First Draft: November 6, 2008
This Draft: July 18, 2009

Do Banks Produce Private Information?
Bank Screening and Ex-Post Small Firm Performance☆

Kaoru Hosono*Peng Xu**

Abstract

This paper examines whether commercial banks screen loan applications based on
private information on firms’ future profitability, and consequently how banks’
ex-ante private information and screening decisions affect firms’ ex-post profitability.
Using a dataset of banks’ loan application screenings and the ex-post firm
performance for SMEs, we obtained strong evidences suggesting that banks’ ex-ante
private information was related to firms' ex-post performance. We found thisrelationship to be especially strong for small, mature firms, which supports therelationship-lending hypothesis.

Key Words: Private information, relationship lending, information monopoly, andloan screening.
JEL Classification Codes: G21, G32.

*Gakushuin University, email: kaoru.hosono@gakushuin.ac.jp**Hosei University, email: pxu@hosei.ac.jp

Do Banks Have Private Information?
Bank Screening and Ex-Post Firm Performance

1. Introduction
Modern banking theory posits that banks have private information on
borrowers’ creditworthiness and financial conditions, and that they utilize it inscreening and monitoring borrowers (e.g., Diamond, 1984; Rajan, 1992). However,few studies obtain direct evidence on the presence of banks’ private information. We
try to fill in the void between the theory and evidence.
Specifically, we investigate banks’ private information using a survey ofJapanese small firms on whether firms’ loan applications were accepted or rejected.
This survey is similar to the National Survey of Small Business Finance (NSSBF)
for U.S. firms. Combining the survey data with the firms’ financial statement data,
we analyze the ex-post firm performance of loan applicants.
Banks decide whether to make loans or not according to their judgment on theapplicants’ creditworthiness. Their judgment is based on borrowing firms’ financial
statements and other public information, and possibly on the private information thatthe banks have accumulated through their transactions with the borrowing firms.
Though outsiders, including researchers, cannot directly observe banks’ private
information, we can estimate its usefulness and accurateness by observing the
ex-post performance of borrowing firms after bank screenings.
One may simply think that if the ex-post performance of rejected firms turns out
to be worse than that of accepted firms after controlling various financial variables,
one can conclude that the bank’s private information was useful in predicting thefirm performance. However, we should make a distinction between two different
1channels that potentially work between banks’ loan screening and borrowing firms’
ex-post performance.
First, a bank screens borrowing firms using its private information to forecast
the borrowers’ profitability and default probability. For example, the bank may havethe information on the market value of the firm’s real estate and other collateralized
assets, credit guarantees and other off-balance-sheet liabilities, and thecreditworthiness of the firm’s clients. The bank rejects the application for loans froma firm whose profitability and default probability are forecast to worsen. If thebank’s forecast is correct, the rejected firm will perform worse than the acceptedfirm after controlling for the firm characteristics contained in public information. We
call the banks’ ability to forecast the borrowers’ future profitability the information
production effect.
Second, rejected firms may find it difficult to obtain other sources of funds, facea liquidity shortage, miss out on profitable opportunities and see its profits
deteriorate. We call this financial constraint effect faced by rejected borrowers theinformation monopoly effect. This effect works if other banks do not have enoughinformation on the creditworthiness of the rejected firm to lend to it. Though otherbanks can access public information, it may take considerable time and effort forthem to process it before deciding whether to make a loan to the firm. Actually,small- and medium-sized enterprises (SMEs) rarely disclose audited and reliablefinancial statements. Consequently, the rejected firm may experience a liquidity
shortage.
We present an empirical approach from which we can distinguish between theinformation production effect and the information monopoly effect, and we apply it
to a unique dataset of Japanese SMEs. Specifically, we compare the ex-post2performance between rejected firms and accepted firms to investigate the two effectsof banks' private information.
We also investigate whether the above two bank information effects aredifferent among various types of firms. In particular, we examine how firm age andsize affect the accumulation of private information on the part of banks. Literatureon relationship lending (e.g., Boot, 2000) emphasize that banks accumulate private
information through a long-run relationship with their client firms. In this case,banks are likely to accumulate the private information of mature firms rather than
young firms. In addition, bank information monopoly theory (e.g., Rajan, 1992)
stresses that banks have an incentive to accumulate information on informationallyopaque firms, because those firms find it difficult to raise funds in the publicfinancial markets. In this case small firms, which tend to be informational opaque,are more likely to be monitored by banks than large firms.
Some preceding studies examine the ex-post firm stock price or operating
performance after bank failures. If banks monopolistically own private information
on borrowing firms, borrowing firms will face difficulty in raising external financeand see their performance deteriorate. Most of the existing evidences support thisidea (Slovin et al.,1993; Bae et al., 2002; Yamori and Murakami,1999), though somestudies found only small or negligible effects (Ongena et al., 2003; Brewer et al.,
2003; Hori, 2005). These studies focus on the information monopoly effect.
To reveal the signal effect of bank private information, the response of stock
prices to the announcement of new bank loans has been investigated. James (1987)
and Lummer and MacConell (1989) find that following the announcement of newbank financing, the stock prices of the firms rise, suggesting that bank loans serve as
favorable signals of the firms’ performance. However, they do not compare the3performance between those that can borrow from banks and those that cannot. In adifferent context, Puri (1996) investigates the signal effect of bond underwriting bybanks. Focusing on the U.S. bond market before the implementation of the
Glass-Steagall Act, she found that banks’ underwriting raised the bond prices ascompared with security companies’ underwriting. This finding also suggests thatprivate information owned by banks serves as a positive signal.1 While she detects
the banks’ information production effect in the securities underwriting business, we
directly investigate the roles of bank private information in the bank loan business.
To our best knowledge, this is the first study that provides large-sample
evidence on bank information production and the ex-post firm performance for small
and medium-sized firms. Many testable implications are most relevant to time series
data when relating bank decisions to post-decision firm performance. This paper
contributes to the literature on relationship lending (Petersen and Rajan, 1994;
Angelini et al., 1998; Cole, 1998; Harhoff and Körting, 1998; Boot, 2000). Existingstudies on relationship lending examine the effects of bank-firm relationships on theavailability and price of credit, obtaining positive results in most cases. These studies
judge the accumulation of private information on the part of banks by the availability
and price of credit using cross sectional data. However, a positive correlation
between the close bank-firm relationship and the availability of credit does notnecessarily imply that banks produce private information thorough relationships.
Peek and Rosengren (2005), for example, document a perverse incentive of Japanese
1

Puri (1996) analyzes only the information production effect and implicitly assumes noinformation monopoly effect, which seems reasonable for firms that issue public bonds.
However, for SMEs we cannot a priori neglect the possibility of the information monopoly
effect.
4

banks to extend loans to almost insolvent borrowers, especially to closely related
borrowers. On the other hand, we use a panel data set to judge the accumulation ofprivate information by investigating the ex-post performance of borrowers. Thus we
can test the hypotheses that banks produce private information and that they utilize it
to screen creditworthy borrowers. We also investigate whether information effects
are stronger on young or small firms than on mature orlarge firms to test the

hypothesis that banks produce private information through long-term relationships
with informational-opaque firms.
The rest of the paper is composed as follows. In Section 2, we present anempirical method that enables us to detect the information production effect and theinformation monopoly effect of bank private information. In Section 3, we describe
our dataset. In Section 4, we present our empirical results. Section 5 concludes.

