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The Paradox of Poverty Reduction: How Digital Economy Development Widens the Urban–Rural Income Gap in China

Xiaokun Ding et al.

Management and Organization Review2026https://doi.org/10.1017/mor.2025.10121article
AJG 3ABDC A
Weight
0.50

Abstract

Coordinated urban and rural development is crucial for reducing inequality and achieving common prosperity. Using panel data from 57 counties in Zhejiang Province from 2015 to 2019, this study examines the impact of digital economy development on the urban–rural income gap, with particular attention to its role in digital poverty alleviation and the underlying transmission mechanisms. The results show that while digital economy development contributes to poverty alleviation by significantly increasing household income in both urban and rural areas, it simultaneously widens the urban–rural income gap. The analysis of mechanisms reveals that nonagricultural employment (NAE), innovation, and entrepreneurship mediate this relationship, disproportionately benefiting urban areas. The heterogeneity analysis further indicates that human capital (HC) and business environment (BE) disparities amplify the widening effect of digital economy development on the urban–rural income gap, while improved infrastructure reduces barriers to resource flow, mitigating the gap. These findings highlight the dual effects of digital economy development, offering critical policy insights for promoting digital poverty reduction while addressing regional disparities to achieve common prosperity.

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https://doi.org/https://doi.org/10.1017/mor.2025.10121

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@article{xiaokun2026,
  title        = {{The Paradox of Poverty Reduction: How Digital Economy Development Widens the Urban–Rural Income Gap in China}},
  author       = {Xiaokun Ding et al.},
  journal      = {Management and Organization Review},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1017/mor.2025.10121},
}

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

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