The Role of Collateral and Personal Guarantees in Relationship Lending:
Evidence from Japan’s Small Business Loan Market*

Arito Ono†
Mizuho Research Institute

Iichiro Uesugi¶
Research Institute of Economy, Trade and Industry

October, 2007*

The views expressed in this paper are ours and do not necessarily reflect those of Mizuho Research Institutenor Research Institute of Economy, Trade and Industry (RIETI). We thank Tetsuro Furuta, Shinji Mizukami,
Yutaka Soejima, Kotaro Tsuru, Noboru Yamada, seminar participants at Chuo University, Hitotsubashi
University, Research Institute of Capital Formation of Development Bank of Japan, the RIETI Policy
Symposium “Japan’s Financial System: Revisiting the Relationship between Corporations and Financial
Institutions,” and the members of Corporate Finance Study Group in RIETI for many helpful comments. Anyremaining errors remain our responsibility.
†
Email: arito.ono@mizuho-ri.co.jp¶
Email: uesugi-iichiro@rieti.go.jp

Abstract
This paper investigates the role of collateral and personal guarantees in small business
lending using the unique data set of Japan’s small business loan market. Consistent withconventional theory, collateral is more likely to be pledged by riskier borrowers, implying they may
be useful in mitigating debtor moral hazard. Contrary to conventional theory, we find that banks
whose claims are either collateralized or personally guaranteed monitor borrowers more frequently.
We also find that borrowers who establish long-term relationships with their main banks are more
likely to pledge collateral. Our empirical evidence thus suggests that collateral and personal
guarantees are complementary to relationship lending.

JEL classification number: D82, G21, G30
Keywords: collateral, personal guarantees, relationship lending11 Introduction
Recent literature on financial intermediation has focused on the role of banks as relationshiplenders. Relationship lending has also received close attention by policymakers and the business
community in Japan recently, following publication of the “Action Program Concerning
Enhancement of Relationship Banking Functions” by Japan’s Financial Services Agency in March
2003. The background paper of the Program argues that relationship banking has not been working
effectively in Japan and it cites the intensive use of collateral and personal guarantees in small
business lending as a typical example of Japanese banks’ incompetence. The implicit assumption inthis argument is that collateral and personal guarantees are incompatible with relationship lending,which requires intensive screening and monitoring of borrowers on whom information tends to be
scarce and opaque. This assumption is based on the widely held view that during the “bubble
economy” of the late 1980s when real estate prices were escalating rapidly, Japanese banks relied
too heavily upon the value of real estate collateral in making loans to businesses and hence lost theirscreening and monitoring ability. On the other hand, the conventional wisdom among Japanesebankers is that banks do screen and monitor borrowers more intensively following the bursting of
the bubble economy because they can no longer rely on the value of real estate collateral. If thisconventional wisdom is true, collateral is likely to be used as a tool for credit enhancement, which
may complement rather than substitute for screening and monitoring activities by banks.
This paper studies the role and determinants of collateral and personal guarantees inrelationship lending using the unique data set of Japan’s small business loan market. In particular,
we are interested in whether the use of collateral and personal guarantees is in fact incompatible
with screening and monitoring by the relationship lender. We argue that collateral and personal
guarantees do play a positive role in relationship lending.
The intensive use of collateral and personal guarantees in small business lending is not2

uncommon in other developed countries. For instance, using the 1993 “National Survey of Small
Business Finances in the United States,” Berger and Udell (1998) argue that most small businessloans are personally guaranteed by the business owners and in many cases, the business assets aswell as the personal assets of insiders are explicitly pledged as collateral to back the loan.
A number of theoretical and empirical studies have examined the uses of collateral andpersonal guarantees in loan contracts. Given asymmetric information between creditors andborrowers, collateral and personal guarantees may mitigate the problem of adverse selection (Bester,
1985; 1987) and the problem of moral hazard (Bester, 1994; Boot, Thakor, and Udell, 1991).
Collateral and personal guarantees also affect the incentives of creditors, as they will either
substitute for or complement information production by financial intermediaries (Manove, Padilla,
and Pagano, 2001; Rajan and Winton, 1995; Boot 2000; Longhofer and Santos, 2000). The presenceof collateral and personal guarantees may also depend on the length and intimacy of the relationshipbetween creditors and borrowers (Boot, 2000; Boot and Thakor, 1994; Sharpe, 1990). The use ofcollateral and personal guarantees, how it relates to the characteristics of borrowers and lenders, andthe relationship between the two parties remain unclear; empirical research has yet to reach decisive
conclusions about the nature of this relationship.
This paper seeks to contribute to the existing literature on collateral and personal guarantees
using the unique data set of Japan’s small business loan market. We are interested in how the use ofcollateral and personal guarantees affects the incentives of debtors, lenders, and the relationshipbetween them. More specifically, the paper examines the following three conventional theories.
First, the paper examines whether collateral and personal guarantees are required for riskier
borrowers in order to limit the extent of debtor moral hazard after loans are made. Although it does
not necessarily contradict the conventional wisdom, some argue that collateral and personal
guarantees are more likely to be pledged by less risky borrowers so as to prevent the problem of3

adverse selection in loan contracts. We test both hypotheses in turn. Second, the paper investigates
whether collateral and personal guarantees may substitute for the bank’s information production
activities, such as screening and monitoring of borrowers (the “lazy bank” hypothesis). Contrary tothis conventional theory, several theoretical studies, which we review briefly below, argue thatcollateral and personal guarantees may instead complement screening and monitoring activities by
the lender. We empirically assess which hypothesis holds in Japan’s small business lending market.
Third, we examine the correlation between the use of collateral and personal guarantees and thecloseness of the bank-borrower relationship. The current belief among Japanese policymakers andbusiness leaders is that there is less use of collateral and personal guarantees if loans are based onsolid relationships between lenders and borrowers. This belief is reflected in the Japan Financial
Services Agency’s Action Program. On the other hand, if collateral and personal guarantees arecomplementary to screening and monitoring by the relationship-lender, it would be natural to see
the opposite correlation. We also test these hypotheses.
The paper is organized as follows. Section 2 reviews theoretical and empirical literature on
collateral and personal guarantees in loan contracts. Section 3 describes the data used in this paper.
Section 4 presents our empirical results. First, we take preliminary overviews on how the risk of theborrower, the degree of screening and monitoring by the lender, and the relationship between thetwo would affect the use of collateral and personal guarantees. Then, we present our estimation
models and empirical results. Section 5 summarizes our key findings.

2 Literature review on the role of collateral and personal guarantees
2.1 Role of collateral under perfect information
The role of collateral and personal guarantees differs completely, depending upon whether or
not there is information asymmetry between creditors and borrowers. To see this point, it is useful to4

consider first the situation where creditors have perfect information on borrowers. For convenience,
we use the term “collateral” for both collateral and personal guarantees1.
The exposition is a simplified version of the seminal work by Bester (1985). We consider two
types of risk-neutral entrepreneurs, G and B, whose projects are distinguished by their riskiness.
Both projects require the same amount of capital to carry out, and we assume the required amountof capital is a unity. The returns to the projects are RG and RB ( RG
they fail. The probability of success is given by PG and PB ( PG

RB ) if they succeed and 0 if

PB ), respectively. Entrepreneurs

finance their projects by borrowing from a bank whose loan contract is specified by the interest
rate r and the amount of collateral C . Entrepreneurs face collateralization costs, which are assumed
to be proportional to the amount of collateral by a factor k . For simplicity, there is only one
risk-neutral bank that gains all social surpluses.
The expected profits of an entrepreneur i ( iXi

Pi ( RiriG, B) are given by:kCi ) (1 Pi )(1 k )C i

(1)

The expected profits of a bank for a loan contract with entrepreneur i are given by:
Zi

Pi ri

(1 Pi )Ci

(2)

Because the bank is a monopolist, it will set the terms of loan contracts to maximize theexpected profit, making the expected profit of each entrepreneur zero. Because the bank can
distinguish the riskiness of borrowers under perfect information, the bank will charge different
1

To be precise, collateral is typically physical assets or securities that the creditor can sell in the event of theborrower’s default. In many cases, the assets or securities pledged as collateral are owned by the borrowing
firm (inside collateral) and hence do not increase the potential losses that the borrower may suffer. Inside
collateral mainly defines the order of seniority among creditors in the case of bankruptcy, although it also
deters the use of perks by the borrower. On the other hand, a personal guarantee refers to a contractual
obligation of the business owner or other third parties, such as the relatives of the owner or directors of theborrowing firm, to repay the principal in the event of a default. If the borrowing firm is a limited liability
entity, a personal guarantee functions as outside collateral, except that it does not give control over specificassets. Most of the theoretical literature, as well as our exposition below, explicitly or implicitly assumes
collateral is outside, but it is often difficult to discriminate between inside and outside collateral in empiricalanalyses due to the lack of information.

5

interest rates and impose different collateral requirements on each entrepreneur. If there is nocollateral, the interest rate is set at ri

Ri and the bank will earn Pi Ri ( X i

if the bank requires a positive amount of collateral C i
ri

Ri

Ci

(1 k )C i
Pi

and the bank will earn Pi Ri

0 ). On the other hand,

0 , the interest rate is set at

kCi . Hence, under perfect information, thebank will not require collateral. This result obtains because a social cost accrues in registering andmanaging collateral.
Alternatively, the bank will not require collateral under perfect information when there is nocollateralization cost but entrepreneurs are risk averse, because, from the viewpoint of anentrepreneur, increasing the loss if the project fails by providing collateral and increasing gain if the
project succeeds by reducing interest payments is incompatible with risk-averseness (Bester, 1987).
In other words, risk-averse entrepreneurs would buy complete insurance if there were actuariallyfair insurance available. In the absence of such insurance, an entrepreneur would prefer a loan
contract without collateral as a second-best choice so as to minimize the difference between the
payoff if the project succeeds and the payoff if the project fails.

