IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2605.25505.html

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

Author

Listed:
  • Xiliu He
  • Haoxiang Zhao
  • Mingyi Ma
  • Edward Wen Chuan Lai
  • Koei Enomoto
  • Anni Hu
  • Jiatong Li
  • Lingyun Chu
  • Yuan Lai

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.

Suggested Citation

  • Xiliu He & Haoxiang Zhao & Mingyi Ma & Edward Wen Chuan Lai & Koei Enomoto & Anni Hu & Jiatong Li & Lingyun Chu & Yuan Lai, 2026. "Generative AI impacts on intra-urban inequality and skill premium in Beijing," Papers 2605.25505, arXiv.org.
    • Handle: RePEc:arx:papers:2605.25505
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2605.25505
    File Function: Latest version
    Download Restriction: no
    ---><---

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:arx:papers:2605.25505. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.