Small and medium-sized financial institutions and the loans of small and medium-sized enterprises

LIU Chang1 LIU Chong2 MA Guangrong3

(1.Guanghua School of Management, Peking University 100871)
(2.School of Economics, Peking University 100871)
(3.School of Finance, Renmin University of China 100872)
【Knowledge Link】non-performing loans

【Abstract】The Chinese economy has long suffered from the financing problem of small and medium-sized enterprises (SMEs), which has attracted the attention of policy makers and industrial participants. The literature on the financial constraints of SMEs is still growing. Theoretical research has praised the advantages of large banks in collecting “hard information” (e.g. enterprises’ economic and credit status and audit information) due to their economies of scale, although they perform poorly in collecting “soft information” (e.g. relationships) and delivering information due to their complex internal structures (Stein, 2002; Berger et al., 2005; Berger and Udell, 2006; De la Torre, 2010; Berger et al., 2014). In a highly influential Chinese study, Lin and Li (2001) propose the hypothesis on the comparative advantages of small and medium-sized banks (SMBs) based on China’s financial institutional background. However, the evidence (especially micro-evidence from a large representative sample throughout China) is still too inadequate to make a conclusion on this hypothesis. We use county-level data on China’s bank branches from the China Banking Regulatory Commission (CBRC) between 2006 and 2011. We focus on four main types of banks: large state-owned commercial banks, joint-equity commercial banks, city commercial banks and rural financial institutions. We exclude Tibet from the sample due to its special economic and social conditions. The Agricultural Bank of China notably stripped out approximately CNY 750 billion non-performing loans in November 2007 before entering the stock market. Therefore, we exclude branches of the Agricultural Bank of China in each county for comparability across years. Our empirical findings suggest that an increase of CNY 1 in the loans granted by large state-owned commercial banks is accompanied by an increase of CNY 0.0568 in the loans toward SMEs. In contrast, an increase of CNY 1 in the loans granted by joint-equity commercial banks, that in the loans granted by city commercial banks and that in the loans granted by rural financial institutions are accompanied by an increase of CNY 0.1, CNY 0.199 and CNY 0.248 in their loans toward SMEs, respectively. These findings are highly consistent with a battery of robustness tests. In order to deal with potential endogeneity concerns, we utilize the number of newly established branches of specific categories of banks as an instrumental variable (Ⅳ). The Ⅳ results resemble our baseline ordinary least squares results. We further use the plausible exogenous Ⅳ approach proposed by Conley et al. (2012) to strengthen confidence in our Ⅳ estimations. We contribute to the literature in two ways. Firstly, with detailed county-level data, we provide solid empirical evidence for the hypothesis on the comparative advantages of SMBs in Lin and Li (2001), namely that SMBs reveal unparalleled advantages in terms of providing SMEs with loans. Secondly, our analysis documents the correlation between the increase in financial institutions’ loans and the increase in their non-performing loans. A simple back-of-the-envelope calculation suggests that SMBs can use higher interest rates to make up for their excess non-performing loans stemming from more lending toward SMEs. Our findings yield important policy implications. Firstly, the Chinese government should encourage the development of SMBs to support the real economy. Secondly, in accordance with Wang (2004), we recommend that policy makers should not urge large state-owned commercial banks to increase their lending toward SMEs through administrative means, as such means would run counter to the comparative advantages of large state-owned commercial banks and may lead to an increase in non-performing loans or even induce a financial crisis. Our findings offer insights encouraging policy makers to rely on SMBs to serve for SMEs’ financing. Thirdly, China’s preliminarily completed interest rate marketization reform should further raise SMBs’ risk pricing capability and contribute to relieving SMEs’ financial constraints. Branch-level micro-data should be collected to better reveal the micro-foundations of the hypothesis on the comparative advantages of SMBs that underlie lending technologies and internal organization structures.

【Keywords】 small and medium-sized financial institutions; loans of small and medium-sized enterprises; non-performing loans;

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    [1]. ① Several Opinions of the State Council of China on Further Promoting the Development of Small and Medium-sized Enterprises (No. 36 document issued by the State Council of China in 2009).

    [2]. ② Using data of more than 2000 Chinese enterprises, Ayyagari et al. (2010) find that enterprises able to win support from formal finance have better performance.

