Credit access of unlisted companies

Listed companies, on average, are much larger and have better financial performance than unlisted companies. Therefore, only investigating listed companies may bias our conclusion on how the bank ownership reform affected SOBs’ credit allocation to corporate borrowers. We now use data on a universe of unlisted manufacturing firms to complement

6The quantitative effect is -0.028 [=-0.154×(0.70−0.23)×(0.65−0.26)].

7The quantitative effect is -0.016 [=−0.09×(0.70−0.23)×(0.65−0.26)].

8The quantitative effect is -0.045 [=-0.244×(0.70−0.23)×(0.65−0.26)].

Table 3.5: Bank ownership change and corporate debt

(1) (2) (3) (4)

Dependent Variable ST loans_i,t/assets_i,t−1 LT loans_i,t/assets_i,t−1 loans_i,t/assets_i,t−1 other liability_i,t/assets_i,t−1

Big Three exposurec,t −0.154^∗∗∗ −0.091^∗∗ −0.244^∗∗∗ 0.071

×Debt ratio_i,t−1 (0.056) (0.036) (0.073) (0.074)

Controls Y Y Y Y

Year fixed effects Y Y Y Y

Firm fixed effects Y Y Y Y

Observations 7,733 7,733 7,733 7,733

R-squared 0.09 0.02 0.07 0.732

Notes: This table explores the effect of the ownership reform in three of China’s largest SOBs (Bank of China, China Construction Bank, and Industrial and Commercial Bank of China) on listed com-panies’ term and long-term outstanding loans. The dependent variables are the ratio of short-term outstanding loans to initial assets (ST loansi,t/assetsi,t−1) in column (1), the ratio of long-term outstanding loans to initial assets (LT loansi,t/assetsi,t−1) in column (2), the ratio of total outstand-ing loans to initial assets (loansi,t/assetsi,t−1) in column (3), and the ratio of other liabilities on the balance sheet to initial assets (other liability_i,t/assetsi,t−1) in column (4). The time-variant city-level exposure (Big Three Exposure_c,t) is measured by the sum of each SOB’s loan share in 1999 inter-acted with the indicator for the postreform period. All columns include the control variables from table 3.2, column (2), and all other terms (Debt ratioi,t−1, Debt ratioi,t−1 ×T{year > 2003} and Debt ratioi,t−1×Big Three Share_c,1999) are as in equation 3.5. Robust standard errors (clustered at the city level) are in parentheses. We use ***, **, and * to denote statistical significance at the 1%, 5%, and 10% levels, respectively.

our study, although the ASIF only provides a rough proxy for corporate credit access for these firms.

Table 3.6 reports the results using the same specification as before, based on the assumption that the bank ownership reform did not change corporate credit demand.

Column (1) shows that the coefficient ofBig Three Exposure_c,tis negative and statistically insignificant, similar to in column (1) in table3.2, meaning that the bank ownership reform did not change the mean difference in the share of firms receiving loans between cities with greater exposure and cities with less exposure. In column (2), we control for firm’s future credit demand using a set of one-year lagged firm characteristics including firm ROA, (log) assets, debt ratio, inverse liquid ratio, (log) employment, age, a 0-1 dummy of whether the firm is an SOE, a 0-1 dummy of whether the firm receives subsidies, and (log) total factor productivity (TFP). In addition, we include industry-year fixed effects to eliminate any industry-specific shocks that could influence credit demand. The results show that the average effect on the firms’ credit access is still statistically nonsignificant.

Hence, these results confirm that the ownership change of the three SOBs did not influence their total bank lending.

In column (3), we again explore which type of corporate borrowers had better credit access after the bank ownership reform, as in column (7) in table 3.2. The results are similar in that more-indebted firms still experienced a larger decline in credit access in cities with greater exposure than in cities with smaller exposure. The difference in the share of firms receiving loans between firms at the 90^th and firms at the 10^th percentile of the debt ratio increases by approximately 1.8 percentage points in cities at the 90^th percentile relative to cities at the 10^th percentile of exposure to the three SOBs after

Table 3.6: Bank ownership change and unlisted companies’ credit access

(1) (2) (3) (4) (5) (6) (7)

Dependent Variable loan_i,t

Big Three Exposure_c,t −0.012 −0.015 0.026 −0.005 −0.073^∗∗ −0.021 −0.022 (0.033) (0.031) (0.061) (0.030) (0.029) (0.030) (0.030)

Big Three Exposurec,t×ROA_i,t−1 0.083

(0.084)

Big Three Exposure_c,t×Debt ratio_i,t−1 −0.067^∗∗∗ −0.068^∗∗∗ −0.068^∗∗∗

(0.021) (0.020) (0.019)

