DISCUSSION PAPER SERIES
October 2013

No.2013-06

The Effect of Bank Competition on Firm Entry, Growth,and Exit: Evidence from Japanese Industry Data
Masayo Shikimi

The Effect of Bank Competition on Firm Entry, Growth, and Exit: Evidence fromJapanese Industry Data*
Masayo Shikimi†
Abstract

This paper empirically investigates the effects of bank competition in the financial sector on
the creation, growth, and destruction of establishments in the local Japanese market. It is foundthat concentration in the banking sector negatively (positively) affects the creation(destruction) and average size of establishments in industries that have greater dependence on
external financing and a greater value of intangible fixed assets. These results suggest that inconcentrated banking markets, potential entrants are more likely to face difficulty in obtaining
credits, less likely to grow, and more likely to exit as compared with competitive banking
markets.

Keywords: Bank Market Power, Small Business Financing, Asymmetric Information, Firm
Dynamics
JEL Classification: D4, G21, L11*

I am grateful to Yoshiaki Ogura, Shigeru Uchida, and other conference participants at the annual

conference of the Japan Society of Monetary Economics and the 8th international conference on Asianfinancial markets and economic development for their valuable and helpful comments. However, any
mistakes that remain are my own. This research is supported by Grants-in-Aid for Scientific Research(#23530371) from the Ministry of Education, Culture, Sports, Science and Technology, Japan.
†

Associate Professor, Faculty of Economics, Nagasaki University, 4-2-1, Katafuchi, Nagasaki

850-8506, Japan. Phone: +81-95-820-6323; Fax: +81-95-820-6370; E-mail: mshikimi@nagasaki-u.ac.jp

1 Introduction
Small- and medium-sized firms are a key engine for growth of economies. Policies boosting
the entry of new firms can make an important contribution for stimulating growth. In fact, somelocal governments set numerical targets for start-up rates to stimulate growth. A growing
number of empirical studies explore factors affecting start-up activities and growth of local
economies. Most of these empirical studies are based on traditional industrial organization
theory, which has found that demand for goods and services, industry agglomeration, humanresources, and industry entry barriers have affected the star-up ratios and industrial growth inlocal markets. On the other hand, some recent studies found that competition not only in goods
markets but also in local financial markets has affected firm entry and the distribution of firm
size in local economies. (Cetorelli and Gambera 2001; Cetorelli and Strahan 2006).
Informational asymmetry may explain differences in these factors.
In the absence of informational asymmetry problems between lenders and borrowers, newentrants are able to gain finance for their projects at lower costs in competitive financial markets
than in less competitive markets. This leads to more new firm entries in the competitive
financial market. In the presence of informational asymmetry, however, entrepreneurs encounter
difficulties in obtaining credit. Theories on banking explain that long-term exclusive banking
relationships can relax credit constraints on start-up firms. In other words, in the presence of1

informational asymmetry, firms’ availability of credit differs within the same market, depending
upon whether they have a banking relationship or not.
This study investigates the effects of interbank competition on firm entry, growth, and exit inlocal credit markets. The analysis here differs from preceding studies in three ways. First, thisstudy examines the effect of bank competition on not only firm entry and growth but also firm
exit in Japan, whereas most preceding analyses have investigated that on firm entry and growthin the U.S. and Italy but not in Japan. Moreover, few studies have explored the effect of interbank competition on firm exit. Second, most previous studies limit their analysis to the
manufacturing sector, despite the fact that start-up rates in mature economies such as the U.S.
and Japan are higher in non-manufacturing sectors than in manufacturing sectors.1,2 Moreover,
informational asymmetric problems are more severe in non-manufacturing sectors since firms inthose sectors have fewer tangible fixed assets (or collateral) that can serve as incentives fordeterring moral hazard and adverse selection problems. This study expands this analysis towhole industries. Third, this study investigates the effects of bank competition on firm entry

1

An exception is Bonaccorsi Di Patti and Dell’ Ariccia (2004). They used data on start-up rates
for the 22 industries including some non-manufacturing sectors in Italy.
2
According to the Establishment and Enterprise Census issued by the Ministry of Internal
Affairs and Communications conducted in year 2006, industries with most new entry of
establishments were wholesale and retail trade, followed by services, eating and drinking places,
and accommodations. The share of new establishments in the manufacturing sector is only 5.5%.
Industries with the most start-up rates were compound services (the start-up rate is 71.3%),
followed by information and communications (49.96%), and medical, health care, and welfare(39.5%). On the other hand, the start-up rate of manufacturing is 11.6%.
2

(exit) and growth using aggregated census data at an industry and prefecture level. Since we use
aggregated data, it is difficult to test how competition among banks operating in a local market
affects firms’ banking relationships. However, the advantage of using census data is that we can
explore the effects of bank competition on the number of new (exiting) establishments, on startup (exit) rates, and on the distribution of establishment sizes. This enables us to capture theimpact of interbank competition on the industrial organizational structure of a local economy. It
is fairly difficult to obtain data on small- and medium-sized enterprises (SMEs) in Japan. Evenif such data is available, the sample size is extremely small and coverage is limited to relatively
to that of larger SMEs. Moreover, data collected by survey questionnaire has disadvantages suchas potential in recognizing individual respondent firms as representatives of the local market.
Data used in this study is census data that includes all small establishments.
The remainder of the paper is organized as follows. Section 2 reviews previous theoreticaland empirical studies and presents hypotheses testing. Section 3 describes our sample data andthe basic estimation strategy. Section 4 presents the results of the estimation of the effects ofinterbank competition on the number of new establishments, on start-up (exit) rates and on the
distribution size of establishments in local markets. It interprets the results by comparing themto preceding studies. The summary and conclusions are presented in Section 5.

