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What stay-at-home orders reveal about dependence on transportation network companies

Author

Listed:
  • Lily Hanig

    (Carnegie Mellon University)

  • Destenie Nock

    (Carnegie Mellon University
    Carnegie Mellon University)

  • Corey D. Harper

    (Carnegie Mellon University
    Carnegie Mellon University)

Abstract

Transportation Network Companies (TNC) such as Uber and Lyft set out to provide transportation not fulfilled by private vehicles or public transit. The social value of TNCs for essential trips (i.e., necessary trips that cannot be fulfilled by another mode of transportation) is difficult to discern in normal conditions. The COVID-19 stay-at-home order is used as a natural experiment to investigate the heterogeneous ability to avoid TNCs by income areas of trip origins. We measure the sensitivity of different populations’ ability to respond to policies and to avoid TNC trips (e.g., early stay-at-home orders) using a difference-in-difference style regression. Previous studies have indicated that under normal conditions TNCs primarily serve high-income areas, indicating that TNCs may not be improving transportation equity but instead serve as an additional mode of transportation for passengers with multiple options. We fill a gap in the literature by evaluating the role TNCs play in serving unavoidable and essential trips. We find that high-income community areas showed greater sensitivity to the stay-at-home order with a 56% greater decrease in TNC ridership during the stay-at-home order compared to low-income community areas. Specifically, TNC trips from high-income areas decreased by 80%. This indicates that although riders from high-income community areas might make up the majority of trips in normal conditions, low-income community areas are less able to adapt to stay-at-home orders because of a higher degree of non-flexible and essential jobs or less access to TNC alternatives like private vehicles and public transit.

Suggested Citation

  • Lily Hanig & Destenie Nock & Corey D. Harper, 2025. "What stay-at-home orders reveal about dependence on transportation network companies," Transportation, Springer, vol. 52(2), pages 381-412, April.
    • Handle: RePEc:kap:transp:v:52:y:2025:i:2:d:10.1007_s11116-023-10425-w
    DOI: 10.1007/s11116-023-10425-w
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    References listed on IDEAS

    as
    1. Li, Sen & Tavafoghi, Hamidreza & Poolla, Kameshwar & Varaiya, Pravin, 2019. "Regulating TNCs: Should Uber and Lyft set their own rules?," Transportation Research Part B: Methodological, Elsevier, vol. 129(C), pages 193-225.
    2. Rebecca Brough & Matthew Freedman & David C. Phillips, 2021. "Understanding socioeconomic disparities in travel behavior during the COVID‐19 pandemic," Journal of Regional Science, Wiley Blackwell, vol. 61(4), pages 753-774, September.
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    4. Rick Grahn & Corey D. Harper & Chris Hendrickson & Zhen Qian & H. Scott Matthews, 2020. "Socioeconomic and usage characteristics of transportation network company (TNC) riders," Transportation, Springer, vol. 47(6), pages 3047-3067, December.
    5. Nelson Erik & Sadowsky Nicole, 2019. "Estimating the Impact of Ride-Hailing App Company Entry on Public Transportation Use in Major US Urban Areas," The B.E. Journal of Economic Analysis & Policy, De Gruyter, vol. 19(1), pages 1-21, January.
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