2. Hypotheses
In this section, we present a simple model of the relationship between bankscreening and firm profitability. Based on this model, we conduct an empiricalanalysis to test whether a bank produces private information on the borrowing firmin the next section.
A firm applies for a loan from a bank. The firm’s next-period profit depends onwhether or not the bank accepts the loan application, as well as the firm’s current
profit and other characteristics of the firm. If the bank rejects the loan application,
the firm may find it difficult to obtain financing from other financial institutions, fall
into liquidity shortage, and miss out on profitable opportunities. We call this channelof bank private information on firm profitability the information monopoly effect.
We specify the firm profit as5

(1)Fit +1 = β 0 Fit − β1Bitf − β 2 REJECTit + vit +1 ,where t denotes the year when firm i applies a loan. The dependent variable Fit +1
denotes firm i ’s profit in year t + 1 . Among the dependent variables, REJECTit isa dummy variable that takes the value of one if the bank rejects the loan applicationin year t and zero otherwise. If the information monopoly effect works, thecoefficient on REJECT turns out to be negative ( β 2 is positive). Fit denotes firmi ’s profit as of year t . If the firm’s profits are positively correlated over time, β 0
is positive. Bitf denotes a vector of firm characteristics that is in the public
information set, such as leverage and size. vit +1 is a random shock that affects thefirm’s next-period profit but is not included in the public information set.
The bank receives an imperfect signal, uit , of the firm’s random shock vit +1 . We
assume uit and vit +1 to be drawn from the joint normal distribution with eachmean zero, each variance σ u2 and σ v2 , respectively, and the correlation coefficientρ ≥ 0 . ρ represents the accuracy of the bank’s private information on the firm’s
next-period profit. If ρ is positive, we say that the information production effect ofbank private information works. The bank decides whether to accept the loan
application or not based on the signal uit . Specifically, the bank that receives thesignal uit accepts the loan application if the firm’s expected profit, conditional on
the bank granting the loan, exceeds some threshold value, Fit +1 .
(2a) REJECTit = 0 if E[ Fit +1 REJECTit = 0, uit ] = β 0 Fit − β1Bitf + E[vit +1 uit ] > Fit +1 ,(2b) REJECTit = 1 otherwise.
From the assumption of the joint normal distribution for uit and vit +1 ,(3) E [vit +1 uit ] = ρσνu .
σ u it

The threshold value is assumed to depend on the bank’s capitalization and the6bank-firm relationship.
(4) Fit +1 = −γ 0 Bitm − γ 1 X it + γ 2COLit + ε it ,where Bitm is a vector of the bank’s capitalization variables, X it is a vector of thebank-firm relationship variables, and COLit is a dummy variable that takes unity if
the firm pledges collateral to the bank and zero otherwise. We briefly discuss therationale for these variables.
If a less-capitalized bank tends to apply a more stringent standard to accept theloan application (creditl crunch), the coefficient on a variable in Bitm will be
negative ( γ 0 is positive). Some theoretical and empirical studies support the credit
crunch hypothesis positing that a deterioration of bank capital results in a decrease in
the supply of loans. The asymmetric information between banks and investors makes
issuing new equity costly (Myers and Majluf, 1984; Stein, 1998; Diamond and Rajan,
2000). As a result, poorly-capitalized banks have to curtail their loans either becauseof their own incentive problems or because of capital adequacy requirements.
Holmstrom and Tirole (1997) develop an incentive model of financial intermediationand find that when bank capital decreases, capital-poor firms are the first to getsqueezed. Thakor (1996) shows that capital requirements linked solely to credit risktighten equilibrium credit rationing. Many empirical studies focus on the 1990-91
recession in New England and obtain evidence supporting the credit crunch
hypothesis (Bernanke and Lown, 1991; Peek and Rosengren, 1995; Hancock andWilcox, 1998, among others). There are also some empirical studies suggesting thecredit crunch by Japanese banks during the 1990s, though the exact period of credit
crunch detected varies (Ito and Sasaki (2002) for the early 1990s, Woo (2003) for the
1997 crisis period, and Montgomery (2005) throughout the 1990s).
There are several reasons why the bank-firm relationship affects the threshold7

value, Fit +1 . The firm establishes a business relationship with a bank in order toincrease the likelihood that it will have access to bank loans in tough times (Petersen
and Rajan, 1994). Jiangli et al. (2008) obtained evidences supporting this idea for
Korean and Thai firms during the Asian financial crisis. In Japan, the bank affiliatedwith the firm may want to rescue the firm even in an almost insolvent state bylending on favorable conditions (Peek and Rosengren, 2005). In addition, if the bankcan gain profits besides loans from a wide-range of transactions with the firm, it may
apply a looser standard to accept the loan application. For any case, the coefficient
on a variable in X it is expected to be negative ( γ 1 to be positive). ε it is a random
shock to the threshold value set by the bank that is drawn from a normal distribution
with mean zero, variance σ ε2 , and no correlation with v it +1 or uit .
There are two competing theories concerning the role of collateral in SME financing.
Bester (1985) posits that a low risk firm pledges collateral to distinguish itself from a highrisk firm, while Boot et al., (1991) insists that a bank demands a high risk firm to pledge
collateral to curb the firm’s moral hazard. In the former case, a bank is more likely to lend
to a firm that offers collateral, because the bank considers such a firm to be less risky. On
the other hand, in the latter case a bank is less likely to lend to a firm that offers collateralbecause the bank considers such a firm to be more risky. There are also competing theories
concerning the role of collateral in bank monitoring. Rajan and Winton (1995) argue thatcollateral can increase a lender’s incentive to monitor under certain conditions. In theirmodel, when banks demand collateral it signals that a firm is in bad shape. On the other
hand, Manove et al., (2001) posit that strong creditor protection may result in an
inefficiently low level of project screening by banks, suggesting that a low quality firm isless likely to post collateral and more likely to be rejected. Though empirical evidence forthe role of collateral in the U.S. SME financing is mixed (Berger and Udel, 1990, 1995;
8

Pozzolo, 2004; Krahnen, 2000), Ono and Uesugi (2009) found a positive correlation
between the borrower’s risk and collateral. Based on their evidence, we may presume thatthose firms that offer collateral are more likely to be rejected. If this is the case, γ 2 is
expected to be positive.
Substituting (3) and (4) into (2a) and (2b), we can summarize the bank decisionas(5) REJECTit* = − β 0 Fit + β1Bitf − γ 0 Bitm − γ 1 X it + γ 2COLit + u~it ,⎧⎪1(6) REJECTit = ⎨⎪⎩0where u~it ≡ ε it − ρ

if REJECTit* ≥ 0
if REJECTit* < 0,σv
u it is a random shock drawn from the normal distribution
σuwith mean zero, variance σ u~2 ≡ σ ε2 + ρ 2σ v2 , and(7) ρ~ ≡ corr (u~ , v
itit + s)=−ρ 2σ vσ ε2 + ρ 2σ v2Equation (7) shows that ρ~ is monotonically decreasing in ρ and takes the valueof zero if ρ is zero. ρ~ ≈ − ρ if σ ε2 is sufficiently small.

Now we can summarize two testable hypotheses concerning the effects of the bank’s
private information on the firm’s profit.

Hypothesis 1: Information production effect.
If the bank decides whether to accept the loan application or not based on theprivate signal that is informative to the borrower’s future profit, ρ~ < 0 .

Hypothesis 2: Information monopoly effect.
9

If the bank’s rejection of the firm’s loan application causes the firm’s future
profits to deteriorate due to the bank’s information monopoly, the coefficient on
REJECT in Equation (1) is negative ( β 2 > 0 ).

To further clarify the above two effects, we derive the difference between the
expected profits of the firm whose loan application is accepted and the firm whose
loan application is rejected. Defining(8) φit = φ[(− β 0 Fit + β1Bitf − γ 0 Bitm − γ 1 X it + γ 2COLit ) / σ u~ ] , and(9) Φ it = Φ[(− β 0 Fit + β1Bitf − γ 0 Bitm − γ 1 X it + γ 2COLit ) / σ u~ ] ,where φ and Φ denote the marginal and cumulative density functions of thestandard normal distribution, we getφ(10) E[ Fit +1 | REJECTit = 1] = β 0 Fit − β1 Bitf − β 2 + ρ~σ v it , andΦ it⎡ − φit ⎤(11) E[ Fit +1 | REJECTit = 0] = β 0 Fit − β1Bitf + ρ~σ v ⎢⎥.
⎣1 − Φ it ⎦From Equations (10) and (11), we obtain(12) E[ Fit +1 | REJECTit = 1] − E[ Fi , t +1 REJECTit = 0] = − β 2 + ρ~σ v

φit
Φ it [1 − Φ it ].

(see Green, 2008, for example). Equation (12) shows that the difference between the
expected profit of the rejected and accepted firms depends on the informationmonopoly effect, − β 2 , and the information production effect, ρ~σ v

3. Empirical Methodology and Data

3.1 Methodology
10

φitΦ it [1 − Φ it ].

