Baseline specification

The bank ownership reform may have made SOBs more stringent in their overall lending, implying a decline in credit supply that reduced all firms’ credit access. To test this hypothesis, we use the following specification:

loan funding_i,t/assets_i,t−1 =βExposure_c,t+γX_i,t−1+µ_i+δ_t+ϵ_i,t (3.4)

whereidenotes the firm. On the left-hand side, the dependent variable (loan funding_i,t/assets_i,t−1) is the ratio of loan funding in yeartto the initial assets (end of period assets in year t−1),

which measures corporate credit access. Scaling loan funding by initial assets is reason-able, as firms with larger initial assets should have greater credit demand. On the right-hand side, the sole explanatory variable that we are interested in is Exposure_c,t (given by equation3.3), asβ tells us whether firms in cities with greater exposure to the three SOBs experience a larger increase or decrease in credit access than those in cities with lower exposure after the bank ownership reform. X_i,t−1 is a set of firm-level control variables including firm ROA (return on assets), EPS (earnings per share), debt ratio (liabilities to assets), inverse liquid ratio (liquid liabilities to liquid assets), (log) book value of assets, and firm age in year t−1. µ_i denotes firm fixed effects, which capture all time-invariant

differences across firms; δ_t denotes year fixed effects, which capture specific time shocks affecting all firms; and ϵ_i,t represents standard errors clustered at the city level.

A more interesting question that we want to address is how the bank ownership reform changed SOBs’ lending behavior—that is, whether certain types of firms faced easier or more constrained credit access after bank reform. Since these three SOBs had a large number of NPLs before the bank reform, it is natural to conjecture that the ownership change may have caused loan officers to pursue safer borrowers to avoid further NPL accumulation. The Chinese interest rate control regime could strengthen this dynamic due to the ceiling on bank lending rates: banks cannot charge arbitrarily high interest rates on risky firms (Porter et al., 2009).

We consider several popular indicators that might be used by loan officers in the internal rating process. These include measures of firm profitability, debt level, and size, as higher profitability, lower debt, and larger size can all reflect lower credit risk. By incorporating these firm features into our DiD setting, we can explore whether firms with specific characteristics benefited from the bank ownership reform. The main specification is given below:

loan funding_i,t/assets_i,t−1 = β₁Exposure_c,t×Firm type_i,t−1+β₂Exposure_c,t (3.5) +β₃Firm type_i,t−1+β₄Big Three Share_c,1999×Firm type_i,t−1 +β5Firm type_i,t−1×T{year >2003}+γXi,t−1+µi+δt+ϵi,t

On the right-hand side, the main explanatory variable we are concerned with is the interaction of (time-variant) local exposure to the three SOBs (given by equation 3.3) with the one-year lagged measures of firm characteristics (Exposurec,t×Firm typei,t−1) mentioned above. Since both Exposure_c,t and Firm type_i,t−1 are not traditional binary dummies that define the treatment and control groups, the interpretation of the results becomes more complicated.

Here, we take firm profitability as an example to explain the different items in the regression. In this setting, Exposure_c,t captures the variation in the mean difference of the credit access changes between less profitable firms in cities with greater exposure and their counterparts in cities with less exposure in the pre- and postreform peri-ods. Firm type_i,t−1 captures the mean difference in credit access between more prof-itable firms and less profprof-itable firms in cities with lower exposure before the bank reform.

Big Three Share_c,1999×Firm type_i,t−1 then captures whether the mean difference in credit access between more profitable firms and less profitable firms is different between cities with greater exposure and cities with smaller exposure before the bank reform. Finally, Firm type_i,t−1 ×T{year > 2003} captures the mean difference in credit access of more profitable firms in cities with lower exposure before and after the bank reform.

Essentially, equation 3.5 provides a DDD (difference-in-difference-in-difference) iden-tification strategy, which involves three mean differences: the mean difference between firms of different types (i.e., more profitable vs. less profitable), the mean difference be-tween cities with greater exposure and cities with less exposure to the three SOBs, and the mean difference between the pre- and postreform periods. Finally, the coefficient of Exposure_c,t ×Firm type_i,t−1, β₁, captures whether the change in the mean difference in corporate credit access between firms of different types from the pre- to the postreform period is different between cities with greater exposure and cities with lower exposure.

If β₁ is positive (negative), then it means that bank ownership reforms increase (reduce)

SOBs’ lending to more-profitable firms, more-indebted firms, or larger firms, depending on the firm characteristic that we use.

