analyze the effect of real estate as collateral on bank lending.

5

The original NEEDS Company Data Base includes 150 banks in total: 3 long-term credit banks,
11 city banks, and 129 regional banks including the member banks of the Second Association ofRegional Banks. We exclude only 11 banks that experienced mergers during the sample period.
They are Sakura bank, Asahi bank, Sumitomo bank, Tokai bank (4 city banks), Nishinippon bank,Michinoku bank, Sanin-Godo bank, Iyo bank, Kumamoto Family bank, Hokuto bank, and Akita
Akebono bank (7 regional banks).

6

Small firms are defined as the corporations with a capital less than 100 million yen or withregular employees less than 300 persons (wholesalers capitalized at less than 30 million yen orwith regular employees less than 100 persons and retail trade, eating and drinking place andservice industries capitalized at less than 10 million with regular employees less than 50 persons)and unincorporated enterprises.

7

All the data series are seasonally adjusted by the Census X-12 ARIMA method. The detailed
procedures for constructing the consistent data series are described in Ogawa (1999).

8

The interest rate on time deposit had been regulated until 1993. Even after the uplift ofregulation, the interest rate on deposits does not exhibit large variability across banks to induce
shift of deposits. It justifies our assumption that deposit is exogenous to banks. The share pricealso had been stable until 1983, which discouraged banks from issuing new equities. Thisjustifies our assumption that bank capital is exogenous to banks.

9

As for the borrowings from the Bank of Japan, the official discount rate is usually set lower
than the call rate, inter-bank market rate, so that we assume that the banking firm alreadyborrows as much as possible from the Bank of Japan at the current discount rate.

10Note that call money borrowing refers to debit items of the banks, while call loan refers tocredit items. In our model, call loan enters as a negative figure in debit items.

11

Gross increase of loans is more closely associated with the agency cost than the net increase of34

loans given that all the new loans are for new customers. However, the unavailability of grossloan data forces us to use the flow loan data.
12

In a static model, Yamazaki and Takeda(1997) include land price as a variable to affect theadjustment cost of loans and derive the optimal loan supply equation. It is shown that the optimalloan supply is a function of the interest differential between the loan rate and the call rate and theland price. Our model is interpreted as an extension of their model in the dynamic setting.

13

Kashyap and Stein(1995) run a similar kind of regression of the growth rate of loans on that ofcore deposits.

14

Honda(1997) also investigates the implications of the Basle Accord using the panel data ofbanks as well as the quarterly aggregate data. Instead of estimating the structural equation, heestimates the reduced-form equation of bank loans. He finds that the introduction of the Basle
Accord had significant impacts on bank credit. Moreover, it is found that the reaction of thosebanks with the international standard is significantly different from that with the domestic
standard. Ito and Sasaki(1998) also examine how the Basle Accord influenced major Japanesebanks’ lending behavior between 1990 and 1993. They find that the effect of the risk-based
capital (RBC) ratio on lending for city banks is positive and significant, but not different fromzero for trust or regional banks. They conclude that the risk-based capital requirement was aserious hurdle only for banks with internationally active banks.

15

The deposit variable we employ is core deposit excluding the CD’s. We exclude the CD’s since
the banks can influence the interest and terms on the CD’s. In testing the imperfection of capital
market facing banks, we need the deposit variable exogenous to banks.

16

The land price index of six largest cities is also applied to the prefectures adjacent to six largest
cities, where the land price soared in the bubble period.

17

The original sample period covers from 1976 to 1995. However, the first five years are
exclusively for instruments of 1981, while the last year is used as a lead variable in 1994
regression.

18

The effects of the growth rate of deposits on bank loans are little affected even if the bankdebentures are added to the deposit variable.

19

Our evidence is consistent with the findings by Berger and Udell(1995) for the U.S. case. Theyfind that borrowers with longer banking relationships are less likely to pledge collateral.

20

The 82 banks include 7 city banks, 3 long-term banks, 7 trust banks, and 65 regional banks. We
exclude the following sample (Hyogo bank in 1994) since the BIS standard capital-asset ratio is
not reported for this year.

21

Since the lagged investment rate is included in the regressors, the significance test of thedeposit growth rates is equivalent to the Granger causality test from loan supply to investment. In
the simulation analysis below we also show the evidence that the supply conditions of loans isvery important in determining the observed growth rate of loans.

22

See Data Appendix Table A-2 in Ogawa et. al (1994).

23

See Abel and Blanchard(1986) for more details on the procedure to construct the marginal q
series.

35

24

It might be argued that measurement error of the operating surplus, constituent of marginal q,
is larger for small firms. If it is the case, then the coefficient estimates of investment function
will be inconsistent. To account for measurement error of marginal q in small firm group, we
employ the marginal q series of large firm group as instruments of marginal q in small firm group.
It turns out that the coefficient estimates of marginal q and bank loan growth with t-value inparentheses are 0.0079 (1.01) and 0.1946 (2.34) for manufacturing industries and 0.0292 (3.40)
and 0.1411 (1.93) for non-manufacturing industries, respectively. Although the marginal q loses
its significance in manufacturing industries, all the other parameters remain significant.

25

Our results are consistent with Motonishi and Yoshikawa (1998). They find that financial
constraints are important for investment decision of small firms in manufacturing as well as nonmanufacturing industries.

26

The annual growth rate of fixed investment would have been raised by 8.8% if the increment ofinvestment in the first quarter of 1998 had continued throughout 1998. This is close to the figure
obtained by Motonishi and Yoshikawa’s study, 10%.

36

References[1] Anderson, T.W. and C. Hsiao(1981).” Estimation of Dynamic Models with Error Components,
Journal of the American Statistical Association 76, pp.598-606.

[2] Berger, A.N. and G.F. Udell(1995).” Relationship Lending and Lines of Credit in Small Firm
Finance,” Journal of Business Vol.68, No.3, pp.351-381.

[3] Elyasiani, E., Kopecky, K.J. and D. Van Hoose(1995).” Costs of Adjustment, Portfolio
Separation, and the Dynamic Behavior of Bank Loans and Deposits, “ Journal of Money, Credit,and Banking Vol.27, No.4, pp.955-974.

