Applied Mathematics and Nonlinear Sciences
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Journal

Applied Mathematics and Nonlinear Sciences


Volume
& Issue

Volume 9, Issue 1


Published
on

May 30, 2024


Pages


DOI

Article

A study on the impact of digital economy on driving up the income of rural residents in the context of big data

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Authors

Yiqun Li Affiliation:
Huzhou Vocational and Technological College, Huzhou, Zhejiang, 313000, China.
and Yun Fei Affiliation:
The National University of Malaysia, Malaysia.


Abstract

The digital economy is promoting rural development and rural residents’ income growth and realizing the improvement of supply efficiency and the change of factor power. In this paper, we first construct the measurement system of rural residents’ income level and digital economy development level, respectively, and then use big data technology to collect relevant data from 2013 to 2020 and calculate the development level of the two by using principal component analysis and entropy weight method respectively. Then, the level of digital economy development was taken as an explanatory variable. Rural residents’ income was taken as an explanatory variable. Empirical regression analysis was carried out to explore the impact of digital economy on the improvement of rural residents’ income. The mediation effect and regional heterogeneity test were carried out. The results show that the coefficients of the digital economy are always significant and all positive at a 1% level when control variables are added gradually, and the Sobel statistic of entrepreneurial activity is 0.061 and 0.045. This study provides theoretical support for the analysis of how the rural digital economy can promote the revitalization of rural industries and promote the commonwealth of farmers and rural areas.


Keywords

Principal component analysis, Entropy weight method, Regression analysis, Mediation effect, Digital economy, 97P10


Citation

Li, Y. & Fei, Y. (2024). A study on the impact of digital economy on driving up the income of rural residents in the context of big data. Applied Mathematics and Nonlinear Sciences, 9(1). https://doi.org/10.2478/amns-2024-1271
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