Priority of Unemployed Immigrants? A Causal Machine Learning Evaluation of Training in Belgium
81 Pages Posted: 20 May 2020
Date Written: 2020
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.
Keywords: policy evaluation, active labour market policy, causal machine learning, modified causal forest, conditional average treatment effects
JEL Classification: J680
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