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

Bart Cockx, Michael Lechner, Joost Bollens

arXiv 30 Dec 2019 · Econometrics · publishedLabour Economics (2022) · 29 citations (OpenAlex)

arXiv:1912.12864 · PDF · DOI · OpenAlex

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 percent more (less) time spent in (un)employment within a 30 months window. A shallow policy tree delivers a simple rule that realizes about 70 percent of this gain.

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	Citing paper	Intensity	Mentions	Sections
1	Meta-Learners for Estimation of Causal Effects: Finite Sample Cross-Fit Performance	0.405	1	1
2	Peer Effects in Labor Market Training	0.405	1	1
3	Macroeconomic Effects of Active Labour Market Policies: A Novel Instrumental Variables Approach	0.405	1	1
4	Policy Learning With Rare Outcomes	0.405	1	1
5	Transparency challenges in policy evaluation with causal machine learning –- improving usability and accountability	0.405	1	1