Exploring the impact of the internet on income inequality in rural households

FANG Shunchao, ZHU Pingfang

Systems Engineering - Theory & Practice ›› 2024, Vol. 44 ›› Issue (5) : 1450-1467.

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Systems Engineering - Theory & Practice ›› 2024, Vol. 44 ›› Issue (5) : 1450-1467. DOI: 10.12011/SETP2023-2467

Exploring the impact of the internet on income inequality in rural households

  • FANG Shunchao, ZHU Pingfang
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Abstract

This article aims to explore the impact of the internet on income inequality among rural households. Through the analysis of data from China Family Panel Studies, it is found that although the internet can significantly alleviate the inequality in total income and wage income among rural households, its effect on alleviating inequality in entrepreneurial income is limited, and it may exacerbate inequality in household property income. Based on this finding, this article analyzes the mechanism of its impact from the perspective of household income sources, revealing that the internet mainly reduces the wage income gap by pulling rural labor force into the non-agricultural sector, thereby alleviating household income inequality. Meanwhile, households with original capital accumulation are more likely to benefit from the internet, which exacerbates property income inequality. In addition, this article introduces the causal forest algorithm and, from the perspective of human capital, analyzes the heterogeneous effects of the internet on individual-level inequality in wage income and property income among rural households. The results show that the alleviation of wage income inequality is mainly manifested in households with low human capital, while the exacerbation of property income inequality is mainly manifested in households with high human capital.

Key words

rural household income inequality / source of income / individual heterogeneity / causal forest

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FANG Shunchao , ZHU Pingfang. Exploring the impact of the internet on income inequality in rural households. Systems Engineering - Theory & Practice, 2024, 44(5): 1450-1467 https://doi.org/10.12011/SETP2023-2467

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Funding

National Social Science Foundation of China (23BTJ015); National Natural Science Foundation of China (72273091, 72303155); Shanghai Soft Science Research Base—Research Center of Shanghai Science and Technology Statistics and Analysis Project Supported by the Shanghai Foundation for Development of Science and Technology
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