2011-34
Soft Information Management Effects
on Lending Credit Terms in Japan
Tadanori Yosano Takayoshi Nakaoka

Soft Information Management Effects on Lending
Credit Terms in Japan

VERY PRELIMINARY VERSION
Please do not cite or quote without the authors’ permission
COMMENT WELCOME

Authors
Yosano, Tadanori and Takayoshi Nakaoka**
Second Draft: 2011, 5
1.

PurposeThe purpose of this paper is to investigate how an organization’s soft information management
affects the lending credit terms with Japanese banks and cooperative financial institutions. Prior
research suggests that small lenders have an advantage in accumulating and utilizing soft
information through relationship lending with smaller, less transparent borrowers. Large banks, incontrast, have the advantage of using lending technologies based on hard, quantitative financial
information (Stein, 2002; Berger and Udell, 2002; Cole et al., 2004; Berger et al., 2005; Liberti andMian, 2009). However, Japanese lenders, who come from a long history of specific lending
technologies part of the main bank system (Aoki and Patrick; 1994), have had a comparable
advantage in constructing a platform for personal, face-to-face communication with borrowers.
Therefore, they have been able to lend to borrowers with a comparably more long-term perspective;these lenders are more adept to use a long-term lending history towards a longer lending term.
Meanwhile, small lenders have voiced difficulties in building a close relationship with borrowers
and accessing the creditworthiness rating using other types of information that are not consideredfinancial simply due to their organizational strength (SMRJ, 2008). Hence, we must first analyze
whether the current Western paradigm concerning large banks, mentioned above, holds true forJapanese lenders. Secondly, we must explore the implication of an influence from the lender’smanagement on substantial lending decisions.
Some existing research regarding Japanese lender relationships provide evidence supporting theGraduate School of Business Administration of Kobe University, 2-1, Rokko-dai cho, Nada-ku,
Kobe, 657-8501, Japan**
SMBC Nikko Securities Inc. Industrial Research Department, 1-5-1, Marunouchi, Chiyodaku,
Tokyo, 100-6524, Japan1

current Western paradigm showcasing small lenders with a comparative advantage when using soft
information (Uchida et al, 2008; Kano et al., 2010; Uchida, 2011). This literature is based on
empirical results from the borrower, such as the lending demand’s data. On the other hand, our paper
is based on data from the "Organization for Small & Medium Enterprises and Regional Innovation,
JAPAN" (SMRJ), which conducted a comprehensive analysis to all the lending institutions in Japan
2008. Thus, our paper is based on the lender, such as the supply-side data, and casts a new light on
the existing research.
The SMRJ survey includes 428 banks and cooperative financial institutions that can all be
categorized by one of the four following typologies: Mega Banks (Total Asset Size is on averaged ,69300 billion Yen), Tier I Regional Banks(Total Asset Size is on averaged , 3760.6 billion Yen), Tier
II Regional Banks(Total Asset Size is on averaged , 1104.1 billion Yen), Community Banks(Total
Asset Size is on averaged , 410.76 billion Yen), and Credit Unions(Total Asset Size is on
averaged ,110.67 billion Yen). The SMRJ survey data allows us to address four important issues
associated with relationship lending. First, does soft information actually have an affect on thesubstantial credit terms? Second, does an hierarchical organizational structure hamper the substantial
usage of soft information. Third, does an organizational endeavor, such as a form of systematicmanagement that helps to build up their commonly-shared knowledge for gathering, accumulating
and utilizing soft information in lending decisions, encourage a substantial level of soft information
usage. Fourth, is the facilitation of a standardized soft information list within the organization
effectively beneficial during the lending decision.
Therefore, this paper aims to make four main contributions to the already existing literature
regarding relationship lending. The first is to provide Japanese empirical evidence from lender-sidedata to show that the utilization of soft information has a positive effect on the credit decision. As of
2008, more than 60% of all Mega and Regional banks, including both Tier I and Tier IIclassifications, and more than 50% of Community Banks and Credit Unions, utilize soft informationin evaluating a borrower’s creditworthiness, which is further reflected in credit term decisions.
However, these observations also run contrary to the current small business lending paradigm where
Community Banks and Credit Unions focus on the utilization of soft information lending
technologies, such as relationship lending, rather than hard-information technologies, such as thecredit score (Berger and Udell, 2006). Amongst Japanese lenders, the soft information usage level ismuch smaller within Community Banks and Credit Unions compared to that found in the Megabanks and Tier I, Tier II Regional banks.
The second contribution of this paper is to study the effects of an organizational structure on credit
term decisions. The SMRJ survey data includes the lending managers’ opinions regarding theinfluence soft information has on credit terms, such as the amount offered, the collateral amount, theinterest rate, and the length of financing. We specifically viewed the amount offered as a function of2

the organization’s size. Here, we assume two things: 1) that large organizations have an hierarchicalstructure, and 2) that the soft information substance will become ineffective when it is transferredfrom the loan officers to the lending managers. The results, however, reveal that an organizational
structure does not actually affect substantial lending decisions, and that the utilization of soft
information in lending decisions requires a comparatively large organizational strength. In Japan,
where there is a great number of Community Banks with a diversified organizational size, adifferentiation in firm size leaves small Community Banks incapable of using soft information,
while relatively large Community Banks are able to utilize soft information in a credit decision.
Our third contribution is to study whether a standardized soft information list has an affect on
shifting a loan officer’s assessment more towards relationship lending. Facilitating a standardized
form actually limits soft information that is inherently diversified amongst borrowers. However, a lotof professional affiliations, such as the Japanese Institute of Certified Public Accounts, andGovernment affiliations, such as the Ministry of Economic and Trade Industry, have proposed thefacilitation of a standardized form to generate lender technologies used in relationship lending. We
found that the standardized list curbs the affect of relationship lending upon the lending decisionespecially for the comparably larger structured banks. For small lenders, however, especially at thestarting point, a standardized method would be beneficial in order to generate relationship lending
technologies.
Our final contribution is to pioneer an examination of systematic management effects on soft
information utilization in substantial lending decisions. Such an analysis was not possible in earlier
studies, because the organizational management is an inherent part of the organization’s strategy
making it difficult to use outside information to assess whether lenders purposefully facilitate thatmanagement style or not. A comprehensive SMRJ survey would enable us to identify the form ofsystematic management. The results suggest that systematic management hinders the affects ofrelationship lending upon the lending decision. These results, however, appear to be limited to thecomparatively larger structured banks. Systematic management would be beneficial for small
lenders who have a lower soft information usage level, because it can be useful for implanting soft
information in a loan officers’ common practice in accessing borrowers and assessing their
creditworthiness.
The remainder of this paper is organized as follows: Section 2 provides our four hypotheses
associated with relationship lending. Section 3 also gives SMRJ survey data characteristics anddescriptive statistics of five categorized lenders. Section 4 presents our estimated results along with
our empirical methodologies used in this paper, and Section 5 ties everything together in ourconclusion.

2.

Soft Information Management toward building a lender-borrower relationship3

2.1 Regulatory Shift: Loosening the Controls from the Japanese Financial Services Agency

Until the early 1990s, all banks and credit unions in Japan had been under the control of theJapanese Financial Services Agency (FSA). The management of lending institutions had always
been under the strict control of FSA, and every significant decision required FSA approval. Japanesebanks and credit unions had little discretionary power, especially with financial services other thancredit lending. Typical examples of firms under government administration include banks, securities,
and insurance companies, which are separately governed by their respective ministries. These
industries were unable to enter other financial sectors before 1997. Previous to this, all financial
institutions were restricted through government administration, but they were also ironicallysheltered from bankruptcy by the same government protection. Consequently, bankruptcy withfinancial intermediations was very rare before the early 1990s.

