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

Michael Knaus, Michael Lechner and Anthony Strittmatter

Journal of Human Resources, 2022, vol. 57, issue 2, 597-636

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

JEL-codes: C21 H43 J68 (search for similar items in EconPapers)
Date: 2022
Note: DOI: 10.3368/jhr.57.2.0718-9615R1
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Citations: View citations in EconPapers (14)

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Related works:
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)
Working Paper: Heterogeneous Employment Effects of Job Search Programmes: A Machine Learning Approach (2017)
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Persistent link: https://EconPapers.repec.org/RePEc:uwp:jhriss:v:57:y:2022:i:2:p:597-636

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