3

2 The effect of interbank competition on firm entry and growth: Theory and evidenceThis section presents the theoretical literature on the effects of bank competition on firm entry
and size. In addition, it discusses previous empirical studies on these issues. From a theoreticalperspective, bank competition can have both positive and negative effects on firm creation.
Industrial organization theory says that, in general, bank market power reduces credit
availability for firms. Thus, a negative relationship between the number of new establishments(or firm size) and bank concentration in a local market is expected.
On the other hand, bank concentration in local markets disproportionately affects credit
availability of firms where informational asymmetry between borrowers and lenders exists. It is
well known that informationally opaque SMEs are more likely to encounter credit rationing.
This is a problem that can be overcome through banking relationships, which, over time, help
the bank to obtain information on the borrowing firm’s unobservable qualities and thus, mitigate
credit rationing. The benefit of a long-term exclusive banking relationship is that it enables
banks to internalize the cost of soft-information gathering. Additionally, it enables firms to offer
their private information to the bank without revealing it to rival firms. On the other hand, such
relationships potentially allow banks to extract rent by exploiting the informational monopoly
power they possess over a firm (Sharpe 1990; Rajan 1992; von Thadden 1995). In thisenvironment, small- and medium-sized firms that heavily rely on bank finance can protect
4themselves from this hold-up problem by establishing a second banking relationship or byswitching banks. When local credit markets are competitive, firms more easily find othersources of financing after they grow. Thus, banks are discouraged from investing in relationshiplending in competitive financial markets. On the other hand, in concentrated markets, a firm’s
costs of switching to a new bank are relatively high. In other words, banks are more likely tofinance start-up firms because they can expect to gain from rent exploitation by charging higher
interest rates on loans once these firms grow. Therefore, start-up rates are expected to be higher
in concentrated financial markets. On the other hand, firm size is relatively small inconcentrated markets because firm growth is hampered by the hold-up problem.
On the other hand, Boot and Thakor (2000) predict a positive correlation between bankcompetition and relationship lending. They argue theoretically that as bank competition
increases, banks make more relational loans than transaction loans. Since relational loans are
relationship-specific, sector-specialized credit, they are less likely to be affected by bankcompetition than are transaction loans. Thus, a positive relationship is expected between bankcompetition and firm entry. On the other hand, bank competition is expected to have both
positive and negative effects on firm growth. A negative effect is due to hold-up problems.
Relationship banking is expected to have a positive effect on firm growth when hold-up
problems are mitigated by increased bank competition. Boot and Thakor (2000) argue that even5though interbank competition may lead to more relationship lending, the profitability of thistype of lending is reduced because of interbank competition. Thus, banks’ monitoring efforts,
which in turn improve a firm’s performance, are expected to decline. This leads to fewer holdup problems. At the same time, firms can easily find other financing sources in competitivebank markets. Therefore, banks in competitive markets will most likely commit themselves to
not extracting rents to preserve their reputations and potential clients. In such cases, it is notalways true that relationship banking entails hold-up problems. Thus, firm growth is expected tobe high in competitive banking markets.
The empirical literature has produced mixed results regarding the effects of interbank
competition on firm entry and growth. For example, using U.S. small firm data, Petersen andRajan (1994, 1995) find that a higher concentration in local credit market leads to less credit
rationing for young firms. Zarutskie (2006), using panel data for U.S. firms, finds that younger
firms are less capable of obtaining credit after a regulation to limit interbank competition in
local credit markets is lifted. These studies suggest that interbank competition leads to lesscredit for young firms. Ogura (2007), using survey data for 1500 SMEs in Japan, finds that
relationship banking has a positive effect on credit availability for start-up firms. Moreover, hefinds a negative relationship between interbank competition and relationship banking.
Bonaccorsi Di Patti and Dell’Ariccia (2004), using aggregated data of industry by geographic
6

location in Italy, find a nonlinear relationship between interbank competition and start-up rates.
Moreover, they find that start-up rates decline in more informationally opaque industries asinterbank competition increases. These studies suggest that firm entry is more observed inconcentrated bank markets.
On the other hand, Cetorelli and Strahan (2006), using aggregated data at an industry andstate level in the U.S., find that interbank competition increases the total number of
establishments and reduces the size of typical establishments in bank-dependent industries.
These results suggest that smaller firms are credit rationed in concentrated banking markets.
Black and Strahan (2002), using an aggregated panel data at a state level, find a positive
relationship between the number of start-up firms and interbank competition. Shikimi (2013),
using data on SMEs in Japan, finds a positive relationship between the credit availability offirms and interbank competition in local financial markets. These studies suggest a positive
relationship between firm entry and interbank competition in local credit markets.
However, interbank competition has conflicting implications with regard to firm growth.
Fernández de Guevara and Maudos (2009), using data on Spanish firms, find a U-shaped
relationship between firm growth and interbank competition in local financial markets. Cetorelli
and Gambera (2001), using international data at an industry level, find an inversely U-shaped
relationship between industrial growth and an interaction term of concentration in the banking
7sector and industry's financial dependency. They interpret these results to show that industries
more dependent on bank financing reap the benefits of relationship banking from concentratedbanking markets, while industries less dependent on bank financing can reap the benefits ofbank competition. On the other hand, Claessens and Laeven (2005) find higher growth inindustries with a greater dependence on external finance as bank competition increases.
Other studies argue that bank competition has no effect on firms’ survival rates or firm growth.
Okamuro (2007), using survey data for 2100 start-up firms in Japan, finds that local credit
market conditions have little effect on firm survival rates and firm growth.
Studies using aggregated data at an industry and regional level test whether the effect of bankcompetition on firm entry and growth varies according to the level of a sector’s dependence on
external financing and on the degree of informational opaqueness. Most previous studies
assume that industries with a higher dependence on external finance are informationally opaqueand more likely to be affected by interbank competition. However, firms’ sources of externalfinancing are not limited to bank loans; stock issues and public debt are other viable options.
Only those firms who encounter difficulties in financing from capital markets because ofasymmetric information problems turn to bank loan financing. Most previous studies use firmsize, firm age, and the value of a firm’s intangible assets ratio as proxies for informational
asymmetries. In general, these studies show a positive correlation between a firm’s bank loan
8

ratio and the value of its collateral. Firm-specific assets, such as intangible fixed assets, areconsidered difficult to evaluate and are not, generally, accepted as collateral. Thus, a high valueof intangible asset ratio does not always reflect a firm’s dependency on bank financing. In otherwords, a firm’s bank loan dependence does not always reflect its degree of informational
opaqueness.
There are two types of bank lending: relationship lending and transaction lending. In theformer type of lending, banks use both a firm’s soft and hard information when deciding toextend a loan, whereas in the latter case they mostly rely on a firm’s hard information to makethese credit decisions. Therefore, firms more dependent on bank loans are not always theinformationally opaque firms that rely heavily on relationship lending. Considering such cases,
this study focuses on informationally opaque industries with a higher dependence on externalfinancing. We assume the effect of interbank competition varies according to an industry’s
dependence on external financing and to the degree of the industry’s informational opaqueness.
As to firm exit, there are few theoretical or empirical studies on the effects of interbank
competition on firm exit. If the relationship banking alleviates the credit constraints of firms
which are temporally shortage of liquidity and hence avoid inefficient corporate failures, it isexpected the same mechanism will work for firm exit.
The theoretical literature and previous empirical studies on the effects of interbank
9competition on firm creation and growth can be summarized in the following partly conflicting
hypotheses:
H1: Concentration in bank financing leads to an increase in relationship lending. Thus,
concentration in bank financing markets has a positive (negative) effect on start-up (exit) rates
of external-finance dependent industries with more informational asymmetry, and a negative
effect on establishment size in those industries due to hold-up problems (Petersen and Rajan
1994, 1995; Bonaccorsi Di Patti and Dell’Ariccia 2004).
H2: Interbank competition leads to an increase in relationship lending (Boot and Thakor
2000). Thus, increased concentration in banking sectors has a negative (positive) effect on startup (exit) rates in industries with more informational asymmetry, and has a positive effect on thesize of establishments in those industries.
H3: Interbank competition leads to an increase in relationship lending (Boot and Thakor
2000). Thus, an increase in interbank competition has a positive (negative) effect on the start-up(exit) rates of establishments in industries with more informational asymmetry. Additionally, it
has a positive effect on the size of establishments in those industries because hold-up problems
are mitigated due to a decline in the costs incurred by firms from switching to other banks(Claessens and Laeven 2005).

