2011-41
The Roles of Relationship Lending and Utilizationof Soft Information on Bank Performancein Competitive Local Markets
Tadanori Yosano Takayoshi Nakaoka

The Roles of Relationship Lending and Utilization of Soft Information
on Bank Performance in Competitive Local Markets
Tadanori Yosano☆
Graduate School of Business Administration of Kobe University, 2-1, Rokko-dai cho, Nada-ku, Kobe,
657-8501, Japan
Takayoshi Nakaoka
SMBC Nikko Securities Inc. Industrial Research Department, 1-5-1, Marunouchi, Chiyodaku, Tokyo,
100-6524, Japan要約
わが国では，2003 年の金融審議会報告『リレーションシップバンキングの機能強化に向けて」以降，中小企業金融に対するひとつのビジネスモデルとして注目を集めるようになっており，わが国においてもマイクロデータを用いた研究が蓄積されている．しかしながら，これは企業側のデータを用いた分析であり，銀行がリレーションシップバンキングによるソフト情報の利用によって，パフォーマンスを向上できるのかどうかは分析されていない．
そこで本稿では，地域金融機関（地銀，第二地銀，信用金庫）に対するアンケートデー
タを用いて，リレーションシップバンキングによるソフト情報の利用が，貸出パフォーマ
ンスに影響を及ぼすか否かを分析している．ここでは銀行のアンケート回答を因子分析することによって，3 つのソフト情報の利用に関する潜在変数（企業の組織形態に関するソフ
ト情報，企業の取引関係に関するソフト情報，企業の事業と経営者に関するソフト情報）を抽出し，貸出収益率と不良債権比率との関係を分析している．
分析の結果，ソフト情報の利用は，銀行の貸出収益率を向上させ，また統計的に有意ではないが不良債権比率を低下させることが発見された．とくに抽出された３つの潜在変数の中でも，収益性の向上において，企業の取引関係に関するソフト情報が最も重要なソフ
ト情報となっていることが示されている．また，競争的な市場において，ソフト情報を利用することによって貸出における収益性を確保できることも示された．
これらの結果は，リレーションシップバンキングの研究における新たな発見事実であり，貸出技術の発展による他業態の競争圧力の増加に対して，リレーションシップバンキング
によるソフト情報を利用した貸出方法が，地域金融機関の収益確保に寄与していることを示している．
〔付記〕本稿は，（財）全国銀行学術研究振興財団の助成金（代表・與三野禎倫）を受けた研究成果の一部である．
Key words: Relationship lending, soft information, inter-bank competition, lending profitability,
credit risk rating, regional banks
JEL classification code: G21, G33, L14, L25☆

Corresponding author. Graduate School of Business Administration of Kobe University, 2-1,
Rokko-dai cho, Nada-ku, Kobe, 657-8501, Japan E-mail address: yosano@kobe-u.ac.jp

SMBC Nikko Securities Inc. Industrial Research Department, 1-5-1, Marunouchi, Chiyodaku,
Tokyo, 100-6524, Japan1

1. Introduction
Berger and Udell (2002) have argued that soft information, defined as unquantifiable, unverifiable
and undocumentable information production is an essential part of relationship lending. For the past
two decades, whether relationship lending benefits both lenders1 and borrowers has been examinedby many researchers. Before the early 2000s, much literature has discussed the influence of using
soft information on credit conditions (credit availability, interest rate, and collateral securities, etc).
However their results showed a mixed picture across countries. The effect differed across the bank’s
lending technologies and the borrower’s characteristics (Petersen and Rajan, 1994; Berger and Udell,
1995; Petersen and Rajan, 1995).
In recent years, researchers have focused more on the role that relationship lending has oninter-bank competition. For example, Boot and Thakor (2000) suggested that inter-bank competition
would accelerate relationship lending, but that beneficial added value for borrowers might decreaseas lenders shift their business from transaction lending to relationship lending. On the other hand,
Dinc (2000) suggested that inter-bank competition would lower the threshold of credit availability ofborrowers, but diminish the lenders’ enthusiasm for costly commitment with borrowers. Hauswald
and Marquez (2006) similarly suggested that inter-bank competition would reduce banks’ investment
in information acquisition because of the decrease of the lower expected interest rate. Previous
theoretical research reported that lenders benefit from monopolistic rent and higher interest rates byrelationship lending (Sharpe, 1990; Rajan, 1992).
However there has been little empirical evidence which supports this claim. Moreover, almost all
previous research investigated this claim using proxies of relationship lending: duration (the numberof years since lender granted the first loan to the borrower), scope of varieties of financial services
from lenders, and distance between headquarters of the lender and location of the borrower. The
reason why previous research used proxies for relationship lending is attributed to the difficulty ofmeasuring soft information directly. One exception was Schwarze (2007) who investigated the direct
influence of using soft information on lending performance by sending questionnaires to Germanbanks. Our research expands on Schwarze’s by identifying the components of soft information thatare beneficial to lenders. This paper is the first one to analyze the type of soft information that is
important and should be collected by banks to raise their profitability and improve their judgment ofcredit risk. It also describes the benefit of utilizing soft information especially if the bank faces
strong inter-bank competition.
Japanese Financial Services Agency introduced the action program which requires regional banks
to shift from transaction banking to relationship lending. “General supervisory guidance forsmall/medium-sized and regional financial banks” (FSA guideline, July 2008) clearly emphasized
the need for improvement in the ability to evaluate the importance of soft information. Against sucha background, the “Organization for Small & Medium Enterprises and Regional Innovation, JAPAN”
1

Note that the definition of the term “soft” is not identical to private or proprietary, while theseterms often show similarities. In this case, however, only the realm of ``soft'' information, including
the similarities with private and proprietary information, has been used in the relationship lending
research. Hard proprietary and private, such as information regarding personal manager assets,
are, therefore, excluded from research analysis in this specific study. For further detailed discussionof the information characteristics, see Petersen (2000).
2(SMRJ2) set up a working group for strengthening finance based on the soft information of small andmedium-sized businesses. The SMRJ carried out a survey in 2008, on the use of soft information in
the decision process used by lenders, especially for small and medium-sized enterprises. The results
are reported in “Investigation research business for the use of intellectual capital by small andmedium-sized enterprises—a chapter of the investigation and research of financing based on
intellectual capital” (SMRJ report, September 2008).
We investigated three factors of soft information: organizational systems, networks oralliances/partnerships, and business/management leadership on the basis of the SMRJ data. We
analyzed the relationship between financial metrics of lenders’ performance and soft information
factors both separately and jointly. We also examined whether these soft information factors havesignificant effects in an inter-bank competitive market. This paper contributes greatly to research on
relationship lending because we showed what kind of soft information is vital for banks to boost
their profitability and improve their judgment skill of credit risk especially if the bank faces strong
inter-bank competition.
The remainder of this paper is structured as follows. Section 2 introduces previous research andshows how our hypotheses were developed and tested. Section 3 shows our survey data and related
micro/macro data in detail and describes how we extracted soft information factors from our survey
data. Section 4 and 5 report the results of univariate and multivariate analyses, respectively. Finally,
section 6 reviews our results, summary, and conclusion.

2. Theory and Hypotheses
2.1 Information asymmetry and informational lock-in
Small businesses face difficulties raising long-term funds from capitalists due to asymmetricalinformation between lenders and borrowers. Typically, the financial information of small businesses
is not audited by accountants. Therefore, its level of reliability is inferior, by comparison, to big
businesses. In addition, even if small business financial information is reliable, the profitability andsolvency still lags behind that of big businesses. As a consequence, small businesses are more
inclined to rely heavily on banks, especially in Japan, where third-party investment capital is still in
beginning stages. In the absence of reliable financial information, soft information can often assure
the borrowers’ trustworthiness, which plays an important role in the lending market. Ideally, lenders
would start by conducting relation specific investments, in order to acquire the borrowers’ relevant
soft information. A commitment to future long-term lending can be made when lenders become
familiar with the borrower’s (typical) business practices. Soft information, established throughlong-term, personal relationships with borrowers, have specific characteristics. It is not only difficult
to quantify, verify, and document, but access is often restricted to others. Therefore, soft information
2

SMRJ is an affiliated association of Ministry of Economy, Trade, and Industry in Japan. SMRJ)
was officially launched on July 1st, 2004. SMRJ was formed through a merging of Japan Small andMedium Enterprise Corporation (JASMEC), Japan Regional Development Corporation (JRDC),
and Industrial Structure Improvement Fund (ISIF). They are expanding and strengthening networks
with private and public SME supporting organizations, such as Local Government Units, Chamberof Commerce and Industry, Society of Commerce and Industry, financial institutions andUniversities, to provide optimum support.
3

provides a certain level of exclusivity allowing lenders to enjoy advantageous monopolistic rents
from secure informational relationships ((Greenbaum(1989), Sharpe(1990), Rajan(1992), Hauswald
and Marquez (2006)). So we propose Hypothesis 1 as follows:
Hypothesis 1: The highly marginalized lending market creates monopolistic rents allowing
lenders who strategically collect and use soft information to achieve a higher
lending performance.
There is little previous empirical research that examines the influence of the utilization of soft
information on lenders’ performance. Schwarze (2007) found that qualitative information, such as
customer relationships and internal ratings generated by sales departments, has a positive impact on
two types of lenders’ performance: return on equity and operational margin, where operational
expenditure represents the ratio of administration effort plus loan losses to operation income.
Contrary to our hypothesis, some research suggests negative effects from relation-based lending
upon lenderothesis, some research suggests negative effects from relation-basedity an the expected
pay-off of lenders involved in relationship lending. He insists that even if the project requires
long-term funding, lenders actually prefer to create contracts with recurring short-term loan
increments rather than one long-term lump sum. Short-term contracts with recurring pay increments
create an atmosphere of pressure and responsibility amongst borrowers. Lenders are able to better
control the progress of their investments with the financial manipulation of capital, and borrowers
are compelled to communicate constantly eliminating the moral hazard inherent in long-term
relationships. The control of financial capital by lenders, however, holds up borrowers fromprogressing efficiently and discourages borrowers negating their motivation and incentives. This
controlling and oppressive relationship harms the success of the business, and, in turn, also limits thefinancial returns for lenders. Rajanhip harms the success of the business, and, in turn, also limits
theard inherent in long-termnal expenditure represents the ratio of administration effort plus loan
losses to operation incomeween the nature of the lender-borrower relationship and the profitabilityof the investment which can only be studied accurately through empirical data.
2.2 Inter-bank competition, shift to relationship lending, and the effect of relationship lending on
profitabilityPetersen and Rajan (1995) insist that inter-bank competition has the effect of lowering market
interest rates pressuring lenders towards the relationship lending option. Considering a model wherelenders are engaged in either transaction lending or relationship lending, not both, lower market
interest rates adversely hinder the lenders (banks) own future investment opportunities into soft
information (relationship lending). Even though, Boot and Thakor (2000) support Petersen andRajan’s model with the specific stipulation that lenders are engaged only in relationship lending,they propose, on the other hand, that if lenders are engaged in both transaction lending andrelationship lending, a substitution effect arises. Although, inter-bank competition causes a decreasein both lending types’ interest rates, lenders are motivated to lean more towards relationship lending,because the accumulation of soft information and opportunity for controlled involvement insulate
lenders from the effects of a pure price competition market.
4The substitution effect urges lenders to shift away from transaction lending towards relationshiplending for business. Meanwhile, the insulation effect protects the lender’s privilege to borrowers’
private information through constructed barriers for potential competitors and minimizes the lenders’risk of profit loss. Therefore, we propose Hypotheses 2, 3 and 4 as follows:
Hypothesis 2: Lenders in local markets with high inter-bank competition face lower profitability.
Hypothesis 3: Local market competition is the main motivation for lenders to acquire an
investment advantage through exclusive relationship lending which obtains anduses soft-information.
Hypothesis 4: Even though inter-bank competition has negative effects, a bank in a competitive
local market can acquire an informational advantage to limit its own loss.
In fact, some previous research of borrowers’ data has provided empirical evidence of a positive
correlation between inter-bank competition and relationship lending. For example, Degryse andOngena (2007) deduced evidence from a sample of 645 Bergin’s borrowers that lenders facing
inter-bank competition are more likely to engage in relationship lending.