[4] Hansen, L.P.(1982).” Large Sample Properties of Generalized Method of Moments Estimator,”
Econometrica 50, pp.1029-1054.

[5] Hatakeda, T.(1997).” Nippon ni okeru Ginko Hakyu Keiro no Jyuyosei (On Importance of Bank
Lending Channel in Japan),” Finance Kenkyu No.22, pp.15-31.

[6] Hiroshima, T.(1997).” Chushou Kigyo muke Kashidashi to Jittai Keizai Katsudo ni tsuite(Loans to Small Firms and Real Activity),” Working Paper 97-4, The Bank of Japan.

[7] Honda,Y.(1997).” Some Implications of the Basle Accord: The Case of Japan,” mimeographed.

[8] Ito,T. and Y.N. Sasaki(1998).” Impacts of the Basle Capital Standard on Japanese Banks’Behavior,” Discussion paper Series A No.356, The Institute of Economic Research, Hitotsubashi
University.

[9] Kashyap, A.K. and J.C. Stein(1994).” Monetary Policy and Bank Lending,” in Mankiw,
N.G.(ed.), Monetary Policy, The University of Chicago Press, pp.221-256.

37

[10] Kashyap, A.K. and J.C. Stein(1995).” The Impact of Monetary Policy on Bank Balance
Sheets,” Carnegie-Rochester Conference Series on Public Policy 42, pp.151-195.

[11] Kashyap, A.K. and J.C. Stein(1997).” What Do a Million Banks Have to Say about theTransmission of Monetary Policy?” NBER Working Paper No.6056.

[12] Miyagawa, T. and H. Ishihara(1997).” Kin’yu Seisaku･Ginko Kodo no Henka to Macro Keizai(Monetary Policy, Change in Bank Behavior and the Macroeconomy) in Asako, K.,

Yoshino, N.

and S. Fukuda (eds.) Gendai Macro Keizai Bunseki, University of Tokyo Press, pp.157-191.

[13] Motonishi, T. and H. Yoshikawa(1998).” Causes of the Long Stagnation of Japan during the1990’s: Financial or Real?” mimeographed.

[14] Ogawa, K., Kitasaka, S.,Watanabe, T.,Maruyama, T.,Yamaoka, H. and Y. Iwata(1994). Asset
Markets and Business Fluctuations in Japan, The Keizai Bunseki (The Economic Analysis),Economic Research Institute, Economic Planning Agency.

[15] Ogawa, K. and S. Kitasaka(1998) Shisan Shijyo to Keiki Hendo – Gendai Nippon Keizai no
Jissho Bunseki (Asset Markets and Economic Fluctuations – An Empirical Analysis of Modern
Japanese Economy), Nippon Keizai Shinbunsha.

[16] Ogawa, K.(1999).” Monetary policy, Credit and Real Activity: Evidence from the Balance
Sheet of Japanese Firms,” mimeographed.

[17] Shimizu, Y.(1995).” Ginko no Jyoho Seisan to Tochi Tanpo Tsuki Kashidashi (Production ofInformation by Banks and Lending Secured by Land),” Business Review Vol.43, No.3, pp.52-66.

[18] Sui,Q.(1995).” Kin’yu Chukai Katsudo to Keiki Hendo (Financial Intermidiation and Business
Fluctuations,” Nippon Keizai Kenkyu No.29, pp.31-49.

38

[19] Tanaka,K.(1997).” Ginko Shinyo to Kin’yu Seisaku no Koka (Bank Credit and Effects ofMonetary Policy),” mimeographed.

[20] Ueda,K.(1993).” A Comparative Perspective on Japanese Monetary Policy: Short-Run
Monetary Control and the Transmission Mechanism,” in Singleton, K.J.(ed.) Japanese Monetary
Policy, University of Chicago Press, pp.7-29.

[21] Yamazaki, F. and Y. Takeda (1997).” Tochi Tanpo no Kachi to Ginko no Kashidashi Kodo (TheCollateral Value of Land and Bank Lending),” in Asako, K. and M. Otaki(eds.) Gendai Macro
Keizai Dogaku, University of Tokyo Press, pp.351-375.

39

Table 1 Summary Statistics: Growth Rate of Bank Loans

1977-86

1987-90

1991-95(%)
whole sample period

city banks

10.58

14.07

1.07

8.81

long-term
credit banks

10.67

12.62

-0.02

8.27

trust banks

10.17

11.31

0.06

7.75

regional banks

9.65

10.50

3.81

8.29

total

9.75

10.77

3.40

8.29

Notes: The figures are average values of the sample period shown.
Source: Nikkei NEEDS Company Data Base

Table 2 Summary Statistics: Proportion of Bank Loans to Small Firms(%)
whole sample period

1977-86

1987-90

1991-95

city banks

38.55

48.33

52.17

44.20

long-term
credit banks

27.97

34.57

38.40

32.11

trust banks

31.30

43.39

43.74

37.12

regional banks

77.97

82.26

80.49

79.54

total

72.55

77.57

76.31

74.60

Notes: The figures are average values of the sample period shown.
Source: Nikkei NEEDS Company Data Base

Table 3 Summary Statistics: Proportion of Bank Loans Secured by Real Estate(%)
whole sample period

1977-86

1987-90

1991-95

city banks

17.33

19.79

21.94

19.06

long-term
credit banks

36.68

23.03

27.31

31.34

trust banks

32.33

24.22

25.53

28.83

regional banks

37.94

38.97

39.42

38.55

total

36.59

36.91

37.58

36.92

Notes: The figures are average values of the sample period shown.
Source: Nikkei NEEDS Company Data Base
Table 4 Summary Statistics: Growth Rate of Bank Loans by Industries
1977-86

1987-90

1992-95(%)
whole sample period
excluding 1991

manufacturing

5.29

1.333

1.147

3.39

construction

11.01

6.51

7.30

9.09

wholesale andretail trade

12.36

3.11

0.64

7.46

financial
institutions

30.55

25.03

-0.74

21.96

real estate

13.27

17.61

5.40

12.45

service

14.99

13.75

5.22

12.42

personal andothers

12.21

13.43

1.85

10.08

personal excluding
housing loans

17.06

23.61

-1.45

14.28

Notes: The figures are average values of the sample period shown.
Source: Nikkei NEEDS Company Data Base

