Welcome to Chinese Agricultural Science Bulletin,

Chinese Agricultural Science Bulletin ›› 2021, Vol. 37 ›› Issue (25): 157-164.doi: 10.11924/j.issn.1000-6850.casb2020-0572

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Credit Risk Assessment of Farmer Loans: Based on CFPS2018 Data

Liang Weisen1,2(), Fang Wei3()   

  1. 1College of Economics, Jinan University, Guangzhou 510632
    2Postdoctoral Programme, Guangdong Shunde Rural Commercial Bank Company Limited, Foshan Guangdong 528300
    3Institute of Agricultural Economic and Information, Guangdong Academy of Agricultural Sciences, Guangzhou 510640
  • Received:2020-10-20 Revised:2021-06-24 Online:2021-09-05 Published:2021-09-23
  • Contact: Fang Wei E-mail:nj_sunshine@163.com;fangwei@gdaas.cn

Abstract:

The aim of the study is to improve the accuracy of credit risk measurement of farmer loans, reduce the non-performing rate of banks’ agricultural loans and promote the bank's credit coverage to farmers. We selected default indicators from the four aspects of householder characteristics, assets and liabilities, income and expenditure, and willingness to repay, and used a combination of factor analysis and Logistic regression to build the credit risk assessment model for farmer loans. The empirical analysis took CFPS2018 data as a sample. The asset status of farmers was the most important factor affecting loan default, and the asset-liability ratio was positively correlated with the credit risk of loans. The more households spend on consumption, the more likely their loans will default. Recognition of trust is an important factor, the higher the trust of farmers in cooperation with others, the lower the risk of default. Moreover, the prediction accuracy of the constructed model exceeded 90%, which was universally applicable. We encourage qualified rural small and medium-sized banks to implement internal credit risk rating to improve the credit coverage of farmers. At the same time, banks should strengthen related supporting constructions, such as optimizing the risk organization structure, improving the risk management system, and building the risk control talent team.

Key words: farmer loans, credit risk, probability of default, Logistic model, factor analysis

CLC Number: