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

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.