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

Daniel Goller, Tamara Harrer, Michael Lechner and Joachim Wolff

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.

Date: 2021-06, Revised 2023-05
New Economics Papers: this item is included in nep-big
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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:arx:papers:2106.10141

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