2002. 4
What are the determinants of the number ofbank relations of Japanese firms?
Elmer Sterken

得津 一郎

What are the determinants of the number of bankrelations of Japanese firms?
Elmer Sterken
Department of Economics
University of Groningen
The Netherlands
PO Box 800, 9700 AV Groningen, The Netherlands
e-mail: e.sterken@eco.rug.nl

Ichiro Tokutsu
Institute of EBA studies
Konan University
8-9-1 Okamoto Higashi-nada KOBE 658-8501
Japan
e-mail: tokutsu@center.konan-u.ac.jp

March 2003

Abstract
We explore the determinants of the number of long-term bank relations of listed Japanesefirms in a unique data set covering 1982-1999. Japanese listed firms had about sevenlong-term bank loan relations on average, but show a large variation around the average.
We analyze the determinants of the choice for the number of bank relations. We use
special data on loan and equity ownership to address the impact of the Japan-specificcorporate governance structure on loan decisions. Having a relation with a top-equity
holding bank reduces the number of bank relations, while debt-rich and cash-poor firms
have more bank relations.
Keywords: Bank relations, Single versus Multiple Borrowing, Governance, Discrete
Choice Models
JEL: G21, G32
Acknowledgements: We are grateful to the Zengin Foundation for Studies on
Economics and Finance for financial support, and thank Elena Carletti, Hans Degryse,
Maarten Gelderman, Robert Lensink, Steven Ongena and participants at seminars at the
University of Amsterdam, De Nederlandsche Bank and at the 2002 Venice meetings ofthe Econometric Society for valuable comments.

1

Introduction

In this paper we analyze in detail the endogeneity of the number of long-term banking
relations Japanese listed firms had in the period 1982-1999. These two decades are of
special interest, since they represent the so-called Japanese bubble- and post-bubble
periods. Contrary to earlier studies on the optimal number of bank relationships (see e.g.
Detragiache et al., 2000, and Farinha and Santos, 2000) we exploit the time variation in
our unique data set. Moreover, we are able to include information with respect to theJapanese corporate control mechanism via identification of top loan and equity ownershipon the firm level. This feature is especially relevant to the Japanese case of industrial
group structures (keiretsu). Our study intends to provide new insights into Japaneseborrowing decisions by presenting both descriptive statistics and various econometricborrowing choice models.

Why is it interesting to analyze the long-term borrowing decisions of Japanese firms?
First, it is widely believed that especially long-term loans were essential to enhance therapid Japanese economic growth in the 1970s and 1980s. The role of the financial system
in providing financial means for investment that generated the high GDP growth rates has
been crucial. As known, in post-war Japan long-term bank loans were the number one
source of external funds for almost all firms (see Fukuda, 2001). Except for a few cashrich firms internal financing was limited in general. As Ito (1992) shows, internalfinancing in the 1960s and 1970s was about 20% of the total financial needs (ascompared to 50% for the U.S.) This is even true for firms within the business groups,
wherein banks play a well-known key position in providing external finance. The
dominant role of long-term loans in external finance is reflected in the fact that until the
mid-1980s bond financing was strictly regulated (even after 1985 only very large firms
were able to issue bonds). So private long-term loans formed the financial core of theinvestment-led Japanese growth. It is good to illustrate that the major providers of thelong-term loans in the 1980s and 1990s were the three long-term credit institutions, socalled city banks (about 10 on average in the last two decades), and for smaller firms theabout 120 local (or regional) banks. Secondly, the long-term loans are also seen as the2key to the current economic depression in Japan. The bad loan problem has a serious
impact on real economic activity since the beginning of the 1990s. When the bubble burst
in 1990 the average quality of especially the long-term loans appeared not to be as good
as expected. Third, as explained above banks play a key role in the Japanese industrial
structures. Lending activity, combined with equity ownership, is therefore relatively more
important than in any other market economy.

The theoretical background of our paper is a key problem in financial economics: what is
the optimal number of creditors? These creditors can be holders of either public or private
claims. In this paper we analyze the private component, namely the number of bankcontacts per firm. Our goal is to get a deeper understanding of the motives of Japanesefirms to contact more than one bank or in some cases even more than 10 banks (andabout 6 as a median value). For other countries similar work has been carried out (see e.g.
Ongena and Smith, 2000a, for an international comparative study on 20 countries).
Ongena and Smith (2000b) report an overview of studies for various individual countries
on this issue. They illustrate that for instance in Norway the number of bank relations isvery low (with a median of one), while for Italy median values of 11 relations arereported. Ongena and Smith (2000a) argue that firms in countries with stable andunconcentrated banking systems maintain more banking relations, while firms incountries with strong judicial systems and stronger creditor protections maintain fewer
relations. Volpin (2000) adds that countries with low shareholder protection allow forhigher private benefits of control and through that allow for more banking relations.

Horiuchi (1993, 1994) presents the most detailed descriptive analysis for the borrowing
decisions by Japanese firms up to now. Horiuchi (1993) reports for 1990 an average
number of bank relations for 126 firms with less than 300 employees of 3.4 and for 309
firms with more than 300 employees an average number of relations of 7.7. Horiuchi(1994) reports for 1992 an average (and median) number of 3 relations for 364 firms(including small firms with less than 10 employees). So indeed Japanese firms do havemultiple banking contacts on average and the question to be answered is why. In our

3study we extend Horiuchi’s work by updating statistical evidence and presenting models
that explain the number of banking relations.

For Japanese firms who belong to a keiretsu structure a strong and long-lasting relation
with the bank that belongs to the group seems to be natural. But having this line of credit
it might also been easier to attract more loans from other banks. It is known in theliterature that long-lasting relations with principle banks could lead to higher interest
rates to be paid by Japanese firms (see Kaplan and Minton, 1994). So firms might weightthe benefits of having one bank (and keep their information secret) or try to let banks bid
for the lowest interest rates. It might also be true that some firms want a portfolio of loanproviders in order to reduce the risk of shortage of financial capital (see Section 2 for amore extensive review of the theoretical literature).

The rather unique feature of our data is the time-series information for long-term loans:
1982-1999. As known, this period covers the 'bubble' and the post-'bubble' years. One of
the items covered by us is the analysis of these two rather distinct periods.
Macroeconomic conditions might affect individual borrowing decisions. On the other
hand, loans are essential in explaining macroeconomic fluctuations (the ‘credit view’). So
it is natural to analyze the relation between macroeconomic conditions and the number ofbanking contacts firms tend to have on average. How is the macro bad loan problem
affecting individual firm decisions to contact banks? We illustrate this in Figure 1 thatgives the percentage of single-bank relations of 14055 firm-year observations for theyears 1982-1999. Figure 1 suggests that during an economic expansion firms tend to rely
on a single relation, while in the period of downturn the average percentage of multiple
contracts seems to increase. So, current profitability seems to limit the number of bankrelations. Without giving a detailed explanation here, this finding stresses the need forour analysis.

Insert Figure 1 about here4We proceed as follows. First we give a review of the theoretical and empirical literatureon the determination of the optimal number of bank relations. This literature is largely
based in the theory of corporate finance. In this literature one is interested in the gamebetween the provider of capital and the firm regarding the control rights that belong to theassets. This game can cover the choice between equity and debt, the rights of equity
holders (Shleifer and Vishny, 1997), or the composition of external financing (Bolton andScharfstein, 1996). We review the relevant literature and derive the variables that might
influence the choice of the number of bank contacts. Next we describe the data we use.
The data are provided by the Development Bank of Japan and form an unexplored rich
set of detailed balance sheet and profit-loss account data as well as indicators ofownership of both (long-term) loans and equity. We give an extensive descriptive
overview of the variables of interest in Section 3. In Section 4 we present an econometricanalysis of he decision to borrow from different banks. Since our main dependent
variable, the number of bank relations, is a discrete variable we estimate several types of
discrete choice models. Moreover, we present results for the explanation of the loan
concentration ratio (measured by the Herfindahl index). In the last section we summarize
and conclude.

2

Theory and empirical evidence on the optimal number of banking relations

One of the most interesting fields in finance is the topic of coordination problems
between suppliers of capital. These problems hold with respect to owners of equity(which lead to the governance problems like described by Shleifer and Vishny, 1997) andthe suppliers of debt. Such coordination failures can be harmful and lead to takeover
failures (like the depositors in the Diamond-Dybvig (1983) model) or renegotiation
problems (see Bolton and Scharfstein, 1996). For debt it is natural to distinguish between
public debt (bonds) and credit. By definition, by selecting private credit the firm opts fora higher concentration of claims (see Bris and Welch, 2001).

