Discussion Paper No.

584

FINANCIAL DISTRESS ANDCORPORATE INVESTMENT:THE JAPANESE CASE IN THE 90s

Kazuo Ogawa

June 2003

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

June 2003

Financial Distress and Corporate Investment:
The Japanese Case in the 90s*

Kazuo Ogawa
Institute of Social and Economic Research,
Osaka University* This paper is part of the research project where the author was a chief researcheconomist at Research Office, Director-General’s Secretariat of Small and Medium
Enterprise Agency. The author was permitted by Statistical Bureau of Ministry of Public
Management, Home Affairs, Posts and Telecommunications to use the micro-data of theAnnual Report of Financial Statements of Incorporated Business for the purpose notoriginally intended when the data was collected. Early version of the paper waspresented at monetary economics workshop at Kobe University. We are grateful to
Toshiki Jinushi, Shin-ichi Kitasaka, Ryoichi Mikitani, Ryuzo Miyao and the participantsat seminar for helpful comments and suggestions. The author also thanks the research
stuff for useful suggestions and comments in the course of the project. This research
was partially supported by Grants-in-Aid for Scientific Research 12124207 of the
Ministry of Education. Any remaining errors are the sole responsibility of the author.

Abstract

We examine quantitatively the extent to which financial distress in the 90s
affected Japanese corporate investment. Based on the firm-level data that includes small,unlisted firms, we estimate investment function to measure the impact of financial
distress on investment. We find that the firm’s ratio of debt to total asset exerts asignificantly negative effect on investment of small firms. We also find that lending
attitude of financial institutions did affect investment behavior irrespective of firm size.
The impact of lending attitude on investment is notably large for 1998 labeled “credit
crunch.”JEL Classification Number: E22, E44, and E51
Keywords: Financial distress, Debt hangover, Investment, External finance premium
Lending channel

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. Introduction
Financial liberalization in the 80s prompted Japanese large firms in
manufacturing industries to shift from bank borrowing to direct financing in capital
market. On the other hand, small firms, especially in nonmanufacturing industries,
increased borrowings enormously, mostly collateralized by land. However, land pricekept on falling in the 90s, which plunged a number of firms into insolvency. Looking at
the problem from the lender’s side, the loans they made became non-performing andcaused severe damage to the bank’s balance sheet.
It is often argued that massive debt outstanding in the corporate sector and theassociated bad loan problems are the main cause of long stagnancy of the Japanese
economy in the 90s. The purpose of this paper is to examine the impact of financial
distress on the real economy quantitatively. Specifically we estimate the extent to which
firm’s investment is affected by high leverage in the corporate sector and bad loan
problems in the banking sector.
There are three features in this study. First of all, we analyze the relationshipbetween debt accumulation and investment using the firm-level data. The micro data set
we utilize is taken from the Annual Report of Financial Statements of Incorporated
Business or Hojin Kigyo Tokei Nenpo of the Ministry of Finance. It includes not only
large firms listed in stock exchange but also unlisted small firms. Since small firms are
more bank-dependent, investment of small firms might be affected more sensitively bydebt outstanding of their own and bad loan problem of banking sector. The sample
period covers the period of 1993 to 1998 that includes the financial turmoil labeled“credit crunch” in Japan. There are none that investigate investment behavior by
utilizing firm-level data including small firms.
Secondly, we deal with not only financial leverage in the corporate sector but also
bad loan problems in the banking sector. Debt accumulation in the corporate sector
might raise the firm’s borrowing cost, while lingering bad loans in the bank’s balance
sheet might lead to a reduction of bank loans, which might directly affect the investment
expenditure of bank-dependent firms. Investigation into these two problems
simultaneously has important policy implications. If high leverage is found to be a1

significant factor to affect investment, policy prescriptions to speed up the restructuringof firm’s balance sheet will be needed. On the other hand, if bad loan problems turn out
to affect investment expenditure, wipeout of bad loans will be an urgent agenda forpolicymakers.
Thirdly, it is quite difficult to estimate the effect of loan supply on investment
from observed data of bank loans due to the identification problem of supply anddemand conditions. Therefore it is necessary to select the variable purely representing
the supply condition of loans. Fortunately the Bank of Japan Tankan (Short-term
Economic Survey of Corporations) records the diffusion index of ‘banks’ willingness tolend’ that can be a good proxy of the supply condition of loans. The data is available byindustry and firm size, so that it is possible to test whether the impact of supply
conditions of loans on investment varies across firms with different size. Motonishi and
Yoshikawa (1999) is the first to estimate the investment function with this diffusionindex as one of the explanatory variables to estimate the effect of credit crunch on
investment. They use the aggregated data and this study is the first attempt to use thesame approach to firm-level data.
We preview the main findings of this study. First the firm’s ratio of debt to total
asset exerts a significantly negative effect on investment for small firms. Furthermore
the effect of debt on investment is statistically smaller for large firms. This finding lends
support to the debt overhang argument that lingering debt chokes off new investment. It
is also consistent with the asymmetric information literature that external financepremium is inversely associated with the collateralized net worth relative to loan size.
Secondly, the lending attitude of financial institutions has significant effect on
investment irrespective of firm size. It implies that bank health is important forinvestment activities of large firms as well as small firms that are bank-dependent. Thisfinding is consistent with the work of Gibson(1995 and 1997) and Kang andStulz(2000) that are based on micro data of large listed companies. The effect of lending
attitude of financial institutions on investment is notably large for 1998 often identified
as the year of credit crunch.
This paper is organized as follows. The next section reviews the theoretical and2empirical literature on the impact of financial distress on investment. Section 3 specifies
investment function to be estimated and explains the data set used for empirical analysis.
Section 4 explains the estimation results of investment function and discusses theimplications derived from the estimation results. Section 5 gives concluding remarks.

