Geographical segmentation in Japanese bank loan markets

Regional Science and Urban Economics 33 (2003) 157–174www.elsevier.com/ locate/econbase

Geographical segmentation in Japanese bank loanqmarkets

a b ,*Masaji Kano , Yoshiro TsutsuiaFaculty of Business Administration and Information, Setsunan University, 17-8 Ikeda-Nakamachi,

Neyagawa City, Osaka 572-8508, JapanbGraduate School of Economics, Osaka University, 1-7, Machikaneyama, Toyonaka 560-0043,

Japan

Received 14 November 2000; received in revised form 21 January 2002; accepted 12 February 2002

Abstract

This paper examines whether Japanese bank loan markets are segmented geographically.Studying regional banks and shinkin banks, we examine whether the demand and supplyfactors of each prefecture have an effect on the interest rates of that prefecture. The resultssuggest that the markets for regional banks are not segmented, but those of shinkin banksare. We also find that interest rates of adjacent prefectures do not differ very much, whilethose between distant prefectures do. We suggest that relaxation of regulation on geographi-cal operations should promote competition and lower loan interest rates in rural Japan. 2002 Elsevier Science B.V. All rights reserved.

Keywords: Bank loan market; Segmentation; Japan; Market structure-performance hypothesis

JEL classification: R51; G21; L13

1. Introduction

This paper explores whether Japanese bank loan markets are segmented

qAn earlier version of this paper was presented Monetary Economics Workshop and Workshop onRegional Finance.

*Corresponding author. Tel.:181-6-6850-5223; fax:181-6-6850-5274.E-mail address: [email protected] (Y. Tsutsui).

0166-0462/02/$ – see front matter 2002 Elsevier Science B.V. All rights reserved.PI I : S0166-0462( 02 )00009-1

158 M. Kano, Y. Tsutsui / Regional Science and Urban Economics 33 (2003) 157–174

geographically. Segmentation of loan markets by prefecture may result in reducedcompetition and lower social welfare. Thus, the investigation is essential toelucidate the efficiency of the loan markets.

The investigation is also important because many cross-sectional analyses on theJapanese loan markets become invalid, if the markets are not segmented. Mori andTsutsui (1989) and Alley (1993) examined the market structure-performancehypothesis in Japan, assuming that the loan markets are segmented by prefecture.Tsutsui and Matsuura (1993) estimated the size of dead-weight loss of theJapanese loan markets based on the same assumption. Constructing an inter-regional macroeconomic model of Japanese economy, Fujita and Takahashi (1992)assume that the Japanese loan markets are segmented between advanced anddeveloping regions.

Segmentation of Japanese loan markets has often been accepted as a factwithout conducting statistical analysis (Tatsumi, 1984). Viewing a plot of the loaninterest rates by prefecture, some studies have argued that Japanese loan marketsare segmented (Horiuchi, 1988; Kanou, 1998). However, plotting the interest ratesdoes not offer adequate evidence. A statistical test of differences in rates is calledfor, which is done in this paper.

Osborne (1988) examined whether US loan markets were segregated into sixregions. Estimating demand elasticity of each region and examining the correlationof risk premiums between regions, he found that for larger loan classes US loanmarkets were integrated geographically except for the Southeastern region, butthey are segregated for the smallest loan-size class.

In Japan, private depository financial institutions are classified into sevencategories: city banks, long-term credit banks, trust banks, regional banks, shinkinbanks (formerly credit associations), credit cooperatives, and agricultural and

1forestry cooperatives. What is important in the context of this paper is that theinstitutions in the latter four categories have their operations restricted to a certainarea, while the former three have a nation-wide branch network. Regional banks,shinkin banks, credit cooperatives, and agricultural and forestry cooperativesusually operate within a single prefecture: on average, 81.1% of regional bankbranches locate within the same prefecture of their head office; 95.8% of shinkinbank branches operate in their home prefecture. This suggests that bank depositand loan markets may be geographically segmented in Japan.

One of the important features of the Japanese loan market is ‘the imbalance ofbank liquidity’ between city and local banks, whereby the city banks arepersistently borrowing and the other financial institutions are persistently lendingin the call market. (Suzuki, 1980, 1987). This implies that there is an imbalance infunding and lending opportunity between city and local banks, but the imbalanceis adjusted through call market. One might think that the adjustment through call

1See Japanese Bankers Association (2001) for explanation of these institutions.