Because the bank’s decision and the firm’s future profitability depends on eachother, as we have shown in Section 2, we perform the simultaneous estimation of
Equations (1) and (5) using the maximum likelihood estimator.2 Hypothesis 1 (theinformation production effect) can be tested by determining whether or not thecorrelation coefficient of the error terms of the two equations is negative. Hypothesis
2 (the information monopoly effect) can be tested by checking whether or not thecoefficient of REJECT in Equation (1) is negative.

3.2 Data
Data Source
We use the Corporate Finance Survey published by the Small and Medium Enterprise
Agency in December, 2001. This Survey is similar to the NSSBF1993 (the National Surveyof Small Business Finance) for similar size U.S firms. The sample firms are randomly
drawn from the firms contained in the TSR (Tokyo Shoko Research) database belonging to
all industries except agriculture, fishery and forestry, financial services, and public services.
Large firms are included in the sample so that they account for 10 percent of the total ineach industry. The inquiries cover the three-year period from January 1999 to December
2001.
The Survey contains information on whether the firms’ applications for loans had been
rejected by their main banks in the preceding three years. The Survey also includes
information on the number of years the firms have been in business, the numbers of
employees, the numbers of years over which they have been transacting with their mainbank, the year when they changed their main banks, the number of financial institutions
that provide the firm with banking services, and their industries as of the end of October
2

This is a so-called treatment effect model.
11

2001. In addition, the Survey contains information on whether the firms’ businessconditions were good, unchanged, or bad for each of the last three years.
We obtain information about the firms’ financial statements and their main banks fromthe TSR database. The firms’ main banks are identified as the first financial institutions
listed in the TSR database.3 The Survey data is linked to the main banks’ financial
statements available in the Nikkei Needs database.
In the National Survey of Small Business Finance, all other data are available for only
one year, with the exception of sales figures (Peterson and Rajan, 1997). One advantage of
our survey that is different from the NSSBF is that subsequent firm performance isavailable. This enables us to investigate the implications of the information hypotheses as
they pertain to the changes of the firm profitability from the pre-screening level to thepost-screening one.

Sample Selection
We use the data as of years 2000 and 2001 for the years of bank screening (year t in
Equations (1) and (5)) and examine the ex-post performance of firms in years 2001 and2002. From detailed information for year 2001 that included the number of years firms had
been transacting with their main banks, the number of financial institutions that firms were
3

The Corporate Finance Survey does not contain information on the firms’ main banks.

The first financial institution listed in the TSR database has been determined by TSR
researchers to be the most important one, based on the information they obtain from thefirm managers. As such, it should coincide with the one firm managers also regard as themost important. However, if a firm changes its main bank during the inquiry period, the"most important bank" may be different. It is because of this reason we exclude those firmsthat changed main banks during the inquiry period (Jan. 1999 - Dec. 2001).
12

transacting with, and the share of loans that were extended by their main banks, we wereable to infer those same pieces of information for year 2000.
The sample consists of the firms that satisfy the following four conditions. First, they
are classified as small or medium-sized enterprises according to the Small andMedium-Sized Firm Fundamental Law. 4 Second, their main financial institutions aremajor banks, long-term credit banks, trust banks, first-tier regional banks, second-tierregional banks or credit banks (shinkin) whose financial statements are available. Third, we
exclude those firms whose loan applications were rejected consecutively for two years or
more. Thus we can accurately identify the effects of a firm's loan rejection in one year on
its profit in the following year. Finally, to identify the firms’ main banks correctly, we
restrict our sample firms to those who did not change their main banks during 1999-2001.
The number of remaining firms that satisfy these conditions is 3,173. Loan rejections
account for a 3.4% share of the 4,687 firm-year samples.

Variables
We first describe the variables in the loan rejection equation (5), and then those in the
4

The Small and Medium-Sized Firm Fundamental Law defines SMEs, in principle, as

those firms whose equity is ¥300 million yen or less, or who have 300 employees or less.
However, the maximum capital amounts are set to ¥100 million for wholesale industriesand ¥50 million yen for retail and service industries, and the maximum number of
employees is set to 100 for wholesale and service industries and 50 for retail industries.
6

We add the logarithm of the number of years a firm has been in business as an

explanatory variable of Equation (5), finding that it takes an insignificant coefficient andthat it is highly correlated with the number of years a firm has been transacting with its
main bank.
13

firm profit equation (1).
As a measure of firm profit, Fit , we use EBTDA (Earnings before interest, taxes,
depreciation and amortization) as a proportion of total assets. EBTDA represents the firm’s
cash flow and is therefore an appropriate measure of firm profitability. On the other handand in spite of being widely used in other studies of Japanese firms, ROA (after-tax returnon assets) is problematic for our purpose because a firm whose loan application is rejected
may reduce investment and hence capital stock and depreciation, which has an increasing
effect on ROA. It is expected that a bank is more likely to deny the loan applications fromless profitable firms.
As firm characteristic variables, Bitf , we use the debt-to-asset ratio (DEBT), businesscondition dummy (BUSINESS), sales-to-asset ratio (SALES), and logarithm of the numberof employers (SIZE).6 BUSINESS takes the value of one if the firm responded “good” or“unchanged” to the question of business conditions, and the value of zero if it responded“bad." A high DEBT means a high default risk and hence is likely to result in a highprobability of rejection, implying that it takes a positive sign in the loan rejection equation(5). On the other hand, high SALES and BUSINESS are expected to take negative
coefficients in the loan rejection equation (5).
However, until the middle of the 1990s, the Japanese government denied the existenceof nonperforming loans, and its complicity enabled banks to pursue forbearance-lending
policies. If banks liquidate impaired firms, the difference between the face value of loans
and the value of the foreclosed assets become losses on their own balance sheets. Likewise,
debt forgiveness results in losses from nonperforming loans. Indeed, Hoshi and Kashyap(2004) and Caballero, Hoshi and Kashyap (2008) show how the dysfunctional Japanesebanking system misallocates funds by keeping many insolvent firms in business. Similarly,
Peek and Rosengren (2005) examine the misallocation of credit in Japan associated with14

the perverse incentives of banks to provide additional credit to the weakest firms. It is
possible that a bank accepts the loan applications from less profitable or unhealthierindustrial firms but denies the loan applications from more profitable or healthier industrial
firms.
Appropriate measures for bank capitalization, Bitm , are difficult to find because theaccounting capital and the risk-adjusted regulatory capital ratio under the Basel standard(the so-called BIS capital ratio) are often prone to discretion and not coincident with theeconomic value of capital (e.g., Shrieves and Dahl, 2003). Given no one perfect measure,
we use three alternative variables. One is the difference between the BIS ratio and theminimum requirement level (8 percent for internationally active banks and 4 percent fordomestic banks) (e.g., Peek and Rosengren, 2005). We call it CAPITAL MARGIN. Anotheris the share of non-performing loans, i.e., risk-management loans,7 in total assets (NPL),
which is supposed to be negatively correlated with the economic value of capital. The last
one is the growth rate of deposits outstanding (DEPOSIT). If a large amount of deposits are
drawn from a poorly capitalized bank, the bank may face a liquidity shortage and be forced
to reduce loans by applying strict standards in screening loan applications. In the loan
rejection equation (5), CAPITAL MARGIN and DEPOSIT are supposed to take negative
coefficients, while NPL is supposed to take a positive coefficient. In the case of DEPOSIT,
however, it may take a positive coefficient if depositors draw deposits from banks withloose screening standards because they consider such banks to be risky. In addition, if apoorly-capitalized bank tried to collect a large amount of deposits by raising depositinterest rates in order to invest in risky loans (Hellman, Murdock and Stiglitz, 2000; Gan
2004), then DEPOSIT would take a positive coefficient. We suppose that this is not
7

