Generative AI impacts on intra-urban inequality and skill premium in Beijing
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866914599266156544 |
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| author | He, Xiliu Zhao, Haoxiang Ma, Mingyi Lai, Edward Wen Chuan Enomoto, Koei Hu, Anni Li, Jiatong Chu, Lingyun Lai, Yuan |
| author_facet | He, Xiliu Zhao, Haoxiang Ma, Mingyi Lai, Edward Wen Chuan Enomoto, Koei Hu, Anni Li, Jiatong Chu, Lingyun Lai, Yuan |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2605_25505 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Generative AI impacts on intra-urban inequality and skill premium in Beijing He, Xiliu Zhao, Haoxiang Ma, Mingyi Lai, Edward Wen Chuan Enomoto, Koei Hu, Anni Li, Jiatong Chu, Lingyun Lai, Yuan Computers and Society Artificial Intelligence General Economics Economics Physics and Society 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. |
| title | Generative AI impacts on intra-urban inequality and skill premium in Beijing |
| topic | Computers and Society Artificial Intelligence General Economics Economics Physics and Society |
| url | https://arxiv.org/abs/2605.25505 |