M. Kano, Y. Tsutsui / Regional Science and Urban Economics 33 (2003) 157–174 159

market equalizes the loan interest rates between the city and rural areas, but this isnot true (see Appendix A). The loan interest rates may differ after the adjustmentthrough call market, if the loan markets are segmented. Therefore, the analysis ofthis paper on the segmentation still has its substance in spite of the operation of thecall market.

Even if the markets are segmented, the segmentation can not be perfect becausebank operations interact through the branch network of city banks in the largecities, and adjacent prefectures through the activities of the branches of the localinstitutions that have their head offices in the adjacent prefectures. In other words,bank markets partially overlap. Considering these facts, we test whether or not theloan interest rates differ among prefectures.

If loan markets are segmented by prefecture, the loan interest rate is determinedby the demand for and the supply of loans for each prefecture. Thus, a test ofsignificance of the demand and supply factors of each prefecture in determiningthe interest rates of that prefecture will be another way of testing the segmentationhypothesis.

The rest of the paper is organized as follows: in the next section, we test thedifference in loan interest rates across prefectures. In Section 3, we investigatewhether the demand and supply factors of each prefecture determine interest rates.In Section 4, we propose two hypotheses about the segmentation and examinethem. Section 5 concludes.

2. Test of the difference in the interest rates among prefectures

2.1. Regional banks and shinkin banks

We select regional banks and shinkin banks as samples. City banks, long-termcredit banks, and trust banks are excluded from the analysis because theiroperation is nation-wide and data on their operation in each prefecture are notavailable. Inclusion of credit cooperatives and agricultural and forestry coopera-tives may be interesting future work, but their exclusion may be justified becausethey are tiny institutions and their customers are tiny firms and farmers, most ofwhom are distinct from the customers of regional banks and shinkin banks.

Regional banks, together with city banks, form ‘ordinary banks’: legally, theyare not distinguished from city banks, but they form their own business

2organizations. They differ from city banks in that they are much smaller andbasically operate in a restricted area. In 1996, there were 129 regional banks

2Regional banks consist of regional banks and second regional banks. The latter were called mutualbanks until 1989 and were specialized institutions for small- and medium-sized firms.


3whose average outstanding loans is 1466 billion yen. On average, they have 98branches and hire 1901 employees. Their share of the loan market in Japan is

433.1%.Shinkin banks are a type of commercial bank specialized for small- and

medium-sized firms that are not stock companies but cooperative institutions. In1996, there were 410 shinkin banks whose average amount of outstanding loans is171 billion yen. On average, they have 21 branches and hire 372 employees. Theirshare of the Japanese loan market is 12.7%. According to the value of outstandingloans, the average size of a shinkin bank is about 1/8 of a regional bank.

We first examine whether the interest rates of regional banks and shinkin bankscan be regarded as samples drawn from the same population. In Fig. 1, we show afrequency polygon of the loan interest rates of regional banks and shinkin banks:those of the shinkin banks are clearly higher than regional banks. The means are3.01 and 3.70%, respectively, and thet-statistic of the tests of same means is 17.3,implying a rejection of the null of the same mean at a very low significance level.

Fig. 1. Frequency polygon of the interest rates of regional banks and Shinkin banks. Note: The samplesize is 410 (Shinkin Banks) and 128 (Regional Banks), respectively.

3In what follows, we exclude Wakashio Bank from the sample, which was established in September1996 to inherit the operations of bankrupted Taiheiyo Bank. Thus, our sample size is 128.

4Here, city banks, long-term credit banks, trust banks, regional banks, shinkin banks, and creditcooperatives are considered to form the Japanese loan markets.


Thus, we conclude that the regional banks’ and shinkin banks’ interest rates aredifferent.

In addition, from the time-series regression, Fujino (1987) finds that the regionalbanks adjust their interest rates responding to the excess demand for loans, but

5shinkin banks do not. This suggests that the regional banks and shinkin banksform different market structure. Therefore, in what follows, we analyze with thesetwo samples separately.

