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

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  • Cockx, Bart
  • Lechner, Michael
  • Bollens, Joost

Abstract

We investigate heterogenous employment effects of Flemish training programmes. Based on administrative individual data, we analyse programme effects at various aggregation levels using Modified Causal Forests (MCF), a causal machine learning estimator for multiple programmes. While all programmes have positive effects after the lock-in period, we find substantial heterogeneity across programmes and types of unemployed. Simulations show that assigning unemployed to programmes that maximise individual gains as identified in our estimation can considerably improve effectiveness. Simplified rules, such as one giving priority to unemployed with low employability, mostly recent migrants, lead to about half of the gains obtained by more sophisticated rules.

Suggested Citation

  • Cockx, Bart & Lechner, Michael & Bollens, Joost, 2020. "Priority to unemployed immigrants? A causal machine learning evaluation of training in Belgium," Economics Working Paper Series 2001, University of St. Gallen, School of Economics and Political Science.
  • Handle: RePEc:usg:econwp:2020:01
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    3. Ulrike Unterhofer & Conny Wunsch, 2022. "Macroeconomic Effects of Active Labour Market Policies: A Novel Instrumental Variables Approach," Papers 2211.12437, arXiv.org.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. Nora Bearth & Michael Lechner, 2024. "Causal Machine Learning for Moderation Effects," Papers 2401.08290, arXiv.org, revised Apr 2024.
    9. 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.
    10. 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.
    11. Daniel Boller & Michael Lechner & Gabriel Okasa, 2021. "The Effect of Sport in Online Dating: Evidence from Causal Machine Learning," Papers 2104.04601, arXiv.org.
    12. 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.
    13. 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.
    14. Gert Bijnens & Shyngys Karimov & Jozef Konings, 2023. "Does Automatic Wage Indexation Destroy Jobs? A Machine Learning Approach," De Economist, Springer, vol. 171(1), pages 85-117, March.
    15. 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.
    16. Michael Lechner & Jana Mareckova, 2022. "Modified Causal Forest," Papers 2209.03744, arXiv.org.
    17. 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.
    18. 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.
    19. Martins, Pedro S., 2021. "Employee training and firm performance: Evidence from ESF grant applications," Labour Economics, Elsevier, vol. 72(C).
    20. Körtner, John & Bonoli, Giuliano, 2021. "Predictive Algorithms in the Delivery of Public Employment Services," SocArXiv j7r8y, Center for Open Science.
    21. 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).

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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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