2.2 Role of collateral under asymmetric information
Riskiness of the borrower
The above result will be quite different if the bank cannot discern the riskiness of theentrepreneurs (hidden information). Under informational asymmetry, collateral can serve as ascreening device in order to discern the riskiness of entrepreneurs. This follows from theobservation that the marginal rate of substitution (willingness to exchange interest payment forcollateral loss if default occurs) for the riskier entrepreneur is higher (in absolute value) than that of
the less risky entrepreneur:

6

MRS i1 k PiPi(3)

Hence, the lower-risk entrepreneur G has a relatively larger incentive to pledge collateral than
the risky entrepreneur B , because of his lower probability of failure and loss of collateral.
The incentive compatibility constraint requires the bank to offer the first-best contract to theriskier entrepreneur, B, who has an incentive to act as if he were type G. Hence, as is the case under
perfect information, the loan contract with the riskier entrepreneur entails no collateral. Regarding
the type G borrower, the bank will offer a contract with “minimum” collateral2 so as to satisfy tothe following incentive compatibility constraint of the type B borrower:
X B (rG , CG )X B (rB

RB , C B

0)(4)

As a result, collateral serves as a screening device to discriminate based on the riskiness ofthe borrower, and to mitigate the adverse selection problem. The lower-risk borrower will choose
the contract with collateral, in order to take advantage of the lower interest rate.
On the other hand, when the information asymmetry is in the form of hidden action, in which
the lender cannot observe actions taken by a borrower after the loan is originated, collateral can beused as an incentive to mitigate the moral hazard problem. For example, Boot, Thakor, and Udell(1991) argue that if a project’s probability of success depends on the degree of effort by theborrower — which is unobservable by the creditor — and the marginal impact of effort on theprobability of success decreases with borrower quality (that is, riskier entrepreneurs have a higher
marginal “return” to effort), then it is optimal for the lender to require collateral from the riskier
borrowers in order to limit moral hazard (a lack of effort on the part of the borrower). Similarly,

2

In our setting, this reduces to C G

PB PG ( RB R G ). The interest rate for the type G borrower is set at(1 k )( PG PB )PB ( RB RG )PG
, which is lower than the first-best interest rate (without collateral)
1
PG PB
1 k
under perfect information. Note also that it is lower than the interest rate charged to the type B borrower.
rG

RG

7

Bester (1994) considers the situation where the creditors cannot directly observe the project
outcome and hence cannot distinguish whether the borrower defaults strategically or because he isactually unable to meet his debt obligations. Under this constraint, collateral reduces the debtor’s
incentive for voluntary default. Because in equilibrium the incentive to strategically default isnegatively correlated with risk, the riskier borrower is more likely to be financed thorough loan
contracts that entail collateral than the lower-risk borrower. Contrary to the literature on hidden
information, theoretical models of hidden action argue that the riskier entrepreneurs will obtainloans with collateral while the less risky ones obtain loans without collateral. One should note,however, that the theories of hidden information, in which the borrower’s risk is unobservable, andtheories of hidden action, in which the borrower’s risk is observable but its actions are unobservable,
are not mutually exclusive because they are based on different assumptions about the informational
structure.
Screening and Monitoring by the lender
Recent literature on financial intermediation has focused on the role of banks as information
providers: that is, screening the borrower’s project and monitoring its performance. Manove, Padilla,
and Pagano (2001) argue that, from the banks’ point of view, collateral can be considered a
substitute for evaluation of the actual risk of a borrower. Thus, banks that are highly protected bycollateral may perform less screening of the projects they finance than is socially optimal (the lazybank hypothesis). This idea is reflected in the Financial Services Agency’s Action Program, whichurges banks to promote “lending activities [by] placing emphasis upon cash flow from business
operations and by avoiding excessive dependence upon collateral and guarantees.”
However, several theoretical studies argue that collateral may complement screening andmonitoring activities by the lender. For instance, in the presence of other claimants, the lender’sincentive to monitor the borrower is reduced due to the informational free-rider problem, among8others. In order to enhance the lender’s incentive to monitor, loan contracts must be structured in a
way that makes the lender’s payoff sensitive to the borrower’s financial health. Rajan and Winton(1995) argue that collateral may serve as a contractual device to increase the lender’s monitoring
incentive, because collateral is likely to be effective only if its value can be monitored. Moreover,the use of collateral as an incentive will be more extensive when the value of such collateral
depreciates rapidly according to business conditions (e.g., accounts receivable and inventories), thanwhen the value of collateral is relatively stable3 (e.g., real estate). As a corollary, Rajan and Winton
also show that if the value of collateral is too high relative to the lender’s claim, the lender has no
incentive to monitor because its claim is fully secured regardless of the borrower’s businessconditions.
The paper by Longhofer and Santos (2000) provides another explanation of how collateral
may be complementary to screening and monitoring by banks. They point out that banks usuallytake senior positions on their small business loans. They further argue that the relationship-lending
equilibrium, in which the lender establishes firm bank-firm relationships by investing in costly
information production activities such as screening and monitoring, is more likely to exist when therelationship-lender (the bank) is senior to the firm’s other creditors, because the lender’s incentive
to make such investments depends crucially on its payoff. If the bank is junior to other creditors, it
gains little from additional investment in information production activities on the firm during timesof poor performance, and hence has little incentive to make such investments. By making its loan
senior to other creditors’ claims, the bank is able to reap the benefits of its relationship-building
investments. Because taking collateral effectively raises the lender’s priority, this argument caneasily apply to the use of collateral.

3

Another related benefit of using inventories and accounts receivable as collateral is that they may reveal
valuable information about the business (Boot, 2000). This also shows the complementarities between
collateral and information production by the financial intermediary.

9Relationship between the borrower and the lender
Banks and firms often maintain their relationship through multiple interactions over time andacross products. Such relationships often involve borrower-specific information gathered by thebank through screening and monitoring. This information is thus proprietary and exclusive in nature.
As a result, the borrower tends to maintain relations with only one bank. This type of lending isreferred to as “relationship banking”4 (Boot, 2000).
By establishing a solid relationship with the borrower, the lender learns about the hidden
attributes and actions of the borrower and reduces the information asymmetry. Hence the terms ofloan contracts may become more favorable to the borrower if the firm has transactions with a
specific relationship-lender over time. For example, Boot and Thakor (1994) construct a model inwhich collateral requirements are negatively correlated with the duration of bank-borrower
relationship: Borrowers pledge collateral early in the relationship but do not pledge collateral after
they have demonstrated success with several projects.
Alternatively, relationship lending may enhance the use of collateral due to the hold-up
problem: As the bank obtains the proprietary information about the borrower, the bank exerts its
information monopoly by charging higher interest rates or requiring more collateral (Sharpe, 1990).
In contrast, the strand of literature that emphasizes the complementarities between collateraland screening and monitoring activities by the lender discussed in the previous subsection argues
that collateral is an intrinsic component of relationship lending. This literature treats collateral as anecessary condition for the lender to invest in information production. Boot (2000) highlights
another contribution of collateral in relationship lending: its role in mitigating the soft-budget
constraint problem. The soft-budget constraint problem refers to a situation where the lender has
4

Relationship lending is quite different from “transactions-based lending,” where a lender focuses on a single
transaction and hence maintains an arm’s-length relationship with a borrower. In the realm of small business
lending, Berger and Udell (2002) cite financial statement lending, asset-based lending, and credit scoring
lending as three forms of transactions-based lending.

10difficulty in enforcing the loan contracts that may come with relationship lending. For example,
consider the case where a borrower in difficulty asks the bank for more credit and reduced interest
obligations in order to avoid default. Although a transaction-based lender would not lend to such aborrower, a relationship-lender that has already made loans might extend additional credit and lower
the interest rate in the hope of recovering its previous loan. Once the borrower realizes he canrenegotiate the loan contract relatively easily, he has an incentive to misbehave ex ante, such as byfailing to make sufficient efforts to prevent the bad outcome (dynamic inconsistency). In such cases,
collateral will increase the ex-post bargaining power of the lender and hence mitigate thesoft-budget constraint problem because collateral makes the value of lender’s claim less sensitive tothe borrower’s total net worth. The bank can credibly threaten to call in the loan and thus prevent
misbehavior by the borrower.

2.3 Empirical evidence
Because collateral has little role under perfect information, most empirical literature on
collateral investigates the role of collateral under asymmetric information. This subsection reviews
some of these empirical studies.
Riskiness of the borrower
There are several empirical studies that examine the relationship between collateral and theriskiness of the loan or the borrower. Berger and Udell (1990) investigate the relationship between
collateral and credit risk by estimating the differences in risk premiums between secured andunsecured loans. If collateral serves as an incentive device that is designed to solve the problem ofadverse selection, then the risk premium of the loan should be negatively correlated with thelikelihood of collateral being pledged because a low-risk borrower would choose a contract withcollateral, in order to take advantage of the lower risk premium. On the other hand, if the lender11

observes the ex-ante risk of the borrower and requires a higher-risk borrower to pledge collateral,then there should be a positive relationship between the risk premium and the presence of collateral.
Berger and Udell (1990) find a positive association between use of collateral and risk premiums,
which is consistent with the hypothesis that collateral reduces debtor’s moral hazard. Similarly,
Berger and Udell (1995) find a positive relationship between the leverage of the borrower, which isa proxy for borrower risk, and collateral, and thus confirm their earlier result.
Pozzolo (2004) focuses on possible differences between the roles of inside and outside
collateral (see footnote 1 for the definition of these terms). He argues that outside collateral is more
effective in dealing with debtor incentive problems because it increases the value of assets that thelender can withhold in the event of default. Because it is difficult in practice to distinguish between
inside and outside collateral due to data limitations, the study considers collateral (such as physicalassets or equities) as inside and personal guarantees as outside. Pozzolo (2004) uses a credit score,
which measures the risk profile of the borrower, as a proxy for ex-ante borrower risk. The study
finds no statistically significant relationship between collateral and borrower risk and interprets thisresult as potentially consistent with the idea that collateral mitigates the adverse selection problem,
which deals with the unobservable riskiness of the borrower. The study also argues that collateral is
not used as an incentive device for the moral hazard problem, presumably because such collateral isinternal to the borrowing firm. The idea that internal collateral cannot serve as an incentive is
supported by Elsas and Krahnen (2000), who find no statistically significant relationship between
collateral and borrower quality5. In the case of personal guarantees (outside collateral), Pozzolo(2004) finds positive association between the two, implying that personal guarantees are used as anincentive in the presence of moral hazard.

