Generative AI impacts on intra-urban inequality and skill premium in Beijing

2026-05-25

AI Summary

This study investigates the mechanisms through which generative artificial intelligence (AI) affects intracity inequality and skill premia in Beijing. Leveraging 5 million job postings from 2018 to 2024, the authors construct a neighborhood-level AI exposure index and employ task-based evaluations from five leading large language models alongside a difference-in-differences design to identify causal effects. Findings reveal that, since 2023, neighborhoods with high AI exposure have experienced concurrent wage stagnation and agglomeration of high-skilled workers—a paradox driven by task deskilling and labor market congestion. This phenomenon uncovers a “high-skill trap” in urban cores, challenging conventional skill-biased technological change theory and offering new insights for inclusive AI governance.

Abstract

Generative artificial intelligence (GenAI) is the first automation wave to reach high-cognitive tasks at scale, yet its effects on intra-urban inequality remain largely unknown. Using 5 million job postings from Beijing (2018--2024), we construct a neighborhood-level GenAI Exposure Index by aggregating task-level assessments from five leading large language models. We examine the spatial, structural and causal mechanisms of this shock. We find that GenAI exposure is highly concentrated in the city's core districts, deepening the intra-urban AI divide. Since 2023, high-exposure neighborhoods have experienced wage stagnation even as they continue to attract high-skilled workers -- a "high-skill trap." This wage penalty is driven by task de-skilling and intensified labor-market crowding. A difference-in-differences design centered on ChatGPT's release supports a causal interpretation. These findings challenge the prevailing theory of skill-biased technological change and provide a basis for inclusive AI governance in global technology hubs.

Problem

• Generative AI
• intra-urban inequality
• skill premium
• wage stagnation
• AI divide

Innovation

• Generative AI
• Task-level exposure index
• Intra-urban inequality
• Difference-in-differences
• Skill premium