A machine learning approach to improving occupational income scores

Martin Saavedra () and Tate Twinam

Explorations in Economic History, 2020, vol. 75, issue C

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.

Keywords: OCCSCORE; Occupational income score; LIDO Score; Machine learning; Lasso; Non-classical measurement error; Occupation; Earnings gaps (search for similar items in EconPapers)
JEL-codes: C21 J71 N32 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (31)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0014498319300646
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:exehis:v:75:y:2020:i:c:s0014498319300646

DOI: 10.1016/j.eeh.2019.101304

Access Statistics for this article

Explorations in Economic History is currently edited by R.H. Steckel

More articles in Explorations in Economic History from Elsevier
Bibliographic data for series maintained by Catherine Liu ().