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Statistical profiling in public employment services: An international comparison

Author

Listed:
  • Sam Desiere
  • Kristine Langenbucher
  • Ludo Struyven

Abstract

Profiling tools help to deliver employment services more efficiently. They can ensure that more costly, intensive services are targeted at jobseekers most at risk of becoming long term unemployed. Moreover, the detailed information on the employment barriers facing jobseekers obtained through the profiling process can be used to tailor services more closely to their individual needs. While other forms of profiling exist, the focus is on statistical profiling, which makes use of statistical models to predict jobseekers’ likelihood of becoming long-term unemployed. An overview on profiling tools currently used throughout the OECD is presented, considerations for the development of such tools, and some insights into the latest developments such as using “click data” on job searches and advanced machine learning techniques. Also discussed are the limitations of statistical profiling tools and options for policymakers on how to address those in the development and implementation of statistical profiling tools.

Suggested Citation

  • Sam Desiere & Kristine Langenbucher & Ludo Struyven, 2019. "Statistical profiling in public employment services: An international comparison," OECD Social, Employment and Migration Working Papers 224, OECD Publishing.
    • Handle: RePEc:oec:elsaab:224-en
    DOI: 10.1787/b5e5f16e-en
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    Citations

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    Cited by:

    1. Emile Cammeraat & Brinn Hekkelman & Pim Kastelein & Suzanne Vissers, 2023. "Predictability and (co-)incidence of labor and health shocks," CPB Discussion Paper 453, CPB Netherlands Bureau for Economic Policy Analysis.
    2. Kerstin Bachberger-Strolz, 2020. "Profiling, Targeting, Algorithmen, künstliche Intelligenz – über die Irrwege einer Debatte in der Arbeitsmarktpolitik," Wirtschaft und Gesellschaft - WuG, Kammer für Arbeiter und Angestellte für Wien, Abteilung Wirtschaftswissenschaft und Statistik, vol. 46(3), pages 329-363.
    3. 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.
    4. Körtner, John & Bonoli, Giuliano, 2021. "Predictive Algorithms in the Delivery of Public Employment Services," SocArXiv j7r8y, Center for Open Science.
    5. Berman, Alexander & de Fine Licht, Karl & Carlsson, Vanja, 2024. "Trustworthy AI in the public sector: An empirical analysis of a Swedish labor market decision-support system," Technology in Society, Elsevier, vol. 76(C).

    More about this item

    Keywords

    active labour market policy; caseworkers; employment barrier; jobseekers; selection; statistical profiling; targeting; unemployment;
    All these keywords.

    JEL classification:

    • J64 - Labor and Demographic Economics - - Mobility, Unemployment, Vacancies, and Immigrant Workers - - - Unemployment: Models, Duration, Incidence, and Job Search
    • J68 - Labor and Demographic Economics - - Mobility, Unemployment, Vacancies, and Immigrant Workers - - - Public Policy

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