Priority to unemployed immigrants? A causal machine learning evaluation of training in Belgium
This research addresses policy optimization for unemployed immigrants, offering incremental improvements in training program effectiveness through data-driven reassignment.
The study evaluated the labor market effects of three training programs for unemployed immigrants in Belgium using causal machine learning, finding that reassigning participants to programs based on individual gains could increase employment time by up to 20% over 30 months.
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 percent more (less) time spent in (un)employment within a 30 months window. A shallow policy tree delivers a simple rule that realizes about 70 percent of this gain.