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The Heterogeneous Effects of Active Labour Market Policies in Switzerland

Author

Listed:
  • Federica Mascolo
  • Nora Bearth
  • Fabian Muny
  • Michael Lechner
  • Jana Mareckova

Abstract

Active labour market policies are widely used by the Swiss government, enrolling more than half of unemployed individuals. This paper analyses whether the Swiss programmes increase future employment and earnings of the unemployed by using causal machine learning methods and leveraging an administrative dataset that captures the population of unemployed and their labour market histories. The findings indicate a small positive average effect on employment and earnings three years after starting a specific Temporary Wage Subsidy programme. In contrast, we find negative effects for Basic Courses, such as job application training, on both outcomes three years after starting the programme. We find no significant effect for Employment Programmes which are conducted outside the regular labour market and Training Courses, such as language and computer courses. The programmes are most effective for individuals with lower education levels and with a migration background from non-EU countries. Last, shallow policy trees provide practical guidance on how the allocation of individuals to programmes could be optimised.

Suggested Citation

  • Federica Mascolo & Nora Bearth & Fabian Muny & Michael Lechner & Jana Mareckova, 2024. "The Heterogeneous Effects of Active Labour Market Policies in Switzerland," Papers 2410.23322, arXiv.org.
  • Handle: RePEc:arx:papers:2410.23322
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    References listed on IDEAS

    as
    1. Lechner, Michael & Wunsch, Conny, 2013. "Sensitivity of matching-based program evaluations to the availability of control variables," Labour Economics, Elsevier, vol. 21(C), pages 111-121.
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    3. Stefan Wager & Susan Athey, 2018. "Estimation and Inference of Heterogeneous Treatment Effects using Random Forests," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 113(523), pages 1228-1242, July.
    4. Michael C Knaus & Michael Lechner & Anthony Strittmatter, 2021. "Machine learning estimation of heterogeneous causal effects: Empirical Monte Carlo evidence," The Econometrics Journal, Royal Economic Society, vol. 24(1), pages 134-161.
    5. Hugo Bodory & Federica Mascolo & Michael Lechner, 2024. "Enabling Decision-Making with the Modified Causal Forest: Policy Trees for Treatment Assignment," Papers 2406.02241, arXiv.org.
    6. Martin, John P. & Grubb, David, 2001. "What works and for whom: a review of OECD countries' experiences with active labour market policies," Working Paper Series 2001:14, IFAU - Institute for Evaluation of Labour Market and Education Policy.
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    12. Goller, Daniel & Harrer, Tamara & Lechner, Michael & Wolff, Joachim, 2021. "Active labour market policies for the long-term unemployed: New evidence from causal machine learning," Economics Working Paper Series 2108, University of St. Gallen, School of Economics and Political Science.
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