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

Author

Listed:
  • Susan Athey

    (a Department of Economics, Stanford University, Stanford, CA 94305;; b National Bureau of Economic Research, Cambridge, MA 02138;)

  • 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;)

  • Tobias Schmidt

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.

Suggested Citation

  • Susan Athey & Billy Ferguson & Matthew Gentzkow & Tobias Schmidt, 2021. "Estimating experienced racial segregation in US cities using large-scale GPS data," Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, vol. 118(46), pages 2026160118-, November.
    • Handle: RePEc:nas:journl:v:118:y:2021:p:e2026160118
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    Cited by:

    1. Paul Blanchard & Douglas Gollin & Martina Kirchberger, 2023. "Perpetual Motion: High-Frequency Human Mobility in Three African Countries," Trinity Economics Papers tep0823, Trinity College Dublin, Department of Economics.
    2. Yuhei Miyauchi & Kentaro Nakajima & Stephen J. Redding, 2021. "The Economics of Spatial Mobility: Theory and Evidence Using Smartphone Data," NBER Working Papers 28497, National Bureau of Economic Research, Inc.
    3. Coven, Joshua & Gupta, Arpit & Yao, Iris, 2023. "JUE Insight: Urban flight seeded the COVID-19 pandemic across the United States," Journal of Urban Economics, Elsevier, vol. 133(C).
    4. Almagro, Milena & Coven, Joshua & Gupta, Arpit & Orane-Hutchinson, Angelo, 2023. "Disparities in COVID-19 risk exposure: Evidence from geolocation data," Regional Science and Urban Economics, Elsevier, vol. 102(C).

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