INFORMATION FROM RELATIONSHIP LENDING: EVIDENCE
FROM LOAN DEFAULTS IN CHINA*
Chun Chang
Department of Finance and Accounting
China Europe International Business School
[email protected]
Guanmin Liao
School of Accountancy
Central University of Finance and Economics
[email protected]
Xiaoyun Yu†
Indiana University and
Shanghai Advanced Institute of Finance
[email protected]
Zheng Ni
Hanqing Advanced Institute of Economics and Finance
Renmin University of China
[email protected]
This version: August, 2009
*
We thank Sumit Agrawal, Utpal Bhattacharya, Martin Brown, Hans Degryse, Nandini Gupta, Charles Kahn,
Paolo Emilio Mistrulli, Steven Ongena, Zacharias Sautner, Greg Udell, Marc Umber, and seminar
participants at Indiana University, Renmin University, Tsinghua University, Xiamen University, Shanghai
Winter Finance Conference, 3rd CAF-FIC-SIFR Emerging Market Conference (Hyderabad), 2nd Swiss
Conference on Banking and Financial Intermediation (Hasliberg), and European Financial Management
Association Annual Meeting (Milan) for helpful comments. We thank Nicholas Korsakov for editorial
assistance. This project was supported by the National Natural Science Foundation of China, Grant #
70773122.
†
Contacting author. Address: Department of Finance, Kelley School of Business, Indiana University, 1309
East 10th Street, Bloomington, Indiana 47405. Telephone: (812) 855-3521.
INFORMATION FROM RELATIONSHIP LENDING: EVIDENCE FROM
LOAN DEFAULTS IN CHINA
ABSTRACT
We study the economic role of banks’ soft information, which evolved from repeated lending
relationships, in predicting loan default. Using a proprietary database from one of the largest stateowned commercial banks in China, we find that the bank’s internal credit rating scores play a
significant role in default prediction. While the internal credit rating incorporates firm-specific hard
information such as financial ratios, it is the soft information component of these ratings that
contributes to the improvement in assessing credit quality. More importantly, the relative
importance of soft information over hard information depends on the depth of the lending
relationship. When evaluating loan delinquency, a sustained lending relationship allows soft
information to substitute for, rather than complement, the role of hard information, especially hard
information that is subject to easy manipulation.
Key words: Debt default, internal credit ratings, credit risk, relationship lending, soft information
JEL Classification: G21, D81, D82, D83, F34
INFORMATION FROM RELATIONSHIP LENDING: EVIDENCE FROM LOAN
DEFAULTS IN CHINA
1. INTRODUCTION
The theoretical literature on financial intermediation has long recognized the superior ability of
banks in acquiring information or knowledge beyond that which is available to ordinary financial market
participants (e.g., Ramakrishnan and Thakor 1984, Diamond 1984, Boyd and Prescott 1986, and Dow and
Gorton 1997). Many researchers emphasize the “soft” nature of this special knowledge in the notion that
soft information is not easily and accurately conveyed, verifiable, or transferable. In contrast to the “hard”
information derived from firms’ financial statements or industrial data, most researchers attribute soft
information to banks’ relationships with borrowing firms (e.g., Peterson 2004). In light of the central role
of the banking system in channeling capital to the real economy, however, few studies have directly
examined the nature and role of this special knowledge in predicting defaults on commercial loans.
In this paper we investigate to what extent this special knowledge can predict loan defaults, and
whether it acts as a complement or a substitute for any particular type of hard information in accessing
credit delinquency. Using a proprietary dataset from a major Chinese state-owned bank containing
information on all loans offered to firms in the five largest manufacturing industries in China from 2003
to 2006, we first document a substantial decline in loan defaults after the implementation of an internal
credit rating system in 2004. Internal credit ratings are significantly related to the commonly used firmspecific financial ratios in predictable ways, and changes in these financial ratios lead to changes in credit
ratings. These findings suggest that, at least with regard to credit ratings, loan decisions by Chinese banks
are based on commercial principles instead of government policies, which may have contributed to the
overall performance improvement of Chinese banks in recent years.1
1
Since 2002, major state-owned commercial banks in China have embarked on a series of reforms, which have
generally gone through the following four stages: financial reorganization, injection of new capital by the state,
introduction of foreign strategic investors, and eventual IPOs. Amid the banks’ financial reorganization efforts is the
introduction of an internal credit rating system. Concurrently, the average non-performing loan (NPL) ratio of the
1
Furthermore, our analysis reveals that the bank’s internal credit ratings largely subsume firmspecific hard information, as the majority of the commonly used financial ratios are no longer significant
in predicting loan defaults after including these ratings. Therefore, we next investigate to what extent the
improvement in loan quality is due to the bank’s soft information arising from extensive
borrowing/lending relationships, instead of relying on firm-specific hard information.
To extract the firm-specific soft information component from the bank’s private credit rating
score, we follow Agarwal and Hauswald (2008) and orthogonalize the credit rating with the firm’s
financial factors. To capture the nature of soft information generated from a repeated lending relationship,
we construct three proxies to identify the depth of banking relationship. Our first proxy is based on how
frequent a firm borrows from the bank. Our second proxy is based on how long a firm has maintained its
relationship with the bank. Our last proxy is based on a firm’s ownership, in which we classify a firm as
either state-owned or non-state-owned. Since a state bank’s lending relationship with state-owned firms is
historically mandated by the Chinese government, this proxy is relatively exogenous and thus mitigates
the endogeneity of matching between a firm and its bank that typically affects such studies (Berger,
Miller, Petersen, Rajan, and Stein 2005).
We find that the bank’s internal credit ratings contain useful information beyond that which is
conveyed by the commonly used financial and industrial variables. This soft information, captured by the
residual component of the internal credit rating that is unpredictable by those variables, is statistically and
economically significant in forecasting loan defaults. Our result thus provides evidence in support of the
theoretical arguments that banks possess special knowledge in assessing credit quality.
More importantly, for firms that borrow more frequently from the bank, have a longer term of
banking relationship, or state-owned firms, the majority of proxies for hard information are no longer
significant in predicting loan default once the soft information component of internal credit rating is
included. By contrast, for firms that borrow less frequently from the bank, have a shorter period of
major commercial banks in China decreased from 17.9% in 2003 to 6.7% in 2007. In a companion paper, we
investigate other potential factors responsible for the decline of NPL ratio in Chinese banks.
2
banking relationship, or are non-state-owned, most proxies for hard information remain significant even
in the presence of the bank’s soft information. Our findings indicate that the extent to which soft
information dominates hard information depends on the depth of the lending relationship, and that an
extensive lending relationship allows soft information to substitute for, rather than complement, the role
of hard information in evaluating loan delinquency.
Interestingly, for firms that maintain a long-term or frequent banking relationship as well as stateowned firms, it is the hard information that can be easily manipulated by Chinese firms—such as ROA
and cash holding—that is displaced by the bank’s soft information. In contrast, the hard information that
is not subject to easy manipulation remains significant in predicting loan defaults even after the inclusion
of the bank’s soft information. Our analysis on earnings management further confirms the role of soft
information in the presence of less credible firm-specific hard information. The economic impact of soft
information is significantly more pronounced among firms with a higher level of earnings management
than among firms that are less likely to manipulate their earnings.
Our paper contributes to the finance literature that analyzes the role of hard and soft information
in bank lending.2 Most of the existing literature focuses on small business financing, loan underwriting or
pricing (e.g., Petersen and Rajan 1994, Berger and Udell 1995, Scott 2004, Uchida, Udell and Yamori
2007, and Cerqueiro, Degryse and Ongena 2008). Instead, we study the role of soft information in the
context of loan default. Our research design and unique dataset allow us to disentangle the soft
information that is ascertained through repeated lending relationships from that which is driven either by
bank competition and relative size, or by geographical proximity. In addition, we directly assess the
importance of banks’ soft information for large firms and industrial loans, which is usually absent from
the literature.
Our paper is related to Grunert, Norden, and Weber (2005) who find that the combined use of
financial and non-financial factors of credit rating scores predicts loan defaults more accurately by
German firms than the use of either financial or non-financial factors alone, and to Agrawal and
2
See Gorton and Winton (2003) for a survey on this literature.
3
Hauswald (2008) who document that the soft information component of a credit rating predicts loan
defaults of small firms. Differing from the former, we show that soft information evolved through
extensive lending relationships not only improves default prediction, but also prevails over the effect of
financial factors. Differing from the latter, we establish that soft information plays an important role for
large firms and commercial loans, despite the fact that there tends to be more hard information about large
firms. In addition, we show that the effect of soft information in predicting default varies with the depth
of the lending relationship.
Our paper is also related to the literature analyzing how financial and industrial factors predict
corporate bankruptcy (e.g., Altman 1968). Instead, we focus on loan default. Our findings complement
this literature by indicating that hard information, derived from firm’s financial statements, predicts not
only a firm’s likelihood of bankruptcy but also that of short-term loan delinquency.
The rest of the paper is organized as follows. Section 2 discusses institutional details about
China’s banking system and recent banking reforms, and the uniqueness of our research setting. Section 3
describes our sources of data. Section 4 reports the results on the determinants of loan default and internal
credit rating. Section 5 examines the role of soft information. Section 6 extends to the nature of soft
information and its role in the presence of less credible hard information. Section 7 discusses various tests
for robustness. Section 8 concludes.
2. CHINA’S BANKING SYSTEM AND RESEARCH SETTING
2.1 The Role of the Big Four
In an attempt to emulate the Soviet Union in which a centralized banking system is used to
support a central planning economy, China established the People’s Bank of China (PBOC) in 1948. Prior
to 1978, the PBOC served as both a central bank and a commercial bank.
In 1978, China embarked on a market-oriented economic reform. Accordingly, four state-owned
banks—Agricultural Bank of China, Bank of China, China Construction Bank, and Industrial and
Commercial Bank of China—were established during the period of 1979-1984. The so-called “big four”
4
were set to serve financing needs of four sectors, respectively: agriculture, foreign trade, infrastructure
construction, and manufacturing industries. After 1984, however, each of the “big four” was allowed to
broaden the scope of their operations into other banks’ sectors amid China’s effort to introduce
competition among banks.
Throughout this time, the firm-bank relationship was mandated by the government instead of
driven by commercial principles. Many of the state-owned banks’ loans were originated to state-owned
enterprises (SOEs) based on political and policy considerations. With the concerns regarding social
instability accompanied with rising unemployment, loans were continuously granted by the banks to pay
workers’ compensation despite the unprofitability and non-competitiveness of SOEs. Consequently, nonperforming loans (NPLs) piled up on banks’ financial statements.
