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 (RLE), is more sensitive to financial innovations due to geographical and other constraints. This paper investigates how digital inclusive finance affects RLE 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. Focusing on Jiangsu Province, China, this study uses rural labor economic indicators to examine the underlying influence mechanisms of digital finance on labor dynamics in rural regions. 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. The study provides a data-driven framework for understanding and enhancing rural labor development through digital financial innovation.