Discussion Papers In Economics
And Business

Has Competition in theJapanese Banking Sector Improved?

Hirofumi Uchida
Yoshiro Tsutsui
Discussion Paper 02-09-Rev.

Graduate School of Economics andOsaka School of International Public Policy (OSIPP)
Osaka University, Toyonaka, Osaka 560-0043, JAPANHas Competition in theJapanese Banking Sector Improved?

Hirofumi Uchida
Yoshiro Tsutsui
Discussion Paper 02-09-Rev.
Sep 2003

この研究は「大学院経済学研究科・経済学部記念事業」
基金より援助を受けた、記して感謝する。

Graduate School of Economics andOsaka School of International Public Policy (OSIPP)
Osaka University, Toyonaka, Osaka 560-0043, JAPANHas Competition in theJapanese Banking Sector Improved?*
Hirofumi Uchida(Indiana University and Wakayama University)
Yoshiro Tsutsui †(Osaka University)

Abstract
This paper investigates whether competition in the Japanese banking sector has improved in
the last quarter of the 20th century.

By estimating the first order condition of profit

maximization, together with the cost function and the inverse demand function, we foundthat competition had improved, especially in the 1970s and in the first half of the 1980s.
The results fail to reject a Cournot oligopoly for city banks for most of the period, while
they do reject it for regional banks for the overall period. This suggests that competition
among city banks was stronger than that among regional banks.

JEL Classification number: G21, L13
Keyword: Japanese banks, degree of competition, loan market.

*

An earlier version of this paper was presented at the Autumn Meeting of the Japanese Economic
Association, Contract Theory Workshop, Monetary Economics Workshop, and the Center for
Economic Institutions at Hitotsubashi University. The authors are grateful for comments made by
Tatsuya Kikutani, Kohsuke Oya, Seki Obata, Hideshi Itoh, and Juro Teranishi. Uchidaacknowledges the financial support from the Zengin Foundation for Studies on Economics andFinance and Wakayama University Research Fund.
†
Corresponding author: Graduate School of Economics, Osaka University, 1-7, Machikaneyama,
Toyonaka, 560-0043 Japan. Phone: +81-6-6850-5223. Fax: +81-6-6850-5274.
E-mail: tsutsui@econ.osaka-u.ac.jp

1. Introduction
The purpose of this paper is to investigate whether competition in the Japanese banking
sector has improved in the last quarter of the 20th century. Over this period, a regulated
system for the Japanese banking sector that had dominated in Japan over the high growth
period has been substantially liberalized.

This period includes the bubble period of the late1980s, and the long stagnation in the 1990s in which private banks struggled with huge
non-performing loans.

As a result, we expect that the degree of competition in theJapanese banking sector has changed throughout this period.
There have been a few studies, to our knowledge, that have investigated the degree ofcompetition in Japanese financial industries. Molyneux, Thornton, and Lloyd-Williams(1996) estimated the Panzar and Rosse (1987) H-statistic of Japanese banks in 1986 and1988.

They found that the Japanese banking sector was in monopoly equilibrium in 1986,

whereas in 1988 it was in monopolistic competition equilibrium.1

Following Clark andDavies (1982), Alley (1993) estimated a degree of collusion in the Japanese banking sector
to find a high degree of collusion in 1986 and 1987.

Souma and Tsutsui (2000) applied an

approach similar to that of this paper to the Japanese life insurance industry from 1986 to
1997 to find that the degree of competition was low, but that the industry became morecompetitive after 1995 when the New Insurance Industry Law was promulgated.
Kamesaka and Tsutsui (2002) estimated Panzar-Rosse’s H-statistic for the Japanese
1

De Bandt and Davis (2000) argue that estimates of cross-section analysis often change sharply, so
1securities industry to find that the industry was in monopoly equilibrium in the 1980s andwas in monopolistic competition equilibrium in the 1990s.
We assess the degree of competition by direct estimation of the first order condition of
profit maximization.

This is essentially the method that is formalized by Bresnahan (1982)

and Lau (1982), and has been applied to many studies.2

For example, Shaffer (1989, 1993),

Shaffer and DiSalvo (1994), and Zardkoohi and Fraser (1998) applied this method to thebanking industries in the U.S. and Canada.

The method uses time-series data, andtherefore only elucidates a long-run average degree of competition.

In contrast, we use

panel data, which enable us to investigate short-run changes in the degree of competition.3
The next section reviews the history of the liberalization in the Japanese banking
sector. In section 3, we present a model for an estimation of the degree of competition.
In section 4, we explain our data and present the empirical results.

Section 5 is devoted tochecking the robustness of the results. In section 6, we investigate what elements had
critically affected the degree of competition.

2.

Section 7 provides the conclusion.

Liberalization of the Japanese Banking Sector

The liberalization of the Japanese banking sector was caused, at least partially, by theenormous issuance of government bonds from the early 1970s.

Initially all the newlythat the results may suffer from some problems.
2
See Bresnahan (1989, 1997) for the development of this method.
3Angelini and Cetoreli (1999) analyzed Italian banks with panel data for 1983-97 to estimate theLerner index.
2issued government bonds were bought by private banks, and one year later the Bank ofJapan purchased all of them.

Therefore, few bonds circulated outside the banking sector.

After 1970, however, the issuance of government bonds became so large that the Bank ofJapan could not purchase all of them.

The government bonds were issued at a high price,and therefore the private banks that bought them suffered enormous losses.

The banksdemanded that the Ministry of Finance (MOF) deregulate the selling of bonds to the market,
and the MOF began to relax the restriction in 1977.
government bonds rapidly emerged.

Consequently, a secondary market for

The emergence of a large free financial market was amajor event that significantly affected the Japanese banking sector in that the market
enabled the securities companies to offer a mutual fund that could compete with deposits.4
This situation might have promoted some dis-intermediation.

