The impact of COVID-19 on the ride-sharing industry and its recovery: Causal evidence from China

Wei Wang, Wei Miao*, Yongdong Liu, Yiting Deng, Yunfei Cao

*Corresponding author for this work

    Research output: Contribution to journalArticlepeer-review

    28 Citations (Scopus)

    Abstract

    The COVID-19 pandemic has brought unprecedented disruptions to many industries, and the transportation industry is among the most disrupted ones. We seek to address, in the context of a ride-sharing platform, the response of drivers to the pandemic and the post-pandemic recovery. We collected comprehensive trip data from one of the leading ride-sharing companies in China from September 2019 to August 2020, which cover pre-, during-, and post-pandemic phases in three major Chinese cities, and investigate the causal effect of the COVID-19 pandemic on driver behavior. We find that drivers only slightly reduce their number of shifts in response to increased COVID-19 cases, likely because they have to make a living from providing ride-sharing services. Nevertheless, conditional on working, drivers exhibit strong risk aversion: As the number of new cases increases, drivers strategically adjust the scope of their search for passengers, complete fewer trips, and as a result, make lower daily earnings. Finally, our heterogeneity analyses indicate that the effects appear to vary both across drivers and over time, with generally stronger effects on drivers who are older, more experienced, more active before the pandemic, and higher-status within the firm. Our findings have strong policy implications: These drivers tend to contribute more to the focal company, and also rely more on providing ride-sharing services to make a living. Therefore, they should be prioritized in stimulus plans offered by the government or the ride-sharing company.

    Original languageEnglish
    Pages (from-to)128-141
    Number of pages14
    JournalTransportation Research Part A: Policy and Practice
    Volume155
    DOIs
    Publication statusPublished - Jan 2022

    Keywords

    • COVID-19
    • Causal inference
    • Instrumental variables
    • Recovery
    • Ride-sharing

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