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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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    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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