Table 5
Descriptive Statistics of Fixed Investment Rate by Firm Size(%)
Firm size in termsof capital(million yen)
Manufacturing
Industries
10-10075:2-86:4

87:1-91:1

91:2-98:1

75:2-98:1

4.10

4.47

2.69

100-1000

3.72

4.03

2.69

1000-

3.58

3.90

3.00

3.74
(0.98)
3.47(0.74)
3.46(0.63)

Non-manufacturing
Industries
10-100

4.19

5.40

3.19

100-1000

4.24

4.38

3.29

1000-

4.02

4.78

3.77

4.11
(0.99)
3.98(0.69)
4.09(0.58)

Notes: The values in parentheses are standard deviation.
Source: Quarterly Report of Financial Statements of Incorporated Business

Table 6 Summary Statistics: Growth Rate of Deposit(%)
whole sample period

1977-86

1987-90

1991-95

city banks

11.46

16.22

-3.12

8.62

long-term
credit banks

14.93

16.34

-5.85

9.76

trust banks

16.32

20.32

-11.189.92

regional banks

9.96

11.13

3.02

8.38

total

10.46

11.96

1.80

8.50

Notes: The figures are average values of the sample period shown.
Source: Nikkei NEEDS Company Data Base

Table 7
Comparison of Selective Balance Sheet Items between Major Banks and Regional Banks:- Sample Mean for the Period of 1976 to 1995 Based on Our Panel Data Set -total assets*

Regional banks
1285.1

Major banks
16091.6

stock of call loan*

45.0

366.0

stock of call money
borrowing*

20.3

951.9

share of loans in
total assets**

66.4

81.8

share of securities intotal assets**

16.0

17.8

share of time deposits
in total liabilities**

56.5

30.2

share of CD in total
liabilities**

0.83

2.88

Notes: Unit is billion yen for the items with * and percent for the items with **.
Source: Nikkei NEEDS Company Data Base

Table 8-1
Estimation of the Euler Equation (4): Total Loans(Major Banks)

1981-94

1981-86

1987-90

1991-94

Reciprocal

Interest

Gross rate

Growthof grossdifferentialof change

rate ofJ-statistics

Number of

discount(bank loan

in landdeposits

p-value

observationsrate- call rate)238-3.2064*** 2.3144-0.6182**

28.3716(-2.65)(1.44)(-2.21)0.000-2.1452

2.6070**0.4207*** 27.8479(-0.99)(2.29)(4.15)

0.0365-2.9085-1.1958***

0.5558*** 11.2832(0.02)(-1.38)(-3.19)(5.46)

0.000

17.2155(-4.26)(8.18)

0.004-4.8894**

3.0331***0.3215***6.0052(-2.50)(3.78)(5.20)

0.306-4.3535**

3.6523*** 0.9381***0.3306***9.8755(-2.53)(4.82)(6.11)

0.130

7.5419-20.3551*** 2.2145***2.4708(1.16)(-2.86)0.781-7.1335

2.47270.6583***

6.9249(-1.42)(0.50)(4.94)

0.226(3.00)(5.64)

4.4940-12.8865** 1.2410***0.4106***

4.2212(0.82)(-2.09)(3.47)(2.97)

0.647-2.5150

6.8559**0.052410.5602(-1.54)(2.10)(0.55)

0.061-1.9849**

4.6974***0.1367**8.4688(-2.28)(2.71)(2.31)

0.132-3.7212**6.8685*** -0.1128

0.2030**7.7422(-2.43)(2.70)(2.38)

0.258(-1.00)

238

0.080

-8.1256*** 5.5050*** 0.7093***(3.75)

238

102
102
102

68
68
68

68
68
68

Notes: The values in parentheses are t-values.
*** significant at the 1 % level,
level

**

significant at the 5 % level.

*significant at the 10 %

Table 8-2
Estimation of the Euler Equations (4): Total Loans(Regional Banks)

1981-94

Reciprocal

Interest

Gross rate

Growthof grossdifferentialof change

rate ofJ-statistics

Number of

discount(bank loan

in landdeposits

p-value

observationsrate- call rate)-0.2533***

35.98871708(-3.34)0.000-1.9236*** 0.2752(-8.55)

1981-86

1987-90

1991-94(1.04)-1.4792*** 0.8653***

0.165666.2134(-5.66)(1.39)

0.000(5.52)-1.4758*** 0.3202-0.1933***

0.2469**57.7906(-6.03)(-2.69)(2.42)

0.000(1.19)-1.2868*** 1.2097*** -0.0690

61.5576(-4.69)(9.68)

0.000-1.0934*

0.6962**0.4214**50.6181(-1.92)(2.31)(2.42)

0.000

2.5176*** -1.9024*** -1.2432***

1.6914***2.4046(3.31)(-3.73)(-4.90)(6.73)

0.879-1.1620-1.7456

0.5704***11.1374(-0.57)(-1.03)(10.74)0.049(-0.73)-19.7876*** 13.4483***

0.9624*** 22.1158(-6.03)(7.58)

0.000(5.34)-7.9713*** 3.7159*

0.4192***0.4169***1.7880(-3.05)(6.67)(3.70)

0.938(1.77)-3.9890*** 5.7609*** 0.2687***

19.5619(-6.81)

0.002(6.50)(8.28)-2.9222*** 3.8038***

0.5671*** 13.8737(-5.19)(4.59)(10.42)0.016-3.6398*** 5.1065*** 0.1087**

0.4199***12.0190(-6.34)(5.43)

0.062(5.85)

Notes: See the notes in Table 7-1(2.56)

1708
1708

732
732
732

488
488
488

488
488
488

Table 9
Estimation of the Euler Equation (3): Loans by Firm Size

Interest differential (bank loan – call rate)
Loans toLoans toJ-statistics

Number ofsmall firms

large firms

p-value

observations

238

Major Banks
1981-94
1981-86
1987-90
1991-94

0.32404.4579***37.9297(0.60)(5.98)

0.000

0.6488**4.3452***23.9677(2.16)(7.28)

0.008

12.4853***

15.5274***

15.3013(3.75)(4.96)

0.121

3.8692**

4.0373**14.8872(2.49)(2.44)

