The Role of Digital Finance in Shaping Agricultural Economic Resilience: Evidence from Machine Learning

Chun Yang (), Wangping Liu and Jiahao Zhou
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Chun Yang: College of Management, Sichuan Agricultural University, Chengdu 611130, China
Wangping Liu: College of Management, Sichuan Agricultural University, Chengdu 611130, China
Jiahao Zhou: Research Institute of Electronic Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China

Agriculture, 2024, vol. 14, issue 10, 1-15

Abstract: This study offers detailed recommendations on strengthening government support without harming digital finance benefits, especially in negatively affected areas, which is critical for enhancing the inclusiveness of the digital financial landscape and reducing social disparities. This paper uses year 2011–2022 panel data from China’s 31 provinces to empirically analyze digital finance’s effects, mechanisms, and heterogeneity on agricultural economy resilience with a two-way, fixed-effect model. It further explores each feature’s impacts using machine learning methodologies like the random forest, GBRT, SHAP value method, and ALE plot. The findings show that digital finance boosted agri-economy resilience, varying by food-producing status and marketization. Among all the features analyzed, government input, urbanization level, and planting structure emerged as the most critical factors influencing agri-economy resilience. Notably, government input negatively moderated this relationship. The ALE plot revealed non-linear effects of digital finance and planting structure on agri-economy resilience.

Keywords: digital finance; agricultural economic resilience; machine learning; moderating effects; China (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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