Uber is a platform that connects riders to independent drivers (“driverpartners”) who are nearby. Riders open the Uber app to see the availability of rides and the price and can then choose to request a ride. If a rider chooses to request a ride, the app calculates the fare based on time and distance traveled and bills the rider electronically. In the event that there are relatively more riders than driverpartners such that the availability of driverpartners is limited and the wait time for a ride is high or no rides are available, Uber employs a “surge pricing” algorithm to equilibrate supply and demand. The algorithm assigns a simple “multiplier” that multiplies the standard fare in order to derive the “surged” fare. The surge multiplier is presented to a rider in the app, and the rider must acknowledge the higher price before a request is sent to nearby drivers.Essentially, surge pricing is used to manage excess demand - when the quantity of Uber rides demanded by users exceeds the quantity of rides available from drivers at that time. In other words, there is a shortage of available Uber drivers. Simple demand-and-supply tells us that this occurs when the price is below equilibrium, as in the diagram below. At the price P0, the quantity of Uber rides demanded is QD, but the number of Uber rides available is just QS, and the difference is the shortage (or excess demand). In this situation, the price should rise towards the equilibrium price (P1), which is what surge pricing achieves.
A traditional taxi firm would typically remain at their standard pricing, which means the excess demand is managed instead by people waiting for a taxi to become available. With surge pricing, two things happen: (1) since the price is higher, fewer people demand rides from Uber (they choose some alternative form of transport instead); and (2) more Uber drivers make themselves available (to take advantage of the higher potential earnings). Notice that at the price P1, the quantity of Uber rides demanded (Q1) is less than QD, and the quantity of Uber rides supplied (Q1) is greater than QS.
Anyway, the Hall et al. paper shows both of these effects, using data from two nights in the area surrounding Madison Square Garden in New York: (1) March 21, 2015, the night of a sold-out Ariana Grande concert at the Garden; and (2) New Years Eve 2014-15, when a software problem caused the surge pricing to fail for 26 minutes between 1am and 2am.
They find, for the first night:
...efficiency gains came from both an increase in the supply of driverpartners on the road and from an allocation of supply to those that valued rides the most.And for the second night:
...we saw that in the absence of surge pricing, key indicators of the health of the marketplace deteriorated dramatically. Drivers were likely less attracted to the platform while, at the same time, riders requested rides in increasing numbers because the price mechanism was not forcing them to make the proper economic tradeoff between the true availability of driverpartners and an alternative transportation option. Because of these problems, completion rates fell dramatically and wait times increased, causing a failure of the system from an economic efficiency perspective.[HT: Marginal Revolution]