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

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  • Michael C. Knaus
  • Michael Lechner
  • Anthony Strittmatter

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.

Suggested Citation

  • Michael C. Knaus & Michael Lechner & Anthony Strittmatter, 2022. "Heterogeneous Employment Effects of Job Search Programs: A Machine Learning Approach," Journal of Human Resources, University of Wisconsin Press, vol. 57(2), pages 597-636.
    • Handle: RePEc:uwp:jhriss:v:57:y:2022:i:2:p:597-636
    Note: DOI: 10.3368/jhr.57.2.0718-9615R1
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    JEL classification:

    • J68 - Labor and Demographic Economics - - Mobility, Unemployment, Vacancies, and Immigrant Workers - - - Public Policy
    • H43 - Public Economics - - Publicly Provided Goods - - - Project Evaluation; Social Discount Rate
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models

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