2. Financial Distress and Investment: Theory and Evidence
Before proceeding to the formal analysis, we review the theoretical arguments
and empirical evidence in Japan how the balance sheet conditions of banks as well as
firms affect corporate investment. It is well known that the balance sheet conditions ofdebtor affect the cost of raising external funds under capital market imperfections.
When there exists asymmetric information between debtors and creditors, it will drive awedge between the cost of external finance and internal finance, called external financepremium. External finance premium reflects the creditor’s cost of collecting the debtor’s
information and monitoring the debtor’s behavior and the cost arising from lemon
problem or moral hazard problem. The premium for external funds influences the costof external funds and thereby affects economic activities of the debtor.
The importance of this view has been frequently stressed in investment literaturesince it cast serious doubt on the celebrated Modigliani-Miller theorem that the financial
policy is irrelevant in investment decision. Contrary to the verdict of the theorem, thecost of internal fund is lower than that of external fund and thus investment is
influenced by the availability of internal fund. 1 Furthermore, the external financepremium is inversely associated with the borrower’s collateralizable net worth relative
to the loan. An adverse shock to the borrower’s net worth increases the external financepremium and reduces borrowings as well as investment. 2

Note that the externalfinance premium is not equally imposed on borrowers. The external finance premiummight be higher for small borrowers for several reasons. First, large borrowers havelarge collateralizable net worth that helps to diversify unobservable idiosyncratic risk,
while small borrowers do not. 3 Secondly, as is discussed below, a number of large
firms in Japan belong to industry groups known as keiretsu, where main bank plays acentral role in mitigating the informational asymmetry between lenders and borrowers.
3

Another channel through which corporate debt affects investment is by creatingdebt overhang. Debt overhang is defined as deterrence of new investment by debt
outstanding. It occurs when the debt outstanding is greater than the net present value ofinvestment project since the benefits from new investment will go to the existing
creditors rather than to the new investors. 4
It is often pointed out that the damaged balance sheet of borrowers is one of thefactors to explain the long stagnancy of investment by Japanese firms in the 90s. The
story goes as follows. The borrowing of Japanese firms increased enormously in the late
80s secured by land. Land used to be perfect for collateral in Japan under the
expectation that the land price would never fall. In other words land can be a very usefuldevice to reduce the external finance premium. In fact, based upon the aggregatedtime-series data, Ogawa et al. (1996) finds that the external finance premium is reduced
by appreciation of land value in the late 80s for non-manufacturing industries that arecomposed of a number of small firms.5 Contrary to the expectations, land price fell
sharply in the 90s, which eroded the firm’s collateralizable net worth with the loan
outstanding almost intact, which raised the external finance premium considerably andthereby decreased investment. Moreover the debt overhang argument above lends
further support to the stagnancy of new investment due to excessive debt outstanding in
the corporate sector.
Recently attention has been also paid to the balance sheet of banks as one of theimportant determinants to affect the real activities of firms. 6 Banks play anintermediary role in mitigating asymmetric information between lenders and borrowers.
Banks examine the request for loans by borrowers by collecting the information ofborrowers and monitor the borrowers so that the contract may be fulfilled. However, if
there are a large number of lenders, free-rider problem discourages individual banksfrom monitoring the borrowers. Thus a single “delegated monitor” can conserve on
aggregate monitoring costs.7 In particular the saving of monitoring cost is large forsmall and medium-sized firms for which there are few alternative ways of raisingexternal funds.
It is also noted that in Japan institutional device called main bank system is potent
4

in narrowing the informational asymmetry between lenders and borrowers. Informationof borrowers is accumulated in main banks through long-term, stable relationships offirms with their main banks. Moreover, bank employees often hold management
positions in the financially troubled firms for the purpose of direct monitoring.8
However, note that accumulation of borrower’s information into a single bankcreates an informational monopoly with respect to its client. This lock-in effect makes it
difficult for borrowers to switch lenders.9
If a shock to the bank’s balance sheet affects lending, it will influence the real
activities of bank-dependent borrowers. Suppose that an adverse shock hits the bank’sbalance sheet. It raises the cost of external finance for the bank for the same reason as
was discussed in the case of non-financial firm and thereby restrains the bank’s lending
activity. Then it will lead to aggravation of the real activities of the bank-dependent
borrowers.
Empirical studies on the effects of balance sheet shock on bank lending havemainly examined two types shocks on the bank’s balance sheet. One type of shock ismonetary policy shock. Kashyap and Stein(1995,2000) examine how the monetary
policy change affects the bank’s lending. They find that the response of lending to themonetary policy change varies across banks with different attributes. The impact ofmonetary policy on lending activities is more pronounced for smaller banks with lessliquid balance sheets.
Another strand of research examines the causes of contraction of bank lending in
the period labeled “credit crunch.“ It is not hard to identify the period of credit crunch inseveral countries. For instance, the early 90s in the US has been labeled credit crunch
and a number of hypotheses on the decline in bank credit have been advocated andtested. Among them include the shocks that affect bank capital, such as implementationof the Basel-Accord risk-based capital standards, depletion of bank capital from loan
loss experiences in the late 80s. 10
As for Japan, there are also similar studies that examine whether the shocks tobank capital affect lending behavior of banks. In Japan the late 90s is often identified as
the period of credit crunch when large financial institutions went into bankruptcy due to5severe bad loan problems. Yoshikawa et al. (1994) tests whether the bad loan problem
led to contraction of bank credit to small firms and found no correlation between thelevel of bad loan and bank lending. Peek and Rosengren(1997) showed that contractionof bank lending by Japanese banks in the US resulted from the binding risk-based
capital requirements associated with the Japanese market decline. Honda (1996), Ito andSasaki (1998), and Ogawa and Kitasaka (2000) analyze the effect of risk-based capitalrequirements on bank lending. They found that the introduction of risk-based capitalrequirements affects bank lending of city banks. Woo(1999) found a positive andstatistically significant correlation between bank capital and lending growth in 1997 andconcludes that the shortage of bank capital is mainly responsible for contraction of banklending.
Combining the evidence that the shocks to bank’s balance sheet affects lending
behavior with the existence of bank-dependent borrowers, it follows that the balance
sheet conditions of banks influence the real activities of firms.
Gibson(1995 and 1997) and Kang and Stulz(2000) conduct direct tests of the
impact of bank health on investment activities. The former studies found that the impactof bank health on investment was small, while the latter found that morebank-dependent firms invested less in early 90s. Note that these two studies are likely tounderestimate the impact of bank health on investment since they use individual firm
data listed in Tokyo Stock Exchange. Those firms are relatively large and notnecessarily bank-dependent borrowers.
Motonishi and Yoshikawa (1999) and Ogawa and Kitasaka (2000) show that banklending exerts a significant effect on investment. Using the Bank of Japan diffusionindex of ‘banks’ willingness to lend,’ Motonishi and Yoshikawa obtain the evidence thatbank lending is a significant determinant of business investment of small firms, but notlarge firms. Ogawa and Kitasaka also show that expenditures on fixed investment are
sensitively affected by bank loans for small firms that do not have close substitutes ofbank loans. Note that the empirical evidence of these two studies is based on theaggregated data.

