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A Machine Learning Approach to Improving Occupational Income Scores

Version
2
Resource Type
Dataset
Creator
  • Saavedra, Martin (Oberlin College)
  • Twinam, Tate (The College of William & Mary)
Publication Date
2018-12-31
Free Keywords
Occupational Income Scores; OCCSCORE; Intergenerational Mobility
Description
  • Abstract

    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.
Geographic Coverage
  • United States
Availability
Download
Relations
  • Cites
    DOI: 10.2139/ssrn.2944870 (Text)
Publications
  • Saavedra, Martin Hugo, and Tate Twinam. “A Machine Learning Approach to Improving Occupational Income Scores.” SSRN Electronic Journal, 2017. https://doi.org/10.2139/ssrn.2944870.
    • ID: 10.2139/ssrn.2944870 (DOI)

Update Metadata: 2019-08-05 | Issue Number: 1 | Registration Date: 2019-08-05

Saavedra, Martin; Twinam, Tate (2018): A Machine Learning Approach to Improving Occupational Income Scores. Version: 2. ICPSR - Interuniversity Consortium for Political and Social Research. Dataset. https://doi.org/10.3886/E111103V2