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Journal of Official Statistics

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Three Methods for Occupation Coding Based on Statistical Learning

Hyukjun Gweon
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  • Department of Statistics and Actuarial Science, University of Waterloo, 200 University Avenue West, Waterloo, Ontario N2L 3G1 Canada
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/ Matthias Schonlau
  • Department of Statistics and Actuarial Science, University of Waterloo, 200 University Avenue West, Waterloo, Ontario N2L 3G1 Canada
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/ Lars Kaczmirek / Michael Blohm / Stefan Steiner
  • Department of Statistics and Actuarial Science, University of Waterloo, 200 University Avenue West, Waterloo, Ontario N2L 3G1 Canada
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Published Online: 2017-02-21 | DOI: https://doi.org/10.1515/jos-2017-0006


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, Volume 33, Issue 1, Pages 101–122, 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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