Default risk conditional on entrusted lending

To verify prediction 6 that shadow creditors face a higher default risk, we need to define our proxies of default risk. As is common in the literature, we select Altman’s Z-score, free cash flow and interest coverage ratio as our proxies of success probability (default risk).

Altman’s Z-score is the formula for predicting the probability of firm bankruptcy within two years. It uses a linear combination of 5 ratios: working capital to total assets, retained earnings to total assets, earnings before interest and taxes to total assets, market value of equity to book value of total liabilities, and sales revenue to total assets. The

Table 2.3: Interest rates conditional on entrusted lending Interest Rate

(1) (2) (3)

Policy 0.151^∗∗∗ 0.187^∗∗∗ 0.176^∗∗∗

(0.031) (0.030) (0.029)

Asset 0.233^∗∗∗ 0.245^∗∗∗ 0.241^∗∗∗

(0.018) (0.018) (0.018)

RPT Loan_t−1 −0.002

(0.003) Policy× RPT Loan_t−1 0.008^∗∗∗

(0.003)

RPT Loan in Financet−1 −0.001

(0.004) Policy× RPT in Finance_t−1 0.001

(0.005)

RPT Loan in Real-estate_t−1 −0.009

(0.008) Policy× RPT in Real-estatet−1 0.019^∗∗

(0.008)

Firm FE YES YES YES

Observations 34,488 34,488 34,488

Adjusted R² 0.342 0.342 0.342

Note: ^∗p<0.1; ^∗∗p<0.05; ^∗∗∗p<0.01

five ratios separately measure asset liquidity, profitability, operating efficiency, market fluctuation, and total asset turnover. Free cash flow (FCF) is calculated as the difference between cash flows from operations and investment cash flow in operating capital, which represents the availability of cash for distribution among all securities holders of a corpo-rate entity. The interest coverage ratio is calculated as EBIT divided by the total interest payable to measure a company’s ability to honor its debts. When the interest coverage ratio is smaller than one, a company is not generating enough cash from its operations EBIT to meet its interest obligations.

To construct a similar DID regression model, in model 2.34, we consider two RPT proxies, RPT loans and RPTs in finance, interacted with the policy dummy. Conditional on total assets and firm fixed effects, the three proxies of default risk are evaluated in terms of their relationship with RPTs.

Def aultRisk_ijt=α+β₁P olicy+β₂RP T_jt+β₃P olicy×RP T_jt+γAsset+η_j+ε_ijt (2.34) The results in table 2.4 confirm prediction6across the measures of RPTs and default risk. Before 2008, only the interest coverage ratio was negatively affected by RPT loans or RPTs in finance, but there was no significant effect on the Z-score and free cash flow. After 2008, when banking-sector competitiveness increased, RPT loans and RPTs in finance displayed strong and significant negative impacts on firms’ default risks across the board. Since a lower value implies a greater default risk on all three risk measures, the parameters of the interaction terms can be interpreted as more entrusted lending by listed firms nonnegligibly increasing firms’ default risk.

Table 2.4: Default risk conditional on entrusted lending

Default Risk

Z-Score Free Cash Flow Interest Coverage Ratio

(1) (2) (3) (4) (5) (6)

Policy 3.759^∗∗∗ 3.459^∗∗∗ 0.091^∗ 0.078 0.271^∗∗∗ 0.247^∗∗∗

(0.600) (0.588) (0.047) (0.052) (0.032) (0.030)

RPT Loan_t−1 0.010 0.001 −0.016^∗∗∗

(0.022) (0.003) (0.003)

Policy×RPT Loan_t−1 −0.119^∗∗∗ −0.007^∗∗ −0.012^∗∗∗

(0.027) (0.004) (0.004)

RPT in Finance_t−1 0.027 0.008^∗ −0.003

(0.029) (0.005) (0.006)

Policy×RPT in Finance_t−1 −0.111^∗∗∗ −0.013^∗∗∗ −0.013^∗

(0.032) (0.004) (0.007)

Asset −1.585^∗∗∗ −1.681^∗∗∗ 0.939^∗∗∗ 0.932^∗∗∗ −0.268^∗∗∗ −0.303^∗∗∗

(0.164) (0.162) (0.025) (0.025) (0.012) (0.012)

Industry FE YES YES YES YES YES YES

Observations 18,749 18,749 15,518 15,518 16,624 16,624

Adjusted R² 0.094 0.092 0.581 0.581 0.056 0.049

Note: ^∗p<0.1;^∗∗p<0.05;^∗∗∗p<0.01

2.8 Conclusion

We build a model to characterize the endogenous generation of an important type of shadow banking in China: entrusted lending between firms. The model shows that shadow lending is a market reaction to increased competition in the banking sector. If the banking sector is not competitive, shadow lending does not exist.

