Digital Inclusive Finance Data Mining and Model-Driven Analysis of the Impact of Urban-Rural Income Gap

Abstract

Digital inclusive finance, rural human capital, and agricultural green total factor productivity are simultaneously incorporated into the framework of urban-rural income gap analysis. Based on digital inclusive finance indices from several provincial panel data in China, a systematic generalized moment estimation method is used to the moderating effect of rural human capital and the mediating role of agricultural green total factor productivity. Annual time series data of foreign trade indicators and urban-rural income gap indicators are estimated by building a state-space model and applying Kalman filter to investigate the effects of these factors on the urban-rural income gap. The results show that the impact of urban-rural fixed asset investment ratio and foreign trade indicator on the urban-rural income gap in China tends to increase gradually, while the impact of rural financial development indicator is decreasing but still positive. The study finds that the development of digital inclusive finance significantly contributes to the reduction of the income gap between urban and rural residents; the breadth of coverage of digital inclusive finance and the degree of digitalization help to suppress the widening of the income gap between urban and rural residents; and rural health-based human capital, education and training-based human capital, and migration-based human capital not only have a convergence effect on the income gap between urban and rural residents themselves.