Author(s):	Joost Boolens, Bart Cockx, Michael Lechner
Publication Date:	January 2020
Keyword(s):	active labour market policy, Causal machine learning, conditional average treatment effects, modified causal forest, policy evaluation
JEL(s):	J68
Programme Areas:	Labour Economics
Link to this Page:	cepr.org/active/publications/discussion_papers/dp.php?dpno=14270

We investigate heterogenous employment effects of Flemish training programmes. Based on administrative individual data, we analyse programme effects at various aggregation levels using Modified Causal Forests (MCF), a causal machine learning estimator for multiple programmes. While all programmes have positive effects after the lock-in period, we find substantial heterogeneity across programmes and types of unemployed. Simulations show that assigning unemployed to programmes that maximise individual gains as identified in our estimation can considerably improve effectiveness. Simplified rules, such as one giving priority to unemployed with low employability, mostly recent migrants, lead to about half of the gains obtained by more sophisticated rules.