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

Bart Cockx; Michael Lechner; Joost Bollens

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arXiv · 1912.12864

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

Bart Cockx·Michael Lechner·Joost Bollens

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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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Bart Cockx, Michael Lechner, Joost Bollens. 2019-12-30. Priority to unemployed immigrants? A causal machine learning evaluation of training in Belgium. https://arxiv.org/abs/1912.12864

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