Risk-management loans are defined as the sum of loans to borrowers in legal bankruptcy,

past due loans three months or more in arrears, and restructured loans.
15plausible during the sample period when the regulators strengthened the enforcement of thecapital adequacy requirements and the deposit interest rates at most banks were very close
to zero.
For the firm-bank relationship measure, X it , we use the number of years the firm has
been transacting with its main bank (RELATIONSHIP YEARS) and the number of financial
institutions that the firm transacts with. These measures are widely used in the literature on
relationship lending, especially for SME finance (Petersen and Rajan, 1994; Angelini et al.,1998, Cole, 1998; Harhoff and Körting, 1998, among others). The firms that have beentransacting with their main banks for a long time and those that transact with a small
number of financial institutions are supposed to have a strong relationship with their mainbanks. Considering that the effect of the number of financial institutions on the mainbank’s decision on loan screening is supposed to be nonlinear (Hosono et al., 2004), we use
a dummy variable that takes the value of one if the number of the transacting banks is threeor more, and the value of zero otherwise (BANKS>2). The threshold value of three ischosen based on the median of the number of the transacting banks.
In the loan rejection equation (5), RELATIONSHIP YEARS and BANKS>2 aresupposed to take negative and positive coefficients, respectively.
Finally, the dummy (COLATTERAL), the dummy(GUARANTEE) takes the value of
one if the firm provides its main bank with collateral, guarantee and the value of zerootherwise respectively. In the firm profit equation (1), the profit, Fit , is EBITDA, as in theloan rejection equation (5). If EBITDA is serially correlated, its coefficient is positive. As
firm characteristics variables, we use BUSINESS, SIZE, and the logarithm of the firm age(AGE). BUSINESS is supposed to take a positive coefficient, while AGE is supposed totake a negative coefficient if a young firm is likely to grow rapidly.8
8

We add the debt-to-asset ratio and sales-to-asset ratio to the explanatory variables of16

Descriptive Sample Statistics
Table 1 presents the descriptive sample statistics of all the samples and sub-samples for
accepted firms and rejected ones. One feature of Japanese firm-bank relationships is thatmost firms, even SMEs, transact with multiple banks. The mean and median numbers ofbanks that firms transact with are 3.38 and 3 respectively. More than half of our sample
firms transact with theree or more banks. There is a large variation in the average numberof bank relations across countries. Detragiache, Garella and Guiso (2000) report that single
banking is prevalent in the U.S. 9 On the other hand, Ongena and Smith (2000) and Jiangli,
Unal and Yom (2008) find that single banking is relatively uncommon in Europe andcrisis-hit Asian countries, respectively.10
The proportion of the firms that pledge collateral to tis main bank is 84.5 percent,Equation (1), finding that neither of them has a significant coefficient and that no othervariables change the significance levels of the coefficients. We also add nine regionaldummies and 4 industry dummies to Equations (1) and (5), finding that few of them aresignificant.
9

Detragiache, Garella and Guiso (2000) report that the proportion of single banking is44.5 percent of the U.S. SMEs based on the 1988-89 NSSBF.
10

Ongena and Smith (2000, Table 1) report that the average number of bank relations

varies from 2.3 for Norway to 15.2 for Italy, with the cross-country average of 5.6. Jiangli,
Unal and Yom (2008) find that the average (median) numbers of bank relations are 1.93 (1)for Indonesia, 5.96 (4) for Korea, 3.06 (2) for the Philippines, and 2.29 (2) for Thailand.

17which is much higher than those reported for the U.S and some European countries11. Also
78% of the firms provide their main banks guarantees.
Comparing the accepted and rejected firms, we see that rejected firms exhibit a high
DEBT, a low EBITDA, low SALES, bad BUSINESS, a young AGE, short RELATIONSHIPYEARS, a small SIZE, a high likelihood of COLLATERAL, and a high likelihood of
GUARANTEE. Rejected firms’ next-period EBITDA is lower than accepted firms.

4. Estimation Results

4.1 Results for all sample firms
Table 2 presents the estimation results for all sample firms. We first look at the loan
rejection equation (5). EBITDA has a negative and significant coefficient, as is expected.
Among the firm characteristics variables, BUSINESS and SIZE have negative andsignificant coefficients, while DEBT has a positive and significant coefficient. As for thebank capitalization variables, neither CAPITAL MARGIN nor NPL is significant,suggesting no evidence of a capital crunch. DEPOSIT has a positive and significant
coefficient, suggesting that depositors withdraw deposits from a bank that adopts a loose
standard for loan screening. Looking at the firm-bank relationship variables,
RELATIONSHIP YEARS has a negative and significant coefficient, and BANKS>2 has apositive and significant coefficient, as expected. Finally, COLLATERAL takes a positive

11

Using the 1988-89 NSSBF, Berger and Udell (1995) find that 53 percent of the U.S.

small businesses pledge collateral to banks. Jimenez, Salas and Saurina (2006) examine
loans to Spanish firms and find that the proportion of loans with collateral is 30.5 percent
in short-term loans and 50.8 percent for long-term loans. Harhoff and Korting find that forGerman firms, this figure is 62 percent.
18and significant coefficient, suggesting that a bank tends to demand collateral from a risky
borrower and tends to reject such a firm’s loan application. It is consistent with Gan (2006,2007) that firms invest less if the value of landholding drops more. This suggests that after
the burst of asset price bubble in the early 1990s, the banks are less likely to lend to firmsthat have pledged collateral. GUARANTEE takes a positive but insignificant coefficient.
Our finding that banks deny loan applications from less profitable and more indebted
firms appears to be inconsistent with Hoshi and Kashyap (2004), Peek and Rosengren(2005), and Caballero, Hoshi and Kashyap (2008), who find the misallocation of credit inJapan associated with the perverse incentives of banks to provide additional credit to theweakest firms in the late 1990s. Some factors may account for the difference. First, banks
had only to extend credit to cover interest payments in order to underreport
non-performing loans. They did not necessarily extend new loans to impaired borrowers.
Second, while the perverse incentive of banks is found for loans towards listed firms, oursample firms are small- and medium-sized firms. It seems natural that banks tended toevergreen the firms to which they had a large exposure. Finally, while the perverse
incentive of banks was detected for the 1990s, our sample period for the bank screening
covers 2000 and 2001. The Japanese supervisors became more and more stringent towindow-dressing and other practices to conceal non-performing loans after the regulatory
reform in 199812.Gan (2006, 2007) finds that Japanese firms rapidly reduced investments
12

In 1998, Financial Supervisory Agency was established as an independent organizationin charge of financial supervision, for which Ministry of Finance had been responsible
until then. The Financial Reconstruction Act and the Rapid Recapitalization Act werepassed also in 1998, which enabled the government to inject public money to banks and tonationalize failing banks. Based on these Acts, two major banks were nationalized andpublic funds were injected to other major banks. See Sakuragawa and Watanabe (2009) for19

in the late 1990s due to severe credit constraints. In particular, special inspections towards
major banks conducted from March 2000 to September 2001 prevented banks tounderreport or evergreen non-performing loans.
Turning to the firm profit equation (1), EBITDA and BUSINESS take positive andsignificant coefficients, as expected. SIZE takes positive and significant coefficients,suggesting that larger firms are more likely to increase profits. AGE takes a negative andsignificant coefficient, suggesting that the profits of matured firms are more likely to drop.
Looking at the two-bank private information effects, we see that the informationmonopoly effect is not significant, while the information production effect is significant, as
can be seen in the loan rejection dummy in Equation (1) and the correlation coefficient of
the residuals from Equations (1) and (5), respectively. In Japan, most SMEs transact withmultiple financial institutions, which may result in our findings against the informationmonopoly effect.13

4.2 Firm Size, Age and Relationship-Lending
Bank private information effects may depend on firm age and size. Firm age may
matter because relationship lending theory posits that banks acquire and accumulate
private information through long-run relationships with client firms. Banks may not yet
have enough information for young firms. If this is the case, the information production
effect is negligible for young firms. Firm size may also matter because more and moremedium-sized firms are beginning to access market-based financing sources, including
details.
13