2.2 The Heavy Recession in the 1990s and the Deregulation of Financial Institutions

The main bank relationship is narrowly defined as the long-term transaction relationship between acertain firm and a respective bank that has the largest share in the borrower’s lending (Aoki et al.,
1996, p. 16). However, the main bank relationship is also widely defined asa main bank that

constitutes a prominent role in the borrower’s management, and market participants and financial
regulators are expected to monitor the borrower exercising their right to intervene when the
borrower faces certain financial distress (ibid., pp. 16-17) (which is different from Anglo-Saxon
market-based governance). This intervention is supported by cross-holdings with the borrowers’
shares, which are capped at a maximum 5% of the total shares for banks(Antitrust Act. It has been
capped at a maximum 10% until 1987)1. Traditionally, a lender-borrower long-term transaction
relationship involves cross-share holdings, management resource offerings, the dispatch of
executives, various financial service offering, lending and offering other credit, commitment in thefirm’s business, and sometimes the role of co-signer insurance. After the 1990s, it also involves
settling a variety of transactions, such as everyday expenses and foreign currency transactions,
underwriting of corporate bonds, acting as an investment bank, and advising in security issuancesand corporate takeovers (ibid., p. 16).
The main bank system had functioned well until the 1980s, but it involved concentrating financial
risk in the banks. Therefore, under a financial crisis, banks incurred damages far greater than any
other financial systems who were able to share financial risks with other parties, such as investors
1

Main Banks are an exception, because they are allowed to hold firms' shares only in Japan andGermany. In Anglo-Saxon countries like U.S. and U.K., a cross-holding is prohibited.
4and creditors in a direct funding market. This prompted the Japanese government to deregulate thebusiness transactions of financial institutions and remove the barriers for single service financialtransactions2. Following this deregulation, banks were able to diversify and enter into all types offinancial services.
However, after deregulation there was also an enormous increase in both inter-bank and marketdriven competition. The deregulation of boundaries in franchising areas increased inter-bankcompetition, especially in the local market. Deregulation also caused a shift in the source of funding.
Larger firms gradually shifted from main bank lending towards direct funding. Therefore, larger
Mega banks increased their SME lending to compensate for losing large firm borrowers.

2.3 Introduction of the Government Action Program to Promote Relationship Lending

The increase in inter-bank and market driven competition threatens the survival of regional banks
and credit unions. After the 1998 deregulation, bank infrastructures were further reduced, and theoccurrence of mergers and acquisitions increased. Therefore, in March 2003, the FSA introduced an
action program to prevent bankruptcy (“Program for Strengthening Relationship Banking
Function.”) This program encouraged regional banks and cooperative financial institutions to moveaway from transaction lending and move toward relationship lending3.
The FSA believes that strengthening profitability and reducing insolvency through relationshiplending enables lenders to overcome competition. However, whether the relationship lending
business model is in accordance with FSA’s purpose is still unknown and should be investigated.
“Relationship” in the banking context implies close lender-borrower connections. Therefore, we
define “relationship lending” as all mutually beneficial banking transactions that occur through theaccumulation of soft information based on closer lender-borrower connections. Using survey data2

We currently believe that the reduction of financial risk for banks by spreading the risk to allparticipants in the monetary market (not just banks) had limited success. In 2007, the withdrawals
from three investment funds by the BNP Paribas triggered a financial crisis and the world economy
went into recession; however, the frequency of bank bankruptcy in Japan was still low compared to
the E.U. and the U.S.. In fact, after 2007, Japanese financial institutions even expanded their global
market. For example, in September, 2007, Nomura Securities acquired the former powerhousebrokerage firm, Lehman Brothers, and in September, 2008, the Bank of Tokyo-Mitsubishi UFJ
completed a tender offer for UnionBanCal in the U.S.
3
The Action Program included a schedule for implementing the policy in stages. The first round(2003 to 2004) was meant to expand lending to SMEs, many of which were damaged during a stiff
recession in the 1990’s. The second round (2005 to 2006) aimed at popularizing relationship lending
for Regional banks and Credit Unions. Relationship lending is viewed as the only survival tactic for
Credit Unions. Relationship lending, furthermore, is naturally selective of borrowers, because of
an increased level of discretion through the use of soft information. Additionally, relationshiplending, which uses soft information, requires specialized lending techniques simply because of its
fragile characteristics. The last round (2007 to present) has been to continue the adoption policies set
forth by the second round.

5

collected from all types of banking institutions, this paper investigates how lenders utilize andmanage soft information4, which we hope will shed more light on relationship lending in Japan.

2.4 Hypotheses development

Prior research suggests that smaller lenders have an advantage in accumulating and utilizing soft
information through relationship lending with smaller borrowers. Large banks, in contrast, have theadvantage of using lending technologies based on hard financial information. However, Japaneselenders are at an advantage with their experience with specific lending technologies inherent of theoriginal main bank system, and have a much more efficient time creating a method that promotes
personal, face-to-face communication with borrowers. Meanwhile, smaller lenders have expressed
difficulties in building intimate relationships with borrowers, and suffer from a weak organizational
structure that prevents them from accessing the creditworthiness rating through non-financial typesof information. Thus, the following hypotheses, if not rejected, will provide a current, preliminary,and contrary, Western paradigm, which acts as evidence in support of the experimental advantage oflarge Japanese lenders with relationship lending. Our hypotheses will also reveal the controversial
issues surrounding the disadvantageous small Japanese lenders with regards to relationship lending(see Figure 1).

H1. (a) Large Japanese lenders have an advantage with the accumulation and utilization of soft
information, and their soft information usage has a positive effect on the credit decision.
H1. (b) Small Japanese lenders have a disadvantage with the accumulation and utilization of soft
information, because they have a weaker organizational structure, which further leads to a nonpositive effect on the credit decision from soft information usage.

A loan officer is primarily responsible for gathering soft information from the long term
relationship with a firm, the owner of a SME business, or a local community, etc. However, soft
information is peculiarly characterized by invisibility, and therefore, is very difficult to verify andconvey within lender organizations. This non-permeable situation inherent with soft information
further causes problems within the agency, especially between the loan officers and the lending
managers, who hold the final authority over loans. Stein (2002) notes that actual soft information
values can be overlooked during loan officer-lending manager communications within larger
hierarchies, which explains why larger and more hierarchical lenders tend to rely more heavily on
4

The Mega banks (our sample included 6 banks, such as Bank of Tokyo-Mitsubishi UFJ, Mizuho
Bank, Sumitomo Mitsui Bank) were not affected by the Action Program promoting relationshipbanking. However, as Table 2 shows, they have been participating heavily in SME loans, and are
therefore, included in our sample.
6

hard information.. Berger and Udell (2002) also reported that smaller lending organizations have lessmanagerial layers, and are able to curb this tension. Scott (2004) rationalizes that community
financial institutions, in which a loan officer also fulfills the lending manager role, are more readilyable to access and use soft information5 to enhance the credit available to small businesses. Thisevidence is consistent with Berger and Udell’s (2002) findings. Thus, the following hypotheses, even
if hypotheses 1. (a) and 1. (b) are not rejected, will reveal the root cause of the management
challenge faced by larger banks executing the credit decision through relationship lending (seeFigure 1).