In this paper we add to the empirical literature on creditor concentration in a very specificway: we focus on concentration of the most concentrated debt component: bank loans. So

5

we do not contribute to a large extent to the work on the choice between public andprivate finance. Still, bank loan concentration is in itself an interesting phenomenon.
Across the globe it is widely observed that firms deal with more than one bank. Ongena
and Smith (2000b) present an overview of studies of various countries and find a range ofthe average number of banking relationships between 1.6 for small US firms in 1987 toeven 33.2 for Italian firms with a credit line over 500 billion Lira in 1993. Ongena andSmith (2000a) observe that larger firms (as measured by sales) hold more bank relations,but firms that do more foreign business typically have less domestic banking contacts.
Moreover, there seem to be cross-country financial system variables that are relevant to
explain national differences. Firms that reside in countries with poor creditor rights andinefficient judicial systems typically have more banking relationships. If the banking
sector is lowly concentrated but stable and private bond markets are effective, the numberof relationships per firm is higher. These statistical observations demand an explanation.
Why would a firm operate via more than one bank?

2.1

The theory of multiple bank relationships

The most intuitive explanation of single banking is based on cost minimization. To deal
with more than one bank is costly. First, transaction costs increase, because bothscreening and monitoring costs are duplicated. It is more expensive to market debt claims
to multiple creditors (see Bris and Welch, 2001). These arguments are at the core of the
Diamond (1984) delegated monitoring model. The Diamond model predicts a firm todeal with a single bank that pools the costs of asymmetric information. A single bankmoreover avoids free-riding problems by private investors. So in all activities prior andduring the loan contract it would be cheaper to deal with a single bank. But also in expost cases, like in the case of bankruptcy, multiple relations will increase the costs of e.g.
handling debt renegotiation (see Boot and Thakor, 1994, and Bolton and Scharfstein,1996).

The second determinant of the number of banking relationships is the degree ofcompetition on the banking market. If competition is low (a few institutions dominate the6market) it is likely that the number of banking relationships drops. On the other hand ifcompetition is fierce and a large number of competing banks fight for new loans, firms
will try to benefit and increase the number of bank contacts.

Third, and related to the second item, is the hold-up problem. If a relationship bank is notaffected by heavy competition it might consider using the acquired private corporate
information to extract rents, thus distorting entrepreneurial incentives and causing
inefficient investment choices (see Sharpe, 1990, and Rajan, 1992). Indeed, there is someevidence that the longer the credit relation exists, the higher the costs of the credit line are(Kaplan, 1994). Carletti (2003) presents a theoretical monitoring model to explain this.
Multiple banking entails duplication of effort and sharing of benefit, which lead to a
reduction in the overall monitoring intensity but not necessarily to higher loan rates, due
to the presence of diseconomies of scale in monitoring. Another form of the holdup
problem might also exist. In a competitive banking environment a high-quality firm thattries to switch from its previous to a new loan provider gets pooled with low-quality
firms and might be forced to pay too high an interest rate. This prevents a high-quality
firm from increasing the number of banking relationships. How do these issues affect thedesired quantity of banking relations of a firm? A firm that faces a monopolistic banking
industry might want to increase the number of contacts and try to force banks to compete
in making offers (see Von Thadden, 1994). This is true for symmetrically informed banks.
If we have the opposite case, an inside bank that competes with outside banks, this mightchange. If outside banks start to compete, the inside bank can use its knowledge on thequality of firms to select the good firms and leaving the lemons as leftovers to the outsidebanks. This might lead to too high interest rates and a reduction of the number of credit
lines. So it is relevant to determine the nature of the existing firm-bank relationships.
Petersen and Rajan (1995) give a final argument to the competition issue. They argue thatborrowing from banks with great market power facilitates intertemporal sharing of therent surplus and through that stimulates a single banking relation. Competition in credit
markets hinders this process. It might even be so that competition forces rents to the point
where it is no longer in the interest of any bank to lend to the firm. Petersen and Rajan

7

(1995) argue that the intertemporal rent sharing is especially crucial to smaller andyounger firms.

A fourth class of arguments against the case of single banking relates to using multiple
contacts as insurance against liquidity or liquidation risk. The worst case for the firm isthat a profitable project has to be liquidated prematurely. Suppose that the loan includes arefinancing stage. If the relation bank cannot rollover their initial loan the firm inliquidity need has to apply for loans from non-relation banks (arm’s-length financiers).
These banks probably think that the applying firms have ‘lemon’ projects (see also
Detragiache et al., 2000).

A fifth class of arguments is formed by the ability among lenders to coordinate activities
in an environment with so-called soft-budget constraints. In a largely decentralized
economy banks cannot commit to finance unprofitable long-term projects because
dispersed banks with limited capital will find it costly to coordinate actions (Dewatripont
and Maskin, 1995). Bolton and Scharfstein (1996) and Bris and Welch (2001) give asimilar argument. In the Bolton-Scharfstein-model the manager has an incentive tostrategically default the project (e.g. by diverting cash to herself). Coordinating withmultiple lenders disciplines the manager. On the other hand it might be the case thatfewer creditors have more incentives to check managers. Such creditors have an incentive
to invest more in monitoring activity (see Bris and Welch, 2001). Writing debt contracts
with multiple lenders is costly though (see the first class of arguments). In any case, adecrease of default risk will increase the number of lenders. The same holds to the degreeof synergy between the assets of the firm (the degree to which the assets are worth more
together than apart) or the liquidation value.

Finally, the type of business activity might affect the number of creditors. Take theexample of a highly innovative, high quality firm that invests to a large extent in R&D. If
this firm believes that it will be successful, it will not be willing to give all theinformation to multiple financiers (see Yosha, 1995). Low-quality firms on the other
hand might want to contact multiple banks. Von Rheinbaben and Ruckes (1998) analyze

8a model that includes the competition on the output market for firms. The main point isagain that leakage of information is detrimental to a firm’s success on the output market.
The firm can avoid this in two ways. First, it decides on the amount of information given
to creditors, and second, it can change the number of contacts. If a firm gives more
information to a bank and its quality is high, it can get a lower interest rate. Morecreditors again intensify competition. Highly rated firms optimally try to deal with manybanks and will disclose as little information as they can. Bhattacharya and Chiesa (1995)
stress the point that it might be optimal for a bank to inform competitors of the innovating
firm with respect to the new technology in order to avoid financial distress. Bolton andScharfstein (1996) also predict that firms in non-cyclical industries will chose a lower
number of lenders.
2.2

Empirical evidence

The empirical literature on explaining the number of banking contacts is typically moreconcentrated than its theoretical equivalent. In Table 1 we present an overview of theresults of five relevant studies: Detragiache et al. (2000) for Italian firms, Farinha andSantos (2000) for Portugese data, Ongena and Smith (2000a) for multiple countries,
Degrijse and Ongena (2001) for Norwegian firms, and Houston and James (2001) for U.S.
firms. We classify the determinants of single-banking relationships along the six
theoretical classes presented above (so a + in Table 1 is a positive stimulus for single
banking). The classification of variables is in some cases arbitrary, but illustrative for our
purposes.

There is at least mixed evidence for the first class: cost minimization. The age of the firmis only found to be important in the Portuguese case. The evidence on firm size is mixed.
With respect to the industrial organization of the banking market (which we combinewith the hold-up problem) there seem to be clues that a more concentrated banking
market predicts single relations. Not all the studies present results with a straightforward
interpretation though. By far the most important category is the class ofliquidity/liquidation risk. Here we do find some evidence of its relevance. The
coordination problems seem to be less relevant. With respect to the business activity there9

is not much hard statistical evidence to be favored. An important characteristic is
profitability. Degryse and Ongena (2001), using data for Norwegian publicly listed firms
for the period 1979-1995, find a robust and economically relevant negative two-way
correspondence between the number of relationships and sales profitability. They alsofind that firms replacing a single relationship are on average smaller and younger thanthose firms choosing not to replace a single relationship.

Insert Table 1 about here

3

Description of the data

The primary source of the data used is the Financial Statement Data (FSD) and Sourcesof Loans Data (SLD) of individual firms. Both sets are provided by the Development
Bank of Japan. The FSD includes more than 500 items in balance sheet accounts, profit
and loss accounts and cash flow statements. Moreover, the set contains other qualitative
information on stock ownership, like the names of the top-10 shareholders and theirholding share of equity. SLD indicates from which financial institution the firms attract
their long-term loans.1 This implies that we know the identity of the top-10 equity andloan owners (we will use this hereafter). The FSD data covers more than 2000 firms
listed on the main Japanese stock markets (Tokyo, Osaka, Nagoya, etc.) from 1957
onwards. The SLD data is, however, available only after 1982. We combine both sources
and transform all available information into firm-year observations. We checked whether
our data reflect the industrial sector of the Japanese economy by mapping our sample on
the SNA-classification. Indeed we have a representative sample, although listed firms
have an overrepresentation in manufacturing (see Table A in the Appendix).

