IDEAS home Printed from https://ideas.repec.org/p/ven/wpaper/202625.html

Competition in Creative Tasks, Gender Gaps and the Mitigating Role of AI: Experimental Evidence

Author

Listed:
  • Federico Atzori

    (Sapienza University)

  • Luca Corazzini

    (University of Milan - Bicocca)

  • Valeria Maggian

    (Ca’ Foscari University of Venice)

  • Filippo Pavesi

    (LIUC University)

  • Massimo Scotti

    (LIUC University)

Abstract

We examine gender differences in competitiveness when performance depends on creativity, which is central to many high-skilled jobs, and investigate how generative AI may affect them. Our study is based on a laboratory experiment in which participants complete a creative task and then choose whether to enter a tournament compensation scheme, with access to ChatGPT varying across treatments. While men and women achieve similar levels of creative performance, they differ in competition entry, with women displaying a lower willingness to compete in environments without AI assistance. This gender gap in competition becomes smaller when AI is available. To investigate the mechanisms behind this pattern, we construct a measure of underconfidence based on the difference between participants’ beliefs about their relative performance and their actual rank. We show that underconfidence predicts competition entry and that, besides improving creative performance, AI is associated with lower underconfidence, particularly among women.

Suggested Citation

  • Federico Atzori & Luca Corazzini & Valeria Maggian & Filippo Pavesi & Massimo Scotti, 2026. "Competition in Creative Tasks, Gender Gaps and the Mitigating Role of AI: Experimental Evidence," Working Papers 2026: 25, Department of Economics, University of Venice "Ca' Foscari".
  • Handle: RePEc:ven:wpaper:2026:25
    as

    Download full text from publisher

    File URL: https://www.unive.it/web/fileadmin/user_upload/dipartimenti/DEC/doc/Pubblicazioni_scientifiche/working_papers/2026/WP_DSE_atzori_corazzini_maggian_etal_25_26.pdf
    File Function: First version, anno
    Download Restriction: no
    ---><---

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    JEL classification:

    • C91 - Mathematical and Quantitative Methods - - Design of Experiments - - - Laboratory, Individual Behavior
    • J16 - Labor and Demographic Economics - - Demographic Economics - - - Economics of Gender; Non-labor Discrimination
    • J24 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Human Capital; Skills; Occupational Choice; Labor Productivity
    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes

    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:ven:wpaper:2026:25. 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: Sassano Sonia (email available below). General contact details of provider: https://edirc.repec.org/data/dsvenit.html .

    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.