    [3]. ① The conclusion of Lin and Li (2001) is basically the same as but earlier than the conclusion of Berger in a series of theoretical discussions. Under the same analytical framework, Li (2002) predicts through a theoretical model that the increase in the number of small and medium-sized financial institutions has a positive effect on the total loans of SMEs. Lin et al. (2009) further regard whether the financial system of an economy is dominated by large banks or small banks as an important feature of financial institutional arrangements. Based on provincial panel data, Lin and Jiang (2006) and Lin and Sun (2008) find that when the concentration of banks is at a lower level or SME loans account for a higher proportion, the regional economic growth is faster.

    [4]. ② Among earlier studies, Petersen and Rajan (1995) have also found a banking competition effect in China.

    [5]. ③ Another type of studies indirectly reflects the differences between different types of banks from the perspective of structural changes of the regional banking industry. For example, based on the data of enterprises listed on the SME board, Yao and Dong (2015) find that the structural changes of the banking industry caused by the entry of small and medium-sized banks significantly ease the financing constraints faced by enterprises listed on the SME board, while the relationship between the financing constraints faced by SMEs and the regional financial development level measured by indicators such as the proportion of the deposits (loans) of financial institutions in GDP and the proportion of stock market value (trading value) in GDP is not robust.

    [6]. ④ Firstly, because the loan balance data of enterprises are not reported in the database of Chinese industrial enterprises, researchers usually use indicators such as interest expense to measure the financing of enterprises, which leads to a serious problem of measurement error. Secondly, the vast majority of enterprises in the database of Chinese industrial enterprises are large and medium-sized enterprises. The use of data about listed enterprises in research has the same problem. Since listed enterprises are usually large with the stock market as a natural financing channel, the empirical research with these enterprises as samples may have serious sample selection bias. Finally, it is not clear that to what extent the conclusions drawn from some regional survey data can be applicable to other regions. In order to get a conclusion with more clear policy implications, it is necessary to use a representative large sample.

    [7]. ① In the original data provided by the CBRC, the data of Shaanxi in 2006 are missing.

    [8]. ②

    [9]. ① The Agricultural Bank of China is not included here. Before 2006, all the large state-owned commercial banks except the Agricultural Bank of China had stripped out the non-performing loans before getting listed on the stock market.

    [10]. ① Due to the merger and abolition of branches, layout adjustments and some other reasons, the increment in the number of branches with loan functions may be negative.

    [11]. ② According to the Measures on the Implementation of Administrative Licensing Matters Concerning Domestically Funded Commercial Banks revised and issued by the CBRC in each year during the sample period, this requirement did not change during the sample period.

    [12]. ① We also use bootstrap technology similar to the approach in Figure 1 for a statistical test of the differences between coefficients. Due to the space limitation, these results are not presented here.

    [13]. ② It is inappropriate to interpret the results of instrumental variable regression as the causality between the independent variable and the dependent variable, because there is no direct causality mechanism between them.

    [14]. ① Due to the space limitation, the test results are not presented here.

    [15]. ② In Conley et al. (2012), two approaches have been provided. The first one is the UCI approach, which assumes the upper and lower bounds of γ and further derives the robust confidence interval of β. The second one is the Local to Zero (LTZ) approach, which considers γ as a value very close to (but not equal to) 0 and assumes it follows a specific normal distribution to derive the robust confidence interval of β.

    [16]. ③ The confidence interval obtained by the LTZ approach is very similar. Relevant results are not presented here due to the space limitation.

    [17]. ④ For the technical details, see Conley et al. (2012).

    [18]. ① The reports provide the respective proportion of different lending rate intervals in provincial-level regions. According to common practices in statistics, we adopt the middle point of each interest rate interval as the approximate average of the interval and calculate the overall average after the estimation of provincial data.

    [19]. ② Due to the lack of data about the average lending rates of different types of financial institutions in 2011, the data of the period from 2006 to 2010 are used for estimation in the following analysis.

    [20]. ① 30668 × (0.0382 − 0.00613) = 983.52.

    [21]. ② This number is obtained by adding up the year-end total loans from 2006 to 2010.

    [22]. ③ Between 2006 and 2010, the benchmark interest rate level was adjusted several times. The average value of the benchmark interest rates published by the People’s Bank of China during this period is adopted here.

    [23]. ④ Assuming that large state-owned commercial banks implemented the benchmark interest rate, we obtain 191748 × 0.064 × 0.444 = 5448.71.


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This Article


CN: 11-1081/F

Vol 52, No. 08, Pages 65-77

August 2017


Article Outline



  • 1 Introduction
  • 2 Data and empirical method
  • 3 Loan growth and SME loan growth of different types of financial institutions
  • 4 Correlations between the growth of loans and the growth of non-performing loans in different types of financial institutions
  • 5 Conclusion
  • Footnote