The model shows that shadow creditors are firms that have large capitalizations or that can provide large collateral. Shadow debtors are small and medium-sized firms (SMEs).

The model features moral hazard on the part of entrepreneurs. Banks not only offer credit but also provide monitoring services to improve the probability of success of in-vestment projects. The model shows that credit rationing exists. Small firms with low capitalizations are not able to obtain bank loans. The effort an entrepreneur exerts de-pends on the return she can derive from the project. Small firms need large bank loans, so they have to pay high returns to banks, and little remains to the entrepreneurs that lead the firms. This low return to entrepreneurs leads to less effort exerted on the project, which further reduces the probability of success. Therefore, banks are not willing to lend to small firms.

Credit rationing is not reduced by shadow lending since shadow banks have no com-parative advantage over traditional banks in providing monitoring services. However, one exception to this rule is entrusted lending to affiliates. Because affiliated loans are usually made by a parent firm to a subsidiary or between customers and suppliers, the asymmetric information between the firms is very small, and the monitoring cost for the lending firm may be smaller than that faced by the bank. Under this circumstance, shadow lending reduces credit rationing by providing credit to affiliated small firms.

Another important issue is whether the existence of shadow lending affects the moni-toring service provided by banks, as well as the bank lending rate. The model shows that, in the presence of shadow banking, total effort, i.e., the probability of success of firms engaged in shadow lending, is reduced. Therefore, the existence of shadow lending could reduce real efficiency and impair financial stability.

However, shadow lending improves the profits of the involved firms. Shadow creditors earn more profit by lending their extra capital at a higher shadow rate than the bank rate. Shadow debtors save by substituting some of their bank loans with cheaper shadow loans.

Overall, we show that one type of shadow banking, i.e., entrust lending between firms, arises due to bank competition. Large firms that have access to cheap bank credit tend to overborrow and then lend the extra capital to SMEs. The existence of shadow banking re-duces the total effort of entrepreneurs, resulting in a higher default risk. However, shadow banking can stimulate economic growth by providing cheaper financing to corporations.

Did China’s Bank Ownership

Reform Improve Credit Allocation?

This chapter studies the impact of the 2004 reform of the ownership of three of China’s largest state-owned banks (SOBs) on credit allocation. We use firm-level data from 2000 to 2007 to show that listed companies experienced a more substantial decline in credit access in cities more exposed to these SOBs after the bank reform. This indicates that the change in bank ownership pushed loan officers to consider more credit risks in the lending process.

We further extend our study to a universe of unlisted manufacturing companies and find that the bank ownership reform also reduced bank lending to state-owned enterprises (SOEs) and less productive firms in addition to more-indebted firms. The negative effect of the reduced SOB lending on firm performance in terms of profitability and investment is only prominent for SOEs and less productive firms.

Keywords: Bank Ownership Reform, Credit Access, Firm Performance

3.1 Introduction

Large state-owned banks dominate the banking sector in many developing countries (Clarke et al., 2005). However, state-owned banks (SOBs) are less efficient, as they play a critical role in supporting the state’s political goals—namely, funding specific firms and industries favored by the government. Therefore, many developing countries have partially or fully privatized state-owned banks to improve bank performance during the past decades (Megginson, 2005). While most bank-level studies have found that privati-zation can raise bank efficiency, one remaining question to be answered is whether bank privatization can also improve the efficiency of the whole economy by improving credit allocation. Does a change in bank ownership reduce lending to firms with high credit risk? Does the resulting bank lending harm the growth of the affected firms?

Motivated by these questions, this paper uses the ownership structure reform of three of China’s largest SOBs to study the impact of bank ownership changes on corporate credit access and firm performance. Four giant SOBs dominate the Chinese banking system: the Bank of China (BOC), the China Construction Bank (CCB), the Industrial and Commercial Bank of China (ICBC), and the Agricultural Bank of China (ABC). In 2000, these SOBs held more than 60% of total loans, but the ratio of nonperforming loans (NPLs) in these banks in the same year ranged between 20% and 40%. To turn these SOBs into efficient commercial banks, the Chinese central government initiated an ownership

structure reform in 2003 with the goal of achieving public listing and introducing foreign strategic investors. By 2004, the BOC and CCB had established a joint-equity ownership structure, which the ICBC did so one year later and the ABC finished the reform late, in 2008, due to a delay in NPL disposal. Since the implementation of the reform in the ABC occurred after our sample period (2000-2007) and this period overlaps with that of the disbursement of the 4 trillion RMB stimulus package (2007-2008), we exclude this bank from our primary analysis.