63. Specification of Investment Function and Data Set Description
Specification of Investment Function
The basic investment function to be estimated is a q-type investment function
with the variables representing the degree of financial distress. The main driving force
of investment is marginal q ( Mq ), present discounted value of maximized profit rate
divided by the investment goods price. In other words, the marginal q is defined as:

Mqt =

∞

1
j
E
t  ∑ β t + j (1 − δ ) π t + j Ipt j =0(1)where ptI ： price of investment goods in period tj

β t + j = ∏ (1 + rt +i ) −1 ,i =1

( j = 1,2, L), β t ≡ 1

rt +i ：discount rate in period t+iδ ：depreciation rate

π t + j ：profit rate, defined as the maximized profit divided
by the capital stock at the end of t+j-1 period

Et [ ] ：expectation operator conditional on the information
set available for the firm in period t

We also add the cash flow variable (CFLOW) to measure the availability of internalfunds to the explanatory variables. Under asymmetric information between lenders andborrowers, it is expected that cash flow will exert a positive effect on investment.11
There are two explanatory variables regarding the severity of financial distress.
One is related to the firm’s balance sheet condition and is represented by the debt size
relative to total asset (DEBT). Higher debt-asset ratio implies a higher cost of externalfinance. It might also create debt overhang. Both will decrease investment. The other isbank’s willingness to lend (LEND). The adverse shock such as bad loan withers thebank’s willingness to lend and hence will reduce investment expenditure ofbank-dependent borrowers.
As was discussed in the previous section, it is quite plausible that the asymmetric7

information problem is more severe for small and medium-sized firms than for large
firms. Therefore it might be the case that investment of small and medium-sized firms ismore sensitive to the degree of financial distress. To account for the differential impactof financial distress on investment, we introduce two dummy variables on the firm size.
One dummy (DUMMY1) takes unity when the firm’s equity capital is between one
hundred million yen and one billion yen and zero otherwise. The other dummy(DUMMY2) takes unity when the firm’s equity capital is above one billion yen and zerootherwise. Then we add the cross terms of two dummy variables with each explanatory
variable. This specification takes into consideration the possibility that the coefficientestimates vary across three different firm groups, each of which corresponds to small,medium, and large firm group, respectively. The basic investment function to be
estimated is written as follows:It= α 0 + α1 Mqt + α 2 Mqt × DUMMY 1t + α 3 Mqt × DUMMY 2 t
K t −1+ α4

CFLOWt
CFLOWt
CFLOWt+ α5× DUMMY 1t + α 6
× DUMMY 2 t (2)K t −1
K t −1
K t −1+ α 7 DEBTt −1 + α 8 DEBTt −1 × DUMMY 1t + α 9 DEBTt −1 × DUMMY 2 t+ α10 LENDt + α11 LENDt × DUMMY 1t + α12 LENDt × DUMMY 2 t + ut
where I t : gross investment in period t
K t −1 ：capital stock at the end of t-1 period
ut : disturbance term in period t
Data Set Description
The firm data set we use is taken from the Annual Report of Financial Statementsof Incorporated Business or Hojin Kigyo Tokei Nenpo (abbreviated as ARFS) of the
Ministry of Finance. It records individual items of firms’ balance sheet as well as profit
and loss statement. The virtue of this data set is an extensive coverage of corporations
with a variety of firm size for all the industries except financial and insurance industries.
The coverage of firms is much wider than the firm database provided by NIKKEI and
8

Development Bank of Japan, both of which include only the listed large firms.
The sample period is from the fiscal year of 1993 to 1998 and covers the periodof financial turbulence in 1997 and 1998. The number of observations in the original
data set is 26040, 26218, 26594, 25691, 25394, and 25505 from 1993 to 1998,
respectively. Unfortunately there is no identifying the sampled firms, so that the data set
cannot be utilized as a panel data set. However the end-of-period value as well as the
beginning-of-period value of each balance sheet item is available for each firm.
Therefore we can compute the flow values from the stock values for individual firms.
Now we describe the procedure to construct the variables used in estimation.
Gross investment ( I t ) is defined as the increment of tangible fixed asset excluding
land, with depreciation allowance added, divided by investment goods deflator. Capitalstock ( K t ) is defined as the tangible fixed asset excluding land divided by theinvestment goods deflator in the year when the capital stock was installed. The
installation year of the capital stock is identified by the information of the average years
elapsed since installation, which is taken from the 1998 White Paper on the Japanese
Economy. Cash flow (CFLOWt ) is defined as net income plus depreciation allowanceminus bonus to directors, and dividends including interim dividends. The real cash flow
is obtained by dividing the cash flow by the value-added deflator of the industry thefirm belongs to. The debt-asset ratio ( DEBTt −1 ) is defined as the ratio of borrowings
and bonds payable to total assets at the end of period t-1. The lending attitude offinancial institutions ( LENDt ) is taken from the Short-term Economic Survey of All
Enterprises called Tankan, conducted by the Bank of Japan. It is the diffusion index andrepresents the proportion of entrepreneurs feeling the present lending attitude offinancial institutions to be “accommodative” minus those feeling the present lending
attitude of financial institutions to be “severe”. The data are available by industries andthree firm-size groups (small, medium and large firm group) classified by regular
employees.12
In constructing the marginal q ( Mqt ) series special attention should be paid tothe stochastic property of the two underlying factors: discount rate ( rt ) and profit rate
(π t ) . The discount rate is computed as the interest and discount paid divided by the sum
9

of discount of notes receivable, short-term loans payable, long-term loans payable andbonds payable. The profit rate is defined as the ratio of operating profit to thebeginning-of-period capital stock. It is assumed that the discount rate and the profit rate
follow random walk independently. In other words,
rt +1 = rt + ut +1(3)

π t +1 = π t + vt +1(4)

where ut +1 , vt +1 ：stationary white noise
Then it can be shown that the marginal q is simply written asMqt =π t 1 + rt
ptI rt + δ(5)The depreciation rate δ is assumed to be 7.72 % per annum. 13
We cannot utilize all the observations mainly due to unavailability of balance
sheet items. Therefore we choose the observations for estimation based on the following
three criteria. First, we exclude the firms in agriculture, forestry, fishery and mining
industries since the data of lending attitude of financial institution are not available forthese industries. We also exclude the firms in electric power and gas industries due toregulatory nature of these industries. Secondly, some of the balance sheet items are
unreported for a number of firms, notably small firms. Therefore we choose only thefirms that report the following balance sheet items: tangible fixed asset excluding landand construction in progress, total assets, equity capital, sales, depreciation allowance,and number of employees. Thirdly, we retain the observations that satisfy all of thefollowing conditions: 1) absolute value of the ratio of investment to thebeginning-of-period capital stock is less than unity