This result suggests that the markets of regional banks and shinkin banks aresegmented. It is generally accepted that the customers of regional banks andshinkin banks are somewhat differentiated, though some of them, of course, areoverlapped. One of the reason of the differentiation is the difference of the banksize, as mentioned above. According to the risk cost hypothesis by Baltensperger(1972a,b), the optimal loan size is proportional to the cubic root of the bank size,and the hypothesis is supported in Japanese banks (Hirota and Tsutsui, 1999).

2.2. Tests of the difference in interest rates across prefectures

The loan interest rate of banki, r , is defined to be the interest revenue of bankii

divided by the value of its outstanding loan at the end of March 1997 (the end ofthe fiscal year of 1996). Thus,

Ii]r ; (1)i li

whereI is the loan interest revenue andl is the value of outstanding loans of banki i

i. Then, we calculate the average loan interest rate of prefecturej, R , byj

O Iii[Pj]]R 5 , (2)j O lii[Pj

whereP is the partition of regional banks or shinkin banks in prefecturej. This isj

the weighted average of the interest rates of individual banks, weights being loanvolume.

We analyze whether the interest rates of 47 prefectures are the same or not bythe following two methods. The one is to regress the interest rates of banks ondummy variables representing each prefecture. The other method we employ is theBonferroni multiple comparisons (Miller, 1981). The multiple comparisons are astatistical method that generalizes the test of same means into the case of the

5The authors are grateful to a referee for informing them of this literature.


comparisons among many means. Since 47 prefectures exist, the number ofcomparisons between a pair of prefectures is C5 1081.47 2

2.3. Results of the difference in interest rates: regression analysis

Using the data on shinkin banks, we regress interest rates on the prefecturedummies with the median prefecture, Fukuoka, as the benchmark. TheF-testrejects the null hypothesis that all the dummy variables are zero at the 1%significance level. Thus, we conclude that loan interest rates differ acrossprefectures. The interest rates of 14 out of 46 prefectures are statistically different

6at the 1% significance level from that of Fukuoka prefecture.Conversely, when the sample consists of regional banks, theF-test takes on the

value of 0.71, so that the null hypothesis that interest rates are the same overprefectures is not rejected. No prefectures differ from the median prefecture,Ehime, at the 1% significance level. This result is reasonable because, on average,regional banks have 18.9% of their branches outside of their home prefecture, sotheir operational areas overlap and they compete each other. Meanwhile, this

7channel is quite weak for shinkin banks.

2.4. Results of the multiple comparisons

The results of the multiple comparisons for shinkin banks are shown in Fig. 2.In this figure, each cell shows the result of the test of the difference in interestrates for the prefectures in each row and column. Of the 1081 total combinations,185 are significant at the 1% level. Thus, 17% of the total pairs of prefecturesdiffer in their interest rates.

The figure reveals that Miyazaki, Kochi, and Aomori, whose interest rates arethe highest among all, are different from more prefectures than Aichi, which isfamous for its low interest rate. Although Aichi is often quoted as a typicalexample of the market segmentation (e.g. Tatsumi, 1984), the more persuasiveexamples are those of the higher interest rates prefectures. This observation maybe important in evaluating geographical segmentation as an oligopoly problem.

Multiple comparisons are not possible for the whole sample of regional banksbecause three prefectures have only one regional bank. Thus, we conduct theanalysis excluding Saitama, Yamanashi and Tottori. The results show that there are

6When the prefecture of the lowest interest rates, Aichi, is used as the benchmark, 35 out of 46prefectures are statistically different from it.

7Some may argue that we should adjust the interest rates with quality of loans. We adjust the interestrates with own capital ratio (equity / total asset), the composition of industries, bank size, average loansize (regional banks only), and ratio of small- and medium-sized firm loans to total loans (regionalbanks only). The results of the regression analysis are essentially unchanged.


Fig. 2. Results of Bonferroni multiple comparisons tests: unadjusted interest rates of Shinkin banks.Notes: (1) Each cell shows the result of a same means test of the interest rates for the prefectures ofeach row and column. (2) The prefectures are ordered geographically from north to south.

no combinations among the remaining 44 prefectures with different means of8interest rates.

We stratify the 47 prefectures into five groups according to loan interest ratesfor shinkin banks. Fig. 3 displays a map of Japan in which prefectures that havehigher interest rates are more heavily shaded. Note that the central area of Japan ischaracterized by lower interest rates.

