Estimating experienced racial segregation in US cities using large-scale GPS data

Susan Athey, Billy Ferguson, Matthew Gentzkow and Tobias Schmidt
Additional contact information
Billy Ferguson: c Stanford Graduate School of Business, Stanford University, Stanford, CA 94305
Matthew Gentzkow: a Department of Economics, Stanford University, Stanford, CA 94305;; b National Bureau of Economic Research, Cambridge, MA 02138;

Proceedings of the National Academy of Sciences, 2021, vol. 118, issue 46, e2026160118

Abstract: Racial segregation shapes key aspects of a healthy society, including educational development, psychological well-being, and economic mobility. As such, a large literature has formed to measure segregation. Estimates of racial segregation often rely on assumptions of uniform interaction within some fixed time and geographic space despite the dynamic nature of urban environments. We leverage Global Positioning System data to estimate a measure of segregation that relaxes these strict assumptions. Experienced segregation according to our measure is substantially lower than standard measures would suggest. By decomposing segregation by functions of a city, like entertainment, restaurants, and retail, we facilitate targeted policy making where segregation matters most.

Keywords: racial segregation; isolation; mobility (search for similar items in EconPapers)
Date: 2021
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Working Paper: Estimating Experienced Racial Segregation in U.S. Cities Using Large-Scale GPS Data (2021)
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