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 Department of Finance Indiana University and
Shanghai Advanced Institute of Finance [email protected]
Zheng Ni
Gao Hua Securities Company Limited [email protected]
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†
Department of Finance Indiana University and
Shanghai Advanced Institute of Finance [email protected]
Zheng Ni
Gao Hua Securities Company Limited [email protected]
Working paper, April 2010
* We thank Sumit Agrawal, Utpal Bhattacharya, Martin Brown, Hans Degryse, Joseph Fan, Nandini Gupta, Charles Kahn, Paolo
Emilio Mistrulli, Steven Ongena, Zacharias Sautner, Greg Udell, Marc Umber, and seminar participants at Australian National University, Chinese University of Hong Kong, City University of Hong Kong, Indiana University, National University of Singapore, Renmin University, Tsinghua University, Xiamen University, Shanghai Jiaotong University, Shanghai University of Finance and Economics, University of Minnesota, 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,
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 state-owned 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. The economic effect of soft information is also stronger in predicting default for firms who are more likely to manipulate their earnings. These findings are consistent with the implications of theoretical studies on relationship banking. Key words: Debt default, internal credit ratings, credit risk, relationship lending, soft information JEL Classification: G21, D81, D82, D83, F34
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 (e.g., Peterson 2004). Thus soft information resembles similarly to the concept of “observable but unverifiable information” in the contracting literature (see, e.g., Hart and Holmstrom 1987). 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: due to its relationship with a company, a bank can observe certain information that is very difficult to be verified by an independent third party (e.g., Berger and Udell, 1995, Boot 2000, and 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 firm-specific 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
Furthermore, our analysis reveals that the bank’s internal credit ratings largely subsume firm-specific 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 Liu and Thakor (1994) and 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.
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 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.
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 that is not easily and accurately conveyed, verifiable, or transferable.
More importantly, the economic impact of soft information is greater for firms that borrow more frequently from the bank, have a longer term of banking relationship, or state-owned firms. In addition, 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 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 state-owned 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 that soft information plays a greater role 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.
To sum up, we find evidence that is consistent with theoretical implications in banking literature about soft information. The role of soft information in predicting defaults depends on the depth of lending relationship, and the effect of soft information is more pronounced in the presence of less credible hard information. Our findings are robust to various sample restrictions, alternative proxies for relationship lending and firm-specific hard and soft information, and alternative definitions of default. In particular, we design three sets of tests to take into account omitted hard information variable problems, and find similar results.
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 by German firms more accurately than the use of either financial or non-financial factors alone. We show that soft information evolved through extensive lending relationships not only improves default prediction, but also prevails over the effect of financial factors. Our paper also compliments to Agrawal and Hauswald (2008), who document that the soft information component of a credit rating predicts loan defaults of small firms. While the role of soft information in loan pricing and defaults is well documented for small firms, the importance of relationship lending and the economic significance of soft information for large firms remain to be explored. Our results indicate that a bank’s 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” 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 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, non-performing 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 state-owned 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 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).
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.
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%.
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 (García-Herrero, 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.
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.
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.
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, 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.
6 The five manufacturing industries include: Steel, Automobiles and Transportation Equipment, Paper, Non-ferrous
Metals, and Construction Materials and Manufacturing.
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.
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 explicit loan rollovers based on traditional definitions are extremely rare in China. This is because after the banking system reform, Chinese firms who would like to extend their loans are required to physically repay their loan obligations to their banks so that banks can close the record for their loans before any new loans can be originated, even if the principal and terms of the new loan remain exactly the same, which in fact constitutes a loan rollover. We are unable to identify explicit loan rollovers in our sample. As a result, if some of the loans were rolled over to avoid default, we might under-estimate the default rate. Nevertheless, as will become clear later, underestimating default works against us in finding the results. In Section 7.2, we also conduct a robustness test taking into account possible implicit loan rollovers.8
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. For our main analyses, we begin by extracting a sub-sample of loan data based on the following filtering criteria.
There are 13 ownership categories for the borrowing firms in our sample, including state-owned, owned, state-controlled (in which the state has a controlling stake), collectively-controlled (in which a collectively-owned entity has a controlling stake), foreign-owned and joint ventures, privately-owned, proprietorship, and joint-stock companies. We remove 403 collectively-owned and collectively-controlled firms (and 2,040 loans they borrowed) due to their ambiguous nature.9 We further remove 4,163 firm-year observations (18,716 loans) with missing information on ownership, internal rating scores, financial statement information, and/or sales growth rate. This exclusion includes firms borrowing from the bank in 2003, which do not have an assigned internal credit rating, are excluded.
Note that some loans initiated in 2006 require payment information in 2007 but our loan sample ends in 2006. Therefore, we remove 7,173 loans to restrict our regression analysis to the sample containing loans originated after 2003, with a maturity date no later than 2006.
8 Note also that in China, a loan officer of a state-owned bank bears the so-called “life responsibility” for loans that
he or she originates. That is, the officer is penalized 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 within the bank. If a loan default occurs due to poor judgment, bonus, salary, and/or future promotion of the loan officer are affected. If multiple loan defaults occur that trigger a corruption investigation, the loan officer is also subject to legal penalty. Therefore, our definition of loan default is unlikely to underestimate actual default.
9 The ambiguous nature of collectively-owned firms and their unique ownership arrangements are discussed and
We focus on defaults 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 4,169 short-term and long-term loans.
