Heterogeneous Employment Effects of Job Search Programmes: A Machine Learning Approach

Michael Lechner, Anthony Strittmatter and Michael Knaus

No 12224, CEPR Discussion Papers from C.E.P.R. Discussion Papers

Abstract: We systematically investigate the effect heterogeneity of job search programmes for unemployed workers. To investigate possibly heterogeneous employment effects, we combine non-experimental causal empirical models with Lasso-type estimators. The empirical analyses are based on rich administrative data from Swiss social security records. We find considerable heterogeneities only during the first six months after the start of training. Consistent with previous results of the literature, unemployed persons with fewer employment opportunities profit more from participating in these programmes. Furthermore, we also document heterogeneous employment effects by residence status. Finally, we show the potential of easy-to-implement programme participation rules for improving average employment effects of these active labour market programmes.

Keywords: Machine learning; Individualized treatment effects; Conditional average treatment effects; Active labour market policy (search for similar items in EconPapers)
JEL-codes: C21 H43 J68 (search for similar items in EconPapers)
Date: 2017-08
New Economics Papers: this item is included in nep-big and nep-lab
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Citations: View citations in EconPapers (33)

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Related works:
Journal Article: Heterogeneous Employment Effects of Job Search Programs: A Machine Learning Approach (2022)
Working Paper: Heterogeneous Employment Effects of Job Search Programmes: A Machine Learning Approach (2018)
Working Paper: Heterogeneous Employment Effects of Job Search Programmes: A Machine Learning Approach (2017)
Working Paper: Heterogeneous Employment Effects of Job Search Programmes: A Machine Learning Approach (2017)
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