Priority to unemployed immigrants? A causal machine learning evaluation of training in Belgium

Bart Cockx, Michael Lechner, Joost Bollens

Research output: Working paperProfessional

41 Downloads (Pure)

Abstract

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.
Original languageEnglish
Place of PublicationMaastricht
PublisherMaastricht University, Graduate School of Business and Economics
Number of pages81
DOIs
Publication statusPublished - 12 May 2020

Publication series

SeriesGSBE Research Memoranda
Number015
ISSN2666-8807

JEL classifications

  • j68 - Mobility, Unemployment, and Vacancies: Public Policy

Keywords

  • policy evaluation
  • active labour market policy,
  • causal machine learning
  • modified causal forest
  • conditional average treatment effects

Cite this

Cockx, B., Lechner, M., & Bollens, J. (2020). Priority to unemployed immigrants? A causal machine learning evaluation of training in Belgium. Maastricht University, Graduate School of Business and Economics. GSBE Research Memoranda, No. 015 https://doi.org/10.26481/umagsb.2020015