From 1986 to 1996, approximately 11 more banks, including the Bank of Communication, China
Merchants’ Bank, Pudong Development Bank, and Shenzhen Development Bank, were established to
increase the competitiveness of China’s banking industry. These banks are usually jointly owned by
several legal entities, such as local governments and enterprises. Although the legal entities are usually
state-owned, these “joint-stock” banks are smaller, albeit more efficiently run, than the big four stateowned banks.3
In 1995, China passed the “Central Bank Law” and “Commercial Bank Law”, explicitly
specifying the functions, rights and duties between the central bank (PBOC) and commercial banks. In
2003, China established the Banking Regulatory Commission to take over part of the regulatory duties
previously held by the PBOC. In turn, the PBOC focuses on its macroeconomic and monetary
responsibilities.
China permits foreign banks to conduct business in the mainland China starting 1979. Initially,
most of the foreign banks’ business is restricted to foreign currency exchange. As a precondition to join
3
Prior to 1995, small credit unions also existed, some of which were transformed into city cooperative banks
through equity contributions from local governments, enterprises, and local citizens. In 1995, the State Council of
China announced that credit unions can no longer be transformed into city cooperative banks through equity
contributions.
5
the WTO, China pledged the commitment to open its domestic currency (RMB) business to all foreign
banks by 2006. Foreign banks were also allowed to take limited equity positions in Chinese banks (see
the next section).
2.2 The Reform of China’s Commercial Banks
Mounting non-performing loans have long plagued the financial statements of China’s
commercial banks, especially the “big four”. In 1997, 30% of all the loans outstanding were NPLs. By
2003, this ratio was still as high as 20%. The high percentage of NPLs was usually attributed to: (1) the
government’s direct or indirect ownership and control of commercial banks to pursue its political and
policy agendas, (2) inefficient operations and soft budget constraints associated with some SOE
borrowers, and (3) ineffectiveness in enforcing the bankruptcy law.
The Chinese government has since initiated a series of reforms to curb the increasing risk
associated with the high level of NPLs. In 1998, 270 billion RMB was injected by the Finance Ministry to
replenish the deteriorating capital of the big four state-owned banks, followed by a transfer of 1.4 trillion
RMB NPLs (at their face value) from these banks to four corresponding newly created Assets
Management Companies in 1999.
As the next step of the reform, the government “corporatized” the state-owned banks by
introducing foreign strategic investors and then listing these banks on the Hong Kong Stock Exchange
and/or the Shanghai Stock Exchange. In late 2003, the government injected $22.5 billion each into the
Bank of China and China Construction Bank as equity capital, and corporatized the two as joint-stock
commercial banks. In 2004, Royal Bank of Scotland, UBS, Bank of America, and TEMASEK took
minority equity positions in these two banks as strategic investors. China Construction Bank went public
in 2005. The state retained a controlling stake (59.12%) of the bank after its listing on the Hong Kong
and Shanghai Stock Exchanges. After the Bank of China’s IPO in both Hong Kong and Shanghai in 2006,
the state’s equity stake was 67.49%.
6
In 2005, $15 billion were used to capitalize the Industrial and Commercial Bank of China (ICBC),
which was then reorganized and corporatized. Goldman Sachs, Allianz, and American Express bought a
total of 8.45% of its equity as strategic investors. ICBC became publicly traded on the Hong Kong and
Shanghai stock exchanges in 2006. After its IPO, the state controlled 72.47% of the shares.
Many believe that banking reforms since 2003, including bank restructuring, introduction of
strategic investors, and public listings, have fundamentally changed the corporate governance and risk
management practices of Chinese state-owned banks. As a result, loan originations have relied more on
commercial principles instead of government policies, and NPL ratios have declined substantially. By the
beginning of 2008, ICBC overtook Citigroup as the world’s largest bank in terms of market capitalization.
The other two of the “big four”, Construction Bank of China and Bank of China, ranked the 4th and 5th,
respectively.4
2.3 Chinese Banks as a Research Setting
Using Chinese banks as a research setting offers several unique features. First, banks play a
dominating role in China’s financial system (Allen et al., 2008). In 2004 alone, bank loans accounted for
83% of external capital raised by non-financial firms, in comparison with 5% of external capital raised
from the equity market and 12% from the public debt market. Unlike many developed countries where
bank loans are predominant among mostly small businesses, Chinese firms rely mainly on bank financing
regardless of the scope of their businesses and the scale of their operations.
Second, within China’s banking system, the big four state-owned banks dominate the loan market.
By the end of 2004, the “big four” accounted for 55% market share in terms of asset scale (GarcíaHerrero, Gavilá and Santabárbara, 2006). Since each of the big four banks specializes in a specific
lending area, the impact of competition from other banks within a lending area remained relatively
marginal at the time. This mitigates the issue of bank size and competition that commonly affect such
studies.
4
“ICBC Deposes Citigroup as Chinese Banks Rule in New World Order”, Bloomberg.com, February 4, 2008.
7
Third, the unique setting of China’s banking system also allows us to concentrate on the nature
and role of firm-specific information obtained by the bank from its long-term and repeated lending
relationships in predicting loan defaults. As discussed later in Section 5, one of our proxies for the depth
of banking relationship is based on whether or not a firm is state-owned.5 Since state banks’ relationships
with state-owned firms were historically mandated by the Chinese government, this proxy is relatively
exogenous and consequently, mitigates the endogeneity of matching between a firm and its bank that
typically affects such studies (Berger, Miller, Petersen, Rajan, and Stein 2005).
Lastly, state-ownership historically mandated the mapping of a nationwide distribution of bank
branches. Since the backbone of our bank’s branch network was originally set up exogenously, instead of
evolving endogenously based on the regional economic development as in previous studies for developed
economies, the soft information in our analysis is less likely to be driven by distance, and more so by
repeated lending.
3. DATA DESCRIPTION
3.1 Data Sources
We obtain a large dataset from one of the big-four state-owned commercial banks in China,
whose lending scope and practices have concentrated on manufacturing industries. The dataset consists of
year-end information on all the outstanding loans made to 15 subcategories of five largest manufacturing
industries from 2003 to 2006. 6 The industry classification system used by the bank is similar to the
Industrial Classification for National Economic Activities from the Bureau of Statistics of China.
For each loan outstanding, our dataset contains information on its principal amount, maturity date,
the province in which the loan was originated, interest rate, the borrowing firm’s financial statements,
5
Our paper studies how information arising from long-term or repeated lending relationships affects defaults on
outstanding loans. The impact of information on loan approvals or rejections, though an interesting issue, is beyond
the scope of the paper. By focusing on the prediction of loan defaults instead of loan approvals, state-ownership as a
proxy for lending relationship is not affected by whether loans are granted for political or policy considerations.
6
The five manufacturing industries include: Steel, Automobiles and Transportation Equipment, Paper, Non-ferrous
Metals, and Construction Materials and Manufacturing.
8
ownership classification, and the industry where the firm operates. More importantly, our dataset contains
the repayment status if the loan is due during the year. Specifically, for each loan outstanding, its
repayment status will be noted by the bank at the end of the following year in one of the following three
categories: repaid, unpaid, or written off.
Starting 2004, the bank implemented an internal credit rating system.7 For a given year, the bank
follows an internal set of guidelines and assigns a credit score to a borrowing firm at the time when it
applies for the first loan of that year. The bank then rates the borrower based on its credit score. The credit
rating ranks from one to 12, with one being the lowest (poorest credit quality) and 12 the highest (highest
credit quality). Our dataset thus also contains all the annual rating information between 2004 and 2006. In
Appendix I, we provide a detailed description of this internal credit rating system. As Appendix I
indicates, a borrowing firm’s internal credit rating reflects both the objective and subjective evaluations
from the bank.
Appendix II and Tables A1 and A2 describe the loan characteristics such as size, maturity and
default rates, as well as internal credit ratings for firms of the five manufacturing industries in this dataset.
There is evidence that the introduction of foreign strategic investors and listing on overseas stock
exchange dramatically improves the bank’s performance over time, resulting in a significant decrease in
loan defaults and an increase in credit ratings. In addition, loans originated to the state-owned enterprises
experienced a substantial decline during our sample period. All these suggest that loan decisions over the
sample period gravitate towards commercial principles instead of government policies.
7
Most banks in the United States have had internal credit rating systems since at least the 1980s (see, e.g., English
and Nelson (1999) for a description). Note also that internal credit rating differs from credit scoring. Small business
credit scoring (SBCS) in the United States was introduced in 1995 and applies to only micro business loans. By
basically adapting consumer lending practices to micro business lending, credit scoring is mostly focused on using
mercantile ratings and consumer credit bureau reports on the entrepreneur. It is best viewed as a subset of internally
rated loans. In the case of SBCS, the entire loan underwriting process is limited to the score. Several studies have
shown that the implementation of SBCS improves small business lending (e.g., Frame, Srinivasan, and Wooseley
2001 and Berger, Frame and Miller 2005). China introduced the internal credit rating system to its banks following
economic reforms. However, there is still a lack of development of credit scoring for small businesses and
consumers.
9
3.2 Loan Default
To ensure the conservativeness of our analysis, we consider a loan in the default stage if the
principal is unpaid or written off by the due date. Therefore, our definition of loan default is essentially
restricted to whether the loan is repaid on time, which is narrow in the sense that other violations of loan
covenants are not considered as default. Note that loan rollovers are extremely rare in China. This is
because Chinese firms are required to physically repay their loan obligations to their banks before they
can borrow any new loans, even if the principal and terms of the new loan remain exactly the same, which
in fact constitutes a loan rollover. Note also that in China, a loan officer of a state-owned bank bears the
“life responsibility” for loans that he or she originates. That is, the officer bears penalty in the event of
default even if he or she no longer works in loan origination or he or she has transferred to a different
department. Therefore, our definition of loan default is unlikely to underestimate actual default. In
addition, as will become clear later, underestimating default works against us in finding the results.
3.3 Sample Selection
Our dataset contains 40,740 bank loans made to 4,624 Chinese firms from five manufacturing
industries between 2003 and 2006. There are 13 ownership categories for the borrowing firms in our
sample, including state-owned, collectively-owned, state-controlled (in which the state has a controlling
stake), collectively-controlled (in which a collectively-owned entity has a controlling stake), foreignowned and joint ventures, privately-owned, proprietorship, and joint-stock companies. We remove 2,667
loans borrowed by collectively-owned and collectively-controlled firms due to their ambiguous nature,8
as well as loans borrowed by firms with missing ownership information.
