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

Daniel Goller, Michael Lechner, Tamara Pongratz and Joachim Wolff

Labour Economics, 2025, vol. 94, issue C

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. In addition to estimating average effects, causal machine learning enables the systematic analysis of effect heterogeneities, thereby facilitating the development of more effective personalized allocation strategies for long-term unemployed. On average, 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. The data-driven rules we propose for the allocation of unemployed people to the available labor market programs, which could be employed by decision-makers, show a potential to improve the effects by 6 - 14 percent.

Keywords: Policy evaluation; Active labor market programs; Conditional average treatment effect (CATE) (search for similar items in EconPapers)
JEL-codes: J08 J68 (search for similar items in EconPapers)
Date: 2025
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Related works:
Working Paper: Active labour market policies for the long-term unemployed: New evidence from causal machine learning (2023)
Working Paper: Active Labour Market Policies for the Long-Term Unemployed: New Evidence from Causal Machine Learning (2021)
Working Paper: Active labour market policies for the long-term unemployed: New evidence from causal machine learning (2021)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:labeco:v:94:y:2025:i:c:s0927537125000533

DOI: 10.1016/j.labeco.2025.102729

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