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| The Effect of SMEs' Formal Financing Based on the Matching Model |
| FANG Xin, ZHANG Baiyang
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| Chengdu Branch, the People's Bank of China |
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Abstract As important part of the economy, small and micro-enterprises (SMEs) play a critical role in providing employment opportunities, developing technological innovation, promoting economic growth and maintaining social stability. However, the development of SMEs has been troubled by financing difficulties. With the shocks of the 2019, China-U.S. trade tensions and the 2020 COVID-19 pandemic, the situation seems to be deteriorating further. China has rolled out multiple measures to address SMEs' financing difficulties. Formal financing for SMEs has shown steady growth in recent years. However, certain questions require further investigation, such as whether the formal credit demand of SMEs is satisfied by financing, whether formal financing promotes SME development and the extent to which such promotion is effective. Finding the answers to these questions will greatly help not only to address the financing difficulties of SMEs, but also to objectively formulate and evaluate relative credit supporting policies and further promote SME development.
Objectively evaluating the impact of formal financing for SMEs is not an easy task. In addition to data restrictions, the effectiveness of controlling sample selection can greatly affect the research outcomes. For example, SMEs' access to financing may not be random. Those that operate well are more likely to obtain bank loans, causing endogenous problems in SMEs' financing choices. Ignoring such nonrandom selection bias would lead to mistakes, such as attributing the good performance of SMEs to financing or exaggerating the impact of formal financing on SMEs.
We aim to provide an unbiased estimate of the effectiveness of formal financing for SMEs and draw a reliable conclusion. We focus on whether the formal credit demand of SMEs is satisfied by financing and explore how and to what extent formal financing affects the operation of SMEs. Based on survey data on 3,134 SMEs in Sichuan Province, we adopt the matching method to address our research questions. First, we define and identify the types of formal credit demand of SMEs based on data characteristics and further analyze the basic features of the credit market. We find that up to 83.83% of SME credit demand may be satisfied through formal financing. Thus, the conclusion that financing is difficult seems to be inaccurate and oversimplified. However, financing difficulties mainly manifest in insufficient effective credit demand, especially for SMEs in non-manufacturing industries. The distribution difference of the main characteristic variables, including operating duration, number of employees, total assets and income, is significant between the samples with financing and those without. This shows that bank loan acquisition is not random, which may cause endogeneity problems. To scientifically evaluate the impact of formal financing on SMEs, we analyze the factors influencing financing for SMEs using the GBM model and probit regression. Furthermore, we construct matching concomitant variables to control the endogeneity problems caused by selection bias via the matching method. Formal financing significantly improves SMEs' profitability, but is not conducive to employment in the short term. We also test the balance of the matched samples using a variety of statistical indicators to ensure the effectiveness of the matching process. Propensity score matching and alternative samples are also used in the robustness tests to further enhance the reliability of the research findings. Finally, we address the situation by industry and find that the inhibitory effect of formal financing on the employment of SMEs is mainly manifested in the manufacturing industry.
We make three contributions. First, we reevaluate the financing difficulties of SMEs. Based on the credit demand characteristics of SMEs, we evaluate the degree to which they experience financing difficulties by distinguishing effective credit demand from potential credit demand. This reflects the basic situation of the credit market for SMEs more objectively. Second, we help narrow the research gap regarding the analysis of formal financing effectiveness for SMEs in China. No research in this field has used the matching method. Third, we provide policy makers with valuable information regarding how to solve SMEs' financing difficulties and the financing dysfunction in the credit market.We verify the need to optimize credit supporting policies for SME financing and innovatively propose suggestions for classifying and implementing these policies in combination with credit policy objectives.
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Received: 19 June 2019
Published: 02 October 2020
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