Degryse and Ongena(2007) argue that the two most relevant variables for determining relationship lending situations are1) the length of the loan period and 2) the geographic proximity of the lenders to the borrowers.
Specifically, relationship lending is characterized by a longer term loan period and a closer
geographical distance between lenders and borrowers. Data shows that these characteristics,
signifying the presence of relationship lending, correlate with a higher degree of inter-bankcompetition measured by the density of lenders within a borrower’s district.
Contrary to the data aforementioned, there has been no empirical evidence supporting aninter-bank competition effect upon lenders’ profitability. This gap is primary due to the difficulties in
obtaining lenders’ data regarding relationship lending. This is the first study providing empiricalevidence of a direct correlation between inter-bank competition and lenders’ profitability.
2.3 Information advantage of relation-based lending and skill of credit analysis of regional
banks
Information advantage from collecting and using soft information might enable banks to improvetheir ability to accurately predict the risk of bankruptcy of potential borrowers, and reduce loan
losses from bankruptcy proceedings. Moreover, through the monitoring process of relationshiplending, the bank could observe the borrower’s business operation and investigate the efficiency ofthe loans over a long period (Berger and Udell, 2006). The efficient use of loans might lower
probability of bankruptcy. So we provide Hypothesis 5 as follows:
Hypothesis 5: Lenders who place a great importance on collecting and using soft information
obtain a lower bad loan ratio, on average.
2.4 Inter-bank competition and the effect of relationship lending on bad loan ratios
5In the previous section, we exposed that inter-bank competition decreases the overall rents for
lenders. However, a shift towards relationship lending and away from transaction lending, may curba more drastic decrease in lenders’ profitability, because it is characteristic for relationship lending tomaintain profitable under the pressures of competition. The decline of rents lowers banks’ financial
ability to participate fully in information investment, and drives down the threshold of credit
availability for borrowers, as described by Dinc (2000). While the shift towards relationship lending
can balance the decreased engagement in transaction lending, the competition for soft information
results in a lower standard for borrower contracts. These two factors, the financially restrained
information investment and lower threshold of credit availability, may result in a higher incidence ofbad loans. So we propose Hypotheses 6 and 7 as follows:
Hypothesis 6: Lenders in higher inter-bank competition local markets proportionally have higher
bad loan ratios.
Hypothesis 7: Furthermore, banks within a competitive local market that acquire an information
advantage become insulated from inter-bank competition, despite the lower average
quality of loans.

3. Data and Characteristics of Japanese Banks
3.1 Survey description and sample
The data in this study come from a survey of a working group that examined financing using thesoft information of small and medium-sized businesses.

This survey was conducted by the“Organization for Small & Medium Enterprises and Regional Innovation, JAPAN” (SMRJ) in early
2008. Before sending questionnaires to the banks, we conducted primary interview surveys with six
regional banks in late 2007, and we collected comprehensive soft information items which are
generally used in credit decision making and/or business support for borrowers. Soft information
items were assigned to seven categories: (1) manager, (2) internal/external business environments,(3) business contents, (4) customers/suppliers, (5) employees, (6) systems that form the bases oforganizations, and (7) risk management/corporate governance. Questionnaires were mailed to 575
banking institutions and responses were received from 428 (6 city banks, 76 regional banks, 244
cooperatives banks, 98 credit unions3, and 4 others), with a response rate of 76.3%. To improve theresponse rate, a follow-up phone call was made to all banks that did not respond by the deadline.
Trust banks and urban banks are totally different from regional and cooperative banks. Credit
3

Cooperative banks (Shinkin banks, based on the Law on Cooperatives of Small and Medium
Enterprises) and credit unions (Shinkumi banks, based on the Law on new Credit Unions) areJapanese banks which are legally limited to specializing in local small and medium-sizedbusinesses. While both are based on not-for-profit and mutual-aid membership schemes, there aresome differences in business areas which are outlined in their respective laws. Cooperative banks
are supposed to lend to firms that are slightly larger than the firms that credit unions serve. (Ulrike,2005).
6

unions are prohibited from serving a broad clientele. Therefore, we excluded these three types of
institutions from our analysis. We constructed our sample with 76 regional banks and 244
cooperative banks, totaling 310 banks.
Table 1 shows the level of use by banks of soft information in three finance processes: credit
ratings, assessment of borrowers and loan determination, and monitoring policies. S= “Strong use”,
M= “Medium use”, W= “Weak use”, N= “No use”, and NA= “No reply.” The credit rating stage has
the highest percentage of “Strong use” of soft information, 18.8%. This figure suggests that a large
minority of regional banks in Japan use soft information positively in making financial decisions.
Table 2 shows how soft information influences credit conditions (interest rate, amount offered,length of financing, and collateral amount). The table shows that banks utilized soft information themost on the amount offered (28.36%). The second highest percentage, 26.09% was utilized in thearea of interest rate. Table 2 shows that 19.16% and 15.23% of the banks used soft information for
determining the collateral amount and the length of financing, respectively. These descriptive results
are consistent with the findings of previous research such as Petersen and Rajan (1994) and Berger
and Udell (1995), which analyze the benefits of relationship lending. Their findings asserted that
relationship lending leads to lower interest rates, amplifies credit availability, and reduces collateral
securities.
The survey utilized a five-point likert scale to determine credit lending (1=No use, to 5=
Considerable use). Soft information items, mean values, and standard deviations of our sample foreach SMRJ category are given in Table 3.
3.2 Factor analysis for soft information and descriptive data of our sample
We conducted factor analysis in order to determine what categorized information the lenders placea great importance on in the credit decision. Many previous researchers (Scott, 2004; Ogura and
Uchida, 2007; Uchida et al., 2008) conducted principal component analysis to integrate their data.
However, we extracted latent factors based on the lenders’ view of soft information. The procedureof extracting latent factors from the lenders’ responses was done in order to identify the kind of soft
information that is beneficial to lenders, when it is collected and utilized in credit decisions.
Our survey was conducted after the Financial Services Agency (FSA) promoted relationshiplending in banks. We thought that, even though the action program issued by FSA was notcompulsory, Japanese banks would conform to the guidelines for relationship lending. We were in
the position of determining the precise value of soft information in the banks’ credit making
decisions.
In our procedure of extracting latent factors, we computed the correlation coefficients between the
SMRJ categorized items and the major potential factors by varimax rotation, and we finally
extracted the three principal factors: (1) organizational systems, (2) business and management
leadership, and (3) networks or alliances/partnerships. To test the convergent and discriminant
validity of the multiple-item scale of the three factors, we performed the confirmatory maximum
likelihood factor analysis. The resulting three factors explained 67.82% of the total variance, hadeigenvalues of 6.973, 2.709, and 1.227, respectively, and had an average communality of 0.460. The
loadings for all items within a factor exceeded. 4 and were statistically significant. Taken together,
these findings strongly support the identification of our three main factors. Additionally, no item
7

cross-loaded on another factor at a level was higher than .40. The procedure used to derive the factor
score is as follows: the average number of each SMRJ item was multiplied by the corresponding
factor loadings of the variables for the given factors. The resulting products for each item within afactor were summed and this sum comprises the factor score. See Table 3 for the results of the factor
analysis and the detailed items of all the soft information factors. Our detailed procedure is also
explained in the Appendix A.
Organizational systems. The seven items comprising the factor organizational systems(Cronbach's alpha = .878) were based on the work of Nonaka and Takeuchi (1996) and Edvinsson
and Malonc (1997). They originally discussed the key concept of an organization’s ability toappropriate and store the knowledge of workers in their physical organization, and to subsequently
share the organizational knowledge by facilitating interactions and exchanges among workers.
Organizational systems involve the following SMRJ categories: personnel evaluation systems (.868),
in-house improvement proposal systems/number of improvement suggestions (.819), incentive
systems (.718), corporate education systems (.674), IT system implementation (.640), healthy
management-labor relations (.611), and expertise employees (.535).
Networks or alliances/partnerships. Likewise, the seven items defining networks oralliances/partnerships (Cronbach's alpha = .860) were based on the following concepts: customer
scope and relation-specific knowledge in the network and the coordination with the suppliers in the
network (Nobeoka and Dyer, 2002). We also referred to the more general knowledge management
literature by Nonaka (1994), in order to describe this factor. We thought that an organizational ability
to share and disseminate knowledge among networks of customers, suppliers, and managers
determines the extent the organization can improve their collaboration. Therefore we constructed
networks or alliances/partnerships with the following SMRJ categorized items: relationship with
suppliers (.807), relationship with customers (.727), suppliers and their status (.706), customers andtheir status (.623), seniority of management (.506), length of senior management in the sector (.497),
and networks of management (.456).
Business and management leadership.

Finally, the eight items measuring business andmanagement leadership (Cronbach's alpha = .801) were based on the following concepts: theselection and concentration of businesses by analyzing their strengths and weaknesses (Porter, 1980;
Porter 1985), the focus on the economics of scope and the generation of technological innovation(Teece, 1980; Teece, 1986), and the management leadership which drives the business and makes
their profit (Nonaka et al., 1996). We constructed networks or alliances/partnership with thefollowing SMRJ categorized items: capability of management (.590), business schedules (.569),
character of management (.547), superiority of main business (.509), leadership of management(.501), management philosophy (.496), superiority of technologies (.461), and management trainingof successors (.445).
Bank performances: profitability and bad loan ratio. The assessment of the regional banks’profitability in the lending operation can be measured by multiple financial metrics, such as the ratio
of interest income from loans and discounts to total assets (Loan Profitability), the ratio of net
interest income from loans and discounts to total assets, and return on assets (ROA). We adopted
Loan Profitability in our analysis because our purpose was to measure the performance of the bank’s
credit operation. Therefore we did not use ROA metric because the numerator involves revenue
outside the lending operation. Net interest income is defined as the interest income from loans and8discounts minus loan losses and minus provision for possible loan losses. Many researchers inaccounting pointed out that the figure of the provision for possible loan losses is often arbitrarily
decided by the management; therefore we did not use Net Loan Profitability.
The regional banks’ ability to utilize the credit analysis can be measured by the ratio of bad loans
to total assets (Bad Loan Ratio). However, there are several ways to define bad loans in Japan. One
is the balance of the loans to bankrupt borrowers (or in bankruptcy proceedings) under the bankaccounts. Those have been reported in the statement of risk-controlled loans in Japan since 1997.
The other is the balance of non-accrual delinquent loans under the bank accounts. Those had beenreported in the statement of risk-controlled loans until 1997. Accordingly, we used both bad loan
definitions in our analysis, but reported only the results from the first one. However the results fromthe second one showed almost the same as the first one. All the data used to calculate theperformance and the bad loans ratio were obtained through the Nikkei NEEDS-Financial QUEST by
Nikkei Media Marketing, Inc. All independent variables are measured in the 2007 accounting period,
the same period in which the survey was conducted.
Herfindahl-Hirschman index. In our study, the number of bank branches in the local market wasused to calculate the Herfindahl-Hirschman index. A higher Herfindahl-Hirschman index indicates agreater bank monopoly in the local market. According to the traditional viewpoint ofcompetition-fragility, higher competition encourages the banks to take higher risk, because it gives
rise to diminishing monopolistic rent. In this scenario, riskier lending due to higher competition
results in a higher bad loan ratio. On the other hand, according to the modern viewpoint ofcompetition-stability, a monopolistic environment encourages the banks to take higher risks.
However, riskier lending practices result in adverse selection and increase the bad loan ratio. Thisview suggests that monopolistic rent from low competition increase the moral hazard of the bank. In
order to determine which viewpoint best accounts for our data, we included the square term of
Herfindahl-Hirschman index in our analysis.
Control Variables. There has been little research which examined the relationship between soft
information and banks’ performance. Therefore, we controlled for all the variables that might
influence the banks’ performance in order to exclude the possibility of covariation between thedependent variables and the missing control variables in our multivariate regression analysis. Thereis much empirical evidence which showed that large banks recorded more profit than smaller ones
(Stiroh and Rumble, 2006). On the other hand, there is also some empirical evidence which showed
the opposite results (Carter et al., 2004). Therefore we controlled for any potential extraneous effectsof economies of scale and/or any potential differences in credit risk management techniques between
the organizational size and its growth (Bank Size, Extent of Growth).
We included the square term of Extent of Growth to take diseconomies of scale into consideration(Stiroh and Rumble, 2006).
We also controlled for the following variables:(1) The ratio of branches to total assets (Branch Ratio) was used due to its potential effect onregional networks. Franchising neighborhood bank offices in the local area strengthens thecommunity knowledge of those banks and improves their judgment in assessing
credit-worthiness of loan applicants. Expanding the market share by franchising their branches
results in improving their ability to decide on credit-worthiness and reduces risk of bankruptcy.
This method is independent of risk reduction obtained by a diverse lending portfolio (Berger
9and Deyoung, 2001; Bofondi and Gobbi, 2006).
(2) The ratio of net assets to total assets (Capital) was used due to its potential function as barrier
and/or a buffer. Capital functions as an entry barrier for credit lending, and also functions as abuffer when a bank decides to take a risk. Only banks with great capital are in the position toundertake the risk and to extend credit to SME lending.
(3) The ratio of SME lending to total lending (SME Lending Ratio) was used to control for higher
interest rate of SME lending. The higher the SME Lending Ratio, the higher the interest rate of
SMEs lending, because SME lending has a larger risk than big business lending. Previous
research pointed out that community banks take advantage of the rent from small business
lending and suggested that banks should focus on these types of loans to survive in acompetitive market (Carter et al., 2004; Carter et al., 2005).
(4) The local market share of the loan (Local Share) was used as a proxy variable, representing thebank’s familiarity of the local market characteristics and the bank’s reputation from within thelocal community (Bharath et al., 2007). Local share also represents monopolistic power(Bofondi and Gobbi, 2006) as well as the Herfindahl-Hirschman Index described above.
Additionally, the performance of the banks and the default risk of the borrowers mightsystematically depend on the local macro economic circumstances. Therefore, we controlled thefollowing four regional economic characteristics:
The growth rate of the land price (Land Price) is linked to the collateral value of the lending
property. An increase in the collateral value facilitates the lending because the banks will able torecover the principle if the firm faces bankruptcy. Land Prices is also a lagged indicator of the local
economic climate.
The descriptions of all dependent and independent variables, including Cooperative Dummy,
Merger Dummy, and Urban Dummy, and Soft Collecting are shown in Table 5.