Although the bank ownership reform was implemented nationwide, firms in cities with different ex ante exposure to the reform should display unequal effects. Naturally, firms in cities with a larger share of loans from the three SOBs should depend more on bank lending, and we thus use the SOBs’ loan share in each city in 1999 as the baseline local exposure. Specifically, the difference in the loan share between the 90^th percentile and the 10^th percentile is 39%, providing considerable variation in city-level exposure, which is the key to our identification strategy. Analyzing the panel of Chinese listed companies throughout 2000 to 2007 and the local exposure and timing of the reform while including firm and year fixed effects allows us to use a difference-in-difference (DiD) method to estimate the average effect of the bank ownership reform on corporate credit access, measured in terms of the ratio of loan funding to initial assets. The effect on average credit access is negative but economically small and statistically nonsignificant, suggesting that the bank ownership reform did not change SOEs’ total credit supply to corporate borrowers.

While total bank lending was unaffected, we find that the bank ownership change reduced bank lending to more-indebted listed companies. Corporate borrowers’ default risk might be reflected in various features of the firm, such as low profitability, high leverage, or small asset size. We thus extend the basic DiD framework into a DDD setting by further interacting the measures of firm characteristics to explore which types of firms are favored by SOBs after the bank ownership reform. It turns out that only firms with high debt, measured as the ratio of total liabilities to total assets (debt ratio), relative to that of other firms experience a significant decline in corporate credit access.

Specifically, the difference in the ratio of loan funding to initial assets between firms at the 90^thpercentile and firms at the10^thpercentile of the debt ratio increases by2.9percentage points in cities at the 90^th percentile relative to cities at the10^th percentile of exposure to the three SOBs after the ownership reform; this increase represents10% of the average of this difference in the full sample. Including industry-year fixed effects changes the main results only slightly, indicating that the bank ownership reform influenced intra- rather than interindustry credit reallocation. In addition, outstanding loans on a firm’s balance sheet have a greater impact than other liability components (e.g., accounts payable) on reductions in external credit access. These results imply that the bank ownership reform caused the SOBs’ loan officers to place much more importance on credit risk in the lending process.

The dynamic pattern confirms that the observed relative decline in bank lending to risky firms is directly linked to the change in the banks’ ownership structure but not to other policy shocks. We first show that the mean difference in the negative effect of a high debt ratio on corporate credit access between cities with high exposure and low exposure is not prominent before the bank ownership reform. Rather, it suddenly becomes significant afterward. The magnitude of the negative credit effect on highly indebted firms increases over time: the magnitude of the effect in 2007 is 51% larger than that in 2004, where 2003 is the base year, implying that the bank ownership reform

changed SOBs’ lending behaviors gradually. Moreover, we show that the effect of the BOC and CCB reforms started in 2004, while the effect of ICBC reform started in 2005, which is consistent with the timing of the establishment of the joint-equity ownership structure.

The separate dynamic patterns thus demonstrate that the main result is attributable neither to the disposal of nonperforming loans, which occurred before the reform, nor to the introduction of foreign investors and public listing, which occurred after the reform.

Additionally, we include the ABC as a placebo test by assuming it also implemented the reform in 2004. In contrast, its dynamic pattern does not reveal any significant credit effect related to local exposure to its loans.

We further find that the bank ownership reform reduced long-term corporate credit access more than short-term credit access. Specifically, the difference in the ratio of outstanding long-term loans to initial assets between firms at the 90^th percentile and firms at the10^thpercentile of the debt ratio is1.6percentage points larger in cities at the 90^th percentile relative to cities at the10^th percentile of exposure to the three SOBs after the bank ownership reform; this increase represents 24% of the average of this difference in the full sample. The corresponding figures for the effect on the ratio of outstanding short-term loans to initial assets are 2.8 percentage points and 13%, respectively. This result is consistent with the explanation that the higher debt ratio reflects corporate borrowers’

credit risk and concerns about their long-run solvency.