2) absolute value of the ratio ofcash flow to the beginning-of-period capital stock is less than 5
to the beginning-of-period capital stock is less than 503) the ratio of sales4) marginal q is less than 105) the ratio of borrowings and bonds to total assets is less than unity 6) the sum of10

interest and discount paid is positive.
The information of the sample finally chosen is shown in Table 1. The figures inparentheses at the bottom row show the proportion of the observations used for
estimation out of total observations. It is the lowest for small firm group, varying from34%(1998) to 41.7%(1993), while it is the highest for large firm group, from
68.2%(1996) to 72.6%(1993). It is in between for medium firm group. The
distributional information of the sampled firms across industries is also shown in thetable. The proportion of manufacturing firms hovers around 40% for small and medium
firm groups. On the other hand it is much higher for large firm group, varying from
51.1%(1998) to 55.8%(1993). The proportion of firms in construction industry is
notably high around 16% for small firm group, while it is at most 6% for medium andlarge firm groups. In the medium firm group the proportion of firms in service
industries is second highest (around 15%) to that in manufacturing industries.
Table 2 shows the median and mean values of gross investment, capital stock andtotal assets for the sample firms. The mean value is much higher than the median value
for all the variables, implying that the distribution of firms is skewed to the right. The
median of capital stock and total assets of large (medium) firms is 80 to 100 (17 to 20)
times larger than that of small firms. As for the median of gross investment of large(medium) firms, it is 180 to 240 (24 to 32) times larger than that of small firms.
Figure 1 to 4 depicts the median values of gross investment rate, ratio of cash
flow to the beginning-of-period capital stock, marginal q, and debt-asset ratio for eachfirm group from 1993 to 1998. The gross investment rate is always highest for large
firm group, followed by medium firm group. The trend of the gross investment rate iscommon to all the firm groups. The gross investment rate increases gradually up to1997 and declines in 1998. The ratio of cash flow to capital stock is highest for small
firm group, though it declines gradually over time. This ratio is rather stable for medium
and large firm groups. We observe the common declining trend of marginal q from 1996
to 1998 for every firm group. From 1993 to 1996 the marginal q exhibits an increasing
trend for large firm group, although it is rather stable for small and medium firm groups.
The debt-asset ratio is highest for small firm group, ranging from 0.47 (1993) to11

0.51(1996,1998), followed by medium firm group hovering around 0.42 to 0.43. It is by
far lowest for large firm group from 0.31(1997) to 0.33(1994). The debt-asset ratio
exhibits a gradual declining trend for large firm group, while it increases slightly overtime for small and medium firm groups.
Figure 5 shows the diffusion index of lending attitude of financial institutions forthree firm groups in the period of 1993 to 1999. It is clear that the lending attitude
becomes very severe in the last quarter of 1997. Note that large financial institutions
such as Yamaichi Securities and The Hokkaido Takushoku Bank went into bankruptcy
on November in 1997. The proportion of “severe” respondents has by far exceeded thatof “ accommodative” respondents since the first quarter of 1998.

4. Estimation Results of Investment Function and Their Implications
Estimation Results of Basic Investment Function
Equation 2 is estimated separately for each year since the lending attitude offinancial institutions changed drastically across years, as was seen in the previous
section, and the response of investment to lending attitude might change accordingly.
Dummy variables to represent individual industry effect are added to the explanatory
variables in estimation. Estimation results of q-type investment function are shown in
Table 3. It is found that the investment behavior is quite different across different sizegroup. In none of the sample years the marginal q exerts a significantly positive effecton investment for small firm group. On the other hand, the effect of marginal q on
investment for large firm group is statistically larger than that for small firm group for
all the sample years. The total effect of marginal q on investment for large firm group as
well as medium firm group is computed by adding the coefficient estimate of the crossterm of marginal q with firm group dummy to that of marginal q. They are shown in
Table 4. Note that the effect of marginal q on investment for large firm group is positive
for all the sample years. This is quite contrasted with the negative marginal q
coefficients of small firm group for all the sample years. The effect of marginal q on
investment for medium firm group is also significantly larger than that for small firmgroup for 1993, 1994, 1996 and 1997. The total effect of marginal q on investment for12medium firm group is also positive for all the sample years.
Contrary to the role of marginal q in investment behavior, cash flow exerts asignificantly positive effect on investment for small firm group for all the sample years.
The effect of cash flow on investment for large firm group is significantly lower thanthat for small firm group for all the sample years. Table 5 shows the total effect of cash
flow on investment. The size of cash flow coefficients varies from 0.1354 (1998) to
0.2279 (1993) for small firm group, while it is only from 0.0073 (1998) to 0.0890(1996) for large firm group.
As for the effect of debt-asset ratio on investment, it is significantly negative forsmall firm group for all the sample years. It is consistent with debt overhang hypothesis
and the theoretical verdict from asymmetric information literature that higher debt-asset
ratio raises the cost of external finance and hence decreases investment. Note that theeffect of debt-asset ratio on investment is significantly smaller for large firm group for
1993, 1994, 1995 and 1997. The total effect of debt-asset ratio on investment, shown in
Table 6, varies from –0.1069(1997) to –0.0668(1998) for small firm group, while it is–0.0878 (1998) to –0.0240 (1993). Our findings indicate that the agency problem isparticularly severe for small firms that have few alternative ways other than bank loans
to finance investment.
Lending attitude of financial institutions has significantly positive effects on
investment of small firm group for all the sample years. Furthermore, the cross terms of
the firm size dummy with the lending attitude variable are insignificant for most of thecases. In fact no systematic difference can be detected from total effects of lending
attitude on investment for three firm groups classified by firm size, shown in Table 7,
except for 1997 and 1998. It implies that lending attitude of financial institutions does
influence firm’s investment activities irrespective of firm size. Small and medium firms
are mostly bank-dependent, so that it is quite plausible that lending attitude of financial
institutions has significant effect on investment. However, our results show that it is also
the case with large firm group. This finding supports the evidence by Gibson(1995 and1997) and Kang and Stulz(2000) that bank health is important for investment of listed
firms. As was shown in Figure 5, the lending attitude of financial institutions was13accommodative during 1993 to 1996. Even under such accommodative circumstances,lending attitude of financial institutions did matter for investment. Then it is expectedthat lending attitude of financial institutions would be all the more important forinvestment activities for the period of 1997 and 1998 labeled “credit crunch” when thebank health was impaired by mounting non-performing loans and the steep drop in
Japanese stock prices, which turned lending attitude of financial institutions very severe.
In fact our evidence shows that this is the case. The effect of lending attitude on
investment of small firm group jumped to 0.2285 in 1998 from 0.0842 in 1997.
However, the cross terms of firm size dummy with lending attitude in 1998 are
significantly negative for medium and large firm groups, indicating that the effect oflending attitude on investment was somewhat mitigated for larger firm groups.