8We conduct the multiple comparison tests with quality adjusted interest rates (see footnote 7). Ofthe 1081 possible combinations, 119 were different in the case of shinkin banks at the 5% significancelevel. As for regional banks, the same results as the unadjusted case are obtained.


Fig. 3. Interest rate map of Japan: unadjusted interest rates of Shinkin banks. Notes: (1) 47 prefecturesare stratified into 5 groups according to the level of the unadjusted interest rates. (2) Lighter colorssignify lower interest rates.

3. Are the loan markets segmented?

3.1. Demand and supply factors for loans

If the loan market is segmented by prefecture, the loan interest rate,R , isj

determined by the demand for and the supply of loans of each prefecture. In thissection, we derive a reduced form of loan interest rates assuming that the marketsare segmented by prefecture, and construct our test of the segmentation hypothesis.

DLet us assume that the loan demand of prefecturej, L , is described asj


DL 5a 2a R 1a Y , a ,a .0 (3)j 0 1 j 2 j 1 2

9whereY is the income of the prefecturej.j

We derive the loan supply function from the profit maximization problem of abank in an oligopolistic market. Let us assume that there areI banks in aprefecture. Banki raises its funds from deposits,d , and lends it as loans,l , andi i

call loans,c . Thus, the bank’s budget constraint isi

c 1 l 5 d . (4)i i i

Profits of the bank areC D

p 5R (L )l 1R c 2R d 2 f(l ,d ), (5)i j j i i j i i i

whereR (L ) is the inverse demand function, andL 5o l , the total amount ofj j j i[P ijC Dloans of prefecturej; R and R are the call rate and the deposit interest rate,

respectively, andf(l , d ) is the operating cost function. Differentiating (5) withi i

respect tol subject to the the budget constraint, we obtaini

≠Ll ≠f(l , d )ji i iC]] ]]]R 2 2R 5 . (6)j a ≠l ≠l1 i i

2 2 2 2The operating cost function is assumed to have≠f/≠l , ≠ f/≠l , ≠f/≠d , ≠ f/≠d .i i i i20, and≠ f/≠l ≠d ,0. For simplicity, let us assume thati i

2 2f(l , d )5 a 1 a l 1 a l 2 a l d 1 a d 1 a d , a , . . . ,a .0.i i 0 1 i 2 i 3 i i 4 i 5 i 0 5

Then, (6) can be rewritten

≠L a a1 1 1 j 3 1C] ]] ] ] ]l 5 (R 2R )2 l 1 d 2 . (7)i j i i2a 2a a ≠l 2a 2a2 2 1 i 2 2

Summing Eq. (7) over the banks in prefecturej, we obtain

≠L a a II 1 1 j 3 1C] ]] ] ] ]L 5 (R 2R )2 O l 1 D 2 , (8)S Dj j i j2a 2a a ≠l 2a 2a2 2 1 i 2 2i[Pj

where D ;o d stands for the total deposits of the prefecturej. Assumingj i[P ij

banks increase their loans more in response to an increase in loans made by a2larger bank, specifically dL /dl 5 b(l /L ), b .0, the second term of Eq. (8)j i i j

becomesI 2lb 1 b 1i

]] ] ]]2 O ; 2 HI,S D2a a L 2a a2 1 j 2 1i51

9We focus on the loan markets and assume that income is exogenously determined; this is a partialequilibrium analysis. As for a general equilibrium analysis of the Japanese economy, see Fujita andTakahashi (1992).


and thusSL 5b 1b R 1b D 2b HI , (9)j 0 1 j 2 j 3 j

10whereHI stands for the Herfindahl index of prefecturej, andb , b , b . 0.j 1 2 3

In equilibrium,

R 5k 1aY 2bD 1gHI , (10)j j j j

wherek ; (a 2b ) /(a 1b ), a ;a /(a 1b ).0, b ;b /(a 1b ). 0, g;0 0 1 1 2 1 1 2 1 1

b /(a 1b ). 0.3 1 1

If markets are segmented, the interest rate is affected byY , D , andHI . On thej j j

other hand, if the market is not segmented,R should not depend onY , D andj j j11HI .j

If a andb are significantly positive, the markets are segmented. In addition, ifmarket structure-performance hypothesis is valid,g will be positive.

