Breaking the HISCO Barrier: Automatic Occupational Standardization with OccCANINE

Christian Møller Dahl, University of Southern Denmark
Torben Johansen, University of Southern Denmark
Christian Vedel, University of Southern Denmark

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. Note: At the conference we expect to demonstrate how the method can also be adapted to be used for various other classification systems: OCC1950, PST, etc.

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 Presented in Session 14. Using Data and Language Models