Research on Traditional Rural Finance, Digital Finance, and Agricultural Economic Resilience: Causal Inference Based on Double Machine Learning

Su Li, Changjun Yang, Kexin Li

Agricultural economic resilience (AER) is not only a key pathway for promoting rural revitalization and ensuring food security, but also an important guarantee for sustainable agricultural development. Based on panel data for 1410 counties in China from 2014 to 2023, this study employs the entropy weight method, a double machine learning model (DML), an instrumental variable model, and a panel threshold model to systematically analyze the impact of traditional rural finance (TRF) on AER and its underlying mechanisms. It also examines the threshold effect of digital finance (DF) in the process through which TRF influences AER, and further explores the roles of DF and TRF in narrowing agricultural development disparities, with the aim of providing scientific evidence for rural revitalization and food security in China and other developing countries, and contributing to the sustainable development of agriculture. The results show that (1) TRF can significantly improve AER, with agricultural technological innovation (ATI) and agricultural socialized services (ASS) playing mediating roles; (2) DF and its dimensions, including coverage breadth, usage depth, and degree of digitalization, exhibit threshold effects in the impact of TRF on AER, and as the levels of DF and its dimensions increase, the positive effect of TRF shows a diminishing marginal trend, indicating a competitive crowding-out effect between the two; (3) the promoting effect of TRF on AER exhibits significant heterogeneity, being stronger in agricultural counties and in the eastern, central, and western regions, following a “Central > Eastern > Western” pattern, while it is not significant in the northeastern region; (4) TRF significantly reduces agricultural development disparities, whereas DF overall significantly exacerbates such disparities, although its different dimensions exhibit clear heterogeneity in their effects, with coverage breadth consistently and significantly widening regional agricultural development gaps.