H2. (a) If the organization has a large-hierarchical structure, useful core soft information will leak
during the conveyance process from loan officers to lending managers.
H2. (b) If the organizational structure does not facilitate a managerial layer, then the useful core soft
information may not leak because the loan officer will make the credit decision directly.

Since soft information has the peculiar characteristic of invisibility, and is, therefore, difficult toverify, the useful core soft information is naturally also difficult to quantify and document. Therefore,soft information, by nature, does not fit readily into standardized list, because these forms are limitedin their scope and are likely to neglect certain aspects. Petersen (2004) explores the idea of “lostinformation” during transmission. However, the standardized soft information list is a very goodreminder for lenders, because it is humanistic to overlook a relevant aspect and neglect very crucial
information from the borrowers’ business. In actuality, a lot of professional affiliations, such as theJapanese Institute of Certified Public Accounts, and Government affiliations, such as the Ministry of
Economic and Trade Industry, have focused on this positive aspect and proposed a greater
facilitation of a standardized soft information list to generate lender technologies used in relationshiplending. Thus, the following hypotheses, even if hypotheses 1. (a) and 1. (b) are not rejected, will
also shed light on the management challenge in relationship lending (see Figure 1).

H3. The facilitation of a standardized soft information list will hamper the efficient accumulation
and utilization of soft information towards a lending decision: the lender’s usage level of soft
information will decrease when they rely on the standardized soft information list.

In order to optimize the lender’s ability to make a fully educated judgment about the borrower’s
trustworthiness using soft information, an adequate amount of management is indispensable.

5

Many empirical researchers have also shown evidence that smaller lenders have been lending tosmall business for long terms, which further suggests that small lenders utilize soft information insmall business lending relationships (Haynes et al., 1999; Cole et al., 2004; Berger et al., 2005).

7Effective management creates a corporate culture that encourages the sharing of experience ingathering and accumulating soft information. Furthermore, a corporate culture helps to motivate loan
officers to build their interpersonal discerning skills to improve their final lending decisions. The
systematic management of soft information is often times accompanied with written rules.
However, these written rules will be used to share a loan officer’s knowledge of the clientele’s soft
information within the lending organization, and generate an embedded institutionalized repertoireof relevant soft information despite its non-durability, and inflexibility to be translated into hard
information (Pertersen, 2004, pp. 11-12). A caveat, however, is when the written rules of systematicmanagement mimic the aforementioned standardized information list used for collecting soft
information. In this exceptional case, the written rules also bear the same dilemma as thestandardized soft information list by dampening the use of soft information. Thus, the following
hypothesis, even if hypotheses 1. (a) and 1. (b) are not rejected, will also reveal the management
challenge in relationship lending (see Figure 1).

H4. Ineffective systematic management actually hampers the efficient utilization of soft
information in a lending decision, and reduces soft information usage limiting its impact on thelending credit terms.

3

Survey Data and Descriptive Statistics of Our Sample Data

The primary data used in our analysis comes from a survey data originally from material provided
by a working group called the “Finance Professionals Utilizing Soft Information in Lending toSMEs” formed by the Organization for Small & Medium Enterprises and Regional Innovation,
JAPAN (SMRJ) in early 2008. Before sending questionnaires to the lenders, in late 2007 we
conducted primary, face-to-face interviews with representatives from six lenders, and identified ageneral set of comprehensive hard and soft information used in making credit decisions and offeringbusiness support. Hard information involved thirteen items, while soft information involved twenty
eight items. Soft information items are categorized into four groups: Managers, Business Contents,
Customers and Suppliers, and Organization Basics. Questionnaires were mailed to 575 lenders, andwe received back 428 lenders’ responses (6 Mega Banks, 44 Tier I Regional banks, 34 Tier II
Regional Banks, 234 Community Banks, 92 Credit Unions , and 4 others).

A follow-up phone call

was made to all lenders that did not respond by the deadline in order to improve the response rate,
which eventually was at 76.3%.
The primary survey asked lender representatives to rank the current usage level of hard and soft
information items in regards to the credit decision on a five point Likert Scale. In Panel A of Table 1,
we present the mean and median values from this survey alongside the standard deviation, as a8

function of the financial institution typology.
The primary survey also asked lender representatives to identify if the usage level increased or
decreased for hard and soft information items during the credit decision following the introductionof the “Program for Strengthening Relationship Banking Function” in 2003, and rank theirassessment on the same five point Likert Scale. In Panel B of Table 1, we present the increase or
decrease in the current usage level as a percentage for each hard and soft information item,
juxtaposed to the original usage level and standard deviation, as a function of the financial institution
typology. It can easily be confirmed that the consistent ratios are dominant. Even though theJapanese government introduced the Action Program to enhance the soft information usage level, thelenders’ usage level has regrettably not increased a great deal.
We extracted two principal components from the total 41 items aforementioned in Table 2: hard
information and soft information. Panel A of Table 2 presents the two principal component analysis
scores according to five financial lender attributes, the percentage of lenders who facilitate astandardized soft information list and/or introduce the systematic management, and the percentage oflender representatives who think that soft information actually has an influence on the credit terms,
such as the amount offered, the collateral amount, the interest rate, and the length of financing. The
component scores are standardized and their values are adjusted according to the mean value being
zero. Panel A of Table 2 also gives a correlation matrix of the soft information influence on the four
credit terms.

4Empirical Specification and the Results

Firstly, we executed the ANOVA analysis to determine whether the level of hard and soft
information usage differs across the various Japanese financial institution typologies. Table 3 shows
the ANOVA test results and distribution graph for the hard and soft information component scores
sorted according to each type of financial institution. Panel A of Table 3 shows the ANOVA test
results for only the hard information primary components. The average score for Mega Banks(0.724), Tier I Regional Banks (0.392), Tier II Regional Banks (0.030), Community Banks (-0.012)
and Credit Unions (-0,205) all differ across the financial institution typologies at a 1% significancelevel (p = 0.000). The smaller the organization’s size, the less hard information is used in a credit
decision.
Panel A of Table 3 shows ANOVA test results for only the soft information primary components.
The average score for Mega Banks (1.336), Tier I Regional Banks (0.200), Tier II Regional Banks(0.178), Community Banks (0.009) and Credit Unions (-0,271) also differs across financial
institution typologies at 1% significance level (p = 0.000). The smaller the organization’s size, theless soft information is used in the credit decision, which is consistent with the first half of9

