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

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  • Burlat, Héloïse

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

  • Burlat, Héloïse, 2024. "Everybody’s got to learn sometime? A causal machine learning evaluation of training programmes for jobseekers in France," Labour Economics, Elsevier, vol. 89(C).
    • Handle: RePEc:eee:labeco:v:89:y:2024:i:c:s092753712400068x
    DOI: 10.1016/j.labeco.2024.102573
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    More about this item

    Keywords

    Policy evaluation; Active labour market policy; Continuing vocational training; Causal machine learning; Causal forest; Conditional average treatment effects;
    All these keywords.

    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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