We need to define our interpretation of a bank relation. We define the total number ofbanks that provided long-term loans in year t as the number of long-term loan banking
1

The label financial institution refers to life- and non-life insurance companies as well as
public and private banks. Insurance companies and banks are the main long-term funds

10contacts. For most of those loans it will be likely that the relationship continues up to thenext year, but we do not check whether the same bank actually provides a long-term loan
next year. So suppose that a normal long-term loan will last for three years and a firm has
two providers: bank A grants the loan at t-1, bank B at time t. In our set we observe one
bank contact at time t-1, 2 at times t and t+1 and only 1 at time t+2. So we don't measure
the length of an individual loan relation (which we of course could do with our data).

The total number of firm-year observations in the original dataset from 1982 to 1999 is34939. In combining the two sets, however, some of the observations are excluded
mainly due to inconsistencies between the two data sets. For instance, the outstanding
long-term loans in the SLD data sometimes do not match the balance sheet registration of
long-term loans in the FSD source. Also, the source of the long-term loans is in somecases classified by miscellaneous financial institutions. In this case we cannot identify the
number of bank relations. This is also true for the case of classification as foreign banks;that is to say, the data set does not indicate the specific name of the foreign bank (thisholds for only 5 per cent of the firms at the maximum). Since our main concern is thedeterminants of the number of bank relations, sample firms with above characteristics are
excluded from the analysis. As a result of this data screening, we have 20740 firm-year
observations in terms of unbalanced panel data from 1982 to 1999.2

Table 2 presents the number of long-term bank relations over the sample period. We
show the time series of the number of banking relations for various cases. We
distinguish: no loans (0), a single loan (1), 2 to 4 loans, 5 to 7 loans, 8-10 loans, 11-15
loans, and over 16 loans. In the bottom line of Table 2 we give the percentage of single
loans (see also Figure 1). Table 2 shows that there is a general increase of the number ofsuppliers in Japan. Note that we are not able to identify the identity of foreign banks. In
our sample foreign banks supply less than five per cent of the loans.
2
It should be noted, however, that the calendar year does not correspond to the actual
accounting period of the firm. For example, the firm with the accounting period startingin April 1998 and ending in March 1999 is classified as 1999 in spite that the firm
actually operates 9 months in 1998 and only 3 months in 1999.
11

loans over time. It also appears that especially the classes with multiple loans (over 10loans) seem to increase above average.

Insert Table 2 about here

We also provide a figure of the mean and median number of long-term loan relations(Figure 2). As can be seen from Figure 2 the average number of relations decreases from7.74 in 1982 and it reaches it bottom level 6.65 in 1989. After 1990, it fluctuates around 7
except for the sharp decline in 1997. This means that the concentration of the long-termloans has been gradually promoted towards the bubble period but recovered to theoriginal level with the collapse of the Bubble. As shown in Figure 2, however, the medianof the number of long-term bank relations is quite stable over the sample period. It
remains at 6 except for 1982, 1983, and 1993, where the median is 7.

Insert Figure 2 about here

We also computed the Herfindahl index for long-term loans per firm (see Figure 3). The
average of this index increases from 0.370 in 1982 to its maximum of 0.409 in 1990. The
same tendency can be seen for the median value. The concentration, however, gradually
decreases towards its lowest level 0.335 in 1995 and increases again towards 1997.

Insert Figure 3 about here

We also highlight one specific feature of our data set: financial governance of Japanesefirms. The Japanese industrial organization differs to a large extent from most westernequivalents. Mutual ownership of stock is quite common, especially in the industrial
group structures (keiretsu). Within the group structure long-lived equity holdings andlender relations are the key financial characteristics. As known, banks play a central rolein these business groups, so it is valuable to give some idea of the relation between
simultaneous holdings of loans and equity, especially if we want to test the hypothesisthat firms that have some form of a main bank relation want fewer banking contacts. In

12order to consider the relationship between loan activity and equity ownership, we classify
our firm-year observations into the following seven categories with respect to a so-called
Main Bank Dummy (MBD):

MBD1: if the largest equity owner is also the largest debt owner;
MBD2: if the largest equity owner resorts under the top-3 debt owners;
MBD3: if the largest equity owner resorts under the top-10 debt owners;
MBD4: if the largest debt owner resorts under the top-3 equity owners;
MBD5: if the largest debt owner resorts under the top-10 equity owners;
MBD6: if one of the top-3 equity owners resorts under the top-3 debt owners;
MBD7: if one of the top-10 equity owners resorts under the top-10 debt owners.
Table 3 presents the percentage of firms for the above seven cases in our sample by year.
As is expected, the first class is the most rare case: a little more than 5 percent of thefirm-year observations fall into this class. Although the equity ownership by banks ishighly restricted in Japan 3 , about half of the firm-year observations are classified in
MBD6 and about 90 percent of the firm-year observations in our sample fall in the class
MBD7.
How does the governance variable MBD affect the number of bank contacts? First of all,moving from MBD1 to MBD3 for instance will increase the probability of multiple bankcontacts. Although debt contains also non-loans, the probability of a single lending
relation decreases if the largest equity holder moves from the first debt holder to a top-ten
debt holder. If a top-ten debt holder moves from a top-ten equity holding position to thesingle top equity holder one could argue that the relative probability of a single loan
relation will become larger. The bank can control the firm not only via the supply ofloans, but also as a top equity holder. So the bank is probably willing to offset theliquidity risk the firm faces, lowering the firm's intentions to contact multiple banks. The
3

In Japan the maximum share of equity holding of a bank for one firm is restricted to 5
percent. It should be noted that in our data set the financial institution includes life-13

firm has less costs of asymmetric information and will also prefer a single relationshipmore.

Insert Table 3 about here

Finally we note some basic statistical evidence from our data with respect to thedifferences between the characteristics of the Japanese financial system in the 1980s andthe 1990s. First, profitability of firms decreased. There is also a remarkable reduction in
debt-to-assets and liquid-to-total assets ratio. The Japanese corporate bond market was
not fully developed in the 1980s, but it is in the 1990s. Up to 1990 corporate bonds could
be considered to be loan-like assets. Main banks were also the underwriters of corporate
bonds. After 1990 the corporate bond market became a real financing alternative to loans.
So it could be expected that the borrowing decisions by firms will be affected stronger by
the corporate bond market indicator in the 1990s than in the 1980s. The Japanese
economy invested more in R&D in the 1980s (although this is hard to measure precisely,
since Japanese firms tend to report lower expenditure on R&D than actually purchased).
The strong impetus to growth by investing in R&D seriously changed in the 1990s, so wemight expect to see some differences between the borrowing decisions in the 1980s and1990s due to R&D expenditure shifts.

4

Explaining Japanese multiple bank relationships

We model the number of bank relations from the perspective of the firm. So we argue
that the number of bank contacts is demand driven. One could bring to the fore that
supply arguments might interfere: banks might also refuse loan supply. With our dataset
we are not able to identify these supply arguments, because we do not have bank-specificinformation. We argue that banks will probably want to sell their products, especially inthe last decade, and be certainly interested in supplying services to the large listed

insurance companies as well as private banking companies. The equities held byindividual and institutions through trust banks are classified as individual holdings.
14

companies. Of course banks can control the terms of the contracts, but this will not affectthe number of contracts to a large extent.

We model a demand for banking contacts model. Decomposing the total observed
variance into inter-firm and temporal variance and leads to the conclusion that we reject
the hypothesis that either cross-section or time variance dominates the nature of theobservations (results available upon request). So we proceed by explaining firm-year
observations instead of using a dynamic panel. We do so in three steps. First we estimate
the decision to have a single bank contact versus multiple loans using a simple logit
model. Next, we model the decision to opt for multiple loans in more detail by estimating
a multinomial logit model. Finally we present evidence on the Herfindahl index of loan
concentration using a tobit model. Given the time-series nature of our data for long-termloans, and the macroeconomic bubble-pattern, we use various sub-samples in ourestimation:
1. 1982-1999;
2. 1982-1989 (the ‘bubble’ period);
3. 1990-1999 (the post-‘bubble’ period).
We use the following variables to explain loan concentration:•

A variable that indicates the size of the firm: total real sales (SAL);•

A variable that indicates profitability (ROA). Here we note that in all our results
Tobin’s Q did not play a significant role (results available upon request);•

A variable that indicates solvability (debt-to-assets ratio, DAR);•

A variables that indicates liquidity (liquid-to-total assets, LAR);•

Variables that indicate alternative financing forms. We use the corporate bonds to
debt ratio (CBR) and the short-term loan to debt ratio (SLR);•

A variable that indicates the R&D activity of the firm: R&D expense to total sales.
Moreover, we include industry dummies (not reported in the tables) and year
dummies (not reported in the tables).

•A variable that indicates a relation between top-x loan and top-y equity ownership(MBDi, i=1,…7). We use the 7 indicators as explained above.