We begin by extracting a sub-sample of loan data based on the following filtering criteria. Note
that firms borrowing from the bank in 2003 do not have an assigned internal credit rating, and some loans
initiated in 2006 require payment information in 2007 but our loan sample ends in 2006. Therefore, we
8
The ambiguous nature of collectively-owned firms and their unique ownership arrangements are discussed and
analyzed in details in Chang and Wang (1994).
10
remove 22,252 loans to restrict our regression analysis to the sample containing loans originated after
2003, with a maturity date no later than 2006.
We focus on short-term loans (maturity of one year or less). This is because to identify the default
status for medium and long-term loans requires information extended beyond one year. However,
concentrating on short-term loans does not bias our analysis. As Appendix II and Table A1 indicate,
short-term loans constitute the major source of funding for Chinese firms. In fact, 95% of loans in our
overall sample have a maturity of one year or less, accounting for 84% of aggregate outstanding
principals. In addition, for short-term loans, default more likely arises from failing to meet principal
repayment requirements rather than from violating other loan covenants; our definition of loan default—
whether or not the principal is repaid on time—thus matches this focus well.
With only the end-of-year information available in our data, short-term loans made and repaid in
the same calendar year are not included in the database unless there is a default. Therefore, for loans with
a maturity less than one year, we only include those initiated on and after July 1st of years 2004 or 2005
with a maturity of six months or longer, which allows us to identify their default status during the period
of 2005-2006. This approach ensures the conservativeness of our analyses and avoids over-estimating
default rate. These sample restrictions lead to an exclusion of 5,452 short-term and long-term loans.
We further remove 815 firm-year observations (1,727 loans) with missing internal rating scores,
financial statement information, and/or sales growth rate. Our final sample thus contains 8,642 loans from
1,450 firms (2,063 firm-year observations).
Table 1 summarizes the characteristics between firms that defaulted on their loans and those that
did not. The detailed variable descriptions are provided in Appendix III.
From Table 1, there is preliminary evidence that the bank’s internal credit rating predicts loan
default as the rating differs depending on whether or not the loans are in subsequent default stage. For
example, among loans initiated in 2004, those that were in default in 2005 had an average internal credit
score of 5.38, compared to the average score of 8.30 for those that were not in default. Firms defaulting
11
on their loans also have a significantly higher degree of leverage, poorer profitability (measured by return
on assets, or ROA), lower asset turnover, and smaller cash reserves.
Interestingly, Panels B and C show that the above observed default characteristics are similar
between state-owned firms and non-state-owned firms. This suggests that most of the firm-specific
fundamental factors that affect loan default are relatively universal across Chinese firms.
Lastly, both the average annual book value of total assets and annual number of employees in
Table 1 indicate that our sample is not dominated by small manufacturing firms. For example, firms that
borrowed short-term loans in 2004 and then defaulted in 2005 on average have 1,160 employees and total
assets of RMB 371 million, approximately $46.14 million. 9 Unlike small businesses analyzed by the
majority of previous studies, our sample firms have a relatively large operating scale and asset base.
In addition, our sample is not dominated by micro-loans. Although not tabulated, the average
outstanding loan principal per sample firm is RMB 47.036 million ($5.85 million). Our study therefore
sheds light on the characteristics and economic impact of relationship lending associated with commercial
loans and large industrial firms.
4. LOAN DEFAULT AND INTERNAL CREDIT RATING
We now evaluate the economic role of the bank’s information, captured by its private credit
rating score, in the context of predicting loan default. We first identify firm-specific factors that can
potentially affect the incentive to default. We then investigate whether credit rating scores have additional
predictive power after controlling for firm-specific factors known to affect default propensity.
Next, we explore the information content of the bank’s internal credit rating by examining
whether these ratings take into account a firm’s fundamentals, and whether there is any evidence that the
bank possesses additional proprietary information in evaluating credit delinquency. By parsing the rating
score into a “hard” information component and a “soft” information component, based on the predicted
and unpredicted components of credit rating score with respect to a firm’s fundamentals, we examine
9
Based on an average exchange rate between 2003 and 2006 of $1 = RMB 8.04.
12
whether the soft information component has any predictive power. Since a bank’s soft information
evolves from its lending relationship with the firm, we further investigate whether the role of soft
information differs depending on the depth of lending relationship.
As discussed in Appendix I, a credit rating score is assigned to a borrowing firm when it applies
for its first loan of the year. Since the bank’s internal credit rating is assigned to the borrower instead of to
individual loans, we conduct our regressions at firm-level to ensure the conservativeness of our analysis.
Consequently, for each year, we define a firm as in a loan default stage if at least one of its previously
borrowed short-term loans is written off or unpaid. This definition includes firms that default some, but
not all, of their loans. Among the 2,063 firm-year observations, 172 firm-year observations are classified
as default. 124 out of 172 (72%) involve firms that default all their loans, whereas 28% involves firms
that default some of their loans. In the robustness section, we discuss the results based on the loan-level
analyses and based on alternative definitions of loan default.
4.1 Can Hard Information and Internal Credit Rating Predict Loan Default?
We start with a correlation analysis to identify the relationship between firm-specific hard
information—including fundamental factors derived from their financial statements—and the subsequent
loan defaults. As indicated in Table 2, a high incidence of default is correlated with small asset base, high
leverage, being state-owned, and poor operating performance in terms of ROA, asset turnover, cash
reserve, and sales growth. A high loan default rate is also correlated with a low internal credit rating.
We now explicitly explore this relationship with the following probit regression model:
Pr(Default) =f(firm-specific hard information variables, γindustry, λyear)
[1]
Our dependent variable is a dummy equal to one if a firm is in the stage of defaulting its loan, and
zero otherwise. Our independent variables include lagged firm-specific factors that could affect the
default propensity. Specifically, we include a firm’s key financial statement information such as size,
leverage, return on assets, asset turnover, cash reserve, and sales growth. Other characteristics include a
13
dummy variable equal to one if the firm is state-owned,10 the interaction of the dummy with the firm’s
size, average loan maturity, a dummy equal to one if the firm has previously defaulted on its loans, and a
dummy variable equal to one if the firm is publicly traded. We include log(GDP) to control for potential
clustering of bank branches and borrowing firms based on local economic conditions. Lastly, we control
for both industry and year fixed-effects, γ industry and λ year .
Table 3 Column 1 presents the results for the probit regression model [1]. Controlling for industry
and year fixed effects, hard information, captured by a firm’s fundamentals, can significantly predict loan
default. Specifically, firms with a larger asset base, lower leverage, higher profitability, larger cash
reserves, and operating in regions of more advanced economic development tend to have a lower
propensity of default. Firms that previously defaulted on their loans also tend to default on current loans.
In addition, the dummy variable for state-owned firms is positive and significant, but the
coefficient for the interaction term between the dummy and size is negatively significant. This suggests
that while state-owned firms tend to have a higher probability of default, this probability declines if such
firms have a large asset base.
To examine whether the bank’s internal credit rating, implemented nation-wide for all its
branches, has any additional predictive power for loan default, we next include the bank’s internal credit
rating score in the regression model [1].
Columns 2 and 3 of Table 3 suggest that internal credit rating is significantly negatively related to
the probability of default, regardless of whether or not the proxies for firm-specific hard information are
included. Specifically, one level increase in the internal credit rating (higher score) leads to a 1.6% lower
probability of default (Column 3).
Since the occurrence of loan default constitutes a relatively rare event for our sample, we evaluate
the overall improvement in prediction accuracy by comparing the fitted probabilities between Columns 1
and 3 with respect to actual default. Among firms that actually default on their loans, we count the
10
In 2005, 12 firms changed their ownership from state-owned to non-state-owned. Excluding these 12 firms does
not alter our findings.
14
number of firms whose predicted probability of default based on Column 3 is greater than the one based
on Column 1. Among firms that actually do not default on their loans, we count the number of firms
whose predicted probability of default based on Column 3 is smaller than the one based on Column 1. We
find that the predicted probability overall improves from Column 1 for 70.14% of sample observations,
after including the internal credit rating variable.
Interestingly, Column 3 reveals that once the internal credit rating is included, most of the proxies
for firm-specific hard information—fundamental factors identified in Column 1 to help predict the
probability of loan default, such as firm size, leverage, profitability, and previous default records—are no
longer statistically significant. This suggests that internal credit rating scores subsume the effect of these
factors.
We also observe from Column 3 that the coefficients associated with the dummy for state-owned
firms and the interaction term are no longer significant after including the internal credit rating variable.
This is in contrast with the results of Column 1 where, in the absence of rating, both coefficients are
significant at the 5% level. This comparison provides preliminary evidence that internal credit rating is
more informative about state-owned firms, with whom the bank tends to have a long-term relationship.
4.2 The Information Content of Internal Credit Rating
The results from Table 3 show that the bank’s internal credit rating scores are significantly related
to the probability of default. Most of the fundamental factors are no longer significant after including
these rating scores, which suggests that internal credit rating scores incorporate the majority, if not all, of
firm-specific hard information.
We next regress internal credit rating scores against these proxies for firm-specific hard
information identified in the probit regression model [1]. The OLS results from Table 4 Panel A presents
evidence that internal credit ratings do take into account firm-specific fundamental factors expected to
affect loan default. Not surprisingly, factors such as larger asset base, lower leverage, greater profitability,
faster asset turnover, higher level of cash reserve and sales growth, and a previous non-default record lead
15
to better credit quality and a more favorable credit score. While state-owned firms on average are
associated with low credit rating scores, this effect is more pronounced for firms of smaller sizes, as the
coefficient associated with the interaction term is positive and significant.
Table 4 Panel A Column 1 shows that these proxies for firm-specific hard information together
explain approximately 44% of a firm’s internal credit rating score. The adjusted R2s for Columns 2 and 3
indicate that there is a difference between state-owned and non-state-owned firms: for state-owned firms,
firm-specific fundamentals are able to explain over 63% of the internal credit ratings, whereas they can
explain only 38% for firms without state ownership. This suggests that financial information is more
credible for firms that maintain a long-term banking relationship, and therefore is taken into account to a
greater extent by the bank. In contrast, financial information from firms that do not maintain a long-term
banking relationship is less influential when the bank sets its internal credit rating scores.
Table 4 Panel B adopts a difference-in-difference analysis and examines whether a change in
firm-specific hard information such as a firm’s fundamental characteristics leads to a subsequent change
in the internal credit rating. In the OLS regression (Column 1), the dependent variable is the change in the
internal credit rating. In the ordered probit regression (Column 2), the dependent variable takes a value of
1 if a firm’s internal credit rating improves from 2004 to 2005, -1 if it deteriorates, and 0 otherwise.
