Discussion Paper No. 680

CREDIT ALLOCATION OF
JAPANESE BANKS IN THE 1990s:
EVIDENCE FROMTHE SHORT-TERM ECONOMIC SURVEY
OF ENTERPRISES

Kazuo Ogawa

January 2007

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

Discussion Paper No. 680

CREDIT ALLOCATION OF
JAPANESE BANKS IN THE 1990s:
EVIDENCE FROMTHE SHORT-TERM ECONOMIC SURVEY
OF ENTERPRISES

Kazuo Ogawa

January 2007

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

May 2005
Revised December 2006

Credit Allocation of Japanese Banks in the 1990s:
Evidence from the Short-term Economic Survey of Enterprises*

Kazuo Ogawa

Institute of Social and Economic Research,
Osaka University* Earlier version of this paper was presented as an invited lecture at 2005 Spring Meetingof the Japan Economic Association held at Kyoto Sangyo University in June 4 and 5,
2005. This research was partially supported by Grants-in-Aid for Scientific Research
12124207 and 16330038 of the Ministry of Education. Any remaining errors are the sole
responsibility of the author.

Abstract

This study is an empirical attempt to investigate the effects of balance sheet deteriorationof Japanese firms and banks during the 1990s on credit allocation using the Short-term
Economic Survey of Enterprises. This survey includes a unique item: the proportion offirms perceiving the lending attitude to be severe. After developing a theoretical model tolink this item with the balance sheet conditions of borrowers and lenders, we estimatedthe relationships that we derived from our model. We found that credit was reduced when
the balance sheet of firms and banks deteriorated. The effects are particularly significant
for non-manufacturing industries.

JEL Classification Number: E44, E51 and G21
Keywords: Tankan Survey, Credit crunch, Ever-greening, Non-performing loans,
Corporate Debt

Correspondence to:
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1. Introduction
It is often argued that credit misallocation was responsible for the long stagnation of
the Japanese economy during the 1990s. The case is set out as follows. Banks hadconcentrated their managerial resources around the disposal of massive, non-performing
loans, which had prevented banks from playing the intermediary role of allocating funds
more efficiently. Thus, the malfunction of the credit allocation system plunged theJapanese economy into a severe, decade-long stagnation. While few would refute thisinterpretation, a wide spectrum of opinion remains about the manner in which credit was
misallocated in the economy.
One view is focused on what has been termed a “credit crunch” in the 1990s. The banks,
burdened with bad loans, reduced the amount of bank loans granted to firms.
Bank-dependent firms were forced to cut spending on fixed investments, which lowered
aggregated production.
Another view is that associated with the “evergreening” of loans. It is profitable for abank to continue, or even to increase, lending to insolvent firms if the bank has alreadyspent a large sum of money on screening and monitoring loans to these firms, given thatpast expenses have already been sunk into this firm. Furthermore, additional loans to
insolvent firms can delay the realization of loan losses. In other words, it is perfectly
rational for a bank to continue to grant loans to inefficient firms in order to prevent therealization of loan losses. This is a well-known soft budget problem.1 From a theoreticalperspective, it is difficult to settle the dispute over which interpretation of events is moreplausible. Therefore, it is an issue that must be settled empirically.
This paper is an empirical attempt to shed light on this issue based on survey data fromthe Short-term Economic Survey of Enterprises (or the Tankan survey), reported by the
Bank of Japan. The Tankan survey is unique in the sense that it contains data concerning
firms’ judgments on the credit conditions currently faced. The firms were asked to answer
the following question: How do you perceive the current lending attitude of financial
institutions? Firms were asked to choose one of the following three items: severe, not sosevere, and accommodative. Each item reflects the credit condition the firm perceived
itself to face. The Tankan survey reports the proportion of firms responding to each2

choice by aggregating the responses of individual firms.
Two benefits of the Tankan survey deserve special mention. First, it provides us withinformation on credit allocation viewed from the firms’ perspective. It enables us toincorporate demand as well as supply factors associated with loans in analyzing credit
allocations. Secondly, survey results are available by industry and firm size, so that we
can examine differences in credit allocations across industries and firm size. The main
contribution of this study is that it relates information on credit allocations in the Tankan
survey to factors underlying loan demand and supply. In particular, we are interested in
the ways in which deterioration of the balance sheets of firms and banks in the 1990s
affected the allocation of credit.
To preview our main findings, the deterioration of firms’ balance sheets in the 1990s
raised the proportion of firms perceiving the lending attitude of financial institutions to be
severe. This is most notable for non-manufacturing industries. Massive non-performing
loans in the banking sector significantly raised the proportion of firms perceiving thelending attitude of financial institutions to be severe. This tendency is also notable fornon-manufacturing industries, especially the real estate industry. Our results areirrespective of the choice of balance sheet variable with respect to the banks. Ourevidence indicates that the credit crunch interpretation is more relevant than theevergreening interpretation in describing credit allocations at the industry level in theJapanese economy of the 1990s.
The paper is organized as follows. Section 2 reviews studies dealing with credit
allocation and its association with the balance sheet conditions of Japanese firms andbanks after the 1990s. We summarize previous findings on this issue. Section 3 sets up a
simple theoretical model to analyze the relationship of credit allocations deduced fromthe Tankan survey with the factors underlying loan demand and supply. Section 4 derives
equations that link the extent to which firms perceive the lending attitude of financial
institutions to be severe with some underlying factors, including balance sheet variablesof firms and banks. The data set we use is also explained in this section. Section 5 shows
the results of our estimations and explains the implications of the manner in which credit
was allocated in the 1990s at the industry level. Section 6 presents our conclusions.
32. Credit Allocation and Balance Sheet Conditions of Firms and Banks: Literature Survey
Here, we review empirical studies dealing with the effects of balance sheet conditionsin Japanese firms and banks with respect to credit allocations after the 1990s. The
empirical studies are divided into two positions. One position supports the idea that thedeterioration of balance sheets in firms and banks had negative effects on credit allocation.
These studies are numerous, including Yoshikawa et al. (1994), Miyagawa et al. (1995),
Economic and Social Research Institute (1995), Maeda (1996), Peek and Rosengren(1997, 2000), Woo (1998), Ito and Sasaki (2002), Horie (2001), Honda (2002), Hosono(2006), Ohkusa (2002), and Ogawa (2003).
There are several characteristics common to these studies. First, almost all of the studies
investigated the effects of banks’ capital adequacy ratio and ratio of non-performing
loans to total loans on credit allocation. Many of the studies obtained significantly
positive effects with respect to the capital adequacy ratio and significantly negative
effects with the non-performing loans ratio in relation to bank loans. In other words, thecredit crunch hypothesis is supported by these studies. It is true that, in these studies, thefactors affecting demand for bank loans were partially controlled for; however, they only
estimate the supply schedule of bank loans. One exception is Ohkusa’s study (2002), in
which the level and growth rate of firms’ profits are shown to be important factors that
influence the lending attitude of financial institutions. Given that it is not an easy task tothoroughly identify demand and supply with respect to bank loans, it would be preferable
to include not only factors affecting bank loan supply but also those affecting demand forbank loans.
Second, most of these studies examine total bank loans, with the exception of Miyagawa
et al. (1995), Maeda (1996) and Ogawa (2003). They divide loans into several
components and pay special attention to the relationships between banks’ balance sheet
conditions and bank loans to small firms. Ogawa’s study (2003) revealed evidence
suggesting that the effect of the non-performing loans ratio is particularly strong for loans
to small firms. Moreover, Ogawa also examined the effect of the non-performing loans
ratio on bank loans by industry and reported that the effect of the non-performing loans
4ratio on bank loans is larger for non-manufacturing industries.
The other position supports the explanation of the evergreening of loans. The studies
providing evidence for this condition are relatively new. For example, Hoshi’s study(2000) offers the seminal argument that evergreening of loans is apparent in the realestate industry. Studies by Fukao (2000) and Sakuragawa (2002) are in the same vein.
The former shows that the average borrowings per firm increased for the real estateindustry in the late 1990s, and the latter reports that the correlation between the rate ofchange in land price and lending to the real estate industry vanished in the late 1990s.
More formal empirical works estimate the supply equation of bank loans. The balance
sheet variables commonly used in these studies are the ratio of non-performing loans tototal loans, banks’ capital adequacy ratio, and firms’ debt-asset ratio. Sasaki (2000) foundthat banks with a higher non-performing loans ratio tend to increase loans to theconstruction industry while Sugihara and Fueta (2002) also found that non-performing
loans to the real estate industry exert a significantly positive effect on loans granted to thisindustry. Tsuru (2001) found that the capital adequacy ratio had significantly negative
effects on loans made to the real estate industry. Hosono and Sakuragawa (2003) argued
that banks with a lower capital adequacy ratio tend to increase real estate loans. The
studies of Hibara (2001), Koyabashi et al. (2002), and Peek and Rosengren (2005) are
unique in that they used matched samples of individual firms and bank lenders’transactions. By examining the relationship between the non-performing loans ratio andthe debt-asset ratio, Hibara (2001) found that banks with higher non-performing loans
ratios tended to use less restraint in making loans to firms with high debt-asset ratios.
Koyabashi et al. (2002) showed that beyond a certain point of debt-asset ratio, a rise in thedebt-asset ratio corresponded with an increase in lending to firms in the construction andreal estate industries. Peek and Rosengren (2005) offer the most comprehensive micro
study. They demonstrated that Japanese banks increased loans to less profitable firms in
the 1990s. Moreover, they found that this was especially so for banks with balance sheet
deterioration and loans to affiliated firms.
To summarize, first, the evergreening phenomena is not prevalent across industries but
can be observed in certain, non-manufacturing industries (construction and real estate)5that are burdened with excessive debt. Second, all of the studies that incorporate firms’balance sheet variables rely on micro data from banks and firms. Since listed firms’
information constitutes the micro data, the evidence supporting the evergreeningargument is confined to large firms, with no information concerning small or
medium-sized firms included.2 It is worth noting that small or medium-sized firms are
more dependent on bank loans, so investigation into how credit allocation is influenced
by the balance sheet conditions of firms, as well as that of banks, is important. The benefitof examining micro data from firms and banks in the context of this study is that it enables
us to make clear estimates of the effects of balance sheet conditions of firms and banks on
credit allocation. However, we fail to obtain macro or industry level implications for
credit allocations from micro level evidence. It is true that credit is constrained for somefirms with heavy debts and is expanded for some firms with excessive debts, but microlevel evidence is silent as to the aggregated consequences of credit allocations on theindustry level. In the subsequent sections, we investigate how credit is allocated for small
and medium-sized firms as well as large firms. The Tankan survey is an ideal data sourcethat provides us with the information necessary for conducting empirical analysis along
these lines.