A greater degree of

disintermediation did not occur because the issuance amount of the mutual fund wasregulated so that securities companies were prevented from meeting the potential demandsuch funds.

Nevertheless, it is probable that the emergence of the secondary government

bond market applied greater competitive pressure on the Japanese banking sector.5
The next important liberalization was that of deposit interest rates. Jumbo timedeposits and Money Market Certificates were introduced in 1985. The interest rates of theformer were not regulated, and those of the latter were tied to market interest rates.

4

The medium-term government bond fund was the main brand of mutual fund.
The threat of dis-intermediation was also created by the revision of the Foreign Exchange andForeign Trade Control Act in 1980, which facilitated investors’ access to foreign markets. Theinternationalization of the financial markets was promoted over 1980–85.
35However, with respect to these deposits, the minimum amount that could be deposited wasrestricted. For example, in 1985, each jumbo time deposit account was required to hold anamount of more than one billion yen.

This restriction was gradually relaxed up until 1992.

In 1994, all the deposit interest rates were deregulated.

However, interest rates fell rapidly

from 1991 to 1996, suggesting that banks might not have been under deposit interest ratecompetition.
On the other hand, loan interest rates seem to have moved more freely since 1973.
Figure 1, we show loan interest rates over the period 1956 to 2001.
apparently began to change more sharply from 1973.

In

Loan interest rates

Table 1 shows that the coefficient ofvariation of loan interest rates jumped from 0.055 in the high growth period (1961–1972) to
0.20 in the low growth period (1973–1983). The correlation coefficient between loaninterest rates and the call rate increased from 0.80 to 0.91 in the same period, while thecoefficient of variation of the call market did not change substantially during this period.
These facts suggest that the market environment for Japanese banks drastically changed in
the late 1970s, which may have affected competition among them.
In 1993, regulation regarding the segregation of business lines in financial industries
was relaxed.

Banks and securities companies were allowed to enter each other’sbusinesses by establishing subsidiaries.

Although banks established many securities

company subsidiaries based on this deregulation, no securities companies established banksubsidiaries.

This is probably because banks had stronger business power than securities
4companies in Japan.

Therefore, this deregulation did not have a significant effect on thebanking sector, contrary to the case for the securities industry.

3.

Model

We assume that a bank i raises funds d i from depositors and invests it to loans qi andgovernment bonds bi .

Thus, the profit of the bank i at time t is:

π i ,t = Pt (Qt ) qi ,t + rib,t bi ,t − ri d,t d i ,t − Ci ,t ( qi ,t , d i ,t ) ,(1)nwhere Pt (Qt ) is the inverse demand function for loans, Qt ≡ ∑ qi ,t , n is the
i =1

number of banks, rib,t is the yields for bonds, ri d,t is the deposit interest rate, andCi ,t (qi ,t , d i ,t ) stands for the operating cost function. The bank’s problem is:Max{ b i ,t , q i ,t , d i ,t }π i , t s .t . b i , t + q i , t = d i , t .

(2)

The first order conditions of the profit maximization are:Pt (1 −1ηt

θ i ,t ) = rib,t +∂ C i ,t∂q i , t

,

(3)

and

rib,t − rid,t −where ηt ≡ −∂Ci ,t∂di ,t

= 0,

(4)

q
Pt ∂Qtis the market demand elasticity for loans, Si ,t ≡ i ,t is theQt ∂PtQt

market share of bank i in loans, and θ i ,t =5∂Qt
S i ,t .
∂qi ,tNote that θ i ,t = 0 for perfectcompetition, whereas θ i ,t = S i ,t for Cournot competition. From (3) and (4), we obtain:6

Pt (1 −1ηt

θ i ,t ) = ri d,t +∂ C i ,t
∂ d i ,t+

∂ C i ,t∂q i ,t.

(5)

On the other hand, if the banks are maximizing their joint profits, represented asnPt (Qt )Qt + ∑ (rib,t bi ,t − ri d,t d i ,t − C i ,t (qi ,t , d i ,t )) , the first order condition can be expressed as
i =1equation (5) with θ i ,t = 1 .

Furthermore, Si ,t < θ i ,t < 1 represents an array of repeatedgame equilibria whose one-shot game is Cournot oligopoly.
Rearranging (5) and defining Ri ,t ≡ Pt qi ,t to represent the revenue of bank i generated
by loans, we obtain the following equation:

Ri ,t =∂Ci ,t∂Ci ,tθt+ qi ,t.
Ri ,t + ri d,t qi ,t + qi ,tηt∂d i ,t∂qi ,t(6)

θ t represents the degree of competition. Note that we have dropped out the subscript ifor θ in order to capture the industry average degree of competition.7
Because the marginal cost is not observable, we assume the translog cost function:111
lnCi,t = a0 + a1ln qi,t + a2 (ln qi,t )2 + a3 ln di,t + a4 (ln di,t )2 + a5 ln wi,t + a6 (ln wi,t )22
2
2+ a7 (ln qi,t )(ln wi,t ) + a8 (ln qi,t )(ln di,t ) + a9 (ln di,t )(ln wi,t ) + εiC,t ,(7)

where wi ,t stands for the wage rate of bank i , ε iC,t is an error term, and variables
with upper bars are the deviations from their means. Given equation (7), our regression

6

Equation (3) can be estimated instead of equation (5). Estimation of equation (3), however, did
not generate reasonable results. The reason for the poor results may be that, in the estimation, we
should use as ri b,t the data for securities including stocks, instead of the data for government bonds,

because the revenues from government bonds are not reported. In Japan, however, considerable
parts of stocks are held as the mutual holding, which is not determined based on the short-run
incentives. In addition, although bond yields should include capital gains and losses, they are notincorporated in the reported data of r b . Therefore, we present the results based on equation (5).
7
See Bresnahan (1989) for this point.
6equation is specified as:

Ri ,t =

where ε i ,tSθt
R + r d q + Ci ,t ( a1 + a 2 ln qi ,t + a7 ln wi ,t + a8 ln d i ,t )
ηt i ,t i ,t i ,tq
+ Ci ,t i ,t ( a3 + a4 ln d i ,t + a8 ln q i ,t + a9 ln wi ,t ) + ε iS,t ,d i ,t(8)

is the error term.

