IDEAS home Printed from https://ideas.repec.org/p/cpr/ceprdp/19864.html

AI, Task Changes in Jobs, and Worker Reallocation

Author

Listed:
  • Gathmann, Christina
  • Grimm, Felix
  • Winkler, Erwin

Abstract

How does Artificial Intelligence (AI) affect the task content of work, and how do workers adjust to the diffusion of AI in the economy? To answer these important questions, we combine novel patent-based measures of AI and robot exposure with individual survey data on tasks performed on the job and administrative data on worker careers. Like prior studies, we find that robots have reduced routine tasks. In sharp contrast, AI has reduced non-routine abstract tasks like information gathering and increased the demand for ‘high-level’ routine tasks like monitoring processes. These task shifts mainly occur within detailed occupations and become stronger over time. While displacement effects are small, workers have responded by switching jobs, often to less exposed industries. We also document that low-skilled workers suffer some wage losses, while high-skilled incumbent workers experience wage gains.

Suggested Citation

  • Gathmann, Christina & Grimm, Felix & Winkler, Erwin, 2025. "AI, Task Changes in Jobs, and Worker Reallocation," CEPR Discussion Papers 19864, Centre for Economic Policy Research.
  • Handle: RePEc:cpr:ceprdp:19864
    as

    Download full text from publisher

    File URL: https://cepr.org/publications/DP19864
    Download Restriction: no
    ---><---

    Other versions of this item:

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. is not listed on IDEAS
    2. Etheridge, Ben & Bharier, David & Morais, Paulo, 2026. "AI adoption and workforce change in SMEs," ISER Working Paper Series 2026-01, Institute for Social and Economic Research.
    3. Ilse Lindenlaub & Ryungha Oh & Mar’a Alejandra Rodr’guez Vega & Laura Veldkamp, 2026. "Beyond Exposure: Predicting AI Adoption Based On Comparative Advantage," Cowles Foundation Discussion Papers 2532, Cowles Foundation for Research in Economics, Yale University.
    4. Meisenbacher, Stephen & Nestorov, Svetlozar & Norlander, Peter, 2025. "Extracting O*NET Features from the NLx Corpus to Build Public Use Aggregate Labor Market Data," MPRA Paper 126336, University Library of Munich, Germany.
    5. Flavio Calvino & Luca Fontanelli, 2026. "Decoding AI: an early look at how French firms use AI," Eurasian Business Review, Springer;Eurasia Business and Economics Society, vol. 16(1), pages 51-93, March.
    6. Milena Nikolova & Anthony Lepinteur & Femke Cnossen, 2025. "Just another cog in the machine? A worker‐level view of robotization and tasks," Economica, London School of Economics and Political Science, vol. 92(368), pages 1101-1148, October.

    More about this item

    Keywords

    ;

    JEL classification:

    • J23 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Labor Demand
    • J24 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Human Capital; Skills; Occupational Choice; Labor Productivity
    • J31 - Labor and Demographic Economics - - Wages, Compensation, and Labor Costs - - - Wage Level and Structure; Wage Differentials
    • J62 - Labor and Demographic Economics - - Mobility, Unemployment, Vacancies, and Immigrant Workers - - - Job, Occupational and Intergenerational Mobility; Promotion

    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:cpr:ceprdp:19864. 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: CEPR (email available below). General contact details of provider: https://cepr.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.