15

First we present a logit-model of the decision to have either a single loan, represented by
Y=1, or to have multiple loans (Y=0). The results are presented in Table 4. In Table 4 wegive three panels (A, B, and C) that describe the two sub-samples as well as the wholesample period. The rows in each panel give the results for each type of main bankdummy variables, say MBDi (i=1,…,7) as listed above. The columns give the various
estimated parameters of the determinants (see also above). In the last column we give thepseudo-R-squared and the Correct Prediction Rate (CPR). The numbers of observations
used in each sub-sample are denoted at the top of each panel. At the bottom of each panel
we also provide the marginal derivatives of the determinants x on the probability of asingle relation P: dP/dx. We shade the significant estimated parameters at the 5%
confidence level (asymptotic t-values between the brackets below the estimatedparameters).

Insert Table 4 about here

Table 4 shows that a higher debt-to-assets ratio (DAR) decreases the probability of asingle loan relation in all cases. This is a natural effect: more debt in the Japanese case
implies a higher loan demand, which increases the probability of multiple loans. It is also
clear that a higher liquid asset ratio (LAR) increases the probability of a single bankrelation. Firms with relatively more liquid assets do not need liquidity insurance and rely
on a single bank. Table 4 also shows that variables that represent the size of the firm
(SAL) and profitability (ROA) do not have a systematic impact on the loan decisions. Of
the alternative financing forms (short-term loans SLR and the corporate bond to total debt
ratio CBR) only the corporate bond to debt ratio has a significant impact on the single
versus multiple loan relation decision. A higher value of CBR indicates two things in thisrespect: more bonds relative to loans will increase the probability of a single loan byitself. But secondly, as explained above, corporate bonds also signal quality in theJapanese case. A better bond rating reduces the need for multiple banking contacts.
All the main bank dummies MBDi have a significant negative impact on the probabilityof a single bank relation. As such this is a counterintuitive result: one would expect that amain bank dominates the firm’s loan market and will use its market dominance. On the16other hand, the firm might use a main bank relation as a signal of quality in attracting
other debt suppliers’ attention. It might be in the interest of the main bank as an equity
holder to e.g. have some liquidity insurance. If we compare the results among the MBDilines one should note that for the cases where the bank is the largest equity holder, theprobability of single borrowing relations decreases with a more modest position of thebank as a top-debt supplier. The other way round, if the bank is the largest supplier ofdebt, the probability of a single relation increases if the bank becomes a more important
equity holder. So there are two effects: a main bank relation leads to a larger probabilityof multiple banking contacts, but equity concentration leads to a relatively higher
probability of a single loan.

Comparing the bubble and post-bubble periods one can observe that there are no real big
differences in terms of marginal derivatives. A 1% increase of the debt-to-assets ratio
decreases the probability of a single relation in the bubble period by 0.5% and by 0.3% in
the post-bubble period. The other derivatives are comparable across sub-periods. The
most striking difference is the impact of R&D-expenses. In the bubble period more R&D
expenses decrease the probability of a single relation: this supports the BhattacharyaChiesa hypothesis (see Section 2). In the post-bubble period this impact of R&D
expenses vanishes. The R-squared is around 0.1, but the correct prediction rate is around
90 percent for all models. In general, we can conceive the relatively stable relationshipbetween the several determinants and the single-multiple decision irrespective of the
choice of MBDi’s.
Next we analyze the decision of multiple loan contacts a little further (see for a similar
approach Detragiache et al., 2000). That is to say, once firms decided to have multipleloans, how many relations do they have? We model five classes:
1. 2-4 relationships (Y=0);
2. 5-7 relationships (Y=1);
3. 8-10 relationships (Y=2);
4. 11-15 relationships (Y=3);
5. 16 and more relationships (Y=4).

17

Table 5 contains the results of the estimated multinomial logit model. We again use thesame structure as presented in Table 4. For each panel we present the estimatedparameters and the dP/dx values. In estimating the model, the parameters for Y=0 (thesmallest number of relations, 2-4 relations) are normalized to zero. So, all parameters
should be interpreted as changes from the base case Y=0. We include only the results forone of the main bank relation variables MBD3 (the other results are available uponrequest).
Insert Table 5 about here

Table 5 shows that in most cases there is a split between less than and more than 8banking contacts. Take for example the impact of the debt-to-assets ratio (DAR). A lower
solvability (higher DAR) decreases the probability of having less than 8 contacts, andincreases the probabilities of the large contact classes. For the liquid assets ratio (LAR)
the reverse holds. Liquidity-rich firms have higher probabilities of having up to 8 bankcontacts. For the main-bank dummy variable MBD3 we find that the probability ofmultiple banking contacts (more than 5) increases. Apart from these main three
determinants, we now also observe that size (SAL), profitability (ROA), and the financing
alternatives matter in some respect. For size we find that larger firms want more bankrelations, especially for the large numbers of banking contacts (more than 11). There isalso some evidence that in the pos-bubble period profitability matters. More profitablefirms want more bank relations, which implies that most of the loss-making firms willtend to have fewer bank relations. Alternative financing forms (corporate bonds, CBR,and short-term loans SLR) tend to make firms opt for a moderate (up to 8 loan contacts)
or extensive number of banking relations. For the first group there could be substitutionof financing means, while for the latter group the signaling function might be relevant.
R&D-intensive firms tend to have a larger probability of having multiple relations (morethan 16). We do not find striking differences between the bubble and post-bubble period
in this model.

The previous results relate to the discrete lending choice. Next we use a continuous
variable as a dependent variable: the Herfindahl-index. This variable is limited in range18

(by definition in the interval [0,1]). Prior to estimating the model we transformed theoriginal index by taking the logarithm and multiplying it by -1. By this transformation thedependent variable lost its upper bound. After this transformation we apply an ordinary
Tobit model with a lower truncation at zero. Table 6 gives the estimation results. It
should be noted that a larger value of the dependent variable implies a lower
concentration (multiple loan contacts). A plus sign in the table therefore indicates that anincrease of the determining variable will lead to more banking relations. Table 6 includes
the same panels and determinants as before. We give the results for all our main bankrelation variables MBDi (i=1,…7).
Insert Table 6 about here

The results in Table 6 confirm the results shown in Tables 4 and 5. Table 6 shows that anincrease in sales (SAL, representing size) leads to a lower concentration of loans in the
post-bubble period only. Higher profitability (ROA) also implies more banking contacts,
especially after 1990. Both the impact of size and profitability did not come to the forethat prominently in the discrete choice models. As in Tables 4 and 5, a higher debt-toassets (DAR) and a lower liquidity (LAR) lead to more banking relations. With respect tothe alternative financing forms we now find some differences between the bubble andpost-bubble periods. It seems that both corporate bonds (CBR) and short-term loans(SLR) developed from complementary assets into true substitutes after 1990. For the
corporate bond market this result coincides with the institutional observation that
corporate bonds developed from loan-like assets into alternative market financing forms
after 1990. As before having a main-bank relation leads to a lower concentration of loans.
Especially, in the post-bubble period a relatively more important equity-holding bankrelation leads to fewer banking contacts. Finally more R&D-intensive firms tend to havea lower loan concentration.

19

5

Summary and conclusions

In this paper we analyze the number of long-term bank relations that Japanese listed firms
hold. Japanese firms have about 6 long-term bank relations (median value). Compared toother countries this is about the average value observed. We present an overview of therather extensive literature in the field of the optimal number of creditors. From thisliterature we retrieve a set of likely candidate variables that might have an impact on thenumber of bank relations. We focus on long-term loans, since these loans play a crucialrole in the functioning of the Japanese economy. We estimate discrete choice models of
the decision for single versus multiple relations, the decision to have a number of bankrelations in certain classes (in a multinomial logit model) and a model with a continuous
measurement of the loan concentration (as measured by the Herfindahl index).

Our general conclusions are as follows. Size, profitability, solvability, liquidity, andalternative financing forms determine the number of banking contacts. These variables
are standard determinants of the number of bank relations in the literature. Our results
support especially the liquidity insurance argument to have multiple relations, as well as
the impact of solvability. We show that size and profitability matter in explaining the
Herfindahl-index of loan concentration. We pay special attention to the impact ofJapanese corporate governance by including indicators of the types of relations Japanesefirms tend to have with their banks. We find on average that firms having a so-called
main bank relation tend to have a preference for multiple loan contacts (which seems to
be counterintuitive). If the bank is a relatively important equity owner there is a relative
decrease of the desire to have multiple relations. These effects tend to hold for the bubble(1981-1989) ad post-bubble (19990-1999) sub-samples. R&D-intensive firms tended towant more bank relations.

The Japanese banking system has shown some drastic changes in the last few years. Bank
concentration increased, so-called bad loans are transferred to special-purpose banks, andsome bank managers have been replaced. Our paper shows that Japanese firms tend tohave important links with multiple banks, which makes Japanese corporate behavior to be20

dependent on the developments in the banking sector. Especially for instance in R&D
intensive sectors the role of banks is big. As Ongena and Smith (2000a) argue, thestability of the banking sector interacts with the bank-firm networking systems. Banks
being central to Japanese development therefore have a leading role in establishingconditions for a recovery of the Japanese economy.

