Machine Learning for Labour Market Matching

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

No 202203, IAB-Discussion Paper from Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, 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))

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 (search for similar items in EconPapers)
JEL-codes: C14 C45 C55 J64 (search for similar items in EconPapers)
Pages: 37 pages
Date: 2022-02-02
New Economics Papers: this item is included in nep-big, nep-cmp, nep-his and nep-lab
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Citations: View citations in EconPapers (3)

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https://doi.org/10.48720/IAB.DP.2203

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Persistent link: https://EconPapers.repec.org/RePEc:iab:iabdpa:202203

DOI: 10.48720/IAB.DP.2203

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