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<p>Achieving sustainable growth in rural residents’ income (RRI) is pivotal for eradicating poverty, alleviating inequality, and achieving common prosperity. However, existing studies predominantly focus on macro-level analyses, leaving a gap in systematic exploration of spatiotemporal patterns and dynamic driving mechanisms at the county level. To address these shortcomings, this paper employs spatial autocorrelation, the Dagum Gini coefficient, and the geographically and temporally weighted regression (GTWR) model to examine the evolution of rural incomes, regional disparities, and driving factors across 1871 Chinese counties from 2010 to 2020. The results found that: (1) The average RRI increased from 5814 to 16,108 yuan, with an annual growth rate of 11.11%, exhibiting a persistent “east-high, west-low” spatial pattern except for some mineral-rich counties, though growth rates were significantly higher in western and central regions. (2) The national Gini coefficient declined by 21.05%, primarily driven by reduced inter-regional disparities, yet inequality increased in ecologically fragile areas like Xizang and Qinghai. (3) Driving mechanism displayed marked spatiotemporal heterogeneity, with per capita GDP and agricultural labor productivity dominating in 2010, while per capita GDP, urbanization, and road density gained prominence by 2020, alongside negative effects from education level and financial expenditure. These findings provide a scientific foundation for region-specific rural revitalization policies tailored to local development stages and resource endowments, fostering balanced and sustainable rural income growth.</p>

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Rural income evolution across Chinese counties: patterns, inequalities and drivers

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年11月18日

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  • Chuanyang Pan,
  • Xu Feng,
  • Kaixin Yang,
  • Ming Nie,
  • Qinyu Song

摘要

Achieving sustainable growth in rural residents’ income (RRI) is pivotal for eradicating poverty, alleviating inequality, and achieving common prosperity. However, existing studies predominantly focus on macro-level analyses, leaving a gap in systematic exploration of spatiotemporal patterns and dynamic driving mechanisms at the county level. To address these shortcomings, this paper employs spatial autocorrelation, the Dagum Gini coefficient, and the geographically and temporally weighted regression (GTWR) model to examine the evolution of rural incomes, regional disparities, and driving factors across 1871 Chinese counties from 2010 to 2020. The results found that: (1) The average RRI increased from 5814 to 16,108 yuan, with an annual growth rate of 11.11%, exhibiting a persistent “east-high, west-low” spatial pattern except for some mineral-rich counties, though growth rates were significantly higher in western and central regions. (2) The national Gini coefficient declined by 21.05%, primarily driven by reduced inter-regional disparities, yet inequality increased in ecologically fragile areas like Xizang and Qinghai. (3) Driving mechanism displayed marked spatiotemporal heterogeneity, with per capita GDP and agricultural labor productivity dominating in 2010, while per capita GDP, urbanization, and road density gained prominence by 2020, alongside negative effects from education level and financial expenditure. These findings provide a scientific foundation for region-specific rural revitalization policies tailored to local development stages and resource endowments, fostering balanced and sustainable rural income growth.