Robustness of Findings
We obtained the findings that firm’s debt-asset ratio as well as lending attitude offinancial institutions did affect the firm’s investment activities. We will show that thesefindings are quite robust with respect to specification of investment function.
Investment function is re-estimated under two different specifications to reexamine theeffects of debt-asset ratio and lending attitude of financial institutions on investment.
First of all, we estimate the accelerator-type investment function where themarginal q variable is replaced by the sales variable. The estimation results are shown in
Table 8. The debt-asset ratio exerts a significantly negative effect on investment forsmall firm group in all the sample years. Moreover, the effect of debt-asset ratio on
investment is significantly less for large firm group in 1993, 1994, 1995 and 1997. The
lending attitude of financial institutions also affects investment of small firm group in asignificantly negative manner for all the sample years. The magnitude of the effect oflending attitude on investment is not discernibly different between small firm group andlarger firm groups except for a few years. In 1998 the effect of lending attitude on
investment becomes notably large for small firm group, while it is significantly less for
medium and large firm groups.
In the second specification of investment function, we specify the coefficient of
14

each variable as a function of firm size, represented by the logarithm of total asset. In
the previous specifications we incorporated into the analysis the possibility that thecoefficients varied discontinuously depending on the firm’s equity capital. Here it isassumed that the coefficients change continuously as a function of the logarithm offirm’s total asset. Specifically investment function is written as follows:It
CFLOWt= α 0 + α1 Mqt + α 2 Mqt × log(TASSET ) t −1 + α 3
K t −1
K t −1+ α4

CFLOWt× log(TASSET ) t −1 + α 5 DEBTt −1 + α 6 DEBTt −1 × log(TASSET ) t −1 (6)K t −1+ α 7 LENDt + α 8 LENDt × log(TASSET ) t −1 + ut
where TASSETt −1 : total asset of firm at the end of period t-1
The estimation results of equation 6 are shown in Table 9. It is found that thecross term of firm size with debt-asset ratio is significantly positive in all the sample
years but 1998. It implies that the effect of debt-asset ratio on investment decreases as
the firm becomes larger. To put it differently, the impact of debt burden on investment
would be significantly less for larger firms. On the other hand, the effect of lending
attitude of financial institutions on investment depends inversely on firm size in asignificant manner only in half of the sample years or 1993, 1995 and 1998.
To evaluate the impact of debt-asset ratio and lending attitude of financial
institutions on investment quantitatively under three different specifications ofinvestment function, we make comparison of the marginal effect of debt-asset ratio andlending attitude of financial institutions on investment for small, medium and large firmgroups, respectively. Under the last specification of investment function, the marginal
effect depends on the firm’s total asset, so that we compute the average of the marginal
effects for each firm group classified by firm’s equity capital. The marginal coefficients
under three different specifications are shown in Table 10 and 11. Table 10 shows themarginal effects of debt-asset ratio on investment. They are quite alike in terms ofmagnitude across different specifications of investment function. They are the largest
15

for small firm group and the smallest for large firm group. Common to eachspecification is an increasing trend in absolute value of the effect of debt-asset on
investment for large firm group, indicating that the impact of debt burden on investment
is increasing for large firm group over the sample period.
The marginal effects of lending attitude of financial institutions on investment are
shown in Table 11. Here we also observe that the magnitude of the marginal effects isquite alike across different specifications of investment function for small and medium
firm groups. The spike of the coefficient estimate in 1998 is commonly observed forsmall firm group. For large firm group, the size of marginal effects is similar under thetwo specifications where the size effect is captured by the dummy variables for firm’s
equity capital. However, when the size effect is specified as a function of the logarithmof firm’s total asset, the marginal effects become less than those under the otherspecifications.
To sum up, our findings that debt-asset ratio as well as lending attitude offinancial institutions does affect firm’s investment behavior is quite robust irrespectiveof the specification of investment function.

Impact of “Credit Crunch” on Investment: Quantitative Evaluation
It is frequently asserted that the “credit crunch” in the late 90s, triggered by thebankruptcy of large financial institutions, is mainly responsible for the stagnancy ofcorporate investment. We evaluate this assertion based on the coefficient estimates ofinvestment function obtained above in two different ways.
Firstly, we compute to what extent investment is increased when the proportionof accommodative respondents rises by 10 percentage points. Table 12 shows annually
by how much investment will increase for each firm group in percentage terms. The
figures are based on the specification of marginal q-type investment function where firm
size effect is represented by the dummy variables for firm’s equity capital. They are
aggregated values of all the sample firms. Investment of small firm group increases by
23.45% in 1998, which is far larger than any other year. Percentage increase ofinvestment is also largest in 1998 for medium firm group (7.41%). On the other hand,16the percentage increase of investment for large firm group is only 3.52% in 1998, whichis the second smallest in the sample years.
The second exercise computes by how much investment would have been
increased if the financial institutions in 1998 had made loans under the lending attitudein 1996. The results are shown in Table 13. The table shows the increment of investment
by industry and firm size in percentage terms. The investment of small firm groupwould have increased by as much as 84.9%. It should be noted that the pattern ofinvestment increment is not uniform across industries. The percentage increase ofinvestment is notably high for real estate (270.1%), transportation and communication(185.9%) and wholesale trade (129.9%), while it is lowest for manufacturing industry(59.8%). The investment of medium firm group would have increased by 34.5%. The
percentage increase of investment is high for real estate (84.7%), construction (65.1%)
and wholesale trade (63.7%), while it is the second lowest for manufacturing industry(36.3%). For large firm group the percentage increase of investment is lowest (18.8%)
among three firm groups. The percentage increase of investment is high for real estate(50.7%) and construction (47.0%). Large increase of investment in real estate andconstruction industries is common to all the firm groups. It is shown that investment in
total would have increased by 21.3% in 1998 under accommodative attitude of financial
institutions in 1996. If the aggregate investment had increased by this proportion, theincrement of investment in 1998 would amount to 16361.4 billion yen. Then the GDP
growth rate in 1998 would have been 1.4% instead of –1.9%. Therefore it is concluded
that the impact of “credit crunch” in the late 90s on corporate investment was
substantial.

5.Concluding Remarks
Mounting debt in the corporate sector and related bad loan problems of thefinancial sector had been a big concern for the domestic economists as well as abroad. It
is frequently asserted that this is the main cause of the stagnancy of the Japanese
economy. We made an attempt to evaluate this assertion rigorously by estimating theinvestment function. The novelty of this study is an empirical examination of the17

association of investment with financial distress based on the firm-level data in the 90s
including unlisted small firms. This point is very important since it is often argued thatwhat suffers most from financial distress is small firms rather than large firms. However
existing empirical literature along this line failed to shed light on this aspect due to theaccessibility to the micro data of small firms.
Our main findings are that financial distress seriously hurt firm’s investment
behavior in the 90s. Lingering debt on the corporate sector exerted a negative effect on
investment of small firms on the one hand, and severe lending attitude of financial
institutions also deterred investment of small firms on the other hand. Furthermore, we
also find that the impact of “credit crunch” in the late 90s had non-negligible effect on
aggregate investment. A number of policy prescriptions have been proposed to boost up
the Japanese economy. Our evidence indicates that serious tackle of mounting debt in
the corporate sector and non-performing loans in the financial sector deserves firstpriority in policy agenda.