Our findings against the information monopoly effect are also consistent with Hosono etal., (2004), suggesting that multiple financial institutions share borrowers’ information
through transactions.
20

privately placed bonds and syndicated loans, for which multiple financial institutions andinvestors share information on the firms. For medium-sized firms, public information is soabundant that banks have little incentive to accumulate private information on them.
Considering such heterogeneity among SMEs, we divide the sample firms into three
categories: young firms, small and mature firms, and medium-size mature firms. We regard
firms that have been in business for thirteen years or more, which is the fifth percentile, as
mature firms and the other firms as young firms. We consider those firms with at least 87
employees, which is the seventy-fifth percentile, as medium-sized firms and the others as
small firms.
Tables 3A, 3B and 3C describe the sample statistics for each sub-sample of firms. The
proportion of loan rejections was 4.4%, the highest, for young firms, 3.9% for small andmature firms, and 1.7%, the lowest, for medium-sized mature firms. In each group,
rejected firms display a high DEBT, a low EBITDA, low SALES, short RELATIONSHIPYEARS, small SIZE, a high likelihood of COLLATEAL and a high likelihood of
GUARANTEE. While the AGE of rejected firms is less than that of accepted medium-sized
mature firms, no such tendency can be observed for young firms or small and mature firms.
Tables 4A, 4B and 4C present the estimation results for each subgroup. Table 4A shows theresults for young firms. In the loan rejection equation (5), there is no variable that takes asignificant coefficient if all the explanatory variables are included (in columns 1 to 3).
Taking into consideration the possibility that young firms’ financial statements are often
opaque and unreliable, we omit the financial statement variables and re-estimate the model,
finding that BUSINESS and SIZE take negative and significant coefficients in the results
for all sample firms. In the firm profit equation (1), EBITDA and BUSINESS take positiveand significant coefficients, while SIZE is not significant. AGE takes positive andsignificant coefficients, unlike the all-sample results, implying that the relationship21

between firm age and profit is non-linear. Looking at the correlation coefficient of theresiduals, we observe no information production effect. This result is consistent with therelationship-bank theory, which posits that banks accumulate private information throughtheir long-running relationships with firms (e.g., Boot, 2000). As for the informationmonopoly effect, the coefficient for REJECT is negative but not significant, which may be
the result of a relatively small sample size.
Table 4B shows the results for medium-sized mature firms. In the loan rejection
equation (5), EBITDA, SIZE and RELATIONSHIP YEARS take negative and significant
coefficients and DEBT takes a positive and significant coefficient, like the results for all
sample firms. Unlike the results for the entire sample, however, BANKS>2 takes a negative
but insignificant coefficient. In the firm profit equation (1), EBITDA and BUSINESS takepositive and significant coefficients. Importantly, we observe no information monopoly
effect or information production effect, consistent with the casual observation thatmedium-sized mature firms can access various financing sources in the markets.
Finally, Table 4C shows the results for small mature firms. The overall results are
similar to those for all sample firms. In the loan rejection equation (5), EBITDA,
BUSINESS, SALES, SIZE, RELATIONSHIP YEARS and BANKS>2 take negative andsignificant coefficients while DEBT, COLLATERAL, and DEPOSIT take positive andsignificant coefficients. In the firm profit equation (1), EBITDA and BUSINESS takepositive and significant coefficients, and AGE takes a negative and significant coefficient.
The information production effect is significant, as it is for the full sample results. On theother hand, the coefficient on REJECT in Equation (1) is positive and significant at the
10% level, which is not consistent with the information monopoly effect.

22

We can summarize the sub-sample results with the explanation that the full-sample
results for the bank information production effect are brought about mainly by small,mature firms.

4.3 Comparisons with the existing literature
Some previous studies examine the information monopoly effect by investigating theeffects of bank failures on client firms. Slovin et al. (1993) found that the stock prices ofclient firms moved down when the Continental Illinois Bank was on the verge of failure,
and then moved up when Federal Deposit Insurance Corporation began to rescue the Bank.
Bae et al. (2002) show that adverse shocks to Korean banks during the 1997-98 period had
a negative effect on the values of their client firms and that this adverse effect on firm
value is a decreasing function of the financial health of both the banks and their client
firms.
On the other hand, Ongena et al., (2003) used the near-collapse of the Norwegianbanking system during 1988-1991 to investigate the impact of bank distressannouncements on the stock prices of firms maintaining a relationship with a distressedbank. They found that firms faced only small and temporary changes, on average, in stock
price and that firms with access to unused liquid bank funds and firms that issued equity
just prior to the crisis experienced relatively high abnormal returns.
For the failures of Japanese banks, there are mixed results. Yamori and Murakami(1999) found that the failure of Hokkaido Takushoku Bank in 1997 had a negative impact
on the stock returns of client firms. Hori (2005) also picked up the failure of Hokkaido
Takushoku Bank and extended sample firms to include unlisted client firms. He found thatno significant difference existed between client firms and non-client firms after the bankfailure, though he also showed that those client firms that had low-grades before the failure
23and those that were not transferred to Hokuyo Bank when it acquired the business of
Hokkaido Takushoku Bank, saw their profits deteriorate. Brewer et al., (2003) examined
the failures of Hokkaido Takushoku Bank, Long-Term Credit Bank of Japan, and Nippon
Credit Bank during 1997-1998, and found that the declines in the stock returns of the client
firms on the dates of the disclosure of failures were not significantly different from those ofnon-client firms.
Given the various events and different time periods or countries, it is difficult to
directly compare these preceding studies. However, the evidence for the informationmonopoly effect in Japan seems to be weaker than it is in the U.S. This may be due to thefact that most Japanese firms transact with many banks. Our evidence against the
information monopoly effect also supports this view.
Some other studies, though few, are concerned with the information production effect.
James (1987) and Lummer and MacConell (1989) find that following the announcement of
new bank financing, the stock prices of the firms rise. Puri (1996) found that banks’
underwriting raised the bond prices as compared with security companies’ underwriting in
the U.S. bond market before the implementation of the Glass-Steagall Act. Though they do
not directly compare the ex-post performance of rejected firms and accepted firms, all of
their findings are consistent with the bank information effect, which we have detected inthis paper.
Evidence found in the relationship lending literature is also related to this paper.
Petersen and Rajan (1994) analyzed data collected in a survey of U.S. small firms (theNational Survey of Small Business Finance (NSSBF) collected by the U.S. Small Business
Administration) and found that the effects of relationship are larger on the availability ofcredit than on the price of credit. In particular, they found that firm age and the length ofthe longest relationship had positive impacts on the availability of credit while the24

BANKS>2 firms borrowed from had a negative impact. The positive firm age effect foundin their study is consistent with our results. Berkowitz and White (2002) also used datafrom the 1993 NSSBF and found that the years of the bank relationship were negatively
correlated with the credit constraint under which firms were discouraged or denied. They
also found that the owner’s age was negatively correlated with credit constraint in the caseof non-corporate firms, and that the firm size (measured by employment) was negatively
correlated with it in the case of corporate firms.14
Angelini et al. (1998) used a sample of small Italian firms. They found that withbanks other than cooperative banks, lending rates tend to increase with the length of therelationship for all customers, whereas with local cooperative banks this is the case fornon-member customers only. Their result, which is in line with bank capture or
informational monopoly theories (Sharpe, 1990; Rajan, 1992), may be surprising given that
small Italian firms tend to deal with multiple banks (Detragiache et al., 2000). Harhoff andKörting (1998) analyzed data in a survey of small- and medium-sized German firms andfound that relationship variables have a bearing on loan collateralization and availability.
Finally, we point out that our estimation results on the role of collateral in bankscreening are consistent with Ono and Uesugi (2009), who found a positive correlation
between the borrowers’ risk and the presence of collateral. On the other hand, empiricalevidence for U.S. SMEs is mixed. Berger and Udel, (1990, 1995) and Pozzolo (2004)
found a positive correlation between borrowers’ risk and the presence of collateral, while
Krahnen (2000) found no significant correlation between them.
14

Berkowitz and White (2002) find that high homestead exemptions tend to lead to credit

constraint under which firms are either discouraged or denied. For the effects ofbankruptcy laws on small firm finance and entrepreneurial activity, see Gropp et al. (1996)
and Fan and White (2003), respectively.
25

5. Conclusion

Observing the bank screening of loan applications and the ex-post firm performance,
we investigated the accuracy of bank private information. Specifically, we examined
whether banks screen loan applications based on private information on firms’ future
profitability (the information production effect), and whether rejected firms cannot be
refinanced and consequently experience reduced profits (the information monopoly effect).
Using a dataset for Japanese SMEs, we obtained strong evidence supportive of theinformation production effect, while we found no evidence to support the informationmonopoly effect. We also found that this result is mainly driven by relatively small andmature SMEs, for which a strong information production effect can be observed.
Our results are consistent with the relationship-lending theory positing that banks
acquire and accumulate private information through long-run relationships with borrowing
firms, especially with small, mature firms, who face difficulty obtaining financing frommarkets (Petersen and Rajan, 1994; Berkowitz and White, 2002; Harhoff and Körting,1998) . Our results also suggest that the information monopoly problems raised by Rajan(1992) and Sharpe (1990) may not be very serious for Japanese SMEs, most of whichtransact with multiple financial institutions.

