Occupational classifications. A machine learning approach.

Autor/inn/en	Ikudo, Akina; Lane, Julia; Staudt, Joseph; Weinberg, Bruce
Institution	Forschungsinstitut zur Zukunft der Arbeit
Titel	Occupational classifications. A machine learning approach. Gefälligkeitsübersetzung: Berufsklassifikationen. Ein maschineller Lernansatz.
Quelle	Bonn (2018), 46 S. PDF als Volltext kostenfreie Datei Link als defekt meldenVerfügbarkeit
Reihe	IZA discussion paper. 11738
Sprache	englisch
Dokumenttyp	online; Monographie
Schlagwörter	Prozessproduzierte Daten; Künstliche Intelligenz; Algorithmus; Automatisierung; Berufsbezeichnung; Berufsklassifikation; Universität; Hochschulpersonal; Klassifikation; USA
Abstract	"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." (Author's abstract, IAB-Doku).
Erfasst von	Institut für Arbeitsmarkt- und Berufsforschung, Nürnberg
Update	2019/1