3.2. Test results of the segmentation hypothesis

The estimation results are in Table 1. We use gross product of a prefecture as12our measure ofY. We use the sum of the deposits of shinkin banks and regional

Table 1Estimation results of Eq. (10)

Variables Expected Shinkin banks Regional bankssigns

Coefficient P-value Coefficient P-value21 21Constant ? 0.389310 [0.000]** 0.307310 [0.000]**29 210Y 1 0.179310 [0.000]** 0.853310 [0.015]*29 29D 2 20.675310 [0.000]** 20.253310 [0.032]*22 23HI 1 0.467310 [0.002]** 0.923310 [0.732]

AdjustedR-squared 0.158 0.011

F value 26.6161 [0.000]** 1.47174 [0.226]Sample size 410 128

The dependent variables are the interest rates of shinkin banks and regional banks, respectively.*Significant at the 5% level. **Significant at the 1% level.

10Alternatively, one may think that only Nash equilibrium is realized, so that≠L /≠l 5 1, ;i, j.j i

Then, HI disappears from Eq. (10). We estimate Eq. (10) to see ifHI is significant.11See Modigliani and Sutch (1966, 1967) and Echols and Elliot (1976) for this test applied to the

segmentation hypothesis in term structure of interest rates. Feldstein and Horioka (1980) are based on asimilar idea, although an examination of the relationship between the amount of loans and depositswould be a more direct application of their method.

12We conduct the same estimation using the corporate income of prefecture as the data ofY and getsimilar results.


banks of each prefecture as our measure ofD. HI is calculated for each prefecturebased on the outstanding loans of shinkin banks and regional banks in thatprefecture.

The estimation results for shinkin banks are shown in the left side of Table 1.The coefficients ofY, D and HI show the correct signs and are significant at the5% level. These results imply that (1) the loan markets for shinkin banks aresegmented by prefecture, and (2) the market structure-performance hypothesis is

13,14supported.The results for regional banks are shown in the right side of Table 1. Looking at

the F value of the total regression, we find that the variables have no power toexplain the interest rates (P-value is 0.226). The result implies that the loan

15markets of regional banks are only weakly segmented by prefecture, if at all.Together, the estimation results of Eq. (10) suggest that the loan markets of

shinkin banks are segmented by prefecture, but those of regional banks are not.

4. Two hypotheses on the segmentation

4.1. Are the differences smaller across adjacent prefectures than distantprefectures?

As argued previously, if segmentation exists, it cannot be perfect. Loan marketsinteract with other loan markets through two channels. First, markets areconnected to the adjacent prefectures through the activities of the branches of localfinancial institutions that have their head offices in the adjacent prefectures.Second, the local loan market may be connected to large cities through the branchnetwork of city banks. We use the first observation to construct the followinghypothesis.

Hypothesis A (Adjacent): Interest rates of adjacent prefectures do not differ,while those between distant prefectures do.

We use the sample of shinkin banks because we did not find differences ininterest rates for regional banks. We test Hypothesis A by first choosing aprefecture and classifying all other prefectures into three groups. Group A(Adjacent) consists of the prefectures that are adjacent to the current prefecture.

13When the average interest rates of each prefecture are used as the dependent variable,HI becomesinsignificant.

14Some may argue that loans are heterogeneous goods, so that we should consider the quality ofloans. We run the regression adding the variables representing the quality of loans (see footnote 7). Thefit is greatly improved (R-squared is 0.406) and the coefficients ofY, D and HI become highlysignificant.

15When the variable representing quality of loans are added, the fit is greatly improved (R-squared is0.722). However, the coefficients ofY andD are not significant at all (P-values are over 0.6). Thus, theconclusion is not changed.


The prefectures that are adjacent to the prefectures in Group A are defined asGroup N (Neighborhood). All the other prefectures comprise Group F (Far). Japanconsists of four main islands: Hokkaido, Honshu, Shikoku, and Kyushu. Prefec-tures separated by sea are classified as Group F. Hokkaido, for example, has all theother prefectures included in Group F. Fukuoka has Oita, Kumamoto and Sagaclassified as Group A, and Nagasaki, Kagoshima and Miyazaki as Group N; all ofthe other prefectures form Group F.