3. Credit Allocation and Balance Sheet Conditions of Firms and Banks: Theoretical
Framework
We set up a simple theoretical model to analyze how credit allocation across firms
within an industry is associated with the balance sheet conditions of firms constituting an
industry and banks. We assume that the i-th firm in a certain industry has the following
demand schedule for bank loans:
Ldit = f ( y i ,1t , yi ,2 t , L , yi ,nt )(1)where Ldit : demand for bank loans of the i-th firm in time t

yi ,kt : determinants of loan demand of the i-th firm in time t (k = 1,2, L , n )Denote the supply schedule of loans to the i-th firm by Lsit . 3 The supply schedule of loans6

is given by:
Lsit = g ( yi ,1t , yi , 2 t , L y i ,nt , z1t , , z 2 t , L , z pt )(2)

where z kt :determinants of loan supply in time t (k = 1,2, L , p )It should be noted that the loan schedule is influenced not only by the factors associatedwith banks, such as the banks’ balance sheet, but also by the factors that affect loan
demand. This is because a loan contract is intrinsically determined by negotiation
between creditors and debtors, and information concerning debtors is processed by banks
in determining the supply of loans to the firm.4
When the demand schedule for loans exceeds the supply schedule, the firm may perceive
the current lending attitude of financial institution to be severe. Let θ it be the extent towhich the supply schedule exceeds demand schedule, or:θ it =

Lsit
−1
Ldit

(3)

Then, θ it can capture the extent to which the firm perceives the present lending attitude to
be severe. The Tankan survey we used for data in our empirical analysis reports thepercentage share of firms regarding the current lending attitude as severe, not so severe
and accommodative, respectively. These ratios were easily translated into our framework.
Figure 1 shows the distribution of θ it across firms within a certain industry. We denotethe percentage share of firms feeling the present lending attitude to be severe, not sosevere, and accommodative as S t NS t , At , respectively. Then, we have:

St = ∫− δ1−∞

NS t = ∫f (θ it )dθ it

+δ 2

−δ1

f (θ it )dθ it(4)7

At = ∫+∞+δ 2

f (θ it )dθ it

where δ 1 : threshold to recognize the lending attitude as severeδ 2 : threshold to recognize the lending attitude as accommodative

By specifying the density function, we can obtain more tractable expressions for eq.(4).
We assume that the distribution function of θ it is given by the following logistic
function:

F (θ it ) =

1
1+ e

(5)

−(θit −α ) / β

where α , β :shift parameters β > 0

Since θ it is defined as the ratio of supply to demand for loans, it is a function of thefactors affecting both supply and demand for loans. In other words, the shape of the
distribution hinges upon yi ,1t , , yi , 2 t , yi ,3t , L , yi ,nt , z1t , z 2 t , z3t , L , z pt (i = 1,2, L , I ) , where I
is the number of firms. These variables are the driving force of the shift parameters. Since
it is impossible to incorporate the information of individual firms into our analysis, we
assume that the shift parameter α is a function of the mean of firm attributes. We denotethe mean of yi ,1t , yi , 2 t , yi ,3t , L , yi ,nt across firms by y1t , y 2 t , L , y nt . 5Then, α isexpressed as follows:

α = h ( y1t , L , y nt , z1t , L , z pt )(6)

From this, we can link the proportion of firms perceiving the present lending attitude to be
severe (S t ) with the factors underlying supply and demand for loans.

St =

1+ e

(

1

(7)

)

−( − δ1 − h y1t ,L, ynt , z1t ,L, z pt ) / β

8In our framework, the effects of factors affecting loan demand and supply on S t may beeasily analyzed. For example, the effect of deterioration of a bank’s balance sheet on S t
can be examined in the following manner. Suppose that deterioration of the bank’sbalance sheet makes the bank more reluctant to supply loans to firms. It will lower θ it forsome firms, and the distribution function of θ it will shift to the left, as shown in Figure 2.
Thus, the proportion of firms perceiving the lending attitude to be severe rises.
However, the bank might increase loans as a result of soft budget problems when the
bank’s balance sheet deteriorates. Then, it will raise θ it for some firms, and the
distribution function of θ it will correspondingly shift to the right. This implies that the
proportion of firms perceiving the lending attitude to be severe falls. This case is that of
the evergreening of loans. In actuality, some firms might observe a rise of θ it whileothers might observe a fall of θ it . Therefore, the question of whether the proportion offirms perceiving the lending attitude to be severe increases or not is purely an empiricalissue.
In a similar fashion, we can analyze the effects of factors affecting loan demand on S t .
Suppose that the outlook for the firm’s profitability becomes gloomy. Then, the firm will
decrease demand for loans. This will raise θ it and the percentage share of firms
perceiving the lending attitude to be severe will fall. However, when the bank finds out
the firm’s gloomy profit expectations, a decrease of loan demand might beaccommodated. This might lead to a fall of θ it and the proportion of firms feeling thelending attitude to be severe might rise. This illustrates the importance of firms’
information to the banks’ loan supply decisions; it also shows that the question of whether
banks accommodate a change in loan demand hinges upon firms’ information as well ason the ways in which banks process such information. This is an empirical issue to beinvestigated in subsequent sections.
It should be noted that we can also link the proportion of firms perceiving the present
lending attitude to be accommodative ( At ) to the factors underlying the demand andsupply schedule of loans. It is written as:

9At = 1 −

1+ e

(

1

)

− ( δ 2 − h y1t ,L, ynt , z1t ,L, z pt ) / β

=

1+ e

(

1

)

( δ 2 − h y1t ,L, ynt , z1t ,L, z pt ) / β(8)This equation is estimated jointly with eq.(7).