5

Elsas and Krahnen (2000) argue that their empirical results neither support the role of collateral as asignaling device in the presence of adverse selection nor as an incentive device in the presence of moral
hazard.

12

The study by Jiménez, Salas-Fumás, and Saurina (2004) directly tests the adverse selection
hypothesis and the moral hazard hypothesis by separating the ex-ante and ex-post measures ofborrower riskiness. The ex-ante riskiness of the borrower, which must be distinguished to examine
moral hazard hypothesis, is measured by a default dummy variable that takes the value of one if theborrower had previously defaulted on a loan at the time the new loan was made. As for the expost-riskiness of the borrower, which is used to test the adverse selection hypothesis, the default
variable takes the value of one if the borrower defaults on a loan after it is made. The authors findevidence supporting the moral hazard hypothesis. Brick, Kane and Palia (2004) obtain a similar
result, using a default dummy variable for either the principal owner or the firm as a measure of theex-ante riskiness of the borrower.
Screening and Monitoring by the lenderTo our knowledge, little work has been done to empirically assess whether the use ofcollateral and personal guarantees are substitutive or complementary to screening and monitoringby the lender. Based on the lazy bank hypothesis, which posits collateral as a substitute, Jiménez,Salas-Fumás, and Saurina (2004) examine whether banks with a lower level of expertise in small
business lending use collateral more intensively. Examining Spanish loan data from 1984 to 2002,
they find that loans originated by smaller banks, which are deemed to have fewer resources for
credit evaluation, and by savings banks, which traditionally make loans mainly to households rather
than businesses, are more likely to extend collateralized loans. The authors argue that their findings
suggest collateral is used as a substitute for the evaluation of credit risk, and hence is compatible
with the lazy bank hypothesis.
Relationship between the borrower and the lender
A vast amount of empirical work has investigated how the relationship between a borrower
and a lender may affect the terms of small business lending, such as interest rates, maturity, and13

collateral requirements. The proxy variables for “relationship” vary in the literature, such as theduration of the lender-borrower relationship, the number of financial products the borrower
purchases from the lender (“scope” of relationship), and the number of banks with which theborrower has transactions.
As with the theoretical literature, the empirical results in the above studies are contradictory.
Berger and Udell (1995), Brick, Kane, and Palia (2004), Harhoff and Körting (1998), and Jiménez,Salas-Fumás, and Saurina (2004) find negative relationships between the duration of the bank-firm
relationship and the probability that collateral will be pledged. These findings are consistent withthe model of relationship banking and reputation posited by Boot and Thakor (1994). Berger andUdell (1995) and Harhoff and Körting (1998) also find the interest rate on loans falls with theduration of relationship. But Brick, Kane, and Palia (2004) argue that this result may be due to theendogeneity problem among loan contract terms; they find that endogenizing collateral and fees
eliminates any significant correlation between the interest rate and the duration of the relationship.
Degryse and Van Cayseele (2000) also find a negative relationship between the interest rate
and the duration of the relationship, but their paper argues this result depends on the proxies used
for the relationship: They obtain a positive relationship between the scope of lender-borrower
relations and the collateral requirement, implying that a relationship lender will require morecollateral than a transaction-based lender, presumably because of the hold-up problem.
As for the empirical literature that analyzes the number of banks involved in transactions,Harhoff and Körting (1998) find the incidence of collateralization of credit lines increases as thenumber of financial institutions the firm is borrowing from rises. They argue that concentrated
borrowing represents a strong lender-borrower relationship, and that their results provide evidence
for the claim that such a relationship eases loan conditions for the borrower. On the other hand,Jiménez, Salas-Fumás, and Saurina (2004) find that the use of collateral decreases with the number

14

of financial institutions used by the borrower. Interestingly, they interpret the number of lenders as
an increase in the bargaining power of the borrower, and hence reach the same conclusion
qualitatively as Harhoff and Körting (1998). Petersen and Rajan (1994) provide anecdotal evidence
from conversations with U.S. bankers that concentrated borrowing implies a “strong relationship.”In contrast to the literature above (except Degryse and Van Cayseele, 2000), Elsas andKrahnen (2000) and Pozzolo (2004) obtain results consistent with the idea that relationship lenders
do require collateral more frequently than other lenders because of the positive role such collateralplays in relationship lending. Using survey data from German banks, Elsas and Krahnen find that“housebanks,” defined as relationship-lenders, have a higher probability of holding loans backed bycollateral and personal guarantees than other banks. Pozzolo (2004) finds a positive relationshipbetween the term of the loan and the probability of collateralization. He finds, however, a negative
relationship between loan term and the probability of the loan being secured by personal guarantees.

3 DataWe use data from the “Survey of Corporate Procurement” (2001), the “Survey of the
Financial Environment” (2002), and the “Survey of the Corporate Financial Environment” (2003)
conducted by the Small and Medium Enterprise Agency of Japan (collectively referred to as “SFEs”
hereinafter). In each of these surveys, a questionnaire was sent to a total of 15,000 companies,
mainly small and medium-size enterprises (SMEs hereinafter), of which around 7,000 to 9,000
firms responded each year. Although the contents of the SFEs vary from year to year, the surveys
ask a number of detailed questions regarding firm characteristics and financial transactions such as
the number of financial institutions the firm deals with; its relationship with its “main bank” (anobvious candidate for the relationship-lender); whether loans are secured by collateral, personal
guarantees, or government-sponsored credit guarantees; and interest rates charged on short-term

15

loans. Because we are interested in the role of collateral and personal guarantees in small business
lending, we restrict our sample to SMEs, that is, enterprises with capital of no more than 300
million yen or no more than 300 regular employees. Unless otherwise stated, we use the 2002 SFE,
which provides the most detailed information on the use of collateral and personal guarantees. We
also use the TSR (Tokyo Shoko Research) database, which consists of financial statements of SMEs,
and we match the data obtained from the TSR with that from the SFE. The TSR database also
provides credit scores for the firms, and we use this variable as a proxy for the borrower’s credit
risk. The score ranges from 0 to 100 points, with a higher credit score implying a lower credit risk
for the firm. The score consists of four components: (i) management ability such as the businessexperience of the manager and outstanding assets that can be collateralized (20 points); (ii) growthpotential of sales and profits (25 points); (iii) stability factors such as firm age, amounts of capital,past payment and credit history of the firm (45 points); and (iv) reputation and disclosure (10points). Based on the total credit score, the firm is judged as either “requiring no caution (80-100),”“safe (65-79),” “requiring little caution (50-64),” “requiring some caution (30-49),” or “requiringcaution (less than 30).” The score is subjective in the sense that each researcher of the TSR grades
the firms for which he is responsible. It should also be noted that the absolute values of the scores
may be of little value in some cases because researchers are asked to assign a score of 50 points tothe “average” firms with which they are working. Despite these shortcomings, the TSR credit scoreis viewed as a typical metric of credit risk for SMEs in Japan and we use the score with care.
Table 1 shows the median values of several variables for the 2002 SFE, depending onwhether the firm pledges collateral, personal guarantees, or has government sponsored credit
guarantees on its loans from the main bank. As shown in the last row, the median amount of capitalfor firms surveyed is over 2 million yen and the median number of employees is 36, which implies16that our sample consists of relatively “large” SMEs6. Note also that the standard deviation of eachvariable is fairly large; thus we report the median rather than the average in the table.
Table 1 reveals that firms which pledge collateral or personal guarantees, or roughly
three-fourths of the sample, are typical SMEs in our sample in terms of size, credit risk, and several
financial ratios. Firms receiving credit guarantees, which account for about half the sample, arerelatively smaller and deemed riskier than the others. They are thus subject to higher interest rates.
Composition of collateral and personal guarantees
Tables 2 and 3 show the composition of collateral by type of assets pledged and thecomposition of guarantors, respectively, using 2001 SFE data. The figures do not add up to 100%
because more than one response is allowed. Table 2 confirms the widely held view that JapaneseSMEs most often pledge real estate as collateral. Financial assets such as deposits, shares, andcommercial bills are the second most common form of collateral and are especially common among
the low-score (high-risk) firms. Account receivables and inventories, which are typical assets
pledged for working capital in the United States, are rarely used. The table does not distinguishbetween inside and outside collateral7.
Table 3 shows that the representative of the firm is the guarantor in most cases. In addition,
directors other than the representative and relatives of the representative occasionally guaranteeloans, especially for low-score firms. Clearly, personal guarantees serve as outside collateral.

4 Empirical Results
4.1 Variables
The terms of the loan contracts we analyze are whether the borrower pledges collateral and/or
6

The average number of employees for all SMEs was seven, according to the Ministry of Internal Affairs andCommunications, “Establishment and Enterprise Census in Japan 2001.”
7
See footnote 1 for the definition of inside and outside collateral. Our conversations with Japanese bankers
suggest that small business owners’ personal assets are pledged as collateral in some cases.

17

personal guarantees to its main bank, and the short-term interest rate charged by the main bank8.
The variable COLL and GUAR are 0/1 variables that take the value of 1 if the borrower pledges
collateral/personal guarantees to its main bank. The variable RATE indicates the short-term interest
rate as of end of October 2002, in tenths of a basis point (i.e., 1000 indicates 1.000%).
We have grouped our explanatory variables into three classes: riskiness of the borrower,
screening and monitoring by the lender, and the relationship between the borrower and the lender.
The riskiness of the borrower is approximated by several variables that help capture the risk
profile of the borrower. The variable SCORE indicates the TSR credit score explained above. Other
than SCORE, we use a number of financial ratios such as LEV (leverage ratio, i.e., debt
outstanding/total assets outstanding), PROFMARG (profit margins, i.e., profits before tax/grosssales), CASHRATIO (cash ratio, i.e., cash holdings/gross sales), and LOGSALES (the logarithm ofsales).
The 2002 SFE asks respondent firms how often they have contact with the loan officers of
their main banks, whether they submit relevant documents such as financial statements and cash
flow forecast tables to their main banks so that banks can assess borrowers’ credit risks, and if so,
how often. Hence, screening and monitoring activities by lenders are captured by the variables DOC,DOCFREQ, CONTACTFREQ. The 0/1 variable DOC takes the value of 1 if the borrower submits
documents to its main bank, and the index variable DOCFREQ shows the frequency, with thelowest value 1 indicating the shortest frequency (1: once every 1-2 months, 2: quarterly, 3:semi-annually, 4: annually). Similarly, the index variable CONTACTFREQ indicates the frequencyof loan officer contact and takes the value of 1-9 (1: every day, 2: weekly, 3: once every 2 weeks, 4:monthly, 5: bi-monthly, 6: quarterly, 7: semi-annually, 8: annually, 9: no contact). We also use theratio of non-performing loans to total loans, NPL, as an ex-post measure of screening and
8

If the borrower has several short-term loans with its mainbank, the loan with the highest interest rate isreported.

