Three Methods for Occupation Coding Based on Statistical Learning

Journal article | Zeitschriftenartikel

Occupation coding, an important task in official statistics, refers to coding a respondent's text answer into one of many hundreds of occupation codes. To date, occupation coding is still at least partially conducted manually, at great expense. We propose three methods for automatic coding: combining separate models for the detailed occupation codes and for aggregate occupation codes, a hybrid method that combines a duplicate-based approach with a statistical learning algorithm, and a modified nearest neighbor approach. Using data from the German General Social Survey (ALLBUS), we show that the proposed methods improve on both the coding accuracy of the underlying statistical learning algorithm and the coding accuracy of duplicates where duplicates exist. Further, we find defining duplicates based on ngram variables (a concept from text mining) is preferable to one based on exact string matches.

Object Details

• ISSN: 2001-7367
• Extent: Seite(n): 101-122
• Language: Englisch
• Notes: Status: Veröffentlichungsversion; begutachtet (peer reviewed)

References and Relationships

• Bibliographic citation: Journal of Official Statistics, 33(1)

Classification and Topics

• Subject: Sozialwissenschaften, Soziologie ; Erhebungstechniken und Analysetechniken der Sozialwissenschaften ; Codierung ; Beruf ; Algorithmus ; ALLBUS ; amtliche Statistik ; Methode

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Gweon, Hyukjun ; Schonlau, Matthias ; Kaczmirek, Lars ; Blohm, Michael ; Steiner, Stefan
• Event: Veröffentlichung
• (where): Deutschland
• (when): 2017

Further information

• DOI: https://doi.org/10.1515/JOS-2017-0006
• Rights: GESIS - Leibniz-Institut für Sozialwissenschaften. Bibliothek Köln
• Last update: 21.06.2024, 4:27 PM CEST