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<p>Since the 1980s, urban income inequality (UII) has become a primary source of global inequality. Understanding the sources of UII is crucial for achieving common prosperity. This study, from the perspective of skill biased technological change (SBTC) and industry diversification, employs machine learning methods to reveal the current spatial pattern of UII in China and analyzes the impact of SBTC on UII. The findings are as follows: (1) UII in China exhibits a fluctuating pattern of “rising first, then falling, and then rising again”. (2) The overall spatial pattern of UII in China is characterized by “higher in the west and lower in the east, higher in the north and lower in the south,” gradually shifting from an inverted “U” shape to a monotonically decreasing trend in both the east-west and north-south directions. (3) SBTC has widened UII, with a more significant effect in cities with a higher proportion of non-routine cognitive labor, lower proportions of non-routine manual and routine labor, as well as in smaller cities and lower-tier cities. (4) SBTC can lead to an increase in UII by promoting both related and unrelated industry diversification. The research conclusions provide empirical evidence for narrowing the income gap, promoting common prosperity, and coordinated regional development.</p>

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Skill-biased technological change, industrial diversification and urban income inequality: empirical evidence from machine learning

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月20日

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  • Zuge Xing,
  • Weilong Kong,
  • Yuxin Pan

摘要

Since the 1980s, urban income inequality (UII) has become a primary source of global inequality. Understanding the sources of UII is crucial for achieving common prosperity. This study, from the perspective of skill biased technological change (SBTC) and industry diversification, employs machine learning methods to reveal the current spatial pattern of UII in China and analyzes the impact of SBTC on UII. The findings are as follows: (1) UII in China exhibits a fluctuating pattern of “rising first, then falling, and then rising again”. (2) The overall spatial pattern of UII in China is characterized by “higher in the west and lower in the east, higher in the north and lower in the south,” gradually shifting from an inverted “U” shape to a monotonically decreasing trend in both the east-west and north-south directions. (3) SBTC has widened UII, with a more significant effect in cities with a higher proportion of non-routine cognitive labor, lower proportions of non-routine manual and routine labor, as well as in smaller cities and lower-tier cities. (4) SBTC can lead to an increase in UII by promoting both related and unrelated industry diversification. The research conclusions provide empirical evidence for narrowing the income gap, promoting common prosperity, and coordinated regional development.