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Occupational Classifications: A Machine Learning Approach

Author

Listed:
  • Ikudo, Akina

    () (University of California, Los Angeles)

  • Lane, Julia

    () (New York University)

  • Staudt, Joseph

    (U.S. Census Bureau)

  • Weinberg, Bruce A.

    () (Ohio State University)

Abstract

Characterizing the work that people do on their jobs is a longstanding and core issue in labor economics. Traditionally, classification has been done manually. If it were possible to combine new computational tools and administrative wage records to generate an automated crosswalk between job titles and occupations, millions of dollars could be saved in labor costs, data processing could be sped up, data could become more consistent, and it might be possible to generate, without a lag, current information about the changing occupational composition of the labor market. This paper examines the potential to assign occupations to job titles contained in administrative data using automated, machine-learning approaches. We use a new extraordinarily rich and detailed set of data on transactional HR records of large firms (universities) in a relatively narrowly defined industry (public institutions of higher education) to identify the potential for machine-learning approaches to classify occupations.

Suggested Citation

  • Ikudo, Akina & Lane, Julia & Staudt, Joseph & Weinberg, Bruce A., 2018. "Occupational Classifications: A Machine Learning Approach," IZA Discussion Papers 11738, Institute for the Study of Labor (IZA).
  • Handle: RePEc:iza:izadps:dp11738
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    File URL: http://ftp.iza.org/dp11738.pdf
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    References listed on IDEAS

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    1. Fisher, Jonathan D. & Houseworth, Christina A., 2013. "Occupation inflation in the Current Population Survey," Journal of Economic and Social Measurement, IOS Press, issue 3, pages 243-261.
    2. William Kerr & Ufuk Akcigit & Nicholas Bloom & Daron Acemoglu, 2012. "Innovation, Reallocation and Growth," 2012 Meeting Papers 1137, Society for Economic Dynamics.
    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. Carol Corrado & Jonathan Haskel & Cecilia Jona-Lasinio, 2017. "Knowledge Spillovers, ICT and Productivity Growth," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 79(4), pages 592-618, August.
    5. Olga Alonso-Villar & Coral Del Rio & Carlos Gradin, 2012. "The Extent of Occupational Segregation in the United States: Differences by Race, Ethnicity, and Gender," Industrial Relations: A Journal of Economy and Society, Wiley Blackwell, vol. 51(2), pages 179-212, April.
    6. Katharine G. Abraham & James R. Spletzer, 2009. "New Evidence on the Returns to Job Skills," American Economic Review, American Economic Association, vol. 99(2), pages 52-57, May.
    7. Mellow, Wesley & Sider, Hal, 1983. "Accuracy of Response in Labor Market Surveys: Evidence and Implications," Journal of Labor Economics, University of Chicago Press, vol. 1(4), pages 331-344, October.
    8. Schierholz, Malte, 2014. "Automating survey coding for occupation," FDZ Methodenreport 201410_en, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].
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    Cited by:

    1. Glennon, Britta & Lane, Julia & Sodhi, Ridhima, 2018. "Money for Something: The Links between Research Funding and Innovation," IZA Discussion Papers 11711, Institute for the Study of Labor (IZA).

    More about this item

    Keywords

    UMETRICS; occupational classifications; machine learning; administrative data; transaction data;

    JEL classification:

    • J0 - Labor and Demographic Economics - - General
    • J21 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Labor Force and Employment, Size, and Structure
    • J24 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Human Capital; Skills; Occupational Choice; Labor Productivity

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