Then, picking a prefecture, we count, for each group, the number of combina-tions in which the interest rates are statistically different at the 5% and 1%significance levels. We repeat this for every prefecture and total the counts for eachgroup to get the number of ‘different means combinations’. Hypothesis A predictsthat the ratio of ‘different means combinations’ to all the combinations is thehighest for Group F, the second highest for Group N, and the lowest for Group A.

The results are summarized in Table 2. Of 213 pairs in which interest ratesdiffer at the 5% significance level, seven belong to Group A, five to Group N, andthe other 201 pairs belong to Group F. The ratio of ‘different means pairs’ to thetotal pairs is 8.0, 4.6 and 22.7%, respectively. While the result that the ratio forGroup F is much larger than Groups A and N supports Hypothesis A, the result

16that the ratio for Group N is smaller than that for Group A contradicts it.Hypothesis A can also be evaluated by comparingt-values of the test of same

means for the prefectures by group. Hypothesis A predicts that thet-values ofGroup A are the lowest and that of Group F is the highest. Note that theset-valuesare from the Bonferroni tests. The mean and standard deviation of thet-values foreach group are shown in the upper panel of Table 3. The mean of Group F is thehighest and the mean of Group A is the lowest.

We apply the test of the same means to thet-values of Groups A, N and F to see

Table 2Number of different means prefecture pairs within Groups A, N and F

Group A Group N Group F Whole sample

Number of observations 87 108 886 1081

Significant at 1% 6 (6.9%) 5 (4.6%) 174 (19.6%) 185 (17.1%)Significant at 5% 7 (8.0%) 5 (4.6%) 201 (22.7%) 213 (19.7%)

(1) ‘significant at 1% (5%) means the number of pairs which are significant at the 1% (5%) for thewhole Bonferroni comparison tests. (2) Figures in parentheses are the ratio of the significant cases tothe number of observations.

16When adjusted interest rates are used, the results become consistent with our hypothesis. The ratioof ‘different means pairs’ to the total pairs is 3.4, 7.4 and 12.2% for Groups A, N and F, respectively.


Table 3Analysis of t-values of the comparison tests of Groups A, N and F

Group Mean of Standardt-values deviation

of t-values

A 1.690 1.390N 1.860 1.517F 2.863 2.433

Combination P-value

A–N 0.60525*A–F 0.573310 *24*N–F 0.189310 *

Mean and standard deviation oft-values by the Bonferroni comparison tests of Groups A, N, and F.Note: A–N is the test of same means of thet-values of Groups A and N shown in the upper panel, andsimilarly for A–F and N–F. **Significant at the 1% level of the whole Bonferroni comparison tests.

if the difference is statistically significant. These results are shown in the lowerpanel of Table 3. The means of thet-values are not different between Groups Aand N, but they do differ between Groups A and F and Groups N and F. Thus, weconclude that the difference within Group F is statistically larger than withinGroup A or Group N, but the difference within Group A and Group N is nearly thesame.

Our results suggest that a difference in the interest rates across the adjacentprefectures is quite rare, but when compared to distant prefectures, a differenceoften emerges. Thus, Hypothesis A is supported.

4.2. Are the interest rates in large prefectures more homogeneous than ruralprefectures?

As the second channel of the interaction, a local loan market is connected tolarge cities through a branch network of city banks. Therefore, if local markets, i.e.shinkin banks, are not completely isolated from the markets of city banks, localmarkets in prefectures where city banks have their branches interact with eachother through the activity of city banks. As a result, we examine the followinghypothesis.

Hypothesis C (City): Interest rates of prefectures that have many branches ofcity banks do not differ from each other. Conversely, interest rates differ for ruralprefectures.

In order to test Hypothesis C, we define the large prefectures, which we callGroup L (Large), as those that have more than 50 branches of city banks:


Table 4Results of tests of same means of the interest rates between Groups L and S

L S

Number of observations 167 243Mean 0.0352 0.0382Standard deviation 0.0034 0.0053

211*P-value 0.489310 *

(1) Mean and standard deviation are of the interest rates for shinkin banks belonging to Groups Land S, respectively. (2) TheP-value is of the tests of same means of the interest rates between GroupsL and S. **Significant at the 1% level.

