Active labour market policies for the long-term unemployed: New evidence from causal machine learning

TL;DR

This paper uses causal machine learning to evaluate the effectiveness of active labor market policies for long-term unemployed in Germany, finding positive and lasting effects, especially for women, and proposes data-driven allocation rules.

Contribution

It introduces a novel application of causal machine learning to assess and improve active labor market policies using administrative data.

Findings

Participants benefit from positive long-term effects across programs.

Placement services are the most effective intervention.

Women benefit more under better local labor market conditions.

Abstract

Active labor market programs are important instruments used by European employment agencies to help the unemployed find work. Investigating large administrative data on German long-term unemployed persons, we analyze the effectiveness of three job search assistance and training programs using Causal Machine Learning. Participants benefit from quickly realizing and long-lasting positive effects across all programs, with placement services being the most effective. For women, we find differential effects in various characteristics. Especially, women benefit from better local labor market conditions. We propose more effective data-driven rules for allocating the unemployed to the respective labor market programs that could be employed by decision-makers.