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

Heloise Burlat

Post-Print from HAL

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.

Keywords: Continuing vocational training; Conditional average treatment effects; Policy evaluation; Active labour market policy; Causal machine learning; Causal forest (search for similar items in EconPapers)
Date: 2024-08
References: Add references at CitEc
Citations:

Published in Labour Economics, 2024, 89, pp.102573. ⟨10.1016/j.labeco.2024.102573⟩

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
Journal Article: Everybody’s got to learn sometime? A causal machine learning evaluation of training programmes for jobseekers in France (2024)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-05117416

DOI: 10.1016/j.labeco.2024.102573

Access Statistics for this paper

More papers in Post-Print from HAL
Bibliographic data for series maintained by CCSD ().