The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research. NBER working papers are circulated for discussion and comment purposes. They have not been peer-reviewed or been subject to the review by the NBER Board of Directors that accompanies official NBER publications.
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 significant 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.JEL codes: C21, J71, N32
(A. Shertzer). The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research. NBER working papers are circulated for discussion and comment purposes. They have not been peerreviewed or been subject to the review by the NBER Board of Directors that accompanies official NBER publications.
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