Machine Learning for Occupation Coding—A Comparison Study
Abstract Asking people about their occupation is common practice in surveys and censuses around the world. The answers are typically recorded in textual form and subsequently assigned (coded) to categories, which have been defined in official occupational classifications. While this coding step is o...
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| Published in: | Journal of survey statistics and methodology Vol. 9; no. 5; pp. 1013 - 1034 |
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| Main Authors: | , |
| Format: | Journal Article |
| Language: | English |
| Published: |
Oxford University Press
01.11.2021
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| Subjects: | |
| ISSN: | 2325-0984, 2325-0992 |
| Online Access: | Get full text |
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| Summary: | Abstract
Asking people about their occupation is common practice in surveys and censuses around the world. The answers are typically recorded in textual form and subsequently assigned (coded) to categories, which have been defined in official occupational classifications. While this coding step is often done manually, substituting it with more automated workflows has been a longstanding goal, promising reduced data-processing costs and accelerated publication of key statistics. Although numerous researchers have developed different algorithms for automated occupation coding, the algorithms have rarely been compared with each other or tested on different data sets. We fill this gap by comparing some of the most promising algorithms found in the literature and testing them on five data sets from Germany. The first two algorithms we test exemplify a common practice in which answers are coded automatically according to a predefined list of job titles. Statistical learning algorithms—that is, regularized multinomial regression, tree boosting, or algorithms developed specifically for occupation coding (algorithms three to six)—can improve upon algorithms one and two, but only if a sufficient number of training observations from previous surveys is available. The best results are obtained by merging the list of job titles with coded answers from previous surveys before using this combined training data for statistical learning (algorithm 7). However, the differences between the algorithms are often small compared to the large variation found across different data sets, which we ascribe to systematic differences in the way the data were coded in the first place. Such differences complicate the application of statistical learning, which risks perpetuating questionable coding decisions from the training data to the future. |
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| ISSN: | 2325-0984 2325-0992 |
| DOI: | 10.1093/jssam/smaa023 |