4. Framework of Empirical Investigation and Data Set
In the previous section, we derived the basic equation to link the proportion of firms
perceiving the lending attitude to be severe or accommodative with the factors underlying
loan demand and supply. The purpose of this section is twofold. One is to modify thebasic equations and derive the equation to estimate the effects of the factors underlying
supply and demand for loans on credit allocation. The other is to explain the data set weused in the estimation as well as the procedures for constructing the variables.

A. Specification of Lending Attitude Equation
In estimating eqs.(7) and (8), the shift parameter of α is specified as a linear functionof the factors underlying loan demand and supply. We selected the following variables
that affect loan demand. They are liquidity measure (LIQ), growth potential (GROWTH),
and balance sheet condition of firms (FBALANCE). As for the variables that affect loan
supply, we chose the balance sheet variable of banks (BBALANCE).
Specifically, the equation linking the proportion of firms perceiving the lending
attitude of financial institutions to be severe ( S t ) with the factors underlying loan demandand supply is written as:

St =1
1 + e Φt

(9)

where
Φ t = γ 1 + γ 2GROWTH t + γ 3 LIQt + γ 4 FBALANCEt ++ γ 5 BBALANCEt + γ 6 FBALANCEt × BBALANCEt

γ1 =

δ1
β

10

An increase of firms’ liquidity may decrease the demand for loans and lead to a fall in
S t by way of a rise in θ it . Thus, the coefficient γ 3 is positive. Higher growth potential

will increase the demand for loans. It will lower θ it and S t will rise as long as banks do
not accommodate the increase of loan demand. On the other hand, when banks
accommodate an increase in loan demand, it might be the case that θ it rises and S t falls.
Therefore the sign of γ 2 is indeterminate a priori.
Deterioration of firms’ balance sheets, leading to the reduction of demand for loans, mayalso reduce loan supply if banks are aware of firms’ balance sheet deteriorations. Whenthe reduction of loan supply by banks is larger than that of loan demand, θ it will fall and
S t will rise. In this case, the coefficient γ 4 is negative. However, if the firms increase

their demands for loans in spite of the deterioration of their balance sheets, to repay debt,
for example, and banks support this decision by increasing loan supply, it may raise θ it
and S t will fall ( γ 4 > 0) . This case can be interpreted as an example of the evergreeningof loans.
Deterioration of banks’ balance sheets generally reduces loan supply, which lowers θ itand raises S t . In other words, the coefficient γ 5 is negative. However, when the banks
boost loan supply, in spite of the deterioration of their balance sheets, it lowers S t( γ 5 > 0 ). This also corresponds to the case of evergreening of loans. To sum up, we candetermine whether evergreening of loans is prevalent by examining the sign of thecoefficients of FBALANCE and/or BBALANCE.
We added the cross term of the firms’ balance sheet conditions with that of the banks’ tomeasure the interaction between the balance sheets of the two sectors. The cross term
plays an important role in strengthening or weakening the individual balance sheet effects.
When γ 6 is negative, it will enhance the effects of credit crunch, whereas when γ 6 ispositive it will enhance the effects of evergreening.
Lastly, we take account of the differential effects of balance sheet conditions of firms andbanks on credit allocation by industry and firm size. As we pointed out in Section 2, theevergreening phenomenon has been found mainly in non-manufacturing industries
burdened with excessive and outstanding debt. Moreover, small firms are morebank-dependent, so the bank relationship might be special to small firm clients. This11

suggests that evaluation of firms’ balance sheet conditions by banks might have a
differential impact on banks’ lending attitudes toward small firms. To take these
possibilities into consideration, we add the cross term of balance sheet variables of firms
and banks, with dummy variables for industry and firm size, to eq.(9).6 That is, we have:St =1
1 + e Φt

(10)

where
q

Φ t = γ 1 + γ 1,S DUMS t + γ 1,M DUMM t + ∑ γ 1, Ik DUMINDkt
k =1+ γ 2GROWTH t + (γ 3 + γ 3,S DUMS t + γ 3,M DUMM t ) × ∆LIQtq+ (γ 4 + γ 4,S DUMS t + γ 4,M DUMM t + ∑ γ 4,Ik DUMINDkt ) × ∆FBALANCEtk =1q+ (γ 5 + γ 5,S DUMS t + γ 5,M DUMM t + ∑ γ 5,Ik DUMINDkt ) × ∆BBALANCEtk =1q+ (γ 6 + γ 6,S DUMS t + γ 6,M DUMM t + ∑ γ 6, Ik DUMINDkt ) × ∆FBALANCEt × ∆BBALANCEt
k =1

where DUMSt : dummy variable for small firms
DUMM t : dummy variable for medium-sized firms
DUMINDkt :dummy variable for the k-th industry ( k = 1,2, L , q)

It should also be noted that all the explanatory variables except growth potential are in
first difference form.7, 8
In a similar fashion, we can write the proportion of firms perceiving the present lending
attitude to be accommodative ( At ) as a function of factors underlying supply and demandfor bank loans as follows:St =1
1 + e Ψt

(11)

where

12

q

ψ t = ε 1 + ε 1,S DUMS t + ε 1,M DUMM t + ∑ ε 1, Ik DUMINDkt
k =1− γ 2 GROWTH t − (γ 3 + γ 3,S DUMS t + γ 3,M DUMM t ) × ∆LIQtq− (γ 4 + γ 4,S DUMS t + γ 4,M DUMM t + ∑ γ 4, Ik DUMINDkt ) × ∆FBALANCEtk =1q− (γ 5 + γ 5,S DUMS t + γ 5,M DUMM t + ∑ γ 5, Ik DUMINDkt ) × ∆BBALANCEtk =1q− (γ 6 + γ 6,S DUMS t + γ 6,M DUMM t + ∑ γ 6, Ik DUMINDkt ) × ∆FBALANCEt × ∆BBALANCEt
k =1

Note that the coefficient of each explanatory variable in eq.(11) has the reverse sign ofeq.(10).

B. Data Set Description and Construction of Variables
The Tankan survey reports, on a quarterly basis, the proportion of firms perceiving thelending attitude of financial institutions to be severe by industry and firm size. Sample
firms are private firms selected from the population of all firms in Japan employing 50 ormore persons (or 20 or more persons for wholesale, retail, and leasing industries). The
Tankan survey classifies industries into 17 manufacturing and 10 non-manufacturingindustries. We chose 14 manufacturing and six non-manufacturing industries withavailable balance sheet information.9 Sample firms are classified by size into large,
medium, and small firm groups based on the number of employees. Large firms aredefined as those with a number of employees over 1,000. Medium-sized firms are definedas those with a number of employees between 300 and 1,000. Small firms are defined as
those with a number of employees below 300.10 Our sample period covers the second
quarter of 1983 to the first quarter of 2003. In our data, we pooled all of our observations
across industry and firm size, so that observations total 4,800 (= 80 × 20 × 3 ).
The explanatory variables of loan demand are taken from the Financial Statements
Statistics of Corporations by Industry (abbreviated as FSSC), issued by the Ministry of
Finance. A variety of items on firms’ balance sheets as well as profit and loss accounts arereported on a quarterly basis by industry and firm size. One difficulty of matching theTankan survey with the FSSC lies in the difference of size classification. The former
13

