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Ride-hailing demand elasticity: a regression discontinuity method


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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  1. New York City Taxi & Limousine Commission.

  2. Recently Tapsi has improved its pricing algorithm. However, during the time period that we used for our project, pricing was as described above.

  3. Further details and assumptions for this way of measuring consumer surplus using demand elasticities can be found in Cohen et al. (2016).

  4. 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.


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Correspondence to Seyed Ali Madanizadeh.

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Appendix A

Appendix A

See Figs. 7, 8, 9, 10, 11, 12 and Tables 7 and 8.

Fig. 7
figure 7

Ride request of weekend in different surge coefficient level. Note: The vertical axis is the normalized sum of ride requests for a level of surge coefficient at the weekend. The weekend is from Thursday to Friday. We are called from 6 a.m. to 12 p.m. morning, from 12 p.m. to 6 p.m. afternoon, 6 p.m. to 12 a.m. night, and 12 a.m. to 6 a.m. after midnight. Due to privacy policy, the sum of ride requests normalized by the highest amount in the figure

Fig. 8
figure 8

Sum of ride requests in each section of adjacent regions with different surge coefficients changes. Note: The vertical axis is the normalized sum of ride requests in a section. In this figure, in panel (a), just adjacent regions, which one of them has surge coefficient 1.8 and the other one has surge coefficient 2, are considered. Panels bd are for the other Surge coefficient changes. Sections are determined by the division of the closest triangle to the borders of adjacent regions into 50 parts with the same areas (Fig. 4). Distance to Border is the distance of the furthest point of a section normalized by the width of a region. Sections in the regions with lower surge coefficient have negative distances, and sections in the regions with higher surge coefficient have positive distances. Due to privacy policy, the sum of ride requests normalized by the highest amount in the figure

Fig. 9
figure 9

Sum of ride requests in each section of adjacent regions with the same surge coefficients

Fig. 10
figure 10

Normalized average of online taxis in different surge coefficient levels. Note: The vertical axis is the normalized average of online taxis in adjacent regions in different surge coefficient levels. Due to privacy policy, the sum of online taxis normalized by the highest amount in the figure

Fig. 11
figure 11

A map of Tehran with the grids used in the paper

Fig. 12
figure 12

The residual of the sum of ride requests in each section of adjacent regions controlled for all of our control variables, for surge price coefficients from 1.6 to 18

Table 7 Demand elasticity in 1-h periods by a one regression
Table 8 Demand elasticity for similar surge prices

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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).

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