18

monitoring activities by the lender. We assume that the bank’s non-performing loan ratio isnegatively correlated with the intensity of its screening and monitoring efforts.
Finally, we use the following lender-borrower relationship variables: DURATION, whichindicates the duration of the main bank-borrower relationship, SCOPE, which indicates the numberof financial products the borrower purchases from its main bank (“scope” relationship), and BANKS,
which indicates the number of banks with which the borrower has transactions9. Similar to thedefinition of housebanks in Elsas and Krahnen (2000), the definition of a main bank in the SFE issomewhat subjective because each respondent firm is asked to choose its main bank based on its
own perceptions. As for the number of financial products purchased, the 2002 SFE asks firms to list
all products, other than loans, which they purchase from the main banks. We then tabulate thenumber of products that each firm has purchased10.
Table 4 lists the variables used in our empirical analysis and their definitions. Several
unexplained variables will be discussed below.

4.2 Preliminary findings on the role of collateral and personal guarantees
Before explaining our regression models and results, it is useful to provide a preliminary
overview of how the riskiness of the borrower, the degree of screening and monitoring by thelenders, and the relationship between the two affects the share of borrowers who use collateral andpersonal guarantees.
Riskiness of the borrower
As stated above, we use the TSR credit score as a measure for ex-ante risk of the borrower.
Table 5 shows the percentage of borrowers using collateral, the percentage of those using personal9

We also construct an index variable, BANKONE, which takes the value of 1 if the borrower has a soletransaction with its main bank.
10
We exclude the items “purchasing additional stock of the mainbank” and “hiring retired bankers,” whichappear irrelevant to building the relationship.

19

guarantees, and the average short-term interest rates (RATE) paid by collateral/guarantee-user andnon-users, by credit score category (SCORE). Table 5 indicates a negative relationship between
credit score and use of collateral and personal guarantees, and thus favors moral hazard hypothesis.
Interestingly, even within the same credit score category, borrowers who pledge collateral andpersonal guarantees are charged higher interest rates than those who do not. This is consistent withthe finding in Berger and Udell (1990), who argue riskier borrowers more often pledge collateral,but that recourse to collateral less than fully offsets the difference in borrower risk. The interest rate
differential between the two is especially large for low-score (high-risk) borrowers.
We also investigate whether there are any preliminary findings consistent with the adverse
selection hypothesis. First, following the model of Jiménez, Salas-Fumás, and Saurina (2004), welook into the amount of collateral pledged. The 2002 SFE provides a value index of collateral,
which measures the present value of collateral relative to the amount of debt incurred. Table 6
shows that, once the decision has been made to pledge collateral, it is the high-score (low risk)borrowers who pledge more, suggesting the plausibility of the adverse selection model. However,
we attribute this finding to the simple fact that the low-score borrowers often do not have enoughassets to fully secure the loan. The bottom row of Table 6 shows the ratio of borrowers who own
real estate whose value exceeds the amount of short-term and long-term loans11. Naturally, thepercentage rises along with the score of the borrowers.
Second, we examine the relationship between the share of borrowers using collateral andpersonal guarantees in year 2001 and the TSR credit score in 2002 (Table 7). Because the creditscore in 2002 is unobservable by the lender in 2001, we think this is a more appropriate way to test
whether the lender uses collateral and personal guarantees to deal with the problem of adverse

11

We use the value of real estate because this is the most common asset pledged as collateral in Japan (Table2). We have also compared the results against the value of other assets that can be collateralized, such as
securities, and cash. The result is effectively the same.

20

selection, under which the borrower’s riskiness is unknown. Table 7 indicates, however, that therelationship between the share of borrowers using collateral and personal guarantees and the creditscore a year after the loan is made is still negative, which is inconsistent with the adverse selection
hypothesis.
Screening and monitoring by the lender
One important new approach in this paper is our use of direct measures for screening andmonitoring activities by lenders in our examination of the relationship between use of collateral andpersonal guarantees and screening and monitoring. Because it is somewhat subjective to posit a
priori which type of lenders have a relative advantage in evaluating and managing credit risks ofsmall business borrowers (as was done by Jiménez, Salas-Fumás, and Saurina, 2004), our work may
shed light on how collateral may affect the screening and monitoring incentives of the lender.
Table 8 shows the relationship between the frequency of document submission (DOCFREQ)
and the use of collateral and personal guarantees, and average short-term interest rates12. Because
the use of collateral and personal guarantees and the average short-term interest rate are affected byborrower risk, we make observations by credit-score category (the rows in Table 8). In general, thehigher the percentage of loans with either collateral or personal guarantees, the more often
borrowers submit documents. This preliminary result is inconsistent with the lazy bank hypothesis,
which claims that a bank requires collateral as a substitute for screening and monitoring. Interest
rates are somewhat higher for borrowers who submit documents more frequently.
We make a similar tabulation to investigate whether banks that contact their borrowers more
12

We have also investigated the frequency of document submission (DOCFREQ) by each banking sector andchecked whether a particular banking sector monitors the borrowers more frequently (Table 9). Table 9 showsthat the monitoring frequency of Shinkin (credit unions) and Shinkumi (credit cooperatives) are slightly
shorter than the other banking sectors, because their shares of low-score borrowers that require intensive
monitoring are larger than those of the other sectors. Hence, controlling for the effect of borrower risk, we do
not find any relationship between the banking sector and monitoring frequency. This reinforces our empiricalstrategy of not positing a priori which type of lenders have a relative advantage in evaluating and managing
credit risks of small business borrowers.

21

often (CONTACTFREQ) are more likely to have loans with collateral and personal guarantees (table
not reported), and obtain qualitatively the same result as above. We also examine the relationshipbetween the non-performing loan ratio (NPL) and use of collateral and personal guarantees, because
NPL may serve as an ex-post measure for screening and monitoring activities by the lender. We
have found, however, no monotonic relationship between the two (table not reported).
Relationship between the borrower and the lender
Table 10 shows the relationship between the duration of the main bank-borrower relationship(DURATION)13 and the use of collateral and personal guarantees, and the average short-term
interest rate. As the duration increases, the share of collateralized and personally guaranteed loansrises. Note also that the collateral ratio is relatively low for borrowers with main bank relationshipsof less than 15 years, regardless of credit score. This suggests collateral and personal guarantees arecomplementary to the relationship, and is consistent with both the “hold-up” argument (the “darkside” of the relationship) and the “mitigation of the soft-budget constraint” argument (the “brightside” of relationship). Interest rates are somewhat lower for borrowers with longer main bankrelationships, but the correlation is less clear-cut. The finding on interest rates is thus inconsistent
with the hold-up hypothesis.
Tables 11 and 12 make similar observations for the number of financial products purchasedby the borrower from its main bank (SCOPE) and the number of banks the borrower has
transactions with (BANKS). Table 11 leads us qualitatively to the same results as above: Borrowers
with more “scope” in the relationship are more likely to pledge collateral and personal guarantees,
and make slightly lower interest payments (although the relationship is less clear).
Table 12 gives a somewhat different view of the lender-borrower relationship. It shows thatborrowers who establish proprietary (sole) relationships with their main banks pledge collateral and13

Each respondent firm is asked to state the exact number of years it has been dealing with its mainbank.

22

personal guarantees less often than those who do not. In the case of collateral, which is more likely
to be inside than personal guarantees, this preliminary evidence supports the idea that the main
reason for requiring borrowers to pledge collateral is to secure seniority for the main bank’s claims,
because the need to define seniority among creditors would be less in the case of a single
transaction.

4.3 Regression model and results
We estimate the following equation to verify whether collateral and personal guarantee
requirements are greater for riskier borrowers, for banks with more intensive monitoring activities,
and for borrowers with more intimate banking relationships:
Pr(Yijg)f ( RISK j , MONITORINGi , RELATION ij , FIRM j , LENDERi , CONTRACTS ij , OTHERS )where Yij equals 1 if the loan made by bank i to the borrowing firm j is collateralized or
personally guaranteed. RISKj is a vector of variables specifying the risk profile of the borrower.
MONITORINGi is a vector of variables of monitoring activities by banks. RELATIONij is a vector ofvariables indicating the bank-firm relationship. All of these variables are discussed in Table 4.
FIRMj and LENDERi are dummy variables for firm and lender characteristics, where FIRM j
indicates the industry the firm belongs to and LENDERi indicates the sector (such as city bank or
regional bank) that the bank belongs to. Finally, we control for the contracting terms(CONTRACTSij) such as interest rate, RATEij, and whether the borrower pledges a guarantee orcollateral to its main bank. These variables are potentially endogenous, and we discuss how we deal
with the problem of endogenous regressors below. The variables in OTHERS include several
variables that are specifically important determinants for collateral and personal guarantees. For
example, the variable MATURITYj which represents the ratio of short-term loans to long-term loans
is likely to be correlated with whether the loan is collateralized because long-term loans such as23

equipment lending and real estate lending are more likely to be secured by physical assets to be heldby the borrower. The equation above is estimated using probit specification for both collateral andpersonal guarantees.
As in Berger and Udell (1990), we also estimate the following interest rate equation as anindirect test for the determinants of collateral and personal guarantees.
RATEijf ( RISK j , MONITORINGi , RELATION ij , FIRM j , LENDERi , Yij , OTHERS )In particular, if a borrower who establishes a solid relationship with its main bank is more likely topledge collateral and personal guarantees, the interest rate equation verifies whether thecomplementarity between collateral and personal guarantees and the bank-firm relationship is dueto the hold-up problem or to mitigation of the soft-budget constraint.
Estimation strategies
We begin with the probit estimations for collateral and personal guarantees, and the OLS
estimation for the interest rate, assuming that the contract-terms are exogenous explanatory
variables. For example, regarding the probit estimation for collateral, the interest rates and thebinary variable for personal guarantees are assumed to be exogenous.
We then implement the probit estimations for collateral and personal guarantees, treatinginterest rates as an endogenous variable. The estimation will follow the maximum likelihood
estimation procedure with the endogenous variable in Wooldridge (2004, pp.475-476). The idea is
to obtain the parameters of the model and their standard errors by maximizing the likelihood
function of the following equation:f (Yij , RATE | z )f (Yij | RATE , z) f ( RATE | z )where z is a vector of instrumental variables. As shown in the second term of the right-handside equation, interest rates (endogenous variable) are estimated by the instrumental variable. The
instrumental variables of interest rates are measures of market power and the age of the borrowing