0.136

102
68
68

Regional Banks
1981-94
1981-86
1987-90
1991-94

0.82270.189161.4651(1.63)(0.38)

0.000

1.5960***-0.211947.7562(4.59)(-0.63)0.000

13.3425**-10.8655**

114.104(2.55)(-1.99)0.000-4.6797***

5.0465***80.6232(2.75)(3.02)

0.000

Notes: See the notes in Table 7-1

1708
732
488
488

Table 10-1
Estimation of the Euler Equations (4): Major Banks
Joint Estimation of Bank Loan Equation by Firm Size

Firm

Reciprocal

Interest

Gross rate

Growthof grossdifferentialof change

rate ofJ-statistics

Number of

discount(bank loan

in landdeposits

p-value

observationsrate- call rate)Size
1981-94
Small
Large
Small
Large
Small
Large

0.31250.0958-0.0212(0.40)(0.12)(-0.13)32.1447-3.1518*** 2.7279** -0.5375**

0.004(-3.82)(2.36)-0.56070.51490.0046(-0.40)(0.90)(0.07)

36.4250-2.0695

2.5772***

0.3463***0.000(-1.41)(3.06)(4.68)

0.5468-0.5202-0.08240.0949**(0.69)(-0.64)(-0.51)(1.96)

32.1447-1.2688-0.3455-0.6999***

0.3928***0.004(-1.26)(-0.26)(-3.02)(5.56)

238(-2.47)

238

238

1981-86
Small-2.9410*** 0.9674*** 0.4047***(-2.80)Large(2.57)

25.2249-4.8787*** 4.5941*** 0.2395

0.014(-3.07)
Small
Large
Small
Large

(3.14)(8.28)

102(1.07)-3.2753*** 0.9889***-0.0739**(-2.68)(2.88)(-2.13)1.7421

1.7188***0.3867***(1.19)(2.65)(8.29)-2.1958**

1.0036*** 0.2259-0.0476(-2.11)(3.29)(-1.55)-0.3198

2.2340*** 0.5156***

0.3839***(-0.20)(3.40)(8.41)(1.49)(2.72)

22.6166
0.031

102

22.5830
0.067

102

Table 10-1 (continued)
Estimation of the Euler Equations (4): Major Banks
Joint Estimation of Bank Loan Equation by Firm Size

Firm

Reciprocal

Interest

Gross rate

Growthof grossdifferentialof change

rate ofJ-statistics

Number of

discount(bank loan

in landdeposits

p-value

observationsrate- call rate)Size
1987-90
Small
Large
Small
Large
Small
Large

6.3814*-11.3010*** 1.0363***(1.67)(-2.76)(4.69)

8.9156-6.8868*

1.67240.4984***0.710(-1.89)(0.42)(2.78)

0.1813-0.7349

0.2938***(0.07)(-0.30)(4.83)

11.5878-8.7740**

4.78290.3377***0.479(-2.32)(1.37)(3.87)

9.2806**-12.6014*** 0.8338***

0.1631*(2.51)(-3.04)(3.06)(1.81)

11.9630-4.0512-0.2732

0.18780.2944***0.609(-0.89)(-0.05)(0.66)(2.82)

68

68

68

1991-94
Small
Large
Small
Large
Small
Large

0.5102

1.12260.0885(0.43)(0.53)(1.52)

15.3971-4.0082*** 6.2466*** -0.0517

0.220(-3.84)(3.30)-0.4245

2.33280.0624(-0.43)(1.46)(1.44)

14.0234-2.1624**

3.5157**0.04660.299(-2.12)(2.17)(1.21)-0.1527

1.62290.04010.0393(-0.11)(0.79)(0.47)(0.58)

13.6869-5.0037*** 6.8697*** -0.1995**

0.1445*0.473(-3.27)(1.73)(3.16)

Notes: See the notes in Table 7-1

68(-1.06)(-2.00)68

68

Table 10-2
Estimation of the Euler Equations (4): Regional Banks
Joint Estimation of Bank Loan Equation by Firm Size

Firm

Reciprocal

Interest

Gross rate

Growthof grossdifferentialof change

rate ofJ-statistics

Number of

discount(bank loan

in landdeposits

p-value

observationsrate- call rate)Size
1981-94
Small
Large
Small
Large
Small
Large

-0.4576-0.0468-0.1925(-0.68)(-0.07)(-0.93)46.9946-1.4637**

0.3595-0.05680.000(-2.21)(0.58)(-0.28)-0.38110.59920.6191**(-0.53)(1.23)(1.98)

71.3373-1.1598

0.29984-0.4898

0.000(-1.62)(0.61)(-1.61)-0.0460-0.1706-0.25910.7240***(-0.08)(-0.26)(-1.34)(2.76)

64.1680-1.5071**

0.58860.08620-0.5332**

0.000(-2.49)(0.93)(0.46)(-1.99)

1708

1708

1708

1981-86
Small
Large
Small
Large
Small
Large

-1.8180*** 1.5990*** -0.0759(-3.04)(4.39)(-0.28)75.6583

0.4968-0.3971-0.0185

0.000(0.85)(-1.10)(-0.07)-3.2751**

1.3515*0.0539(-2.25)(1.69)(0.11)

58.57472.2179-0.70100.39550.000(1.53)(-0.90)(0.78)

1.2466-1.1539-0.9963***

1.3886***(1.21)(-1.63)(-2.81)(4.03)

20.6269

0.7843-0.3775-0.1559

0.10490.112(0.94)(-0.65)(-0.53)(0.37)

732

732

732

Table 10-2 (continued)
Estimation of the Euler Equations (4): Regional Banks
Joint Estimation of Bank Loan Equation by Firm Size

Firm

Reciprocal

Interest

Gross rate

Growthof grossdifferentialof change

rate ofJ-statistics

Number of

discount(bank loan

in landdeposits

p-value

observationsrate- call rate)Size
1987-90
Small
Large
Small
Large
Small
Large

-11.5974*

7.06990.7395***(-1.89)(1.41)(4.59)

27.2353

10.5931*-8.8476*-0.18160.007(1.67)(-1.71)(-1.14)-30.7559*** 23.4534***

1.06931***(-3.94)(3.81)(4.05)