We identify θ t from ηt using information from the loan demand function. The
following inverse loan demand function is assumed:8
ln Pi ,t = b0 −

1

ηt

(+)

ln Q t + b2 ln IIPt + b3 ln ASLi ,t + b4 ln SMSFi ,t + b5 ln OPLi ,t + ε iD,t ,
(+)

( −)

(+)

( −)

(9)

where IIPt is the index of industrial production, ASLi ,t the average size of loansof bank i , SMSFi ,t the ratio of the amount of loans to small- and medium-sized firms tototal loans, OPLi ,t the ratio of the amount of loans to operation funds to the total loans, andε i,Dt is a disturbance term.9 Simultaneous estimation of (7), (8), and (9) gives the estimatesof θ t and ηt .10
Expected signs are shown under the coefficients.

The expected sign of b3 isnegative because increasing the loan size lowers average operation costs, resulting in lower

8Here, we assume that loans are heterogeneous, so that their interest rates differ based on their size,
riskiness, and maturity. This assumption contradicts the derivation of equation (3), which assumes
homogeneity of loans. At the sacrifice of this theoretical disparity, we obtain an identification ofθ from. η .
9Total loans are divided into two parts: operation funds and equipment funds. While borrowers
use the former for the short-term operation of their businesses, the latter are used for capital
investment.
10
We conducted the analysis using GDP instead of IIP. The results were quite similar.
7interest rates.

The sign of b4 is expected to be negative, because loans to smaller firms

are often riskier. Since operation funds are generally advanced by shorter loans thanequipment funds, the sign of b5 depends on the shape of the yield curve of the term
structure of interest rates. As the yield curve is usually increasing, we expect that it isnegative.
A merit of the estimation method of this paper is that by using panel data, it gives theestimates of the degree of competition every year, which enables us to investigate theshort-term changes in the degree of competition. On the other hand, the method proposedby Bresnahan (1982) and Lau (1982) only reveals the average degree of competition for along period, because it uses aggregated time series data.

4. Results
4.1 Data
We select regional banks and city banks as samples. Long-term credit banks, trust banks,and smaller cooperative institutions such as shinkin banks are excluded from the analysis forthe following reasons. The former two types of banks are not ‘ordinary banks’ and theiroperations are substantially different from those of city and regional banks. The smaller
cooperative institutions generally operate in small areas. Kano and Tsutsui (2003) foundthat loan markets of shinkin banks are segmented by prefecture, implying that they compete
within each prefecture. Other cooperative institutions, credit cooperatives and agricultural
8

and forestry cooperatives, are much smaller, so they probably compete within more limitedareas. Therefore, the analysis of these institutions should require a somewhat different
methodology than that applied in this paper, which should be interesting as a future study.
Regional banks, together with city banks, constitute ‘ordinary banks’.

Legally,regional banks are not distinguished from city banks, but they form their own businessorganizations.11

They differ from city banks in that they are much smaller and basicallyoperate in restricted areas. However, Kano and Tsutsui (2003) found that the loan markets
of regional banks are not segmented by prefecture. In 1996, their share of the loan market
in Japan was 33.1%.12
City banks have nation-wide branch networks and operate diversified businesses,
including international bank business, while regional banks tend to be specialized in
traditional deposit-loan business. In 1996, city banks’ share of the loan market in Japanwas 49.6%. Thus, by analyzing regional and city banks, we cover 82.7% of the total
amount of loans of Japanese banks. We estimate the model with separate samples because
city and regional banks have different production functions and their markets are somewhat
segregated.
We used panel data of city banks and regional banks for the estimation. All the data
were extracted from Nikkei NEEDS Company (Bank) Data File, except for those of IIPt ,11

Regional banks consist of regional banks and second regional banks. The latter were converted
from mutual banks during 1989–92, and had previously been specialized financial institutions forsmall- and medium-sized firms.
12
The total loan outstanding in Japan is defined as the sum of city banks, long-term credit banks,
9which were extracted from the Nikkei NEEDS Macro Data File. The list of the data used
for each variable is:

Ri ,t : loan interest revenue for bank i at time t ,qi ,t : outstanding balances of loan for bank i at time t ,Qt : sum of qi ,t over city banks or regional banks at time t ,d i ,t : outstanding balances of deposit for bank i at time t ,C i ,t : operating costs for bank i at time t ,ri d,t = (interest paid for deposit) / (total amounts of deposit) for bank i at time t ,wi ,t = (personnel expenses + welfare expenses) / (the number of employees) for bank i attime t ,Pi ,t ≡

Ri ,t: loan interest rates for bank i at time t ,qi ,tASLi ,t = qi ,t / (the number of loans for bank i at time t ),SMSFi ,t ≡ (the amount of loans to small- and medium-sized firms) / qi ,t ,OPLi ,t ≡ (the amount of loans for operation funds) / (the amount of loans for operation
funds + that for equipment funds) for bank i at time t ,IIPt : index of industrial production at time t .
The estimation period is from fiscal 1974 to fiscal 2000.

