2017/04/02 by Martin Saavedra, Martín Saavedra, Saavedra, Martin +2 · 34 citations
Economics, Econometrics and Finance · Mathematics · Social Sciences · #Applications (stat.AP) #Census #Computer science #Construct (python library) #Demographic economics #Demographics #Demography #Earnings #Econometrics #Economics #FOS: Computer and information sciences #Finance #Intergenerational and Educational Inequality Studies #Labor market dynamics and wage inequality #Labour economics #Population #Sociology #Urban, Neighborhood, and Segregation Studies #stat.AP
paper · pdf · doi:10.48550/arxiv.1704.08299
published in arXiv (Cornell University) (Cornell University)
openalex publication_date 2017/04/02 · arxiv created 2019/09/20 · arxiv updated 2019/09/24 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.