10

3 Econometric procedure and Data
3.1 Econometric procedure
This section analyzes the effect of interbank competition in local financial markets on start-up(exit) rates and the size of establishments. The creation (destruction) and growth of
establishments are determined by the degree of interbank competition in local financial markets,by the level of an industry’s maturity, and by other factors. It is possible that interbank
competition does not always has a homogenous effect on the creation (destruction) and growthof establishments; instead it has a heterogeneous effect according to the degree of externalfinancial dependence of industries and to the level of their informational asymmetry.
The baseline empirical model can be written as follows3:

Y jpt  1  1  ( Dependence j  Concentrat ion pt )   1  Concentrat ion pt  1  Share jpt Γ  Industry effect jt  κ  Control pt   jpt ,(1)

where Y jpt is the number of new (exiting) establishments, start-up (exit) rates, or ln (numberof employees per establishment) for industry i in prefecture p in year t in separate regression
models. Dependence

represents the industry’s dependence on external financing, its

informational asymmetry, or a cross term of those factors. Concentrat ion represents thedegree of concentration in the banking sector. Share represents the industry’s employment

3

For examples of this specification, see Rajan and Zingales (1998), Cetorelli and Gambera(2001), Cetorelli and Strahan (2006), and Bonaccorsi Di Patti and Dell’Ariccia (2004).
11

share, to control for the significance of a given sector in prefecture p in year t . The expected
sign of this variable is negative since opportunities for new business or firm growth are limited
in already grown-up industries (Rajan and Zingales 1998; Cetorelli and Gambera 2001;
Cetorelli and Strahan 2006) 4 . Industry effect is a cross term of industry dummy and time
dummy variables to control time-variant industry effects. Control contains variables to control
local market demand such as industry accumulation and market sizes.
Two problems arise in the estimation of equation (1). The first is a multicolinearity problem
that stems from the serial correlation between Concentration and Control because of banks
being dense in populous areas. The second is an endogeneity problem. When unobservablefactors determine the industrial structure of local markets as well as the level of interbank
competition, concentration in banking markets and dependent variables become endogenous. A
traditional remedy for this problem is to use instrumental variable methods or a fixed-effects
estimator. Following Cetorelli and Strahan (2006), a prefecture fixed effect is used for thisendogeneity problem.
The empirical model can be rewritten as follows:

Y jpt   2   2  ( Dependence j  Concentrat ion pt )  Φ  Market effect pt   2  Share jpt Λ  Industry effect jt   jpt ,4(2)

On the other hand, a negative sign is also expected because declining industries have smaller
shares, fewer new entrants, and less growth.
12where Market effect is a cross term of prefecture dummies and year dummies to control fortime-variant regional effects. Omitted variables problems and endogeneity problems are solved
by including both Marketeffect and Industryeffect . However, the direct effect ofConcentration cannot be estimated because it is now omitted in equation (2). 5
A summary of the hypotheses and the expected signs are provided on Table 1.

3.2 Data
Following previous studies, the aggregated data at the prefecture and industry level is used forthe regression analysis. Data on the number of new establishments and employees are takenfrom the Establishment and Enterprise Census issued by the Ministry of Internal Affairs andCommunications. This survey is conducted every five years and a less refined survey isconducted in the middle of each five-year period. Data used in this study are taken from thesurveys that are conducted on July 1, 1991; April 8, 1994; October 1, 1996; July 1, 1999;
October 1, 2001; June 1, 2004; and October 1, 2006.6 Since the surveys for new establishments
are conducted only in years 1994, 1999, and 2004 for the sample periods, estimations of thenumber of new entrants and of start-up (exit) rates are limited to those years. To investigate the

The effect of Concentration cannot be identified since it takes the same value within
prefecture*year and it is absorbed in Market effect .
6
The survey is conducted approximately every two and a half years (+/− three months).
13
5

effects of interbank competition, the sample is limited to privately owned establishments. Dataon the number of new establishments and employees at the two-digit industry and prefecturelevel are used for the regression analysis.
Some might argue that a small firm is an establishment by itself; however, large firms are notconsidered as establishments because they generally comprise a head office and many branches.
According to the survey of the Establishment and Enterprise Census conducted in year 2004,
the share of single-unit establishments over total establishments is 75.5%. Therefore, mostestablishments are small firms. The benefits of using data on establishments are two-fold. First,
since small firms usually consist of one establishment only, it is possible to capture the trend of
the start-up rates of small businesses in this data. Second, firms tend to rely on internal funds forbusiness expansion, while they are more likely to depend on external funds for creating new
establishments. Moreover, an increase in the number of establishments will most probably be
driven not by the expansion of existing establishments; instead, by the creation of new
establishments. Thus, an increase in the number of establishments is more likely to be affectedby the competition in the credit market (Rajan and Zingales 1998). In case of Japan, according
to Useful Labour Statistics: 2011 issued by the Japan Institute for Labour Policy and Training,for the period 1992–1996, the start-up rate of new establishments through the expansion ofexisting businesses is 1.0%, while start-up rate through new firm creation is 2.2%. The start-up
14

rate of new establishments by existing firms is 4.1%, and the start-up rate of new establishments
through the creation of new firms rose to 8% from 2001 to 2004. Thus, the number of new
establishments reflects the trend in the creation of small firms in Japan. Therefore, we use theestablishment data to compare our results with previous studies.
The distribution of establishments by size is provided in Table 2. The share of establishmentswith fewer than 10 employees is 80.5%. This suggests that most establishments are small firms.
Summary statistics of the variables used in this study are presented in Table 3. The median
number of employees per establishment is 11.
The proxies of independent variables are explained in detail as follows.

