Using the unique pricing method of Tapsi, the second-largest ride-hailing company in Iran, we estimate the price elasticity of demand for Tapsi rides. Tapsi mechanically divides Tehran, the largest city of Iran, into 256 regions using a 16 × 16 matrix of straight lines, to implement surge pricing in response to excess regional demand or supply. Surge multiplier works for all ride requests within each region, independent of supply and demand in neighboring regions or rides characteristics. We exploit this sharp discontinuity in pricing by running a regression discontinuity to estimate the causal effect of the price change on the number of ride requests, i.e., price elasticity of demand. Using information of more than 10 million unique ride requests, we estimate not only average price elasticity, but also price elasticity at different levels of surge multiplier. Moreover, we measure price elasticity for 1-h and 6-h horizons and estimate the price elasticity of − 0.25 and − 0.54 for each horizon, respectively. This can be explained by the fact that in longer horizons, customers can more easily choose alternative modes of transport. This finding supports the very fundamental economic principle of higher elasticities in the long-run than in the short-run. Furthermore, we find that dynamic pricing generated by Tapsi creates consumer surplus as large as 6.5 times the total revenue of Tapsi.
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New York City Taxi & Limousine Commission.
Recently Tapsi has improved its pricing algorithm. However, during the time period that we used for our project, pricing was as described above.
Further details and assumptions for this way of measuring consumer surplus using demand elasticities can be found in Cohen et al. (2016).
Due to confidentiality of the data, we can report the nominal values of the welfare gains in dollars or Rial (Iranian currency). Results exist with the authors.
Alexander, L.P., González, M.C. (2015). Assessing the impact of real-time ridesharing on urban traffic using mobile phone data. Proc. UrbComp, 5, 1–9.
Berry, S., Levinsohn, J., & Pakes, A. (1995). Automobile prices in market equilibrium. Econometrica Journal of the Econometric Society, 1995, 841–890.
Castillo, J.C., Knoepfle, D., Weyl, G. (2017). Surge pricing solves the wild goose chase. In Proceedings of the 2017 ACM Conference on Economics and Computation, pp. 241–242. ACM. In S.-U. M. Center (Ed.) Shared mobility and the transformation of public transit. American Public Transportation Association.
Chen, M.K., Sheldon, M. (2015). Dynamic pricing in a labor market: Surge pricing and the supply of uber driver-partners. In University of California (Los Angeles) Working Paper http://citeseerx.ist.psu.edu/viewdoc/download.
Cohen, P., Hahn, R., Hall, J., Levitt, S., Metcalfe, R. (2016). Using big data to estimate consumer surplus: The case of uber. In Technical report, National Bureau of Economic Research.
Greenwood, B. N., & Wattal, S. (2015). Show me the way to go home: an empirical investigation of ride sharing and alcohol related motor vehicle homicide. Fox School of Business research paper, (15-054).
Hall, J.V., Horton, J.J., Knoepfle, D.T. (2017). Labor market equilibration: Evidence from uber. http://john-joseph-horton.com/papers/uber_price.pdf, working paper.
Lam, C. T., & Liu, M. (2017). Demand and consumer surplus in the on-demand economy: The case of ride sharing. SSRN Journal. https://doi.org/10.2139/ssrn.2997190
Li, Z., Hong, Y., & Zhang, Z. (2016). Do ride-sharing services affect traffic congestion? An empirical study of uber entry. Social Science Research Network, 2002, 1–29.
Nevo, A. (2000). Mergers with differentiated products: The case of the ready-to-eat cereal industry. The RAND Journal of Economics, 31, 395–421.
Petrin, A. (2002). Quantifying the benefits of new products: The case of the minivan. Journal of Political Economy, 110(4), 705–729.
Wallsten, S. (2015). The competitive effects of the sharing economy: How is uber changing taxis. Technology Policy Institute, 22, 3.
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Madanizadeh, S.A., Joshaghani, H. & Moradi, R. Ride-hailing demand elasticity: a regression discontinuity method. J. Ind. Bus. Econ. 50, 907–932 (2023). https://doi.org/10.1007/s40812-022-00225-1
- Estimating demand elasticity
- Regression discontinuity design
- Ride-hailing applications
- Two-sided markets