Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/215271 
Year of Publication: 
2019
Series/Report no.: 
IZA Discussion Papers No. 12875
Publisher: 
Institute of Labor Economics (IZA), Bonn
Abstract: 
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.
Subjects: 
policy evaluation
active labour market policy
causal machine learning
modified causal forest
conditional average treatment effects
JEL: 
J68
Document Type: 
Working Paper

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