26

Acknowledgements
We thank Michiru Sawada for providing us with the Corporate Finance Survey dataset.
We also thank Tsutomu Watanabe and other participants in RIETI's Corporate Finance andNetwork Study Group, Corporate Governance Seminar at the Tohoku University. Allremaining errors are ours. Xu thanks RIETI and Hosei University for research support andhe is also grateful to the Grant-in-Aid for Scientific Research (B) (19330066).

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32

Table 1. Descriptive Sample Statistics
variable
Loan Screening
REJECT
Firm Characteristics
EBITDAEBITDA(t+1)
SALES
DEBT
NO. OF WORKERS
AGE
BUSINESS
BUSINESS(t+1)
Bank Characteristics
DEPOSITNPLCAPITAL MARGIN
Firm-Bank Relationships
RELATIONSHIP YEARSNO. OF BANKSBANKS>2
COLLATERALGUARANTEE
Firm Characteristics
EBITDAEBITDA(t+1)
SALES
DEBT
NO. OF WORKERS
AGE
BUSINESS
BUSINESS(t+1)
Bank Characteristics
DEPOSITNPLCAPITAL MARGIN
Firm-Bank Relationships
RELATIONSHIP YEARSNO. OF BANKSBANKS>2
COLLATERALGUARANTEE
Firm Characteristics
EBITDAEBITDA(t+1)
SALES
DEBT
NO. OF WORKERS
AGE
BUSINESS
BUSINESS(t+1)
Bank Characteristics
DEPOSITNPLCAPITAL MARGIN
Firm-Bank Relationships
RELATIONSHIP YEARSNO. OF BANKSBANKS>2
COLLATERALGUARANTEE

no. of obs.

mean
medianA. All firms

sd

6695

0.034

0.000

0.181

6695
6695
6695
6695
6695
6695
6695
6695

5.179
4.954
1.469
0.763
71.17342.407
0.685
0.583

4.327
4.159
1.269
0.792
41.00039.000
1.000
1.000

7.571
6.830
1.111
0.227
89.51022.183
0.464
0.493

6695
6695
6695

1.681
4.023
4.745

1.691
3.646
4.290

3.006
1.911
2.102

6695
6695
6695
6695
6695

31.81030.000
3.384
3.000
0.576
1.000
0.845
1.000
0.781
1.000
B. Accepted firms

15.030
2.563
0.494
0.362
0.414

6469
6469
6469
6469
6469
6469
6469
6469

5.304
5.064
1.475
0.758
72.19942.500
0.693
0.593

4.417
4.227
1.275
0.788
42.00039.000
1.000
1.000

7.533
6.745
1.119
0.226
90.51522.209
0.461
0.491

6469
6469
6469

1.669
4.020
4.746

1.650
3.646
4.290

3.009
1.916
2.097

31.93530.0003.396043
3.000
0.576
1.000
0.842
1.000
0.777
1.000
C. Rejected firms

15.035
2.586
0.494
0.365
0.416

6469
6469
6469
6469
6469

226
226
226
226
226
226
226
226

1.616
1.785
1.309
0.896
41.81939.739
0.482
0.296

2.566
2.681
1.074
0.897
27.50035.000
0.000
0.000

7.801
8.322
0.839
0.217
44.16221.313
0.501
0.458

226
226
226

2.043
4.113
4.715

1.934
3.682
4.170

2.880
1.748
2.252

226
226
226
226
226

28.226
3.168
0.580
0.929
0.876

29.000
3.000
1.000
1.000
1.000

14.462
1.873
0.495
0.257
0.330

33

Table 2. Estimation Results for All Firms***, ** and * represent the significance levels at 1%, 5% and 10%, respectively. athrho is the inverse
hyperbolic tangent of ρ: athrho=0.5*ln((1+ρ)/(1-ρ)), where ρ is the correlation coefficient of the residuals of
Eq. (1) and (5) 3. lnsigma is ln(σ), where σ is the standard error of the residual of Eq. (1). 4. LR-test is theΧ-square statistics of the null hypothesis that ρ is zero.
1
EBITDA
BUSINESS (t+1)
SIZE
AGE
REJECT
Constant

0.333***(32.985)
2.403***(15.806)
0.221***(2.640)-0.650***(4.615)0.03(0.044)3.886***(6.689)

2
3
EBITDA(t+1): Eq. (1)0.333***
0.333***(32.966)(32.965)
2.402***
2.403***(15.800)(15.800)
0.220***
0.220***(2.625)(2.626)-0.650***-0.650***(4.618)(4.617)-0.05-0.052(0.074)(0.076)3.896***
3.896***(6.706)(6.705)REJECT: Eq. (5)40.333***(32.975)
2.403***(15.802)0.220***(2.633)-0.650***(4.616)-0.019(0.028)3.892***(6.699)Firm Characteristics
DEBT
EBITDA
BUSINESS
SALES
SIZE

0.621***(5.072)-0.020***(4.180)-0.315***(4.895)-0.114***(2.842)-0.171***(4.978)0.627***(5.098)-0.020***(4.059)-0.315***(4.892)-0.116***(2.873)-0.171***(4.946)

0.623***(5.076)-0.020***(4.073)-0.314***(4.881)-0.114***(2.844)-0.170***(4.928)0.646***(5.357)-0.020***(4.079)-0.318***(4.947)-0.116***(2.928)-0.168***(5.125)

Bank Characteristics
DEPOSIT0.026***(2.639)0.026***(2.616)(0.010)(0.621)NPL(0.007)(0.459)CAPITAL MARGIN

Firm-Bank Relationships
RELATIONSHIP YEAR -0.169***
BANKS>2
COLLATERALGUARANTEE

Constant
athrholnsigma
No. of Observations
Log Likelihood
Wald
Prob
LR-testProb>chi2(3.329)0.079(1.186)
0.394***(3.295)0.126(1.357)-1.296***(4.961)-0.102**(2.467)1.798***(207.244)
6695-22424.3
1859.46
0
4.61
0.032

-0.166***(3.276)0.082(1.226)
0.385***(3.229)0.119(1.281)-1.209***(4.478)-0.096**(2.289)1.798***(207.318)
6695-22427.4
1859.622
0
4.049
0.044

34

-0.164***(3.234)0.079(1.177)0.383***(3.211)0.12(1.294)-1.224***(4.556)-0.096**(2.283)
1.798***(207.319)
6695-22427.5
1859.689
0
4.023
0.045

-0.169***(3.340)0.429***(3.696)-1.205***(4.754)-0.098**(2.373)
1.798***(207.296)
6695-22426
1859.503
0
4.304
0.038

Table 3A. Descriptive Sample Statistics for Young Firms (Age<=13)variable

no. of obs.

Loan Screening
REJECT
Firm Characteristics
EBITDAEBITDA(t+1)
SALES
DEBT
NO. OF WORKERS
AGE
BUSINESS
BUSINESS(t+1)
Bank Characteristics
DEPOSITNPLCAPITAL MARGIN
Firm-Bank Relationships
RELATIONSHIP YEARSNO. OF BANKSBANKS>2
COLLATERALGUARANTEE

0.044

0.000

0.205

388
388
388
388
388
388
388
388

6.784
6.301
2.144
0.883
37.693
9.464
0.753
0.673

4.975
4.648
1.892
0.873
22.00010.0001.000
1.000

18.665
9.961
1.442
0.370
50.187
2.908
0.432
0.470

388
388
388

1.651
4.016
4.605

1.636
3.628
4.160

2.927
2.118
2.164

35

10.36910.000
3.003
2.000
0.472
0.000
0.629
1.000
0.729
1.000
B. Accepted young firms

