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

Author

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  • Julia Manzella
  • Evan Totty
  • Gary Benedetto
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.

Suggested Citation

  • Julia Manzella & Evan Totty & Gary Benedetto, 2019. "A Task-based Approach to Constructing Occupational Categories with Implications for Empirical Research in Labor Economics," Working Papers 19-27, Center for Economic Studies, U.S. Census Bureau.
    • Handle: RePEc:cen:wpaper:19-27
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    File URL: https://www2.census.gov/ces/wp/2019/CES-WP-19-27.pdf
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    References listed on IDEAS

    as
    1. Barry T. Hirsch, 2004. "Reconsidering Union Wage Effects: Surveying New Evidence on an Old Topic," Journal of Labor Research, Transaction Publishers, vol. 25(2), pages 233-266, April.
    2. Francine D. Blau & Lawrence M. Kahn, 2017. "The Gender Wage Gap: Extent, Trends, and Explanations," Journal of Economic Literature, American Economic Association, vol. 55(3), pages 789-865, September.
    3. David H. Autor & Frank Levy & Richard J. Murnane, 2003. "The skill content of recent technological change: an empirical exploration," Proceedings, Federal Reserve Bank of San Francisco, issue Nov.
    4. Hirsch, Barry & Manzella, Julia, 2014. "Who Cares – and Does It Matter? Measuring Wage Penalties for Caring Work," IZA Discussion Papers 8388, Institute of Labor Economics (IZA).
    5. John M. Abowd & Martha H. Stinson, 2013. "Estimating Measurement Error in Annual Job Earnings: A Comparison of Survey and Administrative Data," The Review of Economics and Statistics, MIT Press, vol. 95(5), pages 1451-1467, December.
    6. Bruce D. Meyer & Wallace K. C. Mok & James X. Sullivan, 2015. "Household Surveys in Crisis," Journal of Economic Perspectives, American Economic Association, vol. 29(4), pages 199-226, Fall.
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    Cited by:

    1. Speer, Jamin D., 2020. "STEM Occupations and the Gender Gap: What Can We Learn from Job Tasks?," IZA Discussion Papers 13734, Institute of Labor Economics (IZA).
    2. Thomas B. Foster & Marta Murray-Close & Liana Christin Landivar & Mark deWolf, 2020. "An Evaluation of the Gender Wage Gap Using Linked Survey and Administrative Data," Working Papers 20-34, Center for Economic Studies, U.S. Census Bureau.

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