Abstract
This paper estimates the social welfare effect of China’s largest online ride-sharing platform. Under the plausible assumption that consumers would change from traditional transportation to online ride-sharing when the marginal benefit of saved time outgrows the additional cost, we calculate the distribution of implied wage rate of passengers. We then use the passenger wage rate to calculate the social welfare generated by the decrease in waiting time and the reduction of waiting uncertainty brought about by the ride-sharing platform. Our estimate suggests that the ride-sharing platform created a total of 130.5 billion Yuan of social welfare in the three years between 2016 and 2018, and the consumer surplus and producer surplus created by an average transaction are 5.4 Yuan and 2.5 Yuan, respectively. The robustness test finds that our results were insensitive to the assumed risk aversion coefficient in the model, the subsample number used for each city, and the inclusion of nonlinear terms in the model. Alternative hypotheses, such as learning effect, seem unable to explain our result.
Highlights
The online car-hailing business has expanded rapidly, but its utility remains controversial
The consumer surplus and producer surplus created by a single order are 5.4 Yuan and 2.5 Yuan, respectively
We use the difference between the time of passengers entering the online car-hailing market and traditional transportation options to calculate the wage rate of passengers entering the market
Summary
The online car-hailing business has expanded rapidly, but its utility remains controversial. To estimate the social welfare created by ride-hailing and the impact of regulation, we make use of a highly unique dataset coming from the largest national ride-hailing platform in China. Cohen et al (2016) used the data of Uber, the largest online car-hailing platform in the United States, to calculate the social welfare of ride-sharing. They used the mark-up behavior of passengers in different road scenarios to infer their willingness to pay and their social welfare. The rest of our paper is arranged as follows: Section 1 describes the model and method of online car-hailing welfare estimation, Section 2 gives the sample data description and empirical analysis results, Section 3 offers the more detailed robustness test results, and the last Section summarizes the whole article and discusses the relevant policy implications
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