Hypotheses 1. (a) and 1. (B). Figure2 illustrates the result.
Table 4 shows the results from the univariate test that calculates the impact of the soft information
on respective credit terms: the amount offered by Mega Banks. Table 5 also shows the results fromthe probit regressions representing the impact of soft information on the respective credit terms: theamount offered across all institutional typologies other than Mega Banks. Our results show that
Mega banks, Tier II Regional banks, Cooperative banks, and Credit Unions actually consider soft
information when determining credit terms, which is consistent with the second half of Hypothesis 1.
(a) but contrary to the second half of Hypothesis 1. (b). Relatively small organizations, such as
Cooperative banks and Credit Unions, face difficulties in accumulating soft information, because it
requires a certain level of organizational capacity for investment in order to achieve the maximum
utilization and deliberation of soft information during credit decisions.
Table 5 shows the actual influence of the organization’s size upon lending decisions in four
institutional typologies, including Regional Banks Tiers I and II, Community Banks, and Credit
Unions. We do not have these results for Mega Banks, because the sample numbers were notsubstantial enough to perform the regression. In the probit regression, the dependent variable is theamount offered and the independent variables include the following: 1) the primary soft information
usage score, 2) the logarithm of the organization’s total assets (size), 3) the multipliers of the total
assets’ Dummy variables (Dummy variable equals 1 if the lender’s assets are larger than the medianvalue, and is simplified to 0 if the assets are equal or less than the median value) and primary soft
information usage score. This creates an additional composite variable that considers both thelender’s usage and the organizational hierarchical structure.
The results, which contradict Hypothesis 2. (a) and 2. (b), reveal that an organizational structure
does not actually affect the amount offered, but rather that the utilization of soft information in
lending decisions requires a comparatively large organizational strength. In Japan, where there are agreat number of Community Banks of various organizational sizes, large Community Banks have an
advantage in utilizing soft information in a credit decision.
Table 6 shows the actual influence of the standardized soft information list on lending decisions infour institutional typologies, including Regional Banks Tiers I and II, Community Banks, and Credit
Unions. We do not have results for Mega banks, because the sample numbers needed were not met
to perform the regression. In the probit regression, dependent variable is the amount offered andindependent variables are the following: 1) the primary soft information usage score, 2) the
standardized soft information list’s Dummy variables (Dummy variable equals 1 if the lenderfacilitate the standardized soft information list, and 0 otherwise), 3) the multipliers of thestandardized soft information list’s Dummy variables and the primary soft information usage score.
This creates an additional composite variable that considers both the lender’s usage and thestandardized soft information list’s affect. We found that the standardized list curbs the affect of10

relationship lending on the lending decision especially for the comparably larger structure in Tier I
regional banks, consistent with Hypothesis 3. We found a negative effect from standardized list itself
toward the credit decision (coefficient for the standardized list is -0.7724, at significance level of10%) by curbing soft information utilization (coefficient for the intersection between the soft
information usage level and standardized list is -0.7552, at significance level of 10%). However, in
Regional Tier II banks with a smaller structure, the standardized list enhances the utilization of soft
information, contrary to Hypothesis 3. We found a positive effect toward the credit decision byenhancing the utilization of soft information (coefficient for the intersection between the soft
information usage level and standardized list is 0.8372, at significance level of 10%). Lastly, forCommunity Banks (facilitation percentage is 52.4%) and Credit Unions (facilitation percentage is
43.5%), whose soft information usage level is considerably low, facilitating a standardized soft
information list doesn’t have any impact in the credit decisions.
Table 7 shows the actual influence of a systematic soft information management on lending
decisions in four institutional typologies including Regional Banks Tiers I and II, Community Banks,
and Credit Unions. We do not have results for Mega banks, because the sample numbers were not
met in order to perform the regression. In the probit regression, the dependent variable is the amountoffered and the independent variables are the following: 1) the primary soft information usage score,2) the soft information’s systematic management Dummy variables (Dummy variable equals 1 if thelender facilitates the systematic management of soft information, and 0 otherwise), 3) multipliers of
the soft information’s systematic management Dummy variables and the primary soft information
usage score. This creates an additional composite variable that considers both the lender’s usage andthe effect of a standardized soft information list. We found that soft information systematicmanagement curbs the affect of relationship lending in the lending decision, especially for thecomparably larger structures of Tier I regional banks, consistent with Hypothesis 4. We found anegative effect on the credit decision by curbing the utilization of soft information (coefficient for
the intersection between the soft information usage level and systematic management is -2.0914, at
significance level of 5%). However, for Regional Tier II banks with a smaller structure, a form ofsystematic management does not hamper the utilization of soft information, contrary to Hypothesis 4.

5

Conclusions

Our results suggest the following. First, the usage level of soft information in small business credit
decisions is strongly associated with a lending organization’s size. Large lenders have a great
advantage in the usage of soft information. This evidence shows a contradictory perspective to thecurrent Western paradigm where smaller lenders have an advantage in utilizing soft information
through relationship lending with a smaller borrower,.

11

However, these results shed light on thelarger Japanese lenders experience from the Japanese traditional main bank systems in constructing avenue for personal, face-to-face communication with borrowers. However, small Japanese lenders
especially, such as Community Banks and Credit Unions, have faced difficulties in creating lending
skills not heavily dependent on hard information, and the collateral amount. This dilemma has
possibly been due to their organizational strength, even though this could be an adverse effect fromthe governmental action of implementing the “Shared Responsibility Credit Guarantee Scheme” thatwarrants 100% SME lending. SME lending has a maximum limit at 200 million yen, which was
supported for SME liquidity by the government budget until 2007 August. . This could hamper small
lenders from establishing relationship lending technologies. The Japanese Financial Service Agency(FSA) actually introduced the “Program for Strengthening Relationship Banking Function” in 2003
to encourage creating lending skills especially amongst small lenders from the lending demand
perspective. However, using the lending supply side data, we found that this action program has not
functioned well; the usage level of soft information for small businesses has not increased. From thelending supply side viewpoint, utilizing relationship lending technologies needs a certain amount of
the lending volume, which is suggested by Berger and Black (2010) and Berger and Rice (2010).
Therefore, building up relationship lending technologies within small lenders needs to beapproached from the perspective of the large volume lending from small businesses where small
lenders cover the informational investment cost incurred as they develop a close relationship withsmall business borrowers and judge their creditworthiness.
Secondly, the results reveal that an organizational structure does not actually affect substantial
lending decisions. Third, facilitating a standardized form actually limits soft information that isinherently diversified amongst borrowers, especially for large lenders. Last, the results suggest thatsystematic management hinders the affects of relationship lending upon the lending decision,
especially for large lenders. The last two findings show the challenges in developingrelationshiplending technologies within organizations. On the other hand, our findings also suggest that forsmall lenders, especially at the starting point, a standardized soft information list would be beneficial
in order to generate relationship lending technologies, consistent with professional affiliation
proposals, such as the Japanese Institute of Certified Public Accounts and the Ministry of Economic
and Trade Industry. The adequate management of soft information is challenging. However, if
lender organizations hope to create relationship lending technologies within the organization, a formof systematic soft information management is inevitable. Written rules can be used to share a loan
officer’s knowledge of the clientele’s soft information within the lending organization, and build an
institutionalized repertoire of relevant soft information. This will help to control soft information’s
non-durable nature, and inflexibility for translation (Petersen, 2004, pp. 11-12). An exception,
however, is when the written rules of systematic management mimic the aforementioned
standardized information list used for collecting soft information. In this situation, the written rules

12take on the same dilemma as the standardized soft information list in dampening the use of soft
information. Our data shows that lenders who introduce the systematic soft information management
are also more inclined to facilitate a standardized soft information list. Lenders should be careful
about falling into this situation. Even if lenders generate the credit culture using relationship lending
in the organization, without sharing knowledge and an institutionalized repertoire at the judgment
lending level, the final decision will rely heavily on the loan officer’s personal experience (Berger
and Black, 2010). Creating relationship lending technologies and establishing an institutionalized
repertoire requires organizational rules. It is inevitable for creating an embedded certain norm andcustoms in the organization which has influence over soft information usage.