We observe that a change in asset base, leverage, and operating performance is significantly
related to a subsequent change in internal credit rating. The ordered probit result suggests that an increase
in operating performance measured by ROA leads to a higher internal credit rating, while an increase in
leverage leads to a lower rating.
To summarize, our multivariate regression analysis and difference-in-difference analysis indicate
that the bank’s internal credit rating takes into account firm-specific hard information, such as
fundamental factors previously identified to predict loan default. In addition, these factors matter more if
the firm has a long-term relationship with the bank.
16
5. THE ROLE OF SOFT INFORMATION
Results from Table 3 indicate that most known fundamental factors are no longer statistically
significantly in predicting loan default once the international rating scores are included in the probit
regression. Table 4 Panel A reveals that, being able to explain less than 44% of the internal credit rating,
firm-specific hard information—captured by firms’ fundamentals—is not sole determinant of the bank’s
internal credit rating. This suggests that the bank possesses superior informational advantage when
evaluating loan defaults.
5.1 Proxies for Soft Information and the Depth of Lending Relationship
We follow an approach similar to Agarwal and Hauswald (2008) and parse the internal credit
rating into a hard information component and a soft information component, which we define statistically
based on the firm-specific fundamental information available during the period the rating score is
assigned. Specifically, for each sample firm we obtain the fitted value and residual of its internal credit
rating score from Table 4 Panel A Column 1. In this respect, Bank Specialty, measured by the residual
component of the internal credit rating, captures the soft information arising from the bank’s own
assessment, monitoring, knowledge and experiences that cannot be predicted by hard information proxies.
If the bank’s firm-specific soft information arises from its lending relationship with the firm, then
the role of Bank Specialty in predicting default may vary between firms that have a sustained banking
relationship and those do not. On the other hand, if Bank Specialty captures omitted firm-specific hard
information instead of the bank’s own information that is not easily verifiable, conveyed, or transferable,
then we should not expect its role to vary with the depth of lending relationship. In Section 7, we also
discuss our robustness tests with respect to omitted hard information.
To identify whether or not the bank’s soft information is pertinent to relationship lending, we
adopt three proxies for the depth of relationship. Our first proxy is based on the borrowing frequency over
the sample period. For each firm, we compute the total number of loans outstanding with the bank over
the sample period. We then classify a firm as an infrequent borrower if its number of loans is less than or
17
equal to the sample median of 12. A firm is a frequent borrower if its number of loans outstanding is
greater than 12.
Our second proxy is based on the duration of the banking relationship. Starting in 2004, our bank
assigns its annual internal credit rating score at the time when it grants the first loan to the firm. So for
each firm in each year (2004 or 2005), we identify the month when it obtains its first loan. We then trace
back the firm’s loan information prior to this month. The duration variable is then calculated as the
difference between the current month and the earliest recorded time in our database among all the loans
borrowed by the firm prior to the current month. We classify a firm as having a long-term relationship
with the bank if the duration of its banking relationship is more than 23 months (sample median in 2005).
Otherwise, the firm is classified as having a short-term banking relationship.11
Our last proxy is based on whether a firm is owned or controlled by the state. Since the Chinese
government historically mandates the banking relationship with state-owned firms, the bank has more
interactions with state-owned firms than non-state-owned firms. More importantly, this relationship is
forged exogenously and is therefore, not subject to the doubt-matching endogeneity problem commonly
seen in the existing literature. On the other hand, since our focus is loan default instead of loan origination,
the results with respect to this proxy for lending relationship is not affected by whether loans are
originated for commercial principles.
5.2 Soft Information, Relationship Lending, and Loan Default
To examine whether the bank’s soft information matters in predicting loan defaults, we include
Bank Specialty instead of internal credit rating and re-run the probit regression [1] as follows:
11
Alternative cutoff based on sample median of 2004-2005 yields similar results. Note that this proxy is measured
against the short sample period; it tends to be noisy in capturing the interaction between the firm and the bank than
the other two proxies. A firm might have secured loans from the bank prior to the beginning of our sample period
and beyond the records that are traceable, and had little borrowing activities since then. It is possible that such firms
are classified as being associated with a short-term banking relationship. However, this type of misclassification
works against us in finding the difference between firms of short- and long-term banking relationships. In addition,
our focus on short-term lending activities, instead of long-term loans, helps to mitigate this potential
misclassification problem.
18
Pr(Default) =f(Bank Specialty, firm-specific hard information variables, γindustry, λyear)
[2]
We run the regression model [2] for the two sub-samples of firms with and without a profound
banking relationship, respectively. Since one of our proxies is state ownership, we remove the State
dummy and its interaction with Size from the set of hard information proxies. Including or excluding the
two variables when estimating [2] for sub-samples classified by borrowing frequency or duration does not
alter our findings. Table 5 presents the probit regression results for all three proxies, respectively.
Bootstrapped standard errors are reported in parentheses.12
Table 5 shows that Bank Specialty is negatively related to default propensity, suggesting that
more favorable soft information leads to a lower probability of default. This relationship is statistically
significant, regardless of a firm’s ownership, borrowing frequency, or length of banking relationship.
On the other hand, the role of soft information relative to hard information also varies depending
on the depth of the lending relationship. Columns (2), (4) and (6) of Table 5 reveal that for frequent
borrowers, firms that begin their banking relationship early, and state-owned firms, the majority of hard
information proxies are no longer significant after including Bank Specialty. This suggests that the bank’s
soft information, arising from a long-term and repeated lending relationship, substitutes most hard
information, and is almost capable of predicting loan defaults alone.
In contrast, Columns (1), (3) and (5) of Table 5 indicates that in the absence of such a profound
lending relationship, Bank Specialty is unable to prevail over most hard information. For infrequent
borrowers, firms that started their banking relationship more recently, and non-state-owned firms, half of
the hard information proxies continue to significantly predict default even in the presence of the bank’s
soft information. This suggests that soft information accumulated through a tenuous lending relationship
is less capable of evaluating loan defaults by itself alone.
12
The probit estimates could potentially be biased due to violations of distributional assumptions under which the
regression models are estimated. We therefore apply the bootstrap methodology (Efron, 1979) to determine the
statistical significance of the estimated coefficients. The bootstrapped standard errors are based on 500 random
draws with replacement. Using robust standard errors instead of bootstrapped standard errors yields the same results.
Therefore, these results are not tabulated.
19
Interestingly, when analyzing what kind of hard information is subsumed by soft information, we
observe from Table 5 that ROA and Cash consistently remain insignificant for firms that have a sustained
banking relationship, but are statistically significant for firms lacking a profound banking relationship.
Relative to other hard information proxies, these two are more difficult to verify and can be easily
manipulated by Chinese firms.13 This highlights the importance of bank’s soft information in replacing
the type of hard information that is subject to easy manipulation.
6. EXTENSIONS
6.1 The Contents of Soft Information and Loan Default Prediction
Table 5 reveals that the bank’s soft information significantly predicts loan default. Intuitively,
loan default occurs when the deterioration in credit quality of the borrowing firm exceeds a certain level.
This implies that the relationship between Bank Specialty and incidence of default may be nonlinear:
when the bank is already very positive about the borrowing firm, more favorable information may not
significantly lead to a substantial decline in default propensity.
To investigate to what extent the bank’s soft information predicts subsequent loan default, we
employ a piecewise linear estimation—a spline.14 A spline specification allows the slope coefficient to
vary with different levels of soft information. We choose the spline cutoff points based on the terciles of
Bank Specialty: -0.387, and 0.902.
Table 6 Column 1 reports the probit regression results for the overall sample and Column 2
reports the spline regression results. Table 6 Column 1 reveals that for the overall sample, Bank Specialty
is negatively related to default propensity after controlling for firm-specific hard information proxies and
year and industry fixed effects, suggesting that more favorable soft information is associated with a lower
incidence of default.
13
From an accounting perspective, cash manipulations are relatively limited compared to the options to manipulate
ROAs. Nevertheless, cash manipulations are widespread among Chinese firms, even among publicly traded
companies.
14
For a detailed description of spline regression, see Garber and Poirier (1974) and Poirier (1974).
20
The results from the spline regressions indicate that the overall sample findings are driven by less
favorable soft information. We observe from Table 6 Column 2 that controlling for firm-specific hard
information and year and industry fixed effects, the coefficient estimates for Bank Specialty remain
negative for all three tercile levels. However, the relationship between the bank’s soft information and
default propensity is statistically significant only for the bottom tercile. This suggests that, ceteris paribus,
when the bank’s soft information is already very favorable (the middle and top terciles), an increase in the
bank’s optimism about the borrowing firms does not contribute to a significant decline in the incidence of
default. On the other hand, when the bank’s soft information is relatively negative about the borrower (the
bottom tercile), default propensity decreases significantly if soft information becomes more positive.
6.2 The Economic Impact of Bank Specialty: The Case of Earnings Management
While various proxies for firm-specific hard information capture different dimensions of a firm’s
characteristics, they are not equally precise and credible. Table 5 suggests that soft information can
subsume hard information proxies that are subject to easy manipulation. We now explore the role of soft
information in default prediction from a different but related angle: earnings management. If a firm tends
to manipulate its hard-information and thus its hard information is less credible, then the bank should rely
more on its soft information rather than the firm’s reported hard information.
In this respect, China as a research setting offers a unique advantage. Because of the underdevelopment of its stock market and constraints on other external financing sources, most Chinese firms,
regardless of their sizes and scopes of business operations, rely on bank financing. In particular, the
majority of Chinese firms are not publicly traded. In contrast to other motives to manipulate earnings
among public firms, the primary purpose of earnings management of the private firms is to obtain or
maintain bank financing capacity and to delay the consequences associated with loan defaults.
To explore the role of soft information in the presence of earnings management, we first remove
publicly traded firms from our sample. Next, we employ three earnings management measures for our
analysis. Our first measure follows Leuz, Nanda, and Wysocki (2003) and uses the magnitude of accruals
21
as a proxy for the extent to which a firm exercises discretion in reporting earnings. Specifically, we
compute this firm-specific proxy as the ratio of a firm’s absolute value of accruals and the absolute value
of the cash flow from operations, where accruals are calculated as: (Δtotal current assets - Δcash) - (Δtotal
current liabilities- Δshort-term debt - Δtaxes payable) - depreciation expense.
