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Priority to unemployed immigrants? A causal machine learning evaluation of training in Belgium

Author

Listed:
  • Bart Cockx

    (Department of Economics, Ghent University)

  • Michael Lechner

    (Swiss Institute for Empirical Economic Research (SEW), University of St. Gallen)

  • Joost Bollens

    (Vlaamse Dienst voor Arbeidsbemiddeling en Beroepsopleiding (VDAB))

Abstract
Based on administrative data of unemployed in Belgium, we estimate the labour market effects of three training programmes at various aggregation levels using Modified Causal Forests, a causal machine learning estimator. While all programmes have positive effects after the lock-in period, we find substantial heterogeneity across programmes and unemployed. Simulations show that “black-box” rules that reassign unemployed to programmes that maximise estimated individual gains can considerably improve effectiveness: up to 20% more (less) time spent in (un)employment within a 30 months window. A shallow policy tree delivers a simple rule that realizes about 70% of this gain.

Suggested Citation

  • Bart Cockx & Michael Lechner & Joost Bollens, 2020. "Priority to unemployed immigrants? A causal machine learning evaluation of training in Belgium," LIDAM Discussion Papers IRES 2020016, Université catholique de Louvain, Institut de Recherches Economiques et Sociales (IRES).
    • Handle: RePEc:ctl:louvir:2020016
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    References listed on IDEAS

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    Citations

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    Cited by:

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    2. Martins, Pedro S., 2021. "Employee training and firm performance: Evidence from ESF grant applications," Labour Economics, Elsevier, vol. 72(C).
    3. Ulrike Unterhofer, 2022. "Peer Effects in Labor Market Training," Papers 2211.12366, arXiv.org, revised Jun 2023.
    4. Nora Bearth & Michael Lechner, 2024. "Causal Machine Learning for Moderation Effects," Papers 2401.08290, arXiv.org, revised Apr 2024.
    5. Bert van Landeghem & Sam Desiere & Ludo Struyven, 2021. "Statistical profiling of unemployed jobseekers," IZA World of Labor, Institute of Labor Economics (IZA), pages 483-483, February.
    6. Mueller, Andreas I. & Spinnewijn, Johannes, 2023. "The Nature of Long-Term Unemployment: Predictability, Heterogeneity and Selection," CEPR Discussion Papers 17913, C.E.P.R. Discussion Papers.
    7. Steffen Mink & Daria Loginova & Stefan Mann, 2024. "Wolves' contribution to structural change in grazing systems among swiss alpine summer farms: The evidence from causal random forest," Journal of Agricultural Economics, Wiley Blackwell, vol. 75(1), pages 201-217, February.
    8. Michael Lechner & Jana Mareckova, 2024. "Comprehensive Causal Machine Learning," Papers 2405.10198, arXiv.org.
    9. Michael C Knaus, 2022. "Double machine learning-based programme evaluation under unconfoundedness [Econometric methods for program evaluation]," The Econometrics Journal, Royal Economic Society, vol. 25(3), pages 602-627.
    10. Daniel Boller & Michael Lechner & Gabriel Okasa, 2021. "The Effect of Sport in Online Dating: Evidence from Causal Machine Learning," Papers 2104.04601, arXiv.org.
    11. Ulrike Unterhofer & Conny Wunsch, 2022. "Macroeconomic Effects of Active Labour Market Policies: A Novel Instrumental Variables Approach," Papers 2211.12437, arXiv.org.
    12. Daniel Goller & Tamara Harrer & Michael Lechner & Joachim Wolff, 2021. "Active labour market policies for the long-term unemployed: New evidence from causal machine learning," Papers 2106.10141, arXiv.org, revised May 2023.
    13. Kleifgen, Eva & Lang, Julia, 2022. "Should I Train Or Should I Go? Estimating Treatment Effects of Retraining on Regional and Occupational Mobility," VfS Annual Conference 2022 (Basel): Big Data in Economics 264069, Verein für Socialpolitik / German Economic Association.
    14. Michael Lechner & Jana Mareckova, 2022. "Modified Causal Forest," Papers 2209.03744, arXiv.org.
    15. Ulrike Huemer & Rainer Eppel & Marion Kogler & Helmut Mahringer & Lukas Schmoigl & David Pichler, 2021. "Effektivität von Instrumenten der aktiven Arbeitsmarktpolitik in unterschiedlichen Konjunkturphasen," WIFO Studies, WIFO, number 67250, April.
    16. Michael Lechner, 2023. "Causal Machine Learning and its use for public policy," Swiss Journal of Economics and Statistics, Springer;Swiss Society of Economics and Statistics, vol. 159(1), pages 1-15, December.
    17. Körtner, John & Bonoli, Giuliano, 2021. "Predictive Algorithms in the Delivery of Public Employment Services," SocArXiv j7r8y, Center for Open Science.
    18. Patrick Rehill & Nicholas Biddle, 2023. "Transparency challenges in policy evaluation with causal machine learning -- improving usability and accountability," Papers 2310.13240, arXiv.org, revised Mar 2024.
    19. Kelvin Mulungu & Zewdu Ayalew Abro & Wambui Beatrice Muriithi & Menale Kassie & Miachael Kidoido & Subramanian Sevgan & Samira Mohamed & Chrysantus Tanga & Fathiya Khamis, 2024. "One size does not fit all: Heterogeneous economic impact of integrated pest management practices for mango fruit flies in Kenya—a machine learning approach," Journal of Agricultural Economics, Wiley Blackwell, vol. 75(1), pages 261-279, February.
    20. Strittmatter, Anthony, 2023. "What is the value added by using causal machine learning methods in a welfare experiment evaluation?," Labour Economics, Elsevier, vol. 84(C).
    21. Phi-Hung Nguyen & Jung-Fa Tsai & Ihsan Erdem Kayral & Ming-Hua Lin, 2021. "Unemployment Rates Forecasting with Grey-Based Models in the Post-COVID-19 Period: A Case Study from Vietnam," Sustainability, MDPI, vol. 13(14), pages 1-27, July.

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    More about this item

    Keywords

    Policy evaluation; active labour market policy; causal machine learning; modified causal forest; conditional average treatment effects;
    All these keywords.

    JEL classification:

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

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