[2011.6.2 1051]

13

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・Berger, A. N., N. H. Miller, M. A. Petersen, R. G. Rajan and J. C. Stein, “Does function follow
organizational form? Evidence from the lending practices of large and small banks,” Journalof Financial Economics, 76, 2005, pp. 237-269.
・Cole, R. A., “The importance of relationships to the availability of credit,” Journal of Banking& Finance, 22, 1998, pp. 959-977.
・Cole, R.A., L. G. Goldberg, and L. J. White, “Cookie-cutter versus character: The micro
structure of small-business lending by large and small banks,” Journal of Financial
Quantitative Analysis, 39, 2004, pp. 227–252.
・Haynes, G. W., C. Ou, and R. Berney, “Small business borrowing from large and small banks,”
in Business Access to Capital and Credit, edited by J. L. Blanton, A. Williams and S. L. W.
Rhine, AFederal Reserve System Reserch Conforence, 1999, pp. 287-327.
・Jimenez, G., V. Salas, and J. Saurina, “Organizational distance and use of collateral forbusiness loans,” Journal of Banking & Finance, 33, 2009, pp. 234-243.
・Kano, M., Uchida, H., Udell, G. F., and Watanabe, W., Information verifiability, bankorganization, bank competition and bank–borrower relationships in Japan. Working paper 06E-003, Research Institute of Economy, Trade, and Industry (REITI), 2006..
・Liberti, J. M. and A. R. Mian, “Estimating the Effect of Hierarchies on Information Use,”
Review of Financial Studies 22 ,2009, pp. 4057–4090.
・Petersen, M. A., “Information: Hard and Soft,” Preliminary and Incomplete Comments
Welcome, July 2004.
・Scott, J. A., “Small Business and the Value of Community Financial Institutions,” Journal of
Financial Services Research, Vol. 25, No. 2-3, pp. 207-230, 2004.
・SMRJ, “Investigation research business for the use of intellectual capital by small and mediumsized enterprises—a chapter of the investigation and research of financing based on intellectual
capital,” SMRJ report, September 2008.
・Stein, J. C., “Information production and capital allocation: decentralized vs. hierarchicalfirms,” Journal of Finance, 57, pp. 1891-1921, 2002.
・Uchida, H., G. F. Udell, and W. Watanabe, “Bank size and lending relationships in Japan,”
Journal of the Japanese and International Economies, 22, 2008, pp. 242-267.

14

Figure 1 Hypotheses Sketch：Branch Manager：Lending Managers
Hierarchical Organization: Mega Banks, Regional Banks：Loan Officers：Conveyance of soft information
Effective systematicmanagements (H4)

Decentralized Organization: Community banks, Credit Unions

Conveyance of soft
information (H2)

Assess the creditworthiness using soft information
through relationship lending (H1)

Assess the creditworthiness using soft information
through relationship lending (H1)

Sometime using standardized soft
information list (H3)

Sometime using standardized soft
information list (H3)

Borrower Firms

15

Soft Information ( Primary Component )Figure 2: Distribution of Soft Information Primary Components

Bank Size (Ln (Total Assets))16

Table 1 :Survey Data
Panel A :Mean Value of Responses from Lender Representatives regarding Current Hard and Soft Information Usage Levels during theCredit Decision on a Five Likert Scale, as a Function of Financial Institution Typologies
Mega Banks
Regional Banks (Tier I) Regional Banks (Tier II) Community BanksSMRJ Categories
N=6
N=44
N=34
N=234
Mean
Sales
Ordinary Earnings
CapitalCurrent Ratio
Ratio of Fixed Assets to Fixed Liability
Capital to Asset Ratio
Cash Flow
Capability of debt Maturity

Financial
Information

Capital toOrdinary Profit Ratio
Sales to Ordinary Profit Ratio
Inventory Turnover Periods
Sales Growth
Earnings GrowthManagement Capability
Starategic Planning Ability
Management LeadershipManagement Traits
Experience in the Sector

Manager

Experience in Management
Level of Industry Network
Quality of Management Succession
Health of Top Manager
Superiority of Technologies

Business Contents

Customers and their Status
Suppliers and their Status
Relationship with Customers

Customers/Suppliers

Relationship with Suppliers
Sales Promotion and Advertising Campaign Activities
Incentive System
Know-How of Employees
Management Philosophy
Business Schedules
Corporate Brand Name Recognition
Business Model
Quality of Management-Labor Relations
Suggestion Box and Number of Suggested
Improvements
Quality of Personnel Evaluation System
Level of Corporate Education
Introduction of IT System to Employees
Internal Infrastructure Type
Status of Research and Development

Basis of
Organizations

Std. Dev. Median

Mean

Std. Dev. Median

Mean

Std. Dev. Median

Mean

Credit Unions
N=92

Std. Dev. Median

Mean

Std. Dev. Median

4.00
4.33
4.33
4.00
4.00
4.50
4.50
4.50
3.83
4.00
3.83
3.67
3.83
4.17
3.67
4.17
4.17
4.00
3.83
3.50
3.83
3.83
4.17
4.17
4.17
4.00
4.00
3.50
3.17
3.67
3.67
4.17
4.00
4.33
3.83

0.89
0.52
0.52
0.89
0.89
0.55
0.55
0.84
0.98
0.89
1.33
1.21
1.17
0.75
1.03
0.75
0.75
1.10
0.98
0.84
0.98
0.98
0.75
0.75
0.75
0.89
0.89
0.84
0.41
0.82
1.03
0.98
0.89
0.82
0.98

4
4
4
4
4
4.5
4.5
5
3.5
4
4
3.5
4
4
3
4
4
4
3.5
3
3.5
3.5
4
4
4
4
4
3
3
3.5
3
4.5
4
4.5
3.5

3.91
4.20
4.11
3.57
3.41
4.18
4.57
4.59
3.66
3.86
4.16
3.66
3.68
4.09
3.23
3.52
3.82
3.52
3.50
3.32
3.91
3.50
4.00
3.80
3.57
3.57
3.43
2.91
2.70
3.07
3.43
4.07
3.61
3.91
3.11

0.56
0.59
0.65
0.70
0.62
0.50
0.50
0.54
0.64
0.55
0.61
0.64
0.67
0.47
0.64
0.59
0.62
0.59
0.55
0.56
0.56
0.63
0.43
0.59
0.62
0.62
0.66
0.47
0.63
0.62
0.59
0.59
0.54
0.52
0.58

4
4
4
4
3
4
5
5
4
4
4
4
4
4
3
3
4
4
4
3
4
3
4
4
4
3.5
3
3
3
3
3
4
4
4
3

3.91
4.09
3.94
3.44
3.38
4.03
4.56
4.38
3.53
3.53
3.79
3.50
3.44
3.76
3.47
3.59
3.82
3.47
3.50
3.26
3.74
3.56
3.85
3.74
3.59
3.53
3.53
3.15
2.82
3.26
3.47
3.85
3.50
3.53
3.15

0.67
0.75
0.65
0.56
0.55
0.63
0.66
0.65
0.71
0.71
0.73
0.71
0.75
0.61
0.56
0.50
0.67
0.51
0.51
0.57
0.51
0.56
0.56
0.62
0.50
0.51
0.51
0.66
0.52
0.57
0.71
0.61
0.62
0.71
0.70

