IDEAS home Printed from https://ideas.repec.org/a/eee/eecrev/v184y2026ics0014292125002958.html

Child penalties in labour market skills

Author

Listed:
  • Jessen, Jonas
  • Kinne, Lavinia
  • Battisti, Michele

Abstract

This paper estimates child penalties in labour-market-relevant cognitive skills, such as numeracy but also literacy and problem-solving competencies. We use international PIAAC data and adapt a pseudo-panel approach to a single cross-section covering 29 countries. Numeracy scores, which are associated with the largest returns to skills and pronounced gender differences, decline by 0.11 standard deviations for fathers and an additional 0.07 for mothers. We find no evidence of a deterioration in the occupational skill match for either mothers or fathers. Our findings suggest that changes in general labour market skills such as numeracy competencies explain at most 10% of child penalties in earnings. We additionally show that cross-sectional estimates of child penalties can be sensitive to controlling for predetermined characteristics that vary across cohorts, in our case education.

Suggested Citation

  • Jessen, Jonas & Kinne, Lavinia & Battisti, Michele, 2026. "Child penalties in labour market skills," European Economic Review, Elsevier, vol. 184(C).
    • Handle: RePEc:eee:eecrev:v:184:y:2026:i:c:s0014292125002958
    DOI: 10.1016/j.euroecorev.2025.105245
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0014292125002958
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.euroecorev.2025.105245?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to look for a different version below or

    for a different version of it.

    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. David Dorn & Florian Schoner & Moritz Seebacher & Lisa Simon & Ludger Woessmann, 2024. "Multidimensional Skills on LinkedIn Profiles: Measuring Human Capital and the Gender Skill Gap," Papers 2409.18638, arXiv.org, revised May 2025.

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    JEL classification:

    • I20 - Health, Education, and Welfare - - Education - - - General
    • J13 - Labor and Demographic Economics - - Demographic Economics - - - Fertility; Family Planning; Child Care; Children; Youth
    • 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

    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:eee:eecrev:v:184:y:2026:i:c:s0014292125002958. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/eer .

    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.