Big Three Exposurec,t×lnAssets_i,t−1 −0.002 (0.005)

Big Three Exposurec,t×SOE_i,t−1 −0.039^∗∗∗ −0.027^∗∗ −0.026^∗∗

(0.013) (0.013) (0.013)

Big Three Exposurec,t×lnTFP_i,t−1 0.058^∗∗∗ 0.052^∗∗∗ 0.053^∗∗∗

(0.016) (0.015) (0.017)

Controls Y Y Y Y Y Y

Year fixed effects Y Y Y Y Y

Firm fixed effects Y Y Y Y Y Y Y

Industry×Year fixed effects Y Y

Observations 965,650 965,650 965,650 965,650 965,650 965,650 965,650

R-squared 0.00 0.01 0.01 0.01 0.01 0.01 0.01

Notes: This table explores the effect of the ownership reform of three of China’s largest SOBs (Bank of China, China Construction Bank, and Industrial and Commercial Bank of China) on unlisted companies’

credit access. The dependent variable is a 0-1 dummy of whether a firm has bank loans (loani,t). The time-variant city-level exposure (Big Three Exposure_c,t) is measured by the sum of each SOB’s loan share in 1999 interacted with the indicator for the postreform period. Big Three Share_c,1999 is the sum of the three SOBs’ loan shares in 1999. The firm type categories include firm profitability, measured by the return on assets (ROAi,t−1); firm debt level, measured by the ratio of total liabilities to total assets (Debt ratioi,t−1); firm size, measured by the (log) book value of assets (lnAssetsi,t−1); firm ownership, measured by a 0-1 dummy for whether firm is a SOE (SOEi,t−1); and firm productivity, measured by (log) total factor productivity (lnTFPi,t−1). The controls include firm ROA, (log) assets, debt ratio, liquid ratio, (log) employment, age, dummy for whether the firm is a SOE, dummy for whether the firm receives subsidies, and (log) TFP at lag1. All other terms (Firm type_i,t−1,Firm type_i,t−1×Big Three Share_c,1999, and Firm type_i,t−1×T{year > 2003}) are as in equation 3.5. Robust standard errors (clustered at the city level) are in parentheses. We use ***, **, and * to denote statistical significance at the 1%, 5%, and 10% levels, respectively.

the bank ownership reform ⁹; however, this effect represents only 3% of the average differential debt effect in this sample. This quantitative effect is much smaller than that in the previous results for listed companies. The difference in the measures of corporate credit access between the two samples implies that the bank ownership reform reduced the volume of bank lending to more-indebted firms rather than completely cutting off the bank-firm relationship.

Further, we study whether the bank ownership reform contributed to a positive link between firm productivity and credit access, which matters for resource reallocation effi-ciency and long-run economic growth. We first explore a firm’s state ownership, as SOEs are less productive than private firms (Hsieh and Song, 2015; Huang et al., 2017). Col-umn (4) shows that SOEs indeed experienced a decline in credit access due to the bank reform, as the coefficient of Big Three Exposure_c,t×SOE_i,t−1 is negative and statistically significant. Hence, it suggests that the bank ownership reform weakened the link be-tween SOBs and SOEs, preventing the latter from enjoying easy credit from the national banking system as they had before. We further use total factor productivity (TFP) as a precise measure to differentiate productive firms. Column (5) shows that the bank ownership reform increased bank lending to more productive firms, as the coefficient of Big Three Exposure_c,t×lnTFP_i,t−1 is positive and statistically significant at the 1% level.

The difference in the share of firms receiving loans between firms at the 90^th and firms at the 10^th percentiles of (log) TFP increases by 2.1 percentage points in cities at the 90^th percentile relative to cities at the 10^th percentile of exposure to the three SOBs after the bank ownership reform¹⁰, which again represents a small economic magnitude, indicating that loan funding is a better measure than the loan dummy for credit access.

In case the firm debt ratio, state ownership, and TFP are correlated and only one characteristic dominates the results, column (6) pools these three firm features together in the same regression. It shows that the main results remain, though the SOE effect becomes weaker and less significant. Since TFP is not entirely comparable across differ-ent industries, column (7) adds industry-year fixed effects so that we only compare firms within the same two-digit manufacturing industry, and the results do not change. The results are striking, as loan officers can only read a firm’s financial reports but cannot estimate firm productivity themselves. Moreover, even if loan officers know firm produc-tivity, they do not have any incentive to lend money to more-productive firms. Therefore, it suggests that banks might also use other information, such as business reputation and investment project NPV, in the bank lending process and that some of these figures are positively correlated with firm productivity but negatively correlated with credit risk.