TY - UNPB
T1 - Priority to unemployed immigrants? A causal machine learning evaluation of training in Belgium
AU - Cockx, Bart
AU - Lechner, Michael
AU - Bollens, Joost
PY - 2020/5/12
Y1 - 2020/5/12
N2 - 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.
AB - 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.
KW - policy evaluation
KW - active labour market policy,
KW - causal machine learning
KW - modified causal forest
KW - conditional average treatment effects
U2 - 10.26481/umaror.2020006
DO - 10.26481/umaror.2020006
M3 - Working paper
T3 - ROA Research Memoranda
BT - Priority to unemployed immigrants? A causal machine learning evaluation of training in Belgium
PB - ROA
CY - Maastricht
ER -