How Ride‐Sharing Is Shaping Public Transit System: A Counterfactual Estimator Approach

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.

The complementary effect of ride-sharing on public transit: evidence from a natural experiment

“…, 2018; Lee et al. , 2022; Pan and Qiu, 2022), with little attention paid to complementary effects. Therefore, we found that ride-sharing has significant complementary effects on public transit, which fills the theoretical gap in this literature stream.…”

“…Ngo et al. (2021) and Pan and Qiu (2022) focus on the impact of ride-sharing services on city bus, results show that the entry of ride-sharing platforms is associated with a decrease in bus ridership. Considering that each city and public transit mode has its own unique characteristics, Babar and Burtch (2020) investigated the heterogeneous effect of ride-sharing services on public transit use and showed that ride-sharing services significantly reduce the use of bus services, increase the use of commuter rail services and have no significant effects on light rail and subway services.…”

“…, 2018; Lee et al. , 2022; Pan and Qiu, 2022), paying little attention to complementary effects due to the lack of reliable trip-level fine-grained data. This study fills this gap by investigating the complementary effects of ride-sharing on public transit using a difference-in-differences approach, based on a unique fine-grained trip-level data set from a large ride-sharing platform.…”

“…, 2018; Lee et al. , 2022; Pan and Qiu, 2022). However, these studies have revealed only the net effect of ride-sharing services on transit use, partly because of the poor availability of reliable disaggregated data and their conclusions provide competing arguments regarding the effect that ride-sharing services may have on public transit.…”

Putting analytics into action in care coordination research: Emerging issues and potential solutions

“…Unlike a conventional two‐way fixed effects model, counterfactual estimators take observations under the treatment condition (in our context, $$VerInt{e}_{it} = 1$$) as missing data and directly estimate their counterfactuals: the readmission rates for the treated hospitals if they have not experienced vertical integration. Essentially, counterfactual estimators convert the original causal identification problem into a “prediction” problem: Using the observations in untreated conditions (in our context, $$VerInt{e}_{it} = 0$$) to predict the missing outcomes of treated observations in counterfactual conditions (Liu et al., 2021; Pan & Qiu, 2022). This approach addresses unobserved, time‐varying confounders using a latent factor approach.…”