24

firm (FIRMAGE). We use the Herfindahl Index for small business lending in the prefecture of thefirm, denoted as HHI, as our market power measure. Because HHI is computed based on the shareof small business lending of regional banks, second-tier regional banks, Shinkin (credit unions), andShinkumi (credit cooperatives), we also include the aggregated share of city banks in small business
lending in the prefecture of the firm (CITYSHARE). These market power variables are taken fromthe Kinyu Journal, “Regional Finance Map.” The information obtained from the interest rates
equation as well as information from the collateral (personal guarantees) equation is simultaneously
used in estimating the parameters.
It may be preferable to endogenize personal guarantees (collateral) as well as interest rates in
estimating the probability of collateral (personal guarantees) pledged. However, the number ofendogenous variables that we can handle in the full maximum likelihood procedure is constrainedby the computational difficulties in getting the iterations to converge. Alternatively, we follow thetwo-stage conditional maximum likelihood method (Wooldridge, 2004, pp.472-475), in which theinterest rates and personal guarantees (collateral) are estimated by ordinary least squares with
instrumental variables in the first step, and then the probit model for collateral (personal guarantees)
is estimated in the second step, using the fitted values and the standard errors obtained in thefirst-step estimations. The instrumental variable used in the first-step estimation for personal
guarantees is the share of equity holdings held by the owner and his relatives in the previous year,
2001: the index variables OWNERRATIO take the value of 1-7, with the highest value 7 indicating
the 100% equity holdings (1: 0%, 2: 1-5%, 3: 6-25%, 4: 26-50%, 5: 51-74%, 6: 75-99%, 7: 100%).
The instrumental variable for collateral is the ratio of real estate holdings to total assets(LANDRATIO).
Based on these three types of estimations, we will make our inference on how the riskiness ofthe borrower, the degree of screening and monitoring by the lenders, and the relationship between

25

the two affects the share of borrowers who use collateral and personal guarantees.
Estimation results
Tables 13, 14, and 15 report our estimation results for collateral, personal guarantees, andinterest rates equations, respectively. The first column of each table provides the estimates of thebenchmark models in which all the terms of loan contracts are assumed to be exogenous. The
second column shows the estimates of the full maximum likelihood models with one endogenous
variable (interest rates), and the third column shows the estimates of the two-step maximum
likelihood models with two endogenous variables. Coefficients for firm and lender characteristics
dummy variables are omitted from the tables.
We first investigate the COLLATERAL equation (Table 13). In the basic probit estimation forcollateral, GUAR and RATE are positively correlated with the probability of collateral being
pledged. The SCORE receives an insignificant negative coefficient, but the coefficient of LEV ispositive and significant, suggesting that collateral is used to suppress the debtor’s moral hazard. The
negative and significant coefficient of DOCFREQ indicates the complementarity between
monitoring by the lender and collateral requirements. The positive coefficients of the relationshipvariables (DURATION, SCOPE) show that collateral is also complementary to the relationship.
In the second column of Table 13, the full maximum likelihood estimation with interest rates
treated as endogenous exhibits similar results. In order to achieve the convergence, we dropped
several explanatory variables that are less relevant14. A Wald statistics test of exogeneity rejects thenull hypothesis of the interest rate being exogenous. Once we control for the endogeneity of theinterest rate, its coefficient becomes significantly negative. This indicates that borrowers who
pledge collateral to their main bank are charged lower interest rates, presumably because collateralensures the seniority of main bank’s claims and limits debtor moral hazard. Compared with the14

Firm and lender characteristics dummy variables are also excluded in this estimation.

26

basic probit estimation, two variables are worth noting. SCORE has a significantly negative sign,
thus strengthening the robustness of the moral hazard hypothesis. The sign of SCOPE becomes
negative, although it is insignificant. Nevertheless, the coefficient of DURATION is still positiveand significant, suggesting the complementarity between collateral and the bank-borrower
relationship.
The third column of Table 13 presents the estimation results for the two-step procedure inwhich personal guarantees as well as interest rates are endogenous. Although we lose efficiency inthe coefficients for most variables to a certain degree, the results obtained in the previous
estimations remain valid qualitatively.
In parallel fashion, Table 14 provides the estimation results for personal guarantees. The
second and third columns of Table 14 show estimates for full and two-step maximum likelihood
procedures, respectively, but the Wald statistics test of exogeneity cannot reject the null hypothesisof interest rate and collateral being exogenous. Hence, we will make our inference based on thebasic probit estimation results (first column, Table 14). Somewhat surprisingly, many credit risk
variables correlated with the probability of collateral being pledged are insignificant in the
GUARANTEE equation. SCORE and LEV are insignificant, while LOGSALES has a significant andnegative sign, indicating firms with larger sales are less likely to pledge personal guarantees. These
results, however, are likely to be due to multicollinearity among SCORE, the terms of loan contracts,
and other financial ratios. Once we drop the loan-contract and other financial ratio variables,
SCORE has a significantly negative sign, suggesting that personal guarantees are also used tocontain the debtors’ moral hazard.
The complementarity of personal guarantees to monitoring and the borrower-lender
relationship becomes weaker but is maintained in the GUARANTEE equation. The coefficient of thefrequency of document submission (DOCFREQ) is significantly negative, which implies that27

personal guarantees are complementary to monitoring by lenders. SCOPE is significantly positive,
also suggesting the complementarity of personal guarantees and the borrower-lender relationship.
An exception is the negative and significant coefficient of ONEBANK.
Finally, Table 15 shows estimation results for the interest rate. The second and third columns
are “first-step” estimation results in COLLATERAL equations. The negative and significant
coefficient of SCORE indicates that the borrowers’ risk is negatively correlated with the interest
rates charged by the main bank. The negative coefficient of SCOPE and the positive coefficient of
BANKS are inconsistent with the hold-up hypothesis.
As a robustness check, we divide our sample into credit guarantee users and non-users andre-estimated for the latter firms for the following reason: The Japanese government has credit
guarantee programs designed to mitigate the financial difficulties faced by SMEs, and more thanhalf of our sample firms use such credit guarantees. In the event a borrower cannot repay its debt tothe bank, the credit guarantee corporation covers the debt, whereupon it receives a claim against theborrowing firm. In principle, the credit guarantee corporation guarantees 100% of the loans
outstanding, meaning the bank bears no credit risk. As shown in Table 17, the share of borrowers
who use credit guarantees is inversely related to the credit score and the share of borrowers who
pledge collateral is very high among credit guarantee users, irrespective of the credit score15. Thus,
the use of credit guarantees may have influenced our inference above. Estimation for the
non-guarantee users excludes such distortions, which is shown in Table 16. We posit the probit and
OLS estimation results for collateral, personal guarantees, and the interest rates. Most of thesignificant coefficients in Tables 13, 14, and 15 are qualitatively unchanged in the table, while a few
of them lose their significance, possibly due to the smaller number of observations.
15

The maximum loan amount for the most common credit guarantee program is 200 million yen andcollateral can be required for loans of more than 80 million yen. This collateral requirement is deemed toprevent moral hazard. Thus, users of credit guarantee programs may pledge collateral for institutional
requirements rather than for the economic reasons we have discussed.

28

5 Concluding Remarks
This paper investigates the role of collateral and personal guarantees in small business
lending using the unique data set of Japan’s small business loan market. Consistent withconventional theory, collateral is more likely to be pledged by riskier borrowers, implying suchcollateral may be useful in mitigating debtor moral hazard. Contrary to conventional theory, we findthat banks whose claims are either collateralized or personally guaranteed monitor borrowers more
frequently. We also find that borrowers who establish long-term relationships with their main banks
are more likely to pledge collateral. Our empirical evidence thus suggests that collateral andpersonal guarantees are complementary to relationship lending.
Our findings have the following implications for the current debate on strengthening thefunction of relationship banking in Japan. In the bubble economy period, real estate was consideredsuch a safe collateral asset that the incentive of financial institutions to monitor the borrowers wasundermined insofar as loans were collateralized. This phenomenon may be behind the widely heldview that accepting collateral hinders the banks’ monitoring effort and thus undermines relationshiplending in Japan. However, our evidence suggests this may no longer hold true. Based on data fromthe early 2000s, we find that lenders who require borrowers to pledge collateral and personal
guarantees are more likely to monitor intensively. Therefore, emphasizing only the negative side ofcollateral and recommending non-collateral loans, such as the rapidly expanding credit scoringloans, may in fact hinder screening and monitoring activities by lenders that are inherent inrelationship lending. We also find that collateral is more likely to be pledged by borrowers whoestablish long-term relationships with their main bank. This evidence reinforces our argument.
There are a few caveats and further issues to be addressed. First, the sample firms in our
analysis are rather large, which may bias our empirical results. In Japan, small businesses with nomore than 20 employees make up more than 70% of all firms, while the median firm in our sample

29

has 36 workers. Hence, it may be the case that only fairly sizable, well-established SMEs with
sufficient collateral benefit from relationship lending, while their smaller counterparts that are shortof collateral face strict borrowing constraints. Second, our estimation may have been biased due toinaccurate assessments of firms’ credit risk. We employ the TSR credit scores and balance sheet
items to measure the credit risk of a firm. However, if there is private information to which only
banks have access, our empirical results may be biased due to the problem of measurement error.
We may be able to better approximate the actual credit risk of firms by employing future creditscores or the firms’ default measures; we need to examine this point more closely. Finally, we findevidence that weakly rejects the hold-up hypothesis: Interest rates are relatively lower for borrowers
who establish a solid relationship with their main banks. However, it is beyond the scope of thispaper to investigate whether collateral mitigates the soft-budget constraint problem in relationshiplending. In order to evaluate the efficiencies of Japan’s credit market, it is important to further
investigate the motivations of banks in accepting collateral and personal guarantees. In order tomake a welfare judgment, it is also necessary to evaluate whether the enhanced bargaining power ofthe lender, be it due to hold-up of the borrower or mitigation of the soft budget constraint, facilitates
the screening and monitoring activities of the lender and thereby increases the availability of credits
for small businesses. This issue should be addressed in more detail in future studies.