17Hokkaido, Chiba, Saitama, Kanagawa, Tokyo, Aichi, Kyoto, Osaka and Hyogo.The other prefectures are named Group S (Small). Hypothesis C predicts that theinterest rates of the prefectures belonging to Group L do not differ from each othervery much, while those of Group S differ from each other more. It also predictsthat the interest rates of the prefectures in Group S are different from those inGroup L.

First, we compare the means of the interest rates for Groups L and S. Accordingto Table 4, the mean of Group L is lower than Group S, and the difference issignificant at the 1% level.

Next, we examine whether the interest rates in Group L are more homogeneousthan in Group S. The numbers of ‘different means pairs’ inside Group L (L–L),inside Group S (S–S), and of inter-Groups (L–S) are summarized in Table 5. The

Table 5Number of different means prefecture pairs of Groups L and S

L–L S–S L–S Whole sample

Number of observations 36 703 342 1081

Significant at 1% 5 (13.9%) 105 (14.9%) 75 (21.9%) 185 (17.1%)Significant at 5% 6 (16.7%) 124 (17.6%) 83 (24.3%) 213 (19.7%)

(1) L–L is a pair of prefectures that belong to Group L. (2) S–S is a pair of prefectures that belongto Group S. (3) L–S is a pair of prefectures with one prefecture in Group L and the other in Group S.(4) ‘Significant at 1% (5%)’ means the number of pairs which are significant at the 1% (5%) for thewhole Bonferroni comparison tests. (5) Figures in parentheses are the ratios of the significant cases tothe number of observations.

17If the definition of Group L is changed to those prefectures having more than 100 branches of citybanks, Kyoto is excluded from Group L. While the results with the new definition using unadjustedinterest rates are essentially same, results with adjusted interest rates are somewhat changed (seefootnote 18). We also conduct the same analysis excluding Hokkaido prefecture from the abovesamples in the text, so that three metropolitan areas form Group L. However, the results are notchanged significantly.


Table 6Analysis of t-values of the comparison tests of Groups L and S

Number of Mean of Standard deviationcomparisons t-values oft-values

L–L 36 2.381 1.827S–S 703 2.474 2.209L–S 342 3.097 2.543

Combination P-value

(L–L) vs. (S–S) 0.813(L–L) vs. (L–S) 0.076

26*(S–S) vs. (L–S) 0.456310 *

Mean and standard deviation oft-values by the Bonferroni comparison tests among prefecturesbelonging to Groups L and S. (1) See notes of Table 6. (2) (L–L) vs. (S–S) is a test of same means fort-values of the test of same interest rates between L–L and S–S. (3) (L–L) vs. (L–S) is a test of samemeans fort-values of the test of same interest rates between L–L and L–S. (4) (S–S) vs. (L–S) is atest of same means fort-values of the test of same interest rates between S–S and L–S. **Significant atthe 1% level of the whole Bonferroni comparison tests.

ratio of the ‘different means pairs’ to total pairs does not vary much across groups:L–L (16.7%), S–S (17.6%) and L–S (24.3%).

The mean and standard deviation of thet-values of the Bonferroni comparisontests of L–L, S–S and L–S are in the upper panel of Table 6. Here, L–L (S–S)means the comparison within Group L (S), and L–S means the comparisonbetween the prefectures that belong to Groups L and S. Hypothesis C predicts thatthe mean of thet-values of L–L combinations should be the lowest. The panelshows that this prediction holds, while the difference of the means oft-valuesbetween L–L and S–S is small.

Test results of the same means oft-values of L–L, S–S and L–S pairs areshown in the lower panel of Table 6. The means of thet-values of pairs withinGroup L and Group S are not statistically different, but the means of thet-valuesof pairs within Group S are significantly different from the inter-Group pairs

18(L–S).The empirical results for Hypothesis C are mixed. While the interest rates for

Group L are lower than the interest rates for Group S, whether the interest ratesinside Group L are more homogeneous than Group S is still somewhat ambiguous.These results suggest that the markets of shinkin banks are fairly segmented from

18When adjusted interest rates are used, L–L and L–S differ at the 5% level and theP-value of thecomparison of L–L vs. S–S is reduced 81% to 44%. Still, the result is not fully consistent withHypothesis C. When the criterion of large prefecture is set to having 100 branches, theP-value of thecomparison of L–L vs. S–S is reduced to 4.1% for adjusted interest rates, which is still insignificantdue to the criterion of the multiple comparisons. In the case that interest rates are not adjusted, theresults are not changed dramatically.


those of larger banks. The pressure from city banks on shinkin banks is weak, if atall.