4. Data and Empirical Analysis
4.1 Descriptive statistics and univariate test
Descriptive statistics are shown in Table 5. We also show three principal factors: organizational
systems, business/management leadership, and networks or alliances/partnerships. These three
metrics are normalized. The mean value of Loan Profitability is 1.412%. The mean value of bad loan
ratios is 7.051%.
We crossed loan profitability and Bad Loan Ratio with two levels of soft information use (high andlow). We analyzed the factors into two groups (see Table 6 left side). In this dichotomy, scores wereassigned to high score group if they were above the median and low factor score group if they were
below the median. We also analyzed the factors into three groups (see Table 6 right side). We
compared the top tertile with the bottom tertile and ignored the middle tertile in our analysis. Thus
groups were either high or low on Factor Scores 1, 2, and 3.
First, we analyzed the factors by dividing them into two groups (see Table 6 left half). In thisdichotomy, scores were assigned to High Factor Score group if they were above the median and Low
Factor Group if they were below the median. We also analyzed the factors by dividing them into
three groups (see Table 6 right side). We compared the top tertile with the bottom tertile and ignored
10

the middle tertile in our analysis. So groups were either High or Low on Factor Scores 1, 2, and 3.
We crossed Loan Profitability and Bad Loan Ratio with two levels of soft information use (High
Factor Score and Low Factor Score). Table 6 summarizes the mean values and the results of thedifference of means test for both groups using a t test.
In Hypothesis 1, we predicted higher profitability when the lenders use more soft information in thecredit decision process. In particular, we found Loan Profitability as a function of the level of use of
Network information supported Hypothesis 1. This was significant at the 1% level with a one-tailed
test and 5% level with a two-tailed test, respectively, both in the dichotomized and tertile groups.
The other two main factors, Organizational Systems and Business/Leadership, were not statisticallysignificant. Regarding Loan Profitability as a function of the level of use of Organizational Systems,
the mean score of the High Factor Score group was lower (but not significantly) than the mean scoreof the Low Factor Score group in both the dichotomized and tertile analysis. Regarding Loan
Profitability as a function of the level of use of Business/Leadership, the mean score of the High
Factor Score group was lower (but not significantly) than the mean score of the Low Factor Score
group in the dichotomized and analysis. The mean score of the High Factor Score group was higher
than the mean score of the Low Factor Score group (consistent with our hypothesis, but notsignificant) in the tertile analysis.
In Hypothesis 5, we predicted a lower Bad Loan Ratio effect when the lenders use more soft
information in the credit decision process. In particular, Loan Profitability as a function of the levelof use of Network information strongly supported Hypothesis 1, at the 1% level both with a
one-tailed test and a two-tailed test, for both the dichotomized and tertile groups. The other two main
factors, Organizational Systems and Business/Leadership were not statistically significant.
Regarding the Bad Loan Ratio as a function of the level of use of Organizational Systems, the meanscore of the High Factor Score group was higher (but not significantly) than the mean score of the
Low Factor Score group in both the dichotomized and tertile analysis. Regarding Bad Loan Ratio asa function of the level of use of Business/Leadership, the mean score of the High Factor Score group
was lower (which was consistent with our hypothesis, but not significant) than the mean score of the
Low Factor Score group in both the dichotomized and tertile analysis.
Second, we analyzed interbank competition by dividing Herfindahl-Hirschman indexes into twogroups (see Table 7 above side). In this dichotomy, Herfindahl-Hirschman indices were assigned tobanks that fell into the High Competition group if they were below the median and to the Low
Competition group if they were above the median. We also analyzed interbank competition by
dividing Herfindahl-Hirschman indices into three groups (see Table 7, Panel B). We compared the
top tertile with the bottom tertile and ignored the middle tertile in our analysis. We crossed Loan
Profitability and Bad Loan Ratio with two levels of interbank competition (High Competition and
Low Competition). Table 7 summarizes the mean values and the results of the difference of means
test for both groups using a t test.
In Hypothesis 2, we predicted lower profitability when the lenders face strong interbank
competition. We found Loan Profitability as a function of interbank competition strongly supported
Hypothesis 2, at the 1% level both with a one-tailed test and a two-tailed test, for both thedichotomized and tertile groups. In Hypothesis 6 we also predicted a higher bad loan ratio when thelenders face strong interbank competition. However, this prediction was not borne out. In fact, wefound the opposite results. We obtained a low Bad Loan Ratio as a function of interbank competition11

at the 1% level both with a one-tailed test and a two-tailed test, for both the dichotomized groups andtertile groups, a finding which was contrary to Hypothesis 6. These results suggests that the lenders
who enjoyed a monopolistic environment were inclined to undertake riskier lending practices that
resulted in a higher bad loan ratio, a finding consistent with the modern viewpoint ofcompetition-stability described in Berger et al. (2009). We discuss these results in detail in the later
section of multivariate analysis.
Finally, we crossed three principle factors (together with their component items) with two levels ofinterbank competition (High Competition and Low Competition). Panel A and Panel B of Table 8summarize the mean values and the results of the difference of means test for both the dichotomized
and tertile groups using a t test, respectively.
In Hypothesis 3 we predicted higher use of soft information Factor Scores when interbank
competition is higher in. In particular, Factor Scores of Business/Leadership as a function ofinterbank competition supported Hypothesis 3, at the 10% level with a one-tailed test in the tertile
groups. In the dichotomized analysis, the mean score of High Competition was higher (but notsignificantly) than the mean score of Low Competition. The other two main factors, Organizational
Systems and Networks were not statistically significant. Regarding Organizational Systems as afunction of interbank competition, the mean score of the High Competition was lower (but notsignificantly) than the mean score of the Low Competition for both the dichotomized and tertile
analysis. Regarding Networks as a function of interbank competition, the mean score of the High
Competition group was higher (which is consistent with our hypothesis, but not significant) than themean score of the Low Competition group for both the dichotomized and tertile analysis.
4.2 Multivariate Analysis
In our multivariate analysis, we assumed banks’ profit function include bank-specificcharacteristics as well as regional characteristics, as follows:
Performancei＝f (Soft Information Factorsi, Inter-bank Competitioni, Bank Characteristicsi ,Regional Characteristicsi j,  i）,
where bank characteristics denote an individual banks specific attributes excluding soft informationand the Herfindahl-Hirschman Index (which measures inter-bank competition). i denotes anindividual bank, and j denotes the region where the bank’s headquarters are located.
We correlated all of the control variables with each other and we were concerned with the numberof control variables that were found to have significant correlations. We calculated the varianceinflation factor (VIF) which quantifies the severity of multicollinearity. All the VIFs of the
independent variables in our multivariate regression were below 7. Consequently, we concluded that
our model did not have a severe multicollinearity problem, because in general, this problem occurs
when VIF is over 10. In order to ensure unbiased and consistent estimates, we controlled for all thevariables that might influence the banks’ performance to exclude the possibility of covariation
between the dependent variables and the missing control variables.
4.2.1 Multivariate Analysis and Loan Profitability
Results of our multivariate tests on Loan Profitability are shown in Panel A and B of Table 9. In
12

Hypothesis 1, we had predicted positive coefficients of the three Soft Information Factors(Organizational Systems, Networks, and Business/Leadership) on Loan Profitability. Our prediction
was borne out for two Soft Information Factors: Networks and Business/Leadership. The coefficientof Networks with Loan Profitability was 0.0413 with a t statistic of 3.49 (1% significance level); thecoefficient of Business/Leadership with Loan Profitability is 0.0299 with a t statistic of 2.01 (5%significance level). These results are presented in the Full Model of Panel A.
On the other hand, the coefficient of Organizational Systems with Loan Profitability was negative,-0.0219, with a t statistic of -1.90 (10% significance level), which is contrary to our hypothesis, alsopresented in the Full Model of Panel A. The banks which use soft information regarding
Organizational Systems face lower profitability, on average. The component items of Organizational
Systems relate to organizational stability. Our results suggest that excessive reliance on soft
information regarding the borrowers’ organizational appearance results in slightly less profit. Larger
companies may appear to be better risks, but this is not always the case.
Regarding the interactions of Soft Information Factors, we found that Organizational Systems andBusiness/Leadership was significantly positive at the 10% level (see Interaction Model of Panel A).
Recall that we found a negative effect of Organizational Systems when this factor was examined
independently. However, the interaction of Organizational Systems and Networks was positive(resulting in higher profitability for the bank). This change in direction from negative to positive
suggests that if banks make use of Organizational Systems information in conjunction with
Networks information, the negative impact of Organizational Systems information was diminishedby the powerful positive effects of Network information.
However, we also found that the interaction of Networks and Business/Leadership was
significantly negative at the 10% level (see Interaction Model of Panel A). Recall that we foundpositive effects of both Networks and Business/Leadership when these factors were examined
independently. Therefore, when banks make use of Networks information in conjunction with
Business/Leadership information, they should not expect an additive effect of the two factors.
In Hypothesis 2, we predicted a positive relationship between the Herfindahl-Hirschman Indexand Loan Profitability. Our results strongly support this hypothesis. The coefficient of the
Herfindahl-Hirschman Index is 1.208 with a t-statistic of 4.27, significant at the 1% level, and ispresented in the Full Model of Panel A. In Appendix B, we show the positive linear relationshipwhich was obtained by the regression of Loan Profitability on the Herfindahl-Hirschman Index(measured by the square of the number of branches) and adjusted for covariation with other control
variables.
In Hypothesis 3, we predicted that the utilization of soft information would limit the banks’ losses,
even in a competitive local market. In our regression analysis, we assigned banks to tertile categories
based on their Herfindahl Index. We limited our analysis to the first and third tertile. We assignedbanks that were at or below the first tertile to the High Competition Dummy variable = 1, and banks
that were at or above the first tertile the High Competition Dummy variable = 0. We found thecoefficient of the intersection of Networks and the High Competition Dummy was significantly
positive at the 10% level, and Business/Leadership and the High Competition Dummy was alsosignificantly positive at the 5% level. These findings were consistent with our hypothesis (see Joint
Effects Model of Panel B, Table 9). In general, banks’ profitability suffers in a highly competitivemarket (supporting Hypothesis 1).

