The Unequal Impact of AI on Local Labor Markets: Mechanisms, Evidence, and Policy Insights

• Yiran Li

  Author

Artificial intelligence hits local labor markets unevenly, and the reasons go well beyond simple occupational exposure. By synthesizing task-based models, occupational network theory, and research on labor mobility, this review argues that regional resilience depends largely on how densely local jobs are linked by shared skills and whether workers can move---across occupations or across regions. Cities with thicker skill networks tend to absorb automation shocks better, but often at the price of rising wage inequality. Rural areas face a harsher trap: when low-skill jobs vanish, few nearby alternatives exist, and many workers drop out of the labor force entirely. Migration offers partial relief, yet it can also widen the gap between thriving cores and struggling peripheries. For developing economies, weak digital infrastructure and large informal sectors create a structural dependence on the global AI value chain that looks less like automation risk and more like computing colonialism. The paper closes with region-specific policy directions---investing in infrastructure, thickening skill networks, improving migration support, and building fairer global AI governance.

• JAESD000016.pdf