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

Arbeitspapier

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.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: IZA Discussion Papers ; No. 12875

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): Institute of Labor Economics (IZA)
• (where): Bonn
• (when): 2019

Further information

• Handle: http://hdl.handle.net/10419/215271
• Last update: 10.03.2025, 11:41 AM CET