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Everybody’s got to learn sometime? A causal machine learning evaluation of training programmes for jobseekers in France

Author

Listed:
  • Héloïse Burlat

    (BETA - Bureau d'Économie Théorique et Appliquée - AgroParisTech - UNISTRA - Université de Strasbourg - Université de Haute-Alsace (UHA) - Université de Haute-Alsace (UHA) Mulhouse - Colmar - UL - Université de Lorraine - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement, UGent - Universiteit Gent = Ghent University = Université de Gand)

Abstract

This paper estimates the heterogeneous impact of three types of vocational training- preparation, qualifying, and combined – on jobseekers' return to employment using the Modified Causal Forest method. Analysing data from 33,699 individuals over 24 months, it reveals a short-term negative lock-in effect for all programmes, persisting in the medium term for combined training. Only qualifying training shows a positive medium-term effect. Seniors, low-skilled, foreign-born, and those with poor job histories benefit most, while youth and higher education levels benefit less. Targeting foreign-born individuals could significantly enhance programme effectiveness, as indicated by the clustering analysis and optimal policy trees.

Suggested Citation

  • Héloïse Burlat, 2024. "Everybody’s got to learn sometime? A causal machine learning evaluation of training programmes for jobseekers in France," Post-Print hal-05117416, HAL.
    • Handle: RePEc:hal:journl:hal-05117416
    DOI: 10.1016/j.labeco.2024.102573
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    Cited by:

    1. Di Pietro Giorgio, 2025. "Labour market effects of training programmes in the EU. Findings and policy lessons from a meta-analysis," JRC Research Reports JRC142871, Joint Research Centre.
    2. Federica Mascolo & Nora Bearth & Fabian Muny & Michael Lechner & Jana Mareckova, 2024. "From Average Effects to Targeted Assignment: A Causal Machine Learning Analysis of Swiss Active Labor Market Policies," Papers 2410.23322, arXiv.org, revised May 2025.

    More about this item

    Keywords

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    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • J68 - Labor and Demographic Economics - - Mobility, Unemployment, Vacancies, and Immigrant Workers - - - Public Policy
    • J08 - Labor and Demographic Economics - - General - - - Labor Economics Policies
    • J24 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Human Capital; Skills; Occupational Choice; Labor Productivity

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