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

Item Type Journal paper
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.
Authors Lechner, Michael; Strittmatter, Anthony & Knaus, Michael
Journal or Publication Title Journal of Human Resources
Language English
Subjects economics
social sciences
education
other research area
HSG Classification contribution to scientific community
HSG Profile Area SEPS - Quantitative Economic Methods
Refereed Yes
Date 2020
Depositing User Prof. Ph.D Anthony Strittmatter
Date Deposited 04 Sep 2017 20:02
Last Modified 20 Jul 2022 17:31
URI: https://www.alexandria.unisg.ch/publications/251547

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Lechner, Michael; Strittmatter, Anthony & Knaus, Michael (2020) Heterogeneous Employment Effects of Job Search Programmes: A Machine Learning Approach. Journal of Human Resources,

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https://www.alexandria.unisg.ch/id/eprint/251547
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