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The congestion costs of Uber and Lyft

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  • Tarduno, Matthew

Abstract

I study the impact of transportation network companies (TNC) on traffic delays using a natural experiment created by the abrupt departure of Uber and Lyft from Austin, Texas. Applying difference in differences and regression discontinuity specifications to high-frequency traffic data, I estimate that Uber and Lyft together decreased daytime traffic speeds in Austin by roughly 2.3%. Using Austin-specific measures of the value of travel time, I translate these slowdowns to estimates of citywide congestion costs that range from $33 to $52 million annually. Back of the envelope calculations imply that these costs are similar in magnitude to the consumer surplus provided by TNCs in Austin. Together these results suggest that while TNCs may impose modest travel time externalities, restricting or taxing TNC activity is unlikely to generate large net welfare gains through reduced congestion.

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  • Tarduno, Matthew, 2021. "The congestion costs of Uber and Lyft," Journal of Urban Economics, Elsevier, vol. 122(C).
    • Handle: RePEc:eee:juecon:v:122:y:2021:i:c:s0094119020300899
    DOI: 10.1016/j.jue.2020.103318
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    1. Nicholas Buchholz & Laura Doval & Jakub Kastl & Filip Matějka & Tobias Salz, 2020. "Personalized Pricing and the Value of Time: Evidence from Auctioned Cab Rides," NBER Working Papers 27087, National Bureau of Economic Research, Inc.
    2. Anderson, Michael L. & Lu, Fangwen & Zhang, Yiran & Yang, Jun & Qin, Ping, 2016. "Superstitions, street traffic, and subjective well-being," Journal of Public Economics, Elsevier, vol. 142(C), pages 1-10.
    3. Prud'homme, Rémy & Bocarejo, Juan Pablo, 2005. "The London congestion charge: a tentative economic appraisal," Transport Policy, Elsevier, vol. 12(3), pages 279-287, May.
    4. Jonathan Leape, 2006. "The London Congestion Charge," Journal of Economic Perspectives, American Economic Association, vol. 20(4), pages 157-176, Fall.
    5. Peter A. Diamond, 1973. "Consumption Externalities and Imperfect Corrective Pricing," Bell Journal of Economics, The RAND Corporation, vol. 4(2), pages 526-538, Autumn.
    6. Judd Cramer & Alan B. Krueger, 2016. "Disruptive Change in the Taxi Business: The Case of Uber," American Economic Review, American Economic Association, vol. 106(5), pages 177-182, May.
    7. Kastl, Jakub & Buchholz, Nicholas & Doval, Laura & Matějka, Filip & Salz, Tobias, 2020. "The Value of Time: Evidence From Auctioned Cab Rides," CEPR Discussion Papers 14666, C.E.P.R. Discussion Papers.
    8. Vickrey, William S, 1969. "Congestion Theory and Transport Investment," American Economic Review, American Economic Association, vol. 59(2), pages 251-260, May.
    9. Janet Currie & Reed Walker, 2011. "Traffic Congestion and Infant Health: Evidence from E-ZPass," American Economic Journal: Applied Economics, American Economic Association, vol. 3(1), pages 65-90, January.
    10. King, David & Manville, Michael & Shoup, Donald, 2007. "The political calculus of congestion pricing," University of California Transportation Center, Working Papers qt9js9z8gz, University of California Transportation Center.
    11. Hall, Jonathan D. & Palsson, Craig & Price, Joseph, 2018. "Is Uber a substitute or complement for public transit?," Journal of Urban Economics, Elsevier, vol. 108(C), pages 36-50.
    12. Christopher R. Knittel & Ryan Sandler, 2018. "The Welfare Impact of Second-Best Uniform-Pigouvian Taxation: Evidence from Transportation," American Economic Journal: Economic Policy, American Economic Association, vol. 10(4), pages 211-242, November.
    13. King, David & Manville, Michael & Shoup, Donald, 2007. "The political calculus of congestion pricing," Transport Policy, Elsevier, vol. 14(2), pages 111-123, March.
    14. Peter Cohen & Robert Hahn & Jonathan Hall & Steven Levitt & Robert Metcalfe, 2016. "Using Big Data to Estimate Consumer Surplus: The Case of Uber," NBER Working Papers 22627, National Bureau of Economic Research, Inc.
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    Cited by:

    1. Jindong Pang & Shulin Shen, 2023. "Do ridesharing services cause traffic congestion?," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 56(2), pages 520-552, May.
    2. Vignon, Daniel & Yin, Yafeng & Ke, Jintao, 2023. "Regulating the ride-hailing market in the age of uberization," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 169(C).
    3. Wang, Yiyuan & Shen, Qing, 2023. "An economic analysis of incorporating new shared mobility into public transportation provision," Transport Policy, Elsevier, vol. 141(C), pages 263-273.
    4. Jinwon Kim & Jucheol Moon, 2022. "Congestion Costs and Scheduling Preferences of Car Commuters in California: Estimates Using Big Data," Working Papers 2201, Nam Duck-Woo Economic Research Institute, Sogang University (Former Research Institute for Market Economy).
    5. Yuan Liang & Bingjie Yu & Xiaojian Zhang & Yi Lu & Linchuan Yang, 2022. "The Short-term Impact of Congestion Taxes on Ridesourcing Demand and Traffic Congestion: Evidence from Chicago," Papers 2207.01793, arXiv.org, revised Feb 2023.
    6. Liang, Yuan & Yu, Bingjie & Zhang, Xiaojian & Lu, Yi & Yang, Linchuan, 2023. "The short-term impact of congestion taxes on ridesourcing demand and traffic congestion: Evidence from Chicago," Transportation Research Part A: Policy and Practice, Elsevier, vol. 172(C).

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