34.0880

12.1066*-10.7165*-0.16240.001(1.71)(-1.90)(-0.67)-19.5984*** 13.4277*** 0.5107**

0.5838**(-3.04)(2.57)(2.56)(2.05)

15.6614

11.4605*-9.4875*-0.1127-0.1387

0.609(1.78)(-1.82)(-0.59)(-0.50)

488

488

488

1991-94
Small
Large
Small
Large
Small
Large

3.1643** -2.9668*

0.1211**(2.29)(2.17)(-1.68)31.0682-7.1424*** 8.6832*** 0.1517***0.002(-5.27)(4.97)

0.58340.05700.5742***(0.25)(0.02)(3.44)

29.7900-3.5447

3.7543-0.0258

0.003(-1.51)(1.26)(-0.16)-1.1151

2.1147-0.0996

0.7900***(-0.49)(0.73)(-1.20)(3.52)-2.5250

2.95200.2061*** -0.3738*(-1.11)(1.04)(2.73)

Notes: See the notes in Table 7-1.

488

(2.74)(-1.76)488

22.6460
0.066

488

Table 11-1
Estimation of the Euler Equations (4): Major Banks
Joint Estimation of Bank Loan Equation by Industries

Industry

Reciprocal

Interest

Gross rate

Growthof grossdifferentialof change

rate ofJ-statistics

Number of

discount(bank loan

in landdeposits

p-value

observationsrate- call rate)
1981-94

M
NM
M
NM
M
NM

-0.5707*** 0.5684*

0.0414(-2.85)(1.78)(0.70)

49.0203-0.5745-0.9092-0.9596***0.000(-0.48)(-0.51)(-3.17)

1.1145** -0.1919

0.0752**(2.08)(-0.76)(2.27)

38.5774-3.4985*

2.9634***

0.2967***0.000(-1.88)(2.96)(3.16)-0.4663**

0.5534*0.0370-0.0078(-2.21)(1.83)(0.65)(-0.48)38.11932.0796-4.1086** -1.2936***

0.5664***0.000(1.31)(-2.20)(6.16)(-3.70)

238

238

238

1981-86
M
NM
M
NM
M
NM

-0.3461-0.1155

0.0082(-0.86)(-0.79)(0.10)

34.6627-6.8660*** 4.8061*** 0.5213**

0.001(-3.07)(8.28)

0.9152-0.3361*

0.0191(1.43)(-1.81)(1.03)

24.9661-4.7393**2.4112***0.2785***0.015(-2.49)(3.91)(4.36)-0.6441-0.14240.05820.0025(-1.39)(-0.80)(0.76)(0.15)-1.6035

2.4055*** 0.5887**

0.3716***(-0.97)(4.09)(7.56)

102(1.07)(2.42)

102

28.1423
0.014

102

Table 11-1

(continued)

Estimation of the Euler Equations (4): Major Banks
Joint Estimation of Bank Loan Equation by Industries

Industry

Reciprocal

Interest

Gross rate

Growthof grossdifferentialof change

rate ofJ-statistics

Number of

discount(bank loan

in landdeposits

p-value

observationsrate- call rate)
1987-90

M
NM
M
NM
M
NM

4.6190*** -3.6680*** -0.0887*(5.17)(-3.81)(-1.70)0.0563-12.2940** 1.9411***(0.01)(-2.13)15.40140.220

68(6.56)

3.8959*** -3.5205***-0.0411**(5.62)(-2.19)15.0586-15.6874*** 11.1217***0.5854***0.238(-4.62)(3.42)(7.41)

3.7590*** -3.1744*** -0.0376-0.0168(4.84)(-3.85)(-0.66)(-0.72)-2.6388-8.1926*

1.5411***0.3152***(-0.68)(-1.76)(4.84)(2.89)(-5.53)68

18.62170.180

68

1991-94
M
NM
M
NM
M
NM

-2.9189*** 2.8288*** -0.1133***(-7.13)(5.31)(-5.58)-1.6206*

6.7975*** 0.1162*(-1.68)(3.26)

30.15210.003(1.72)-2.2232*** 2.5891***-0.1564***(-5.05)(4.69)(-7.98)27.7729-2.0962*6.7147***0.1625***0.006(-1.72)(3.64)(3.28)

0.2115-0.1965

0.1530*** -0.2599***(0.31)(-0.22)(3.48)(-7.02)

17.6354-6.1916*** 10.9805*** -0.3182***

0.3942***0.224(-4.01)(4.54)(4.72)(-3.40)Notes: M stands for manufacturing industries and NM stands for non-manufacturing industries.
See also the notes in Table 7-1.

68

68

68

Table 11-2
Estimation of the Euler Equations (4): Regional Banks
Joint Estimation of Bank Loan Equation by Industries

Industry

Reciprocal

Interest

Gross rate

Growthof grossdifferentialof change

rate ofJ-statistics

Number of

discount(bank loan

in landdeposits

p-value

observationsrate- call rate)
1981-94

M-0.6621*** 0.5643*** 0.1418***(-9.48)

NM
M
NM
M
NM

(7.35)(7.36)

140.203-1.1342*** 0.1110-0.3201***(-5.65)(0.47)(-5.12)-0.2863**0.2357***0.1773***(-2.52)(3.56)(3.48)

148.720-1.2937*** 0.7739***-0.00110.000(-5.19)(-0.01)(5.19)

0.000

1708

1708

-1.0001*** 0.9257*** 0.2208*** -0.2933***(-7.29)(7.22)(6.68)(-5.06)-0.3553-0.4488*-0.3636*** 0.5266***(-1.37)(-1.74)(-5.46)92.9575
0.000

1708

(5.10)
1981-86

M
NM
M
NM
M
NM

-0.3933*** 0.0614-0.1177***(-4.15)(-3.58)101.803-1.2500*** 1.2576*** 0.0926

0.000(1.43)(-5.63)(12.11)

0.2379-0.3809***0.3318***(0.92)(-2.69)(4.12)

81.1811-0.9681**0.6919***0.3952***0.000(-2.35)(3.14)(3.12)

1.4890*** -1.3775*** -0.6701***0.7407***(4.19)(-5.49)(-5.57)(5.92)

