Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/298601 
Erscheinungsjahr: 
2024
Schriftenreihe/Nr.: 
EHES Working Paper No. 255
Verlag: 
European Historical Economics Society (EHES), s.l.
Zusammenfassung: 
This paper introduces a new tool, OccCANINE, to automatically transform occupational descriptions into the HISCO classification system. The manual work involved in processing and classifying occupational descriptions is error-prone, tedious, and time-consuming. We finetune a preexisting language model (CANINE) to do this automatically, thereby performing in seconds and minutes what previously took days and weeks. The model is trained on 14 million pairs of occupational descriptions and HISCO codes in 13 different languages contributed by 22 different sources. Our approach is shown to have accuracy, recall, and precision above 90 percent. Our tool breaks the metaphorical HISCO barrier and makes this data readily available for analysis of occupational structures with broad applicability in economics, economic history, and various related disciplines.
Schlagwörter: 
Occupational Standardization
HISCO Classification System
Machine Learning in Economic History
Language Models
JEL: 
C55
C81
J1
N01
N3
N6
O33
Dokumentart: 
Working Paper

Datei(en):
Datei
Größe
1.15 MB





Publikationen in EconStor sind urheberrechtlich geschützt.