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Journal of Official Statistics

The Journal of Statistics Sweden

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2001-7367
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Three Methods for Occupation Coding Based on Statistical Learning

Hyukjun Gweon
  • Corresponding author
  • Department of Statistics and Actuarial Science, University of Waterloo, 200 University Avenue West, Waterloo, Ontario N2L 3G1 Canada
  • Email:
/ Matthias Schonlau
  • Department of Statistics and Actuarial Science, University of Waterloo, 200 University Avenue West, Waterloo, Ontario N2L 3G1 Canada
  • Email:
/ Lars Kaczmirek
  • GESIS – Leibniz-Institute for the Social Sciences, PO Box 12 21 55, D-68072 Mannheim, Germany
  • Email:
/ Michael Blohm
  • GESIS – Leibniz-Institute for the Social Sciences, PO Box 12 21 55, D-68072 Mannheim, Germany
  • Email:
/ Stefan Steiner
  • Department of Statistics and Actuarial Science, University of Waterloo, 200 University Avenue West, Waterloo, Ontario N2L 3G1 Canada
  • Email:
Published Online: 2017-02-21 | DOI: https://doi.org/10.1515/jos-2017-0006

Abstract

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.

Keywords: Automated coding; Machine learning; ISCO-88; ALLBUS

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About the article

Received: 2016-03-01

Revised: 2016-10-01

Accepted: 2016-10-01

Published Online: 2017-02-21

Published in Print: 2017-03-01


Citation Information: Journal of Official Statistics, ISSN (Online) 2001-7367, DOI: https://doi.org/10.1515/jos-2017-0006.

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© by Hyukjun Gweon. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. BY-NC-ND 4.0

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