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The Effects Of Uber’s Surge Pricing: A Case Study

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The Effects Of Uber’s Surge Pricing: A Case Study by Chicago Booth

Jonathan Hall, Cory Kendrick, Chris Nosko

Uber is a platform that connects riders to independent drivers (“driver-partners”) 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 driver-partners such that the availability of driver-partners 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.

The Surge Algorithm in Action

Uber operates in a market with large fluctuations in demand and a variable supply of driver-partners. Driver-partners are free to work whenever they want and must be incentivized to provide services. Under these conditions, economic theory tells us that using prices to signal to riders that rides are scarce and inducing driver-partners to forgo other activities will close the gap between supply and demand and lead to improved outcomes for both riders (as a whole) and driver-partners.

Let’s illustrate the underlying economics by taking a typical example of surge in action. On March 21, 2015, pop superstar Ariana Grande played a sold out show at Madison Square Garden. Attendees attempting to get home after the concert caused a large spike in demand.

Figure 1 shows the number of riders opening the Uber app in the vicinity of Madison Square Garden directly after the concert ended:

App openings are a good representation of those who are in the market for Uber’s services and thus provide a nice measure of demand. As we can see from the red line, the number of riders opening the app after the concert spiked up to 4 times the normal number of app openings.

Because of this increase in demand relative to the number of available Uber cars in the area, surge kicked in, fluctuating between 1 and 1.8x for over an hour after the concert ended.

The first beneficial effect of surge was to increase the number of driver-partners in the area. Surge signaled that this was a valuable time to be on the road, and driver-partner supply
increased by up to 2x the presurge baseline. This increase in driver-partner supply was a net win for riders in the area because more of them were able to take advantage of Uber services. The supply response is shown in Figure 2:


The second effect of surge pricing was to allocate rides to those that value them most. Figure 3 shows that, while the number of app openings increased dramatically, the number of actual requests didn’t increase by as much. This came from riders who opened the app, saw that surge pricing was in effect and decided to take an alternate form of transportation or chose to wait for surge pricing to end. From an economic efficiency standpoint, this was highly beneficial because those that ended up requesting a ride are those for which their outside option was worse, leading them to value Uber more in that particular moment. The gap between the red and blue line could be tentatively interpreted as a measure of this allocative efficiency.


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