How Ride‐Sharing Is Shaping Public Transit System: A Counterfactual Estimator Approach. Issue 3 (1st November 2021)

• Record Type: Journal Article
• Authors: Pan, Yang; Qiu, Liangfei
• Is Part Of: Production and operations management. Volume 31:Issue 3(2022)
• Page Start: 906
• Page End: 927
• Publication Date: 2021-11-01
• Subjects: disruptive technology -- public transportation operations -- sharing economy -- ride‐sharing platform -- counterfactual estimators; Production management -- Periodicals; 658.505
• DOI: 10.1111/poms.13582
• Languages: English
• ISSNs: 1059-1478

Abstract

Abstract : The new sharing economy model has introduced a dramatic, disruptive impact on the traditional industries by matching the demand and supply in real time. In this study, we examine how the entry of Uber, a ride‐sharing services digital platform, brings new disruptive changes in public transportation operations. Significant debate has surrounded whether the new ride‐sharing model siphoned riders from public transit or made public transit feasible for more riders, but no consensus has been reached. One reason could be that the commonly used difference‐in‐differences empirical strategy fails to account for time‐varying unobserved confounders. To address this issue, we introduce a class of counterfactual estimators (CEs) to strengthen our causal identification and perform diagnostic tests to validate model assumptions for each CE. A significant drop in passenger trips with buses is found after Uber entry from both the conventional two‐way fixed effects model and the CEs. Moreover, we provide empirical evidence that the entry of Uber has not significantly affected public demand response transportation, which indicates that Uber is not directly competing with other transportation services that aim to solve the "last‐mile problem." Last, our additional analyses suggest that the effect of Uber entry is not uniform for different urban areas. All these empirical findings can be coherently explained in our framework of the substitution effect of Uber.