Credit market competition
As a proxy for bank concentration in the local credit market, the Herfindahl-Hirschman Index(HHI) of bank loans by prefectures is used. The Index is calculated according to each bank’s
total loan amount in a given prefecture by including only regional and second-tier regionalbanks, credit associations, and credit cooperatives. Data on an individual bank’s total loan
amount by prefecture for city banks, trust banks, and long-term trust banks are unavailable. We
used three different data sources: Kinyu Map published by the Financial Journal Co., Zenkoku
Shinyokinko Zaimu Shohyo, and Zenkoku Shinyokumiai Zaimu Shohyo published by Kinyu
15

Tosho Consultant Co. All independent variables in the previous year are used to avoid
simultaneous problems. Some might argue that geographical area for SMEs bank loan markets
at the prefecture level is too wide and the municipal level is better. However, with data
availability, it is assumed that credit market conditions are the same for all firms within a given
prefecture.7

Dependence on external financing
Two measures of an industry’s dependence on external financing are included. First, theindustry median bank loan/total assets for SMEs. To eliminate temporal shocks to a firms’
loan/asset ratio, each firm’s average loan-to-assets ratio is calculated over the period 1998–
2002. Then, its industry median is used as a proxy for the industry’s external dependence on
financing. Data on industry median loan assets ratios are obtained from Shikimi (2013).8 The
weakness of this measure is that the observed loan amounts are the equilibrium between
demand for and supply of bank loans. There is a possibility that the low bank loan ratio reflects
firms’ credit constraints despite the fact that firms’ demand for bank loans is large. To avoid
7

For the investigation of interbank competition on firm entry at the municipal level, we face
two difficulties. First, the data on establishments at the municipal and industry level areunavailable. Second, it is extremely difficult to obtain data on each bank’s lending at the bankbranch level.
8
Shikimi (2013) investigates the banking relationships of SMEs in Japan using data taken fromthe Japanese Accounts and Data on Enterprises (JADE) database, which covers over 100,000
Japanese firms, including small and medium-sized ones.
16

this identification problem, following Rajan and Zingales (1998) and Cetorelli and Strahan(2006), the second measure used in this study is listed firms' dependence on external financing.
According to Rajan and Zingales (1998), the initial project scale, the time period required for a
project to generate cash flow, and additional financing required for investments and their sizes
vary among industries. Therefore, an industry’s dependence on external financing is
considered to be determined by an industry’s technology for production. Therefore, anindustry’s technological dependence on external financing is used as a proxy for the industry’s
demand of external financing. To calculate this, data on listed firms is used. The pro of using
data for listed firms is that it is unlikely that the listed firms incur credit constraints and theindustry’s actual external financial dependence is reflected. External dependence is defined asa change in total assets minus a change in retained earnings divided by total assets. A positive
value indicates industries that are in financial deficit because they have invested more thantheir internal funds permit. The negative value means that they are in financial surplus since
they have more internal funds than their investment. In the regression analysis, both dummy
variables and the actual level of external dependence are used. To calculate this measure, eachfirm’s external financial dependence is averaged over the period 1991–2006 to eliminate
temporal fluctuations. Then, the industry median is used to eliminate the effect of outliers.
Financial data on listed firms are taken from the Financial Data Bank, published by the17

Development Bank of Japan. Samples are limited to firms that are listed for more than five
years from 1991 to 2006. Initial Public Offer (IPO) firms are eliminated because their financial
needs might be different from other listed firms.

Informational asymmetry
Prior studies on small business financing use firm age or firm size as proxies for
informational asymmetry. These measures vary largely within industries, and the usage of theindustry median is inappropriate. On the other hand, the difference between firms’ production
technology and types of investment projects are relatively large among industries, andrelatively small within industries. In general, the precise value of firms’ R&D investments andinvestments in intangible assets, such as goodwill, is difficult to evaluate for outsiders. Thus,
there are large informational asymmetries in these investments. On the other hand, tangible
fixed assets, such as machines and lands, are less likely to show informational asymmetry andcan serve as collateral for bank loans. Therefore, two measures are used as proxies for
informational asymmetries. The first measure is the industry median intangible fixed assets tototal assets ratio. To calculate this, a firm’s average intangible fixed assets ratio is taken overthe period 1998–2002, and then the industry median is calculated. The data source is the samefor the loan assets ratio.
18

The second measure is industry average Tobin’s q since growing industries have fewer
tangible fixed assets and more serious information asymmetric problems.9 Industries with highq ratios are considered to have more growth opportunities. The industry average q ratio is
calculated using data from all listed firms. The rationale for this approach is that it is
impossible to calculate the q ratio for SMEs because most of them are unlisted firms. Data on
firms’ stock prices and other financial statements are taken from the Financial Data Bank
published by the Development Bank of Japan.
To capture the effects of the relative importance of a given sector, an industry’s employment
share is used as a proxy for Share. Data on employees at an industry and prefecture level istaken from the Establishment and Enterprise Census issued by the Ministry of Internal Affairs
and Communications. Additionally, the number of exiting establishments is included in the
regression model for the number of new establishments. Table 3 shows the sample statistics.

4 Empirical results

4.1 The effect of interbank competition on the number of new establishments and start-up rates

9

The correlation coefficients between the intangible fixed assets ratio and the industry averageq, and between the intangible fixed assets ratio and the industry average sales growth rate are
0.38 and 0.59, respectively.
19