After excluding

observations that lack some data, the number of observations each year is 9–13 for city
banks and 116–131 for regional banks. The total number of observations is 318 for city

trust banks, regional banks, shinkin banks, and credit cooperatives.
10

banks and 3441 for regional banks.13

Until 1980, banks reported financial statements twicea year, in September and March, so we constructed annual data of flow variables as the sumof the semi-annual data. Data at the end of each fiscal year, i.e., March, are used for thestock variables.
Descriptive statistics for these variables are shown in Table 2. The results reveal thatcity banks are much larger than regional banks. For example, city banks have outstandingloans and deposits that are 15 times larger, on average, than regional banks. City banks
earn 15 times as much as regional banks and pay 1.3 times the salary to each employee, on
average. Regional banks tend to lend smaller amounts to each borrower, and are more
likely to lend to small- and medium-sized firms.
We use year dummy variables to estimate θ t in equation (8). To estimate

equations (8) and (9), we use time dummy variables for every two years.14

1

ηt

in

We cannot use

time dummy variables for each year because they are linearly dependent with IIPt .
Equations (7), (8), and (9) are simultaneously estimated by multivariate regression(MVR) and three-stage least squares (3SLS). 15

In 3SLS we use rank variables as

instrumental variables for the terms including qi ,t , d i ,t , Ri ,t , ri d,t , Pi ,t , and C i ,t , the13

For 1999 and/or 2000, there are some observations that lack data of ASL and/or OPL. Deletionof them results in only two observations for city banks in 2000. Therefore, for both city andregional banks, we complement these data by copying the latest available data to include theseobservations in the sample. To check the possible bias due to this expediential method, we conduct
the estimation by deleting the two explanatory variables from the demand function. The estimation
results are essentially unchanged.
14
We use a one-year dummy for 1974.
15
Since equation (7) is not necessary for identifying θ from η , we estimated only equations (8)and (9) jointly. The results were generally unreasonable, suggesting that equation (7) plays an
11exogenous variables, wi ,t , ASLi ,t , SMSFi ,t , OPLi ,t , and Qt , and year dummies.16

4.2 Estimation results of city banks
Results of the simultaneous estimation of equations (7), (8), and (9) for city banks arepresented in Table 3. In the left columns, the estimates by MVR are shown. As for thecost function, four coefficients out of ten are significant at a 5% level of significance. As
for the demand function, the coefficient of IIPt is significantly positive and that of lnQt is
significantly negative, confirming that the demand function (9) is identified from the supply
relation (8). The estimates of1ηttook on a value of around 0.7 and were significant for

all the years. However, the estimate of b4 unexpectedly took on a negative value. Thismay be interpreted as meaning that the small- and medium-sized firms that could obtainloans from city banks were promising firms and not riskier than large firms.
In the right columns of Table 3, the estimates by 3SLS are shown. They are similar
to those by MVR, but the results of the cost function are not convincing: the coefficient of
lnd is non-positive, casting some doubt on the results.
The estimates of θ t by MVR are depicted in Figure 2 together with their 95%
confidence interval. The value of 1/n is also depicted as a proxy of Cournot oligopoly.
The results from 3SLS are quite similar so they are not presented here. For the first two

important role in identifying the marginal cost part in equation (8).
16
The rank variable of Xi is the variable whose value is the order of Xi ranked in ascending order.
(See Maddala, 1977, pp. 297-298.)
12years, 1974 and 1975, the results reject θ = n , θ = 1 , and θ = 0 , at a 5% significance level.

θ t generally had a downward trend, with a spike in 1981, until 1984. It stayed aroundzero for 1984–1989, and rose in the first half of the 1990s, rejecting perfect competition forthese years. In the middle of the 1990s, θ t again took on a value around zero, while itrose again in the final few years. In summary, competition improved until 1984, and thecompetitive environment obtained until 1997. Cournot oligopoly was not rejected for mostof the period. Perfect competition was not rejected for about half of the period.

4.3 Estimation results of regional banks
Results of simultaneous estimation of equations (7), (8), and (9) for regional banks arepresented in Table 4.

The results by MVR are shown in the left columns.

All theestimates of the cost function are significant except for that of the quadratic term of deposits.
A problem is that the coefficients of ln w (and (ln w) 2 ) are negative. We suspected thatthe wage rate might be an endogenous variable and conducted 3SLS estimation using a rank
variable of wi ,t as an instrumental variable. However, the signs of the coefficients did notchange.
The estimates of the demand function significantly satisfy the expected signs except
for that of operation funds. The estimates ofsignificant for all the years.

1ηttook on a value around 0.43 and were

Fits are good for all the equations: the coefficient ofdetermination exceeds 0.93.
13

Figure 3 shows the MVR estimates of θ t together with their 95% confidence interval.
During the entire sample period, θ = n , θ = 1 , and θ = 0 were all rejected. This implies
that the degree of competition was less severe than Cournot oligopoly, although it was more
severe than joint profit maximization.

θ t declined from 1974 to 1987, and the decline was

especially rapid in the 1970s, implying that competition improved during this period.

θtthen rose to around 0.35 and did not change from 1988 to 1998. It rose again markedly
after 1998.
In summary, the degree of competition improved from the 1970s to 1987, and was stable
during the late 1980s and the first half of the 1990s. Competition has recently become
laxer.
The 3SLS estimates shown in the right columns of Table 4 are not remarkably
different from those generated by MVR. However, the 3SLS estimates of θ t are smaller
than the MVR estimates in the early period.
Comparing Figure 3 with Figure 2, we find that competition among city banks has
been stronger than among regional banks. The result is plausible because the businessareas of regional banks are limited, even though they overlap, so that they may possessmonopolistic power in their areas.
The results of the basic estimations are summarized as follows: First, competition in
the Japanese banking sector had improved in the 1970s and in the first half of the 1980s.
Second, the degree of competition is higher for city banks than for regional banks. Third,
14the loan market for city banks has been perfectly competitive during the 1980s and the mid
1990s. Fourth, the loan market for regional banks has never been perfectly competitive ora Cournot oligopoly, while joint profit maximization is also rejected. Fifth, competition
became laxer again after 1998.