We confirm that the bank ownership reform reduced SOBs’ lending to highly indebted firms by extending our study to a universe of unlisted manufacturing firms. While the listed company data only contain approximately 7,700 firm-year observations, the sample of unlisted firms has almost 1 million observations, in which the firms are on average smaller and have worse financial performance; these firms can thus potentially offer a dif-ferent picture of the effect of the bank ownership reform. However, the unlisted company data provide only a rough measure of corporate credit access, namely, a 0-1 dummy for whether firms have loans. We find that more-indebted firms become less likely to receive loans in cities with higher exposure than in cities with lower exposure to the three SOBs after the bank ownership reform, although the magnitude of the effect is small compared to the results for listed companies. This difference implies that the bank ownership reform had the effect of reducing the lending amount that more highly indebted firms can acquire rather than cutting off bank lending to these firms completely.

We also find that the bank ownership reform reduced bank lending to state-owned enterprises (SOEs) and less productive firms. We use data on unlisted manufacturing firms only to explore whether the bank ownership reform curtailed SOB credit allocated to less productive firms, as both SOEs and firms with lower total factor productivity (TFP) can be regarded as less productive firms. We do not use data on listed companies in this exercise, as most listed companies have government capital or political connections and do not provide information on value-added output for the estimation of TFP. The results show that less productive firms do indeed become less likely to receive loans in cities with higher exposure than in cities with lower exposure to the three SOBs after the bank ownership reform. This finding that more productive rather than more profitable firms can acquire credit more effectively is surprising, especially considering that loan officers cannot estimate a firm’s productivity precisely. It suggests that loan officers can review other firm information, such as business reputation and investment project NPV, which reflect both low default risks and high productivity. Overall, these results imply that the bank ownership reform weakened the link between the SOBs and SOEs.

The impact on firm performance due to the SOBs’ reduced lending is unambiguous.

While both more-indebted listed and unlisted manufacturing firms show little decline in the investment rate, they appear to experience an increase in profitability. In contrast, both SOEs and less productive firms experience a noticeable decline in their profitability and investment rate. For example, the bank ownership reform caused the difference in the investment rate between firms at the 90^th and firms at the 10^th percentile of (log) TFP to increase by 2percentage points in cities at the 90^thpercentile relative to cities at the 10^th percentile of exposure to the three SOBs after bank ownership reform, which represents a sizeable economic magnitude (47% of the average difference in these firms’ investment rates in the full sample). One possible explanation is that the reduced bank lending could be regarded as a competitive shock that pushed more-indebted firms to improve their business performance, whereas SOEs and less productive firms were not capable of experiencing this transformation due to their low level of management efficiency.

The bank ownership reform affected bank lending more through the rationing of credit than through the charging of diversified interest rates. We find that the average firm-level loan rate, defined as the ratio of interest payments to total outstanding loans, increased only modestly for more-indebted listed companies in cities more exposed to the three SOBs after the bank ownership reform. We implement this analysis only for listed companies that report the amount of loan debt to calculate firm-level loan rates. The results thus suggest that even if SOBs charged higher interest rates to riskier borrowers, the price increase was limited, which is reasonable in the context of the interest-rate control regime in the China. Since commercial banks can only charge an interest rate within a small range around the baseline rate set by the central bank, it is rational for banks to take more deposits and issue loans to safer borrowers to earn the interest margin (loan rate - deposit rate). Therefore, the results also imply that over the long term, the bank ownership reform might cause corporate underinvestment for firms with risky investment projects.

Changes in credit demand are unlikely to explain our findings. First, we include various lagged firm features and firm fixed effects in the regressions to capture firm credit demand. Second, we include industry-year fixed effects to eliminate any industry-specific (demand) shocks due to unknown policy changes. Third, we include the ratio of total nonloan liabilities to initial assets to conduct a placebo test, and the results show that the bank ownership reform did not influence other liability components (i.e., accounts payable). Fourth, we find that other external financing channels, such as investments and bonds, were unaffected. If the bank ownership reform reduced credit demand among more-indebted firms, then it is unlikely that other liability components and other external financing channels would show a nonsignificant decline. Fifth, although the change in the

Changes in credit demand are unlikely to explain our findings. First, we include various lagged firm features and firm fixed effects in the regressions to capture firm credit demand. Second, we include industry-year fixed effects to eliminate any industry-specific (demand) shocks due to unknown policy changes. Third, we include the ratio of total nonloan liabilities to initial assets to conduct a placebo test, and the results show that the bank ownership reform did not influence other liability components (i.e., accounts payable). Fourth, we find that other external financing channels, such as investments and bonds, were unaffected. If the bank ownership reform reduced credit demand among more-indebted firms, then it is unlikely that other liability components and other external financing channels would show a nonsignificant decline. Fifth, although the change in the