Credit rating, repayment willingness and farmer credit default

This study, grounded in survey data collected from farmers in three northeastern provinces of China-specifically Heilongjiang, Jilin, and Liaoning in 2019-investigates the relationships among credit ratings, repayment willingness, and credit default among farmers. To address the dual sample selection issue stemming from both credit demand and credit accessibility, a three-stage Prohibit model is employed. The research empirically examines the impact of both traditional credit ratings and digital credit ratings on farmer credit default while delving into the interplay between the two. Furthermore, a mediation effect model is utilized to explore how credit ratings influence credit default through their effect on farmers' willingness to repay. The main findings as follows: Firstly, both traditional and digital credit ratings serve to deter farmers' credit defaults. Specifically, as a farmer attains a high-credit status in the traditional sense or achieves a higher digital credit score, the likelihood of credit default diminishes. Secondly, the inhibitory impact of both traditional and digital credit ratings on farmers' credit defaults is mutually reinforcing and complementary. Lastly, the mechanistic analysis reveals that both traditional and digital credit ratings effectively mitigate credit defaults by enhancing farmers' repayment willingness. In this regard, traditional credit ratings operate through the reputation and punishment mechanisms, while the supervision mechanism is not implicated. On the other hand, digital credit ratings operate through the reputation, punishment, and supervision mechanisms. Consequently, a comprehensive approach to reducing rural financial risks involves bolstering the cultivation of a robust reputation atmosphere and credit system within rural financial markets, along with the systematic enhancement of the efficacy of credit rating supervision mechanisms.