18

Footnotes
1

There is a growing body of literature on this issue. See Hubbard (1998) for a survey of investment
behavior under capital market imperfections.

2

Empirical support for negative effect of firm leverage on investment is shown in Lang et al.(1996).
Cantor(1990) and Calomiris et al.(1997) show in a slightly different context that investment ofleverage firm is more responsive to sales and cash flow.

3

Gertler and Gilchrist (1993) emphasizes that asymmetric information problem is more severe forsmall firms than for large firms. See Berger and Udell (1998) for a comprehensive survey of small
business finance.

4

See Myers(1977) and Hart(1995) for more detailed discussion on debt overhang.

5

Suzuki and Ogawa (1997) and Ogawa and Suzuki (1998) also confirm the positive dependence ofcorporate investment on land asset for large, listed corporations by firm-level panel data.

6

Kashyap and Stein (1994) survey theoretical and empirical studies that relate to the propagation
mechanism of monetary policy shock to bank lending. See also Hubbard(1995), Bernanke andGertler(1995) and Mishkin(1996) for comprehensive survey of monetary transmission mechanism.

7

See Diamond (1984) and Boyd and Prescott (1986) for more details on delegated monitoring.

8

Hoshi et al. (1991) obtains the evidence that the firms affiliated with main bank enjoy lower
external finance premium than independent firms using the micro data of firms.

9

See Sharpe(1990) and Rajan(1992) for the theoretical analysis of the association of banking
relationship with information monopoly.

10

To mention a few, Bernanke and Lown(1991), Berger and Udell(1994), Peek and Rosengren(1995) , and Hancock and Wilcox(1998) analyze the effect of credit crunch on bank lending.
11

There is a caveat on the interpretation of cash flow coefficient. Kaplan and Zingales(1997) argue
that positive correlation of cash flow with investment is not the evidence of liquidity constrain, but
simply reflects the future investment opportunities not covered by marginal q.
12

The diffusion index of lending attitude of financial institutions is available on a quarterly basis. In
estimating investment functions, we average out the quarterly data into the annual base.
13

The depreciation rate is taken from TableA-2 in Ogawa et al. (1994).

19

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[24] Lang, L., Ofek,E. and R.M.Stulz(1996).” Leverage, Investment, and Firm Growth,”
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[27] Myers, S.C.(1977).” Determinants of Corporate Borrowing,” Journal of Financial
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1, Economic Planning Agency: Tokyo.

24

1994
Medium
firms
2004
230
518
533
447
733
Large
firms
1699
143
231
284
188
346

Small
firms
2038
851
504
622
208
577

1995
Medium
firms
1936
241
500
548
452
750

Industries
Manufacturing
Construction
Wholesale trade
Reatil trade
Real estate
Services
Transportationand communication
Total
328
4884
(35.3)

Small
firms
2252
906
522
691
236
528

376
400
223
364
426
238
353
406
240
5814
4879
3015
5499
4891
3129
5153
4833
3202
(41.7)(61.4)(72.6)(40.0)(60.4)(71.7)(37.3)(58.5)(70.6)
Data Source: Ministry of Finance, Annual Report of Financial Statements of Incorporated BusinessNotes: The figures in parentheses are the proportion of observations used for estimation in percentage terms.

Large
firms
1682
114
242
276
164
314

Small
firms
2010
807
452
556
216
515

1993
Medium
firms
1993
204
523
559
451
749
Large
firms
1708
153
237
286
206
372

Small
firms
2390
927
561
713
258
589
360
4125
(57.7)

1996
Medium
firms
1655
211
426
472
391
610

Table 1 Number of Sampled Firms

242
3218
(68.2)

Large
firms
1697
165
249
282
214
369
296
4705
(35.0)

Small
firms
1940
772
406
529
203
559
356
4098
(57.3)

1997
Medium
firms
1625
224
410
491
387
605
253
3316
(68.9)

Large
firms
1717
183
234
291
228
410
312
4564
(34.0)

Small
firms
1862
725
411
568
197
489
352
4239
(58.7)

1998
Medium
firms
1672
249
443
512
381
630

250
3345
(68.9)

Large
firms
1708
188
248
297
222
432

0.73
(3.39)

3.23(13.06)Items
Gross investmnet

Capital stock

Total asset

60.36(120.11)14.10(32.17)339.79(1289.25)

73.31(346.04)

Large
firms
8.41
(57.05)

3.23(13.83)0.77(3.57)

Small
firms
0.04
(0.41)

63.28(122.42)

14.57(34.46)

1994
Mediumfirms
0.97(4.94)
0.82(3.77)

Small
firms
0.04
(0.39)

334.92
3.51(1279.57) (14.51)

71.48(337.63)

Large
firms
7.45
(51.13)

Data Source: Ministry of Finance, Annual Report of Financial Statements of Incorporated BusinessNotes: The Figures of capital stock and total asset are those at the beginning of the period.
The numbers in parentheses are sample means.

Small
firms
0.04
(0.36)

1993
Mediumfirms
1.03(5.17)

62.96(124.01)

14.31(34.30)1995
Mediumfirms
1.02(5.86)

332.53(1284.56)

69.46(330.10)

Large
firms
7.61
(53.21)

3.70(14.68)0.88(3.94)

Small
firms
0.04
(0.54)

64.90(135.82)

15.12(37.42)

1996
Mediumfirms
1.22(7.17)

350.20(1328.08)

69.64(333.35)

Large
firms
8.73
(60.57)

Table 2 Descriptive Statistics of Sampled Firms

3.51(14.80)0.83(3.99)

Small
firms
0.04
(0.67)

65.78(134.75)

15.36(37.29)1997
Mediumfirms
1.27(7.21)

347.58(1321.71)

69.77(334.72)

Large
firms
8.73
(60.70)15.25(38.51)

1998
Mediumfirms
0.97(6.68)

69.65(340.21)

Large
firms
7.24
(58.19)3.48
67.35
339.24(13.85) (132.52) (1327.48)

0.86(3.62)

Small
firms
0.03
(0.35)(100 million yen)

Table 3 Estimation Results of q-type Investment Functions
Variables

1993

1994

1995

1996

1997

1998

Marginal q-0.0063***(-3.95)-0.0034*(-1.82)-0.0005(-0.26)-0.0009(-0.44)-0.0049***(-2.60)-0.0003(-0.13)0.0133***(6.39)

0.0065***(2.73)

0.0040(1.56)

0.0051*(1.78)

0.0057**(2.28)

0.0036(1.30)

0.0156***(6.83)

0.0125***(5.02)

