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

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  • Lechner, Michael
  • Strittmatter, Anthony
  • Knaus, Michael C.

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.

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  • Lechner, Michael & Strittmatter, Anthony & Knaus, Michael C., 2017. "Heterogeneous Employment Effects of Job Search Programmes: A Machine Learning Approach," CEPR Discussion Papers 12224, C.E.P.R. Discussion Papers.
    • Handle: RePEc:cpr:ceprdp:12224
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    More about this item

    Keywords

    Machine learning; Individualized treatment effects; Conditional average treatment effects; Active labour market policy;
    All these keywords.

    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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