35.53250.7456*-0.4058-0.5774***

0.8739***0.001(1.77)(-1.44)(-4.00)(6.18)

732(1.13)

732

732

Table 11-2

(continued)

Estimation of the Euler Equations (4): Regional Banks
Joint Estimation of Bank Loan Equation by Industries

Industry

Reciprocal

Interest

Gross rate

Growthof grossdifferentialof change

rate ofJ-statistics

Number of

discount(bank loan

in landdeposits

p-value

observationsrate- call rate)
1987-90

M
NM
M
NM
M
NM

-0.1959

0.1454-0.0568***(-0.43)(0.39)(-4.02)33.7229-1.5636-1.43160.6465***0.001(-0.83)(-0.92)(12.68)1.1584-0.9640-0.0912***(1.53)(-1.62)(-3.12)38.3718-20.4249*** 14.0652***

1.0481***0.000(-5.97)(5.31)(8.12)

0.0971-0.0631-0.0479***-0.0246(0.14)(-0.11)(-2.65)(-0.74)21.6257-7.9932*** 3.7197** 0.4797***0.4508***0.087(-3.46)(4.71)(1.99)(8.07)

488

488

488

1991-94
M-2.1169*** 2.4643*** 0.0631***(-9.19)NM
M
NM
M(5.00)

129.554-1.8417*** 3.1875*** 0.1850***

0.000(-3.52)(4.11)-0.5648**0.2019-0.1451***(-2.48)(0.60)(-5.75)83.9530-2.3032*** 3.5271***

0.6930***0.000(-4.30)(11.46)488(5.92)(4.36)

488-1.6332*** 1.9790*** 0.1689*** -0.3727***(-4.49)NM(7.18)(3.58)(6.42)(-6.96)-1.7380*** 2.9376*** -0.0426

0.7474***(-2.94)(9.21)(3.28)(-1.03)30.0948
0.007

Notes: M stands for manufacturing industries and NM stands for non-manufacturing industries.
See also the notes in Table 7-1.

488

Table 12
Effect of the Basle Accord on Bank’s Lending Activity: 1992-1994
Reciprocal

Interest

Gross rate GrowthBankof grossdifferentialof change

rate ofcapital- J-statistics Number

discount(bank loan

in landdeposits

bank

rate

- call rate)asset

p-valueofobservations

ratio
Total Loans

Major Banks-1.3969*-1.5128-0.3612*** -0.1440

4.7922***2.6907(-1.68)(-0.53)(-2.91)(3.15)

0.847(-0.95)

51

Regional Banks-2.5401*** 3.8531*** 0.0329

0.4256*0.7375

4.2139(-4.43)(1.94)(0.79)

0.648(3.55)(0.46)

Loans by

194

Major Banks

Firm Size
Small
Large

-2.3999**2.3172-0.2900

0.09432.6576(-2.35)(1.10)(-1.54)(0.58)(1.46)

14.3690-3.0845**1.5979-0.3995*** 0.0079

3.2507**0.348(-2.39)(0.87)(-2.59)(2.43)(0.06)

51

Regional Banks
Small
Large

1.22661.42900.2327

0.1981-3.8875(0.84)(0.87)(0.80)(0.43)(-1.09)14.3690-3.8394*** 2.0163-0.25430.24055.42440.383(-2.59)(-0.89)(0.56)(1.53)(1.11)

194

Table 12 (continued)
Effect of the Basle Accord on Bank’s Lending Activity: 1992-1994
Reciprocal

Interest

Gross rate GrowthBankof grossdifferentialof change

rate ofcapital- J-statistics Number

discount(bank loan

in landdeposits

bank

rate

- call rate)asset

p-valueofobservations

ratio
Loans byMajor Banks

Industries
MNM-0.5242**0.2471

0.0674** -0.0807*** 0.9312***(-2.10)(0.64)(2.24)(-3.20)(4.28)

12.21310.510-2.2757*** 1.0478-0.3899** -0.2329

3.3251**(-2.82)(-2.50)(2.09)(0.50)(-1.36)

51

Regional Banks
M
NM-1.2866*** 1.0236*** 0.0792*** 0.0104

1.6571***(-5.73)(2.61)(0.13)(4.33)

27.9712-1.1281**3.0322*** -0.0091

0.3310*-1.4824*0.014(-2.42)(3.25)(1.89)(-1.86)(2.67)(-0.15)

Notes: M stands for manufacturing industries and NM stands for non-manufacturing industries.
See also the notes in Table 7-1.

194

Table 13
Descriptive Statistics of Marginal q by Firm SizeFirm size in termsof capital(million yen)
Manufacturing
Industries
10-10075:4-86:4

87:1-91:1

91:2-98:1

75:2-98:1

1.66

1.89

0.93

100-1000

1.10

1.06

0.64

1000-

0.96

1.04

0.75

1.47
(0.47)
0.95(0.28)
0.91(0.22)

Non-manufacturing
Industries
10-100

1.54

1.91

1.16

100-1000

0.91

0.98

0.48

1000-

0.80

0.92

0.57

Notes: The values in parentheses are standard deviation.

1.49(0.36)
0.79(0.27)
0.75(0.17)

Table 14
Estimation Results of Investment Function by Firm SizeFirm size in termsof capital(million yen)
Manufacturing
Industries
10-100Marginal q
Equation.3 }Growth rate

{ EMBEDof loans

S.E.