6.181
2.349
0.500
0.484
0.445

371
371
371
371
371
371
371
371

6.997
6.555
2.145
0.882
38.491
9.461
0.763
0.687

5.110
4.955
1.873
0.873
23.00010.0001.000
1.000

18.949
9.811
1.455
0.377
50.967
2.908
0.426
0.464

371
371
371

1.643
4.027
4.569

1.636
3.628
4.120

2.967
2.147
2.111

371
371
371
371
371

Firm Characteristics
EBITDAEBITDA(t+1)
SALES
DEBT
NO. OF WORKERS
AGE
BUSINESS
BUSINESS(t+1)
Bank Characteristics
DEPOSITNPLCAPITAL MARGIN
Firm-Bank Relationships
RELATIONSHIP YEARSNO. OF BANKSBANKS>2
COLLATERALGUARANTEE

sd

388

388
388
388
388
388

Firm Characteristics
EBITDAEBITDA(t+1)
SALES
DEBT
NO. OF WORKERS
AGE
BUSINESS
BUSINESS(t+1)
Bank Characteristics
DEPOSITNPLCAPITAL MARGIN
Firm-Bank Relationships
RELATIONSHIP YEARSNO. OF BANKSBANKS>2
COLLATERALGUARANTEE

mean
medianA. All young firms

10.40410.000
3.019
2.000
0.472
0.000
0.623
1.000
0.722
1.000
C. Rejected young firms

6.256
2.382
0.500
0.485
0.448

17
17
17
17
17
17
17
17

2.122
0.759
2.118
0.911
20.294
9.529
0.529
0.353

2.759
3.127
2.277
0.894
10.00011.000
1.000
0.000

9.948
11.8271.141
0.122
22.707
3.002
0.514
0.493

17
17
17

1.824
3.778
5.378

1.861
3.593
4.260

1.894
1.384
3.094

17
17
17
17
17

9.588
2.647
0.471
0.765
0.882

10.000
2.000
0.000
1.000
1.000

4.273
1.455
0.514
0.437
0.332

Table 3B. Descriptive Sample Statistics for Middle-sized Matured Firms(Age>13 and No. of Workers>87)
variable
Loan Screening
REJECT
Firm Characteristics
EBITDAEBITDA(t+1)
SALES
DEBT
NO. OF WORKERS
AGE
BUSINESS
BUSINESS(t+1)
Bank Characteristics
DEPOSITNPLCAPITAL MARGIN
Firm-Bank Relationships
RELATIONSHIP YEARSNO. OF BANKSBANKS>2
COLLATERALGUARANTEE

no. of obs.
mean
mediansd
A. All middle-sized matured firms
1620

0.017

0.000

0.128

1620
1620
1620
1620
1620
1620
1620
1620

5.776
5.316
1.246
0.731
182.72951.539
0.706
0.618

5.038
4.837
1.114
0.763
151.000
48.500
1.000
1.000

6.361
6.672
0.664
0.198
121.224
23.255
0.456
0.486

1620
1620
1620

1.817
3.680
4.405

1.744
3.458
4.110

3.288
1.513
1.666

1620
36.83636.00015.637
1620
4.495
4.000
2.831
1620
0.766
1.000
0.423
1620
0.865
1.000
0.342
1620
0.651
1.000
0.477
B. Accepted middle-sized matured firms

Firm Characteristics
EBITDAEBITDA(t+1)
SALES
DEBT
NO. OF WORKERS
AGE
BUSINESS
BUSINESS(t+1)
Bank Characteristics
DEPOSITNPLCAPITAL MARGIN
Firm-Bank Relationships
RELATIONSHIP YEARSNo. OF BANKSBANKS>2
COLLATERALGUARANTEE

1593
1593
1593
1593
1593
1593
1593
1593

5.833
5.411
1.249
0.729
183.505
51.699
0.709
0.621

5.082
4.860
1.118
0.762
151.000
49.000
1.000
1.000

6.358
6.602
0.660
0.198
121.898
23.331
0.454
0.485

1593
1593
1593

1.807
3.674
4.407

1.744
3.458
4.110

3.266
1.512
1.664

1593
36.99636.00015.632
1593
4.500
4.000
2.838
1593
0.767
1.000
0.423
1593
0.864
1.000
0.343
1593
0.650
1.000
0.477
C. Rejected middle-sized matured firms

Firm Characteristics
EBITDAEBITDA(t+1)
SALES
DEBT
NO. OF WORKERS
AGE
BUSINESS
BUSINESS(t+1)
Bank Characteristics
DEPOSITNPLCAPITAL MARGIN
Firm-Bank Relationships
RELATIONSHIP YEARSNo. OF BANKSBANKS>2
COLLATERALGUARANTEE

36

27
27
27
27
27
27
27
27

2.405
-0.3091.059
0.829
136.926
42.111
0.519
0.407

2.420
0.436
0.788
0.890
126.000
47.000
1.000
0.000

5.714
8.347
0.829
0.165
54.88015.858
0.509
0.501

27
27
27

2.444
4.009
4.248

1.482
3.688
3.600

4.419
1.618
1.772

27
27
27
27
27

27.370
4.222
0.704
0.926
0.667

29.000
4.000
1.000
1.000
1.000

12.977
2.375
0.465
0.267
0.480

Table 3C. Descripteive Sample Statistics for Small Matured Firms (Age>13 and No. of Workers<=87)
variable

no. of obs.

Loan Screening
REJECT
Firm Characteristics
EBITDAEBITDA(t+1)
SALES
DEBT
NO. OF WORKERS
AGE
BUSINESS
BUSINESS(t+1)
Bank Characteristics
DEPOSITNPLCAPITAL MARGIN
Firm-Bank Relationships
RELATIONSHIP YEARSNO. OF BANKSBANKS>2
COLLATERALGUARANTEE

mean
medianA. All small matured firms

sd

4687

0.039

0.000

0.193

4687
4687
4687
4687
4687
4687
4687
4687

4.840
4.717
1.490
0.764
35.38741.978
0.673
0.564

4.052
3.899
1.305
0.795
31.00039.000
1.000
1.000

6.219
6.542
1.177
0.218
22.00119.913
0.469
0.496

4687
4687
4687

1.637
4.142
4.874

1.650
3.688
4.460

2.908
1.999
2.215

4687
31.84730.00013.721
4687
3.031
3.000
2.366
4687
0.517
1.000
0.500
4687
0.856
1.000
0.351
4687
0.830
1.000
0.376
B. Accepted small matured firms

Firm Characteristics
EBITDAEBITDA(t+1)
SALES
DEBT
NO. OF WORKERS
AGE
BUSINESS
BUSINESS(t+1)
Bank Characteristics
DEPOSITNPLCAPITAL MARGIN
Firm-Bank Relationships
RELATIONSHIP YEARSNO. OF BANKSBANKS>2
COLLATERALGUARANTEE

4505
4505
4505
4505
4505
4505
4505
4505

4.977
4.819
1.499
0.758
35.61641.968
0.681
0.575

4.126
3.967
1.314
0.789
31.00039.000
1.000
1.000

6.105
6.461
1.189
0.215
22.04419.877
0.466
0.494

4505
4505
4505

1.622
4.141
4.881

1.636
3.688
4.460

2.916
2.007
2.215

4505
31.91830.00013.703
4505
3.030
3.000
2.386
4505
0.517
1.000
0.500
4505
0.852
1.000
0.355
4505
0.827
1.000
0.378
C. Rejected small matured firms

Firm Characteristics
EBITDAEBITDA(t+1)
SALES
DEBT
NO. OF WORKERS
AGE
BUSINESS
BUSINESS(t+1)
Bank Characteristics
DEPOSITNPLCAPITAL MARGIN
Firm-Bank Relationships
RELATIONSHIP YEARSNO. OF BANKSBANKS>2
COLLATERALGUARANTEE

37

182
182
182
182
182
182
182
182

1.452
2.192
1.270
0.905
29.72042.209
0.473
0.275

2.552
2.705
1.069
0.898
25.00036.000
0.000
0.000

7.878
7.924
0.767
0.230
20.15320.858
0.501
0.448

182
182
182

2.004
4.160
4.722

2.061
3.682
4.225

2.676
1.800
2.221

182
182
182
182
182

30.093
3.060
0.571
0.945
0.907

29.000
3.000
1.000
1.000
1.000

14.069
1.778
0.496
0.229
0.292

Table 4A. Estimation Results for Young Firms(Age<=13)***, ** and * represent the significance levels at 1%, 5% and 10%, respectively. athrho is the inverse
hyperbolic tangent of ρ: athrho=0.5*ln((1+ρ)/(1-ρ)), where ρ is the correlation coefficient of the residuals of
Eq. (1) and (5) 3. lnsigma is ln(σ), where σ is the standard error of the residual of Eq. (1). 4. LR-test is theΧ-square statistics of the null hypothesis that ρ is zero.
1
EBITDA
BUSINESS (t+1)
SIZE
AGE
REJECT
Constant

