My KIPP Blog – Week 6-9 – Knowledge Integration and Project Planning: Data, Inequality and Society (2022-2023)

In my previous blog, I mentioned my research interest in studying the impact of digital inclusive finance on China’s rural-urban income inequality. After reading through some literature, I found that there is still controversy over whether digital inclusive finance can effectively narrow the income gap between rural and urban areas. While some researchers argue that digital inclusive finance has indeed reduced the income gap by lowering transaction costs, reducing barriers to financial services, increasing the possibility of entrepreneurship, and controlling financial investment risks by reducing information asymmetry, others believe that its promoting effect on rural economy is limited and may even widen the income gap between rural and urban areas. This is due to the possibility of digital divide, regulatory loopholes, and the uneven development of digital inclusive finance in China.

Therefore, in addition to theoretical analysis, a quantitative approach is necessary to explore this issue. Firstly, descriptive statistical analysis can be used to depict the development status of digital inclusive finance. Visualization tools such as heat maps can also be utilized to analyze the current situation. Secondly, fixed effects models can be used to analyze the factors that affect the urban-rural income gap, which is the method used in most literature. The dependent variable is the Theil index, which measures income inequality. The explanatory variable I plan to use is the digital inclusive finance index released by Peking University. Furthermore, I am considering using DEA efficiency models to evaluate whether the government’s investment in digitalization and infrastructure construction is sufficiently effective. The input variable is the investment made by each province in digitalization, infrastructure construction, and other relevant areas, while the output variable includes coverage breadth, usage depth, and digitization degree. However, the DEA model requires a lot of data and the correct selection of input and output variables to accurately evaluate efficiency. By exploring these three areas, I hope to identify the key influencing factors and mechanisms, and provide targeted recommendations.

Also, I had the first meeting with my supervisor to establish the timeline and task arrangement. Currently, I need to write a lengthy abstract of the references according to his requirements and provide him with a preliminary outline for feedback.