Working Paper: CEPR ID: DP14270
Authors: Michael Lechner; Bart Cockx; Joost Bollens
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.
Keywords: policy evaluation; active labour market policy; causal machine learning; modified causal forest; conditional average treatment effects
JEL Codes: J68
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| Short-term vocational training (SVT) (M53) | Average time spent in employment (J29) |
| Long-term vocational training (LVT) (M53) | Average time spent in employment (J29) |
| Orientation training (OT) (Y20) | Average time spent in employment (J29) |
| Individualized assignment of unemployed individuals to programmes (J68) | Average time spent in employment (J29) |
| Capacity constraints (D24) | Average time spent in employment (J29) |
| Simpler allocation rules based on employability (J68) | Average time spent in employment (J29) |