3.4 Data

The data we use in this study mainly cover three aspects. First are the data on Chinese listed companies for the period of 2000-2007, which come from the RESSET database, a Chinese data vendor from Beijing. The RESSET database includes balance sheets, income statements, and cash-flow statements of all Chinese companies publicly listed on the Shanghai and Shenzhen Stock Exchanges. Therefore, we can construct our outcome variables of interest—corporate credit access—using loan funding and measures of firm characteristics such as profitability, debt level, and size. We exclude from the sample listed companies in the finance and real estate industries as well as communication services (due to few observations).

Second, we use as complementary data a universe of Chinese unlisted manufacturing firms throughout 2000-2007 from the Annual Survey of Industrial Firms (ASIF). The ASIF contains all state-owned enterprises and non-state-owned enterprises that have annual operating incomes of at least 5 million RMB and at least eight employees. These data constitute an unbalanced panel that covers three categories of industries: (1) mining; (2) manufacturing; and (3) production and distribution of electricity, gas, and water. We only use manufacturing firms, as they are more comparable. The ASIF only reports necessary balance sheet information, so the loan funding in cash-flow statements is missing. Hence, it provides only a rough measure of firm credit access but allows us to exploit more variation in firm characteristics due to a much larger sample. These additional firm data help us check whether the results for a small sample of listed companies also apply to a large sample of small and medium-sized unlisted companies.

The third dataset is the distribution of the three SOBs’ bank branches and loan shares at the province level, which allows us to construct the prereform city-level exposure to the bank ownership reform, given by equation 3.1. The information on the bank branch comes from the China Banking and Insurance Regulatory Commission (CBRC), which reports the branch name, address, foundation year, and organizational level (i.e., savings office, subbranch, city branch, provincial branch, etc.). We exclude branches without authority to issue loans (i.e., savings offices) and branches affiliated with nonbank financial institutions. Specifically, there are several types of banks in the sample: branches of the four largest SOBs, large joint-stock banks, city commercial banks, foreign banks, rural commercial banks, rural credit cooperatives, and three policy banks. In 1999, the branch share of the four largest SOBs was 52.4%, which declined to 51% in 2004. Hence, the relatively stable pattern of bank branches across time mitigates the concern that the bank ownership reform changed corporate outcomes by changing the network size.

Information on the share of the SOBs in 1999 comes from China’s Financial Statis-tical Yearbook (CFSY). In 1999, the average total loan share of the three SOBs that implemented the ownership reform around 2004 across provinces was 47%, with a share of 38.3% at the 10^th percentile and 58.7% at the 90^th percentile. Specifically, the average loan shares of the BOC, CCB, and ICBC are7.1%,12.6%, and27.3%, respectively, so the ICBC is the largest SOB in terms of loan share. In 2003, the average loan share of these banks was still large at 42.2%, indicating that the effect of the bank ownership change could still be prominent, as many corporate borrowers were still seeking loans from these SOBs before the bank reform.

Table 3.1: Summary statistics

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

Obs. Mean SD 10th Median 90th

percentile percentile Panel A: City-level exposure (CBRC & CFSY)

Big Three Sharec,1999 330 0.455 0.164 0.264 0.451 0.649

BOCc,1999 330 0.071 0.044 0.022 0.065 0.125

CCBc,1999 330 0.122 0.062 0.055 0.116 0.203

ICBCc,1999 330 0.262 0.119 0.128 0.253 0.402

Big Three Exposurec,t 2,640 0.195 0.235 0 0 0.627 Panel B: Listed company (RESSET)

loan fundingi,t/assetsi,t−1 7,733 0.296 0.259 0.009 0.253 0.613 ROAi,t−1 7,733 0.027 0.101 −0.014 0.035 0.094 Debt ratioi,t−1 7,733 0.498 0.329 0.230 0.480 0.704 ln Assetsi,t−1 7,733 21.04 1.013 19.86 20.98 22.29 ST loansi,t/assetsi,t−1 7,733 0.205 0.173 0.009 0.183 0.402 LT loansi,t/assetsi,t−1 7,733 0.069 0.020 0 0 0.200 loansi,t/assetsi,t−1 7,733 0.274 0.215 0.128 0.252 0.510 other liabilityi,t/assets_i,t−1 7,733 0.159 0.456 0.041 0.107 0.269 EBTi,t/assetsi,t−1 7,668 0.054 0.090 −0.011 0.048 0.150

∆FAi,t/assetsi,t−1 7,565 0.044 0.102 −0.037 0.014 0.173 interesti,t/loansi,t 6,612 0.148 0.163 0.049 0.088 0.309 Panel C: Unlisted company (ASIF)

loani,t 965,650 0.657 0.475 0 1 1

ROAi,t−1 965,650 0.070 0.122 −0.013 0.034 0.204 Debt ratioi,t−1 965,650 0.584 0.269 0.214 0.597 0.901 ln Assetsi,t−1 965,650 9.875 1.362 8.302 9.693 11.72

