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A Task-based Approach to Constructing Occupational Categories with Implications for Empirical Research in Labor Economics

September 2019

Working Paper Number:

CES-19-27

Abstract

Most applied research in labor economics that examines returns to worker skills or differences in earnings across subgroups of workers typically accounts for the role of occupations by controlling for occupational categories. Researchers often aggregate detailed occupations into categories based on the Standard Occupation Classification (SOC) coding scheme, which is based largely on narratives or qualitative measures of workers' tasks. Alternatively, we propose two quantitative task-based approaches to constructing occupational categories by using factor analysis with O*NET job descriptors that provide a rich set of continuous measures of job tasks across all occupations. We find that our task-based approach outperforms the SOC-based approach in terms of lower occupation distance measures. We show that our task-based approach provides an intuitive, nuanced interpretation for grouping occupations and permits quantitative assessments of similarities in task compositions across occupations. We also replicate a recent analysis and find that our task-based occupational categories explain more of the gender wage gap than the SOC-based approaches explain. Our study enhances the Federal Statistical System's understanding of the SOC codes, investigates ways to use third-party data to construct useful research variables that can potentially be added to Census Bureau data products to improve their quality and versatility, and sheds light on how the use of alternative occupational categories in economics research may lead to different empirical results and deeper understanding in the analysis of labor market outcomes.

Document Tags and Keywords

Keywords Keywords are automatically generated using KeyBERT, a powerful and innovative keyword extraction tool that utilizes BERT embeddings to ensure high-quality and contextually relevant keywords.

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:
survey, agency, employ, employed, labor, job, classified, worker, occupation, union

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:
Internal Revenue Service, Social Security Administration, Stern School of Business, Current Population Survey, Survey of Income and Program Participation, Federal Register, Journal of Economic Literature, Standard Occupational Classification, PSID, Census Bureau Disclosure Review Board

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