5.

Robustness Checks of the Basic Results

In this section, we provide a check on the robustness of the results in the previous section.

5.1 Cost of capital
Although the model in this paper indicates that the deposit interest rate is the relevant
financial cost of the loan, this outcome depends on the assumption that the bank manipulates
only the amount of deposit. If the bank manipulates all the liability side including capital,
the deposit interest rate ri d,t in equation (7) should be replaced with the cost of capital r ,which is defined as:
debt
interest paid for debt
capitalcurrent profit+,
debt + capital amount of debt
debt + capital
capital
where debt consists of deposits, certificate of deposits, call money, borrowing fromother financial institutions, including the Bank of Japan, etc.
The estimates of θ t using r are shown in Figure 4 (city banks) and Figure 5(regional banks). Figure 4 is quite similar to Figure 2 except for the period after 1992.

θ t was significantly negative in the middle of the 1990s and in 1999. Figure 5 shows15

more variability after 1995 than does Figure 3. Together with the fact that the current
profits of many banks were negative in these periods, these results may imply that it isinappropriate to use the cost of capital as calculated above. However, the conclusions in
the previous section are confirmed with this analysis.

5.2 Bank reserves
The model in this paper disregards the existence of reserves for deposits. If we introducebank reserve R and assume that it is a constant ratio of deposit, i.e., R = βD , equation (8)becomes:r qθRi ,t = t Ri ,t + i ,t i ,t + Ci ,t ( a1 + 2a 2 ln qi ,t + a 7 ln w i ,t + a8 ln d i ,t )
ηt
1− β
d

+ Ci ,tqi ,t(1 − β )d i ,t(8)’

( a3 + 2a 4 ln d i ,t + a8 ln q i ,t + a 9 ln w i ,t ) + ε .
Si ,tEstimation of equations (7), (8)’, and (9), θ t for regional banks is shown in Figure 6.
Figure 6 resembles Figure 3, but close inspection reveals that the curve shifts downwards,
so that θ t took on a negative value in 1987. Estimates of θ t for city banks are notreasonable and not presented: θ t is negative for most of the period, and although it
decreases until 1984, it rises after 1990 and takes almost the same value in the 1990’s as in
the 1970s.
We construct the data of β as money plus reserves divided by deposits. The meanof β is about 0.1, which is much larger than the required reserve ratio. This implies that16

banks held large excess reserves. Our assumption of a constant reserve ratio is therefore
irrelevant, which may be the reason we could not obtain reasonable results for city banks.

5.3 Adding a constant term
One might question why equation (8) has no constant term.

Although the theory does notallow for a constant term, it is not unusual to include it in empirical analysis. The fact that
there is no constant term may make θ critically dependent on the level of interest rates inequation (8).
The estimation with the addition of a constant term to equation (8) reveals that thisspeculation is wrong. The basic results, presented in Tables 3 and 4, and in Figures 2 and 3,
are confirmed by the estimation with a constant term.

6.

What Affects the Degree of Competition?

In this section, we investigate what factors relate to the degree of competition.

In

particular, clarifying which element of financial liberalization promoted competition in loan
markets is an interesting agenda. As described in section 2, three important events arerecognized in the financial liberalization in Japan, i.e., the emergence of a huge government
bond market, the deregulation of deposit interest rates, and the deregulation of thesegregation of business lines in the financial sector. We regress the degree of competition
obtained in section 4 on variables representing these elements in order to find their17

influences. As a proxy for the first element, we employ the trading amount of government
bonds (BTRADE).

We adopt the ratio of time deposits with unregulated interest rates to thetotal time deposits (FREEDEP) as the data of the second element.
calculated separately for city banks and regional banks.17

The variable is

As for the third element, we use

a year dummy (SEGDUM), which takes the value of zero until 1993, and unity thereafter.
The degree of competition may be affected by various other factors, which should be
added as explanatory variables in the regression equation. The first candidate for thesefactors is the effect of the business cycle. We use the composite index (CIX) to represent
this factor. The real GDP growth rate (GDP) and inflation rates (INFL) are also examinedas alternatives to CIX. The second element is the market structure of the loan market.
The Structure-Conduct-Performance hypothesis predicts that the less concentrated themarket, the more competitive it becomes. The efficiency structure hypothesis proposed by
Demsetz (1973), however, predicts the opposite relationship.

We use the Herfindahl index

of city banks and regional banks (HI) as a measure of market concentration.
In view of these arguments, our regression equation isθ k ,t = α 0 + α1 BTRADE t + α 2 FREEDEPk ,t + α 3 SEGDUM t+ α 4 CIX t (or GDPt and INFLt ) + α 5 HI k ,t + α 6 REGDUM k ,(10)where k = city or regional banks, and REGDUM is the dummy variable
representing regional banks.

17

For regional banks, only the data for the first regional banks are used, since the second regionalbanks were undergoing conversion from mutual banks, and the data was not available during
1989–1991.
18

The estimation results are shown in Table 5. The benchmark case using CIX isshown in the left columns. The coefficient of BTRADE is significantly negative at a 5%
level, implying that the emergence of a government bond market promoted competition inloan markets. In contrast, the coefficients of FREEDEP and SEGDUM are not significant,suggesting that deregulation of deposit interest rates and the segregation of business lines
did not promote competition in the loan markets.
The business condition, CIX, did not systematically affect the degree of competition.
The coefficient of the Herfindahl index is not significant either.