Our second measure is similar to the industry-adjusted non-operating income measure used in
Chen and Yuan (2004), who show that for Chinese firms, earnings management can be detected from
non-operating income that is in excess of industry norms. For each year, we compute each firm’s nonoperating income by scaling its gain and loss from non-operating activities with the total pre-tax profit
and then subtract the industry median to reach its industry-adjusted non-operating come. We obtain
industry median non-operating income from SINOFIN’s Chinese Industrial Enterprises Database.15
Our third measure uses discretionary accruals as a proxy for earnings management. Firm-specific
discretionary accrual is estimated according to the modified Jones model (Dechow, Sloan and Sweeney,
1995). The industry benchmark is again drawn from SINOFIN’s Chinese Industrial Enterprises Database.
For each earnings management proxy, we classify a firm as the one with a low degree of earnings
management if its proxy value falls below the sample median. We then re-estimate regression [2] for both
the high and low earnings management sub-samples. When hard information is subject to easy
manipulation and thus becomes less credible, the bank more likely relies on its soft information. We
should expect that the bank’s soft information plays a more significant role in predicting default among
firms that tend to manipulate their earnings than those that are less likely to misstate their economic
performance.
Table 7 reports the probit regression results for high and low earnings management sub-samples,
and for each of the three earnings management proxies, respectively. Sample size varies due to missing
values for variables required in calculating a specific measure of earnings management. For brevity, only
15
To the best of our knowledge, the Chinese Industrial Enterprises Database is the only database that provides
industrial level accounting information that contains non-publicly traded firms. Although untabulated, results are
similar if we use sample annual industry median instead.
22
the coefficient and marginal effect associated with Bank Specialty are presented. Firm-specific variables,
as well as industry and year fixed effects, are included in the probit regression but not tabulated.
Table 7 reveals that while Bank Specialty significantly predicts loan default for both sub-samples,
the economic impact of soft information is more prominent for the high-earnings management sub-sample,
as the magnitude of the marginal effect for Bank Specialty is uniformly larger, regardless of the proxies
used for earnings management. For example, when using the magnitude of accruals as a proxy for
earnings management, a 1% increase in bank specialty leads to a 1.2% reduction in loan default
probability for firms in the low earnings management sub-sample, the decline is almost twice—a 2.0%—
among firms in the high earnings management sub-sample.16
To summarize, we find that the bank’s soft information plays a more prominent role in predicting
loan default when the borrowing firm is more likely to manage its earnings. This result is consistent with
the findings in Table 5, suggesting that the bank relies more on its soft information when hardinformation is less credible.
7. ROBUSTNESS
7.1 Omitted Hard Information
Our proxy for soft information, Bank Specialty, is the residual component from regressing the
bank’s internal credit rating score against a set of firm-specific hard information variables (Table 4 Panel
A Column 1). Intuitively, this proxy represents the part of the internal credit rating that is not predicted by
hard information proxies. However, it is possible that instead of the bank’s soft information, Bank
Specialty reflects firm-specific hard information that is not captured by our existing proxies.
16
In untabulated regressions, we further divide the high earnings management sub-sample based on the depth of
lending relationship. We find that the magnitude of marginal effect associated with Bank Specialty is significantly
larger for firms that have a sustained relationship with the bank. For example, when using the magnitude of accruals
as a proxy for earnings management, a 1% increase in Bank Specialty leads to a 1.7% decrease for non-state-owned
firms, but the decline widens to 3.1% for state-owned firms. Using duration of banking relationship instead of stateownership as a proxy for the depth of lending relationship, the marginal effect is -1.4% for firms that have a shortterm banking relationship, compared to -2.6% for firms that have a long-term banking relationship.
23
As we have discussed before, soft information differs from hard information in that it is not easily
and accurately conveyed, verifiable, or transferable. Therefore, if Bank Specialty were merely a proxy for
omitted firm-specific hard information, then its effect on predicting loan default should not vary with the
depth of lending relationship. Instead, Table 5 shows that to the extent that Bank Specialty displaces hard
information depends on whether the borrowing firm has a profound banking relationship.
Nevertheless, we check the robustness of our results with respect to potential omitted hard
information in three separate sets of tests outlined in sub-sections 7.1.1 through 7.1.3. As these robustness
analyses indicate, we find similar results.
7.1.1 Large firm sub-sample
We restrict our sample to large firms whose assets are greater than or equal to the sample median
of RMB 109.07 million. Compared to small firms, firm-specific hard information for large firms is more
readily available, and the information contents are less noisy. Therefore, Bank Specialty less likely
contains hard information factors that are significantly related to loan default, yet completely orthogonal
to our existing hard information proxies. In addition, the impact of factors other than information about
industry, size, and financial statements to predict default—such as background of senior management—is
less prominent within large firms, and hence less likely drives our findings.
We repeat the analyses (as those in Tables 3-5) for the large firm sub-sample. Although our
overall sample is reduced by half, we find similar results. For example, when re-estimating the results in
Table 5, Bank Specialty continues to be negatively related to loan default for all six sub-samples, and is
statistically significant at 1% except for the sub-sample of state-owned firms, where the coefficient is
negative and significant at 10% (p = 0.053). Cash, a proxy for hard information subject to easy
manipulation, is significantly related to the incidence of loan default only when the borrowing firm does
not have a profound banking relationship. ROA, on the other hand, becomes insignificant regardless of
the depth of lending relationship.
24
7.1.2 Bank Specialty based on difference-in-difference analysis
In our main analysis, we construct Bank Specialty as the residual component of Table 4 Panel A
Column 1, where the level of internal credit rating score is regressed against a set of firm-specific hard
information proxies. As a robustness check, we replace Bank Specialty using the residual component from
the difference-in-difference analysis in Table 4 Panel B Column 1. This approach helps to mitigate the
impact of the omitted time-invariate hard information on our results. We then re-estimate the regression
model [2].
Since the bank started its internal credit rating system in 2004 and the difference-in-difference
analysis demands at least two years of data, this approach dramatically decreases the number of
observations. With fewer observations making the estimations difficult to converge, we are only able to
estimate regression [2] for frequency-based proxy for the depth of relationship. Nevertheless, we find
similar results: The coefficient estimate (marginal effect) for Bank Specialty is -0.219 (-0.000) for
infrequent borrowers, and is -0.582 (-0.012) for frequent borrowers, significant at 5% and 1% levels,
respectively.
7.1.3 Sensitivity of soft information to omitted hard information
How sensitive is our soft information proxy to omitted hard information? If the magnitude of the
coefficient estimate and marginal effect for Bank Specialty varies significantly across different sets of
hard information proxies, then it is possible that our soft information proxy is sensitive to the choice of
hard information, thus omitted variables could potentially have a significant effect on our results.
As a robustness check, we randomly exclude two variables from our existing set of hard
information proxies each time and re-estimate our main regressions. We then compare the coefficient and
marginal effect associated with Bank Specialty between our main results and the results based on the subset of hard information proxies.
We find that the magnitude and sign of the coefficient and marginal effect for Bank Specialty
does not vary significantly to the change in the set of hard information proxies. For example, in the
25
analysis where the variables Previous Default and Maturity are excluded, the coefficient estimate
(marginal effect) for Bank Specialty in probit regression model [2] is -0.215 (-0.015) for infrequent
borrowers, and is -0.256 (-0.020) for frequent borrowers; is -0.217 (-0.014) for firms with short banking
relationships, but is -0.250 (-0.017) for firms with long banking relationships. All of the coefficient
estimates are significant at 1% level. In the analysis where hard information variables Asset Turnover and
Sales Growth are excluded, the coefficient for Bank Specialty is -0.211 (-0.014) for infrequent borrowers,
and is -0.257 (-0.019) for frequent borrowers; is -0.255 (-0.015) for non-state-owned firms, and is -0.181
(-0.017) for state-owned firms. In addition, while the coefficients for ROA remain insignificant for firms
with a profound banking relationship, they are now significant at 1% for firms lacking a profound
banking relationship.
Note that the above results are not driven by the potential multicollinearity among hard
information proxies. Multi-collinearity diagnostic test for Table 3 yields a VIF value less than 2.21 for
individual hard information variable (except for State and State×Size)—far below the threshold level of
10—suggesting that our hard information proxies are not highly correlated. In another set of robustness
check, we use alternative specifications for some of the hard information variables (see sub-section 7.2.3
for more details) and find similar results. Together these results suggest that the Bank Specialty variable is
not sensitive to the potential omitted hard information proxies.
7.2 Alternative Variable Specification
7.2.1 Alternative measures for the depth of banking relationship
We repeat the analyses in Table 5 for the following alternative specifications for the depth of
banking relationship proxies: instead of dividing borrowing frequency and duration of banking
relationship based on sample medians, we divide them based on sample terciles. In another alternative
specification, we define a banking-relationship based on a firm’s previous borrowing frequency. Namely,
a firm is classified as a frequent borrower if for its loans originated in 2004 it has borrowed more than 4
times from the bank (the sample median) in 2003, or if for its loans originated in 2005 it has borrowed
26
more than 5 times (the sample median) during the period of 2003-2004. Our results are robust to these
alternative measures.
7.2.2 Alternative definitions of defaults
The bank of our loan sample assigns its annual internal credit rating score to individual firms
instead of to loans. In addition, 72% of our sample firm-year observations are involved with default of all,
rather than some, short-term loans within a year. Therefore, we conduct our main analyses at firm-level
and define default as occurring when at least one of the short-term loans borrowed by the firm in a given
year is marked by the bank as either written off or unpaid.
We now examine how “partial default”—a firm defaults on some, but not all, of its short-term
loans within a year—affects our results. We first re-estimate our results by excluding firms that partially
default on their loans from our sample. Alternatively, we re-define default as when all the short-term
loans borrowed by the firm in a given year are written off or unpaid. Our results remain unchanged for
both cases.
In another robustness check, we separate between loans that are written off and those that are
unpaid. Among 673 defaulted loans within our sample, 16% are marked as written off, and the rest are
recorded as unpaid. We restrict loan default as unpaid loans only, and re-estimate Tables 4 and 5. Our
results hold.
7.2.3 Alternative variable constructions for firm-specific hard information
As a robustness check, we re-estimate our basic models in Tables 3-5 using several alternative
proxies for hard information. Instead of book value of asset, we use the number of employees per firm as
a proxy for size. In our main regressions, we include log(GDP) to control for the degree of regional
economic development. Alternatively, we replace log(GDP) with regional GDP growth to capture macroeconomic uncertainty. Our results are robust to these variations of firm-specific hard information
measures.
