Predicting Job Match Quality: A Machine Learning Approach

Sabrina Mühlbauer and Enzo Weber
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Sabrina Mühlbauer: Institute for Employment Research (IAB), Nuremberg, Germany

No 202409, IAB-Discussion Paper from Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, 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))

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 (search for similar items in EconPapers)
JEL-codes: C14 C45 C55 J64 (search for similar items in EconPapers)
Pages: 25 pages
Date: 2024-07-12
New Economics Papers: this item is included in nep-big, nep-cmp and nep-lab
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https://doi.org/10.48720/IAB.DP.2409

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Persistent link: https://EconPapers.repec.org/RePEc:iab:iabdpa:202409

DOI: 10.48720/IAB.DP.2409

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