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

TL;DR

This paper uses machine learning to analyze how job search programs affect different groups of unemployed workers, revealing significant heterogeneity in outcomes based on timing, opportunities, and residence status.

Contribution

It introduces a novel combination of causal models and Lasso estimators to identify heterogeneous effects in employment programs using rich administrative data.

Findings

Heterogeneous effects are most pronounced within the first six months of training.

Unemployed persons with fewer opportunities benefit more from programs.

Residence status influences employment outcomes after participation.

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.