Arbeitspapier
Machine learning for labour market matching
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 (OLS, Logit) and machine learning (ML) methods. The findings suggest that ML performs best regarding the out-of-sample classification error. In terms of the unemployment rate hypothetically, the advantage of ML compared to the common statistical methods would stand for a diference of 0.3 - 1.0 percentage points.
- Sprache
-
Englisch
- Erschienen in
-
Series: IAB-Discussion Paper ; No. 3/2022
- Klassifikation
-
Wirtschaft
Semiparametric and Nonparametric Methods: General
Neural Networks and Related Topics
Large Data Sets: Modeling and Analysis
Unemployment: Models, Duration, Incidence, and Job Search
- Thema
-
Labour Market
Machine Learning
Matching
Random Forest
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Mühlbauer, Sabrina
Weber, Enzo
- Ereignis
-
Veröffentlichung
- (wer)
-
Institut für Arbeitsmarkt- und Berufsforschung (IAB)
- (wo)
-
Nürnberg
- (wann)
-
2022
- DOI
-
doi:10.48720/IAB.DP.2203
- Handle
- Letzte Aktualisierung
-
10.03.2025, 11:41 MEZ
Datenpartner
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Arbeitspapier
Beteiligte
- Mühlbauer, Sabrina
- Weber, Enzo
- Institut für Arbeitsmarkt- und Berufsforschung (IAB)
Entstanden
- 2022
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