0.108***(4.260)5.498***(5.500)0.422(0.869)3.151***(2.643)-7.98(1.641)-5.235(1.594)

2
3
EBITDA(t+1): Eq. (1)0.107***
0.107***(4.254)(4.247)5.496***
5.476***(5.504)(5.479)0.418
0.412(0.861)(0.851)
3.147***
3.151***(2.640)(2.643)-8.198*-8.528*(1.741)(1.904)-5.195-5.18(1.581)(1.579)REJECT: Eq. (5)40.110***(4.372)5.679***(5.745)3.266***(2.756)-5.25(1.103)-4.146(1.420)Firm Characteristics
DEBT
EBITDA
BUSINESS
SALES
SIZE

-0.236(0.507)-0.016(1.288)-0.375(1.510)-0.001(0.005)-0.205(1.469)-0.24(0.516)-0.016(1.269)-0.371(1.499)0.004(0.045)-0.217(1.530)-0.277(0.572)-0.018(1.462)-0.339(1.350)0.001(0.013)-0.198(1.420)-0.478**(2.014)-0.222*(1.759)Bank Characteristics
DEPOSIT

0.022(0.458)(0.041)(0.612)NPL

0.067(1.421)

CAPITAL MARGIN

Firm-Bank Relationships
RELATIONSHIP YEARS -0.076
BANKS>2
COLLATERALGUARANTEE

Constant
athrholnsigma
No. of Observations
Log Likelihood
Wald
Prob
LR-testProb>chi2(0.291)
0.094(0.358)0.222(0.769)0.334(0.957)-0.905(1.107)0.244(0.987)2.177***(57.870)388-1457.33
110.851
0
0.366
0.545

-0.067(0.258)0.095(0.365)0.23(0.796)0.346(0.983)-0.718(0.837)0.257(1.075)2.178***(57.801)
388-1457.23
111.226
0
0.44
0.507

38

-0.101(0.388)0.156(0.597)0.199(0.687)0.393(1.076)-1.211(1.410)0.278(1.222)
2.179***(57.747)388-1456.47
111.986
0
0.599
0.439

-0.741*(1.857)0.082(0.362)2.174***(60.220)388-1459.95
105.823
0
0.107
0.744

Table 4B. Estimation Results for Medium-size Matured Firms(Age>13 & No. of Worker>87)***, ** and * represent the significance levels at 1%, 5% and 10%, respectively. athrho is the inverse
hyperbolic tangent of ρ: athrho=0.5*ln((1+ρ)/(1-ρ)), where ρ is the correlation coefficient of the residuals of
Eq. (1) and (5) 3. lnsigma is ln(σ), where σ is the standard error of the residual of Eq. (1). 4. LR-test is theΧ-square statistics of the null hypothesis that ρ is zero.
1

EBITDA
BUSINESS (t+1)
SIZE
AGE
REJECT
Constant

2
3
EBITDA(t+1): Eq. (1)0.462***
0.462***
0.462***(20.105)(20.088)(20.094)
2.295***
2.294***
2.294***(7.793)(7.791)(7.791)
0.042
0.04
0.04
(0.134)(0.128)(0.128)-0.183-0.183-0.184(0.525)(0.525)(0.527)-3.108*-3.204*-3.230*(1.652)(1.665)(1.707)
2.218
2.231
2.234
(1.055)(1.060)(1.062)REJECT: Eq. (5)40.464***(20.353)
2.312***(7.902)-2.951*(1.649)1.745***(3.494)

Firm Characteristics
DEBT
EBITDA
BUSINESS
SALES
SIZE

1.025**(2.124)-0.034**(2.075)-0.296*(1.645)-0.146(1.052)-0.541**(2.201)0.999**(2.057)-0.035**(2.082)-0.273(1.505)-0.15(1.082)-0.538**(2.185)1.030**(2.135)-0.033**(2.011)-0.294(1.631)-0.149(1.070)-0.568**(2.280)0.870*(1.892)-0.038**(2.371)-0.276(1.558)-0.594**(2.476)

Bank Characteristics
DEPOSIT

0.025(1.072)0.042(0.834)NPL(0.046)(0.879)CAPITAL MARGIN

Firm-Bank Relationships
RELATIONSHIP YEARSBANKS>2
COLLATERALGUARANTEE

Constant
athrholnsigma
No. of Observations
Log Likelihood
Wald
Prob
LR-testProb>chi2-0.338***(2.705)-0.19(0.985)0.176(0.576)-0.04(0.205)1.361(0.988)-0.047(0.400)1.734***(98.646)
1620-5227.48
618.192
0
0.128
0.721

-0.340***(2.697)-0.166(0.862)0.164(0.539)-0.067(0.348)1.263(0.907)-0.039(0.326)1.734***(98.658)
1620-5227.7
617.908
0
0.088
0.767

39

-0.338***(2.710)-0.197(1.020)0.184(0.599)-0.041(0.211)
1.739(1.225)-0.037(0.318)1.734***(98.663)
1620-5227.64
618.177
0
0.085
0.771

-0.338***(2.755)1.613(1.217)-0.056(0.521)1.734***(98.636)
1620-5229.42
617.717
0
0.211
0.646

Table 4C. Estimation Results for Small Matured Firms (Age>13 and No. of Workers<=87)***, ** and * represent the significance levels at 1%, 5% and 10%, respectively. athrho is the inverse
hyperbolic tangent of ρ: athrho=0.5*ln((1+ρ)/(1-ρ)), where ρ is the correlation coefficient of the residuals of
Eq. (1) and (5) 3. lnsigma is ln(σ), where σ is the standard error of the residual of Eq. (1). 4. LR-test is theΧ-square statistics of the null hypothesis that ρ is zero.
1

EBITDA
BUSINESS (t+1)
SIZE
AGE
REJECT
Constant

2
3
EBITDA(t+1): Eq. (1)0.451***
0.451***
0.451***(32.482)(32.464)(32.464)
2.138***
2.138***
2.138***(12.685)(12.682)(12.682)
0.280**
0.279**
0.279**(2.271)(2.263)(2.264)-1.007***-1.007***-1.007***(5.173)(5.174)(5.173)
1.091*
1.022
1.027(1.743)(1.624)(1.630)4.347***
4.355***
4.352***(5.430)(5.440)(5.436)REJECT: Eq. (5)40.451***(32.479)
2.138***(12.684)
0.280**(2.270)-1.007***(5.173)
1.077*(1.719)4.347***(5.430)Firm Characteristics
DEBT
EBITDA
BUSINESS
SALES
SIZE

0.895***(5.653)-0.026***(4.245)-0.302***(4.106)-0.114**(2.409)-0.154**(2.430)0.904***(5.695)-0.025***(4.104)-0.302***(4.114)-0.117**(2.454)-0.151**(2.388)0.897***(5.663)-0.025***(4.120)-0.301***(4.100)-0.114**(2.403)-0.147**(2.305)0.907***(5.769)-0.026***(4.212)-0.306***(4.171)-0.108**(2.309)-0.154**(2.429)Bank Characteristics
DEPOSIT0.028**(2.434)0.028**(2.417)(0.016)(0.845)NPL(0.012)(0.707)

CAPITAL MARGIN

Firm-Bank Relationships
RELATIONSHIP YEARBANKS>2
COLLATERALGUARANTEE

Constant
athrholnsigma
No. of Observations
Log Likelihood
Wald
Prob
LR-testProb>chi2

0.12(1.608)-0.127**(2.564)0.399***(2.683)0.168(1.418)-1.764***(5.232)-0.122***(3.127)1.731***(166.750)
4687-15448.2
1615.939
0
7.796
0.005

0.121
(1.628)-0.128**(2.572)0.385***(2.597)0.163(1.384)-1.647***(4.756)-0.116***(2.947)1.731***(166.807)
4687-15450.6
1615.98
0
7.015
0.008

40

0.115
(1.542)-0.126**(2.540)0.379**(2.561)0.167(1.418)-1.669***(4.863)-0.116***(2.954)
1.731***(166.802)
4687-15450.7
1616.037
0
7.023
0.008

0.124*(1.664)-0.132***(2.686)0.450***(3.120)-1.665***(5.078)-0.121***(3.092)
1.731***(166.763)
4687-15449.2
1615.942
0
7.636
0.006