Analysing the Impact of Digital Economy Development on the Income Gap Between Urban and Rural Residents Based on Copula Function and Kernel Density Estimation

With the advancement of technology, the digital economy is booming, a change that has many implications for the urban-rural divide. In this study, the Thiel index is used to measure the income gap between urban and rural residents, establish an evaluation system to measure the digital economy development index, and explore the relationship between the two based on the Copula function.Firstly, the marginal distributions ofthe two variables were modelled using non-parametric kernel density estimation and tested for goodness-of-fit using root-mean-square error. The Copula function was then used to simulate the joint distribution, and the tail correlation coefficients were calculated by comparing the function to the original data Spearman rank correlation coefficients and calculating the Euclidean distance to test the goodness-of-fit and hence the tail correlation coefficients. The results show that (1) Epanechnikov kernel function, rectangular kernel function, and t-Copula are the best fits for modelling the digital economic development index, Terre index, and joint distribution, respectively. (2) The upper and lower tail correlation coefficients of the two variables are -0.6931 and -0.6784 respectively, indicating that when there is an extremely large value of the Digital Economy Development Index, the income gap between urban and rural residents tends to be extremely small; and vice versa.The findings ofthis study will be useful in helping policy makers to formulate measures to promote coordinated urban-rural economic development and narrow the urban- rural gap.