The estimation results on the determinants of the number of new entries are presented in Table
4.A. The Poisson estimator is used since the independent variable is expressed as a nonnegative integer. ln (the number of establishments) at the beginning of a period at an industry
and prefecture level is included to control for market size effects. All regression models
include a cross term of industry and year dummies to control for time-variant industry-fixed
effects. Similarly, they also include a cross term of prefecture dummies and year dummies tocontrol for time-variant regional-fixed effects. Models (I) and (II) assume that the effects ofinterbank competition varies according to industry dependence on external financing, whereas
Models (III) and (IV) present the results of how the effects of interbank competition differ
among industries according to their respective level of information asymmetry. The cross termof median bank loan ratio and HHI at t−1 in Model (I) is positive but insignificant. The crossterm of industry dependence on external finance and HHI is also positive but insignificant. In
Model (III), the coefficient on the industry median intangible fixed assets ratio*HHI isnegative and significant at the 1% level. This suggests that the number of new establishments
in concentrated bank markets is higher in industries with a higher value of intangible fixed
assets ratio. These results are consistent with hypotheses 2 and 3. Model (IV) includes the q
ratio as a proxy for information symmetries. The coefficient on the q ratio*HHI is negative and20insignificant. Model (V) includes the interaction term of an external financial dependence
dummy, an intangible fixed assets ratio, and HHI. An external financial dependence dummytakes a value of one when an industry’s external financial dependence is positive and zerootherwise. The results show that the coefficient of this cross term is negative and significant at
the 1% level and confirms H2 and H3. The results remain the same when outliers are dropped
from the sample. Model (VI) includes a level of an industry’s dependence on an externalfinancing*intangible fixed assets ratio *HHI. In addition, the coefficient of this cross term isnegative and significant at the 1% level, suggesting that the results in Model (V) are robust.
As for the industry’s market share variable, the industry’s employment share is
unexpectedly positive and significant at the 1% level. This result can be interpreted to indicate
few firm entries existing in declining industries. In addition, it can be interpreted to indicatethat industries with a larger market share can provide good conditions for new start-ups
because their infrastructures are already developed. The coefficient on In (the number of
establishments) at the beginning of the year is expectedly positive and significant at the 1%
level. The coefficient on the number of exiting establishments is insignificant.
To draw the implication for economic policy of the local economy, the regression results on
start-up rates are presented in Table 4.B. The independent variable is ln (the year average
number of new establishments/total number of establishments). The denominator is the total
21

number of establishments at an industry and prefecture level at the beginning of the year.
Industries with zero new entries at the prefecture level are omitted from the sample in thisregression. However, the estimation results are almost the same when industries with zero new
establishments at the prefecture level are included in the sample. In Model (I), the cross termof bank loan dependence and HHI is positive but insignificant. The coefficient on thedependence on external finance*HHI is negative but insignificant. The coefficient of theintangible fixed assets ratio*HHI is negative and significant at the 1% level in Model (III),supporting H2 and H3. The result remains qualitatively the same when the q ratio is used as aproxy for informational asymmetry in Model (IV). The coefficient of the external financedependence dummy*the intangible fixed assets ratio*HHI, and that of the level of externalfinancial dependence*the intangible fixed assets ratio*HHI are both negative and significant at
the 1% level in Models (V) and (VI). These results are the same as in Table 4.A.
To summarize the estimation results, the start-up rates in concentrated markets are lower inexternal financial dependent industries with a higher value of intangible fixed assets or q ratio.
These results suggest that the increase in bank market power has a negative effect on start-up
rates in external financial dependent sectors with less collateral and greater growth
opportunities. The results imply that credit constraints of firms in these industries are lesslikely to be relaxed in concentrated banking markets.
22

To evaluate the economic significance, Table 6 presents the relative effect of an increased
concentration in credit markets on start-up rates, when the level of information asymmetries
and external dependence on financing is changed. The first row in Table 6 reports thepercentage differences in start-up rates between industries with low external financial
dependence (the 25th percentile) and industries with high external dependence (the 75th
percentile), when the concentration of markets is increased from the 25th (0.21) to the 75th
percentile (0.39). The coefficients from Models (I) to (V) in Table 4.B are used forcomputation. Table 6 shows that start-up rates of industries with a higher dependence are lower
than those of industries with a lower dependence by 0.78% as local financial markets become
more concentrated. An increase in bank market power reduces start-up rates by 3.04% in
industries with a higher value of intangible fixed assets ratio relative to industries with a lower
value of intangible fixed assets ratio. Reduced interbank competition leads to a decline in startup rates of industries with a high q ratio by 0.83% than that of industries with a low q ratio.
The last row of Table 6 shows the effect of increased concentration in local credit markets on
dependent sectors with a higher intangible fixed assets ratio relative to nondependent sectors
with a lower intangible fixed assets ratio. Increased bank market power reduces the start-up
rates by 3.18%. These economic effects are not small since the median start-up rates at anindustry and prefecture level is 4.2%.
23

4.2 The effect of interbank competition on exit rates
Next, the effect of interbank competition on exit rates is investigated. Estimation results arepresented in Table 4.C. The independent variable is ln (the year average number of exiting
establishments/total number of establishments). The coefficient on the dependence on externalfinance*HHI is positive and significant at the 1% level. This result implies that exit rates inexternal financial dependent industries are lower in competitive financial markets because
relationship banking alleviates the financial constraints of firms. On the other hand, contrary to
the results of start-up rates, the coefficients of the cross terms of asymmetric information andbank concentration variables are insignificant in Models (III) and (IV). The coefficient of theexternal finance dependence dummy*the intangible fixed assets ratio*HHI, and that of thelevel of external financial dependence*the intangible fixed assets ratio*HHI are both positiveand significant at the 1% level in Models (V) and (VI). These results support H2 and H3.
The economic significance of an increased concentration in credit markets on exit rates isshown in the second column of Table 6. It shows that exit rates of industries with a higher
financial dependence are higher than those of industries with a lower dependence by 1.74% aslocal financial markets become more concentrated. The last row shows the effect of increased
concentration in local credit markets on dependent sectors with a higher intangible fixed assets
24

ratio relative to nondependent sectors with a lower intangible fixed assets ratio. Increased bankmarket power increases the exit rates by 1.21%.

4.3 The effect of interbank competition on establishment size
Next, the effect of interbank competition on establishment size is investigated. Estimation
results are presented in Table 5. The dependent variable is ln (employees per establishment).
The coefficient on the bank dependence*HHI variable is insignificant in Model (I). On theother hand, the coefficients of the external dependence*HHI, the intangible fixed assets
ratio*HHI, and the q*HHI are negative and significant at the 1% level (Models (II)–(IV)). The
coefficient on the external dependence*the intangible fixed assets ratio*HHI is negative andsignificant at the 1% level in Model (V). The highly significant negative effect of the increased
concentration on the establishment size in dependent sectors with a high q ratio is also found in
Model (VI). Taken together with the results on start-up rates, these results are consistent with
H3 in that increased interbank competition has a positive effect on both start-up rates and on
establishment sizes in dependent sectors with high informational asymmetries.
The economic significance of an increase in bank market power on establishment size isshown in the second column in Table 6. The coefficients from Models (I) to (V) in Table 5 areused for calculation. Increased concentration reduced average establishment size by 4.1% in25

industries with higher intangible fixed assets ratio relative to industries with a lower intangible
assets ratio. Similarly, an increase in concentration decreases the average size by 1.78% in
dependent sectors relative to nondependent sectors, and by 1.37% in dependent sectors withhigher intangible assets ratio than in nondependent sectors with lower intangible fixed assets
ratio. These results suggest that firm growth is hampered among those in financially dependent
sectors with less collateral due to credit constraints.