classifies by the number of employees, while the latter classifies by equity capital. We
assume that the firms whose equity capital exceeds one billion yen correspond to large
firms in the Tankan survey. Similarly, the firms whose equity capital is between 0.1 and 1
billion yen correspond to medium-sized firms in the Tankan survey, and the firms whose
equity capital is below 0.1 billion yen correspond to small firms in the Tankan survey.
Although the two definitions of firm size are different, it turns out that they are closely
related. In the FSSC, the average number of employees in the fiscal year of 2003 is 16.3,
167.6, and 1192.1 for small, medium-sized, and large firm groups, respectively, across allindustries.
Liquidity is measured by the ratio of cash, deposit, and securities balance plus ordinary
income and depreciation allowance, to sales. Growth potential (GROWTH) is measured
by the growth rate of real sales. The balance sheet variable of the firm (FBALANCE) ismeasured by the ratio of short-term and long-term borrowings to market-valued land
stock. Land stock is constructed by the perpetual inventory method.11 Since land is themost popular form of collateral in Japan, this ratio reflects the extent to which actual
borrowings exceed firms’ borrowing capacities or the degree of excess borrowings. Thebalance sheet variable of the bank (BBALANCE) is the ratio of allowance for doubtful
loans to total loans. This variable is a proxy for the ratio of non-performing loans to totalloans and is the only one available on a quarterly basis. The balance sheet variables we
use are dated at the beginning of the period to avoid simultaneity bias. The variables offirm attributes are on a per firm basis and all of the variables are seasonally adjusted. 12
Table 1 shows the sample mean of the ratio of borrowing outstanding to market-valued
land stock. The ratio is higher for non-manufacturing industries. It hovers at around unity
for small firms, except for the service industry. The ratio tends to be highest for large
firms, except for those in retail and the real estate industries. For manufacturing industries,
there is less regularity on the magnitude of the ratios across industry and firm size. Table
2 shows the average growth rate of real sales. Negative sales growth was dominant inmost of the industries, reflecting severe stagnation in the 1990s. In particular, the growthrate of real sales was negative, irrespective of firm size, for food, steel, fabricated metals,
construction, and retail industries. In contrast with stagnant industries, there were14

industries that exhibited positive growth rate, irrespective of firm size. These werechemicals, electrical machinery, transportation machinery, and transportation andinformation communication industries.

5. Estimation Results and Their Implications for Credit Allocation
A. Estimation Results of Lending Attitude Equation
Here, we turn to our estimation results that indicate some important implications for
credit allocation. Eqs.(10) and (11) are jointly estimated by nonlinear least squares.
Estimation results are shown in Table 3.13 As we expected, the coefficient estimate ofliquidity is positive and statistically significant at the 1% level. Increase of firms’
liquidity reduces demand for loans and hence decreases the proportion of firms
perceiving the lending attitude to be severe. There was no discernible difference in theeffects of liquidity on the proportion of firms perceiving the lending attitude to be severe
among small, medium-sized, and large firms. The coefficient estimate of sales growthrate is significantly positive. It implies that higher growth potentials decrease theproportion of firms perceiving the lending attitude to be severe by inducing banks toaccommodate firms’ increased loan demand.

B. Implications for Credit Allocation: Credit Crunch or Evergreening?
Based on the coefficient estimates of the balance sheet variables of firms and banks,
we can infer how changes in the balance sheet conditions of firms and banks affect credit
allocation. If deterioration of balance sheet conditions tightens credit allocations, then we
observe a credit crunch. On the other hand, if a deterioration of balance sheet conditions
loosens credit allocations, then we observe evergreening. Our estimation results have theadvantage that we can examine the effects of balance sheet conditions on credit allocation
by industry and firm size.
We conducted our investigation in three different ways. First, we examined thecoefficient estimates of the cross term of firms’ balance sheet conditions with those ofbanks. 14 It is easily shown that the effects of change in the balance sheet conditions offirms and banks regarding credit allocation is calculated from eq.(10) as:
15

q
⎧
∂S t
= − S t (1 − S t )⎨γ 4 + γ 4,S DUMS t + γ 4,M DUMM t + ∑ γ 4, Ik DUMINDkt∂ (∆FBALANCEt )
k =1
⎩
q
⎫
+ (γ 6 + γ 6,S DUMS t + γ 6,M DUMM t + ∑ γ 6, Ik DUMINDkt ) × ∆BBALANCEt ) ⎬
k =1
⎭

q
⎧
∂S t
= − S t (1 − S t )⎨γ 5 + γ 5,S DUMS t + γ 5,M DUMM t + ∑ γ 5, Ik DUMINDkt∂ (∆BBALANCEt )
k =1
⎩
q
⎫
+ (γ 6 + γ 6,S DUMS t + γ 6,M DUMM t + ∑ γ 6, Ik DUMINDkt ) × ∆FBALANCEt ) ⎬
k =1
⎭

(12)

When the cross term coefficient, the last term of the R.H.S. of eq.(12), is positive,
deterioration of the balance sheet of one sector will mitigate the tightening effect of thebalance sheet deterioration of the other sector on credit allocation. Thus, it is interpreted
as evergreening. Table 4 shows the coefficient estimates of the cross term by industry andfirm size. The significant cross terms are all negative for large firms. There is only one
significantly positive cross term (petroleum and coal products industry) for
medium-sized firms. For small firms, there is also one industry (industrial machineryindustry) that has a significantly positive cross term. Judging from the cross termcoefficients of firms’ balance sheets and banks’ balance sheets, evergreening does notseem to be prevalent in the economy.
The second way to examine these effects of balance sheet conditions of firms andbanks on credit allocation is to calculate directly the effects of changes in the balance
sheet conditions of firms and banks on the proportion of the firms perceiving the lending
attitude to be severe, as is shown in eq.(12). We evaluate eq.(12) by industry and firm size
based on the sample mean of S t , ∆FBALANCEt and ∆BBALANCEt over the fourth
quarter of 1997 to the first quarter of 2002, a period of financial turmoil. Tables 5 and 6
show the estimates of

∂S t
∂S t
and
calculated in this∂ (∆FBALANCEt )∂ (∆BBALANCEt )16manner.
Deterioration of firms’ balance sheet conditions significantly increases the proportion offirms perceiving the lending attitude to be severe, across all industries, for large firms.
This implies that credit allocation tightens as the balance sheets of large firms deteriorate.
A similar tendency is observed, though less dramatically, for medium-sized and small
firms. For medium-sized and small firms, we observed negative effects of firms’ balance
sheet deteriorations on the proportion of firms perceiving the lending attitude to be severe
for petroleum and coal products, non-ferrous metals, and service industries. It issignificant only for small firms in the petroleum and coal products industries. In 18 out of
20 industries, we observed significantly positive effects for medium-sized firms, while
we observed significantly positive effects in 11 industries for small firms. It should be
noted that the estimates are notably large for retail, construction, and fabricated metals
industries, irrespective of firm size.
A rise of the non-performing loan ratio makes banks’ lending attitudes more severe
for large and medium-sized firms across all industries, apart from the lumber and wood
products industry. The rise of the non-performing loan ratio makes banks’ lending
attitudes more accommodative for small firms in lumber and wood products, fabricatedmetals, industrial machinery, transportation machinery, and construction industries.
However, the effect is significant only in the lumber and wood products industry. Credit
contraction effects are significant in four, 11, and 10 industries out of 20 for small,medium-sized, and large firms, respectively. The effects are particularly significant in thereal estate industry, in which a rise of the ratio of allowance for doubtful loans to totalloans by a single percentage point increases the proportion of firms perceiving the lending
attitude to be severe by 15.1, 20.4 and 19.9 percentage points for small, medium-sized,
and large firms, respectively. In the late 1990s, evergreening was seldom observed on anindustry level, irrespective of firm size. Rather, we observed a serious credit crunch
situation, especially in non-manufacturing industries.
Lastly, we show inter-industry evidence on the effects of firms’ balance sheet
conditions on credit allocation. This can be accomplished by calculating the correlation17

coefficient of the

∂S t
∂S t
and
estimates obtained above∂ (∆FBALANCEt )∂ (∆BBALANCEt )with the firms’ balance sheet conditions across industries. When credit crunch isprevalent, we will see that the effects of change in balance sheet conditions on lending
attitude is particularly severe for industries with deteriorating balance sheets. That is, wewill observe positive correlations between the estimates of∂S t∂ (∆FBALANCEt )

and

∂S t
with the firms’ borrowings-land ratio. On the other hand, when
∂ (∆BBALANCEt )evergreening is prevalent, deterioration of balance sheets will mitigate the effects ofchange in balance sheet conditions on lending attitude. In this case we will observe
negative correlations. The correlation coefficient between the∂S t∂ (∆BBALANCEt )estimate and the firms’ balance sheet conditions, calculated by combining all theobservations across firm size, is 0.3314, and is significant. 15 It indicates that thecredit-tightening effect is more severe for industries with firms showing more balance
sheet deterioration. This is evidence for a credit crunch condition, not for an evergreeningof loans situation.