0.0082***(3.16)

0.0054*(1.91)

0.0133***(5.23)

0.0096***(3.50)

0.2279***(10.99)0.2030***(8.67)

0.1887***(7.67)

0.1590***(5.22)

0.1552***(5.88)

0.1354***(3.63)-0.1708***(-6.38)-0.0516(-1.61)-0.0446(-1.19)-0.0123(-0.28)-0.0632*(-1.72)-0.0741*(-1.65)-0.2121***(-6.88)-0.1448***(-4.38)-0.1435***(-3.81)-0.0700*(-1.65)-0.1150***(-3.24)-0.1281***(-3.30)-0.0941***(-10.34)-0.0862***(-9.09)-0.1042***(-10.28)-0.0805***(-7.81)-0.1069***(-10.59)-0.0668***(-6.30)0.0303***(2.98)

0.0091(0.88)

0.0155(1.38)-0.0050(-0.43)0.0203*(1.77)-0.0136(-1.04)0.0701***(5.42)

0.0317**(2.39)

0.0433***(3.11)

0.0076(0.57)

0.0264**(2.07)-0.0210(-1.38)0.1167**(2.28)

0.0814**(2.43)

0.1579***(4.75)

0.1743***(5.33)

0.0842**(2.27)

0.2285***(5.81)-0.0066(-0.16)-0.0370(-1.17)-0.0604**(-2.02)-0.0432(-1.46)-0.0670*(-1.82)-0.1001***(-3.07)-0.0298(-0.0298)

0.0142(0.39)-0.0385(-1.20)0.0024(0.07)-0.0593(-1.44)-0.1683***(-4.80)0.06370.19090.09110.18230.08900.1877

0.09470.18890.08090.18850.07260.1774

Dummy for medium
firms x Marginal q
Dummy for large firms x
Marginal q

Cash flow
Dummy for medium
firms x Cash flow
Dummy for large firms x
Cash flow

Debt ratio
Dummy for medium
firms x Debt ratio
Dummy for large firms x
Debt ratio

Lending attitude
Dummy for medium
firms x Lending attitude
Dummy for large firms x
Lending attitude

Adjusted R-squaredStandard error

Notes: The figures in parentheses are t-values.
The coefficient estimates of constant term as well as industry dummy variables are suppressed.
* significant at the 10% level** significant at the 5% level*** significant at the 1% level

Smallfirms-0.0063-0.0034-0.0005-0.0009-0.0049-0.0003-0.0027

1993
1994
1995
1996
1997
1998

Average

0.0037

Mediumfirms
0.00700.00310.00350.00420.00080.00330.0081Large
firms
0.00930.00910.00770.00450.00840.0093

Table 4 Coefficient Estimatesof Marginal q

Average 0.1782

1993
1994
1995
1996
1997
1998

Small
firms
0.22790.20300.18870.15900.15520.1354
0.1088

Mediumfirms
0.05710.15140.14410.1467
0.09200.06130.0426Large
firms
0.01580.05820.04520.08900.04020.0073

Table 5 Coefficient Estimatesof Cash Flow

Average -0.0898

1993
1994
1995
1996
1997
1998
-0.0804-0.0634

Small Medium Large
firms
firms
firms
-0.0941 -0.0638 -0.0240-0.0862 -0.0771 -0.0545-0.1042 -0.0887 -0.0609-0.0805 -0.0855 -0.0729-0.1069 -0.0866 -0.0805-0.0668 -0.0804 -0.0878

Table 6 Coefficient Estimatesof Debt-Asset Ratio

Average 0.1405

1993
1994
1995
1996
1997
1998

Small
firms
0.11670.08140.15790.17430.08420.22850.0881

Mediumfirms
0.1101
0.0444
0.09750.13110.01720.12840.0940Large
firms
0.08690.09560.11940.17670.02490.0602

Table 7 Coefficient Estimatesof Lending Attitude

Table 8 Estimation Results of Accelerator-type Investment Functions
Variables

1993

1994

1995

1996

1997

1998

Sales

0.00003(0.09)

0.0013***(2.93)

0.0017***(3.84)

0.0009**(2.01)

0.0015***(3.31)

0.0014***(2.74)

0.0021***(3.99)-0.0002(-0.38)-0.0009(-1.61)0.0002(0.36)-0.0001(-0.23)0.00001(0.05)

0.0027***(4.01)

0.0006(0.92)-0.0001(-0.13)-0.0001(-0.19)0.0006(0.83)

0.0009(1.33)

0.1950***(10.98)0.1732***(9.13)

0.1625***(7.89)

0.1438***(5.63)

0.1103***(5.43)

0.1165***(3.79)-0.1307***(-5.36)-0.0132(-0.49)-0.0059(-0.18)0.0120(0.31)-0.0250(-0.81)-0.0540(-1.39)-0.1565***(-5.62)-0.0839***(-2.79)-0.0989***(-2.83)-0.0419(-1.09)-0.0489(-1.55)-0.0948***(-2.89)-0.0975***(-10.45)-0.0867***(-9.00)-0.1016***(-9.72)-0.0801***(-7.52)-0.1111***(-10.91)-0.0643***(-6.01)

0.0421***(4.09)

0.0155(1.47)

0.0172(1.52)-0.0017(-0.14)0.0282**(2.46)-0.0140(-1.06)0.0796***(5.99)

0.0375***(2.78)

0.0437***(3.08)

0.0081(0.59)

0.0443***(3.40)-0.0239(-1.56)0.1025**(2.02)

0.0568*(1.71)

0.1379***(4.13)

0.1644***(5.02)

0.0812**(2.19)

0.2445***(6.29)-0.0108(-0.26)-0.0074(-0.23)-0.0239(-0.76)-0.0275(-0.90)-0.0645*(-1.75)-0.1170***(-3.61)-0.0203(-0.44)0.0689*(1.87)

0.0099(0.28)

0.0332(1.01)-0.0388(-0.94)-0.2094***(-6.00)0.06140.19110.08990.18240.08950.1876

0.09450.18890.07980.18900.07210.1774

Dummy for medium
firms x Sales
Dummy for large firms x
Sales

Cash flow
Dummy for medium
firms x Cash flow
Dummy for large firms x
Cash flow

Debt ratio
Dummy for medium
firms x Debt ratio
Dummy for large firms x
Debt ratio

Lending attitude
Dummy for medium
firms x Lending attitude
Dummy for large firms x
Lending attitude

Adjusted R-squaredStandard error

Notes: See the notes in Table 3.