4.4 Interpretation
The estimation results are summarized as follows: the increased concentration has negative(positive) effects on start-up (exit) rates and establishment sizes in financially dependent
sectors with a higher value of intangible fixed assets ratio. These results are consistent with H3.
In this subsection, the empirical results above are interpreted by comparing them with thestudies surveyed in Section 2. The positive effect of increased concentration in local credit
markets on firm exit in dependent sectors is consistent with the empirical findings of Shikimi(2013) that interbank competition relaxes credit constraints on SMEs in Japan. Taken together,
the results obtained here and in the previous study imply that an increase in interbank
competition leads to more relationship lending and the relaxation of credit constraints on
SMEs. The negative effect of increased concentration in bank financing markets on firm
26

growth in informationally opaque industries suggests that firms are less likely to be locked intobank relationships in competitive markets and that they achieve higher growth because thehold-up problems are mitigated. Moreover, this result is consistent with the findings of Shikimi(2010) that a switch from a main bank to other banks is less likely to occur in concentrated
markets and in concentrated banking relationships. On the other hand, the results obtained here
contradict the empirical findings of Petersen and Rajan (1994, 1995) and Ogura (2007). The
negative effect of increased concentration in the credit market on firm entry is consistent withthe empirical findings of Cetorelli and Strahan (2006); however, the results on establishment
size contradict each other in these studies. Mixed results are obtained even in the same country.
One of the possible reasons for these differences is the data coverage of the sample in addition
to the differences between aggregated data and micro data. Special attention must be paid tointerpret the results obtained using the survey questionnaire data because sample firms are
sometimes not representative firms. Obtaining data on SMEs is extremely difficult. Even if it isavailable, firms who report financial data are limited relative to large SMEs. Therefore, thepossibility that a survey questionnaire includes a large number of small firms is very low. On
the other hand, the negative aspect of using aggregated data is that it is impossible to control
for the firm’s heterogeneity within industries.
The negative effect of increased concentration on firms’ entry into industries with a higher
27

value of intangible assets ratio contradicts the findings of Bonaccorsi Di Patti and Dell’Ariccia(2004). The results obtained in this study indicate that entrepreneurs with investment opportunities
but less collateral experience difficulties in beginning a new business.

5. Conclusion

This paper empirically investigated the effects of interbank competition in local credit
markets on firm entry, growth, and exit. Using aggregated establishment data at an industry
and prefecture level, it is found that an increased concentration in credit markets has a negative
effect on start-up rates and the average establishment size and a positive effect on exit rates infinancially dependent sectors with a higher value of intangible assets ratio. These results imply
that possible new entrants in industries with greater information asymmetries face difficulties
in obtaining credit for a positive Net Present Value (NPV) project in a concentrated credit
market.
The results obtained in this paper suggest that policy makers must keep in mind that effects
of interbank competition on start-up (exit) rates and firm growth vary according to industry
characteristics. Increased interbank competition relaxes the credit constraints of industries withhigh information asymmetries; it increases the number of firm entries and stimulates firm28

growth, but decreases firm exit rates. However, policy makers and entrepreneurs cannot
control the level of interbank competition. Economic policy to support possible new entrants
with less collateral in concentrated banking markets needs to be designed to increase start-up
rates and hence, stimulate economic growth in local markets.
Using the census data, this paper captured the overall effect of interbank competition in
local credit markets on start-up (exit) rates and on the average size of establishments. However,the limitations of this study are that the mechanism through which interbank competition
affects a firm’s decision to start-up, exit, and firm growth are not directly explained in theaggregated data. Investigation by using micro data is needed to further explore this mechanism.
Moreover, it is interesting to know the reason why mixed results are found within countries.
Explaining these issues are left for future research.

References
Black, Sandra E. and Philip E. Strahan (2002) “Entrepreneurship and Bank Credit Availability,”
Journal of Finance, Vol. 57, No. 6, 2807–2833.
Bonaccorsi Di Patti, Emilia and Giovanni Dell’Ariccia (2004) “Bank Competition and Firm
Creation,” Journal of Money, Credit, and Banking, Vol. 36, No. 2, 225–51.
Boot, Arnoud W. A. and Anjan V. Thakor (2000) “Can Relationship Banking Survive
29Competition?,” Journal of Finance, Vol. 55, No. 2, 679–713.
Cetorelli, Nicoka and Michele Gambera (2001) “Banking Market Structure, Financial
Dependence and Growth: International Evidence from Industry Data,” Journal of Finance,
Vol. 56, 617–648.
–––and Philip E. Strahan (2006) “Finance as a Barrier to Entry: Bank Competition and Industry
Structure in Local U.S. Markets,” Journal of Finance, Vol. 61, No. 1, 437–461.
Claessens, Stigin and Luc Laeven (2005) “Financial Depdendence, Banking Sector Competition,
and Economic Growth,” Journal of European Economic Association, Vol. 3, No. 1, 179–
207.
Fernández de Guevara, Juan and Joaquín Maudos (2009) “Regional Financial Development andBank Competition: Effects on Firms’ Growth,” Regional Studies, Vol. 43. No. 2, 211–
228.
Ogura, Yoshiaki (2007) “Lending Competition, Relationship Banking, and Credit Availability
for Entrepreneurs,” RIETI Discussion Paper Series 07-E-036.
Okamuro, Hiroyuki (2007) “Sonzoku, Seicho to Chiiki Tokusei (Survival, Growth, andRegional Factors),” Highch, Y., eds. Shinkikaigyokigyou no seicho to tettai (Growth andexit of new start-ups), 95–122, Keiso shobou, Tokyo (in Japanese).
Petersen, Mitchell A. and Raghuram G. Rajan (1994) “The Benefits of Lending Relationships:
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Evidence from Small Business Data,” Journal of Finance, Vol. 49, No. 1, 3–37.
––– and–––(1995) “The Effect of Credit Market Competition on Lending Relationships,” The
Quarterly Journal of Economics, Vol. 110, No. 2, 407–443.

Rajan, Raghuram G. (1992) “Insiders and Outsiders: The Choice between Informed and Arm’sLength Debt.” Journal of Finance Vol. 47, No. 4, 1367–1400.