C. Is Our Evidence Robust?: Estimation under Alternative Specifications
The estimation results obtained so far lend support for the prevalence of credit crunchrather than evergreening on an industry level. We examine the robustness of our evidenceby estimating the lending attitude equation under alternative specifications. We replace
the banks’ balance sheet variable with the rate of change in share price of the banking
sector. The previous measure, the ratio of allowance for doubtful loans to total loan,shows how bad the firms’ balance sheet conditions are on the basis of accounting
information. It is often argued that balance sheet information is not reliable because of“window dressing.” In this case, a market-based measure of balance sheet conditions ismore relevant. It would be legitimate to assume that share prices in the banking sector
reflect the evaluation of bank health by the market. It turns out that the coefficient18

estimates of liquidity and growth rate of sales under these new specifications are quite
similar to those of the previous case. Thus, our focus here is on how the balance sheet
conditions of firms and banks affect credit allocation. This is investigated in threedifferent ways, as in the previous case. Table 7 shows the cross term of the firms’ balance
sheet with the rate of change in share price of the banking sector. Since a rise in the rate ofchange in banks’ share prices reflects the improvement of banks’ balance sheets, anegative cross term corresponds to the evergreening case. It is only in the real estateindustry that we observe evergreening, irrespective of firm size. 16 Our evidence forevergreening in the real estate industry lends empirical support to previous studies.
However, it should be noted that the prevalence of evergreening, judging from the sign ofthe cross term, is weak, given that even if the cross term is negative, the total effects ofbalance sheet deterioration on credit allocation calculated by eq.(12) could also support
the credit crunch hypothesis. In fact, the deterioration of balance sheet conditions on thepart of banks and firms leads to a more severe lending attitude for the real estate industry,irrespective of firm size, as will be seen below.
Here, we examine the

∂S t
∂S t
and
estimates, shown∂ (∆FBALANCEt )∂ (∆BBALANCEt )in Tables 8 and 9. Deterioration of firms’ balance sheets significantly increases the
proportion of firms perceiving the lending attitude to be severe for large firms across allindustries. For medium-sized and small firms, we obtained negative estimates of∂S t
in petroleum and coal products, precision machinery, and service∂ (∆FBALANCEt )industries. All of the significant estimates are positive except for the petroleum and coalproducts industry, indicating the prevalence of credit crunch. As for the effects of banks’share price change on the credit allocation, a drop in the rate of change in banks’ shareprices increases the proportion of firms perceiving the lending attitude to be severe acrossall industries, except for the lumber and wood products industry, irrespective of firm size.
In particular, the estimates are significantly negative and large in absolute value for thereal estate industry, irrespective of firm size.

19Lastly, we calculate the correlation coefficients of the∂S t∂ (∆FBALANCEt )

and

∂S t
estimates with the firms’ balance sheet conditions across industries.
∂ (∆BBALANCEt )The correlation coefficient between the∂S testimates and the firms’∂ (∆BBALANCEt )borrowings-land ratio, calculated by combining all the observations across firm size, is-0.2938, and is statistically significant at the 5% level.17 Once again, the credit crunch
hypothesis is supported by inter-industry comparison.
To sum up our evidence, balance sheet deterioration of firms and banks in the late 1990s
reduced credit ubiquitously across industries. It should be noted that our evidence is notinconsistent with past studies that have argued for the evergreening case. This is because
most of the studies that have obtained evidence favorable to evergreening have been
conducted using micro data from firms. Our study uses more aggregated industry level
data. As we pointed out in Section 3, it is true that deterioration of balance sheets has
adverse effects on credit allocation to some firms, while other firms will perceive that thelending attitude gets more severe. Our examination of the proportion of firms perceiving
the lending attitude to be severe, using evidence at the industry level, simply
demonstrates that the number of firms suffering from the credit crunch condition isgreater than those benefiting from evergreening.

6. Concluding Remarks
Empirical data on how balance sheet deterioration of firms and banks affected credit
allocation in the 1990s revealed that credit allocation was reduced as a whole. The extent
to which each industry suffered from credit reduction depends on the attributes ofindividual industries as well as the banking sector. The effects were notably large for the
non-manufacturing sector, although the effects are not uniform across industries in the
manufacturing sector. There remains another, more important, task to be completed: aninvestigation into how credit allocation in the 1990s affected the resource allocation of
the economy. This is a promising avenue for future work, one that could lead to a deeper
20

understanding

of

the

economicmechanisms

21

of

the

last

decade.

18

Footnotes1

See Dewartripont and Maskin (1995) and Berglof and Rӧland (1995,1997) for detailed
discussions of the soft budget problem.
2
An exception is the study by Fukuda et al. (2005) in which the reduced form of the loan
equation is estimated using a matched sample of unlisted firms and banks. The results are
mixed. Deterioration of firm health leads to reduction of bank loans, while deteriorationof bank health increases bank loans.
3
For simplicity, we assume that there is one representative bank that provides loans to allof the firms in the economy.
4
Interest payment is also an endogenous variable determined as a part of loan contract, sothat interest rate does not appear as an explanatory variable in the loan demand andsupply schedule.
5
The variables z1t , L , z pt are associated with the representative bank, so that they can beinterpreted as the mean of the attributes of total banks.
6
The effect of liquidity on demand for bank loans might vary across firms of different
sizes, so that we add the cross term of liquidity with dummy variables for firm size.
7
We also add dummy variables of firm size and industry to the constant term of Φ t .
8
As will be explained below, growth potential is measured by the growth rate of real
sales. Therefore, we do not take first difference of it.
9
These are textiles, lumber and wood products, paper and pulp, chemicals, petroleum andcoal products, ceramics, stone and clay products, steel, non-ferrous metals, food,fabricated metals, industrial machinery, electrical machinery, transportation machineryand precision machinery for manufacturing industries and construction, real estate,wholesale, retail, transportation and information communication, and service fornon-manufacturing industries.
10
For the wholesale industry, medium-sized firms are defined as those with employees
between 100 and 1,000, while small firms are defined as those with employees between
20 and 100. For retail, service, and lease industries, medium-sized firms are defined as
those with employees between 50 and 1,000, while small firms are defined as those with
employees between 20 and 50.
11
See Ogawa(2000) for detailed procedures for constructing market-valued land stock.
12
For the judgment questions in the Tankan survey, the respondents are asked to answer
on the lending attitude of financial institutions excluding seasonal factors. That is thereason why we do not seasonally adjust the proportion of firms perceiving the lending
attitude to be severe.
13
Constant terms including dummies for industry and firm size are suppressed in thetable.
14
Hibara (2001) takes a similar approach to test the prevalence of evergreening of loans.
∂S t
15
The correlation coefficient between theestimates and the firms’∂ (∆FBALANCEt )borrowings-land ratio calculated by combining all the observations across firm size is
0.0735 and insignificant.

22

16

A statistically negative coefficient of the cross term is also obtained in petroleum andcoal products, precision machinery, and service industries for medium-sized firms.
∂S t
17
The correlation coefficient between theestimates and the firms’∂ (∆FBALANCEt )borrowings-land ratio, calculated by combining all the observations across firm size, is0.0234 and insignificant.
18
Nishimura et al. (2005) and Caballero et al. (2004) are interesting attempts along this
line.

23

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Financial Transition,” Journal of the Japanese and International Economies 9,
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[2] Berglöf E. and G. Roland(1997).” Soft Budget Constraints and Credit Crunches in
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[3] Caballero, R.J., Hoshi,T. and A.K. Kashyap(2004).” Zombie Lending and Depressed
Restructuring in Japan,” mimeographed.

[4] Dewatripont,M and E. Maskin(1995). “ Credit and Efficiency in Centralized andDecentralized Economies,” Review of Economic Studies 62, pp.541-555.