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23

Table 1 - Overview of empirical results on single-bank relationships
Class of explanation/variable

DGG

FS

+
0
+
0

+

+
0

+

OS

DO

HJ

-

0

-

+

1 Cost minimalization
Firm size
Firm age
Share of defaulted loans recovered
Nonperforming loans

2/3 Competition on the banking market
and Hold-up problems
Average size of lending banks
Group membership

4 Liquidity risk
Liquidity shocks
Profitability
Coverage ratio++

5 Coordination problems/Soft-budget
Firm leverage
Share of first owner

+
0

-

-

6 Type of business activity
Patents
R&D
Product innovation
Process innovation
Industry comovement
Variability of asset returns
Home sales
Worldwide sales

0
0
0
0
0
0
+

DGG = Detriagiache, Garella, Guiso (2000)
FS = Farinha, Santos (2000)
OS = Ongena, Smith (2000a)
DO = Degrijse, Ongena (2001)
HJ = Houston, James (2001)+ = significant determinant in explaining a choice for a single banking relation;
0 = insignificant determinant;- = significant determinant in explaining a choice for multiple banking

24

Table 2. Number of bank relations with respect to long-term loans (NBL) by year
1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 total
158 200 235 258 283 315 355 391 406 404 409 404 416 446 479 502 516 508 6685
49
59
59
66
70
82
86
90
96
93
69
62
69
65
69
94 10389 1370
142 140 154 158 155 149 149 172 187 191 192 207 189 213 220 260 254 244 3376
162 176 159 160 188 179 184 184 189 202 204 187 207 222 230 236 272 286 3627
117 126 148 144 138 140 126 146 153 159 156 165 140 157 170 168 187 195 2735
104
90
87 10689
96
88
85
94 103 111 126 142 143 134 126 138 157 2019
47
49
48
43
48
44
42
35
42
51
50
57
54
56
54
49
84
75
928
779 840 890 935 971 1005 1030 1103 1167 1203 1191 1208 1217 1302 1356 1435 1554 1554 20740

01
2≤NBL≤4
5≤NBL≤7
8≤NBL≤10
11≤NBL≤15
16≤NBL
total
with long-term
621 640 655 677 688 690 675 712 761 799 782 804 801 856 877 933 1038 1046 14055
loans(percentage) (79,7) (76,2) (73,6) (72,4) (70,9) (68,7) (65,5) (64,6) (65,2) (66,4) (65,7) (66,6) (65,8) (65,7) (64,7) (65,0) (66,8) (67,3) (67,8)
percentage single
7,9
9,2
9,0
9,7 10,2 11,9 12,7 12,6 12,6 11,6
8,8
7,7
8,6
7,6
7,9 10,1
9,9
8,5
9,7
relation25

Table 3 The relationship between stockholders and debt suppliers
(1)
(2)
(3)
(4)
(5)
(6)
(7)
one of the
One of the
top1 longtop1 longtop1 share
top1 share
top1 share
top3 share top10 shareterm debt
holder = top1 holder resorts holder resorts term debt
under top3 under top10 holder resorts holder resorts holder resorts holder resorts
long-term
under top3 under top10 under top3 under top10
long-term
long-term
debt holder
long-term
debt holder debt holder share holder share holder long-term
debt holder debt holder
1982
4.83
8.86
14.33
23.99
56.36
40.74
83.90
1983
4.22
8.28
12.81
25.16
57.03
42.34
84.22
1984
5.19
10.08
14.05
25.95
59.24
43.97
85.34
1985
4.73
10.19
13.59
26.29
59.97
43.87
84.93
1986
4.65
8.72
12.21
26.45
61.63
44.77
85.61
1987
4.78
9.28
12.61
28.41
63.77
45.36
87.25
1988
4.59
8.74
12.30
25.93
63.41
42.96
86.52
1989
5.06
9.13
13.20
30.76
66.57
46.63
87.36
1990
4.99
10.38
14.45
31.27
64.78
46.78
87.78
1991
4.88
10.01
14.02
31.79
63.70
46.56
87.23
1992
3.58
10.23
14.58
31.84
64.45
49.10
88.87
1993
5.22
12.19
16.67
34.95
68.28
51.99
90.17
1994
4.49
10.99
15.61
35.21
69.91
50.81
90.76
1995
5.84
11.68
16.71
32.71
68.69
48.95
90.42
1996
5.02
11.63
15.28
33.30
72.63
48.69
89.85
1997
5.47
10.83
14.68
34.30
72.45
49.30
88.96
1998
5.78
10.50
14.35
35.45
72.93
47.69
89.88
1999
5.83
11.09
14.91
36.81
72.75
49.14
89.39
total
5.01
10.27
14.35
31.16
66.23
46.99
87.96

26

(8)
total

100.00100.00100.00100.00100.00100.00100.00100.00100.00100.00100.00100.00100.00100.00100.00100.00100.00100.00100.00Table 4 Estimation results for the binary logit model
for single (Y=1) and multiple relations (Y=0)
SAL
0.00019(0.85)
0.00033(1.49)
0.00047(2.19)
0.00010(0.45)-0.00015(0.59)
0.00021(0.92)-0.00028(0.95)

ROA-0.0086(0.96)-0.0081(0.92)-0.0076(0.86)-0.0106(1.22)-0.0087(0.98)-0.0133(1.61)-0.0142(1.61)

Panel A. Whole period: 1982-99
DAR
LAR
CBR
SLR
-0.04120.03710.0159 -0.0007(19.6)(15.4)(7.81)(0.30)-0.0406
0.03660.0165 -0.0003(19.2)(15.2)(8.10)(0.13)-0.0401
0.03600.0174 -0.0005(19.0)(15.0)(8.52)(0.20)-0.03960.03670.0153 -0.0013(18.8)(15.2)(7.48)(0.55)-0.0373
0.03610.0142 -0.0013(17.5)(14.8)(6.84)(0.56)-0.03780.03570.0154 -0.0007(17.9)(14.7)(7.41)(0.31)-0.0295
0.03430.01360.0009(12.7)(12.9)(5.89)(0.36)

R&D-0.0973(4.26)-0.0915(4.02)-0.0795(3.46)-0.0927(4.07)-0.1058(4.52)-0.0924(4.07)-0.1155(4.44)

MBD-0.6442(3.67)-1.4830(8.62)-1.9466(11.3)-0.8158(10.6)-1.0356(16.7)-1.5811(20.6)-2.8237(39.6)(1) MBD1(2) MBD2(3) MBD3(4) MBD4(5) MBD5(6) MBD6(7) MBD70.01550.02650.03830.0085-0.01270.0159-0.0179-0.0020-0.0019-0.0018-0.0020-0.0018-0.0020-0.0013-0.0038-0.0038-0.0037-0.0037-0.0034-0.0034-0.0021-0.0001-0.0001-0.0001-0.0001-0.0001-0.0001
0.0000-0.0084-0.0079-0.0069-0.0080-0.0089-0.0077-0.0075-0.0516-0.1215-0.1589-0.0668-0.0842-0.1246-0.1830(1) MBD1

0.00059(2.02)
0.00065(2.20)
0.00075(2.47)
0.00049(1.66)
0.00030(0.99)
0.00048(1.63)
0.00030(0.81)-0.0001(0.01)
0.0001(0.00)-0.0001(0.00)-0.0015(0.10)
0.0029(0.20)-0.0100(0.67)-0.0111(0.71)

Panel B. Bubble period: 1982-89-0.0501
0.03740.0087 -0.0045(13.9)(9.03)(2.22)(1.20)-0.0499
0.03720.0094 -0.0040(13.9)(9.01)(2.38)(1.08)-0.0499
0.03710.0105 -0.0044(13.8)(8.94)(2.67)(1.18)-0.04900.03670.0080 -0.0052(13.6)(8.86)(2.02)(1.37)-0.0449
0.03560.0071 -0.0047(12.3)(8.51)(1.78)(1.26)-0.0480
0.03510.0090 -0.0041(13.0)(8.41)(2.24)(1.09)-0.03540.03170.0050 -0.0033(8.59)(6.74)(1.09)(0.84)-0.1776(4.59)-0.1733(4.46)-0.1603(4.06)-0.1733(4.48)-0.1974(4.98)-0.1646(4.19)-0.1884(4.11)-0.8325(2.81)-1.6331(5.61)-2.1410(7.38)-0.9426(7.02)-1.1117(10.9)-1.7969(13.5)-3.0019(25.3)

0.0156
0.02670.03760.0001-0.01190.0160-0.0182-0.0007-0.0007-0.0006-0.0009-0.0007-0.0010-0.0009-0.0034-0.0033-0.0032-0.0032-0.0030-0.0029-0.0019-0.0079-0.0074-0.0064-0.0075-0.0084-0.0072-0.0074-0.0526-0.1201-0.1562-0.0658-0.0820-0.1227-0.1813(1) MBD1(2) MBD2(3) MBD3(4) MBD4(5) MBD5(6) MBD6(7) MBD7(2) MBD2(3) MBD3(4) MBD4(5) MBD5(6) MBD6(7) MBD7(1) MBD1(2) MBD2(3) MBD3(4) MBD4(5) MBD5(6) MBD6(7) MBD7