–––and Luigi Zingales (1998) “Financial Dependence and Growth,” American Economic
Review, Vol. 88, No. 3, 559–586.
Sharpe, Steven A. (1990) “Asymmetric Information, Bank Lending, and Implicit Contracts: A
Stylized Model of Customer Relationships.” Journal of Finance Vol. 45, No. 4, 1069–1087.
Shikimi, Masayo (2010) “Chushokigyou no gingo kirikae to ginkoukan kyousou, ginkou
gappei, ginkou kiki (Bank Switch, Interbank Competition, Bank Merger, and Bank
Liquidity Problem: Evidence from SMEs in Japan),” Kitamura, Y., eds. Ouyou micro
keiryou keizai bunseki (Applied Microecnometric Analysis), 197–226, Nippon Hyoron
Sha, Tokyo (in Japanese).
––– (2013), “Do Firms Benefit from Multiple Banking Relationships? Evidence from Small andMedium- Sized Firms in Japan,” International Economics and Economic Policy, Vol. 10,
No. 1, 127–157.
31

Von Thadden, E. L. (1995) “Long-Term Contracts, Short-Term Investment and Monitoring.”
Review of Economic Studies Vol. 62, No. 4, 557–575.

Zarutskie, Rebecca (2006) “Evidence on the Effects of Bank Competition on Firm Borrowing
and Investment,” Journal of Financial Economics, Vol. 81, No. 3, 503–37.

32

Table 1 Hypotheses and expected signs
Dependent variables

Hypothesis

H1: Concentration in bank financing leads to an increase in relationship lending.
Thus, concentration in bank financing markets has a positive (negative) effect onstart-up (exit) rates of external finance dependent industries with more informational
asymmetry and a negative effect on establishment sizes in those industries due tohold-up problems (Petersen and Rajan 1994, 1995; Bonaccorsi Di Patti and Dell’
Ariccia 2004).
H2: Interbank competition leads to an increase in relationship lending (Boot andThakor 2000). Thus, increased concentration in banking sectors has a negative(positive) effect on start-up (exit) rates in industries with more informational
asymmetry and has a positive effect on the size of establishments in those industries.
H3: Interbank competition leads to an increase in relationship lending (Boot andThakor 2000). Thus, an increase in interbank competition has a positive (negative)
effect on the start-up (exit) rates of establishments in industries with more
informational asymmetry. It also has a positive effect on the size of establishments in
those industries because hold-up problems are mitigated due to a decline of firms'
switching costs to other banks (Claessens and Laeven 2005).

33

Start-up rates

Exit rates

Ln (employees per
establishment)

β2>0

β2<0

β2<0

β2<0

β2>0

β2>0

β2<0

β2>0

β2<0

Table 2 The distribution of establishment size
Number ofestablishments
Establishments with less than 4 employees
Establishments with 5–9 employees
Establishments with 10–29 employees
Establishments with 30–49 employees
Establishments with 50–99 employees
Establishments with 100–299 employees
Establishments with more than 300 employees
Establishments with only temporaly workers
Total

3,448,156
1,073,979
800,830
143,123
89,617
44,138
10,3495,555
5,615,747

Share
61.40%
19.12%
14.26%
2.55%
1.60%
0.79%
0.18%
0.10%

Sources: The Establishment and Enterprise Census issued by the Ministry of Internal
Affairs and Communications

34

Table 3 Summary statistics
Prefecture/two- digit industry/year
Establishments
Employees per establishment
Number of new establishments
Number of exit establishments
Number of new establishments/total establishments
Number of exiting establishments/total establishments
Employment share (%)

Number ofobservations

Mean

MedianStd. Dev.

25th
75th
percentile percentile

Min

Max

23880
9183
9183
9183
9183
23880

17.63
199.82
270.29
0.05
0.07
1.22

11.05
45.00
69.00
0.04
0.06
0.66

19.81
521.21666.87
0.06
0.05
1.46

6.50
10.00
15.00
0.03
0.05
0.17

20.49
160.00227.00
0.06
0.08
1.55

Industry characteristics
Median loans/assets for SMEs
External financial dependence for mature listed firms
Median intangible fixed assets/assets for SMEs
Average q ratio for listed firms

23880
21714
23880
17108

0.42
0.03
0.32
1.40

0.41
0.02
0.31
1.27

0.12
0.03
0.12
0.45

0.36
0.01
0.21
1.18

0.45
0.04
0.39
1.42

0.13
-0.02
0.07
1.01

0.92
0.17
0.67
4.09

Interbank competition in local markets
Herfindahl-Hirschmann Index of bank loans (HHI)

23880

0.29

0.28

0.13

0.21

0.39

0.00

0.62

35

1
384.250 11600.00
0 12579.00
0
1.10
0
3.71
0.00
10.64

Table 4.A The effect of concentration in banking markets on the number of new establishments
Dependent variable： number of new establishments(I )Median loan/assets for SMEs ×HHI (t–1)

0.234(0.178)(Ⅱ)

External financial dependence×HHI (t–1)

(Ⅲ)

(Ⅳ)

(Ⅴ)

0.787
(0.808)-1.707 ***(0.314)

Median intangible fixed assets/total assets×HHI (t–1)
q×HHI (t–1)-0.031(0.110)-0.526 ***(0.185)External financial dependence dummy×intangible fixed assets ratio×HHI (t–1)-4.506 ***External financial dependence×intangible fixed assets ratio×HHI (t–1)
Employment share (%)
ln(number of establishments) (t–1)
constant
Industry dummies* year dummies
Prefecture dummies* year dummies
Number of sample
Adjusted R2（VI）

0.030 ***
(0.005)0.845 ***
(0.035)-1.440 ***(0.190)
Yes
Yes
9183
-53838.8

0.031 ***
(0.004)0.845 ***
(0.034)-1.423 ***(0.194)
Yes
Yes
9183
-538380.023 ***
(0.005)0.844 ***
(0.034)-1.203 ***(0.180)
Yes
Yes
9183
-53394.4

0.012(0.008)0.899 ***
(0.035)-0.870 ***(0.298)
Yes
Yes
7427
-39468.1

0.027 ***
(0.005)0.845 ***
(0.034)-1.346 ***(0.181)
Yes
Yes
9183
-53744.1

0.028 ***
(0.004)0.845 ***
(0.034)-1.378 ***(0.190)
Yes
Yes
9183
-53795.4

The dependent variable is the number of new establishments. The external financial dependence dummy takes a value of one when industry's external financial dependence ispositive and zero otherwise. The coefficients are estimated by the Poisson estimator. Two way (prefecture*year) cluster robust standard errors are in parentheses. Significance atthe 1% level is denoted by ***.