4
4
4
3
3
4
5
4
4
4
4
4
3.5
4
3
4
4
3
3.5
3
4
4
4
4
4
4
4
3
3
3
3
4
3.5
4
3

3.85
4.17
3.93
3.58
3.41
3.89
4.53
4.12
3.42
3.60
3.64
3.49
3.58
3.91
3.37
3.47
3.72
3.29
3.36
3.26
4.03
3.63
3.86
3.64
3.43
3.40
3.29
3.10
2.78
3.26
3.50
3.97
3.31
3.38
3.09

0.65
0.63
0.66
0.60
0.59
0.64
0.55
0.67
0.60
0.63
0.63
0.64
0.60
0.59
0.53
0.59
0.65
0.60
0.59
0.55
0.54
0.59
0.57
0.59
0.58
0.59
0.51
0.43
0.59
0.62
0.55
0.59
0.54
0.55
0.57

4
4
4
4
3
4
5
4
3
4
4
3
4
4
3
3
4
3
3
3
4
4
4
4
3
3
3
3
3
3
3
4
3
3
3

4.01
4.21
3.80
3.54
3.37
3.61
4.48
4.00
3.33
3.53
3.52
3.49
3.50
3.91
3.32
3.37
3.83
3.29
3.47
3.15
3.88
3.62
3.66
3.46
3.24
3.28
3.16
3.18
2.71
3.15
3.30
3.80
3.09
3.17
3.00

0.69
0.64
0.74
0.67
0.62
0.69
0.56
0.63
0.73
0.73
0.73
0.65
0.64
0.57
0.57
0.62
0.66
0.60
0.56
0.57
0.55
0.59
0.68
0.62
0.56
0.58
0.52
0.53
0.66
0.65
0.55
0.60
0.60
0.57
0.61

4
4
4
3
3
4
5
4
3
3
3
3
3
4
3
3
4
3
3
3
4
4
4
3
3
3
3
3
3
3
3
4
3
3
3

3.33

0.52

3

2.75

0.44

3

2.82

0.58

3

2.88

0.56

3

2.86

0.67

3

3.17
3.50
3.50
3.67
4.00

0.41
0.84
0.84
1.03
0.89

3
3
3
3
4

2.68
2.93
2.93
3.34
3.32

0.56
0.59
0.55
0.61
0.56

3
3
3
3
3

2.79
3.09
3.12
3.29
3.21

0.64
0.71
0.64
0.68
0.64

3
3
3
3
3

2.81
3.00
3.06
3.33
3.26

0.55
0.51
0.54
0.61
0.55

3
3
3
3
3

2.80
3.04
3.01
3.04
3.16

0.65
0.59
0.60
0.63
0.62

3
3
3
3
3

17

Panel B: The Increase or Decrease of Current Hard and Soft Information Usage Level in the Credit Decision based on Responses from Lender
Representatives after the Introduction of the “Program for Strengthening Relationship Banking Function” in 2003, as a Function of Financial
Institution Typologies (Shown in a Comparative Percentage)
Mega Banks
N=6

SMRJ Categories
Sales
Ordinary Earnings
CapitalCurrent Ratio
Ratio of Fixed Assets to Fixed Liability
Capital to Asset Ratio
Cash Flow

Financial Information

Capability of debt Maturity
Capital toOrdinary Profit Ratio
Sales to Ordinary Profit Ratio
Inventory Turnover Periods
Sales Growth
Earnings GrowthManagement Capability
Starategic Planning Ability
Management LeadershipManagement Traits
Experience in the Sector

Manager

Experience in Management
Level of Industry Network
Quality of Management Succession
Health of Top Manager
Superiority of Technologies

Business Contents

Customers and their Status
Suppliers and their Status
Relationship with Customers

Customers/Suppliers

Relationship with Suppliers
Sales Promotion and Advertising Campaign Activities
Incentive System
Know-How of Employees
Management Philosophy
Business Schedules
Corporate Brand Name Recognition
Business Model
Quality of Management-Labor Relations
Suggestion Box and Number of Suggested
Improvements
Quality of Personnel Evaluation System
Level of Corporate Education
Introduction of IT System to Employees
Internal Infrastructure Type
Status of Research and Development

Basis of
Organizations

Regional Banks (Tier I) Regional Banks (Tier II)
N=44
N=34

Community Banks
N=234

Credit Unions
N=92

up

unchange

dowm

up

unchange

dowm

up

unchange

dowm

up

unchange

dowm

up

unchange

dowm

0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
33.3%
33.3%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
16.7%
16.7%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
16.7%
16.7%
0.0%
0.0%
16.7%
0.0%
16.7%
0.0%

100.0%
100.0%
100.0%
100.0%
100.0%
100.0%
66.7%
66.7%
100.0%
100.0%
100.0%
100.0%
100.0%
100.0%
83.3%
83.3%
100.0%
100.0%
100.0%
100.0%
100.0%
100.0%
100.0%
100.0%
100.0%
100.0%
100.0%
83.3%
83.3%
100.0%
100.0%
83.3%
100.0%
83.3%
100.0%

0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%

7.0%
7.0%
14.0%
7.0%
7.0%
18.6%
69.8%
60.5%
9.3%
9.3%
18.6%
11.6%
11.6%
27.9%
16.3%
4.7%
4.7%
4.7%
4.7%
2.3%
20.9%
7.0%
32.6%
9.3%
9.3%
11.6%
11.6%
4.7%
2.3%
7.0%
16.3%
39.5%
11.6%
37.2%
7.0%

93.0%
93.0%
86.0%
93.0%
93.0%
81.4%
30.2%
39.5%
88.4%
90.7%
81.4%
88.4%
88.4%
72.1%
81.4%
95.3%
95.3%
88.4%
90.7%
95.3%
79.1%
93.0%
67.4%
90.7%
90.7%
88.4%
88.4%
95.3%
95.3%
93.0%
83.7%
60.5%
88.4%
62.8%
90.7%

0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
2.3%
0.0%
0.0%
0.0%
0.0%
0.0%
2.3%
0.0%
0.0%
7.0%
4.7%
2.3%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
2.3%
0.0%
0.0%
0.0%
0.0%
0.0%
2.3%

11.8%
14.7%
23.5%
14.7%
14.7%
23.5%
79.4%
79.4%
14.7%
20.6%
23.5%
17.6%
14.7%
38.2%
17.6%
17.6%
17.6%
17.6%
11.8%
8.8%
26.5%
17.6%
38.2%
14.7%
14.7%
14.7%
14.7%
11.8%
2.9%
17.6%
14.7%
47.1%
20.6%
32.4%
8.8%

85.3%
85.3%
76.5%
82.4%
76.5%
76.5%
20.6%
20.6%
85.3%
76.5%
76.5%
82.4%
85.3%
61.8%
82.4%
82.4%
82.4%
82.4%
88.2%
85.3%
73.5%
82.4%
61.8%
85.3%
85.3%
85.3%
85.3%
85.3%
94.1%
79.4%
85.3%
52.9%
79.4%
67.6%
91.2%

2.9%
0.0%
0.0%
2.9%
8.8%
0.0%
0.0%
0.0%
0.0%
2.9%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
5.9%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
2.9%
2.9%
2.9%
0.0%
0.0%
0.0%
0.0%
0.0%

18.3%
32.1%
32.9%
20.3%
15.9%
34.1%
81.7%
66.3%
18.3%
22.4%
26.8%
18.3%
23.2%
48.0%
27.6%
24.4%
23.6%
7.7%
9.3%
9.8%
44.3%
19.5%
43.9%
17.5%
10.6%
12.2%
9.3%
9.3%
4.1%
19.1%
27.6%
54.5%
18.3%
29.7%
7.3%