0.0092***(3.51)

0.1675**(2.44)

0.92150.0029100-1000

0.0050*(1.79)

0.2534*(1.69)

0.90230.00251000-

0.0061*(1.95)-0.0627(-0.91)0.91960.00180.0016***(3.60)

0.1640***(3.27)

0.85460.0040100-1000

0.0147***(4.27)

0.0416(1.33)

0.80110.00331000-

0.0002(0.03)

0.1635*(1.76)

0.83640.0024Non-manufacturing
Industries
10-100Notes: The values in parentheses are t-value.
*** significant at the 1% level** significant at the 5% level* significant at the 10% level{ EMBED Equation.3 }: adjusted coefficient of determination
S.E.: standard error of the regression

Table 15
Effects of Bank Failures in November in 1997 on Actual Growth Rate of Loans
Firm size in termsof capital(million yen)

Deviation of loan growth rate from the recent trend(% per quarter)
Fourth quarterof 1997

First quarter theof 1998

Manufacturing
Industries
10-100

0.57

-1.02

100-1000

-0.38

-0.17

1000-

3.04

2.08

Non-manufacturing
Industries
10-100

-1.18

-2.06

100-1000

0.53

-0.20

1000-

0.64

-0.96

Source: Quarterly Report of Financial Statements of Incorporated BusinessTable 16
Simulation Results: The Role of Bank Lending in the Recession of 1991-1993

Small
firms
0.0
(0.0)

Medium
firms
0.0
(0.0)

Large
firms
0.0
(0.0)

(Billion yen at market prices in 1990)
Non-manufacturingindustries
Aggregate
Small
MediumLarge
investment
firms
firms
firms
0.0
0.0
0.0
0.0
(0.0)
(0.0)
(0.0)
(0.0)

91:4

15.7
(1.2)

3.8
(0.5)

-9.6
(-0.2)

231.8
(8.4)

48.8
(3.1)

2.7
(0.0)

808.8
(3.7)

92:1

27.3
(2.2)

8.7
(1.1)

-11.2
(-0.3)

406.5(14.9)

99.7
(6.4)

24.5
(0.5)

1515.5
(7.0)

92:2

46.6
(4.4)

18.5
(2.4)

-31.1
(-0.9)

529.4(18.8)

159.3
(9.7)

88.9
(1.6)

1909.1
(9.1)

92:3

61.3
(5.4)

30.8
(5.1)

-52.0
(-1.6)

600.3(21.5)

215.7(14.4)

192.3
(3.5)

2326.2(11.2)

92:4

76.7
(7.3)

44.8
(7.5)

-77.8
(-2.4)

695.2(25.1)

268.4(17.2)

332.1
(6.6)

2836.2(14.1)

93:1

85.2
(8.5)

60.0
(9.7)

-70.4
(-2.4)

702.7(25.9)

321.2(21.9)

489.5
(9.6)

3149.8(16.1)

93:2

97.8(11.2)

73.1(13.1)-52.6(-1.9)

654.0(21.5)

361.4(30.3)

654.8(13.1)

2964.9(15.7)

Manufacturing industries

91:3

Notes: The values in parentheses are percentage change of fixed investment relative to actual
figures.

Table 17
Simulation Results: The Role of Real Factor in the Recession of 1991-1993

Small
firms
31.8
(2.1)

Medium
firms
17.1
(2.0)

Large
firms
19.6
(0.5)

(Billion yen at market prices in 1990)
Non-manufacturingindustries
Aggregate
Small
MediumLarge
investment
firms
firms
firms
44.5
-2.3
6.5
221.1
(1.6)
(-0.2)
(0.1)
(1.0)

91:4

85.8
(6.8)

44.2
(5.4)

61.2
(1.5)

129.1
(4.7)

-0.2
(-0.0)

22.9
(0.4)

630.2
(2.9)

92:1

153.6
(12.2)

65.9
(8.4)

119.1
(3.3)

215.7
(7.9)

12.6
(0.8)

50.5
(1.0)

1126.8
(5.2)

92:2

232.0
(22.1)

86.9(11.3)

203.0
(5.7)

336.9
(12.0)

39.6
(2.4)

85.7
(1.6)

1682.5
(8.0)

92:3

328.6
(28.8)

115.2(19.1)

308.0
(9.5)

492.6
(17.7)

78.5
(5.2)

131.5
(2.4)

2502.9(12.1)

92:4

445.3(42.4)

154.8(25.8)

411.8(12.9)

679.8(24.6)

130.9
(8.4)

189.1
(3.8)

3475.3(17.3)

93:1

567.9(56.4)

196.9(31.7)

534.4(18.5)

900.2(33.2)

188.4(12.9)

257.7
(5.1)

4569.2(23.3)

93:2

682.1(78.2)

222.1(39.8)

673.3(24.4)

1057.1(34.8)

254.2(21.3)

329.8
(6.6)

5120.1(27.1)

Manufacturing industries

91:3

Notes: The values in parentheses are percentage change of fixed investment relative to actual
figures.

Table 18
Simulation Results: The Effects of Credit Crunch in 1997-98

Small
firms
0.0
(0.0)

Medium
firms
0.0
(0.0)

Large
firms
0.0
(0.0)

(Billion yen at market prices in 1990)
Non-manufacturingindustries
Aggregate
Small
MediumLarge
investment
firms
firms
firms
0.0
0.0
0.0
0.0
(0.0)
(0.0)
(0.0)
(0.0)

96:3

-4.1
(-0.6)-0.1(-0.0)-7.6(-0.3)

153.8
(6.5)

11.1
(0.7)

0.2
(0.0)

665.5
(3.2)

96:4

2.3
(0.3)

-0.3
(-0.1)-13.1(-0.5)

264.7(11.7)

15.4
(1.0)

1.3
(0.0)

1251.2
(5.8)

97:1

6.3
(0.8)

-1.7
(-0.2)-18.5(-0.6)

252.2
(9.0)

23.7
(1.5)

4.3
(0.1)

992.3
(4.4)

97:2

13.0
(1.9)

2.8
(0.5)

-16.4
(-0.6)

196.6
(8.1)

26.6
(2.0)

8.1
(0.2)

961.9
(4.4)

97:3

18.6
(2.4)

5.2
(0.8)

-19.7
(-0.7)

209.5(10.1)

26.0
(1.8)

6.6
(0.1)

1152.8
(5.2)

97:4

23.4
(3.4)

12.0
(1.9)

-17.6
(-0.6)

297.2(14.5)

29.5
(1.8)

5.1
(0.1)

1637.7
(7.4)

98:1

19.1
(2.9)

18.5
(3.0)

-44.1
(-1.3)

344.2(19.8)

15.7
(1.1)

-5.6
(-0.1)

1945.5(9.3)

Manufacturing industries

96:2

Notes: The values in parentheses are percentage change of fixed investment relative to actual
figures.