The coefficient of

REGDUM is significantly positive, implying that competition among regional banks is laxer

than that among city banks.
In the right columns, the results of the case where GDP and INFL substitute for CIX
are shown. In this case, the coefficient of INFL is significantly positive at a 1% level,
while that of GDP is insignificant. Thus, competition becomes laxer when the inflation
rate is higher. The other results are the same as for the benchmark case.
In summary, the analysis of this section suggests the following. First, the emergenceof the government bond markets promoted competition in loan markets, but the deregulationof deposit interest rates and the segregation of financial business lines did not have an
impact on competition.

Second, competition among regional banks is laxer than thatamong city banks. Third, the business conditions did not systematically affect the degreeof competition except for the inflation rate. Nor did the market structure.
19

7.

Conclusions

In this paper, we investigated whether competition in the Japanese banking sector has
improved in the last quarter of the 20th century. By estimating the first order condition of
profit maximization, together with the cost function and the inverse demand function, we
estimated the degree of competition of city and regional banks. The results reveal thatcompetition had improved during the 1970s and in the first half of the 1980s.

This

corresponds to the period where the secondary market for government bonds emerged. In
support of this view, the follow-up analysis of the factors affecting the degree ofcompetition demonstrates that the trading volume of the government bonds significantly
affected the degree of competition.
The results also reveal that Cournot oligopoly cannot be rejected at a 5% significancelevel for city banks for most of the period, while the results do reject it for regional banks
over the entire period. This suggests that competition among city banks was stronger thanthat among regional banks, which is consistent with our intuition.
The results are richer and more extensive than those obtained in earlier studies on thecompetitiveness of the Japanese banking sector, such as Molyneux, Thornton, and
Lloyd-Williams (1996), and Alley (1993), which analyzed only a short period of time using
different methods.
Souma and Tsutsui (2000) found that although competition in the Japanese life
20

insurance industry became stronger after 1995, it was still close to perfect collusion in 1997.
Kamesaka and Tsutsui (2002) found that the Japanese securities industry was in monopoly
equilibrium in the 1980s and was in monopolistic competition equilibrium in the 1990s. Inview of these results, the banking industry seems more competitive than the securities andlife insurance industries in Japan.
Although most of the results were convincing, there is one question left unanswered:
why did the degree of competition θ t rise again after 1998? One possible interpretationis that Japanese banks suffered from huge non-performing loans in the 1990s, and that they
have been faced with financial crisis since 1997.18

Consequently, they do not have enoughstrength to compete with each other, and want to avoid severe competition, which often
leads to a cut in their profits.

The clarification of this speculation regarding laxer

competition in the late 1990s should constitute an interesting study in the future.

18

Three large banks failed in 1997 and 1998.
21

References

Alley, W.A., 1993. Collusion versus efficiency in the Japanese regional banking industry.
Economic Studies Quarterly 44, 206--215.
Angelini, P., Cetorelli, N., 1999. Bank competition and regulatory reform: the case of the
Italian banking industry. Working Paper Series, Research Department, WP 99-32.
Bresnahan, T.F., 1982. The oligopoly solution concept is identified. Economics Letters 10,
87--92.
Bresnahan, T.F., 1989. Empirical studies of industries with market power, in: Schmalensee,
R., Willig, R. (Eds.), Handbook of Industrial Organization. North-Holland, Amsterdam.
Bresnahan, T.F., 1997. Testing and measurement in competition models, in: Kreps, D.M.,
Wallis, K.F. (Eds.), Advances in Economics and Econometrics: Theory and Applications.
Seventh World Congress, Vol. III. Cambridge University Press.
Clarke, R., Davies, S.W., 1982. Market structure and price-cost margins. Economica 49,
277--287.
De Bandt, O., Davis, E.P., 2000. Competition, contestability and market structure inEuropean banking sectors on the eve of EMU. Journal of Banking and Finance 24,1045--1066.
Demsetz, H., 1973. Industry structure, market rivalry, and public policy. Journal of Law andEconomics 16 (1), 1--9.
Kamesaka, A., Tsutsui, Y., 2002. Competition in the Japanese securities industry. Discussion
Paper 02-10, Osaka University.
Kano, M., Tsutsui, Y., 2003. Geographical segmentation in Japanese bank loan markets.
Regional Science and Urban Economics 33 (2), 157--174.
Lau, L.J., 1982. On identifying the degree of competitiveness from industry price and output
data. Economics Letters 10, 93--99.
Maddala, G. S., 1977. Econometrics. McGraw-Hill Kogakusha Ltd., Tokyo.
22

Molyneux, J., Thornton, P., Lloyd-Williams, D.M., 1996. Competition and market
contestability in Japanese commercial banking. Journal of Economics and Business 48,
33--45.
Panzar, J. C., Rosse, J. N., 1987. Testing for ‘Monopoly’ equilibrium. Journal of Industrial
Economics 35, 443--456.
Shaffer, S., 1989. Competition in the U.S. banking industry. Economics Letters 29,
321--323.
Shaffer, S., 1993. A Test of competition in Canadian banking. Journal of Money, Credit,and Banking 25, 49--61.
Shaffer, S., DiSalvo, J., 1994. Conduct in a banking duopoly. Journal of Banking and
Finance 18, 1063--1082.
Souma, T., Tsutsui, Y., 2000. Recent competition in Japanese life insurance industry.
Discussion Paper 00-19, Osaka University.
Zardkoohi, A., Fraser, D.R., 1998. Geographical deregulation and competition in U.S.
banking markets. Financial Review 33, 85--98.

23

Table 1.

Variation and Correlation of Loan Interest Rates and the Call Rate

High growth period
Variation
Short term loan
Interest rate

Correlation

0.055

Low growth period
Variation

0.20
0.80

Call rate

Correlation

0.21

0.91
0.25

Note: The coefficient of variation is defined as the standard deviation over the mean.