80.1%
67.9%
66.7%
78.9%
82.9%
65.9%
18.3%
33.7%
81.3%
77.6%
73.2%
80.1%
76.8%
51.6%
72.0%
75.2%
75.6%
87.8%
87.0%
89.0%
55.7%
80.1%
56.1%
82.5%
89.4%
87.8%
90.7%
90.7%
94.3%
80.5%
72.4%
45.5%
80.9%
69.9%
92.7%

1.6%
0.0%
0.4%
0.8%
1.2%
0.0%
0.0%
0.0%
0.4%
0.0%
0.0%
1.6%
0.0%
0.4%
0.4%
0.4%
0.8%
4.5%
3.7%
1.2%
0.0%
0.4%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
1.6%
0.4%
0.0%
0.0%
0.8%
0.4%
0.0%

36.5%
44.8%
36.5%
28.1%
17.7%
32.3%
79.2%
57.3%
15.6%
18.8%
24.0%
25.0%
25.0%
53.1%
29.2%
18.8%
31.3%
13.5%
16.7%
12.5%
44.8%
22.9%
38.5%
26.0%
16.7%
20.8%
14.6%
14.6%
7.3%
18.8%
25.0%
49.0%
13.5%
14.6%
5.2%

62.5%
55.2%
63.5%
70.8%
82.3%
67.7%
20.8%
42.7%
81.3%
80.2%
76.0%
74.0%
75.0%
46.9%
70.8%
80.2%
68.8%
83.3%
80.2%
82.3%
54.2%
77.1%
59.4%
72.9%
82.3%
78.1%
84.4%
82.3%
84.4%
78.1%
74.0%
51.0%
83.3%
83.3%
90.6%

1.0%
0.0%
0.0%
1.0%
0.0%
0.0%
0.0%
0.0%
3.1%
1.0%
0.0%
1.0%
0.0%
0.0%
0.0%
1.0%
0.0%
3.1%
3.1%
5.2%
1.0%
0.0%
2.1%
1.0%
1.0%
1.0%
1.0%
3.1%
8.3%
3.1%
1.0%
0.0%
3.1%
2.1%
4.2%

16.7%

83.3% 0.0%

2.3%

95.3% 2.3%

5.9%

94.1% 0.0%

7.3%

91.1% 1.6%

6.3%

89.6% 4.2%

93.0%
90.7%
88.4%
74.4%
88.4%

5.9%
17.6%
23.5%
29.4%
20.6%

94.1%
82.4%
76.5%
70.6%
79.4%

0.0% 100.0% 0.0% 4.7%
16.7% 83.3% 0.0% 7.0%
16.7% 83.3% 0.0% 11.6%
16.7% 83.3% 0.0% 25.6%
16.7% 83.3% 0.0% 11.6%

18

2.3%
2.3%
0.0%
0.0%
0.0%

0.0% 3.7%
0.0% 9.3%
0.0% 20.3%
0.0% 31.3%
0.0% 19.9%

94.3%
90.7%
79.3%
68.7%
80.1%

2.0% 7.3%
0.0% 9.4%
0.4% 16.7%
0.0% 18.8%
0.0% 16.7%

87.5%
87.5%
80.2%
76.0%
80.2%

5.2%
3.1%
3.1%
5.2%
3.1%

Table2: Discriptive Statistics of Bank Characteristics
Panel A :Descriptive Statistics of Lender Characteristics
Mega Banks
N=6
Mean

Std. Dev. Median

Regional Banks (Tier Ⅰ ) Regional Banks (Tier Ⅱ )
N=44
N=34
Mean

Std. Dev. Median

Mean

Std. Dev. MedianCommunity Banks
N=234
Mean

Std. Dev. MedianCredit Unions
N=92
Mean

Std. Dev. Median

Bank Size (ln(Total Assets))
17.75
0.98 18.06 14.88
0.79 15.11 13.69
0.69 13.63 12.46
0.94 12.41 11.18
0.99 11.15
Payroll Costs to Total Assets(%)
0.220% 0.068% 0.239% 0.486% 0.099% 0.473% 0.672% 0.119% 0.671% 0.792% 0.155% 0.777% 0.923% 0.219% 0.937%
Loan Profitability(%)
1.19% 0.20% 1.22% 1.41% 0.22% 1.41% 1.79% 0.27% 1.80% 1.35% 0.34% 1.33% 1.69% 0.62% 1.60%
Bad Loan Ratio(%)
1.56% 0.42% 1.41% 4.03% 2.06% 3.67% 5.29% 2.12% 4.65% 7.85% 3.43% 7.04% 11.11% 5.66% 11.11%
SMEs Loan Ratio(%)
65.41% 21.31% 68.29% 74.19% 7.07% 74.58% 84.86% 4.63% 84.82% 90.32% 6.73% 91.30% n.a.
n.a.
n.a.
Soft Information(premaliry component)
1.34
2.14
0.45
0.20
0.94
0.05
0.18
1.10 -0.09
0.01
0.93 -0.11 -0.28
0.98 -0.26
Hard Information(premaliry component) 0.72
1.75
0.20
0.39
0.85
0.51
0.03
1.12
0.24 -0.01
0.94 -0.04 -0.16
1.04 -0.30
Standardization of Forms/Manuals(%)
83.3% 40.8% 100.0% 58.1% 49.9% 100.0% 52.9% 50.7% 100.0% 52.4% 50.1% 100.0% 43.5% 49.8% 0.0%
Systematic Accumulation
83.3% 40.8% 100.0% 70.5% 46.2% 100.0% 52.9% 50.7% 100.0% 50.0% 50.1% 50.0% 34.8% 47.9% 0.0%
and Utilization of Soft Information(%)
66.7% 51.6% 100.0% 65.9% 47.9% 100.0% 64.7% 48.5% 100.0% 54.7% 49.9% 100.0% 51.1% 50.3% 100.0%
Amount Offered(%)
33.3% 51.6% 0.0% 63.6% 48.7% 100.0% 44.1% 50.4% 0.0% 34.2% 47.5% 0.0% 35.9% 48.2% 0.0%
Collateral Amount(%)
66.6% 51.6% 100.0% 52.3% 50.5% 100.0% 38.2% 49.3% 0.0% 55.1% 49.8% 100.0% 51.1% 50.3% 100.0%
Interest Rate(%)
Length of Financing(%)
50.0% 54.8% 50.0% 54.5% 50.4% 100.0% 41.2% 50.0% 0.0% 25.6% 43.8% 0.0% 26.1% 44.2% 0.0%
Panel B: Correlation Matrix for the Influence of Soft Information on Credit Terms (Spearman Correlation)
Mega Banks
Regional Banks (Tier Ⅱ )Regional Banks (Tier Ⅰ )
N=6
N=44
N=34
(A)
(C)
(I)
(L)
(A)
(C)
(I)
(L)
(A)
(C)
(I)
(L)
Amount Offered (A)
0.50
0.45
0.28
Collateral Amount (C)
0.25
0.50
0.56
0.41
0.33
0.03
Interest Rate (I)Length of Financing (L)
0.71
0.71
0.71
0.60
0.54
0.59
0.37
0.10
0.08
***, **, * denote significant at the 1%, 5%, and 10% level (two-tailed), respectively.