0.00250.00250.00240.00250.00240.00230.00200.00300.00300.00290.00300.00290.00280.0022270.00120.00120.00130.00110.00100.00110.00080.00130.00130.00140.00120.00110.00120.0009-0.0001
0.00000.0000-0.0001-0.0001-0.0001
0.0001

R2/CPR
0.06550.90300.07270.90310.08100.90350.07370.90390.08530.90290.10410.90310.18910.91630.09010.89600.09800.8964
0.10800.89640.09960.89540.11240.89880.13670.89510.23500.9181

Table 4 (continued) Estimation results for the binary logit-model
for single (Y=1) and multiple relations (Y=0)(1) MBD1(2) MBD2(3) MBD3(4) MBD4(5) MBD5(6) MBD6(7) MBD7(1) MBD1(2) MBD2(3) MBD3(4) MBD4(5) MBD5(6) MBD6(7) MBD7

SAL-0.00026(0.73)-0.00008(0.24)
0.00010(0.31)-0.00035(0.99)-0.00068(1.75)-0.00019(0.55)-0.00104(2.20)

Panel C. Post-Bubbles period: 1990-99
ROA
DAR
LAR
CBR
SLR
-0.0160 -0.0363
0.03850.01820.0014(1.53)(13.7)(13.0)(7.56)(0.46)-0.0153 -0.0353
0.03770.01880.0017(1.47)(13.4)(12.7)(7.75)(0.56)-0.0143 -0.0346
0.03690.01950.0017(1.38)(13.1)(12.5)(8.06)(0.56)-0.0178 -0.0346
0.03830.01790.0009(1.78)(13.1)(12.8)(7.38)(0.29)-0.0172 -0.0333
0.03800.01680.0008(1.66)(12.5)(12.6)(6.85)(0.26)-0.0174 -0.0328
0.03730.01780.0015(1.83)(12.5)(12.5)(7.26)(0.50)-0.0179 -0.0268
0.03660.01680.0033(1.71)(9.30)(11.2)(6.23)(1.04)

R&D-0.0485(1.73)-0.0417(1.50)-0.0300(1.07)-0.0445(1.60)-0.0509(1.80)-0.0495(1.80)-0.0721(2.30)

MBD-0.5111(2.34)-1.3819(6.47)-1.8316(8.57)-0.7707(8.08)-1.0050(12.7)-1.4747(15.4)-2.7701(30.2)-0.0203-0.00640.0081-0.0278-0.0523-0.0143-0.0661-0.0013-0.0012-0.0011-0.0014-0.0013-0.0013-0.0011-0.0038-0.0033-0.0023-0.0035-0.0039-0.0038-0.0046-0.0405-0.1088-0.1431-0.0605-0.0775-0.1119-0.1765-0.0029-0.0028-0.0027-0.0027-0.0026-0.0025-0.00170.00310.00300.00290.00300.00290.00280.00230.00140.00150.00150.00140.00130.00140.00110.00010.00010.00010.00010.00010.00010.0002

R2/CPR
0.05560.9066
0.06220.90690.06960.9066
0.06350.90640.07400.90710.08970.90770.16750.9149

CBR = corporate bonds to total debt;
DAR = debt-to-assets ratio;
LAR = liquid assets to total assets ratio;
MBDi = main-bank dummy variable = 1 if a main bank loan supplies are also a main equity
holders (subscript i indicates the relative importance both in loan and equity holding, see
Section 3);
NBL = number of long-term bank loans;
ROA = return on assets (profits after tax / the average of the total asset at the beginning andthe end of period);
R&D = R&D expenditure to total sales;
SAL = total sales, corresponding probability dP/dx is in terms of 103;
SLR = short-term loans to total debt.

28

Table 5 Estimation results multinomial logit-model for number of bank relations(2) 5-7(3) 8-10(4) 11-15(5) 16-(1) 2-4(2) 5-7(3) 8-10(4) 11-15(5) 16-(2) 5-7(3) 8-10(4) 11-15(5) 16-(1) 2-4(2) 5-7(3) 8-10(4) 11-15(5) 16-(2) 5-7(3) 8-10(4) 11-15(5) 16-(1) 2-4(2) 5-7(3) 8-10(4) 11-15(5) 16-

SAL-0.00090(2.40)
0.00045(1.30)
0.00231(7.28)
0.00304(9.18)

Panel A. Whole period: 1982-1999, R2=0.2423
ROA
DAR
LAR
CBR
SLR
-0.0108
0.0217-0.0180-0.00650.0002(1.57)(12.3)(9.29)(3.36)(0.11)
0.01770.0469-0.0364-0.0088-0.0077(2.29)(23.2)(17.1)(4.08)(3.60)
0.01000.0580-0.0386-0.0062-0.0107(1.17)(25.1)(16.5)(2.57)(4.41)
0.02140.0817-0.05840.0014-0.0049(1.92)(24.2)(19.7)(0.44)(1.51)

R&D-0.0277(1.56)
0.0057(0.30)-0.0006(0.03)
0.1223(4.66)

MBD3
0.6715(8.12)
0.9979(11.6)
1.3116(14.6)
1.6701(15.1)-0.0593-0.3494-0.0001
0.26470.1494-0.0006-0.00400.00280.00070.00100.00080.0010-0.0008-0.00100.00000.0006-0.00770.0007-0.00110.0075-0.1654-0.0082
0.04760.07490.0510-0.00146(2.27)
0.00020(0.35)
0.00182(3.33)
0.00221(3.91)

Panel B. Bubble period: 1982-1989, R2=0.2804-0.0355
0.0277-0.0191
0.0030-0.0015(2.62)(8.45)(5.26)(0.69)(0.46)-0.0086
0.0557-0.04110.0122-0.0016(15.0)(10.4)(2.57)(0.47)(0.63)-0.0165
0.0711-0.04390.0053-0.0173(1.03)(16.2)(10.1)(0.94)(4.15)
0.0116
0.0973-0.07140.03400.0033(0.59)(15.8)(13.4)(4.95)(0.62)

0.0180(0.59)
0.0112(0.32)
0.0226(0.58)
0.1938(4.26)

0.9220(6.32)
1.3036(8.67)
1.5435(9.61)
1.7377(8.69)

0.0275-0.4092
0.02100.24150.11920.0035-0.0060
0.0013-0.0004
0.00160.00070.00050.0004-0.00210.0005-0.0046-0.0010-0.0035-0.00140.0105-0.1971
0.00890.07150.07580.0409Panel C. Post-Bubble period: 1990-1999, R2=0.2424
0.0198-0.0196-0.0087
0.00080.0027(9.28)(8.51)(3.98)(0.33)(0.35)
0.0331
0.0442-0.0351-0.0144-0.0101(3.45)(18.02)(13.85)(5.80)(3.69)
0.02670.0529-0.0378-0.0082-0.0069(2.58)(19.05)(13.58)(3.04)(2.27)
0.02880.0764-0.0526-0.0070-0.0084(2.15)(18.55)(14.71)(1.84)(2.01)-0.0519(2.32)
0.0092(0.39)-0.0089(0.35)
0.0829(2.50)

0.5509(5.43)
0.8443(8.00)
1.2121(11.1)
1.6235(12.1)

0.0031-0.01170.0036-0.0006
0.0056-0.1481-0.01640.03480.07600.0536-0.00059(1.25)
0.00058(1.31)
0.00261(6.49)
0.00351(8.39)-0.1115-0.3133-0.0199
0.28170.1629-0.0031-0.0030
0.00370.00160.0007-0.0069-0.00220.00290.00340.0028-0.0079-0.0025
0.00320.00400.0032-0.0065-0.00210.00290.00310.00260.00520.0012-0.0025-0.0019-0.00200.00550.0018-0.0028-0.0020-0.00250.00530.0006-0.0023-0.0019-0.00160.0011-0.0005-0.0009-0.00020.0004-0.0013-0.0009
0.0011-0.0005
0.00160.0018-0.0004-0.0014-0.0001
0.0001

Symbols: See legend Table 4.