Our final sample thus contains 8,642 loans from 1,450 firms, and 2,063 firm-year observations.
3.4 Descriptive Statistics
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.
The second column of Table 1 reveals that our sample is dominated by large manufacturing firms. Our sample firms on average have 2,345 employees, with an average annual book value of total assets of RMB 1,149 million and annual sales of RMB 888 million, corresponding to approximately $142.9 million and $110.4 million, respectively. 10 In comparison, according to the 2003 classification guidelines issued by the State-owned Assets Supervision and Administration Commission of the State Council (SASAC) of China, a firm is classified as a “large” industrial firm if its assets exceed RMB 400 million, sales exceed RMB 300 million, and employees exceed 2,000. 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.
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 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.
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. Next, we investigate whether credit rating scores have additional predictive power after controlling for firm-specific factors known to affect default propensity. We then explore the information content of the bank’s internal credit rating by examining whether these ratings take into account a firm’s fundamentals.
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 7.3.1, 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 dummy variable equal to one if the firm is state-owned,11 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, γindustry and λyear.
11 In 2005, 12 firms changed their ownership from state-owned to non-state-owned. Excluding these 12 firms does
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 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 both 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.
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 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.
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.
In this section, we parse the rating score into a “hard” information component and a “soft” information component, and examine 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.
5.1 Proxies for Soft Information and the Depth of Lending Relationship
To capture the unverifiable and untransferable nature of soft information, we follow an approach similar to Liu and Thakor (1994) and 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 7.1, 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 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.12
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: 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. Columns (1), (3), and (5) of Table 5 present the probit regression results for firms that lack of a sustained banking relationship, measured by three proxies, respectively. Columns (2), (4), and (6) of Table 5 presents the probit regression results for firms that have a sustained banking relationship. To mitigate the bias in the standard errors of parameter estimates in the second stage of regression, we bootstrap standard errors and report them in parentheses.13
12 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.
13 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.
We observe from Table 5 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.
Table 5 also reveals that the role of soft information relative to hard information varies depending on the depth of the lending relationship. Compared to infrequent borrowers, firms that have a short banking relationship, and non-state-owned firms, the marginal effect of soft information tends to be larger among frequent borrowers, firms that begin their banking relationship early, and state-owned firms.
In addition, despite that the default rate is similar (for duration and frequency proxies) or higher (for ownership proxy) between firms lack and have a sustained banking relationship, Columns (2), (4), and (6) show that 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 indicate 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. 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.14 This highlights the importance of bank’s soft information in replacing the type of hard information that is subject to easy manipulation.
14 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.
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.15 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.
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 under-development 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 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 non-operating 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-non-operating income from SINOFIN’s Chinese Industrial Enterprises Database.16
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 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.
16 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.
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.17 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 hard-information is less credible.
17 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 owned firms. Using duration of banking relationship instead of state-ownership as a proxy for the depth of lending relationship, the marginal effect is -1.4% for firms that have a short-term banking relationship, compared to -2.6% for firms that have a long-short-term banking relationship.
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.
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.
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 vary 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 sub-set 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 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 Implicit Loan Rollovers
As discussed in Section 3.2, explicit loan rollovers based on the traditional banking literature are extremely rare in China. Chinese firms who would like to extend their loans are required to physically repay their loan obligations so that banks can close their record for the old loans before any new loans can be originated, even if the principal and terms of the new loan remain exactly the same, which in fact constitutes a loan rollover.
Note that implicit loan rollover in China is not always driven by default concerns. As indicated in Appendix II and Tables A1 and A2, long-term loans are rare in China; many firms frequently roll over their short-term loans to serve their long-term financing needs. However, if some of these loans were rolled over specifically to avoid default, then the true default rate is under-estimated. Nevertheless, underestimating default works against us in finding the results.
We now examine the effect of implicit loan rollovers on our findings. Since we are unable to identify actual loan rollovers, we define that an “implicit” rollover occurs when a subsequent loan with the same principal is originated immediately after a loan repaid at its due date. Out of 1,450 firms, we identify 226 firms (corresponding to 288 firm-year observations and 649 loans) that have had implicit loan rollovers during the sample period.
The average internal credit rating score is 9.19 for firms that had implicit loan rollovers (288 firm-year observations), significantly higher (p = 0.000) than for firms that did not during the sample period (1,775 firm-year observations). Among the 136 firms that borrowed short-term loans in 2004 and implicitly rolled them over in 2005, 4.4% (6 firms) actually defaulted their loans in 2006. These results suggest that defaults are unlikely the main reason for these implicit loan rollovers.
Next, we repeat our main analyses based on a sample excluding the 288 firm-year observations that are associated with implicit rollovers. We find similar results. For example, for Table 5, Bank Specialty continues to be negatively and highly significantly related to the incidence of loan default for all three proxies for relationship lending. Its marginal effect is stronger among firms that have a sustained lending relationship: -0.014 versus -0.020 for infrequent and frequent borrowers, 0.014 versus 0.016 for borrowers of short and longterm banking relationship, and -0.014 versus -0.022 for non-state-owned and state-owned firms. While the coefficients for ROA are significant for firms lack of sound banking relationship (at 10% levels for duration and frequency proxies, and 5% level for ownership proxy), they become insignificant in predicting default in the presence of soft information for firms that have a sustained banking relationship. We find similar results for Cash, another proxy that are often manipulated by Chinese firms.