[5] Economic and Social Research Institute(1995). Keizai Hakusho (Economic White
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[6] Fukao, M.(2000). Kin-yu Fukyo no Jissho Bunseki (Empirical Analyses of Financial
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[7] Fukuda, S., Kasuya, M. and J. Nakajima(2005).” Hijyojyo Kigyo ni “Oikashi” wa
Sonzai Shitaka?” (Did Ever-greening of Loans Exist for Unlisted Firms?) The Bankof Japan Working Ppaer No.05-J-9.

[8] Hibara,N.(2002).” What Happens in Banking Crises? Moral Hazard vs. Credit
Crunch,” mimeographed.

[9] Honda,Y. (2002).” The Effects of the Basle Accord on Bank Credit: The Case ofJapan,” Applied Economics 34, pp.1233-1239.

24

[10] Horie,Y.(2001). Ginko Kashidashi no Keizai Bunseki (Economic Analysis of Bank
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[11] Hoshi,T.(2000).” Naze Nihon wa Ryudosei no Wana kara Nogarerarenainoka?
(Why is the Japanese Economy Unable to Get Out of a Liquidity Trap?)” in Fukao,M.
and H. Yoshikawa (eds.) Zero Kinri to Nihon Keizai (Zero Interest Rate and theJapanese Economy), Nihon Keizai Shimbunsha, pp.233-266,(in Japanese).

[12] Hosono,K.(2006).” The Transmission Mechanism of Monetary Policy in Japan:
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[13] Hosono,K. and M. Sakuragawa(2003).” Soft Budget Problems in the JapaneseCredit Market,” mimeographed.

[14] Ito,T. and Y.N. Sasaki(2002).” Impacts of the Basle Capital Standard on JapaneseBanks’ Behavior,” Journal of the Japanese and International Economics 16,
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[15] Kobayashi, K., Saita,Y. and T. Sekine(2002).” Forbearance Lending: A Case forJapanese Firms,” Research and Statistics Department Working Paper (Bank of Japan)
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[16] Maeda,T.(1996).” Wagakuni Ginko-gyo niokeru Kashidasi Nobinayami nitsuite –Kashishiburi Ron nikan-suru Kosatsu to Jissho Bunseki (Lending Problem ofJapanese Banking Industry – An Empirical Study on Credit Crunch -)”, Financial
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25[17] Miyagawa, T., Nosaka, H. and M. Hashimoto(1995).” Kin-yu Kankyo no Henka to
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[18] Nishimura,G.K., Nakajima,T. and K. Kiyota(2003).” Does the Natural Selection
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[19] Ogawa,K.(2000).” Monetary Policy, Credit and Real Activity: Evidence from theBalance Sheet of Japanese Firms,”Journal of the Japanese and International
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[20] Ogawa,K.(2003) Daifukyo no Keizai Bunseki (Economic Analysis of Depressions),
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[22] Ohkusa,Y.(2002).” Kashishiburi to Shinsa Kino (Credit Crunch and Screening
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[23] Peek J. and E. S. Rosengren(1997).” The International Transmission of Financial
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[24] Peek J. and E. S. Rosengren(2000).” Collateral Damage: Effects of the JapaneseBank Crisis on Real Activity in the United States,” American Economic Review 90,

26

pp.30-45.

[25] Peek J. and E. S. Rosengren(2005).” Unnatural Selection: Perverse Incentives andthe Misallocation of Credit in Japan,” American Economic Review 95, pp.1140-1166.

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[27] Sasaki,Y.(2000).” Jiko Shihon Hiritsu Kisei to Furyo Saiken no Ginko Kashidashi
heno Eikyo (Effects of Regulation on Capital Adequacy Ratio and Non-Performing
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[28] Sugihara,S. and I.Fueta(2002).” Furyo Saiken to Oikashi (Non-Performing Loansand Ever-Greening), Nippon Keizai Kenkyu (JCER Economic Journal) No.44,
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[29] Tsuru, K.(2001).” The Choice of Lending Patterns by Japanese Banks during the
1980s and 1990s: The Causes and Consequences of a Real Estate Boom,” IMES
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[30] Woo,D.(1998).” In Search of “Capital Crunch”: Supply Factors behind the Credit
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[31] Yoshikawa,H., Eto, M. and T. Ike(1994).” Cyusho Kigyo nitaisuru Ginko niyoru
Kashisiburi nitsuite (Credit Crunch by Banks to Small Firms),” Keizai Bunseki,
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27

Figure 1
Density of θ itf (θ it )

St

− δ1

NS t

At

0

+ δ2

θ it

Figure 2
Shift of Density Function of θ itf (θ it )

S t′
St
− δ1

0

θ it

Table 1 Descriptive Statistics:
Ratio of Borrowing Outstandings to Market-Valued Land Stock
Small
firms

Medium-Sized
firms

Large
firms

Manufacturing industries
Food
TextilesLumber and wood products
Paper and pulp
ChemicalsPetroleum and coal products
Ceramics, stone and clay products
Steel
Non-ferrous metals
Fabricated metals
Industrial machinery
Electrical machineryTransportation machinery
Precision machinery

1.18
0.72
0.70
0.82
0.68
0.68
0.99
0.51
0.94
0.90
1.11
0.94
0.70
0.92

0.77
1.23
0.97
0.91
0.80
2.28
0.91
0.86
1.71
0.76
1.13
1.31
0.81
0.72

0.51
1.07
0.57
1.39
0.79
1.29
0.83
1.21
1.71
0.80
0.66
0.85
0.32
0.80

Average

0.84

1.08

0.91

0.98
0.96
0.95
1.00
0.72

1.33
1.31
0.95
1.15
1.59

1.60
2.67
0.50
1.11
3.15

1.00

0.84

1.09

0.93

1.19

1.69

Non-manufacturing industries
Construction
Wholesale
RetailReal estate
Service
Transportation and information
commucication
Average

Data Source: The Financial Statements Statistics of Corporations by Industry

Table 2 Descriptive Statistics:
Growth Rate of Real Sales(%; per annum)
Large
firms

Small
firms

Medium-Sized
firms

Manufacturing industries
Food
TextilesLumber and wood products
Paper and pulp
ChemicalsPetroleum and coal products
Ceramics, stone and clay produc
Steel
Non-ferrous metals
Fabricated metals
Industrial machinery
Electrical machineryTransportation machinery
Precision machinery

-1.34
-3.73
-0.06
-0.94
1.91
-0.82
-0.18
-1.06
-2.13
-2.24
0.18
2.88
3.74
2.18

-1.54
4.09
2.49
0.64
0.55
6.30
0.08
-0.36
0.85
-1.54
0.01
5.65
0.48
-0.39

-0.67
-1.52
-1.68
-0.09
0.65
2.11
-0.44
-0.53
-0.43
-1.19
-0.09
3.28
0.07
1.87

Average

-0.11

1.24

0.10

-4.61
-2.36
-1.46
0.76
-2.24

-1.10
0.28
-0.35
-1.11
-1.75

-2.24
-3.93
-1.69
0.85
0.17

0.15

1.37

4.33

-1.63

-0.44

-0.42

Non-manufacturing industries
Construction
Wholesale
RetailReal estate
Service
Transportation and information
commucication
Average

Data Source: The Financial Statements Statistics of Corporations by Industry

Table 3
Estimation Results of Basic Lending Attitude Equation
Variables
GROWTH
Terms associatedwith ∆LIQ
Constant
DUMS
DUMM
Terms associatedwith ∆FBALANCE
Constant
DUMS
DUMM
DUMIND1
DUMIND2
DUMIND3
DUMIND4
DUMIND5
DUMIND6
DUMIND7
DUMIND8
DUMIND9
DUMIND10
DUMIND11
DUMIND12
DUMIND13
DUMIND14
DUMIND15
DUMIND16
DUMIND17
DUMIND18
DUMIND19
Terms associatedwith ∆BBALANCE
Constant
DUMS
DUMM
DUMIND1
DUMIND2
DUMIND3
DUMIND4
DUMIND5
DUMIND6
DUMIND7
DUMIND8
DUMIND9
DUMIND10
DUMIND11
DUMIND12
DUMIND13
DUMIND14
DUMIND15
DUMIND16
DUMIND17
DUMIND18
DUMIND19

