Examining the Impact of Ridehailing Services on Public Transit Use
54 Pages Posted: 27 Sep 2017
Date Written: September 25, 2017
We examine the impact that ridehailing services, such as Uber or Lyft, have had on the utilization of various modes of public transit in the United States via an agency-level analysis. We first evaluate the effects by exploiting the temporally and spatially staggered entry of Uber across the United States to estimate a difference-in-differences model. In doing so, we introduce a novel panel-data matching approach that explicitly seeks to match treated agencies with control agencies that exhibited similar pre-treatment trendsin transit utilization over the twenty-four months prior to the arrival of ridehailing services. Our estimates indicate that ridehailing service entry has led to significant reductions in the utilization of road-based, short-haul public transit services, namely city buses, while increasing utilization of rail-based and long-haul transit services, such as subway and commuter rail. We further show that the estimated cannibalization and complementarity effects are attenuated and amplified, respectively, by transit agencies’ pre-existing quality of service. We evaluate the robustness of our results in several ways, such as performing permutation tests and accounting for the entry of multiple ridehailing service operators. For estimates of the city bus impacts, we further consider an alternative, second source of identification, namely a natural experiment in which the Google Maps application incorporated ridehailing services directly into users’ transit/direction recommendations. Here, we show that cities that hosted ridehailing services at the time of the Google Maps change exhibited much larger losses in bus utilization relative to cities that had yet to receive such services, consistent with our main findings. We discuss implications for policymakers and transit operators.
Keywords: Uber, Lyft, Public Transit, Difference in Differences, Coarsened Exact Matching, Ridehailing, Transportation
JEL Classification: O33, O51, P25, C23, C40
Suggested Citation: Suggested Citation