From Average Effects to Targeted Assignment: A Causal Machine Learning Analysis of Swiss Active Labor Market Policies

TL;DR

This paper uses causal machine learning to evaluate Swiss active labor market policies, revealing nuanced effects and providing targeted assignment strategies to improve employment outcomes.

Contribution

It introduces a causal machine learning approach to assess and optimize the targeting of Swiss labor market programs, highlighting differential effects across groups.

Findings

Temporary Wage Subsidies have a small positive effect on employment and earnings.

Basic Courses negatively impact employment and earnings.

Programs are most effective for non-EU migrants and lower-educated individuals.

Abstract

Active labor market policies are widely used by the Swiss government, enrolling over half of all unemployed individuals. This paper evaluates the effectiveness of Swiss programs in improving employment and earnings outcomes using causal machine learning and rich administrative data on unemployed individuals in 2014 and 2015, including detailed labor market histories and other covariates. The findings for Swiss citizens and immigrants with permanent residency indicate a small positive average effect of a Temporary Wage Subsidy program on employment and earnings in the third year after program start. In contrast, Basic Courses, such as job application training, exhibit negative effects on both outcomes over the same period. No significant impacts are found for Employment Programs conducted outside the regular labor market or for Training Courses such as language or computer classes. The programs are most effective for individuals with a non-EU migration background, while Temporary Wage Subsidies also benefit those with lower educational attainment. Finally, shallow policy trees provide practical guidance for improving the targeting of program assignments.