Our data shows that banks can offset the loss inherent in a13highly competitive local inter-bank environment if they utilize soft information, especially Networks
and Business/Leadership information.
4.2.2 Multivariate Analysis on the Bad Loan Ratio
Results of our multivariate tests on the Bad Loan Ratio are shown in Panel A and B of Table 10. In
Hypothesis 5, we predicted negative coefficients of the three Soft Information Factors(Organizational Systems, Networks, and Business/Leadership) on the Bad Loan Ratio. However, thecoefficients of the three soft information factors are not significant. The signs of the coefficients ofOrganizational Systems and Networks are in the direction of our hypothesis (negative).
In Hypothesis 6, we predicted a negative relationship between the Herfindahl-Hirschman Index and
the Bad Loan Ratio. However, the coefficient of the Herfindahl-Hirschman Index was positive,
34.48, with a t statistic of 2.73 (1% significance level),, also presented in the Full Model of Panel A.
Regarding the Bad Loan Ratio, we included the square of the Herfindahl-Hirschman Index to
examinethe modern viewpoint of competition-stability, which suggests that a monopolistic

environment encourages banks to take higher risks. The coefficient of the squared
Herfindahl-Hirschman Index was negative, -78.80, with a t statistic of -2.26 (5% significance level).
This

resultshows

aninverseU-shaped

relationship(convexfunction)

betweentheHerfindahl-Hirschman Index and the Bad Loan Ratio. In Appendix B, we present this inverse
U-shaped relationship which was obtained by the regression of the Bad Loan Ratio on the
Herfindahl-Hirschman Index and adjusted for covariation with other control variables. At the peak of
the inverse U, two different things happen.

When banks are located in the local area with a

Herfindahl Index over 0.218, they tend to lower their threshold of credit-worthiness and this result ina higher Bad Loan Ratio, which is consistent with our hypothesis. On the other hand, when banks arelocated in the local area with a Herfindahl Index below 0.218, they enjoy a lower Bad Loan Ratio ina high-competitive market, which is consistent with the competition stability view.
In Hypothesis 7, we predicted that the utilization of soft information would decrease the banks’
Bad Loan Ratio, even in a competitive local market. In our regression analysis, we assigned banks totertile categories based on their Herfindahl Index. We limited our analysis to the first and third tertile.
We assigned banks that were at or below the first tertile to the High Competition Dummy variable =1, and banks that were at or above the first tertile the High Competition Dummy variable = 0.
However, all the coefficients of the three soft information factors were unexpectedly not significant.

5. Robustness Test
5.1 Influence of Soft Information on Loan Profitability
We conducted the F test for the coefficients of three factors in the baseline model in order toquantify the robustness of our multivariate test results. In regards to loan profitability in the baselinemodel, the F test results, shown in the 4th row in Panel A of Table 9, strongly rejects the null
hypothesis that

requires all coefficients of the three factors (organizational system, networks, andbusiness/management leadership) to be zero at a 1% significant level (p value = 0.0003). Therefore,
we can confirm that the utilization of soft information, all-in-all, has an influential power over lender14profitabilities. With respect to the joint effect model of soft information and local competition, the F
test results, shown in the 5th row from the bottom in Panel B of Table 9, strongly rejects the null
hypothesis that requires all coefficient of the three factors to be zero at a 5% significant level (pvalue = 0.0478), which is consistent with the results of the baseline model.

The joint effect model

also strongly rejects the null hypothesis that states all coefficients of the three points of intersection
between soft information factors and local competition are zero at 10% significant level (p value =0.0717).Therefore, we can confirm that the utilization of soft information in the local market, overall,has the power to reduce the lenders’ loss due to the local market competition.

5.2 Influence of Soft Information on Bad Loan Ratio
In regards to the bad loan ratio in the baseline model, the F test results, shown in the 4th row in
Panel A of Table 10, does not reject the null hypothesis that states all coefficients of the three factors
are zero. Therefore, it is difficult for us to insist upon the utilization of soft information’s all-in-all
influence over a lender’s judgment of the borrower’s creditworthiness. With respects to the joint
effect model of soft information and local competition, the F test results, shown in the 5th row fromthe bottom in Panel B of Table 10, also does not reject the null hypothesis which imposes that all
coefficients of the three factors need to be zero. The joint effect model also does not reject the null
hypothesis that also mandates all coefficients of the three points of intersection between soft
information factors and local competition to be zero. Therefore, it is also difficult for us to quantify
definitively the power of soft information utilization, to subdue the lender’s risk taking behavior in
the local market competition.

5.3 Panel Data Analysis Regarding Loan Profitability
To confirm the robustness of our multivariate test results, we also ran the
random-effects of the GLS regression, despite the ordinary regression model, samplingfrom 2004 to 2008, which is the period after the action program was introduced in 2003.
The random effect model estimation enables us to identify the case specific affects of soft
information which is consistent with regards to time. In our GLS regressions, three soft information
factors are considered to be individualized effects constant with time, and are separated andidentified as three soft information effects from other independent variables. In our model
specifications, we included three soft information factors as proxy variables for measuring soft
information usage levels respectively, which further explain the individual firm’s affect in the GLS
regression. Therefore, we can assume that the influence from the omitted individual effects is slightand do not give raise to serious estimation problems (Wooldridge (2001)). Thus, we specified theeconometric model as follow:

performanc eit    f i  x i ,t   z i ,t    i   it . i  1,  , Nt  2004,  ,2007

In our model, we have included the following independent variables: First, the three soft
information factors f i , which highlight the consistent individualistic soft information effects.

15

Second, the bank oriented control variables x i , which show the regional economic effects. We
estimated our GLS regression by controlling the dummy variables by year in order to avoid theinfluence of other factors that are time-specific.
Both GLS test results for the baseline model and the joint effect model regarding soft information
within the local market, shown in Panel A and B of Table 11 respectively, are consistent with theordinary square regression results. The joint effect model also strongly rejects, at 5% significant
level, both the null hypothesis where all the coefficients of the three factors are zero (p value =0.0201), and the null hypothesis where all the coefficients of the three intersections between soft
information factors and local competition are zero (p value = 0.0461). As the baseline model results
show that the utilization of soft information, in general, has an influential power on lender
profitability, and the results of the joint effect model illustrates that the utilization of soft informationin the local market, overall, subdues the lender’s loss due to the local market competition.
Furthermore, Both GLS test results for the baseline model and the joint effect model regarding
soft information within the local market, shown in Panel A and B of Table 11 respectively, are also
consistent with the ordinary square regression results. The baseline model Wald test results does not
reject the null hypothesis that states all coefficients of the three factors are zero. The joint effectmodel Wald test also does not reject the null hypothesis that states all coefficients of the three factors
are zero, and the null hypothesis where all the coefficients of the three intersections between soft
information factors and local competition are zero.

6. The Role of Network Information in Lending Decisions
In the univariate analysis, we found a strong positive association between high loan profitability
and the use of network information. In the multivariate analysis, we also found positive effects of
networks and business/leadership information on loan profitability. The magnitude of the Network
factor (coefficient = 0.0413) is greater than the magnitude of the Business/Leadership factor(coefficient = 0.0299). We also found that network and business/leadership information can mitigate
the reduction of the lenders’ profitability when inter-bank competition is high. In addition, in theunivariate analysis, we also found a strong association between an improved bad loan ratio and theuse of network information. In the multivariate analysis, these two variables co-varied in thepredicted direction but were not statistically significant. On the whole, however, our empiricalresults have suggested that utilizing the soft information allow the banks to attain a more precise
lending decision.
Why does network information play such a substantial role in lending performance? We proposethat network information plays a vital role in determining creditworthiness of small andmedium-sized businesses. Lenders accumulate network information in order to compensate for the
opacity of the information provided by small and medium-sized businesses. Lenders can reach acertain level of confidence if the small and medium-sized businesses have created durable networks
in their community.
Our rationale is consistent with the empirical findings in the area of trade credit (Cunat, 2006;
Giannetti et al., 2008). In the supplier/customer relationship of trade credit, the supplier accumulates
the customers’ private information through a credit trade and an exchange of commercial notes. The
16

supplier is also more aware of the customers’ financial situation through daily trade, evaluating theirsituation through such measures as the punctuality of delivery and quality of products. For example,
Cunat (2006) found that suppliers may act as liquidity providers and insure against liquidity shocks
when customers face financial distress, especially when their customer-supplier relationship isdurable and intermediate products are specific (not commodity products) and difficult to sell to otherparties.

7. Concluding Remarks
In our study, we examined the influence of the utilization of soft information in relationshiplending on lenders’ financial profitability and their ability to determine credit-worthiness of theirborrowers. Soft information is difficult to quantify and document; it is often protected from accessby others. Therefore, it is difficult to directly measure the utilization of soft information. In order todo so, we conducted a questionnaire survey of regional and cooperative banks in Japan. From thisdata, we were able to extract principal soft information types through factor analysis. We identified
three principal factors: organizational systems,

networks or alliances/partnerships, andbusiness/management leadership. These factor scores which were extracted by factor analysis arelatent variable for utilization of soft information.
Regarding loan profitability, we found a strong association between high loan profitability and theuse of network information, in our univariate analysis. We also examined the relationship between
the financial metrics of lenders’ performance and soft information factors (both separately andjointly) in our multivariate analysis. We found that two significant main factors (networks andbusiness/leadership information) individually contribute to the lenders’ profit. However we also
found that the independent utilization of organizational system information hampers theirprofitability. This result suggests that excessive and sole reliance on soft information thatemphasizes the borrowers’ organizational appearance results in slightly less profit. Still, if lenders
make use of organizational system information in conjunction with business/leadership information,
the negative impact of organizational system information was diminished by the powerful positiveeffects of business/leadership information.
Regarding the bad loan ratio, we found a strong association between a low bad loan ratio and theuse of network information, in our univariate analysis. In our multivariate analysis, however, we did
not find that the use of soft information exerts any significant decrease on the bad loan ratio.
We were very interested in the role of soft information in differing levels of inter-bank competition.
Regarding loan profitability, we found that the utilization of networks and business/leadership
information limits the lenders’ losses, even in a competitive local market. In general, lenders’profitability suffers in a highly competitive market, which is supported by our data. Yet our data also
show that lenders can offset the loss inherent in a highly competitive local inter-bank environment if
they utilize network and business/leadership information. Regarding the bad loan ratio, we did not
find that the use of soft information exerted any positive influence on the ability of lenders todetermine creditworthiness of borrowers, particularly when there is high inter-bank competition. Our
results regarding lenders profitability in a competitive market are consistent with Boot and Thakor’s(2000) rationale.
Our findings suggest that information production, especially network information, plays an17essential role in promoting banks’ profitability; this effect is strong even when banks face highinter-bank competition. We propose that network information plays an essential role in determining
creditworthiness of small and medium-sized businesses. How can banks acquire network
information? We believe there is a two-step strategy. First, banks need to franchise their branches
throughout the local area. Second, banks need to invest in and allocate human resources sufficiently
in the franchises to promote better face-to-face communication with borrowers. In this manner,
network information may be obtained. This two-step strategy would not only improve the bankers’
lending techniques, but also foster and enhance their community knowledge and enable them tosurvive in a highly competitive market.

[2011.8.23 1058]

18

Appendix:A
A.1: Factor analysis model
We assumed that x p ( p  1,..., P ) where observed random variables are explained by latentcommon factors,

f m ( m  1,..., M

where M  P ), and a unique factor,  p . Then, we

supposed the mathematic expression as follows:x1  11 f1  12 f 2    1M f M   1

x 2   21 f1   22 f 2     2 M f M   2
x P   P1 f1   P 2 f 2     PM f M   P(A.1)

where the observed random variables are standardized, i.e. E [ x p ]  0 , E [ x 2p ]  1 . The equation(A.1) is the so called “fundamental equation of actor analysis,” and these equations can be expressedin the matrix notation as follows:x  Λf  ε(A.2)

where Λ is the P  M matrix of factor-patterns, f is the M  1 random vector of common
factors, and ε is the P  1 random vector of unique factors. In this matrix, we assume that the
random vector of the unique factor, i.e., ε follows the condition:

 12 0
 0  22E[ε]  0 , Var[ε]  D  

 
0 0


 0

 0
  
  P2 (A.3)

In addition to this assumption, we also assume that the common factors have zero means and unity
variances, in the equations, E[f ]  0, Var[f ]  I , and E[fε ]  O . Since x is the standardized
vector, the correlation matrix of the factor model (A.1) is given as follows:

Var[x]  R x  E[(Λf  ε)(Λf  ε)]  ΛΛ   D
 R *x  R x  D  ΛΛ .

