Previous studies have shown that dispatch systems, which use innovative technology to match riders with taxis, can substantially reduce search frictions and are therefore more efficient than conventional street hails. However, how to design dispatch systems has not been fully investigated in the literature. In this research, we developed and estimated a structural model for the two-sided ride-hailing market to compare the advantages and disadvantages of a bidding-based dispatch system and an allocation-based dispatch system. (with Junhong Chu and Xueli Zhang)
This study takes advantage of an exogenous policy change in which a leading taxi company in Singapore introduced an origin-destination-based flat-fare option to causally evaluate the causal effects of flat-rate pricing the ride-hailing market.
This research examines sellers’ fraudulent behavior in the taxi industry. Specifically, we identify whom taxi drivers tend to overtreat, what types of drivers are likely to overtreat, and when they tend to escalate overtreatment. (with Junhong Chu)
Leveraging a unique quasi-experiment in which a leading Chinese ride-sharing company introduced surge pricing in different cities in waves, this research combines the difference-in-differences estimator with the causal forest algorithm to identify the causal effects of surge pricing on driver behavior in the ride-sharing market.
In this study, we collected detailed trip-level data from a leading ride-sharing company in China from September 2019 to August 2020, which cover pre-, during-, and post-pandemic phases in three major Chinese cities, and apply an instrumental variable method to investigate the causal effect of the COVID-19 pandemic on driver behavior.