Priority to unemployed immigrants? A causal machine learning evaluation of training in Belgium
Based on administrative data of unemployed in Belgium, we estimate the labour market effects of three training programmes at various aggregation levels using Modified Causal Forests, a causal machine learning estimator. While all programmes have positive effects after the lock-in period, we find substantial heterogeneity across programmes and unemployed. Simulations show that “black-box” rules that reassign unemployed to programmes that maximise estimated individual gains can considerably improve effectiveness: up to 20% more (less) time spent in (un)employment within a 30 months window. A sha... Mehr ...
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Dokumenttyp: | workingPaper |
Erscheinungsdatum: | 2020 |
Verlag/Hrsg.: |
ROA
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Schlagwörter: | atira/keywords/jel_classifications/j68 / j68 - Mobility / Unemployment / and Vacancies: Public Policy / policy evaluation / active labour market policy / causal machine learning / modified causal forest / conditional average treatment effects |
Sprache: | Englisch |
Permalink: | https://search.fid-benelux.de/Record/base-26988117 |
Datenquelle: | BASE; Originalkatalog |
Powered By: | BASE |
Link(s) : | https://cris.maastrichtuniversity.nl/en/publications/6a20f86d-b111-41bc-a363-3f65276c6959 |
Based on administrative data of unemployed in Belgium, we estimate the labour market effects of three training programmes at various aggregation levels using Modified Causal Forests, a causal machine learning estimator. While all programmes have positive effects after the lock-in period, we find substantial heterogeneity across programmes and unemployed. Simulations show that “black-box” rules that reassign unemployed to programmes that maximise estimated individual gains can considerably improve effectiveness: up to 20% more (less) time spent in (un)employment within a 30 months window. A shallow policy tree delivers a simple rule that realizes about 70% of this gain.