Parameter
Estimates

t-value***

8.98

1.0404 ***-0.3981-0.3171

4.04
-1.34
-1.01

-3.0099 ***-6.16

2.6054 ***8.67

1.7731 ***6.42

0.5879

1.23

1.045

0.03820.06-0.5075-0.85-0.9486
1.2219

-1.60
***

2.56

-0.3953-0.61-0.8897-1.31

0.47310.91-1.9022 ***-2.73-0.9097-1.56-0.592-0.93-0.9726-1.34

0.07610.13-1.6957 **-2.35-0.1805-0.33-0.4261-0.66-2.6146 ***-3.12

0.9063 *1.70-0.9273-1.53-12.8685-0.80

14.0278

1.60-6.7693-0.80-24.4064-1.13

40.1898 *1.93-7.843-0.36-6.4631-0.31-74.5895 ***-3.50-11.0429-0.48-45.3541

**

-2.04

-22.4138-0.98

1.14670.05-22.6438-1.00-2.552-0.11

3.52770.16-5.5587-0.25

8.54480.39-69.9243 ***-3.59-6.1016-0.27-11.4517-0.51-19.1004-0.86-31.7175-1.39

Table 3 (Continued)
Estimation Results of Basic Lending Attitude Equation
Variables

Parameter
Estimates

t-valueTerms associatedwith ∆FBALANCE×∆BBALANCE-356.8178-1.15
Constant**
498.0376
2.45
DUMS
***
578.465
3.33
DUMM-292.8372-0.94
DUMIND1***-988.9275-3.22
DUMIND2-323.6759-0.76
DUMIND3
194.5783
0.50
DUMIND4
86.4861
0.29
DUMIND5
251.1433
0.59
DUMIND6-573.8991-1.47
DUMIND7-215.7686-0.63
DUMIND8-178.7633-0.39
DUMIND9892.1184 **2.06
DUMIND10-406.2842-0.98
DUMIND11-263.3822-0.47
DUMIND12***-1092.1964-2.86
DUMIND13
85.4867
0.18
DUMIND14-567.9458-1.62
DUMIND15-205.6978-0.53
DUMIND16
328.4504
0.60
DUMIND1765.6554
0.18
DUMIND18
398.8008
0.74
DUMIND19
Notes: DUMIND1 to DUMIND19 corresponds to the following
industries: textiles, lumber and wood products, paper and pulp,
chemicals, petroleum and coal products, ceramics, stone and clay products,
steel, non-ferrous metals, fabricated metals, industrial machinery,electrical machiney, transportation machinery, precision machinery,
construction, real estate, wholesale, retail, service andtransportation and information communication.
*** ,**,* significant at 1%, 5%, and 10% level, respectively

Table 4 Coefficient Estimates of the Cross Termof Balance Sheet Variables of Firms and Banks
Small firms firms
Coefficient
Manufacturing industries
Food
TextilesLumber and wood products
Paper and pulp
ChemicalsPetroleum and coal products
Ceramics, stone and clay product
Steel
Non-ferrous metals
Fabricated metals
Industrial machinery
Electrical machineryTransportation machinery
Precision machinery

Non-manufacturing industries
Construction
Wholesale
RetailReal estate
Service
Transportation and information
commucication*** ,**,*

Medium-Sized firms
t-value

Coefficient

Large firms
t-value

Coefficientt-value

141.2198-151.6175-847.7078 ***-182.4562
335.7981
227.7059
392.3630-432.6793-74.5489-37.5435
1033.3382 ***-265.0644-122.1624-950.9767 ***

0.51
-0.75
-4.35
-0.54
1.13
1.28
1.18
-1.42
-0.30
-0.10
3.01
-0.84
-0.25
-3.36

221.6472-71.1900-767.2803 ***-102.0287
416.2256
308.1333 ***472.7905-352.2518
5.878642.88391113.7656-184.6369-41.7349-870.5492 ***

0.76
-0.56
-7.07
-0.30
1.39
5.15
1.37
-1.28
0.03
0.11
3.10
-0.55
-0.09
-2.91

-356.8178-649.6551 ***-1345.7454 ***-680.4938 *-162.2395-270.3317-105.6745-930.7169 ***-572.5865 **-535.5811
535.3006-763.1020 **-620.2000-1449.0142 ***

-1.15
-3.17
-7.05
-1.94
-0.49
-1.48
-0.29
-2.97
-2.32
-1.32
1.40
-2.15
-1.22
-4.91

226.7064-64.4780
469.6701-426.7261 *
206.8751

0.54
-0.21
0.98
-1.73
0.76

307.1339
15.9494550.0976-346.2986
287.3026

0.75
0.06
1.12
-1.49
1.11

-271.3312-562.5156 *-28.3674-924.7636 ***-291.1624

-0.68
-1.91
-0.06
-3.44
-1.13

540.02061.14

620.4481

1.32

41.9830

0.09

significant at 1%, 5%, and 10% level, respectively

Table 5 Estimates of the Effects of Change inFirms' Balance Sheet Conditions on Lending Attitude
Small
firms
Estimate
Manufacturing industries
Food
TextilesLumber and wood products
Paper and pulp
ChemicalsPetroleum and coal products
Ceramics, stone and clay product
Steel
Non-ferrous metals
Fabricated metals
Industrial machinery
Electrical machineryTransportation machinery
Precision machinery
Non-manufacturing industries
Construction
Wholesale
RetailReal estate
Service
Transportation and information
commucication*** ,**,*

Medium-Sized
firms
t-value

Estimate

Large
firms
t-value

Estimate

t-value
6.22
7.70
6.69
7.62
8.34
5.75
6.35
7.03
7.80
8.17
8.07
6.86
6.33
7.36

0.0575-0.0352
0.09110.1237 **0.1254 ***-0.0637 ***0.12650.2586 **-0.0104
0.3527 ***0.2072 ***0.1771 **0.2436 **0.0914

0.87
-0.53
1.11
2.17
2.97
-3.06
1.51
2.43
-0.16
4.06
2.94
2.08
2.35
1.18

0.1268 ***0.1182 **0.2832 ***0.2353 ***0.1449 ***-0.0069
0.2344 ***0.4294 ***0.0875 **0.3870 ***0.3296 ***0.1820 ***0.3224 ***0.1490 ***

2.67
3.30
3.12
4.04
5.08
-0.20
3.11
3.97
2.45
5.31
4.58
3.73
3.64
3.13

0.2596 ***0.4734 ***0.3398 ***0.2205 ***0.3209 ***0.1966 ***0.3772 ***0.7709 ***0.4398 ***0.9402 ***0.4990 ***0.3904 ***0.5143 ***0.4068 ***0.3161 ***0.14330.6256 ***0.1462 *-0.1065

3.46
1.62
4.09
1.92
-1.48

0.3874 ***0.2474 ***0.7146 ***0.3496 ***0.0579

4.92
3.27
5.11
3.82
0.86

0.7987 ***0.5233 ***0.8969 ***0.7904 ***0.3051 ***

7.84
7.33
7.40
7.63
6.06

0.2110 ***2.85

0.4247 ***4.31

0.6008 ***7.68

significant at 1%, 5%, and 10% level, respectively

Table 6 Estimates of the Effects of Change inBanks' Balance Sheet Conditions on Lending Attitude
Small
firms
Estimate
Manufacturing industries
Food
TextilesLumber and wood products
Paper and pulp
ChemicalsPetroleum and coal products
Ceramics, stone and clay product
Steel
Non-ferrous metals
Fabricated metals
Industrial machinery
Electrical machineryTransportation machinery
Precision machinery
Non-manufacturing industries
Construction
Wholesale
RetailReal estate
Service
Transportation and information
commucication*** ,**,*

Medium-Sized
firms
t-value

Estimate

Large
firms
t-value

Estimate

t-value

0.30985.0672-7.3758 ***0.6296
0.05155.4113 ***2.60009.2257 ***4.1469-0.2983-0.9233
1.0830-0.7582
2.4665

