Ridesharing and Digital Resilience for Urban Anomalies: Evidence from the New York City Taxi Market
Yingjie Zhang, Beibei Li, Sean Qian
Abstract
This article investigates how and why the traditional on-demand service (i.e., taxies) and ridesharing platforms (e.g., Uber) perform in contexts of urban uncertainty. We consider different types of unexpected urban anomalies and collect large-scale trip data on taxi and ridesharing services. Empirically, we employ a difference-in-differences econometric model to compare the platform-level performances (measured by the number of fulfilled trips) of a traditional taxi system and a ridesharing platform after urban anomaly shocks. We observe that the ridesharing platform significantly outperforms the traditional taxi platform in coping with the uncertainties brought about by unexpected anomalies. We conclude, conservatively, that the technological effect and technology-enabled supply elasticity, are the main factors determining the differences between the platforms during an urban anomaly. This work offers important insights into the design of platform strategies, especially for stimulation of the labor supply and incentivization of the adoption and use of technology in urban transportation systems in response to anomalous urban upheavals.