Abstract
Platforms such as Uber, Lyft and Airbnb serve two-sided markets with drivers (property owners) on one side and riders (renters) on the other side. Some agents join multiple platforms (multi-home). In the case of ride-sharing, a driver may drive for both Uber and Lyft, and a rider may use both apps and request a ride from the company that has a driver close by. In this paper, we are interested in welfare implications of multi-homing in such a market. Our model abstracts away from entry/exit by drivers and riders as well as pricing by platforms. Both drivers’ and riders’ surpluses are determined by the average time between a request and the actual pickup. The benchmark setting is a monopoly platform and the direct comparison is a single-homing duopoly. The former is more efficient since it has a thicker market. Next, we consider multi-homing on the rider side, multi-homing on the driver side, and multi-homing on both sides. Relative to single-homing duopoly, we find that multi-homing on either side improves the overall welfare. However, multi-homing drivers potentially benefit themselves at the cost of single-homing drivers. In contrast, multi-homing riders benefit themselves as well as single-homing riders, representing a more equitable distribution of gains from multi-homing.
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Notes
Bryan and Gans (2019) assume that platforms choose “idleness” parameter \(\delta _i\) which benefits the platform by reducing the distance a rider has to travel to join platform i (à la Hotelling). Investment in idleness \(\delta _i\) comes in the form of a fixed cost to the platform. Drivers are reduced to the exogenous parameter of marginal cost w (wage), which is also independent of \(\delta _i\). This stylized treatment of wait time makes the model tractable enough to allow price competition and multi-homing.
As a result, allowing endogenous market division of agents between the platforms (e.g., by allowing price competition) would lead to the following technical problem. The expected wait/pickup time affects drivers and riders’ platform participation decisions, but these platform participation decisions also affect the wait time. That is, the wait time and platform participation decisions are jointly determined. We are unable to solve such a model analytically with multi-homing, and thus have to rely on exogenous market division.
Uber’s surge pricing has been featured in the popular media such as NPR, “Uber Plans to Kill Surge Pricing, Though Drivers Say It Makes Job Worth It,” NPR All Tech Considered, May 3, 2016.
For example, Choi (2010) examines tying in two-sided markets with multi-homing. Jeitschko and Tremblay (2020) endogenize single- vs. multi-homing decisions on both sides. Belleflamme and Peitz (2019) show that the possibility of multi-homing may benefit or hurt the side which can multi-home. In similar spirit, in our model, the impacts of multi-homing depends on which side multi-home and also differ across single- and multi-homing agents on the same side. On the empirical side, Landsman and Stremersch (2011) analyze the seller-level multi-homing decisions in the video game console market. Gal-Or (2021) focuses on single-homing and shows that intensified competition among peer-to-peer lodging platforms may lead to higher prices, lower consumer surplus and higher platform profit.
This may include the time from the dropoff point to a nearby local center, where the driver will wait for the next request.
After a drop off, a driver can drive around or find a free parking spot, while waiting for a ride. See “What Can Uber Drivers Do While Waiting for a Ride? (Other than Scroll Your Facebook Feed),” http://therideshareguy.com/what-can-uber-drivers-do-while-waiting-for-a-ride-other-than-scroll-your-facebook-feed/.
Assuming that \(N_D\) is sufficiently large, the equilibrium in which all riders get a ride exists. That is, the two curves do intersect.
Convexity of curve D in Fig. 1 (i.e., convexity of the right-hand side of (3) as a function of x) is an intrinsic property of the model. If curve D is concave, then the stable equilibrium is the lower intersection point \(\underline{x}\) (i.e., \(x^*=\underline{x}\)). Qualitatively, this equilibrium has the same comparative statics properties as stated in the text (i.e., it also decreases in \(N_R\) and increases in \(N_D\)). Accordingly, all results in the next section continue to hold.
According to Statista.com, in July 2021, Uber’s share of the US ride-sharing market was 69% and Lyft’s was 31%.
To our knowledge, there is no paper which derives the expected wait/pickup time from interactions between drivers and riders, allows multi-homing and considers price competition. If we add price competition to our model, platform participation decisions and the wait time will feed into each other, making it impossible to solve the model analytically.
Both Uber and Lyft have adopted programs to reward active and high-performing drivers. Launched in June 2016, Uber’s Power Driver Plus awards drivers cash bonuses after a set number of trips. Thus, drivers who complete an extra 80–100 trips a week receive 10–20% extra earnings. Surge pricing is another example of complicated pricing schemes. See Cachon et al. (2017) for a theoretical study of pricing schemes on service platforms, including Uber’s surge pricing.
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