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Active Labour Market Policies for the Long-Term Unemployed: New Evidence from Causal Machine Learning

Author

Listed:
  • Goller, Daniel

    (University of Bern)

  • Harrer, Tamara

    (Institute for Employment Research (IAB), Nuremberg)

  • Lechner, Michael

    (University of St. Gallen)

  • Wolff, Joachim

    (Institute for Employment Research (IAB), Nuremberg)

Abstract

We investigate the effectiveness of three different job-search and training programmes for German long-term unemployed persons. On the basis of an extensive administrative data set, we evaluated the effects of those programmes on various levels of aggregation using Causal Machine Learning. We found participants to benefit from the investigated programmes with placement services to be most effective. Effects are realised quickly and are long-lasting for any programme. While the effects are rather homogenous for men, we found differential effects for women in various characteristics. Women benefit in particular when local labour market conditions improve. Regarding the allocation mechanism of the unemployed to the different programmes, we found the observed allocation to be as effective as a random allocation. Therefore, we propose data-driven rules for the allocation of the unemployed to the respective labour market programmes that would improve the status-quo.

Suggested Citation

  • Goller, Daniel & Harrer, Tamara & Lechner, Michael & Wolff, Joachim, 2021. "Active Labour Market Policies for the Long-Term Unemployed: New Evidence from Causal Machine Learning," IZA Discussion Papers 14486, Institute of Labor Economics (IZA).
    • Handle: RePEc:iza:izadps:dp14486
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    Cited by:

    1. Daniel Goller, 2023. "Analysing a built-in advantage in asymmetric darts contests using causal machine learning," Annals of Operations Research, Springer, vol. 325(1), pages 649-679, June.
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    3. Gabriel Okasa, 2022. "Meta-Learners for Estimation of Causal Effects: Finite Sample Cross-Fit Performance," Papers 2201.12692, arXiv.org.

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    More about this item

    Keywords

    policy evaluation; Modified Causal Forest (MCF); active labour market programmes; conditional average treatment effect (CATE);
    All these keywords.

    JEL classification:

    • J08 - Labor and Demographic Economics - - General - - - Labor Economics Policies
    • J68 - Labor and Demographic Economics - - Mobility, Unemployment, Vacancies, and Immigrant Workers - - - Public Policy

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