30References
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32

Table 1 Descriptive Statistics (Median)
CapitalGross sales TSR Interest rate
No. of samples
No. ofprofit(thousands(thousands Credit (0.1 basis(share, %)employees
marginof yen)of yen)Scorespoint）

Capital/
asset
ratio

With Collateral

4,834 (73.9)

197,50938

1,299,848

55

2000

0.01390.2009With Personal Guarantee

4,984 (76.2)

161,01732

1,079,825

55

2100

0.01330.1991

With Credit Guarantee

3,381 (51.7)

96,277

26

873,70553

2375

0.01200.1588With Collateral and Personal Guarantee
And With Credit Guarantee

2,819 (43.1)

104,01528

931,178

53

2400

0.01220.1537

And Without Credit Guarantee

1,413 (21.6)

417,12152

1,939,796

59

1750

0.01600.2966889(13.6)

464,04045

2,098,614

60

1375

0.01820.38606,540 (100.0)

207,012(1,797,737)

36
(155)

1,290,303(5,837,277)

56
(7)

2000
(1204)

0.0143 0.2201(0.2506) (0.3028)

Without Collateral, Personal
Guarantee, or Credit Guarantee
All Samples（standard deviation）

Note: As of 2002 hereinafter, unless otherwise stated.

33

Table 2 Composition of Collateral
Total

-49

50 - 54

TSR Credit Scores
55 - 59
60 - 64

65 - 69

70 -No. of Samples

5,920

850

1,394

1,484

1,032

914

246

(share, %)(100.0)(14.4)(23.5)(25.1)(17.4)(15.4)(4.2)

Percentage of Borrowers with Collateral

77.4

84.5

82.0

80.2

74.8

68.8

54.1

Composition of Collateral (multiple answers allowed, %)real estate

95.9

95.8

96.0

95.5

95.9

96.8

95.5

machinery

5.4

6.3

4.6

5.0

5.8

4.8

10.5

deposits

22.8

29.2

28.4

24.4

16.5

12.2

12.0

equity securities

9.2

11.4

10.9

9.2

7.0

7.0

6.0

commercial bills

6.9

8.4

8.1

7.1

5.2

5.4

2.3

other securities

2.4

3.6

3.4

2.2

1.2

1.4

0.8

proceeds of guarantee

1.2

2.6

1.1

0.8

0.9

0.8

0.8

accounts receivable

0.8

0.8

0.8

0.4

0.9

1.3

0.8

intellectual property

0.1

0.3

0.0

0.1

0.0

0.0

0.0

others

1.9

1.4

2.1

1.5

2.6

1.3

3.8

70 -

Note: As of 2001.

Table 3 Composition of Personal Guarantees
Total

-49

50 - 54

TSR Credit Scores
55 - 59
60 - 64

65 - 69

No. of Samples

5,920

850

1,394

1,484

1,032

914

246

(share, %)(100.0)(14.4)(23.5)(25.1)(17.4)(15.4)(4.2)

Percentages of Borrowers with Personal
Guarantee

72.6

82.7

81.0

76.1

67.5

58.8

40.7

Composition of Personal Guarantee (multiple answers allowed, %)Representative

94.8

95.3

94.5

94.6

95.1

94.4

95.0

Executives other than representative

34.1

45.9

38.2

34.1

25.5

23.5

21.0

Relatives of representative

18.3

30.0

20.5

17.0

14.6

8.0

8.0

Third party (individuals)

2.4

6.4

2.5

2.0

0.7

0.2

0.0

Enterprises with capital relationship

6.3

7.4

6.9

5.9

5.9

5.0

4.0

Enterprises without capital relationship

0.6

1.3

0.5

0.7

0.0

0.2

0.0

Other

1.1

1.0

1.1

1.4

1.4

0.7

0.0

Note: As of 2001.

34

Table 4 Variable Definitions
Variables

Definitions

Terms of loan contracts
COLL

1 if the borrower pledges collateral to its main bank, 0 otherwise

GUAR

1 if the borrower pledges a personal guarantee to its main bank, 0 otherwise

RATE

short-term interest charged by the main bank. If the borrower has several short-term loans with its mainbank, the loan with the highest rate is reported

GOVGUAR

1 if the borrower uses government sponsored credit guarantees for the loans provided by its main bank, 0
otherwise

Riskiness of borrower
SCORE

TSR Credit Score (0-100)

LEV

ratio of total debts outstanding to total assets

PROFMARG

ratio of profits before tax to gross sales

CASHRATIO

ratio of cash holdings to total assets

LOGSALES

gross sales in logarithm

Screening and monitoring by lenderDOC

1 if the borrower submits relevant documents, such as financial statements, to its main bank, 0 otherwise

DOCFREQ

index variable indicating the frequency of document submission to the borrower's main bank:
1: once every 1-2 months, 2: quarterly, 3: semi-annually, 4: annually

CONTACTFREQ

index variable indicating the main bank's frequency of contact with the borrower:
1: every day, 2: weekly, 3: once every 2 weeks, 4: monthly, 5: bi-monthly, 6: quarterly, 7: semi-annually,
8: annually, 9: no contact

NPL

ratio of non-performing loans to total loans

Relationship between borrower and lender
DURATION

duration of the main bank-borrower relationship in years

SCOPE

number of financial products the borrower purchases from its main bank

BANKS

number of banks the borrower has transactions with

ONEBANK

1 if the borrower has a single bank to make transaction with

Other variables
MATURITY

ratio of short-term to long-term loans, where short-term loans are defined as loans with maturity less than
one year

FIRM

borrower dummy variable classified by industry

LENDERlender dummy variable classified by type of banking sector

Instrumental variables
LANDRATIO

ratio of real estate holdings to total assets

OWNERRATIO

index variable for the owner and his relatives' share of equity holdings:
1: 0%, 2: 1-5%, 3: 6-25%, 4: 26-50%, 5: 51-74%, 6: 75-99%, 7: 100%

FIRMAGEage of the borrowing firm

HHI

Herfindahl Index for small business lending in the prefecture of the firm. Herfindahl Index is computed basedon the share of small business lending of regional banks, second-tier regional banks, shinkin, and shinkumi.

CITYSHARE

city banks' share of small business lending in the prefecture of the firm

35

Table 5 Use Rate of Collateral, Personal Guarantees and Average Interest Rates
Total

-49

50 - 54

TSR Credit Scores
55 - 59
60 - 64

65 - 69

70 -No. of Samples

5,380

868

1,521

1,366

850

663

112

(share, %)(100.0)(16.1)(28.3)(25.4)(15.8)(12.3)(2.1)

Percentage of Borrowers
with Collateral

79.7

85.1

82.0

80.7

76.5

71.5

69.6

Average Interest Rate(with Collateral, 0.1 b.p.)

2283

3073

2557

2069

1800

1636

1386

Average Interest Rate(without Collateral, 0.1f b.p.)

1842

2653

2224

1767

1552

1269

1157

Percentage of Borrowers
with Personal Guarantees

81.8

90.6

87.2

83.7

73.6

67.1

64.3

Average Interest Rate(with Personal Guarantees,
0.1 b.p.)

2326

3080

2581

2088

1867

1648

1341

Average Interest Rate(without Personal Guarantees,
0.1 b.p.)

1600

2347

1919

1614

1392

1294

1272

Collateral

Personal Guarantees

Note: Sample in the table is restricted to borrowers who reported short-term interest rates.

Table 6 Value of Collateral and Real Estate Holdings
Total

-49

50 - 54

TSR Credit Scores
55 - 59
60 - 64

65 - 69

70 -No. of Samples

5,380

868

1,521

1,366

850

663

112

(share, %)(100.0)(16.1)(28.3)(25.4)(15.8)(12.3)(2.1)

Percentage of Borrowers with Collateral

79.7

85.1

82.0

80.7

76.5

71.5

69.6

Value of Collateral<< Loans Outstanding

23.0

31.1

24.8

19.1

20.9

18.8

17.9

Value of Collateral< Loans Outstanding

24.1

25.3

26.3

24.4

24.0

17.3

15.4

Value of Collateral= Loans Outstanding

24.7

21.0

25.4

27.7

22.3

25.3

23.1

Value of Collateral> Loans Outstanding

11.8

10.3

11.1

12.6

13.2

12.0

11.5

Value of Collateral>> Loans Outstanding

14.4

10.3

10.7

14.2

17.2

24.9

29.5

9.0

1.8

4.1

8.6

13.7

23.0

35.9

Distribution of Borrowers (%):

Percentages of borrowers whoseamount of real estate holdings exceedloans outstanding

Note: Sample in the table is restricted to borrowers who reported short-term interest rates.

