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Ride-Hailing Demand Elasticity: a Regression Discontinuity Method

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Ride-Hailing Demand Elasticity: a Regression Discontinuity Method Ride-hailing Demand Elasticity: A Regression discontinuity Method ∗ Hosein Joshaghani , Seyed Ali Madanizadeh†, and Reza Moradi‡ September 16, 2020 Abstract 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 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-hour and 6-hour 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. Keywords: Estimating Demand Elasticity, Regression Discontinuity Design, Ride- hailing Applications, Two-sided Markets JEL Codes: R41, R40, L90, L91, C55 ∗ [email protected] † [email protected] ‡ [email protected] 1 Introduction As a new entrant, ride-hailing companies have become serious competitors for the incumbent taxi system in the world. For instance, by the end of Jan 2015, Uber provided about 60,000 trips in New York City, while 460,000 trips were taken by regular taxis. However, by the end of Feb 2020, Uber provided about 542,000 trips, compared to around 231,000 trips by regular taxis.1 The same pattern of market penetration is observed around the world. In 2019, Snapp and Tapsi, the two largest ride-hailing companies in Iran, record more than 2 million rides per day. In order to measure consumer welfare, and in order to implement a more efficient pricing system, it is necessary to measure demand elasticity with an accurate and low-cost method. In this paper, we use a discontinuity in the pricing of Tapsi, the second-largest ride-hailing company in Iran, to estimate demand elasticity for Tapsi rides. The fare that Tapsi charges consists of two separate parts: 1) baseline ride price, which is calculated by unique characteristics of each ride such as ride distance, estimated time of the ride, estimated wait time at the destination for the next ride request, and so on, and 2) surge multiplier coefficient, which is independent of the unique characteristics of the ride and is calculated based on the number of ride requests (demand) and the number of online drivers (supply) in the rides origin. To exploit the surge coefficient, Tapsi divides Tehran into a 16×16 matrix with straight lines into 256 regions with the same areas. Then Tapsi calculates the demand and supply in each of these 256 iso-area regions every 5 minutes to assign a surge multiplier to each of these regions. The primary identification assumption of this paper is that straight borderlines of these 256 regions, separate customers, are located very close to each other and randomly between regions. Two neighbors with similar socioeconomic characteristics can be assigned to two regions, with an independent surge multiplier, only by chance. Therefore, similar customer observes different fare for a similar ride with an origin at each side of the border. We interpret the difference between the number of ride requests on two sides of the border as the 1 New York City Taxi & Limousine Commission 1 causal effect of price difference between two regions. In this regard, we use Regression Discontinuity Design (RDD) to identify the demand elasticity, i.e., the percentage change in demand due to a 1 percent change in price. Having access to the information of more than 10 million unique rides, we can use this regression discontinuity to measure not only average price elasticity of demand, but also measure price elasticity at different levels of surge multiplier. In other words, we estimate price elasticity at different points on the demand schedule. Moreover, we measure price elasticity for a short horizon and longer horizons. For instance, we count the number of ride requests within 1 hour and 6-hour horizons of neighboring regions 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 easier choose alternative modes of transport. This finding supports the fundamental economic principle of higher elasticities in the long-run than in the short run. Also, we show that there will be an increase in demand if passengers observe more online taxis around themselves. Furthermore, the demand of Tapsi, as a ride-hailing application, increases for rides, which has long-distance or other characteristics that affect their base price positively. This paper is continuing the growing literature of studying the impact of ride-hailing companies and their features. The effect of Uber on DUI related death rate of motor vehicle drivers (Greenwood and Wattal (2015)), the relationship between the satisfaction from Uber’s services and lack of taxis in a city (Wallsten (2015)), the opposite effects of Uber on traffic (Alexander and González (2015)), and the empirical study on the effect of Uber on traffic and CO2 emission in a city (Li et al. (2016)) are just some examples of researches that related to the social effects of the ride-hailing market. Moreover, there are works about other aspects of ride- hailing systems, such as pricing and supply-side: the alleviating effect of dynamic pricing on the ”wild goose chase” phenomenon (Castillo et al. (2017)), the effect of dynamic pricing on drivers’ work hours (Chen and Sheldon (2015)), the short-run and long-run effects of sudden fare changes on drivers’ earnings (Hall et al. (2017)) and so forth. 2 In spite of numerous studies on social effects, pricing, and supply side of ride-hailing companies, there are fewer papers about the demand side of these markets. This paper is in the trend of ride-hailing demand studies, using geographical Regression Discontinuity Design on different rich data from Tapsi. Lam and Liu (2017) identify the demand elasticity of Uber by the discrete choice model used in the literature before (Berry et al. (1995), Nevo (2000), Petrin (2002), and so on). Cohen et al. (2016) use Regression Discontinuity in other surge coefficient levels to estimate the demand elasticity of Uber. Compare to the Cohen et al. (2016), our identification considers the location that ride request sent, which is a proxy of the place customers live, work, and even their income, as unobservable characteristics to make the identification more precise. Moreover, the flexibility of our identification leads us to use one regression to estimate all demand elasticities. Therefore, we can use all records in the data to exploit the fixed effects of our estimation effectively. In Section 2, we briefly explain the system of Tapsi and facts about the data, especially the discontinuity between the ride requests of customers on two sides of the border of regions with different surge coefficients. In Section 3, we explain our identification method and how we apply the Regression Discontinuity Design on our data. In Section 4, we show the results and explain them, and in Section 5, we conclude our findings. 2 Data Tapsi, founded in April 2016, is one of the most important ride-hailing firms in Iran. The company provides service in 15 cities in Iran.2 Due to its CEO, in 2018, 600 people were working for Tapsi directly. Moreover, 250,000 drivers signed up which 150,000 of them were active drivers. The CEO also claimed that they have 40% of the market share in 2018.3 2 https://tapsi.ir 3 https://virgool.io 3 At first, a passenger launches Tapsi mobile application and determines her destination after observing near online taxis. Tapsi calculates the price based on the unique characteristics of the ride request along with the demand (number of requests) and supply (number of online taxis) in the initial location. Tapsi divides Tehran into 256 hypothetical regions to apply the demand and supply in those regions to the price. ”Surge coefficient” is calculated by considering the number of ride requests and taxis. If the surge coefficient is more than 1 in a region, there will be a significant excess demand, and surge coefficient less than 1 illustrates excess supply of taxis. The price calculated by the unique characteristics of each ride is multiplied by the surge coefficient to obtain the price that is observed by the customer. The customer decides to send a ride request after observing the price and nearby taxis on a map. This request is received by the two nearest drivers who observe the location, destination, approximate distance to the passenger, and the price. Drivers have 15 seconds to accept or reject the request, and if they do nothing during this time, the application will assume that the request is rejected. If both drivers reject the request, the next two nearest drivers will observe the request. In a period of time, if none of the drivers accepts the request, the application informs the customer that no driver is found. Data used in this paper are Tapsi ride proposals from January to June 2018 in Tehran.
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