- 0 min read

Priority to unemployed immigrants? A causal machine learning evaluation of training in Belgium

Research Memorandum by Bart Cockx, Michael Lechner, Joost Bollens

report01_web.gif

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.

>> Download Research Mremorandum

back to overview