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Title: A Machine Learning Approach to Improving Occupational Income Scores

Citation Type: Miscellaneous

Publication Year: 2018

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Abstract: Historical studies of labor markets frequently suffer from a lack of data on individual income. The occupational income score (OCCSCORE) is often used as an alternative measure of labor market outcomes. Using modern Census data, we find that the use of OCCSCORE biases results towards zero and can frequently result in statistically signif- icant coefficients of the wrong sign. We use a machine learning approach to construct a new adjusted score based on industry, occupation, and individual demographics. Our alternative score substantially outperforms OCCSCORE in both modern and historical contexts. We illustrate our approach by estimating racial and gender earnings gaps in the 1915 Iowa State Census and intergenerational mobility elasticities using linked data from the 1850-1910 Censuses.

Url: https://faculty.washington.edu/twinam/occupational_income_score.pdf

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Authors: Saavedra, Martin; Twinam, Tate

Publisher: Oberlin College

Data Collections: IPUMS USA - Ancestry Full Count Data

Topics: Labor Force and Occupational Structure, Poverty and Welfare

Countries: United States