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

Bart Cockx, Michael Lechner and Joost Bollens (joost.bollens@vdab.be)
Additional contact information
Joost Bollens: Vlaamse Dienst voor Arbeidsbemiddeling en Beroepsopleiding (VDAB)

No 2020016, LIDAM Discussion Papers IRES from Université catholique de Louvain, Institut de Recherches Economiques et Sociales (IRES)

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.

Keywords: Policy evaluation; active labour market policy; causal machine learning; modified causal forest; conditional average treatment effects (search for similar items in EconPapers)
JEL-codes: J68 (search for similar items in EconPapers)
Date: 2020-05-04
New Economics Papers: this item is included in nep-big, nep-eur and nep-lab
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (15)

Downloads: (external link)
https://sites.uclouvain.be/econ/DP/IRES/2020016.pdf (application/pdf)

Related works:
Journal Article: Priority to unemployed immigrants? A causal machine learning evaluation of training in Belgium (2023)
Working Paper: Priority to unemployed immigrants? A causal machine learning evaluation of training in Belgium (2022)
Working Paper: Priority of Unemployed Immigrants? A Causal Machine Learning Evaluation of Training in Belgium (2020)
Working Paper: Priority to unemployed immigrants? A causal machine learning evaluation of training in Belgium (2020)
Working Paper: Priority to unemployed immigrants? A causal machine learning evaluation of training in Belgium (2020)
Working Paper: Priority to unemployed immigrants? A causal machine learning evaluation of training in Belgium (2020)
Working Paper: Priority to unemployed immigrants? A causal machine learning evaluation of training in Belgium (2020)
Working Paper: Priority to unemployed immigrants? A causal machine learning evaluation of training in Belgium (2020)
Working Paper: Priority to Unemployed Immigrants? A Causal Machine Learning Evaluation of Training in Belgium (2019)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ctl:louvir:2020016

Access Statistics for this paper

More papers in LIDAM Discussion Papers IRES from Université catholique de Louvain, Institut de Recherches Economiques et Sociales (IRES) Place Montesquieu 3, 1348 Louvain-la-Neuve (Belgium). Contact information at EDIRC.
Bibliographic data for series maintained by Virginie LEBLANC (virginie.leblanc@uclouvain.be).