A social welfare estimation of ride-sharing in China: evidence from transaction data analysis of a large online platform


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.

First published online 02 February 2022

Keyword : social welfare, online ride-sharing platform, regulation

How to Cite
Wang, B., Shao, Y., & Miao, M. (2022). A social welfare estimation of ride-sharing in China: evidence from transaction data analysis of a large online platform. Technological and Economic Development of Economy, 28(2), 419–441.
Published in Issue
Feb 23, 2022
Abstract Views
PDF Downloads
Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.


Athey, S., & Luca, M. (2019). Economists (and economics) in tech companies. Journal of Economic Perspectives, 33(1), 209–230.

Berger, T., Chen, C., & Frey, C. B. (2018). Drivers of disruption? Estimating the Uber effect. European Economic Review, 110, 197–210.

Burtch, G., Ghose, A., & Wattal, S. (2016). Secret admirers: An empirical examination of information hiding and contribution dynamics in online crowdfunding. Information Systems Research, 27(3), 478–496.

Castillo, J. C. (2020). Who benefits from surge pricing? SSRN.

Chen, M. K., Rossi, P. E., Chevalier, J. A., & Oehlsen, E. (2019). The value of flexible work: Evidence from Uber drivers. Journal of Political Economy, 127(6), 2735–2794.

Cohen, P., Hahn, R., Hall, J., Levitt, S., & Metcalfe, R. (2016). Using big data to estimate consumer surplus: The case of Uber (No. w22627). National Bureau of Economic Research.

Gao, Y., An, J., & Quan, Y. (2016). A study on the impact of online car-booking on transportation mode selection and road traffic operation: A case study of Beijing. In Proceedings of 2016 China Urban Planning Society. Urban Transportation Planning Committee.

Ge, Y., Knittel, C. R., MacKenzie, D., & Zoepf, S. (2020). Racial discrimination in transportation network companies. Journal of Public Economics, 190, 104205.

Goldfarb, A., & Tucker, C. (2019). Digital economics. Journal of Economic Literature, 57(1), 3–43.

Gong, J., Greenwood, B. N., & Song, Y. (2017). Uber might buy me a Mercedes-Benz: An empirical investigation of the sharing economy and durable goods purchase. SSRN.

Grahn, R., Qian, S., Matthews, H. S., & Hendrickson, C. (2021). Are travelers substituting between transportation network companies (TNC) and public buses? A case study in Pittsburgh. Transportation, 48(2), 977–1005.

Greenwood, B. N., & Wattal, S. (2017). Show me the way to go home: An empirical investigation of ride-sharing and alcohol related motor vehicle fatalities. MIS Quarterly, 41(1), 163–187.

Hall, J. V., & Krueger, A. B. (2018). An analysis of the labor market for Uber’s driver-partners in the United States. ILR Review, 71(3), 705–732.

Hall, J. D., Palsson, C., & Price, J. (2018). Is Uber a substitute or complement for public transit? Journal of Urban Economics, 108, 36–50.

Hall, J. V., Horton, J. J., & Knoepfle, D. T. (2017). Labor market equilibration: Evidence from Uber (working paper).

Lam, C. T., & Liu, M. (2017). Demand and consumer surplus in the on-demand economy: The case of ride sharing. Social Science Electronic Publishing, 17(8), 376–388.

Li, Z., Hong, Y., & Zhang, Z. (2021). The empowering and competition effects of the platform-based sharing economy on the supply and demand sides of the labor market. Journal of Management Information Systems, 38(1), 140–165.

Liu, M., Brynjolfsson, E., & Dowlatabadi, J. (2021). Do digital platforms reduce moral hazard? The case of Uber and taxis. Management Science, 67(8), 4643–5300.

Mejia, J., & Parker, C. (2021). When transparency fails: Bias and financial incentives in ridesharing platforms. Management Science, 67(1), 166–184.

Moskatel, L., & Slusky, D. (2019). Did UberX reduce ambulance volume? Health Economics, 28(7), 817–829.

Park, J., Kim, J., Pang, M. S., & Lee, B. (2017). Offender or guardian? An empirical analysis of ride-sharing and sexual assault (Working Paper Series 2017-006, 18-010). KAIST College of Business.

Sadowsky, N., & Nelson, E. (2017). The impact of ride-hailing services on public transportation use: A discontinuity regression analysis (working paper).

Shapiro, M. H. (2018). Density of demand and the benefit of Uber (working paper).

Shen, Q., & Su, D. (2017). An empirical analysis of the impact of online car-hailing on traditional taxi industry. Journal of Henan University of Technology (Social Science Edition), (2).

Yang, H., Zhang, D., & Sun, L. (2020). The impact of ride-hailing on traffic congestion: A complex systems perspective. Systems Engineering, 38(3), 8.

Yu, H., Tian, L., Jiang, G., & Chen, Y. (2018). Sharing economy: Theoretical construction and research progress. Nankai Management Review, (21).

Zhang, Z., & Li, B. (2017). A quasi-experimental estimate of the impact of p2p transportation platforms on urban consumer patterns. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 1683–1692).

Zoepf, S. M., Chen, S., Adu, P., & Pozo, G. (2018). The economics of ride-hailing: Driver revenue, expenses and taxes (CEEPR Working paper 5). Massachusetts Institute of Technology.