Lending Facilities, Collateral Channels and Small and Micro Enterprises Lending—A Study of DID Based on Double Machine Learning

Abstract

Abstract: In order to support the development of real economy and alleviate the financing difficulties of small and micro enterprises (SMEs), on June 1, 2018, the People's Bank of China (POBC) expanded the collateral scope of the medium-term lending facility (MLF) to include high-quality SMEs' loans in the MLF collateral framework. Using this quasi-natural experiment, this paper introduces the Double Machine Learning method into the traditional DID model to study the impact of collateral expansion of MLF on SMEs' loans. The study finds that the inclusion of high-quality SMEs' loans as collateral for MLF by POBC significantly increases the availability of loans to SMEs, and it is mainly achieved by alleviating information asymmetry of SMEs and lowering mortgage guarantee requirements. Heterogeneity analysis shows that the promotion effect on SMEs in eastern regions is greater than that on SMEs in central and western regions, and the promotion effect on SMEs of specialized and special new type is greater than that on SMEs of non-specialized and special new type.

Key words: collateral expansion, SMEs, loan availability, double machine learning, DID

Ouyang Zhigang, Li Wei. Lending Facilities, Collateral Channels and Small and Micro Enterprises Lending—A Study of DID Based on Double Machine Learning[J]. Management Review, 2024, 36(8): 28-38.

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