The highgrowth period is from December 1961 to December 1972, for which the number of observations is
133.

The low growth period is from January 1973 to March 1983, for which the number ofobservations is 123.

24

Table 2.

Variables
R
P
C
q
d

Mean
7562620.059
20590814984300
17077500
d
0.041
r
8.404
w
111.432
ASL
0.478
SMSF
0.740
OPL
Number of Observations

Variables
R
P
C
q
d

Mean
47311
0.058
20636
9623801229090
d
0.031
r
6.278
w
12.079
ASL
0.796
SMSF
0.684
OPL
Number of Observations

Descriptive Statistics
City banks
Standard
Deviation
5421980.020
11270111363200
11938000
0.017
2.738
354.046
0.133
0.086
318
Regional banks
Standard
Deviation
49299
0.019
17365
1070126
1340053
0.015
1.951
5.389
0.118
0.082
3441

Note: Refer to the main text for a definition of the variables.
in one million yen. P, r d , SMSF, and OPL are ratios.

25

Minimum
1178030.020
38034
14539311734864
0.001
3.135
8.224
0.040
0.190

Maximum
29412560.105
52532343751900
46946000
0.101
15.1262185.624
0.785
0.945

Minimum
2209
0.005
817
25922
31388
0.002
1.167
3.384
0.156
0.203

Maximum
632901
0.094
1280859040438
98142170.060
11.89951.7481.894
0.901

R, C, q, d, w, and ASL are measured

Table 3.

Results of simultaneous estimation of eqs. (7), (8), and (9):city banks
Parameter

θ 1974
θ 1975
θ 1976
θ 1977
θ 1978
θ 1979
θ 1980θ 1981
θ 1982
θ 1983
θ 1984
θ 1985
θ 1986
θ 1987
θ 1988
θ 1989
θ 1990θ 1991
θ 1992
θ 1993
θ 1994
θ 1995
θ 1996
θ 1997
θ 1998
θ 1999θ 2000
a0
a1
a2
a3
a4
a5
a6
a7
a8
a9
b0
b2
b3
b4
b5
1/η 1974
1/η 1975-76
1/η 1977-78
1/η 1979-80
1/η 1981-82
1/η 1983-84
1/η 1985-86
1/η 1987-88
1/η 1989-90
1/η 1991-92
1/η 1993-94
1/η 1995-96
1/η 1997-98
1/η 1999-00
R2 for (7)R2 for (8)R2 for (9)MVR
Estimate
0.353
0.348
0.228
0.208
0.097
0.125
0.227
0.245
0.047
0.094
-0.0250.041
0.020-0.036-0.002-0.028
0.042
0.108
0.124
0.184
0.092
-0.005-0.024-0.007
0.151
0.262
0.185
12.0900.678-0.6770.110-1.928-0.459-0.0990.412
1.308-0.540
5.917
1.053
0.000
-0.291
0.070
0.707
0.710
0.723
0.709
0.700
0.704
0.709
0.711
0.691
0.691
0.712
0.729
0.736
0.747
0.961
0.843
0.949

P-value[.001][.001][.007][.013][.199][.047][.001][.000][.217][.026][.446][.208][.547][.258][.941][.188][.012][.000][.000][.000][.006][.862][.475][.822][.002][.000][.001][.000][.000][.326][.166][.007][.000][.611][.148][.059][.061][.014][.000][.780][.000][.470][.000][.000][.000][.000][.000][.000][.000][.000][.000][.000][.000][.000][.000][.000]

26

3SLS
Estimate
P-value
0.297[.006]
0.261[.010]
0.178[.046]
0.115[.176]
0.016[.850]
0.085[.210]
0.157[.011]
0.222[.003]
0.001[.985]
0.072[.132]-0.045[.262]
0.016[.654]
0.002[.951]-0.054[.182]-0.026[.458]-0.027[.372]
0.023[.223]
0.082[.003]
0.101[.004]
0.133[.003]
0.057[.117]-0.056[.167]-0.050[.296]-0.040[.407]
0.136[.044]
0.215[.009]
0.152[.024]
12.094[.000]
0.947[.000]
1.201[.469]-0.113[.629]
0.311[.860]-0.545[.000]-0.171[.741]-0.893[.255]-0.697[.681]
0.687[.396]
5.995[.032]
1.063[.000]
0.000[.633]-0.294[.000]
0.109[.262]
0.715[.000]
0.718[.000]
0.731[.000]
0.717[.000]
0.708[.000]
0.712[.000]
0.717[.000]
0.719[.000]
0.698[.000]
0.699[.000]
0.719[.000]
0.737[.000]
0.743[.000]
0.755[.000]
0.951
0.852
0.950

Table 4.

Results of simultaneous estimation of eqs. (7), (8), and (9):regional banks
Parameter

θ 1974
θ 1975
θ 1976
θ 1977
θ 1978
θ 1979
θ 1980θ 1981
θ 1982
θ 1983
θ 1984
θ 1985
θ 1986
θ 1987
θ 1988
θ 1989
θ 1990θ 1991
θ 1992
θ 1993
θ 1994
θ 1995
θ 1996
θ 1997
θ 1998
θ 1999θ 2000
a0
a1
a2
a3
a4
a5
a6
a7
a8
a9
b0
b2
b3
b4
b5
1/η 1974
1/η 1975-76
1/η 1977-78
1/η 1979-80
1/η 1981-82
1/η 1983-84
1/η 1985-86
1/η 1987-88
1/η 1989-90
1/η 1991-92
1/η 1993-94
1/η 1995-96
1/η 1997-98
1/η 1999-00
R2 for (7)R2 for (8)R2 for (9)MVR
Estimate
0.624
0.511
0.473
0.365
0.407
0.487
0.389
0.264
0.334
0.323
0.239
0.242
0.259
0.180
0.337
0.365
0.282
0.337
0.338
0.274
0.392
0.285
0.391
0.381
0.563
0.693
0.720
9.637
0.178
0.316
0.698
0.146
-0.263-0.3930.226-0.251-0.1803.331
0.360-0.0050.353-0.019
0.428
0.427
0.436
0.429
0.424
0.425
0.427
0.433
0.421
0.417
0.434
0.453
0.461
0.466
0.980
0.964
0.939

