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A machine learning approach to improving occupational income scores

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  • Saavedra, Martin
  • Twinam, Tate

Abstract

Historical studies of labor markets frequently lack data on individual income. The occupational income score (OCCSCORE) is often used as an alternative measure of labor market outcomes. We consider the consequences of using OCCSCORE when researchers are interested in earnings regressions. We estimate race and gender earnings gaps in modern decennial Censuses as well as the 1915 Iowa State Census. Using OCCSCORE biases results towards zero and can result in estimated gaps of the wrong sign. We use a machine learning approach to construct a new adjusted score based on industry, occupation, and demographics. The new income score provides estimates closer to earnings regressions. Lastly, we consider the consequences for estimates of intergenerational mobility elasticities.

Suggested Citation

  • Saavedra, Martin & Twinam, Tate, 2020. "A machine learning approach to improving occupational income scores," Explorations in Economic History, Elsevier, vol. 75(C).
    • Handle: RePEc:eee:exehis:v:75:y:2020:i:c:s0014498319300646
    DOI: 10.1016/j.eeh.2019.101304
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    9. Collins, William J., 2021. "The Great Migration of Black Americans from the US South: A guide and interpretation," Explorations in Economic History, Elsevier, vol. 80(C).
    10. Karen Clay & Ethan J. Schmick, 2020. "The Impact of an Environmental Shock on Black-White Inequality: Evidence from the Boll Weevil," NBER Working Papers 27101, National Bureau of Economic Research, Inc.
    11. Ran Abramitzky & Philipp Ager & Leah Boustan & Elior Cohen & Casper Hansen, 2021. "The Effect of Immigration on Local Labor Markets: Lessons from the 1920s Border Closure," Research Working Paper RWP 21-09, Federal Reserve Bank of Kansas City.
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    15. Ran Abramitzky & Leah Platt Boustan & Elisa Jácome & Santiago Pérez, 2019. "Intergenerational Mobility of Immigrants in the US over Two Centuries," NBER Working Papers 26408, National Bureau of Economic Research, Inc.
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    17. Ran Abramitzky & Leah Platt Boustan & Dylan Connor, 2020. "Leaving the Enclave: Historical Evidence on Immigrant Mobility from the Industrial Removal Office," NBER Working Papers 27372, National Bureau of Economic Research, Inc.
    18. Enrico Berkes & Peter Nencka, 2019. "‘Novel’ Ideas: The Effects of Carnegie Libraries on Innovative Activities," 2019 Meeting Papers 1315, Society for Economic Dynamics.
    19. Trinh, Nhat An & Lersch, Philipp M. & Schunck, Reinhard, 2023. "Dynamics of Wealth Homogamy in Couples," OSF Preprints mxhcp, Center for Open Science.
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    More about this item

    Keywords

    OCCSCORE; Occupational income score; LIDO Score; Machine learning; Lasso; Non-classical measurement error; Occupation; Earnings gaps;
    All these keywords.

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • J71 - Labor and Demographic Economics - - Labor Discrimination - - - Hiring and Firing
    • N32 - Economic History - - Labor and Consumers, Demography, Education, Health, Welfare, Income, Wealth, Religion, and Philanthropy - - - U.S.; Canada: 1913-

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