Modeling Rural Labor Responses to Digital Finance: A Hybrid IGSA-Random Forest Approach

The application of digital inclusive finance in various industries, particularly in rural areas, is gaining significant attention. The traditional agricultural sector, which focuses on rural labor economics, is more sensitive to financial innovations due to geographical and other constraints. This paper investigates how digital inclusive finance affects rural labor economics by integrating the Improved Gravitational Search Algorithm Random Forest (IGSA-RF) with the Gini coefficient, Out-of-Bag (OOB) coefficient, and the Gini-OOB coupling coefficient. For empirical analysis, Jiangsu Province, China, is selected as the research subject. The findings suggest that: (1) Digital inclusive finance has a long-term positive impact on consumption, gross regional product, and the average wage index of rural workers; (2) There is a growing trend in agricultural machinery power over time. However, the study found that gender, age, and the development of labor-intensive industries did not show significant improvement. To further enhance rural labor economics, the study builds a framework based on the comprehensive application of digital finance, focusing on those in rural areas who are vulnerable to fluctuations in financial markets.