36

Table 7 Use Rate of Collateral and Personal Guarantees in 2001: By 2002 Credit Scores
Total

-49

50 - 54

TSR Credit Scores in 2002
55 - 59
60 - 64
65 - 69

70 -Percentage of Borrowers
with Collateral in 2001

76.0

82.6

80.5

77.9

72.7

66.9

50.5

(in 2002)(73.9)(80.4)(78.4)(75.1)(69.0)(64.8)(56.0)

Percentage of Borrowers
with Personal Guarantee in 2001

74.8

85.8

81.5

78.4

68.3

58.9

46.7

(in 2002)(76.2)(87.0)(83.9)(78.3)(67.1)(60.4)(51.6)

Table 8 Use Rate of Collateral, Personal Guarantees and Average Interest Rates:
By Frequency of Document Submission
Frequency of Document Submission

Total

TSR Credit Scores-49

50 - 54

55 - 59

60 - 64

65 - 69

70 -Percentage of Borrowers with Collateralonce every 1-2 months

91.5

92.3

94.8

88.9

89.9

78.1

93.8

quarterly

87.6

88.1

88.6

89.3

83.5

83.6

75.0

semi-annually

75.9

78.8

77.7

77.2

73.7

70.9

72.4

annually

67.2

69.3

69.5

70.1

66.0

63.8

53.3

Percentage of Borrowers with Personal Guarantee
once every 1-2 months

89.7

92.3

91.4

91.4

85.8

71.2

68.8

quarterly

88.4

91.1

93.6

91.9

77.6

69.1

50.0

semi-annually

70.9

82.7

80.4

73.8

59.2

56.3

62.1

annually

75.7

88.0

82.9

78.2

72.0

65.4

55.1

once every 1-2 months

2634

3192

2783

2328

1879

1588

1680

quarterly

2386

3015

2555

2147

1903

1927

1023

semi-annually

1987

2870

2310

1887

1598

1508

1486

annually

1985

2758

2392

1909

1759

1508

1160

Average Interest Rate, 0.1 b.p.

37

Table 9 Composition of the Frequency of Submitting Documents by Banking Sector
Total

-49

50 - 54

TSR Credit Scores
55 - 59
60 - 64

65 - 69

70 -City Banks, Long Term Credit Banks, and Trust Banks
No. of Samples(share, %)1695(100.0)170(10.0)

424(25.0)

472(27.8)

343(20.2)

240(14.2)

46(2.7)

Composition of the Frequency of Submitting Documents (%)
once in 1-2 months

20.8

34.7

28.1

18.0

16.3

12.1

10.9

quarterly

13.4

20.6

14.2

16.7

11.4

4.6

6.5

semi-annually

30.9

21.2

29.5

32.4

36.4

32.9

13.0

annually

34.9

23.5

28.3

32.8

35.9

50.4

69.6

Regional Banks and Second Regional Banks
No. of Samples(share, %)3252(100.0)497(15.3)

858(26.4)

792(24.4)

536(16.5)

462(14.2)

107
(3.3)
10.3

Composition of the Frequency of Submitting Documents (%)
once in 1-2 months

23.2

44.9

30.7

17.9

13.8

9.1

quarterly

10.9

13.5

14.5

10.6

7.3

7.8

4.7

semi-annually

22.4

16.7

21.1

25.4

25.7

22.7

19.6

annually

43.5

24.9

33.8

46.1

53.2

60.4

65.4

Shinkin (credit unions) and Shinkumi (credit cooperatives)
No. of Samples(share, %)706(100.0)200(28.3)

256(36.3)

150(21.2)

62
(8.8)

36
(5.1)

2
(0.3)

12.9

19.4

50.0

Composition of the Frequency of Submitting Documents (%)
once in 1-2 months

28.2

44.0

25.8

24.7

quarterly

13.3

11.0

16.0

13.3

6.5

19.4

50.0

semi-annually

17.8

11.5

19.1

22.0

21.0

61.1

0.0

annually

40.7

33.5

39.1

40.0

59.7

0.0

0.0

38

Table 10 Use Rate of Collateral, Personal Guarantees and Average Interest Rates:
By Duration of Main Bank Relationship
Duration of relationshipwith the main bankTotal

TSR Credit Scores-49

50 - 54

55 - 59

60 - 64

65 - 69

70 -Percentage of Borrowers with Collateralless than 15 years

54.9

53.9

58.3

54.9

54.5

44.4

52.4

15-28 years

73.9

84.1

80.5

73.2

65.2

59.0

49.8

28-40 years

79.8

92.4

87.0

81.2

70.1

68.0

52.2

40 years or more

82.8

92.7

89.2

86.2

80.1

72.2

57.5

Percentage of Borrowers with Personal Guarantees
less than 15 years

71.2

76.8

78.8

71.8

60.4

45.5

42.9

15-28 years

78.0

91.7

85.1

79.7

67.4

56.5

50.0

28-40 years

78.2

92.0

88.8

79.3

67.1

61.5

50.0

40 years or more

78.1

90.0

83.9

82.8

71.1

68.9

56.3

less than 15 years

2375

2987

2556

2047

1970

1769

1382

15-28 years

2351

3118

2622

2112

1828

1636

1568

28-40 years

2193

3079

2499

2050

1702

1530

1254

40 years or more

1963

2857

2319

1870

1628

1410

1286

Average Interest Rate, 0.1 b.p.

Table 11 Use Rate of Collateral, Personal Guarantees and Average Interest Rates:
By Number of Financial Products Purchased from Main Bank
# of financial products
purchased from main bankTotal

TSR Credit Scores-49

50 - 54

55 - 59

60 - 64

65 - 69

70 -Percentage of Borrowers with Collateral
0-1

48.6

54.2

55.7

43.0

41.8

42.9

26.3

2-3

67.1

78.6

72.5

65.5

59.7

53.3

47.7

4

76.0

84.6

78.4

79.9

69.1

66.4

64.4

5 or more

84.9

92.9

90.4

85.6

81.6

76.6

62.7

Percentage of Borrowers with Personal Guarantees
0-1

58.2

72.5

68.6

53.9

39.6

38.6

31.6

2-3

71.1

85.1

82.0

71.4

56.9

48.6

40.9

4

79.5

91.9

84.5

81.8

72.0

64.6

68.9

5 or more

82.6

92.5

89.0

86.6

76.8

70.3

52.0

0-1

2299

2799

2448

2132

1797

1789

1320

2-3

2320

3173

2527

2075

1820

1523

1401

4

2226

3040

2636

2029

1773

1528

1310

5 or more

2060

2896

2385

1962

1663

1505

1284

Average Interest Rate, 0.1 b.p.

39

Table 12 Use Rate of Collateral, Personal Guarantees and Average Interest Rates:
By Number of Banks in Transactions# of banks in transactions

Total

TSR Credit Scores-49

50 - 54

55 - 59

60 - 64

65 - 69

70 -Percentage of Borrowers with Collateral
1

52.0

67.1

56.9

52.9

43.7

42.9

29.2

2

73.6

79.7

71.9

69.4

74.7

73.9

84.6

3-4

79.7

82.7

83.9

81.2

76.4

71.8

63.8

5 or more

82.5

88.2

88.1

84.7

79.4

69.3

58.8

Percentage of Borrowers with Personal Guarantee
1

59.4

78.7

67.9

58.5

50.0

47.5

22.9

2

81.7

89.6

86.1

81.3

78.3

66.7

65.4

3-4

81.5

91.0

86.9

84.7

71.2

68.0

65.5

5 or more

79.2

87.5

88.8

82.2

70.6

60.3

56.9

1

2173

2994

2502

1899

1624

1631

1424

2

2258

2973

2478

2073

1864

1610

1343

3-4

2170

2967

2465

1948

1808

1522

1380

5 or more

2193

3084

2540

2085

1679

1474

1184

Average Interest Rate, 0.1 b.p.

40

Table 13 Determinants of Collateral
Probit(GUAR, RATE exogenous)

Variables

coefficient(z-value)(p-value)

Probit by Full MLE(RATE endogenous)
coefficient(z-value)(p-value)

Probit by two-step MLE(GUAR, RATE endogenous)
coefficient(z-value)(p-value)

Terms of loan contracts
GUAR

0.800

( 12.11 )( 0.000 )0.685( 9.43 )( 0.000 )6.034( 3.21 )( 0.001 )RATE

0.00013( 4.85 )( 0.000 )-0.00082( -15.66 )( 0.000 )-0.00350( -1.65 )( 0.099 )( -0.73 )( 0.468 )-0.050( -14.23 )( 0.000 )-0.137( -1.78 )( 0.075 )0.205( 2.91 )( 0.004 )Riskiness of Borrower
SCORE-0.004LEV

0.239( 2.16 )( 0.030 )0.988( 1.61 )( 0.108 )PROFMARG-0.390( -0.81 )( 0.420 )4.983( 1.82 )( 0.069 )CASHRATIO

0.427( 2.78 )( 0.005 )0.323( 0.85 )( 0.395 )LOGSALES

0.048( 1.65 )( 0.098 )-0.228( -0.69 )( 0.489 )5.766( 1.23 )( 0.217 )Screening and monitoring by the lender
NPL-0.526( -0.61 )( 0.540 )DOCFREQ-0.181( -7.00 )( 0.000 )-0.188( -9.37 )( 0.000 )-0.633( -2.11 )( 0.035 )Relationship between borrower and lender
LOG(DURATION)

0.388( 11.48 )( 0.000 )0.139( 3.01 )( 0.003 )0.274( 1.66 )( 0.096 )

SCOPE

0.090

( 5.29 )( 0.000 )-0.013( -0.86 )( 0.387 )-0.070( -0.86 )( 0.392 )0.007( 1.20 )( 0.232 )0.088( 1.46 )( 0.145 )0.743( 1.80 )( 0.071 )

BANKS

0.003

( 0.33 )( 0.742 )ONEBANK-0.074( -0.85 )( 0.398 )Other variables
MATURITY-0.483( -5.62 )( 0.000 )-0.466( -7.28 )( 0.000 )-0.351( -1.02 )( 0.306 )LANDRATIO

3.323( 13.38 )( 0.000 )1.289( 3.68 )( 0.000 )1.688( 2.25 )( 0.025 )constant- 2.110( -4.32 )( 0.000 )4.527( 11.14 )( 0.000 )13.774( 1.13 )( 0.260 )# ofobservations