27
7.3 Alternative Sample Specification
7.3.1 Loan-level analysis
Since our bank assigns the annual internal credit rating score to individual firms, and among our
sample firms that have defaulted on their loans, 72% of them default all of their loans, our main analysis
is conducted at firm-level. To check the robustness of our results, we repeat our analyses at loan-level for
Tables 4 and 5 (for Table 4, standard errors are clustered at firm-level). Thus default is defined as
occurring if a short-term loan is recorded by the bank as written off or unpaid. We find similar results. For
example, when estimating Table 5 at loan-level, Bank Specialty remains negative in predicting loan
default and is highly significant for all three proxies for the depth of lending relationship. In addition, in
the presence of soft information, ROA is significant at 1% for infrequent borrowers, firms with a short
banking relationship, and non-state-owned firms, while remaining insignificant for frequent borrowers
and firms with a long banking relationship. It becomes marginally significant (10%) for state-owned firms.
The results with respect to Cash are a bit weaker: While the coefficient is significant at 1% for firms
lacking a profound banking relationship, it is only insignificant for frequent borrowers and state-owned
firms.
7.3.2 Matched samples for ownership proxy
One of our proxies for the depth of lending relationship is state ownership. Table 1 indicates that
there is a significant difference in number of firms, loan size, assets, and employees between state-owned
and non-state-owned sub-samples. To check the robustness of our results, we re-estimate the last two
columns of Table 5 using a matched sample. Specifically, for each sample year, we match a state-owned
firm with a non-state-owned firm by industry and size. Match is conducted without replacement. We then
compare the role of the bank’s soft information over the two matched sub-samples.
We find that Bank Specialty continues to be negatively and significantly associated with the
propensity of loan default, and that the magnitude of the marginal effect remains smaller among nonstate-owned firms than among state-owned firms. In addition, our previous results about the depth of
28
lending relationship hold: even for the non-state-owned firms of similar sizes and operating in the same
industries as their matched state-owned firms, nearly half of the firm-specific fundamental factors remain
statistically significant in the presence of Bank Specialty. In contrast, almost all the hard information
proxies become insignificant for state-owned firms.
8. CONCLUSION
In this paper we study the nature and role of banks’ soft information, which evolved from a
sustained lending relationship with firms, in the context of loan default. Using a proprietary database from
one of the largest state-owned commercial banks in China, we first document that proxies for firmspecific hard information, such as financial ratios derived from firms’ financial statements, are
significantly related to the probability of loan default, and that the bank’s internal credit rating scores play
an important role in predicting default.
Further analysis reveals that while the internal credit rating does incorporate firm-specific hard
information, it is the soft information component of these ratings that contributes to the improvement in
assessing credit quality. In addition, we find that to what extent soft information prevails over hard
information depends on the depth of the lending relationship. When evaluating loan delinquency, a strong
repeated lending relationship allows soft information to substitute, rather than complement, the role of
hard information, especially the hard information that is subject to easy manipulation by Chinese firms.
Our findings also indicate that, at least with regard to credit ratings, loan decisions by Chinese banks are
based on commercial principles instead of government policies, which may have contributed to the
overall performance improvement of Chinese banks in recent years.
29
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31
Appendix I. A Description of the Bank’s Internal Credit Rating System
In 2004, our bank implemented an internal credit rating system. Credit rating is done through the
bank’s risk management guidelines, which we describe below. Following these general guidelines, the
officer of the credit and risk management department of the bank uses a rating card to assign a credit
score to a borrowing firm when it applies for the first loan of that year, normally at the beginning of the
year. The rating card contains three general categories: borrower’s financial positions, non-financial
conditions, and financing status. These three categories are further divided into various sub-categories.
To complete the rating card requires information based on the borrower’s financial statements
(including balance sheet, income statement, and cash flow statement), ratio analyses (including short-term
and long-term solvency, and profitability), operating performance and investment, management quality,
internal control system, capital management, corporate strategy, corporate governance, as well as the
officer’s evaluation and forecast on the trend, advantages, risks, and future performance of the borrower.
Some of the categories incorporate the inputs from the loan officer at the local branch where the
borrower submits the loan application. The officer at the credit and risk management department of the
bank then completes the remaining categories with information obtained through his/her independent
research and investigations (including repeated interviews with the borrower), verifies the information
from the local loan officer, and provides his/her own evaluation and forecasts. He/she then rates the
borrower’s quality in each of the three categories.
The general guideline issued by the bank also contains a set of private weights assigned to the
categories of the rating card. These weights are set according to the characteristics of Chinese firms and
past lending experience. The final score is calculated by taking the weighted average.
The bank then rates the borrower based on this credit score. The credit rating ranks from one to
12, with one being the lowest (poorest credit quality) and 12 the highest (highest credit quality). None of
the credit quality information is shared with other banks or sources. The credit rating thus reflects the
bank’s evaluation of the borrower’s overall credit quality. As the above description indicates, the rating
reflects both the objective and subjective estimates from the bank.
32
Appendix II. Loan Characteristics of the Dataset
In this appendix we provide a general description of loan characteristics for the entire dataset,
rather than for the sample used in the regressions. We include all loans in our dataset that are originated
during a given calendar year with a specific maturity date in a different calendar year. With only the endof-year data available, short-term loans made and repaid in the same calendar year are not included in the
sample unless there is a default.
AII.1 Loan Size and Maturity
Table A1 reports the summary statistics for the two main loan characteristics—size and
maturity—over our sample period. We observe that short-term loans constitute the major source of
funding for Chinese firms. In fact, on average, 95% of loans in our sample have a maturity of one year or
less, accounting for 84.45% of the aggregate outstanding principals. By contrast, loans of medium or
long-term maturity, and firms receiving such loans, are much less common. Loans with maturity
exceeding one year account for only 15.54% of total outstanding principal.
Table A1 indicates that, unlike micro loans commonly seen in small business lending practices,
our sample loans are dominated by commercial loans—which are less studied in the literature. For
example, a back-of-the-envelope calculation shows that the average principal of loans with one year
maturity is RMB 47.069 million in 2005 (approximately $5.85 million). For loans with maturity
exceeding one year, the average loan size is even higher: RMB 168.993 million (approximately $21.02
million).
Table A1 also compares the loan characteristics between state-owned and non-state-owned firms.
While the number of non-state-owned firms receiving bank financing far exceeds the number of stateowned firms, and the number of short-term loans (maturity of one year or less) originated to non-stateowned firms is higher, state-owned firms receive on average larger principal amount than non-stateowned firms.
33
Starting 2004, the year when our bank introduced its internal credit rating system for individual
firms, there has been a steady increase in the number of firms without state background securing shortterm, medium-term, and long-term loans, in the number of loans, as well as principal amount initiated for
this type of firm. On the other hand, there is a steady decrease in both number of firms being funded and
number of loans and principal amount initiated for state-owned firms. Despite the fact that state-owned
firms overall borrow at a larger amount than non-state-owned firms, especially for loans of long-term
maturity, the gap between the two diminishes. In 2006, both types borrow an almost equal amount.
AII.2 Crediting Rating and Loan Default Rate
Table A2 reports the summary statistics for the bank’s internal credit rating scores and the
subsequent short-term loan default rates. Since the bank implemented an internal credit rating system in
2004, we restrict the analysis to the 2004-2006 period. Table A2 reveals that the internal credit rating for
firms borrowing short-term loans generally increases over this period. For example, the average rating for
one-year loans increases from 7.9 in 2004 to 8.4 in 2006. Since short-term loans constitute the majority of
loans outstanding, this indicates an overall improvement in loan quality during the sample period,
probably due to a tougher and more skilled screening process by the bank for the borrowers.
When partitioning the sample into state-owned and non-state-owned sub-samples, it is the stateowned firms that attribute to this overall improvement in credit quality. At the time when the bank
implemented the internal rating system, state-owned firms on average had significantly lower rating
scores—therefore poorer credit quality—than non-state-owned firms for loans with a maturity of less than
one year (7.377 for state-owned firms versus 7.921 for non-state-owned firms). Over time, however, loans
initiated to state-owned firms are consistently rated higher than those for non-state-owned firms,
especially over longer maturities. For example, the average internal rating score for loans with medium
maturity (more than one year but less than five years) in 2006 is 1.44 higher for state-owned firms
(10.026 for state-owned firms versus 8.587 for non-state-owned firms). The difference is also statistically
significant.
34
Table A2 reveals that consistent with the improvement in credit rating of short-term loans over
our sample period, there has been a decline in loan defaults for all sample firms. Prior to the
implementation of the internal credit rating system, 14.71% of firms with loans of less than one year
maturity and 16.97% of firms with one year loans originated in 2003 were in default stage by 2004. After
the installment of the internal credit rating system there has been a sharp decline in loan defaults despite
the increase in the number of short-term loans. For example, 13.03% of firms with one year loans
originated in 2004—the year when the internal credit rating system was in place—were in the default
stage by 2005, a 23% drop. Furthermore, only 4.37% of firms with one year loans originated in 2005
were in default in 2006.
Interestingly, the decline is more dramatic for stated-owned firms: 25.7% loans of less than one
year maturities were pass-due in 2004, whereas in 2006 only 3.1% of such loans were pass-due. In
addition, while the default rate on loans originated in 2003 for state-owned firms on average was higher
than for non-state-owned firms, the difference between the two groups of firms is no longer statistically
significant for loans originated in 2005.
35
Table A1. Loan characteristics for Chinese firms
The sample period is 2003-2006. A firm is classified as stated-owned if it is owned or controlled by the government.
Loan maturity
2003
<1 year
1 year
>1 & <=5 years
>5 years
2004
<1 year
1 year
>1 & <=5 years
>5 years
2005
<1 year
1 year
>1 & <=5 years
>5 years
2006
<1 year
1 year
>1 & <=5 years
>5 years
Number of firms
Overall State-owned Non-state-owned
Number of loans
Overall State-owned Non-state-owned
Total principal amount (billion RMB)
Overall State-owned Non-state-owned
1,860
806
263
16
494
333
91
10
1,366
473
172
6
9,793
3,029
768
54
4,665
1,692
367
45
5,128
1,337
401
9
57.32
32.69
15.11
2.1
35.13
25.03
10.57
1.93
22.19
7.66
4.55
0.17
1,512
744
117
9
287
225
56
8
1,225
519
61
1
4,967
2,783
333
64
1,471
1,128
208
62
3,496
1,655
125
2
45.61
30.6
9.84
4.54
26.25
19.52
8.06
4.39
19.36
11.08
1.78
0.15
1,685
619
90
7
213
167
35
5
1,472
452
55
2
5,156
2,149
311
58
1,165
847
164
49
3,991
1,302
147
9
43.37
29.14
13.68
2.04
22.05
18.73
11.1
1.86
21.32
10.41
2.57
0.19
1,968
839
142
6
175
155
38
2
1,793
684
104
4
5,822
2,394
371
21
981
867
142
8
4,841
1,527
229
13
48.7
33.15
10.23
1.4
24.46
21.21
5.54
0.45
24.24
11.94
4.69
0.95
36
Table A2. Internal credit rating and default rate for Chinese firms
The sample period is 2003-2006. Internal credit rating starts in 2004. A crediting rating score ranks from 1 to 12, with 1 being the lowest credit quality and
12 the highest. Short-term loan default rate is based on the fraction of short-term (one year or less) loans that are not paid or are written off at the end of the
subsequent year. Among loans mature in less than one year, only these with a maturity of more than six months and initiated on and after 1st July are
included. A firm is classified as stated-owned if it is owned or controlled by the government. t-statistics testing the difference in mean internal credit rating
between state-owned and non-state-owned firms are based on uneven variance. ***, **, and * indicate significance at 1%, 5% and 10% levels respectively.
