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Predicting Job Match Quality: A Machine Learning Approach

Author

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  • 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 algorithm-based application to improve the match quality in the labor market. We use comprehensive administrative data on employment biographies in Germany to predict job match quality in terms of job stability and wages. The models are estimated with both machine learning (ML) (i.e., XGBoost) and common statistical methods (i.e., OLS, logit). Compared to the latter approach, we find that XGBoost performs better for pattern recognition, analyzes large amounts of data in an efficient way and minimizes the prediction error in the application. Finally, we combine our results with algorithms that optimize matching probability to provide a ranked list of job recommendations based on individual characteristics for each job seeker. This application could support caseworkers and job seekers in expanding their job search strategy." (Author's abstract, IAB-Doku) ((en))

Suggested Citation

  • Mühlbauer, Sabrina & Weber, Enzo, 2024. "Predicting Job Match Quality: A Machine Learning Approach," IAB-Discussion Paper 202409, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].
    • Handle: RePEc:iab:iabdpa:202409
    DOI: 10.48720/IAB.DP.2409
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    Keywords

    Bundesrepublik Deutschland ; Stichprobe der Integrierten Arbeitsmarktbiografien (SIAB) ; IAB-Open-Access-Publikation ; Berufsverlauf ; Datenanalyse ; Datenqualität ; Fehler ; Informationsgewinnung ; Integrierte Erwerbsbiografien ; Lohn ; matching ; Optimierung ; Prognosegenauigkeit ; Prognoseverfahren ; Qualität ; Quote ; statistische Methode ; Machine learning ; Arbeitsmarktforschung ; Arbeitsplatzangebot ; Arbeitsuchende ; 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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