5. Conclusions

This paper investigates whether regional loan markets are segmented byprefecture. Using regional banks and shinkin banks as samples, we reach theconclusion that loan markets for regional banks are not segmented, but the marketsfor shinkin banks are. Since regional banks, on average, have larger-sized loansthan shinkin banks, this result implies that larger loans are integrated but smallerloans are segregated. Thus, our conclusion is consistent with the result of the USby Osborne (1988). Fujino (1987)’s finding that the loan interest rate are adjustedresponding to the excess demands for loans for the regional banks, but not forshinkin banks seems also consistent with our conclusion. The conclusion suggestswe should admit that the markets of regional and shinkin banks are alsosegmented. This is plausible considering the difference in interest rates betweenthese banks shown in Fig. 1 and the finding in Fujino (1987).

We reach this conclusion using two methods. One is to see whether demand andsupply factors of each prefecture significantly affect the interest rate of thatprefecture. The other is to examine possible differences in loan interest rates acrossprefectures. In the first approach, we obtain the results that demand and supplyfactors take on significantly correct signs for shinkin banks. The market con-centration index also has the correct sign, supporting the market structure-performance hypothesis. For regional banks, the regression equation as a whole isnot significant, suggesting that loan markets for regional banks are not segmentedby prefecture. These results are persuasive because regional banks operate 18.9%of their branches outside of their home prefecture, so that their business operationsoverlap geographically, while only 4.2% of the shinkin branches operate outsidetheir home prefecture.

The second approach discovers that the interest rates of Shinkin banks arediffered between some prefectures, while those of regional banks are not differed.

These results reveal that the rural area in Japan is characterized by isolationfrom other prefectures and a lack of competition. Market segmentation and highmarket concentration cause unfavorably high loan interest rates. Given theseobservations, the policy implication is clear and simple: relaxation of thegeographical operation regulation should promote competition and lower loan

19interest rates in rural Japan.

19A different argument can still work, so that we should make a reservation to this statement. It goesthat shinkin banks are subjected to more relaxed regulations than the city and regional banks, and theirsmaller loan-size and deposit-size class causes their inferior cost performances. From this viewpoint,the effect of relaxation of the geographical operation regulation may be limited.


Acknowledgements

The authors are grateful to comments by Masatsugu Tsuji, Kanemi Ban, ColinMcKenzie, Yasuhiko Tanigawa and anonymous referees of this journal.

Appendix A

In this appendix, we show that even if banks fully adjust their liquidity throughthe call market, their loan interest rates can be different under the assumption ofsegmented loan markets. Furthermore, we show that a solution consistent with thereality of Japanese banking is obtained under a reasonable setting: banks in ruralarea consistently lend to those in city area through the call market, and the loaninterest rates in rural area are higher than those in city area.

Suppose, for simplicity, that there are two banks, A and B, which act as amonopoly in each segmented loan market. Also suppose that banks A and B adjusttheir liquidity through the call market. The balance sheets of banks are

i i iC 1L 5D , i 5 A, B,

whereC, L, andD are call loan, loan, and deposit, respectively. Profits of banksare

i i i i ip 5R L 1R C 2R D , i 5 A, B.L C D

Loan demand functions are assumed to bei i i iL 5a 2b R , i 5 A, B.L

From the first order condition of the profit maximization, we obtaini i iL 5b (R 2R ).L C

Therefore, we geti

a 1i ] ]R 5 1 R , i 5 A, B. (A.1)L i C22b

Equilibrium condition of the call market is

A B A A B BD 1D 2b (R 2R )2b (R 2R )50. (A.2)L C L C

A B AFrom (A.1) and (A.2),R , R andR are solved. Calibrating them witha 5 8,L L CB A B A B A

a 56, b 5b 5 1, D 5 4, D 5 2, we obtain the following solutions:R 5LB A B A B4.5, R 5 3.5, R 5 1, C 5 2C 5 0.5, L 5 3.5, andL 5 2.5. That is, underL C

the assumption that bank A locates in the area where deposits are richer, it lendsthe call loan to bank B, and its loan interest rate is higher than that of bank B.


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Geographical segmentation in Japanese bank loan markets