19

Community Banks
N=234
(A)
(C)
(I)
(L)

(A)

Credit Unions
N=92
(C)
(I)

0.16
0.22
0.36

0.23
0.04
0.35

0.17
0.11

0.17
0.16

0.15

0.05

(L)

Table 3: ANOVA Test Results for Hard and Soft Information Primary Compo
Panel A: ANOVA Test Results for Hard Information Primary Components
typologies
Obsavations mean
Mega Banks
6
1.337
Regional Banks (Tier I)
44
0.200
Regional Banks (Tier II)
34
0.179
Community Banks
236
0.010
Credit Unions
93-0.271
F Statistcs
P -Values (Prob>F )

S.D.
2.136
0.936
1.103
0.929
0.979

5.32***
0.0003

Panel B: ANOVA Test Results for Soft Information Primary Components
typologies
Obsavations mean
Mega Banks
6
0.724
Regional Banks (Tier I)
44
0.392
Regional Banks (Tier II)
34
0.030
Community Banks244-0.012Credit Unions
96-0.205F Statistcs
P -Values (Prob>F )

S.D.
1.746
0.854
1.122
0.948
1.039

3.59***
0.00068

20

Table 4: The Results of the Univariate Test Assessing the Impact of Soft Information on the Amount Offered for
Mega Banks and the Results from the Probit Regressions that represent the Impact of Soft Information on theAmount Offered

Mega BanksHigh Score
Total Rate

Amount Offered

66.67%

No. observations

μ1(2.844)

Ho:μ1-μ2=0

Low Score

μ2(-0.171)

100.00%33.30%

3

3

Ha:μ1-μ2≠0

Ha:μ1-μ2>0
p -value

0.116

0.058*

Pseudo R 2

No. observations

0.033

44

Pseudo R 2

No. observations

0.064

34

Pseudo R 2

No. observations

0.027

234

Regional Banks (Tier I)
Total Rate

Amount Offered

α

βsoft

(S.D.)(S.D.)

0.36670.3016(.2018)(.2095)65.91%

Regional Banks (Tier II)
Total Rate

Amount Offered

α

βsoft

(S.D.)(S.D.)

0.34630.3611*(.2311)(.2082)64.71%

Cooperative Banks
Total Rate

Amount Offered

54.70%

α(S.D.)

βsoft(S.D.)

0.12010.2662***(.0832)(.0926)

Credit Unions
Total Rate

Amount Offered

51.09%

Ha:μ1-μ2<0

α(S.D.)

βsoft(S.D.)

Pseudo R 2

No. observations

0.1181(.1422)

0.3410**(.1484)

0.045

92

21

0.942

Table 5: The Effects of an Organizational Hierarchical Structure on the Amount Offered
Mega Banks
βsoftβlogasset βsoft×sizedummyαNo.
Total Rate
Amount Offered

66.7%(S.D.)(S.D.)(S.D.)(S.D.)

n.a.

n.a.

n.a.

n.a.

Pseudo R2

observations

n.a.

6

Pseudo R2No.
observations

0.041

44

Pseudo R2No.
observations

0.079

34

Pseudo R2No.
observations

0.043

234

Pseudo R2No.
observations

0.048

92

Regional Banks (Tier I)

Amount Offered

βlogasset

βsoft×sizedummyα(S.D.)(S.D.)(S.D.)(S.D.)

65.9%

0.3061(3.7307)0.2159(.2825)0.0016(.2515)0.2647(.4118254)

Total Rate

Amount Offered

βsoft

Total Rate

64.7%

α

Regional Banks (Tier II)
βlogasset βsoft×sizedummy
βsoft(S.D.)(S.D.)(S.D.)(S.D.)-2.7842(4.8147)

0.2234(.4182)0.2309(.3523)0.2084(.4940)

Cooperative Banks
Total Rate

Amount Offered

54.70%

αβsoftβlogasset

βsoft×sizedummy(S.D.)(S.D.)(S.D.)(S.D.)-2.1365*(1.1182)

0.3514**(.1404)0.1815**(.0894)-0.1917(.1860)Credit Unions
Total Rate

Amount Offered

51.09%

αβsoftβlogasset

βsoft×sizedummy(S.D.)(S.D.)(S.D.)(S.D.)

0.2545(1.5832)

0.4199**(.2128)0.0124(.1405)-0.1638(.2968)22

Table 6: The Effects of a Standardized Soft Information List on the Amount Offered
City Banks
βsoft βstandard βsoft×standard
α2Total Rate
Amount Offered

66.67%

Total Rate

Amount Offered

65.91%

Total Rate

Amount Offered

64.71%(S.D.)(S.D.)(S.D.)(S.D.)

n.a.

n.a.

n.a.

n.a.

α

Regional Banks (Tier I)
βsoft βstandard βsoft×standard(S.D.)(S.D.)(S.D.)(S.D.)

0.8911**(.8911)0.8969***(.3315)-0.7724*(.4508)-0.7552*(.4316)

αRegional Banks (Tier II)
βsoft βstandard βsoft×standard(S.D.)(S.D.)(S.D.)(S.D.)

0.3234(.3237)-0.1288(.3273)0.0198(.4776)0.8372*(.4508)

Pseudo R No. observations

n.a.

6

2
Pseudo R No. observations

0.116

44

Pseudo R 2 No. observations
0.131734

Cooperative Banks
Total Rate

Amount Offered

54.70%

Total Rate

Amount Offered

51.09%

βstandard βsoft×standard

α

βsoft

(S.D.)(S.D.)(S.D.)(S.D.)

0.0477(.1217)0.2195(.1379)0.1426(.1686)

0.0597(.1883)αCredit Unions
βsoft βstandard βsoft×standard(S.D.)(S.D.)(S.D.)(S.D.)

0.1768(.1897)0.4301**(.2154)-0.1121(.2846)-0.1565(.2874)23

Pseudo R 2 No. observations
0.030

234

Pseudo R 2 No. observations
0.048

92

Table7 : The effects of Systematic soft information management on the Amount Offered
City Banks
βsoft βsystematic βsoft×systematic
α2Total Rate
Amount Offered

66.67%

Total Rate

Amount Offered

65.91%

Total Rate

Amount Offered

64.71%

Total Rate

Amount Offered

54.70%

Total Rate

Amount Offered

51.09%(S.D.)(S.D.)(S.D.)(S.D.)

n.a.

n.a.

n.a.

n.a.

α

Regional Banks (Tier I)
βsoft βsystematic βsoft×systematic(S.D.)(S.D.)(S.D.)(S.D.)-0.2667(.5196)2.0254**(.8161)

0.8320(.5748)-2.0914**(.8562)

αRegional Banks (Tier II)
βsoft βsystematic βsoft×standard(S.D.)(S.D.)(S.D.)(S.D.)

0.2727(.3422)0.1784(.2811)0.2006(.4875)0.4232(.4565)αCooperative Banks
βsoft βsystematic βsoft×systematic(S.D.)(S.D.)(S.D.)(S.D.)

0.0201(.1180)0.2255*(.1268)0.1950(.1676)

0.0512(.1882)αCredit Unions
βsoft βsystematic βsoft×systematic(S.D.)(S.D.)(S.D.)(S.D.)

0.1183(.1741)0.3458*( .1984)-0.0006(.3018)-0.0116(.3018)24

Pseudo R

No. observations

n.a.

6

Pseudo R 2 No. observations
0.174

44

Pseudo R 2 No. observations
0.088

34

Pseudo R 2 No. observations
0.031

234

Pseudo R 2 No. observations
0.045

92