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

Xiliu He, Haoxiang Zhao, Mingyi Ma, Edward Wen Chuan Lai, Koei Enomoto, Anni Hu, Jiatong Li, Lingyun Chu, Yuan Lai · arXiv · 25 May 2026

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Where this sits in the debate

Will AI widen inequality? · its finding supports the claim (why studies differ)

Direction of the finding as read from the abstract by an AI model (AI-written).

Key takeaway AI-written

Using 5 million Beijing job postings (2018-2024) and an LLM-derived neighborhood-level GenAI Exposure Index, the authors show exposure concentrates in core districts and that since 2023 high-exposure neighborhoods have suffered wage stagnation even while attracting high-skilled workers — a "high-skill trap" driven by task de-skilling and labor-market crowding, causally identified with a diff-in-diff around ChatGPT's release. This is a direct empirical challenge to skill-biased technological change as the operative model for GenAI, and one of the first studies of intra-urban (rather than cross-occupation) AI inequality.

The paper in three steps AI-written

Written by an AI agent from the paper's abstract, which is the primary source and is quoted below.

The question

How does exposure to generative AI affect wages and inequality across neighbourhoods within Beijing?

The approach

The authors use 5 million Beijing job postings from 2018 to 2024 to build a neighbourhood-level GenAI exposure index from task assessments by five large language models, and use a difference-in-differences design around ChatGPT's release.

The findings

GenAI exposure is concentrated in the city's core districts. Since 2023, high-exposure neighbourhoods have seen wage stagnation while still attracting high-skilled workers, which the authors call a high-skill trap, driven by task de-skilling and labour-market crowding.

How to read it: observational. Analysis of real-world firm, worker, or market data — realistic settings; causal claims depend on the identification strategy.

Limitations and counterpoints AI-written

The data are job postings from a single city, which may not reflect realised wages or other cities. Exposure is measured through language models' own task assessments.

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.

About this publication

Preprint. A preprint — posted publicly before or without journal peer review; findings may still be revised.

Published: 2026 (5 months ago)
Citations: Not recorded for this source

Citation counts come from OpenAlex; none are recorded for this source — absence of a count is not absence of citations.