29

0.00080.0012-0.0013-0.0004-0.0002

Table 6 Estimation results: Tobit-model for the Herfindahl index(1) MBD1(2) MBD2(3) MBD3(4) MBD4(5) MBD5(6) MBD6(7) MBD7(1) MBD1(2) MBD2(3) MBD3(4) MBD4(5) MBD5(6) MBD6(7) MBD7(1) MBD1(2) MBD2(3) MBD3(4) MBD4(5) MBD5(6) MBD6(7) MBD7

SAL
0.00033(7.28)
0.00029(6.37)
0.00024(5.24)
0.00036(7.77)
0.00040(8.86)
0.00031(6.94)
0.00038(9.08)

ROA
0.0068(4.58)
0.0066(4.51)
0.0066(4.53)
0.0068(4.64)
0.0065(4.44)
0.0069(4.77)
0.0055(4.03)

Panel A. Whole period: 1982-99
DAR
LAR
CBR
SLR
0.0122 -0.0102 -0.0024 -0.0003(30.7)(23.7)(5.36)(0.71)
0.0121 -0.0102 -0.0026 -0.0005(30.5)(23.8)(5.74)(1.03)
0.0119 -0.0101 -0.0029 -0.0004(30.4)(23.8)(6.54)(0.96)
0.0120 -0.0102 -0.0023 -0.0003(30.2)(23.7)(5.17)(0.58)
0.0116 -0.0101 -0.0021 -0.0002(29.4)(23.7)(4.66)(0.52)
0.0116 -0.0100 -0.0025 -0.0005(29.7)(23.7)(5.53)(1.06)
0.0096 -0.0090 -0.0017 -0.0006(26.1)(22.7)(4.11)(1.54)

R&D
0.0168(4.02)
0.0149(3.58)
0.0121(2.93)
0.0164(3.94)
0.0181(4.37)
0.0165(4.01)
0.0166(4.31)

MBD
0.1414(5.29)
0.2373(12.3)
0.3194(19.0)
0.1355(10.7)
0.2325(18.5)
0.2823(24.3)
0.9495(53.5)

LH/σ-14859.0
0.6831-14797.70.6802-14694.9
0.6751-14815.8
0.6812-14702.7
0.6762-14581.4
0.6705-13488.7
0.62600.00004(0.49)
0.00001(0.09)-0.00005(0.58)
0.00006(0.81)
0.00010(1.34)
0.00003(0.40)
0.00008(1.09)

0.0062(2.29)
0.0062(2.28)
0.0062(2.29)
0.0062(2.29)
0.0054(1.98)
0.0070(2.62)
0.0049(1.94)

Panel B. Bubble period: 1982-89
0.0154 -0.0119
0.00200.0014(21.9)(15.6)(2.11)(1.97)
0.0154 -0.0119
0.00190.0013(21.9)(15.7)(2.01)(1.80)
0.0153 -0.0120
0.00150.0014(22.0)(15.8)(1.54)(1.91)
0.0152 -0.0118
0.00200.0015(21.6)(15.5)(2.15)(2.08)
0.0145 -0.0117
0.00200.0014(20.7)(15.4)(2.15)(1.98)
0.0148 -0.0116
0.00170.0013(21.4)(15.4)(1.86)(1.78)
0.0119 -0.0102
0.00240.0009(18.1)(14.5)(2.78)(1.36)

0.0273(3.87)
0.0257(3.66)
0.0238(3.41)
0.0275(3.90)
0.0308(4.41)
0.0257(3.70)
0.0224(3.45)

0.1807(4.18)
0.2347(7.31)
0.3163(11.5)
0.1190(5.70)
0.2115(10.9)
0.2604(14.1)
0.8790(33.3)-5533.0
0.6664-5515.1
0.6643-5476.3
0.6595-5525.5
0.6658-5482.7
0.6609-5443.6
0.6564-5004.1
0.61090.00048(8.51)
0.00043(7.67)
0.00038(6.76)
0.00050(8.88)
0.00055(9.83)
0.00045(8.15)
0.00053(10.2)

Panel C. Post-Bubble period: 1990-99
0.00750.0108 -0.0098 -0.0036 -0.0011(4.25)(22.2)(18.7)(6.94)(1.99)
0.00730.0106 -0.0097 -0.0038 -0.0013(4.15)(22.0)(18.6)(7.32)(2.24)
0.00720.0105 -0.0096 -0.0041 -0.0013(4.15)(21.8)(18.6)(7.99)(2.23)
0.00760.0106 -0.0097 -0.0035 -0.0011(4.31)(21.8)(18.6)(6.76)(1.90)
0.00740.0104 -0.0097 -0.0032 -0.0010(4.26)(21.6)(18.7)(6.19)(1.75)
0.00730.0103 -0.0096 -0.0036 -0.0013(4.24)(21.5)(18.7)(7.04)(2.33)
0.00620.0088 -0.0086 -0.0028 -0.0014(3.84)(19.5)(17.9)(5.89)(2.68)

0.0125(2.40)
0.0104(2.01)
0.0073(1.43)
0.0119(2.31)
0.0126(2.45)
0.0131(2.58)
0.0147(3.07)

0.1192(3.52)
0.2384(9.90)
0.3207(15.1)
0.1415(8.93)
0.2410(14.7)
0.2919(19.6)
0.9950(41.7)-9252.7
0.6875-9210.1
0.6841-9145.4
0.6789-9219.1
0.6850-9151.60.6801-9069.4
0.6737-8415.90.6302

Symbols: see legend Table 4.

30

Table A - Number of sample firms by SNA industry classification and year
(1) Agriculture, forestry and fishery(2) Mining(3) Manufacturing
1 Food and beverages
2 Textiles
3 Pulp, paper and paper products
4 Chemicals
5 Petroleum and coal products
6 Non-metallic mineral products
7 Basic metal
8 Fabricated metal products
9 Machinery
10 Elec. Machinery
11 Transport equipment
12 Precision instrument
13 Others(4) Construction(5) Electricity, gas and water supply(6) Wholesale and retail trade(8) Real estate(9) Trans. And communication(10) Services
Total

1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 total
1
1
1
1
1
2
3
10
1
2
1
2
2
3
1
2
4
4
5
5
5
4
4
4
3
3
55
576 595 626 657 672 678 703 736 761 785 783 782 792 834 846 893 967 958 13644
52
56
52
53
55
62
61
66
66
70
70
69
68
75
77
79
83
82 1196
23
25
35
42
41
45
42
44
45
50
52
54
53
55
58
52
56
56
828
11
13
12
14
15
16
15
18
19
21
20
17
16
15
16
17
22
22
299
66
67
69
78
83
78
85
86
98
92
91
96
94
97
96 106 123 120 1625
2
3
2
2
2
1
1
3
1
1
1
2
4
2
3
9
6
45
33
30
34
35
35
32
35
37
39
40
39
38
40
43
43
44
45
47
689
41
38
38
42
44
39
41
52
48
49
49
47
52
55
51
56
58
60
860
39
38
44
44
49
44
42
47
50
46
47
47
46
48
55
63
65
65
879
97
99
96 105 105 116 114 118 127 131 132 137 143 149 144 147 157 153 2270
85
89
95
97
98
96 102 110 107 116 115 109 112 113 110 114 123 128 1919
63
65
72
69
74
73
77
74
73
76
71
72
64
71
76
82
90
84 1326
18
22
21
23
20
22
23
22
23
22
24
26
27
27
30
29
28
28
435
46
50
56
53
51
54
65
59
65
71
73
69
75
82
88 101 108 107 1273
67
71
80
76
80
85
72
88
93
95
88
89
86
93 105 107
76
82 1533
6
4
4
3
4
4
3
4
5
4
5
6
4
5
6
4
7
8
86
55
82
97 109 125 138 151 167 180 192 191 199 200 226 240 260 308 303 3223
8
8
7
7
8
7
6
10
13
11
12
11
10
11
14
17
19
20
199
46
43
41
46
45
47
48
47
56
52
48
50
49
52
56
54
63
63
906
20
35
34
35
35
43
46
49
55
60
59
65
70
76
84
95 109 114 1084
779 840 890 935 971 1005 1030 1103 1167 1203 1191 1208 1217 1302 1356 1435 1554 1554 20740

31

Table B - Number of bank relations with respect to long-term loans by year
1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999