36

Table 4.B The effect of concentration in banking markets on start-up rates
Dependent variable： ln(number of new establishments/total establishments)(I )Median loan/assets for SMEs ×HHI (t–1)

0.069(0.342)External financial dependence×HHI (t–1)

(Ⅱ)

(Ⅲ)

(Ⅳ)

(Ⅴ)

-1.189(1.060)-0.932 ***(0.347)

Median intangible fixed assets/total assets×HHI (t–1)-0.194 **(0.083)q×HHI (t–1)-0.975 ***(0.263)External financial dependence dummy×intangible fixed assets ratio×HHI (t–1)
External financial dependence×intangible fixed assets ratio×HHI (t–1)
Employment share (%)
ln(number of exiting establishments/ total establishmets) (t–2)
constant
Industry dummies* year dummies
Prefecture dummies* year dummies
Number of sample
Adjusted R2（VI）-0.027 **(0.013)0.289 ***
(0.039)0.580 ***
(0.178)
Yes
Yes
5808
0.7147-0.029 **(0.013)0.283 ***
(0.040)1.474 ***
(0.141)
Yes
Yes
5907
0.7194-0.031 ***(0.012)0.289 ***
(0.039)0.716 ***
(0.179)
Yes
Yes
5808
0.7152-0.028 **(0.014)0.292 ***
(0.045)0.951 ***
(0.158)
Yes
Yes
4606
0.7239-0.032 ***(0.011)0.290 ***
(0.039)0.597 ***
(0.177)
Yes
Yes
5808
0.7157-7.705 ***(2.257)-0.030 **(0.012)0.289 ***
(0.039)0.586 ***
(0.181)
Yes
Yes
5808
0.7152The dependent variable is ln(the number of new establishments/total establishments). The external financial dependence dummy takes a value of one when industry's external financial
dependence is positive and zero otherwise. Two way (prefecture*year) cluster robust standard errors are in parentheses. Significance at the 10%, 5%, and 1% level is denoted by *, **,and ***, respectively.

37

Table 4.C The effect of concentration in banking markets on exit rates
Dependent variable： ln(number of exiting establishments/total establishments)(I )Median loan/assets for SMEs ×HHI (t–1)

0.101(0.228)

(Ⅱ)

(Ⅲ)

(Ⅳ)

(Ⅴ)

2.670 ***
(0.666)External financial dependence×HHI (t–1)
Median intangible fixed assets/total assets×HHI (t–1)

0.186(0.223)q×HHI (t–1)

0.075(0.069)0.371 ***
(0.133)External financial dependence dummy×intangible fixed assets ratio×HHI (t–1)
External financial dependence×intangible fixed assets ratio×HHI (t–1)
Employment share (%)
ln(number of new establishments/ total establishmets) (t–2)
constant
Industry dummies* year dummies
Prefecture dummies* year dummies
Number of sample
Adjusted R2（VI）

0.013 **
(0.005)0.066 ***
(0.016)
2.177 ***
(0.133)
Yes
Yes
5892
0.66360.015 **
(0.006)
0.067 ***
(0.016)2.964 ***
(0.128)
Yes
Yes
5990
0.67360.014 **
(0.006)
0.067 ***
(0.016)2.167 ***
(0.142)
Yes
Yes
5892
0.66370.007(0.005)
0.061 ***
(0.018)2.688 ***
(0.056)
Yes
Yes
4687
0.67290.015 **
(0.006)
0.067 ***
(0.016)
2.152 ***
(0.138)
Yes
Yes
5892
0.664

4.845 ***
(1.470)0.015 **
(0.006)
0.068 ***
(0.016)
2.155 ***
(0.137)
Yes
Yes
5892
0.6641The dependent variable is ln(the number of exiting establishments/total establishments). The external financial dependence dummy takes a value of one when industry's externalfinancial dependence is positive and zero otherwise. Two way (prefecture*year) cluster robust standard errors are in parentheses. Significance at the 10%, 5%, and 1% level is denotedby *, **, and ***, respectively.

38

Table 5 The effect of concentration in banking markets on establishment size
Dependent variable： ln(employees per establishmet)(I )Median loan/assets for SMEs ×HHI (t-1)

0.111(0.186)(Ⅱ)

External financial dependence×HHI (t-1)

(Ⅲ)

(Ⅳ)

(Ⅴ)

-2.734 ***(0.897)

Median intangible fixed assets/total assets×HHI (t-1)-1.264 ***(0.184)q×HHI (t-1)-0.251 ***(0.039)External financial dependence dummy×intangible fixed assets
ratio×HHI (t-1)-0.434 ***(0.150)External financial dependence×intangible fixed assets ratio×
HHI (t-1)
Employment share (%)
constant
Industry dummies* year dummies
Prefecture dummies* year dummies
Number of sample
R2

（VI）

-9.235 ***0.118 ***
(0.004)2.783 ***
(0.121)
Yes
Yes
23792
0.8312

0.117 ***
(0.004)2.764 ***
(0.145)
Yes
Yes
22010
0.791

0.116 ***
(0.004)2.955 ***
(0.121)
Yes
Yes
23792
0.83160.123 ***
(0.005)2.872 ***
(0.115)
Yes
Yes
17330
0.76620.116 ***
(0.006)1.935 ***
(0.022)
Yes
Yes
21427
0.8201(1.825)0.116 ***
(0.006)2.767 ***
(0.031)
Yes
Yes
21427
0.8203The dependent variable is ln(the number of new establishments/total establishments (t-1)). The external financial dependence dummy takes a value of one whenindustry's external financial dependence is positive and zero otherwise. Two way (prefecture*year) cluster robust standard errors are in parentheses. Significanceat the 1% level is denoted by ***.

39

Table 6 Economic significance of an increase in concentration ofbanking markets on start-up rates and establishment size（％）

Start-up rates

Exit ratesln(employees per
establishmet)

Relative effect of a change in HHI from the 25th
percentiles to the 75th percentile
Median loan/assets for SMEs
0.12
0.18
0.19
***
External financial dependence
-0.78
1.74
-1.78 ******Median intangible fixed assets/total assets
-3.04
0.61
-4.12 ***
q
-0.83 **
0.32
-1.07 ********
External financial dependence dummy×
-3.18
1.21
-1.37 *a)intangible fixed assets ratio
This table reports, for example, the percentage differences in start-up rates (or establishment size)
between industries with low external financial dependence (at the 25th percentiles) and industries withhigh external dependence (at the 75th percentile) when the concentration of markets is increased fromthe 25th percentile (0.21) to the 75th percentile ( 0.39).
a)

It shows the effect of increased concentration in local credit markets on dependent sectors with ahigher value of intangible fixed assets ratio relative to nondependent sectors with a lower value of
intangible fixed assets ratio.

40