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A Machine Learning Approach to Improving Occupational Income Scores
Principal Investigator(s): View help for Principal Investigator(s) Martin Saavedra, Oberlin College; Tate Twinam, The College of William & Mary
Version: View help for Version V1
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Project Citation:
Saavedra, Martin, and Twinam, Tate. A Machine Learning Approach to Improving Occupational Income Scores. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2019-08-05. https://doi.org/10.3886/E111103V1
Project Description
Summary:
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These files are the replication files for "A Machine Learning Approach to Improving Occupational Income Scores" by Martin Saavedra and Tate Twinam.
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 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.
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 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.
Scope of Project
Subject Terms:
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Occupational Income Scores;
OCCSCORE ;
Intergenerational Mobility
Geographic Coverage:
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United States
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