total

0

158

200

235

258

283

315

355

391

406 404

409

404

416

446

479

502

516

508

6685

1

49

59

59

66

70

82

86

90

96

93

69

62

69

65

69

94

103

89

1370

2

48

39

50

50

41

48

55

54

73

72

65

68

65

68

82

95

76

85

1134

3

41

51

54

49

54

47

37

60

57

65

60

67

55

67

60

79

89

75

1067

4

53

50

50

59

60

54

57

58

57

54

67

72

69

78

78

86

89

84

1175

5

50

61

63

60

76

65

71

78

70

76

72

66

70

75

75

84

97

95

1304

6

59

59

53

56

59

65

58

55

51

60

66

60

77

82

83

83

96

98

1220

7

53

56

43

44

53

49

55

51

68

66

66

61

60

65

72

69

79

93

1103

8

59

50

53

58

49

52

57

53

55

60

49

57

55

61

70

67

66

67

1038

9

26

40

57

56

58

53

43

59

63

59

66

61

52

53

57

58

68

72

1001

10

32

36

38

30

31

35

26

34

35

40

41

47

33

43

43

43

53

56

696

11

22

29

30

35

23

22

20

21

22

30

35

36

47

43

44

45

40

44

588

12

27

24

23

24

23

28

26

24

27

24

28

31

30

36

30

26

44

50

525

13

20

17

9

15

17

23

22

21

18

19

16

19

23

23

27

27

23

20

359

14

18

8

15

18

13

15

11

10

14

18

23

22

26

23

19

16

21

23

313

15

17

12

10

14

13

8

9

9

13

12

9

18

16

18

14

12

10

20

234

16

14

13

12

14

13

12

11

8

8

10

18

17

19

20

16

11

23

16

255

17

9

7

11

3

7

9

11

8

13

10

8

10

8

5

8

11

15

11

164

18

6

8

2

3

5

7

6

3

6

5

6

8

4

7

5

10

9

10

110

19

3

5

9

10

6

3

2

4

2

5

5

7

5

9

10

5

9

11

110

20

1

2

1

1

2

2

2

2

21

4

4

3

1

4

2

1

22

2

3

2

3

3

23

2

2

3

24

2

1

1

1

25

1

26

3

27

3
2
1

28
29

2

5

2

1

4

7

8

53

5

3

4

3

4

4

52

4

4

3

44

3

5

3

44

3

4

22

3

2

1

5

2

2

2

4

2

2

1

2

2

2

1

1

1

2

1

1

1

1

1

1

3

5

5

1

1

1

1
1

1

1

1

2
1

2
1

1

1

8
1

5
1

2

1

1

1
1

1
1

37

3
1

1

39

1

1

1
1

1

5
1

4
1

1

1
1

45

1

46

1
1

55

1

total

779

3
1

44

52

3
2

1
1

2
5

1

1

2

14
3

1

1

36

10
1

1

1

34

40

1
5

1

31

35

4
1

4

30
32

6
2

1

1
1

2
2

1
1
1

1

2
1

840

890

935

971 1005 1030 1103 1167 1203 1191 1208 1217 1302 1356 1435 1554 1554 20740

33

15
14
13
12
11
% 10
9
8
7
6

Figure 1. Percentage of firms with a single long-term loan relation

34

1999

1998

1997

1996

1995

1994

1993

1992

1991

1990

1989

1988

1987

1986

1985

1984

1983

1982

5

8

7

6
mean

medianFigure 2. Number of bank relations with respect to long-term loan

35

1999

1998

1997

1996

1995

1994

1993

1992

1991

1990

1989

1988

1987

1986

1985

1984

1983

1982

5

0.45

0.40

0.35

0.30

0.25

mean

medianFigure3. Herfindahl index for long-term loans

36

1999

1998

1997

1996

1995

1994

1993

1992

1991

1990

1989

1988

1987

1986

1985

1984

1983

1982

0.20

Discussion Paper
No.
2001･1

AuthorTakashi Hatakeda
Nobuyuki Isagawa

2001･2Michiyasu Nakajima
Takeshi Mizuguchi
Katsuhiko Kokubu
Yasushi Onishi
Koji Okubayashi

2001･3
2001･4

2001･5
2001･6
2001･7

2001･8

2001･9Katsuhiko Kokubu
Akihiro Noda
Yasushi Onishi
Tomomi Shinabe
Kunio Miyashita
Akinobu Sakashita
KOKUBU,
Katsuhiko
NASHIOKA, ErikoDAIKUHARA, Rie
KOKUBU,
Katsuhiko
KURASAKA,
TomokoShuzo Murata

2001･11

Makoto Yano
Fumio Dei
Mahito Okura

2001･122001･13

Mahito Okura
Shuzo Murata

2001･14

Koji Okubayashi
Toshinori Takashina

2001･15
2001･16

Takuji Hara
Shuzo Murata

2001･17

Norio Kambayashi

2001･18
2001･19
2001･20

Yuichi Fukuta
Kazuhiro Tanaka
Kentaro Nobeoka
Kazuhiro Tanaka

2001･21

Mahito Okura

2001･22
2001･23

Kazuhiro Tanaka
Mahito Okura

2001･24

Katsuhiko Kokubu
Akihiro Noda
Yasushi Onishi
Tomomi Shinabe
Akira Higashida

2001･10

Title
Stock Price Behavior Surrounding Repurchase
Announcements:
Evidence from Japan
Material Flow Cost Accounting of IMU (October 2000)

Date
1／2001(in Japanese)

1／2001

Japanese Manufacturers Without Factories:
Cases of Sony, Matsushita, Misumi, People
Determinants of Environmental Report Publicationin Japanese Companies

1／2001

Logistics Strategy of Japanese Port Management
Issues and Method of Organizational Culture Studies:
Functionalism Approach
Environmental Accounting in Japanese Companies as ofNovember 2000

(in Japanese)

2／20013／2001(in Japanese)

3／20012／2001

Corporate Environmental Accounting: A Japanese
Perspective

The Comparative Study of Japanese & U.S. Companies(6) The Health & Long Term Care-The Management
Education
A Trade Model with Vertical Production Chain andCompetition Policy in the Downstream Sector
Risk Classification: A Study of the Tradeoff between
Quantity and Quality of Insured
Is the Separating Equilibrium Really Desirable?
The Comparative Study of Japanese & U.S. Companies(5) The Conflict Among Companies
Research Report on the relations between there-employment process and former job career at thecompulsory retirement of workers of a firm
The Social Shaping of Medicines
The Comparative Study of Japanese & U.S. Companies(7) Toward Restructuring Japanese Corporations
Cultural influences on IT usage among workers:a UK-Japanese comparison
A Test for Rational Bubbles in Stock Prices
Toward the Effective Reform of Japanese Corporate
Governance
Structural Changes in Top Management―The Effect ofCorporate Governance Reform in Japan
In the Separating Equilibrium Really Desirable ?
―revised version of No.2001･12―
Introduction to Corporate Governance
The Role of Some Insurance Contracts in a Subjective
Equilibrium Model with Observable Effort
Environmental Disclosure of Japanese companies

3／2001(in Japanese)

4／2001

12／2000(in Japanese)

5／2001(in Japanese)(in Japanese)

5／20016／2001(in Japanese)

7／2001(in Japanese)(in Japanese)

7／20017／20017／2001(in Japanese)

7／20017／2001(in Japanese)

7／2001(in Japanese)

8／2001(in Japanese)(in Japanese)

8／20018／2001(in Japanese)

8／2001

Discussion Paper
No.
2001･25

2001･26
2001･27

AuthorKatsuhiko Kokubu
Tomomi Shinabe
Akira Higashida
Yasushi Onishi
Akihiro Noda
Katsuhiko Kokubu
Eriko Nashioka
Masakazu Takagi

2001・28 Mahito Okura
2001・29 Atsushi Takao

2001・30 Mahito Okura
2001・31 Masakazu Takagi
2001・32 Norio Kambayashi
Harry Scarbrough2001・33 Fumitoshi Mizutani
Shuji Uranishi
2001・34 Mahito Okura

2001・35 Tsutomu Harada
2001・36 Nobuyuki Isagawa

2001・37 Nobuyuki Isagawa
2002・1

Nobuyuki Isagawa

2002・2

Nobuyuki Isagawa

2002・3Mahito Okura

2002・4

Elmer Sterken
Ichiro Tokutsu

Title
Environmental Reporting of Japanese companies(in Japanese)

Date
8／2001

Environmental Accounting of Japanese companies(in Japanese)

8／2001Elementary Study of East Asian Corporate andManagement System
Vertical Differentiation in the Insurance Market
A Study on the Penetration Process of Modern Insurance
by Evolutionary Economics―Why do the Modern
Insurance and the Premodern Mutual Society coexist?―
An Essay in the Economics of Post-loss Minimisation:
An Analysis of the Effectiveness of the Insurance Law
and Clauses
Co-operation between Hanshin(Osaka-Kobe) Area andSouth-east Asia-Finding business opportunities of mutual
benefits
Cultural influences on IT use amongst factory managers：
A UK-Japanese comparison
The Post Office vs. Parcel Delivery Companies：
Competition Effects on Costs and Productivity
An Essay in the Economics of Post-loss Minimisation:
An Analysis of the Effectiveness of the Insurance Law
and Clauses―revised version of No.2001・30―
An Examination of IT Paradox in Japan
Open-Market Repurchase Announcements, Actual
Repurchases, and Stock Price Behavior in Inefficient
markets
Corporate Financial Strategy and Stock Price Behavior ina Noise Trader Model with Limited Arbitrage
Cross Holding of Shares, Unwinding Cross Holding of
Shares, and Managerial
Entrenchment
A Theory of Stock Price Behavior following Repurchase
Announcements
An Equilibrium Analysis of the Insurance Market
with Vertical Differentiation
What are the determinants of the number of bankrelations of Japanese firms?

9／2001(in Japanese)(in Japanese)

9／20019／20019／2001(in Japanese)

9／2001

10／2001
10／2001
11／2001(in Japanese)

11／2001
12／2001
12／2001(in Japanese)

1／ 2002(in Japanese)

1／ 20022／20023／2002