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Geographic Mobility in America: Evidence from Cell Phone Data

Author

Listed:
  • M. Keith Chen
  • Devin G. Pope
Abstract
Traveling beyond the immediate surroundings of one’s residence can lead to greater exposure to new ideas and information, jobs, and greater transmission of disease. In this paper, we document the geographic mobility of individuals in the U.S., and how this mobility varies across U.S. cities, regions, and income classes. Using geolocation data for ~1.7 million smartphone users over a 10-month period, we compute different measures of mobility, including the total distance traveled, the median daily distance traveled, the maximum distance traveled from one’s home, and the number of unique haunts visited. We find large differences across cities and income groups. For example, people in New York travel 38% fewer total kilometers and visit 14% fewer block-sized areas than people in Atlanta. And, individuals in the bottom income quartile travel 12% less overall and visit 13% fewer total locations than the top income quartile.

Suggested Citation

  • M. Keith Chen & Devin G. Pope, 2020. "Geographic Mobility in America: Evidence from Cell Phone Data," NBER Working Papers 27072, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberwo:27072
    Note: EH LS
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    Cited by:

    1. Victor Aguirregabiria & Jiaying Gu & Yao Luo & Pedro Mira, 2020. "A Dynamic Structural Model of Virus Diffusion and Network Production: A First Report," Working Papers wp2020_2014, CEMFI.
    2. Kim, Kijin & Kim, Soyoung & Lee, Donghyun & Park, Cyn-Young, 2023. "Impacts of social distancing policy and vaccination during the COVID-19 pandemic in the Republic of Korea," Journal of Economic Dynamics and Control, Elsevier, vol. 150(C).
    3. Victor Couture & Jonathan I. Dingel & Allison Green & Jessie Handbury & Kevin R. Williams, 2020. "Measuring Movement and Social Contact with Smartphone Data: A Real-time Application to COVID-19," Cowles Foundation Discussion Papers 2241, Cowles Foundation for Research in Economics, Yale University.
    4. Natsuki Arai & Masashige Hamano & Munechika Katayama & Yuki Murakami & Katsunori Yamada, 2022. "Nightless City: Impacts of Policymakers’ Questions on Overtime Work of Government Officials," Working Papers 2125, Waseda University, Faculty of Political Science and Economics, revised Oct 2023.
    5. Pradhi Aggarwal & Alec Brandon & Ariel Goldszmidt & Justin Holz & John List & Ian Muir & Gregory Sun & Thomas Yu, 2022. "High-frequency location data shows that race affects the likelihood of being stopped and fined for speeding," Natural Field Experiments 00764, The Field Experiments Website.
    6. Lina Hedman & Kati Kadarik & Roger Andersson & John Östh, 2021. "Daily Mobility Patterns: Reducing or Reproducing Inequalities and Segregation?," Social Inclusion, Cogitatio Press, vol. 9(2), pages 208-221.
    7. Li, Teng & Barwick, Panle Jia & Deng, Yongheng & Huang, Xinfei & Li, Shanjun, 2023. "The COVID-19 pandemic and unemployment: Evidence from mobile phone data from China," Journal of Urban Economics, Elsevier, vol. 135(C).
    8. Till Baldenius & Nicolas Koch & Hannah Klauber & Nadja Klein, 2023. "Heat increases experienced racial segregation in the United States," Papers 2306.13772, arXiv.org.
    9. Couture, Victor & Dingel, Jonathan I. & Green, Allison & Handbury, Jessie & Williams, Kevin R., 2022. "JUE Insight: Measuring movement and social contact with smartphone data: a real-time application to COVID-19," Journal of Urban Economics, Elsevier, vol. 127(C).
    10. Kohei Matsumura & Yusuke Oh & Tomohiro Sugo & Koji Takahashi, "undated". "Nowcasting Economic Activity with Mobility Data," Bank of Japan Working Paper Series 21-E-2, Bank of Japan.

    More about this item

    JEL classification:

    • R23 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Household Analysis - - - Regional Migration; Regional Labor Markets; Population

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