Dynamic Nonlinear Connectedness between the Financial Inclusion, Economic Growth, and China’s Poverty Alleviation: Evidence from a Panel VAR Analysis

Abstract

Whether financial inclusion and economic growth can sustainably release poverty alleviation effects in long term has been the focus of academia and government sector. This article uses provincial panel data from 2004 to 2019 to examine the dynamic nonlinear connectedness between the financial inclusion, economic growth, income inequality, and poverty alleviation; the main objective is to reveal the direction and intensity of the long-term and short-term impact of each factor on poverty alleviation. By building a panel vector autoregression model (PVAR), the comparison analyses of national, eastern, central, and western sample groups verify the existence of dynamic nonlinear connectedness among the four variables. The study found that there is complex bidirectional causality between these variables, financial inclusion has the long-term impact on promoting poverty alleviation in China, the impact of economic growth is relatively weak, and income inequality has weakened the positive impact of financial inclusion on poverty alleviation. Through the analysis of impulse response function, variance decomposition, and time-varying nonparametric estimates in different economic regions, we find that the impact of financial inclusion on poverty alleviation presents a U-shaped characteristic, and the contribution of financial inclusion to poverty alleviation in western regions is significant, but poverty reduction in eastern and central region mainly depends on economic growth. For policymakers, financial inclusion can be an effective way to alleviate relative poverty, but the poverty governance should focus more on reducing income inequality in China.

Dynamic Nonlinear Relationship between Digital Transformation, Green Transformation in Manufacturing Industry and Labor Structure: Evidence from Panel VAR Analysis

“…This paper presents an analysis framework that encompasses the digital and green transformation of the manufacturing industry, as well as the labor structure. To achieve this, we have developed a PVAR model (Tzeremes et al, 2023;Chen et al, 2022).…”