Arbeitspapier
Occupational Classifications: A Machine Learning Approach
Characterizing the work that people do on their jobs is a longstanding and core issue in labor economics. Traditionally, classification has been done manually. If it were possible to combine new computational tools and administrative wage records to generate an automated crosswalk between job titles and occupations, millions of dollars could be saved in labor costs, data processing could be sped up, data could become more consistent, and it might be possible to generate, without a lag, current information about the changing occupational composition of the labor market. This paper examines the potential to assign occupations to job titles contained in administrative data using automated, machine-learning approaches. We use a new extraordinarily rich and detailed set of data on transactional HR records of large firms (universities) in a relatively narrowly defined industry (public institutions of higher education) to identify the potential for machine-learning approaches to classify occupations.
- Sprache
-
Englisch
- Erschienen in
-
Series: IZA Discussion Papers ; No. 11738
- Klassifikation
-
Wirtschaft
Labor Force and Employment, Size, and Structure
Human Capital; Skills; Occupational Choice; Labor Productivity
- Thema
-
UMETRICS
occupational classifications
machine learning
administrative data
transaction data
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Ikudo, Akina
Lane, Julia
Staudt, Joseph
Weinberg, Bruce A.
- Ereignis
-
Veröffentlichung
- (wer)
-
Institute of Labor Economics (IZA)
- (wo)
-
Bonn
- (wann)
-
2018
- Handle
- Letzte Aktualisierung
-
10.03.2025, 11:43 MEZ
Datenpartner
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Arbeitspapier
Beteiligte
- Ikudo, Akina
- Lane, Julia
- Staudt, Joseph
- Weinberg, Bruce A.
- Institute of Labor Economics (IZA)
Entstanden
- 2018
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