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

Michael Knaus (), Michael Lechner and Anthony Strittmatter ()

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: active labour market policy; conditional average treatment effects; individualized treatment effects; Machine Learning (search for similar items in EconPapers)
JEL-codes: C21 H43 J68 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-big and nep-lab
Date: 2017-08
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Related works:
Working Paper: Heterogeneous Employment Effects of Job Search Programmes: A Machine Learning Approach (2018) Downloads
Working Paper: Heterogeneous Employment Effects of Job Search Programmes: A Machine Learning Approach (2017) Downloads
Working Paper: Heterogeneous Employment Effects of Job Search Programmes: A Machine Learning Approach (2017) Downloads
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