(A.4)

The principal factor method estimates Λ and D by utilizing the relation of (A.4).
~
from theTo estimate Λ and D , we generally used the estimated correlation matrix Rxobservations, x i ( i  1,...., n ) . In order to estimate the common factor, f , which are called factor

scores, we adopted the least squares method. This estimation procedure is conceptually similar to the
OLS which is the classical econometric method. The least squares method minimizes the following
function, with respect to f , under the assumption that Λ and D are “true” estimators:

LS  tr Eεε   tr E( Y  Λf )( Y  Λf ) 

After executing this procedure, we obtained the following equation:
19

(A.5)

1
fˆ  Λ Λ  Λ x .

(A.6)

The factor scores which are estimated by these procedures are used in our analysis.
A.2 Number of Factors
We chose contains three factors. Although there are some heuristic rules which can be used todecide the number of factors, we adopted the combination of the “Kaiser-Guttman rule” and“Cattell’s screen criterion rule.” The combination of these rules has the following benefits: theeigenvalue of each factor is greater than 1 from the former rule, and the cut-off point is decided by
the minima difference between the eigenvalues of the factors. Following the adoption of these rules,
we identified three principle factors. Figure A illustrates this combination.
Figure A

8
7
6
5
4
3
2
1
0

1
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0

0.67
scree point

Cumulative

Eigenvalue

Factor Analysis

0 2 4 6 8 10 12 14 16 18 20 22

Eigenvalue

No. of Factor

Cumulative20

Appendix:B(1) Linear Relationship between Loan Profitability and the Herfindahl-Hirschman Index (adjusted

.5

1

1.5

2

2.5

for covariation with other control variables)

.05

.1

.15
.2
Herfindahl-Hirschman Index
95% CI
Linear Prediction

.25

.3

Quadratic Prediction
Loan Profitabiity(2) Inverse U-shaped Relationship between the Bad Loan Ratio and the Herfindahl-Hirschman Index

0

5

10

15

(adjusted for covariation with other control variables)

.05

.1

.15
.2
Herfindahl-Hirschman Index
95% CI
Linear Prediction.25

Quadratic Prediction
Bad Loan Ratio

21.3

References
Berger, A. N. and G. F. Udell, “Relationship Lending and Lines of Credit in Small Firm Finance,”
The Journal of Business, Vol. 68, No. 3, July 1995, pp. 351-381.
――――, and G. F. Udell, “Small Business Credit Availability and Relationship Lending: The

Importance of Bank Organisational Structure,” The Economic Journal, Vol. 112, No. 477,
February 2002, pp. F32-F53.
――――, and G. F. Udell, “A more complete conceptual framework for SME finance,” Journal of

Banking & Finance, Volume 30, November 2006, pp. 2945-2966.
――――, L. F. Klapper and R. Turk-Ariss, “Bank Competition and Financial Stability,” Journal of

Financial Services Research, 2009, 35: 99-118.
Bharath, S., S. Dahiya, A. Saunders and A. Srinivasan, “So what do I get? The bank’s view oflending relationships,” Journal of Financial Economics, 85, 2007, pp. 368-419.
Bofondi, M. and G. Gobbi, “Informational Barriers to Entry into Credit Markets,” Review of Finance,
10, 2006, pp. 39-67.
Boot,A. and A.V.Thakor, “Can Relationship Banking Survive Competition?,” Journal of Finance,
Vol.55,2000,pp.679-713.
Carter, D. A., J. E. McNulty and J. A. Verbrugge, “Do Small Banks have an Advantage in Lending?
An Examination of Risk-Adjusted Yields on Business Loans at a Large and Small Banks,”
Journal of Financial Services Research, 25, 2004, pp. 233-253.
Carter, D.A., and J. E. McNulty, “Deregulation, technological change, and the business-lending
performance of large and small banks,” Journal of Banking & Finance, Volume 29, 2005,
pp1113-1130
Cuñat, V., “Trade Credit: Suppliers as Debt Collectors and Insurance Providers,” Review of
Financial Studies, Vol.20, 2007, pp491-527.
Dinҫ, S., “Bank Reputation, Bank Commitment, and the Effects of Competition in Credit Markets,”
Review of Financial Studies, 13, 2000, pp. 781-812.
Edvinsson, L.,and M.S.Malone, “Intellectual capital : realizing your company's true value by finding
its hidden brainpower,” Harper Collins,1997.
Giannetti, M., M., Burkart and T., Ellingsen, “What you Sells Is What you Lend? Explaining Trade
Credit Contracts,” Review of Financial Studies, forthcoming.
Hauswald,R., and Marquez,R., “Competition and Strategic Information Acquisition in the Credit
Markets,” Review of Financial Studies, Vol.19, 2006, pp. 967-1000.
Nobeoka, K. and J. H. Dyer, “The Influence of Customer Scope on Supplier Learning and
Performance in the Japanese Automobile Industry,” Strategic Management Journal, 2002.
Nonaka, I., “A Dynamic Theory of Organizational Knowledge Creation,” Organization Science, Vol.
5, No. 1, 1994, pp. 14-37.
――――, and Takeuchi, H., “Knowledge-Creating Company,” Oxford Univ. Press. 1995.
――――, K. Umemoto, and D. Senoo, “From information processing to knowledge creation: aparadigm shift in business management,” Technology in Society, Volume 18, Issue 2, 1996,
pp. 203-218.
Ogura, Y. and H. Uchida, “Bank Consolidation and Soft Information Acquisition in Small Business
Lending,” RIETI Discussion Paper Series, 2007, E-037.
22

Petersen, M. A., “Information: Hard and Soft,” Preliminary and Incomplete Comments

Welcome, July, 2004.
Petersen, M. A. and R. G. Rajan, “The Benefits of Lending Relationships: Evidence from Small
Business Data,” The Journal of Finance, Vol.49, No.1, March 1994, pp. 3-37.
――――, “The effects of credit market competition on lending relationships,” Quarterly Journal of

Economics, Vol.60, 1995,pp.407-443.
Porter, M., “Competitive Strategy,” Free Press, 1980.
――――, “Competitive Advantage,” Free Press, 1985.

Rajan, R. G., “Insiders and Outsiders: the choice between relationship and arm’s length debt,”
journal of Finance, 47, pp. 1367-1400.
Schwarze, F., “Relationship Banking and profitability – An empirical survey of German banks,”
Goethe University Frankfurt - Department of Finance, January 2007, Working Paper Series.
Scott, J. A., “Small Business and the Value of Community Financial Institutions,” Journal of
Financial Services Research, Volume 25, Numbers 2-3, 2004, pp. 207-230.
Sharpe, S.A., “Asymmetric information, Bank lending, and implicit contracts: A stylized model ofcustomer relationship,” journal of Finance, Vol.XLV, 1990, pp.1069-1087.
Stiroh, K. J. and A. Rumble, “The dark side of diversification: The case of US financial holding
companies.” Journal of Banking & Finance, 30, 2006, pp. 2131-2161.
Teece, D., “Economies of Scope and the Scope of the Enterprise,” Journal of Economic Behavior
and Organization, 1, 1980, pp. 223-247.
――――, “Profiting from Technological Innovation,” Research Policy, 15, 6, 1986, pp. 285-305.

Uchida, H., G. F. Udell, and W. Watanabe, “Bank size and lending relationships in Japan,” Journalof the Japanese and International Economies, 22, 2008, pp. 242-267.
Ulrike. S., “The “Middle-Risk Gap” and Financial System Reform: Small-Firm Financing in Japan”
Monetary and Economic Studies, February, 2005, pp 149-17

23

Credit rating

Assessment of Borrowers

Monitoring& Loan Determination

Policies

Adoption of financeApplication of finance

Table 1 Use level of soft information in the finance process

60.0%
40.0%
20.0%
0.0%

S

M

W

N

NA

18.40% 43.20% 12.40% 10.80% 15.20%

S

M

W

N

NA

14.80%

52.40%

14.40%

3.60%

14.80%

S

M

W

14.00% 48.40% 20.00%

N

NA

1.60%

16.00%

S= “Strongly use”, M= “Use in medium”, W= “Weakly use”, N= “Do not use at all”, and NA=“No reply”.

Table 2 Influence of soft information on credit conditions

6.79%

No reply
Others

4.37%
19.16%

Collateral amount15.23%

Length of financing

28.36%

Amount offered

26.09%

Interest rate
0.0%

5.0%

10.0%

15.0%

24

20.0%

25.0%

30.0%

Table 3 Factor Analysis for Soft Information
For 310 regional and local cooperative banks, we computed the correlation coefficients between the all SMRJ
categorized items and the factors by varimax rotation, and we finally extracted the three principal factors: (1)organizational systems, (2) business and management leadership, and (3) networks or alliances/partnerships. We
performed the iterated principal factor analysis. The convergent and discriminant validity of the multiple-item scaleof the three factors explained 67.83% of the total variance, had eigenvalues of 6.973, 2.709, and 1.227, respectively,
and had average communality of 0.46. Additionally, no item cross-loaded on another factor is higher than .40.
SMRJ is an affiliated association of Ministry of Economy, Trade, and Industry in Japan. In SMRJ survey, soft
information items are original categorized to seven modes: (1) manager, (2) internal/external business environments, (3)business contents, (4) customers/suppliers, (5) employees, (6) basis of organizations, and (7) risk management/corporate
governance.

1

2

M ean valueof five likert
scale

3

Organizational systems
personnel evaluation system
in-firm improvement proposal system/the number of improvements
incentive systemenhancement level of corporate education
introduction of IT system to employees
quality of management-labor relations
expertise of employees

.868

.139

-.011

.819
.718
.674
.640
.611
.535

.146
.098
.113
.032
.237
.147

.023
-.018
.226
.294
.176
.223

Networks or Alliances / Partnerships
relationship with suppliers
relationship with customers
suppliers and their status
customers and their statusseniority of management
experience in the sector
networks of management

.239
.223
.042
-.012
.181
.128
.251

.807
.727
.706
.623
.506
.497
.456

Business and Management Leadershipcapability of managementbusiness plans/schedules
traits of management
establishment/duration of main business
management leadershipmanagement philosophy
superiority of technologies
smooth management succession

.070
.035
.078
.083
.220
.324
.118
.104

eigenvalue
percent of variance
No. observations

Standard
deviation

SM RJ Categories

2.794.548 basis of organizations

2.858.545 basis of organizations

2.774
3.003
3.048
3.100
3.232

.581
.531
.552
.585
.621

employees
basis of organizations
basis of organizations
basis of organizations
employees

.161
.223
.302
.378
.234
.234
.274

3.336
3.439
3.461
3.671
3.390
3.342
3.277

.537
.592
.583
.598
.574
.591
.546

customers / suppliers
customers / suppliers
customers / suppliers
customers / suppliers
managermanagermanager

.107
.200
.265
.375
.272
.173
.211
.231

.590
.569
.547
.509
.501
.496
.461
.445

3.923
3.977
3.748
3.890
3.494
3.490
3.884
3.977

.575
.588
.650
.581
.579
.573
.557
.548

managerbasis of organizations
managerbusiness contents
managerbasis of organizations
business contents
manager

6.973
2.709
43.350% 16.840%

1.227
7.630%
310

25

Table 4 Description of the variables
Table 4 displays the description and sources of the dependent and independent variables. The (D) notation indicates adummy variable. Nikkei stands for the Nikkei NEEDS-Financial QUEST by Nikkei Media Marketing, inc. FM stands for
Financial Map in Japan by the Japan Financial News Co. Ltd. CD stands for Census Data by Ministry of Internal Affairs
and Communications. DJFI stands for Directory of Japanese Financial Intermediations by the Japan Financial News Co.
Ltd. LPS stands for Land Price Survey by Prefectural Governments, by Ministry of Land Infrastructure, Transport andTourism. LS stands for Labor Statistics by Ministry of Health, Labour and Welfare. SAR stands for Statistical Annual
Report by National Tax Agency.
SymbolDescription
Dependent Variables

SourceLoan Profitabilityi

The ratio of interest income from loans and discounts to total assets of banki in the period 2007.