4380

4590

Log likelihood-1544.746-40042.165

Wald test
statistics

38.42

2243

prob=0.0000

41

47.94

prob=0.0000

Table 14 Determinants of Personal Guarantees
Probit(COLL, RATE exogenous)

Variables

coefficient(z-value)(p-value)

Probit by Full MLE(RATE endogenous)
coefficient

Probit by two-step MLE(COLL, RATE endogenous)(z-value)(p-value) coefficient(z-value)(p-value)

Terms of loan contracts
COLL

0.582

( 6.28 )( 0.000 )0.545( 3.68 )( 0.000 )0.915( 1.07 )( 0.286 )RATE

0.00009( 1.74 )( 0.081 )-0.00085( -2.50 )( 0.012 )-0.00122( -0.67 )( 0.504 )( 0.31 )( 0.754 )-0.030( -2.30 )( 0.021 )-0.043( -0.65 )( 0.516 )Riskiness of Borrower
SCORE

0.002

LEV

0.129

( 0.74 )( 0.458 )0.276( 1.99 )( 0.047 )0.409( 0.85 )( 0.393 )PROFMARG-0.413( -0.59 )( 0.555 )0.589( 0.83 )( 0.409 )0.783( 0.37 )( 0.709 )CASHRATIO-0.270( -1.62 )( 0.105 )-0.213( -1.41 )( 0.159 )-0.338( -1.65 )( 0.099 )LOGSALES-0.338( -7.44 )( 0.000 )-0.379( -6.26 )( 0.000 )-0.580( -1.63 )( 0.102 )Screening and monitoring by the lender
NPL-2.445( -1.89 )( 0.059 )-0.280( -0.17 )( 0.864 )-0.659( -0.23 )( 0.816 )DOCFREQ-0.145( -3.84 )( 0.000 )-0.233( -8.11 )( 0.000 )-0.348( -1.04 )( 0.296 )Relationship between borrower and lender
LOGDURATION-0.048( -0.81 )( 0.421 )-0.088( -1.87 )( 0.061 )-0.139( -1.30 )( 0.195 )

SCOPE

0.082

( 3.47 )( 0.001 )0.040( 1.06 )( 0.289 )0.066( 2.03 )( 0.042 )

BANKS

0.004

( 0.28 )( 0.782 )0.024( 1.83 )( 0.067 )0.034( 0.69 )( 0.492 )ONEBANK-0.414( -2.84 )( 0.004 )-0.226( -1.16 )( 0.244 )-0.375( -1.89 )( 0.059 )MATURITY-0.438( -3.27 )( 0.001 )-0.331( -2.00 )( 0.045 )-0.511( -1.45 )( 0.148 )OWNERRATIO

0.158( 7.55 )( 0.000 )0.126( 2.73 )( 0.006 )0.196( 2.00 )( 0.045 )4.524( 5.88 )( 0.000 )8.786( 10.54 )( 0.000 )13.084( 1.04 )( 0.297 )Other variables

constant# of observations
Log likelihood
Wald test
statistics

2294

2243

-736.17755-19052.302
2.37

42

2243

prob=0.1235

2.35

prob=0.3095

Table 15 Determinants of Interest Rates
Variables

OLS(COLL, GUAR exogenous)
coefficient(t-value)(p-value)First step estimation in Full
MLE in COLL equation
coefficient(z-value)(p-value)414.82820( 9.51 )( 0.000 )First step estimation in two-step
MLE in COLL equation
coefficient(t-value)(p-value)

Terms of loan contracts
COLL

200.615( 4.59 )( 0.000 )GUAR

145.04030( 3.13 )( 0.002 )Riskiness of Borrower
SCORE-35.318( -11.44 )( 0.000 )-54.940 ( -20.42 )( 0.000 )-35.100( -9.55 )( 0.000 )LEV

165.056( 2.66 )( 0.008 )123.248( 0.051 )236.323( 2.91)( 0.004 )PROFMARG

645.546( 2.44 )( 0.015 )997.626( 2.93)( 0.003 )CASHRATIO-155.009( -2.31 )( 0.021 )-33.034( -0.40 )( 0.691 )LOGSALES-216.849 ( -12.81 )( 0.000 )-192.818( -8.84 )( 0.000 )1304.938( 2.13)( 0.033 )( 1.95 )Screening and monitoring by the lender
NPL

1,500.960( 2.95 )( 0.003 )DOCFREQ-170.002 ( -11.80 )( 0.000 )-127.848( -9.07 )( 0.000 )-164.908( -9.43 )( 0.000 )Relationship between borrower and lender
LOGDURATION-5.633( -0.22 )( 0.823 )29.463( 1.17 )( 0.240 )-24.862( -0.76 )( 0.445 )SCOPE-27.361( -2.75 )( 0.006 )-50.774( -5.27 )( 0.000 )-9.455( -0.78 )( 0.434 )BANKS

19.050( 3.37 )( 0.001 )3.076( 0.58 )( 0.561 )24.823( 3.70)( 0.000 )ONEBANK-58.537( -1.05 )( 0.296 )46.831( 0.63)( 0.530 )MATURITY-91.799( -1.70 )( 0.090 )-301.796( -5.61 )( 0.000 )-100.960( -1.48 )( 0.139 )FIRMAGE-1.914( -2.18 )( 0.029 )-5.133( -5.64 )( 0.000 )-1.217( -1.14 )( 0.253 )

HHI

0.021

( 1.33 )( 0.182 )0.005( 0.48 )( 0.628 )0.011( 0.55)( 0.584 )

220.551( 1.94 )( 0.052 )-53.311( -0.80 )( 0.421 )166.014( 1.19)( 0.233 )-66.084( -0.55 )( 0.584 )-38.709( -0.26 )( 0.798 )40.576( 3.56)( 0.000 )6642.338( 18.47)( 0.000 )Other variables

CITYSHARELANDRATIOOWNERRATIO
constant

7,100.074# of observations

4278

Adj. R-squared

0.2791( 25.64 )( 0.000 )5,679.272

4590( 28.68 )( 0.000 )2243
0.300843

Table 16 Basic Model without Credit Guarantees
Variables

COLLATERAL equation(probit)coefficient(z-value)(p-value)

GUARANTEE equation(probit)RATE equation(OLS)

coefficient(z-value)(p-value)coefficient(z-value)(p-value)

0.477( 4.24 )( 0.000 )218.460( 4.46 )( 0.000 )87.105( 1.86 )( 0.064 )Terms of loan contracts
COLL
GUAR

0.663

( 8.18 )( 0.000 )RATE

0.00025( 4.65 )( 0.000 )0.00005( 0.80 )( 0.424 )Riskiness of Borrower
SCORE

0.001

( 0.14 )( 0.892 )0.006( 0.66 )( 0.509 )-25.206( -6.63 )( 0.000 )

LEV

0.098

( 0.75 )( 0.456 )0.146( 0.72 )( 0.470 )56.025( 0.87 )( 0.387 )PROFMARG

0.242( 0.34 )( 0.736 )-0.349( -0.45 )( 0.653 )192.067( 0.65 )( 0.518 )CASHRATIO

0.252( 1.46 )( 0.145 )-0.214( -1.17 )( 0.244 )29.125( 0.40 )( 0.691 )LOGSALES

0.065( 1.61 )( 0.108 )-0.324( -5.48 )( 0.000 )-133.147( -6.24 )( 0.000 )Screening and monitoring by the lender
NPL-0.689( -0.54 )( 0.588 )-2.180( -1.31 )( 0.190 )

2,125.051( 3.15 )( 0.002 )DOCFREQ-0.141( -3.57 )( 0.000 )-0.069( -1.40 )( 0.162 )-100.043( -5.02 )( 0.000 )Relationship between borrower and lender
LOG(DURATION)

0.413( 8.00 )( 0.000 )0.081( 1.08 )( 0.281 )-24.129( -0.74 )( 0.458 )

SCOPE

0.085

( 3.62 )( 0.000 )0.043( 1.46 )( 0.144 )-22.464( -1.81 )( 0.071 )

BANKS

0.006

( 0.38 )( 0.703 )0.000( 0.01 )( 0.994 )9.883( 1.20 )( 0.228 )ONEBANK-0.041( -0.34 )( 0.736 )-0.432( -2.38 )( 0.017 )2.759( 0.04 )( 0.968 )-0.258( -1.56 )( 0.118 )-108.607( -1.68 )( 0.094 )0.182( 6.92 )( 0.000 )FIRMAGE-2.338( -2.14 )( 0.033 )

HHI

0.031

( 1.48 )( 0.139 )192.811( 1.27 )( 0.203 )5,006.312( 14.06 )( 0.000 )Other variables
MATURITY-0.607( -5.11 )( 0.000 )LANDRATIO

3.670( 10.13 )( 0.000 )OWNERRATIO

CITYSHARE
constant-2.867( -4.09 )( 0.000 )3.252# of observations

1786

976

1753

Log likelihood /Adj. R-squared-790.20793-475.10355

0.182444( 3.27 )( 0.001 )Table 17 Use Rate of Collateral, Personal Guarantees and Average Interest Rates:
By the Usage of Government-Sponsored Credit Guarantees
Total

TSR Credit Scores
55 - 59
60 - 64-49

50 - 54

65 - 69

70 -

51.7

79.1

68.8

52.0

31.9

18.1

6.5

Percentage of Borrowers with
Collateral

86.9

86.2

86.0

87.1

89.3

90.4

100.0

Percentage of Borrowers with Personal
Guarantees

94.6

94.4

95.2

94.2

94.2

93.6

91.7

Average Interest Rate, 0.1 b.p.

2592

3145

2692

2268

2061

1971

1788

Percentage of Borrowers with
Collateral

60.0

58.8

61.7

62.0

59.5

59.1

52.9

Percentage of Borrowers with Personal
Guarantees

56.6

59.3

59.0

61.1

54.3

53.1

48.8

Average Interest Rate, 0.1 b.p.

1673

2356

1984

1680

1561

1417

1260

Percentage of borrowers using Credit
Guarantees
Users of Credit Guarantees

Non-users of Credit Guarantees

45