P-value[.000][.000][.000][.000][.000][.000][.000][.000][.000][.000][.000][.000][.000][.000][.000][.000][.000][.000][.000][.000][.000][.000][.000][.000][.000][.000][.000][.000][.000][.000][.000][.102][.000][.000][.000][.004][.007][.000][.000][.000][.000][.454][.000][.000][.000][.000][.000][.000][.000][.000][.000][.000][.000][.000][.000][.000]

27

3SLS
Estimate
P-value
0.380[.000]
0.274[.000]
0.226[.000]
0.137[.000]
0.165[.000]
0.261[.000]
0.247[.000]
0.135[.000]
0.200[.000]
0.193[.000]
0.142[.000]
0.160[.000]
0.187[.000]
0.148[.000]
0.307[.000]
0.335[.000]
0.283[.000]
0.314[.000]
0.304[.000]
0.246[.000]
0.376[.000]
0.316[.000]
0.413[.000]
0.417[.000]
0.555[.000]
0.698[.000]
0.722[.000]
9.635[.000]
0.575[.000]
0.837[.000]
0.288[.000]
0.807[.000]-0.239[.000]-0.140[.358]-0.672[.000]-0.809[.000]
0.525[.001]
3.448[.000]
0.356[.000]-0.006[.000]
0.318[.000]-0.071[.008]
0.430[.000]
0.428[.000]
0.437[.000]
0.431[.000]
0.425[.000]
0.427[.000]
0.428[.000]
0.435[.000]
0.423[.000]
0.420[.000]
0.437[.000]
0.455[.000]
0.463[.000]
0.468[.000]
0.975
0.966
0.939

Table 5.

Variableconstant
BTRADE
FREEDEPSEGDUM
CIX
GDP
INFL
HI
REGDUM
Adjusted R2

Effects on the Degree of Competition
Basic equation
Coefficient
P-value
0.426[.015]-9-1.87x10[.021]
0.056[.441]-0.011[.861]-0.003[.108]

0.161
0.294
0.682

[.419][.000]

GDP & INFLATION
Coefficient
P-value
0.074[.184]-9-1.544 x10[.025]
0.012[.832]
0.063[.261]-0.379
1.294
0.167
0.293
0.724

[.637][.003][.369][.000]

Note: Equation (10),θ k ,t = α 0 + α1 BTRADEt + α 2 FREEDEPk ,t + α 3 SEGDUMt + α 4 CIX t (or GDPt and INFLt ) + α 5 HI k ,t + α 6 REGDUMk ,is run by Ordinary Least Squares to find the magnitude of the effects of the variables on the degreeof competition.

The number of observations is 54. For a definition of the explanatory variables,refer to the main text.

28

Note: Average contracted interest rates on loans of all banks are shown.

29

loan interest rate

200111

200012200001

199902

199803

199704

199605199506199407

199308

199209199110199011

198912198901198802198703198604

198505198406

198307

198208

198109198010197911

197812197801

197702

197603197504197405197306197207197108197009196910196811

196712

196701

196602

196503

196404

196305196206196107196008195909195810195711

195612

195601Figure 1. Loan Interest Rate
%

10

9

8

7

6

5

4

3

2

1

0

Figure 2. Estimates of θ t for city banks
0.6

0.5

0.4

0.3
estimates
95% upper
95% lower
1/n

0.2

0.1

0

-0.1

-0.2
1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000

Note: MVR estimates of θ t in Table 2 are shown with their 95% confidence interval.
to perfect competition.

30

Line of 1/n corresponds to Cournot oligopoly, and line at zero

Figure 3. Estimates of θ t for regional banks
1

0.9
0.8

0.7
0.6
estimates
95% upper
95% lower
1/n

0.5

0.4
0.3

0.2
0.1

0
1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000

Note: MVR estimates of θ t in Table 3 are shown with their 95% confidence interval.
perfect competition, and line at unity to joint profit maximization.

31

Line of 1/n corresponds to Cournot oligopoly, line at zero toFigure 4. Estimates of θ t for city banks, with r d in eq. (8) being the cost of capital
0.8

0.6

0.4

0.2
estimates
95% upper
95% lower
1/n

0

-0.2

-0.4

-0.6

-0.8
1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000

Note:

MVR estimates of θ t for city banks are shown with their 95% confidence interval.

2 and 3, the cost of capital is used for the data of r d in the estimation of this Figure.

32

While r d in eq. (8) is the deposit interest rate in Figures

Figure 5. Estimates of θ t for regional banks, with r d in eq. (8) being the cost of capital
1.2

1

0.8

estimates
95% upper
95% lower
1/n

0.6

0.4

0.2

0
1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000

Note:

MVR estimates of θ t of regional banks are shown with their 95% confidence interval.

Figures 2 and 3, the cost of capital is used for the data of r d in the estimation of this Figure.

33

While r d in eq. (8) is the deposit interest rate inFigure 6. Estimates of θ t for regional banks: bank reserves are considered in eq. (8)’
0.9
0.8
0.7
0.6
0.5
estimates
95% upper
95% lower
1/n

0.4
0.3
0.2
0.1
0
-0.1
-0.2
1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000

Note:

Results based on the simultaneous MVR estimation of eqs. (7), (8)’, and (9) are shown.

34