SOEi,t−1 965,650 0.207 0.405 0 0 1

ln TFPi,t−1 965,650 1.051 0.432 0.565 1.129 1.472 EBTi,t/assetsi,t−1 965,650 0.092 0.159 −0.013 0.040 0.262

∆FAi,t/assetsi,t−1 965,650 0.043 0.208 −0.079 0 0.222

Table 3.1 displays the summary statistics of the main variables. Panel A reports local exposure to the three SOBs. The average overall city-level exposure (Big Three Share_c,1999) and separate exposure to each SOB (BOC_c,1999, CCB_c,1999, ICBC_c,1999) are close to the province-level figures, implying that using the city-level branch to construct the city-level exposure yields a reasonable approximation. Figure 3.2 plots the geographical distribu-tion of the total loan share of the three SOBs at the city level in 1999. It shows that there is no prominent concentration of the SOBs, as almost all provinces have cities with high and low exposure, mitigating the concern that confounding factors may have affected local exposure to the three SOBs and firm credit access simultaneously. Meanwhile, our DiD and DDD approaches can still estimate the treatment effect, as a comparison of the pre-and postreform periods can wipe out all time-invariant differences. Big Three Exposure_c,t is the time-variant local exposure, which has a relatively large standard deviation that allows us to identify the effect of the bank ownership reform.

Figure 3.2: Geographical distribution of the city-level loan share of the three SOBs in 1999

Notes: This figure plots the geographical distribution of the total city-level loan share of the three SOBs (BOC, CCB, ICBC) in 1999.

Panel B shows that for Chinese listed companies, the average ratio of loan funding to initial assets (loan funding_i,t/ assetsi,t−1) is almost 30%, and only a few firms do not acquire loans, as the 10^th percentile is still positive. Hence, a 0-1 dummy for whether a listed company receives loans appears to be a poor proxy for corporate credit access. In contrast, using loan funding scaled by assets can better capture credit access, as larger firms have greater credit demand. We use initial assets but not current assets as the denominator, as the loan funding received in yeart can also increase the asset size in year t, which cannot fully capture how corporate credit access changes given the initial firm features. Alternatively, we can also use (log) loan funding as the dependent variable, and the main results do not change substantially.

The firm type variable captures three characteristics: firm profitability, measured by

the ratio of profits to assets (ROA_i,t−1); firm debt level, measured by the ratio of total liabilities to total assets (Debt ratio_i,t−1); and firm size, measured by the (log) book value of assets (lnAssets_i,t−1). To determine which loan type is influenced more, we disen-tangle the total outstanding loans (loans_i,t/assets_i,t−1) on corporate balance sheets into outstanding short-term loans (ST loans_i,t/assets_i,t−1) and outstanding long-term loans (LT loans_i,t/assets_i,t−1). This shows that on average, short-term loans are two times larger than long-term loans (20.5% vs. 6.9%). Moreover, we investigate firm performance in terms of profitability and investment. First, we use EBT to measure firm profitability, which is the ratio of earnings before tax to initial assets (EBT_i,t/assets_i,t−1). Second, we use investment in fixed assets to measure corporate real expansion, measured by the ratio of the change in fixed assets to initial assets (∆FA_i,t/assets_i,t−1). Finally, we construct the firm-level loan rate (interest_i,t/loans_i,t) using the ratio of cash outflow for interest payments and profit distribution to total outstanding loans to explore whether the bank ownership reform caused greater heterogeneity in loan rates for different corporate bor-rowers.

Panel C gives the summary statistics for a universe of Chinese unlisted manufacturing firms. The first prominent feature of these data is the large sample size, with almost 1 million firm-year observations, making it more than 100 times larger than the sample of Chinese listed companies. Since the ASIF does not include the cash-flow statement, we use a proxy for firm credit access: a 0-1 dummy for whether the firm has positive interest payments (loan_i,t). It shows that almost 35% of Chinese manufacturing firms do not have any bank credit access. Similarly, we use firm profitability (ROA_i,t−1), firm debt level (Debt ratio_i,t−1), and firm assets size (ln Assets_i,t−1) using the same definitions as above to define the firm types. It is clear that unlisted manufacturing firms are smaller in size and have higher debt ratios but show slightly higher average ROAs than listed companies. We further examine firm ownership (SOE_i,t−1), measured by whether the share of state capital is above 30%, and firm productivity (ln TFP_i,t−1), measured by total factor productivity calculated using the Olley-Pakes method. This can greatly help us understand whether the SOB ownership reform reduced their lending to less productive firms and thus improved the efficiency of the whole economy. We do not implement this exercise for listed companies, as most of them have government capital or strong political connections and do not have information on value-added output that allows us to estimate firm productivity.