Overall
Obs. Mean
(1)
(2)
2003
<1 year
1 year
>1 & <=5 years
>5 years
2004
<1 year
1 year
>1 & <=5 years
>5 years
2005
<1 year
1 year
>1 & <=5 years
>5 years
2006
<1 year
1 year
>1 & <=5 years
>5 years
Internal credit rating
State-owned
Non-state-owned
t-statistics
Obs. Mean
Obs. Mean
(3)
(4)
(5)
(6)
(4) - (6)
Overall
Obs. Mean
(7)
(8)
Short-term loan default rate
State-owned Non-state-owned
Obs. Mean
Obs. Mean
(9)
(10)
(11)
(12)
χ2
(10) - (12)
1,164 14.71%
806 16.97%
311 25.70%
333 21.90%
853
473
10.70%
13.50%
41.219***
9.753***
1,045
659
95
7
7.775
7.900
9.547
10.429
281 7.377
221 7.891
52 9.750
7 10.429
764
438
43
7.921
7.904
9.302
-2.508**
-0.056
1.114
1,059 7.77%
744 13.03%
198 14.60%
225 14.70%
861
519
6.20%
12.30%
16.246***
0.755
1,641
601
73
4
7.932
8.191
9.397
8.250
209 7.981
163 8.276
31 10.323
3
9.000
1,432
438
42
1
7.925
8.160
8.714
6.000
0.268
0.515
3.531***
1,129 1.90%
619 4.37%
160 3.10%
167 2.40%
969
452
1.70%
5.10%
1.634
2.12
1,966
839
142
6
8.153
8.400
8.972
9.667
175 8.366
155 8.755
38 10.026
2 10.500
1,791
684
104
4
8.132
8.319
8.587
9.250
1.208
2.158**
4.591***
1.263
37
APPENDIX III. Variable Definitions
Variables
Default
Rating
Listed Firm
Size
Leverage
ROA
Asset Turnover
Cash
Sales Growth
Previous Default
Maturity
Log(GDP)
State
Definition
A dummy variable that equals one if a firm defaults on
its short-term loans, and equals zero otherwise. Default
occurs if the short-term loan is unpaid or written off at
the end of the following year.
Bank’s internal credit rating score. The score is 12 for a
firm with the highest credit rating, and 11 for the
second highest credit rating, and so on. It is 1 for the
lowest credit rating.
A dummy variable equal to one if a firm is publicly
traded, and zero otherwise.
The natural log of book value of total assets at the end
of year.
Financial leverage, calculated as total liabilities divided
by total assets at the end of year.
Return on assets, calculated as net income divided by
total assets.
Asset turnover ratio, calculated as total sales divided by
total assets.
Cash reserve ratio, calculated as the sum of cash and
short-term investments divided by total assets at the
end of year.
Sales growth is calculated as the difference in the
natural log of sales between current year and previous
year.
A dummy variable equal to one if a firm has defaulted
loans before, and zero otherwise.
Weighted average of short-term loan maturities
borrowed by a firm in year. The weight is based on
loan principales.
The natural log of GDP per capita of the province
where the loan is originated.
A dummy variable equal to one if a firm is owned or
controlled by the state, and zero otherwise.
38
Measured as of Year
This variable is
measured at one year
after the year when the
loan is originated.
This variable is
measured as of the
year when the loan is
originated.
This variable is
measured at one year
before the year when
the loan is originated.
This variable is
measured as of the
year when the loan is
originated.
Table 1. Descriptive statistics for sample firms
Dummy variable Default equals 1 if at least one of the short-term loans borrowed by a firm in a given year is
in default stage during the subsequent year, and 0 otherwise. Internal credit rating ranks from one to 12, with
one being the lowest credit quality and 12 the highest. Assets are of book value and are in RMB 100 million.
Employees are the total number of employees per firm. Other variables are defined in Appendix III. The tstatistics are based on uneven variance. ***, **, and * indicate significance at 1%, 5% and 10% levels
respectively.
2004
Default = 0 Default = 1
Panel A: Overall sample
Rating
8.30
Assets (in RMB 100 million)
12.40
Number of Employees
2,707
Leverage
0.51
ROA
0.07
Asset Turnover
1.00
Cash
0.07
Sales Growth
0.34
Panel B: State-owned firms
Rating
8.23
Assets (in RMB 100 million)
32.91
Number of Employees
7,720
Leverage
0.55
ROA
0.04
Asset Turnover
0.79
Cash
0.09
Sales Growth
0.23
Panel C: Non-state-owned firms
Rating
8.33
Assets (in RMB 100 million)
4.77
Number of Employees
843
Leverage
0.49
ROA
0.09
Asset Turnover
1.08
Cash
0.07
Sales Growth
0.38
t-statistics
2005
Default = 0 Default = 1 t-statistics
5.38
3.71
1,160
0.56
0.03
0.80
0.05
0.16
11.009***
4.927***
3.667***
-3.015***
8.563***
3.885***
5.149***
2.803***
8.40
12.05
2,252
0.48
0.09
1.25
0.07
0.33
4.55
3.00
789
0.55
0.05
0.90
0.04
0.23
7.218***
5.414***
4.274***
-2.147**
2.796***
4.004***
4.183***
1.233
5.11
7.46
2,467
0.62
0.02
0.71
0.06
0.22
6.533***
4.920***
3.939***
-2.621**
3.599***
1.175
2.556**
0.066
8.32
44.33
8,876
0.54
0.05
0.83
0.09
0.25
4.38
2.12
893
0.66
0.01
0.68
0.04
0.15
3.220**
6.870***
6.107***
-1.779
1.955*
1.639
3.531***
0.723
5.53
1.68
454
0.52
0.04
0.86
0.04
0.13
8.744***
2.753***
4.446***
-1.623
7.317***
3.316***
5.235***
3.217***
8.41
4.25
653
0.46
0.10
1.36
0.07
0.35
4.60
3.24
761
0.52
0.06
0.96
0.03
0.25
6.346***
0.762
-0.415
-1.568
2.332**
3.722***
3.263***
1.013
39
Table 2. Correlation analysis
Dummy variable Default equals 1 if a firm defaults on at least one of its short-term loans during the subsequent year, and 0 otherwise. Internal credit
rating ranks from 1 to 12, with 1 being the lowest credit quality and 12 the highest. Size is the log of book value of total assets. Other variables are
defined in Appendix III. ***, **, and * indicate significance at 1%, 5% and 10% levels respectively.
Rating
Size
Leverage
ROA
Asset Turnover
Cash
Sales Growth
State
Log(GDP)
Default
-0.365***
-0.057***
0.114***
-0.178***
-0.114***
-0.112***
-0.069***
0.059***
-0.187***
Rating
Size
Leverage
ROA
Asset Turnover
Cash
Sales Growth
State
0.323***
-0.247***
0.322***
0.090***
0.183***
0.152***
-0.044**
0.052**
0.289***
-0.280***
-0.320***
0.145***
0.019
0.486***
-0.185***
-0.402***
-0.147***
0.084***
-0.057***
0.208***
-0.107***
0.495***
0.070***
0.180***
-0.297***
0.207***
0.107***
0.175***
-0.230***
0.218***
0.063***
0.115***
0.005
-0.078***
0.047**
-0.306***
40
Table 3. Determinants of loan default
This table reports the probit regression results. The dependent variable is the dummy variable Default equal
to 1 if a firm defaults on at least one of its short-term loans during the subsequent year, and 0 otherwise. Size
is the log of book value of total assets. State is a dummy variable equal to 1 if the firm is either owned or
controlled by the state government and 0 otherwise. Size, ROA, Cash, and Sales Growth are measured as one
year prior to the time the loan was originated and described in Appendix III. Industry classification is based
on five manufacturing industries. Robust standard errors are reported in the parentheses. Marginal effects are
reported in the square brackets. ***, **, and * indicate significance at 1%, 5% and 10% levels respectively.
Constant
(1)
3.675***
(1.17)
Rating
Size
Leverage
ROA
Asset Turnover
Cash
Sales Growth
State
State×Size
Previous Default
Maturity
Listed Firm
log(GDP)
Industry Fixed Effect
Year Fixed Effect
McFadden’s R2
Wald χ2
No. of observations
-0.148***
(0.04)
0.682**
(0.31)
-4.049***
(1.23)
-0.157
(0.11)
-2.725***
(0.85)
-0.034
(0.08)
2.607**
(1.10)
-0.136**
(0.06)
0.524**
(0.20)
0.110***
(0.03)
-0.071
(0.27)
-0.330***
(0.09)
Yes
Yes
0.211
174.5***
2,063
41
(2)
0.595***
(0.14)
-0.229***
(0.02)
[-0.020]
Yes
Yes
0.254
268.0***
2,063
(3)
2.720**
(1.26)
-0.227***
(0.02)
[-0.016]
0.034
(0.04)
-0.385
(0.32)
-0.163
(1.05)
-0.047
(0.10)
-1.891**
(0.86)
0.015
(0.07)
1.759
(1.16)
-0.098
(0.06)
0.053
(0.23)
0.106***
(0.04)
-0.051
(0.30)
-0.380***
(0.09)
Yes
Yes
0.293
298.1***
2,063
Table 4. Does internal credit rating incorporate firm-specific hard information?
In Panel A, the dependent variable is the internal credit rating, ranking from one to 12, with one being the
lowest credit quality and 12 the highest. All the firm-specific hard information variables are described in
Appendix III. Industry classification is based on five manufacturing industries. In Panel B, the dependent
variable in the OLS regression takes a value of 1 if there is a change in credit rating from 2004 to 2005. In
the ordered probit regression the dependent variable takes a value of 1 if a firm’s internal credit rating
improves from 2004 to 2005, -1 if it deteriorates, and 0 otherwise. Robust standard errors are reported in the
parentheses. ***, **, and * indicate significance at 1%, 5% and 10% levels respectively.
