Priority of Unemployed Immigrants? A Causal Machine Learning Evaluation of Training in Belgium

Arbeitspapier

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.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: CESifo Working Paper ; No. 8297

Classification and Topics

• Classification: Wirtschaft ; Mobility, Unemployment, and Vacancies: Public Policy
• Subject: policy evaluation ; active labour market policy ; causal machine learning ; modified causal forest ; conditional average treatment effects

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Cockx, Bart ; Lechner, Michael ; Bollens, Joost
• Event: Veröffentlichung
• (who): Center for Economic Studies and Ifo Institute (CESifo)
• (where): Munich
• (when): 2020

Further information

• Handle: http://hdl.handle.net/10419/219115
• Last update: 10.03.2025, 11:45 AM CET