Nikkei

Bad Loan Ratioi

The ratio of bad loans to total assets of bank i at the end of 2007. Bad loan
is balance of loans to bankruptborrowers (or in bankruptcy proceedings)
under bank accounts reported in the statement of risk-controlled loans inJapan after 1997.

NikkeiIndependent Variables
Bank Variables
Organizational Systems i

Organizational system, which is the one of three principal factors based on
factor analysis for each of soft information items, is factor score of bank i.
Soft information items are surveyed asking about degree of use in credit
decision making by five likert-type scale (1= “ Do not use at all ” , 5=“Considerably use”) in 2007.

Survey data

Networks iNetworks or alliances/partnerships, which is the one of three principal
factors based on factor analysis for each of soft information items, is factor
score of bank i in 2007.

Survey data

Business and Leadershipi

Business and Management leadership, which is the one of three principal
factors based on factor analysis for each of soft information items, is factor
score of bank i in 2007.

Survey data

Bank Sizei

Log of total assets of bank i at the end of 2007.

Nikkei

Growth of Sizei

Growth of total assets of bank i from 2006 to 2007.

Branch Ratioi
SMEs Lending Ratioi
Capitali

Nikkei4

The ratio of branches to total assets of bank i at the end of 2007 × 10 .
The ratio of SMEs lending to total lending of bank i at the end of 2007.
Ratio of net assets to total assets of bank i at the end of 2007.

Soft Collecting (D)Banks' local market share is based on loans share of local market of bank i
at the end of 2007.
Dummy variable equal to 1 if bank i has a hearing sheet of soft information.

Cooperative (D)Dummy variable equal to 1 if bank i is a cooperative bank.

Merger &HC(D)

Dummy variable equal to 1 if bank i experienced merger,or established abank holding company between 2003 and 2007.

Local Sharei

NikkeiFM
Survey data
Annual Report

Herfindahlij

Local Market Variables
Herfindahl index of local market j computed on branches, based on thelocation of the bank i in 2006.

Land Priceij

Growth rate of land price of local market j is differences of log of land pricebetween 2005 and 2006 , based on the location of the bank i.

LPS

Job Offers

Effective ratio of job offers (seasonally adjusted) to applicants of local
market j , based on the location of the bank i in 2006.

LS

Number of Firms

Log of the number of firms in local market j, based on the location of thebank i in 2006.

SAR

Urban (D)Dummy variable equal to 1 if the headquarter of bank i is in the area withthe population of a millions or higher.

CD

26

DJFI

Table 5 Descriptive Statistics

Variables

Mean

S. D.

Minimum

25th Pctile

median75th Pctile

Maximum

Loan Profitability

1.412

0.348

0.355

1.168

1.372

1.662

2.281

Bad Loan Ratio

7.051

3.449

2.129

4.387

6.190

8.742

23.244

0.003

0.949

-4.203-0.181

0.245

0.413

2.242

Dependent variables
Performance variables

InDependent variables
measure of Soft Information
Organizational Systemi
Networksi

0.012

0.900

-2.237-0.669-0.225

0.826

2.585

Business and Leadership i-0.00003

0.861-2.134-0.571

0.013

0.485

2.622

Bank Sizei

12.8981.248

10.57611.90812.80313.682

16.269Groth Sizei

1.988

6.542

-7.513-0.182

1.141

2.777

65.612

Local Sharei

6.549

10.483

0.042

1.010

2.111

5.700

48.300SM Es Loan Ratioi

87.6868.564

58.95982.95489.97293.86599.983

Branch Ratioi

0.824

0.388

0.174

0.547

0.806

0.998

2.571

Capitali

8.080

4.785

0.580

4.850

6.810

10.13031.300Soft Collecting(D)

0.534

0.500

n/a

n/a

n/a

n/a

n/a

Cooperative (D)

0.238
0.143

0.426
0.351

n/a
n/a

n/a
n/a

n/a
n/a

n/a
n/a

n/a
n/a

Herfindahlij

0.152

0.070

0.049

0.080

0.142

0.216

0.295

Land Priceij

1.511

7.076

-7.953-3.572-0.8952.934

20.555

Bank Characteristics variables

M erger &HC(D)Regional Characteristics variables

Job Offers

1.095

0.358

0.430

0.890

1.070

1.300

2.020

Number of Firms

10.859

0.995

9.216

9.965

10.65411.68613.272

Urban (D)

0.169

0.376

n/a

n/a

n/a

n/a

n/a

No. Observations

307

Mean, standard deviation (S. D.), minimum, 25th pictile, medium, 75th pictile, and maximum values of performance,soft information, and bank variables for the period 2007, and local market variables for the period 2006. See Table 4 for adefinition of the variables.

27

Table 6 Univariate Analysis of Performance as a Function of Use of Soft Information (High Use of Soft Information vs. Low Use of Soft Information)
High vs. Low(Dichotomized Groups)

High vs. Low (Tertile Groups)

Panel A Organizational Systems
High Factor Score Low Factor Score

μ1μ2

μ1 - μ2

Panel A Organizational Systems
High Factor Score Low Factor Score

Ho:μ1-μ2=0
Ha:μ1-μ2≠0 Ha:μ1-μ2>0 Ha:μ1-μ2<0μ2

μ1 - μ2

Loan Profitability

1.38981.4477-0.0579

0.20400.89800.1020

Bad Loan Ratio

7.652836

6.93398

0.7188563

0.1046

0.0523*0.9477

No. observations

123

109

1.40111.4232-0.02210.57870.71060.2894

Bad Loan Ratio

7.22586.87510.35070.37380.18690.8131

No. observations

154

153

P e fo rm a nce

P e fo rm a nce

p -value
Loan Profitability

Panel B Networks

Panel B Networks
High Factor Score Low Factor Score

Ho:μ1-μ2=0μ1μ2μ1 - μ2

Loan Profitability

1.47171.35220.11950.0025***0.0012***0.9988Bad Loan Ratio

6.51577.5899-1.07420.0062***0.99690.0031***

No. observations

154

153

Ho:μ1-μ2=0μ1μ2μ1 - μ2

Loan Profitability

1.4772

1.34930.12790.0048***0.0024***

0.9976

Bad Loan Ratio

6.63687.8942-1.2575

0.0064***

0.99680.0032***

No. observations

123

109

Ha:μ1-μ2≠0 Ha:μ1-μ2>0 Ha:μ1-μ2<0p-valueP e fo rm a nc e

P e fo rm a nc e

High Factor Score Low Factor Score

Panel C Business and Leadership

Ha:μ1-μ2≠0 Ha:μ1-μ2>0 Ha:μ1-μ2<0p-value

Panel C Business and LeadershipHigh Factor Score Low Factor Score

High Factor Score Low Factor Score

Ho:μ1-μ2=0μ1μ2μ1 - μ2

Loan Profitability

1.40151.4228-0.02120.59370.70310.2969Bad Loan Ratio

6.9445

7.1582-0.2137

0.58820.70590.2941

No. observations

154

153

28

Ho:μ1-μ2=0μ1μ2μ1 - μ2

Loan Profitability

1.39671.39060.00610.89630.44810.5519Bad Loan Ratio

7.14897.3305-0.18150.70290.6486

0.3514

No. observations

123

109

Ha:μ1-μ2≠0 Ha:μ1-μ2>0 Ha:μ1-μ2<0p-valueP e fo rm a nc e

P e fo rm a nc e

Ho:μ1-μ2=0
Ha:μ1-μ2≠0 Ha:μ1-μ2>0 Ha:μ1-μ2<0p-valueμ1

Ha:μ1-μ2≠0 Ha:μ1-μ2>0 Ha:μ1-μ2<0p-valueTable 7 Univariate Analysis of Performance as a Function of Competition (High Competition vs. Low Competition)

Panel A High Competition vs. Low Competition (Dichotomized Groups)
High Competition

Low Competition

μ1

μ2

μ1 - μ2

Ha:μ1-μ2≠0

Ho:μ1-μ2=0
Ha:μ1-μ2>0

Ha:μ1-μ2<0

Peformancep -value
Loan Profitability

1.3522

1.4732-0.1209

0.0022 ***0.99890.0011 ***Bad Loan Ratio

6.20427.9146-1.7104

0.0000 ***1.00000.0000 ***No. observations

155

152

Peformance

Panel B High Competition vs. Low Competition (Tertile Groups)
High Competiton

Low Competition

μ1

μ2

μ1 - μ2

Loan Profitability

1.35771.5694-0.21160.0000 ***1.00000.0000 ***Bad Loan Ratio

5.75408.2643-2.51030.0000 ***1.00000.0000 ***No. observations

116

108

Ho:μ 1-μ 2=0
Ha:μ 1-μ 2≠0 Ha:μ 1-μ 2>0
p -value29

Ha:μ 1-μ 2<0Table 8 Univariate Analysis of Use level of Soft Information as a Function of Competition (High Competition vs. Low Competition)
Panel A High Competition vs. Low Competition(Dichotomized Groups)

Panel B High Competion vs. Low Competition(Tertile Groups)

High
Competition

Low
Competition

Ho:μ1-μ2=0High
Competition

Low
Competitionμ1

μ2

Ha:μ1-μ2≠0 Ha:μ1-μ2>0 Ha:μ1-μ2<0μ1μ2incentive system

2.742

2.815

0.275

0.863

0.137

incentive system

2.733

2.879

0.071*

0.964

0.036**

know-how of employees

3.271

3.199

0.308

0.154

0.846

know-how of employees

3.317

3.271

0.590

0.295

0.705

smoothness of management-labor relations

3.135

3.066

0.305

0.152

0.848

smoothness of management-labor relations

3.139

3.093

0.575

0.287

0.713

in-firm improvement proposal system/the number of improvement

2.881

2.907

0.747

0.627

0.373

personnel evaluation system

2.802

2.841

0.614

0.693

0.307

Ho:μ1-μ2=0
Ha:μ1-μ2≠0 Ha:μ1-μ2>0

p -value

Ha:μ1-μ2<0

p -value

2.852

2.868

0.799

0.400

0.601

personnel evaluation system

2.794

2.801

0.901

0.549

0.451

enhancement level of corporate education

3.000

3.000

1.000

0.500

0.500

situations of introduction of the IT system

3.058

3.040

0.774

0.387

0.613

Factor Score-0.017

0.023

0.713

0.643

0.357

Organizational Systems

Organizational Systems

in-firm improvement proposal system/the number of improvement

enhancement level of corporate education

2.960

3.037

0.312

0.844

0.156

situations of introduction of the IT system

3.069

3.103

0.661

0.669

0.331

Factor Score-0.009

0.124

0.318

0.841

0.159

3.381

3.318

0.352

0.176

0.824

career in the sector

3.406

3.327

0.363

0.181

0.819

career of management

3.413

3.371

0.519

0.260

0.740

career of management

3.436

3.402

0.673

0.337

0.663

networks of management

3.284

3.278

0.928

0.464

0.536

networks of management

3.238

3.299

0.423

0.788

0.212

customers and their status

3.742

3.609

0.052*

0.026**

0.974

customers and their status

3.782

3.598

0.025**

0.012**

0.988

suppliers and their status

3.516

3.417

0.139

0.070*

0.931

suppliers and their status

3.515

3.402

0.161

0.080*

0.920

Networks

Networks

career in the sector

3.426

3.470

0.510

0.745

0.255

relationship with customers

3.406

3.430

0.771

0.614

0.386

relationship with suppliers

3.329

3.358

0.640

0.680

0.320

relationship with suppliers

3.287

3.364

0.292

0.854

0.146

Factor Score

0.036-0.010

0.658

0.329

0.671

Factor Score

0.000-0.035

0.783

0.392

0.608

capability of management

3.942

3.914

0.670

0.335

0.665

leadership of management

3.355

3.384

0.637

0.682

0.318

character of management

3.516

3.477

0.554

0.277

0.723

management successor's presence

4.039

3.927

0.074*

0.037**

0.963

superiority of technologies

3.845

3.934

0.164

0.918

0.082*

superiority of main business

3.923

3.854

0.308

0.154

0.846

management philosophy

3.497

3.490

0.919

0.459

0.541

business schedules

3.961

3.993

0.636

0.682

0.318

Factor Score

0.060-0.059

0.228

0.114

0.886

Business and Leadership

Business and Leadership

relationship with customers

30

capability of management

3.990

3.907

0.310

0.155

0.845

leadership of management

3.406

3.402

0.958

0.479

0.521

character of management

3.525

3.477

0.558

0.279

0.721

management successor's presence

4.059

3.944

0.116

0.058*

0.942

superiority of technologies

3.891

3.972

0.284

0.858

0.142

superiority of main business

3.921

3.888

0.675

0.338

0.662

management philosophy

3.505

3.523

0.820

0.590

0.410

business schedules

3.901

3.972

0.400

0.800

0.200

Factor Score

0.113-0.0390.190

0.095*

0.905

Table 9 Effects of Soft Information and Interbank Competition on Loan Profitability