Figure 1 Growth Rate of Bank Loans

0.14000.09000.0400-0.01001977

1978

1979

1980

1981

1982

1983

1984

1985

1986

1987

1988

1989

1990

-0.0600
city banks

Source: Nikkei NEEDS Company Data Baselong-term credit banks

trust banks

regional banks

1991

1992

1993

1994

1995

Figure 2 Proporion of Bank Loans to Small Firms
0.85000.75000.65000.55000.45000.35000.25000.1500
1977

1978

1979

1980

1981

1982

city banks

Source: Nikkei NEEDS Company Data Base

1983

1984

1985

1986

long-term credit banks

1987

1988

trust banks

1989

1990

1991

regional banks

1992

1993

1994

1995

Figure 3 Proportion of Bank Loans secured by Real Estate
0.55000.50000.45000.40000.35000.30000.25000.20000.1500
1977

1978

1979

1980

1981

1982

city banks

Source: Nikkei NEEDS Company Data Base

1983

1984

1985

1986

long-term credit banks

1987

1988

trust banks

1989

1990

1991

regional banks

1992

1993

1994

1995

Figure 4 Proportion of Bank Loans to Non-manufacturing Industries
0.95000.90000.85000.80000.75000.70000.65000.60000.5500
1977

1978

1979

1980

1981

1982

1983

city banks

Source: Nikkei NEEDS Company Data Base

1984

1985

1986

long-term credit banks

1987

1988
trust banks

1989

1990

1991

regional banks

1992

1993

1994

1995

Figure 5-1 Fixed Investment Rate by Firm Size:
Manufacturing Industries

0.07000.06000.05000.04000.03000.02000.0100
Jun75

Jun76

Jun77

Jun78

Jun79

Jun80

Jun81

Jun82

Fixed Investment Rate of Small Firms

Jun83

Jun84

Jun85

Jun86

Jun87

Jun88

Jun89

Fixed Investment Rate of Medium Firms

Source: Quarterly Report of Financial Statements of Incorporated Business

Jun90

Jun91

Jun92

Jun93

Jun94

Jun95

Jun96

Fixed Investment Rate of Large Firms

Jun97

Figure 5-2 Fixed Investment Rate by Firm Size:
Non-manufacturing Industries
0.07000.06000.05000.04000.03000.02000.0100
Jun75

Jun76

Jun77

Jun78

Jun79

Jun80

Jun81

Jun82

Fixed Investment Rate of Small Firms

Jun83

Jun84

Jun85

Jun86

Jun87

Jun88

Jun89

Fixed Investment Rate of Medium Firms

Source: Quarterly Report of Financial Statements of Incorporated Business

Jun90

Jun91

Jun92

Jun93

Jun94

Jun95

Jun96

Fixed Investment Rate of Large Firms

Jun97

Figure 6
Balance Sheet of Banking Firm

AssetsLiabilities

Reserves

Deposits

Loans

Call money borrowing

Securities

CapitalFigure 7 Growth Rate of Deposit
0.40000.30000.20000.10000.0000
1977

1978

1979

1980

1981

1982

1983

1984

1985

1986

1987

1988

1989

1990

1991

-0.1000-0.2000-0.3000
city banks

Source: Nikkei NEEDS Company Data Baselong-term credit banks

trust banks

regional banks

1992

1993

1994

1995

Figure 8-1 Marginal q by Firm Size:
Manufacturing Industries
2.50002.00001.50001.00000.5000Marginal q of Small Firms

Marginal q of Medium Firms

7
ec
D

-9

6
ec
D

-9

5
ec
D

-9

4
ec
D

-9

3
ec
D

-9

2
ec
D

-9

1
ec
D

-9

0
ec
D

-9

9
ec
D

-8

8
ec
D

-8

7
ec
D

-8

6
ec

-8

5
D

ec
D

-8

4
ec
D

-8

3
ec
D

-8

2
ec
D

-8

1
ec
D

-8

0
ec
D

-8

9
ec
D

-7

8
ec
D

-7

7
ec
D

-7

6
ec
D

-7

ec
D

-7

5

0.0000Marginal q of Large Firms

Figure 8-2 Marginal q by Firm Size:
Non-manufacturing Industries

3.00002.50002.00001.50001.00000.5000

5

6

7

8

9

0

1

2

3

4

5

6

7

8

9

0

1

2

3

4

5

6

7

-7

-7

-7

-7

-7

-8

-8

-8

-8

-8

-8

-8

-8

-8

-8

-9

-9

-9

-9

-9

-9

-9

-9

ec

ec

ec

ec

ec

ec

ec

ec

ec

ec

ec

ec

ec

ec

ec

ec

ec

ec

ec

ec

ec

ec

ec

D

D

D

D

D

D

D

D

D

D

D

D

D

D

D

D

D

D

D

D

D

D

D

0.0000Marginal q of Small Firms

Marginal q of Medium Firms

Marginal q of Large Firms

Figure 9-1 Effects of Restrictions of Bank Loans to Real Estate Industry: Manufacturing Industries
4

3

2

1
%

-1

-2

-3

Small firm group

Source: Quarterly Report of Financial Statements of Incorporated BusinessMedium firm group

Large firm group

93
nJu

3
-9
pr
A

93
b-

-9
ec
D

Fe

2

2
-9
ct
O

A
ug
-9
2

92
nJu

2
-9
pr
A

92
b-

-9
ec
D

Fe

1

1
-9
ct
O

A
ug
-9
1

91
nJu

1
-9
pr
A

91
b-

-9
ec
D

Fe

0

0
-9
ct
O

A
ug
-9
0

Ju

n-

90

0

Figure 9-2 Effects of Restrictions of Bank Loans to Real Estate Industry: Non-manufacturing Industries
2

1

-1
%
-2

-3

-4

-5
Small firm group

Source: Quarterly Report of Financial Statements of Incorporated BusinessMedium firm group

Large firm group

Jun-93May-93Apr-93Mar-93

Feb-93Jan-93Dec-92

Nov-92Oct-92

Sep-92Aug-92

Jul-92Jun-92May-92

Apr-92Mar-92

Feb-92Jan-92

Dec-91

Nov-91Oct-91

Sep-91

Aug-91

Jul-91

Jun-91

May-91

Apr-91Mar-91

Feb-91Jan-91

Dec-90Nov-90Oct-90

Sep-90Aug-90Jul-90Jun-900