0.11
1.42
-2.72
0.28
0.03
4.64
0.79
2.97
1.31
-0.11
-0.29
0.36
-0.25
0.83

2.08957.5929 **5.1341 *3.7898 *0.954819.6540 ***4.9943 *13.1728 ***4.8145 **2.20945.5964 **2.42352.31522.5005

1.21
2.55
1.68
1.74
0.81
6.66
1.81
4.11
2.47
1.03
1.97
1.41
0.88
1.33

1.33606.9521 **-1.4056
1.8869 *1.66869.4143 ***2.870014.9811 ***9.1931 ***2.30343.8607 *3.1034 *1.6386
7.8878 ***

0.99
2.33
-0.92
1.88
1.38
5.67
1.44
4.83
3.24
0.70
1.77
1.68
0.77
3.64

-1.90510.9141
1.662615.1203 ***3.4292

-0.73
0.32
0.45
5.20
1.03

1.43573.82305.224320.3585 ***8.9656 ***

0.65
1.55
1.58
6.32
2.59

1.80253.37383.901519.9063 ***3.4453

0.60
1.39
1.41
6.61
1.43

5.0051 *1.70

9.3501 ***2.75

6.6592 **2.48

significant at 1%, 5%, and 10% level, respectively

Table 7 Coefficient Estimates of the Cross Termof Firms' Balance Sheet and the Rate of Change in Banks' Share Price
Small firms firms
Coefficient
Manufacturing industries
Food
TextilesLumber and wood products
Paper and pulp
ChemicalsPetroleum and coal products
Ceramics, stone and clay product
Steel
Non-ferrous metals
Fabricated metals
Industrial machinery
Electrical machineryTransportation machinery
Precision machinery

Non-manufacturing industries
Construction
Wholesale
RetailReal estate
Service
Transportation and information
commucication*** ,**,*

Medium-Sized firms
t-value

Coefficient

Large firms
t-value

Coefficientt-value

0.72952.73482.97183.9949 *1.17290.8445-2.4232-3.0210
5.0576 **1.21024.00571.5704-1.4156-2.5848

0.22
1.40
0.77
1.79
0.44
0.55
-0.52
-0.69
2.20
0.40
1.52
0.46
-0.30
-0.83

-3.1461-1.1409-0.90380.1192-2.7027-3.0312 ***-6.2989-6.8967
1.1819-2.6655
0.1300-2.3052-5.2912-6.4605 **

-0.91
-0.83
-0.24
0.05
-1.01
-3.74
-1.36
-1.62
0.52
-0.83
0.04
-0.70
-1.11
-2.06

0.09312.09842.33543.35850.53650.2081-3.0596-3.6574
4.4212 *0.57383.36930.9340-2.0520-3.2212

0.03
0.98
0.58
1.31
0.18
0.11
-0.65
-0.83
1.87
0.17
1.07
0.26
-0.42
-0.98

-1.9316
4.23602.2487-4.6148 *0.4582

-0.46
1.54
0.42
-1.88
0.23

-5.80730.3603-1.6269-8.4905 ***-3.4175 **

-1.40
0.14
-0.30
-3.31
-2.07

-2.5680
3.59961.6123-5.2512 *-0.1782

-0.62
1.44
0.29
-1.82
-0.09

2.94570.64-0.9300-0.20

2.30920.49

significant at 1%, 5%, and 10% level, respectively

Table 8 Estimates of the Effects of Firms' Balance Sheet Conditions
on Lending Attitude: Use of Banks' Share Price as Banks' Balance Sheet Conditions
Small
firms
Estimate
Manufacturing industries
Food
TextilesLumber and wood products
Paper and pulp
ChemicalsPetroleum and coal products
Ceramics, stone and clay product
Steel
Non-ferrous metals
Fabricated metals
Industrial machinery
Electrical machineryTransportation machinery
Precision machinery
Non-manufacturing industries
Construction
Wholesale
RetailReal estate
Service
Transportation and information
commucication*** ,**,*

Medium-Sized
firms
t-value

Estimate

Large
firms
t-value

Estimate

t-value

0.11140.04840.1938 **0.1784 ***0.1226 ***-0.0750 ***0.09350.19380.09740.3377 ***0.2827 ***0.1843 *0.1960-0.1026

1.53
0.74
2.33
2.80
2.58
-3.24
0.97
1.58
1.32
3.16
3.08
1.94
1.63
-1.04

0.1067 **0.0877 ***0.2881 ***0.2179 ***0.1055 ***-0.1432 ***0.12300.2547 **0.0919 **0.3072 ***0.3079 ***0.1326 **0.2031 **-0.0310

2.04
3.63
3.23
3.27
3.38
-4.26
1.42
2.04
2.21
3.46
3.40
2.45
1.96
-0.53

0.2253 ***0.4010 ***0.3192 ***0.1995 ***0.2571 ***0.0984 **0.2719 ***0.5607 ***0.4101 ***0.7782 ***0.4539 ***0.3149 ***0.3839 ***0.1771 ***

4.85
6.12
5.95
6.05
5.93
2.53
4.02
4.43
6.31
5.57
5.90
4.93
4.05
2.63

0.3142 ***0.2336 **0.5690 ***0.0988-0.1315

3.02
2.09
3.16
1.23
-1.36

0.3122 ***0.2449 **0.5601 ***0.1695 *-0.0795

3.48
2.53
3.42
1.70
-0.82

0.6683 ***0.4887 ***0.7330 ***0.5597 ***0.1791 ***

5.82
5.24
5.16
4.89
2.62

0.2647 ***2.67

0.3772 ***2.94

0.5325 ***5.32

significant at 1%, 5%, and 10% level, respectively

Table 9 Estimates of the Effects of the Rate of Change in Banks' Share Priceon Lending Attitude: Use of Banks' Share Price as Banks' Balance Sheet Conditions
Small
firms
Estimate
Manufacturing industries
Food
TextilesLumber and wood products
Paper and pulp
ChemicalsPetroleum and coal products
Ceramics, stone and clay product
Steel
Non-ferrous metals
Fabricated metals
Industrial machinery
Electrical machineryTransportation machinery
Precision machinery
Non-manufacturing industries
Construction
Wholesale
RetailReal estate
Service
Transportation and information
commucication*** ,**,*

Medium-Sized
firms
t-value

Estimate

Large
firms
t-value

Estimate

t-value-0.1346 ***-0.2145 ***0.0556-0.0942 ***-0.0721 ***-0.0911 ***-0.0787-0.0923-0.1530 ***-0.0926 **-0.1680 ***-0.1491 ***-0.1089 **-0.0987 *

-3.62
-3.54
1.02
-2.84
-2.99
-4.39
-1.64
-1.42
-3.15
-2.09
-2.95
-2.90
-2.22
-1.91

-0.1203 ***-0.2196 ***0.0286-0.1320 ***-0.0602 ***-0.2948 ***-0.1035 **-0.1391 **-0.1138 ***-0.0972 ***-0.1771 ***-0.1020 ***-0.1282 ***-0.0914 ***

-4.59
-4.64
0.35
-3.88
-3.31
-5.47
-2.42
-2.13
-3.74
-2.65
-3.66
-3.55
-3.28
-3.07

-0.0951 ***-0.2222 ***0.0076-0.0622 ***-0.0763 ***-0.1537 ***-0.0636 *-0.1240 *-0.1857 ***-0.1524 ***-0.1401 ***-0.1128 ***-0.1045 ***-0.0852 **

-4.37
-4.32
0.23
-3.79
-3.78
-5.09
-1.65
-1.73
-3.74
-2.88
-3.63
-3.53
-2.91
-2.17

-0.1045 **-0.1554 ***-0.1741 **-0.2381 ***-0.1658 ***

-2.22
-3.25
-2.34
-4.10
-3.14

-0.1245-0.1662-0.1946-0.3259 ***-0.2292 ***

0.65
1.55
1.58
6.32
2.59

-0.1443 **-0.1665 ***-0.1641 ***-0.2981 ***-0.1490 ***

-2.53
-4.02
-2.93
-4.77
-4.05

-0.1471 ***-2.73-0.2224 ***2.75-0.1728 ***-2.93

significant at 1%, 5%, and 10% level, respectively