Panel A Baseline Model and Interaction Model : Loan Profitability
Control varibles

Variables

Single Model

Full Model

Interaction Model

Coefficient

Coefficient

Coefficient

Coefficient(t statistics)(t statistics)(t statistics)(t statistics)

Organizational Systems i-0.0193 *-0.0219 *-0.0244 **-0.0274 **(-1.65)(-1.90)(-2.13)(-2.27)(-1.85)0.0437 ***0.0413 ***0.0433 ***0.0394 ***0.046 ***

(3.63)(3.49 )

(3.53)(3.38)(3.81)

0.0344 **0.0299 **0.0285 *0.0321 **0.0303 **(2.30)(2.01 )

(1.93)(2.10)(2.07)

Networks iBusiness and Leadership i
Organ.Systems×Networks i-0.021 *0.0207(1.54 )

Organ.Systems×Busi.&Leader.

0.0240 *( 1.83)

Networksi ×Busi.&Leader.

-0.0192(-1.63)

Joint test on Soft Information: F statistics(p -value)
Herfindahlij
Bank Sizei
Growth Sizei
2(Growth Size-Growth Size) i
Branch Ratio i
SMEs Loan Ratio i
Capitali
Local Sharei
Land Priceij
Job Offers ij
Number of Firms ij
Soft Collecting(D)Cooperative (D)Merger&HC (D)Urban (D)Const.

No. observations
R Squere

6.53(0.0003) *** 5.13(0.0005) *** 5.26(0.0004) *** 5.54(0.0003) ***1.1361 ***1.1763 ***1.1284 ***1.1749 ***1.2083 ***1.2114 ***1.1905 ***1.2109 ***(3.97)(4.09)( 4.01)( 4.12)(4.27 )

(4.26)(4.17)(4.29)

0.00320.00130.000130.0024-0.0026-0.0028-0.00088-0.0011(0.10)( 0.04)(0.00)(0.07)(-0.08)(-0.09)(-0.03)(-0.03)-0.0097 *-0.0094-0.0102 *-0.0102 *-0.0102 *-0.0103 *-0.0101 *-0.0104 *(-1.65)(-1.59)(-1.79)(-1.75)( -1.79)(-1.79)(-1.74)(-1.83)0.000049

0.000045

0.000069

0.000062

0.000074

0.000076

0.000072

0.000080(0.37)(0.34)(0.53)(0.47)( 0.57 )(0.57)( 0.54)( 0.61)0.3165 ***0.3166 ***0.3141 ***0.3266 ***0.3231 ***0.3232 ***0.3234 ***0.3341 ***(3.44)( 3.47)(3.62)( 3.71 )( 3.90)(3.93)(4.00)(3.93)

0.0143 ***0.0144 ***0.0142 ***0.0139 ***0.0140 ***0.0138 ***0.0137 ***0.0139 ***(5.76)( 5.79)(5.85)(5.64)(5.79)(5.71)( 5.66)( 5.77)-0.0195 ***-0.0197 ***-0.0194 ***-0.0194 ***-0.0194 ***-0.0193 ***-0.0201 ***-0.0194 ***(-6.35)(-6.44)(-6.58)(-6.23)(-6.57)(-6.53)(-6.70)(-6.53)-0.00103-0.00089-0.00103-0.00102-0.00086-0.00102-0.00105-0.00068( -0.40)(-0.34)( -0.41)(-0.40)(-0.34)(-0.41)(-0.41)( -0.27)0.00190.00190.00260.00240.00300.00300.00280.0029( 0.70)( 0.73)(0.97)( 0.88)(1.15)(1.15)(1.07)(1.10)-0.3257 ***-0.3219 ***-0.3271 ***-0.3257 ***-0.3227 ***-0.3273 ***-0.3231 ***-0.3162 ***(-7.76)( -7.75)(7.91)( -7.75)(-7.90)(-7.95)(-7.92)(-7.70)0.0748 ***0.0764 ***0.0718 ***0.0766 ***0.0753 ***0.0745 ***0.0745 ***0.0758 ***(2.69)(2.75 )

(2.63)( 2.79)(2.80)( 2.77)( 2.74)( 2.81)-0.0188-0.0174-0.0223-0.0256-0.0266-0.0262-0.0283-0.0243(-0.72)(-0.67)(-0.87)(-0.97)(-1.03)( -1.02)(-1.10)(-0.95)-0.3976 ***-0.3962 ***-0.3847 ***-0.3981 ***-0.3843 ***-0.3847 ***-0.3781 ***-0.3819 ***(-8.18)(-8.27)(-8.14)(-8.03)(-8.10)(-8.00)(-8.04)(-8.15)0.01000.00800.01030.01150.00930.00960.00370.0099(0.25)(0.21)(0.26)(0.30)( 0.25)(0.26)(0.10)( 0.26)

0.0966 **0.0990 **0.0862 *0.0933 **0.0866 *0.0916 **0.0922 **0.0904 **( 2.00)(2.08)(1.83)(1.99)( 1.90)(2.01)(2.02)( 1.99)-0.2974-0.3097-0.2235-0.2848-0.2306-0.2014-0.2088-0.2670(-0.51)(-0.53)(-0.40)(-0.50)(-0.42)(-0.37)( -0.39)(-0.48 )

307
0.627

307
0.630

307
0.639

307
0.634

307
0.647

307
0.649

307
0.651

307
0.650

31

Variables

Control varibles

Single Model

Full Model

Coefficient

Coefficient

Coefficient(t statistics)(t statistics)(t statistics)-0.0254 *Organizational Systems i
Organ. Systems ×high_comp
Joint test : F statistics(p -value)-0.0284 **(-1.81)(-2.00)0.01600.0211( 0.62)(0.83)

1.77(0.1717)

2.09(0.1260)
0.0246 *Networks iNetworks ×high_comp

0.0249 *(1.82)(1.77)

0.0529 *0.0543 *(1.86)(1.95)

7.17(0.0009) ***Joint test : F statistics(p -value)

7.59(0.0006) ***0.0130Business and Leadership iBusi.& Leader.×high_comp( 0.48)0.0571 *0.0631 **(1.79)( 2.03)3.78(0.0241) **Joint test : F statistics(p -value)

0.0091(0.74)

4.36(0.0137) **Joint test on Soft Information: F statistics(p -value)

2.67(0.0478) **Joint test on Local Competition: F statistics(p -value)

2.36(0.0717) *Herfindahlij
Bank Sizei
Growth Sizei
2(Growth Size-Growth Size) i
Branch Ratio i
SMEs Loan Ratio i
Capitali

1.1361 ***

1.1820 ***1.1177 ***1.1497 ***(3.97)(4.11)(3.97)(4.06)( 4.19)0.00320.0016-0.0028-0.0048-0.0133(0.10)(0.05)(-0.09)(-0.15)( -0.43)-0.0104 *-0.0105 *-0.0108 *-0.0097 *-0.0093

1.1761 ***(-1.65)(-1.56)(-1.84)(-1.81)(-1.89)0.000049

0.000043

0.000071

0.000064

0.000079(0.37)(0.32)(0.56)( 0.49)0.3165 ***0.3179 ***0.3088 ***0.3186 ***0.3113 ***(3.44)(3.48)(3.50)(3.51)(3.59)

0.0143 ***0.0143 ***0.0142 ***0.0141 ***0.0141 ***(5.76)(5.76)(5.83)(5.58)(5.68)-0.0195 ***-0.0198 ***-0.0201 ***-0.0199 ***-0.0208 ***(0.61)(-6.35)(-6.37)(-6.86)(-6.36)(-6.99)Local Sharei-0.00103-0.00086-0.00062-0.00030

0.00037( -0.40)(-0.34)(-0.25)( -0.11)(0.15)

Land Priceij

0.00190.00190.00260.00250.0032( 0.70)(0.72)(0.97)(0.92)(1.22)

Job Offers ij-0.3257 ***-0.3227 ***-0.3275 ***-0.3266 ***-0.3257 ***(-7.92)(-7.81)(-7.99)(-7.76)

Number of Firms ij
Soft Collecting(D)Cooperative (D)Merger&HC (D)Urban (D)Const.

No. observations
R Squere( -7.73)

0.0748 ***0.0771 ***0.0756 ***0.0774 ***(2.69)(2.77)(2.76)(2.84)(3.02)-0.0188-0.0185-0.0215-0.0267-0.0293(-0.72)(-0.72)(-0.85)(-1.02)(-1.18)-0.3976 ***-0.3950 ***-0.3817 ***-0.3949 ***-0.3752 ***(-8.18)(-8.16)(-8.09)(-7.76 )(-7.62)0.01000.00650.01080.01670.0139(0.25)( 0.17)

0.0966 **0.0983 **(0.27)

0.0642(0.43)

0.0862 *0.0809 ***(0.35)

0.0541( 2.00)(2.07)(1.31)(1.86)(1.15 )

-0.2974-0.3189-0.2149-0.2102-0.1438(-0.51)(-0.55)(-0.38)(-0.36)(-0.26)

307
0.627

307
0.630

307
0.643

307
0.638

307
0.658

32

Table 10 Effects of Soft Information and Interbank Competition on Bad loan ratio
Panel A Baseline Model and Interaction Model : Bad Loan Ratio
Variables

Control varibles

Single Model

Full Model

Interaction Model

Coefficient(t statistics)

Coefficient(t statistics)

Coefficient(t statistics)

Coefficient(t statistics)

Organizational Systems i-0.2444-0.2381-0.2532-0.2405-0.2383(-1.37)(-1.34)(-1.38)(-1.35)(-1.34)-0.1643-0.1548-0.1423-0.1556-0.1557(-0.96)(-0.90)(-0.80)(-0.91)(-0.90)-0.0006

0.02650.01870.02750.0264(-0.00)(0.16)(0.11)(0.16)(0.15)

Networks iBusiness and Leadershipi
Organ.Systems×Netwarks

0.1249( 0.61)

Organ.Systems×Busi.&Leader.

0.0105(0.06)

0.0039

Networksi ×Busi.&Leader.

