Data Analytics for the Effect Of Digital Inclusive Finance on Farmers' Entrepreneurship Decisions in Rural Areas Using the Supervised Learning-Based Regression in Rural China

Abstract

Farmers' entrepreneurship is essential in developing the countryside and improving farmers' income. Also, with the arrival of the digital economy, rural formal finance (FF), which was brought about by digital inclusion finance (DIF), promoted entrepreneurship among the farmers. Based on data analytics from 2021 to 2023 in China, this paper empirically performs the quantitative analysis of the effect of DIF on farmers' entrepreneurship decisions (FED). Further, it addresses the role of FF as an alternative for informal finance (IF) brought about by the development of DIF in the process of FED from a social network (SN) perspective using the supervised learning-based regression. The results show that DIF can significantly promote farmers' entrepreneurship (FE), especially in self-employed off-farm entrepreneurship. DIF has a positive effect on FE with low levels of education and weak social networks, suggesting that DIF has a truly "inclusive" role to play. Further mechanistic studies have found that DIF, by FF, compensates for the fact that farmers used to rely mainly on SN for financing from IF channels, increasing their financial accessibility, reducing their borrowing costs, and thus promoting their entrepreneurship. Our results from the estimation applied to the supervised learning algorithm considering the instrument variable provide scientific implications for promoting DIF matched with the rural credit system's perfection to improve farmers' production and operation.