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Machine Learning for Labour Market Matching

Author

Listed:
  • Mühlbauer, Sabrina

    (Institute for Employment Research (IAB), Nuremberg, Germany)

  • Weber, Enzo

    (Institute for Employment Research (IAB), Nuremberg, Germany)

Abstract

"This paper develops a large-scale application to improve the labour market matching process with model- and algorithm-based statistical methods. We use comprehensive administrative data on employment biographies covering individual and job-related information of workers in Germany. We estimate the probability that a job seeker gets employed in a certain occupational field. For this purpose, we make predictions with common statistical methods and machine learning (ML) methods. The findings suggest that ML performs better than the other methods regarding the out-of-sample classification error. In terms of the unemployment rate, the advantage of ML would stand for a difference of 2.9 - 3.6 percentage points." (Author's abstract, IAB-Doku) ((en))

Suggested Citation

  • Mühlbauer, Sabrina & Weber, Enzo, 2022. "Machine Learning for Labour Market Matching," IAB-Discussion Paper 202203, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].
    • Handle: RePEc:iab:iabdpa:202203
    DOI: 10.48720/IAB.DP.2203
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    Cited by:

    1. J. van den Berg, Gerard & Kunaschk, Max & Lang, Julia & Stephan, Gesine & Uhlendorff, Arne, 2023. "Predicting re-employment: machine learning versus assessments by unemployed workers and by their caseworkers," Working Paper Series 2023:22, IFAU - Institute for Evaluation of Labour Market and Education Policy.

    More about this item

    Keywords

    Bundesrepublik Deutschland ; IAB-Open-Access-Publikation ; Berufsfelder ; Berufsverlauf ; Datenanalyse ; Datenqualität ; Forschungsansatz ; Integrierte Erwerbsbiografien ; Algorithmus ; künstliche Intelligenz ; matching ; Optimierung ; Schätzung ; Arbeitslose ; Arbeitsmarktchancen ; Arbeitsmarktforschung ; 2012-2017;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • J64 - Labor and Demographic Economics - - Mobility, Unemployment, Vacancies, and Immigrant Workers - - - Unemployment: Models, Duration, Incidence, and Job Search
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis

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