Table 9 Estimation Results of q-type Investment Functions
with Coefficients Specified as a Function of Firm Size
Variables

1993

1994

1995

1996

1997

1998

Marginal q-0.0384***(-6.66)0.0028***(7.39)-0.0221***(-3.22)0.0221***(3.72)-0.0124(-1.56)0.0011**(2.06)-0.0087(-1.09)0.0007(1.44)-0.0198***(-2.87)0.0014***(3.21)-0.0086(-1.38)0.0009**(2.19)

0.5121***(7.46)-0.0272***(-5.92)0.3323***(4.23)-0.0123**(-2.31)0.3167***(2.96)-0.0127*(-1.66)0.1780(1.59)-0.0028(-0.36)0.2182***(2.67)-0.0081(-1.54)0.2385***(3.18)-0.0116***(-2.69)-0.2183***(-7.07)0.0101***(4.98)-0.1994***(-6.36)0.0083***(4.12)-0.2418***(-6.49)0.0103***(4.29)-0.1374***(-3.87)0.0039*(1.69)-0.1937***(-5.85)0.0068***(3.15)

0.0140(0.37)-0.0063**(-2.49)0.3028**(2.32)-0.0142*(-1.91)0.1909**(2.02)-0.0093(-1.61)0.4161***(4.65)-0.0197***(-3.84)0.2004**(2.35)-0.0046(-0.92)0.1339(1.19)-0.0074(-1.07)0.7465***(7.76)-0.0391***(-7.18)0.06010.19120.08970.18240.08740.18780.09400.18890.07930.18900.07240.1774

Firm size x Marginal q

Cash flow
Firm size x Cash flow

Debt ratio
Firm size x Debt ratio

Lending attitude
Firm size x Lending attitude

Adjusted R-squaredStandard error
Notes: See the notes in Table 3.

-0.0638-0.0771-0.0887-0.0855-0.0866-0.0804-0.0766-0.0901-0.0941-0.1100-0.0878-0.1065-0.0659(3)

Average-0.0898-0.0902-0.0924-0.0804-0.0757
Notes: (1) Q-type investment function with dummy variables for firm size(2) Accelerator-type investment function with dummy variables for firm size(3) Q-type investment function with coefficients as a function of firm size

-0.0975-0.0867-0.1016-0.0801-0.1111-0.0643(1)-0.0608-0.0703-0.0808-0.0767-0.0868-0.0840-0.0941-0.0862-0.1042-0.0805-0.1069-0.0668(3)

Medium firms(2)-0.0554-0.0712-0.0844-0.0818-0.0829-0.0783

1993
1994
1995
1996
1997
1998

(1)

Small firms(2)-0.0634-0.0240-0.0545-0.0609-0.0729-0.0805-0.0878(1)-0.0587-0.0179-0.0492-0.0579-0.0720-0.0668-0.0882

Large firms(2)

Table 10 Comparison of Coefficients of Debt-Asset Ratio-0.0662-0.0416-0.0548-0.0617-0.0697-0.0744-0.0951(3)

0.1101
0.0444
0.09750.13110.01720.12840.08650.12340.07330.16480.14210.03950.2471(3)

Average
0.14050.13120.13170.08810.0894
Notes: (1) Q-type investment function with dummy variables for firm size(2) Accelerator-type investment function with dummy variables for firm size(3) Q-type investment function with coefficients as a function of firm size

0.10250.05680.13790.16440.08120.2445(1)
0.08230.04670.10920.12900.01810.13360.11670.08140.15790.17430.08420.2285(3)

Medium firms(2)
0.09170.04940.11400.13690.01670.1275

1993
1994
1995
1996
1997
1998

(1)

Small firms(2)

0.09400.08690.09560.11940.17670.02490.0602(1)

0.10510.08220.12570.14780.19760.04240.0351

Large firms(2)

Table 11 Comparison of Coefficients of Lending Attitude

0.05800.05550.02940.07280.12080.00480.0644(3)

Table 12 Increase of Investment When the Accommodative
Respondents Increase by 10 Percentage Points

1993
1994
1995
1996
1997
1998

Small
firms
11.07
7.03
15.44
12.77
4.99
23.45

Medium
firms
6.85
3.09
5.70
6.84
0.89
7.41

(%)
Large
firms
5.27
6.31
7.40
9.72
1.37
3.52

Table 13 Effect of Credit Crunch in 1997-98
on Investment

Manufacturing
Construction
Wholesale trade
Reatil trade
Real estate
Services
Transportationand communication
Total

Small
firms
59.8
88.9
129.9
74.4
270.1
97.0

Medium
firms
36.3
65.1
63.7
38.2
84.7
15.1

(%)
Large
firms
17.7
47.0
29.3
21.4
50.7
7.2

185.9
84.9

51.3
34.5

26.0
18.8

1993

1994

Small
firms

1995
Mediumfirms

1996

Data Source: Ministry of Finance, Annual Report of Financial Statements of Incorporated Business

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0.16

Large
firms

Figure 1 Gross Investment Rate by Firm Size (1993-1998)

1997

1998

1993

1994

Small
firms

1995
Mediumfirms

1996

Data Source: Ministry of Finance, Annual Report of Financial Statements of Incorporated Business

0.1

0.12

0.14

0.16

0.18

0.2

0.22

0.24

0.26

Large
firms

1997

Figure 2 Ratio of Cash Flow to Capital Stcok by Firm Size (1993-1998)

1998

1993

1994

Small
firms

1995
Medium
firms

1996
Large
firms

Data Source: Ministry of Finance, Annual Report of Financial Statements of Incorporated Business

1

1.1

1.2

1.3

1.4

1.5

1.6

1.7

1.8

1.9

2

Figure 3 Marginal q by Firm Size (1993-1998)

1997

1998

1993

1994

Small
firms

1995
Mediumfirms

1996

Data Source: Ministry of Finance, Annual Report of Financial Statements of Incorporated Business

0.2

0.25

0.3

0.35

0.4

0.45

0.5

0.55

Large
firms

1997

Figure 4 Ratio of Debt to Total Assets by Firm Size (1993-1998)

1998

-30

-20

Small firms

Medium firms

Large firms

3
3
4
4
5
5
6
6
7
7
8
8
4
5
6
7
8
9
3
4
5
6
7
8
-9
-9
-9
-9
-9
-9
-9
-9
-9
-9
-9
-9
-9
-9
-9
-9
-9
-9
-9
-9
-9
-9
-9
-9
r
r
r
r
r
r
c
c
c
c
c
c
p
p
p
p
p
p
n
n
n
n
n
n
a
a
a
a
a
a
e
e
e
e
e
e
Ju
Ju
Ju
Ju
Ju
Ju
Se
Se
Se
Se
Se
Se
D
D
D
D
D
D
M
M
M
M
M
M
-10

0

Data Source: Bank of Japan, Short-term Economic Survey of All Enterprises

%

10

20

30

40

Figure 5 Diffusion Index: Lending Attitude of Financial Institutions