Panel A
Constant
Size
Leverage
ROA
Asset Turnover
Cash
Sales Growth
State
State × Size
Previous Default
Maturity
Listed Firm
log(GDP)
Industry Fixed Effect
Year Fixed Effect
R2
2
Adjusted R
F
No. of observations
Overall sample
(1)
-4.592***
(1.17)
0.816***
(0.03)
-3.727***
(0.30)
9.491***
(0.85)
0.244***
(0.07)
3.507***
(0.52)
0.249***
(0.08)
-4.073***
(1.20)
0.166***
(0.06)
-1.689***
(0.32)
-0.059***
(0.02)
0.209
(0.20)
-0.100
(0.10)
Yes
Yes
State
(2)
-11.788***
(2.28)
1.040***
(0.06)
-5.099***
(0.69)
21.922***
(2.18)
1.180***
(0.26)
2.372**
(1.11)
0.116
(0.11)
Non-state
(3)
-3.650***
(1.32)
0.797***
(0.03)
-3.338***
(0.33)
7.759***
(0.80)
0.145**
(0.07)
3.517***
(0.58)
0.267***
(0.10)
-1.440***
(0.36)
0.049
(0.06)
0.100
(0.23)
-0.029
(0.19)
Yes
Yes
-1.729***
(0.55)
-0.095***
(0.02)
-0.199
(0.48)
-0.117
(0.11)
Yes
Yes
0.44
0.64
0.38
0.43
87.31***
2,063
0.63
62.22***
489
0.38
56.55***
1,574
42
Table 4 continued.
Panel B
Constant
ΔSize
ΔLeverage
ΔROA
ΔAsset Turnover
ΔCash
ΔSales Growth
State
Previous Default
Maturity
Listed Firm
log(GDP)
Industry Fixed Effect
Year Fixed Effect
R2
OLS
(1)
-0.462
(1.29)
0.452**
(0.21)
-2.358***
(0.70)
7.455***
(1.42)
0.292
(0.24)
0.859
(0.79)
0.096
(0.08)
-0.020
(0.15)
-1.063***
(0.39)
-0.043
(0.03)
0.056
(0.18)
0.069
(0.12)
Yes
Yes
Order Probit
(2)
0.156
(0.19)
-1.760***
(0.59)
6.913***
(1.14)
0.093
(0.21)
0.784
(0.73)
0.076
(0.06)
-0.210*
(0.13)
-0.474**
(0.21)
-0.041
(0.03)
0.112
(0.18)
0.006
(0.10)
Yes
Yes
0.15
Pesudo R2
F
0.07
5.827***
2
Wald χ
No. of observations
670
43
78.12***
670
Table 5. Bank specialty, relationship lending and loan default
The dependent variable of the probit regression is the dummy variable Default equal to 1 if a firm defaults on
at least one of its short-term loans during the subsequent year, and 0 otherwise. Bank Specialty is the residual
from the OLS regression Table 4 Panel A Column 1. A firm is stated-owned if it is owned or controlled by
the state government. A firm is classified as a frequent (infrequent) borrower if it has borrowed from the
bank more than (at most) 12 times during the sample period, where 12 is the sample median. For a given firm
in a given year, duration is computed as the difference between the month that the firm obtained its first loan
in that year and the earliest recorded time of its previous loans prior to that month. A firm is classified as
having a long- (short-) term relationship with the bank if the duration is greater than (less or equal to) 23
months. Firm-specific variables are defined in Appendix III. Industry classification is based on five
manufacturing industries. For each regression model, we report the coefficient estimates and bootstrapped
standard errors (in parentheses). Marginal effects are reported in square brackets. ***, **, and * indicate
significance at 1%, 5% and 10% levels respectively.
44
Table 5 continued.
Duration of banking
relationship
Borrowing frequency
Firm’s ownership
Infrequent
Frequent
Short
Long
Non-stateowned
Stateowned
(1)
4.877***
(1.84)
(2)
4.608**
(2.27)
(3)
5.103***
(1.58)
(4)
3.074
(2.51)
(5)
5.050***
(1.55)
(6)
2.535
(2.98)
-0.214***
(0.04)
[-0.014]
-0.254***
(0.04)
[-0.019]
-0.216***
(0.03)
[-0.013]
-0.261***
(0.05)
[-0.015]
-0.251***
(0.03)
[-0.015]
-0.186***
(0.05)
[-0.017]
-0.196***
(0.06)
0.608
(0.46)
-2.839**
(1.44)
-0.090
(0.13)
-3.515***
(1.28)
0.047
(0.11)
0.294
(0.39)
0.110**
(0.06)
-0.227
(0.31)
-0.393***
(0.13)
Yes
Yes
-0.180***
(0.06)
0.359
(0.52)
-1.140
(1.35)
-0.013
(0.21)
-1.849
(1.51)
-0.284
(0.25)
0.453
(0.33)
0.134*
(0.08)
-0.201
(0.40)
-0.403**
(0.17)
Yes
Yes
-0.164***
(0.04)
0.249
(0.39)
-2.132**
(1.05)
-0.044
(0.12)
-2.717***
(0.89)
-0.085
(0.09)
0.430
(0.36)
0.101*
(0.06)
-0.683***
(0.25)
-0.448***
(0.11)
Yes
Yes
-0.292***
(0.07)
0.949
(0.70)
-2.854
(2.30)
-0.226
(0.25)
-3.142
(2.49)
0.159
(0.22)
0.290
(0.29)
0.175*
(0.09)
0.097
(0.45)
-0.084
(0.22)
Yes
Yes
-0.164***
(0.05)
0.230
(0.36)
-2.452**
(1.09)
-0.113
(0.13)
-3.403**
(1.41)
-0.172
(0.15)
0.529
(0.35)
0.110**
(0.05)
-0.112
(0.36)
-0.442***
(0.11)
Yes
Yes
-0.259***
(0.07)
1.053
(0.80)
-2.831
(3.07)
-0.059
(0.29)
-1.232
(1.45)
0.162
(0.18)
0.215
(0.42)
0.109
(0.10)
-0.080
(0.32)
-0.018
(0.26)
Yes
Yes
McFadden’s R2
0.321
0.279
0.292
0.341
0.315
0.307
2
147.80***
1,065
84.37***
998
181.00***
1,367
73.53***
696
187.60***
1,574
60.98***
489
Constant
Soft information proxy
Bank Specialty
Hard information proxies
Size
Leverage
ROA
Asset Turnover
Cash
Sales Growth
Previous Default
Maturity
Listed Firm
log(GDP)
Industry Fixed Effect
Year Fixed Effect
Wald χ
No. of observations
45
Table 6. The content of soft information and loan default
The dependent variable of the probit regression is the dummy variable Default equal to 1 if a firm defaults at
least one of its short-term loans during the subsequent year, and 0 otherwise. Bank Specialty is the residual
from the OLS regression of Table 4 Panel A Column 1. Firm-specific hard information variables are defined
in Appendix III. Industry classification is based on five manufacturing industries. Column 1 reports the
results from the probit regression. Column 2 reports the results from spline regression, where the spline
cutoff points are based on the terciles of Bank Specialty. For each regression model, we report the coefficient
estimates and bootstrapped standard errors (in parentheses). Marginal effects are reported in square brackets.
***, **, and * indicate significance at 1%, 5% and 10% levels respectively.
46
Table 6 continued.
(1)
4.548***
(1.35)
Constant
Soft information proxy
Bank Specialty
(2)
4.395***
(1.27)
-0.227***
(0.03)
[-0.016]
Bank Specialty Spline 1 (lowest)
-0.291***
(0.04)
[-0.021]
-0.059
(0.13)
[-0.004]
-0.169
(0.14)
[-0.012]
Bank Specialty Spline 2
Bank Specialty Spline 3 (highest)
Hard information proxies
Size
-0.189***
(0.03)
0.554*
(0.33)
-2.435**
(0.97)
-0.106
(0.11)
-2.808***
(0.92)
-0.046
(0.09)
0.420*
(0.23)
0.120***
(0.04)
-0.189
(0.30)
-0.370***
(0.10)
Yes
Yes
0.289
252.5***
2,063
Leverage
ROA
Asset Turnover
Cash
Sales Growth
Previous Default
Maturity
Listed Firm
log(GDP)
Industry Fixed Effect
Year Fixed Effect
McFadden’s R2
Wald χ2
No. of observations
47
-0.185***
(0.03)
0.498
(0.34)
-2.514***
(0.96)
-0.105
(0.11)
-2.795***
(0.90)
-0.046
(0.08)
0.373
(0.24)
0.118***
(0.04)
-0.188
(0.32)
-0.375***
(0.10)
Yes
Yes
0.293
269.00***
2,063
Table 7. Bank specialty and hard information subject to manipulation: The case of earnings
management
This table reports the coefficient estimate and marginal effect for Bank Specialty in the probit regression
model [2]. The sample contains private firms only. In Panel A, earnings management is based on the
magnitude of accruals as in Leuz, Nanda, and Wysocki (2003). In Panel B, earnings management is base on
industry-adjusted non-operating income as in Chen and Yuan (2004). In Panel C, earnings management is
based on discretionary accruals as in Dechow, Sloan and Sweeney (1995). The dependent variable in the
probit regression is the dummy variable Default equal to 1 if at least one of the short-term loans borrowed by
the firm is written off or unpaid at the end of the subsequent year, and 0 otherwise. Bank Specialty is the
residual from the OLS regression of Table 4 Panel A Column 1. Industry and year fixed effects as well as the
same set of firm-specific hard information proxies as those in Table 5 are included in the regression analyses
but are not tabulated. Industry classification is based on five manufacturing industries. Bootstrapped standard
errors are reported in parentheses. Marginal effects are reported in square brackets. ***, **, and * indicate
significance at 1%, 5% and 10% levels respectively.
Low Earnings Management
Coefficient
No. of obs.
Panel A. Magnitude of Accruals
-0.223***
982
(0.04)
Bank Specialty
[-0.012]
High Earnings Management
Coefficient
No. of obs.
-0.220***
(0.04)
[-0.020]
981
Panel B. Industry-adjusted Non-operating Income
-0.229***
951
(0.05)
Bank Specialty
[-0.010]
-0.245***
(0.04)
[-0.020]
950
Panel C. Discretionary Accruals
-0.244***
(0.04)
Bank Specialty
[-0.015]
-0.242***
(0.04)
[-0.020]
983
983
48