(0.02)

Jouint test on Soft Information: F statistics(p -value)
Herfindahlij

0.80(0.4964)
34.8551 ***(2.77)

2(2.81)(2.68)( 2.76)0.60(0.6595)

34.4802 ***35.1270 ***34.4628 ***34.4522 ***(2.73)(2.76)(2.72)(2.72)-78.8006 **-80.6281 **-78.7665 **( -2.35)(-2.25)(-2.33)(-2.26)(-2.30)(-2.26)(-2.25)0.23400.21170.24880.23400.22510.22140.22620.2248( 0.81)(0.72)( 0.86)( 0.81)(0.77)(0.75)(0.77)(0.76)-0.1692 **-0.1655 **-0.1672 **-0.1692 **-0.1641 **-0.1643 **-0.1640 **-0.1640(-2.12)(-2.07)(-2.09)(-2.12 )(-2.05)(-2.04)(-2.04)(-2.05)0.0040 **0.0040 **0.0040 **0.0040 **0.0039 **0.0039 **0.0039 **0.0039(2.53)( 2.51)(2.47)(2.53)(2.46)(2.45)(2.45)(2.45)

3.1659 ***3.1735 ***3.1775 ***3.1658 ***3.1915 ***3.1884 ***3.1928 ***3.1892 ***(3.84)( 3.84)( 3.83)(3.81)(3.81)( 3.78)(3.80)(3.72)

0.0887 ***0.0900 ***0.0889 ***0.0887 ***0.0898 ***0.0887 ***0.0898 ***0.0898 ***(3.87)(3.90)(3.88)(3.87)(3.90)(3.81)(3.88)(3.89)

0.00430.00630.00420.00430.00610.00520.00600.0060(0.18)(0.26)(0.17)(0.18)(0.25)( 0.21)( 0.24)( 0.24)-0.0664 *-0.0661 *-0.0700 *-0.0665 *-0.0689 *-0.0684 *-0.0691 *-0.0689(-1.88)(-1.88)(-1.94)(-1.86)(-1.92)( -1.90)(-1.91)(-1.91)0.9294 *0.9786 *0.9366 *0.9294 *0.9838 *0.9542 *0.9839 *0.9825(1.88)(1.95)(1.88)(1.88)( 1.95)(1.89)(1.94)(1.92)

0.19760.21860.20200.19750.22450.22330.22430.2242(0.64)(0.70)( 0.64)(0.64)(0.71)(0.71)( 0.71)( 0.71)-0.1278-0.1127-0.1162-0.1277-0.1071-0.1032-0.1082-0.1076(-0.41)(-0.36)(-0.37)(-0.40)(-0.33)( -0.32)( -0.33)(-0.33)2.3998 ***2.4131 ***2.3526 ***2.3998 ***2.3679 ***2.3663 ***2.3696 ***(4.76)( 4.76)(4.56)( 4.75)(4.57)(4.59)(4.56)(4.56)

Merger&HC (D)0.36700.34240.36280.36700.3406

0.34400.33830.3404(0.69)(0.65)(0.68)(0.69)(0.65)( 0.66)(0.65)( 0.65)

Urban (D)-0.2058-0.1741-0.1638-0.2058-0.1382-0.1101-0.1356-0.1389(-0.48)( -0.39)(-0.37)(-0.48)(-0.31)(-0.25)(-0.30)(-0.31)

Bank Sizei
Growth Sizei
2(Growth Size-Growth Size) i
Branch Ratioi
SMEs Loan Ratioi
Local Sharei
Land Priceij
Job Offers ij
Number of Firms ij
Soft Collecting(D)Cooperative (D)-81.2500 ** -78.5182 ** -81.5100 **0.61(0.6528)(-2.34)

Herfindahl ij-81.5189 **35.2828 *** 33.8476 *** 34.8514 ***0.65(0.6271)

**

**
**

*
*

2.3675 ***-14.4601 ***-14.3191 ***-14.4650 ***-14.4490 ***(-2.79 )(-2.82)(-2.81)(-2.78)(-2.84)(-2.81)(-2.84)(-2.81)

No. observations

307

R Squere

0.471

307
0.475

307
0.472

307
0.471

307
0.477

307
0.477

307
0.477

307
0.477

Const.

-14.0802 *** -14.3039 *** -14.2519 *** -14.0802 ***-78.7217

33

(Table 10 Continue)

Panel B Joint Effects of Soft Information and Competition : Bad Loan Ratio
Variables

Control varibles

Single Model

Full Model

Coefficient(t statistics)

Coefficient(t statistics)

Coefficient(t statistics)-0.3377

Organizational Systems i
Organ. Systems×high_comp-0.3267(-1.34)(-1.29)0.24220.2384(0.72)(0.70)

1.01(0.3657)

Joint test : F statistics

0.93(0.3949)-0.3119

Networks i-0.323(-1.42)(-1.33)

Networks×high_comp

0.442

0.4334(1.30)(1.25)

1.11(0.3297)

0.99(0.3716)

Joint test : F statistics-0.0568

Business and Leadership i

0.0394( -0.24)(0.16)

Busi.& Leader.×high_comp

0.15050.0461(0.48)(0.14)

Joint test : F statistics

0.13(0.8770)

0.09(0.9123)

Jouint test on Soft Information: F statistics(p -value)

1.04(0.3759)

Jouint test on Local Competition: F statistics(p -value)

0.66(0.5770)
34.8551 ***34.3819 ***33.8665 ***35.0636 ***(2.77)(2.73)(2.69)( 2.75)(2.66)

Herfindahl ij-81.5189 **-78.3693 **-78.7097 **-82.2844 **-76.7958 **(-2.34)(-2.27)(-2.26)(-2.34)(-2.18)Bank Sizei

0.23400.22070.23030.21570.2094( 0.81)(0.74)(0.79)( 0.73)( 0.69)-0.1692 **-0.1640 **-0.1701 **-0.1703 **-0.1663 **(-2.12)(-2.04)(-2.11)(-2.12)(-2.04)(Growth Size-Growth Size) i

0.0040 **0.0040 **0.0040 **0.0040 **0.0039 **Branch Ratio i

3.1659 ***Herfindahlij
2Growth Sizei
2(2.53)(3.84)

SMEs Loan Ratio i
Local Sharei
Land Priceij
Job Offers ij

0.0887 ***(2.50)

3.2012 ***(3.83)

0.0896 ***(2.46)

3.1560 ***(3.77)

0.0899 ***(2.53)

3.1501 ***( 3.78)0.0896 ***33.8436 ***(2.43)

3.2009 ***(3.75)

0.0904 ***(3.87)(3.86)(3.88)(3.89)(3.83)

0.00430.00660.00810.00640.0108(0.18)(0.27)( 0.33)(0.26)(0.42)-0.0664 *-0.0671 *-0.0699 *-0.0660 *-0.0692 *(-1.88)(-1.89)( -1.94)(-1.85)(-1.90)0.9294 *0.9690 *0.9367 *0.9274 *0.9730 *(1.88)(1.95)( 1.88)(1.88)(1.93)

Number of Firms ij

0.19760.22390.23940.20260.2702(0.64)( 0.72)(0.75)(0.65)( 0.84)

Soft Collecting(D)-0.1278-0.1323-0.1153-0.1316-0.1325(-0.41)

Cooperative (D)Merger&HC (D)Urban (D)Const.

No. observations
R Squere

2.3998 ***(-0.41)2.4293 ***(-0.36)2.3600 ***(-0.41)2.4035 ***(-0.41)2.3914 ***(4.76)(4.83)(4.55)(4.76)(4.61)

0.36700.31710.36980.38230.3292(0.69)(0.59)( 0.70)(0.71)(0.62)-0.2058-0.1810-0.3435-0.2242-0.3305(-0.48)( -0.41)( -0.73)( -0.51)(-0.68)-14.0802 ***-14.4028 ***-14.4876 ***-13.9881 ***-14.7587 ***(-2.79 )(-2.83)(-2.86)( -2.76)(-2.90)

307
0.471

307
0.476

307
0.475

307
0.471

307
0.480

34

Table 11 Panel Data Analysis

Panel A Baseline Model and Interaction Model
Loan Profitability
Variables

Bad Loan Ratio

Control varibles

Single Model

Full Model

Interaction Model

Control varibles

Single Model

Full Model

Interaction Model

Coefficient

Coefficient

Coefficient

Coefficient

Coefficient

Coefficient

Coefficient

Coefficient(z statistics)(z statistics)(z statistics)(z statistics)(z statistics)(z statistics)(z statistics)(z statistics)

Organizational Systems i-0.0187-0.0213-0.0242 *-0.0257 *-0.0203-0.2531 *-0.2471-0.2840 *-0.2532-0.2533 *(-1.33)(-1.54)(-1.79)(-1.81)(-1.47)(-1.66)(-1.62)(-1.83)(-1.60)(-1.66)0.0457 ***0.0435 ***0.0457 ***0.0420 ***0.0485 ***-0.1453-0.1312-0.1026-0.1332-0.1633(3.16)(3.07)(3.14)(2.96)(3.34)(-0.89)(-0.79)(-0.61)(-0.81)(-0.96)0.0306 *0.02540.02370.02710.0255-0.0458-0.0197-0.0410-0.0174-0.0209(1.86)(1.57)(1.46)(1.67)( 1.59)(-0.27)(-0.11)(-0.24)(-0.10)(-0.12)

Networks iBusiness and Leadershipi
Organ.Systems×Netwarks

0.0226

0.2948(1.40)(1.61)

Organ.Systems×Busi.&Leader.

0.01920.0277(1.27)( 0.18)

Networksi ×Busi.&Leader.
Jouint test on Soft Information

0.1412(-1.44)(0.85)

13.87(0.0031) *** 15.46(0.0038) ***15.15(0.0044) ***16.15(0.0028) ***2: χ statistics(p -value)
Herfindahlij-0.02220.6024 **(2.47)

0.6225 ** 0.6115 ** 0.6233 ***(2.54)(2.53)(2.57)Wald test : χ statistics

1227

1227
1227
1227
838.05 *** 845.81 *** 831.67 *** 843.82 ***3.4(0.4932)

4.24(0.3744)

0.6501 ***0.6521 ***0.6585 ***13.97314.16413.80913.81113.92614.81113.85613.484( 2.70)(2.69)(2.70)(2.73)(1.54)(1.56 )

(1.52)( 1.52)( 1.53)(1.63)(1.52)(1.48)-25.878-25.255-25.212-25.511-24.476-27.225-24.365-23.269(-0.94)(-0.91)(-0.91)(-0.92)(-0.88)(-0.98)(-0.88)(-0.84 )1227
551.86 ***1227
561.07 ***1227
551.66 ***1227
561.37 ***Herfindahl ij

2

5.4(0.2487)

0.6523 ***2

No. observations

3.38(0.3370)

1227
849.31 ***1227
876.63 ***1227
853.45 ***1227
857.45 ***

35

1227

1227
1227
1227
543.06 ** 542.93 *** 546.75 *** 549.63 ***(Table 11 Continue)

Panel B Joint Effects of Soft Information and Competition
Loan Profitability
Control varibles

Variables

Bad Loan Ratio

Single M odel

Full M odel

Coefficient

Coefficient

Coefficient

Coefficient

Coefficient(z statistics)(z statistics)(z statistics)(z statistics)(z statistics)

2

Jouint test : χ statistics(p -value)-0.0164-0.0214-0.3605 **-0.3605 **(-1.16)(-1.56)(-2.07)( -2.07)-0.0058

0.00130.26600.2869(-0.64)( 0.14)(1.37)(1.47 )

2.03(0.3625)

2.44(0.2957)

4.46(0.1076)

4.55(0.1029)

0.0373 **Networks iNetworks ×high_comp-0.1730-0.1562( 2.40)0.0373 **( 2.43 )(-0.87)(-0.75)0.0231 *0.01990.07590.0832( 1.50)(0.31)( 0.33)0.81(0.6662)

0.59(0.7433)( 1.81 )2

14.53(0.0007) ***Jouint test : χ statistics(p -value)
Business and Leadership iBusi.& Leader.×high_comp2

13.63(0.0011) ***0.02240.0186-0.0855-0.0461(1.34 )

(1.12)(-0.43)( -0.22)0.0220 **0.0210 *0.10630.1141( 2.06)(1.88)(0.56)( 0.57)0.34(0.8451)

0.34(0.8419)

7.65(0.0219) **Jouint test : χ statistics(p -value)
Jouint test on Soft Information2: χ statistics(p -value)
Jouint test on Local Competition2: χ statistics(p -value)

0.6024 **0.6206 **0.6118 **(2.47)(2.53)(2.55 )

6.45(0.0397) **9.83(0.0201) ***5.19(0.1584)

8.00(0.0461) **2.46(0.4823)

0.6296 ***0.6683 ***( 2.61 )( 2.80)2

Herfindahl ij

No. observations

Full M odel

Coefficient

Organ. Systems ×high_comp

Wald test : χ 2 statistics

Single M odel(z statistics)

Organizational Systems i

Herfindahlij

Control varibles

1227
838.05 ***1227
847.61 ***1227
850.68 ***1227
852.92 ***1227
882.82 ***36

13.9734

14.14713.78513.837(1.54)(1.56)(1.52)( 1.52)( 1.54)-25.8784-24.738-25.146-25.628-24.090(-0.94)(-0.90)(-0.91)(-0.93)(-0.87)1227
543.06 ***1227
545.16 **1227
551.21 ***1227
555.51 ***1227
546.66 ***13.951