Uber Uses Economics 101 And A Natural Experiment to Justify Surge Pricing
I have several beefs with Uber and its ilk. One beef I do NOT share with some is the controversy over Uber’s surge pricing. Surge pricing sounds exotic, but the pricing process is relatively basic in operation and in principle. It comes from the economics of bringing supply and demand into balance when demand surges beyond available supply.
Some critics say surge pricing is “not fair” as if Uber is providing or controlling a public good. These critics fail to recognize that pricing IS the way to generate fairness ESPECIALLY when resources are scarce. Uber’s latest defense of surge pricing comes in the form of an Economics 101 lesson accompanied by an interesting case study including what is called a natural experiment. A natural experiment is a scenario where circumstances align to provide a control to compare against an object of study. Comparing the object of study versus the control can provide some understanding of the impact of whatever characteristics make the object of study different from the control (the treatment).
In a recent press release called “The Effects Of Uber’s Surge Pricing,” Uber explains the basic economic principle:
“Surge pricing has two effects: people who can wait for a ride often decide to wait until the price falls; and drivers who are nearby go to that neighborhood to get the higher fares. As a result, the number of people wanting a ride and the number of available drivers come closer together, bringing wait times back down.”
Surge pricing delivers improved service levels by working through incentives. When supply and demand are in balance, a person who wants a ride at the given price P can generally get one in just a few minutes. At this price, every driver who wants to drive is theoretically waiting by the Uber app ready to accept a request. The potential drivers who have chosen not to drive have presumably decided that their time is better spent doing something else given current pricing.
When demand surges out of this state of equilibrium, wait times for riders soars as the number of drivers becomes insufficient to deliver the typical high service level. An increase in price constrains demand AND increases supply. As Uber notes, those people who prefer to pay the lower pre-surge pricing will wait out the surge (or find alternatives). Some drivers who previously preferred to do something other than drive will find the higher price attractive enough to get on the road. The surge price continues to increase until demand comes down and supply goes up enough to return service levels to a more reasonable level.
For Uber, this process of surge pricing achieves operational efficiency. It is a particularly important tool for providing incentives for drivers to get on the road when they are most needed. Uber does NOT note that for those riders who decide to wait out the surge, THEIR wait times increase tremendously. It is not clear theoretically or from the accompanying case study whether some customers are unhappy enough about the poorer service level at the non-surge price P to stop using Uber in the future. Given Uber’s on-going success, the answer seems to be “no.” Customers are always free to come back to Uber whenever prices meet their preferences.
The controversy over Uber’s surge pricing is not just peculiar because of the basic economics that underly the practice. I find the controversy particularly peculiar given the market’s ready acceptance of similar pricing practices throughout the economy. Airlines increase airfares during the busy holiday season. In sports, the tickets for playoffs and championships are much higher than the regular season as the demand from fans soars to participate in a unique experience. The most popular concerts command higher ticket prices. In entertainment in general, when performances sell-out, the price of tickets in the “after-market” are typically much higher than the prices from primary vendors. Hotels cost a lot more during busy tourist seasons. The examples go on and on. Uber’s surge pricing is a well-accepted and well-established process for pricing. Uber’s need to defend the practice likely comes from the company’s transparency in using the pricing and the lack of similar pricing in many traditional transportation services.
The accompanying case study, “The Effects of Uber’s Surge Pricing: A Case Study“, is written by researchers from the University of Chicago: Jonathan Hall, Cory Kendrick, and Chris Nosko. The research is called a case study because the data come from just two examples. The paper is not a comprehensive investigation of Uber’s surge pricing. Yet, the work is still powerful in that it compares a typical example of what happens during surge pricing with a time when Uber suffered an outage in its system for surge pricing. The contrast in service levels is clear and demonstrates the usefulness of surge pricing.
The paper shows what happens during a surge in demand at the end of a concert by pop music star Ariana Grande at the Madison Square Garden on March 21, 2015. In the 75 minutes following the concert’s end, demand surged over 4x normal as represented by the number of times users opened the Uber app in a given 1-minute window. Surge pricing kicked in and sent prices as high as 1.8x the pre-surge price. Specifically, Uber surged prices for 35 minutes: 1.2x for 5 minutes, 1.3x for 5 minutes, 1.4x for 5 minutes, 1.5x for 15 minutes, and 1.8x for 5 minutes. The supply of drivers increased as much as 2x during this same time period.
As a result of bringing demand and supply closer, the percentage of requested rides that resulted in a completed trip (the completion rate) remained unchanged and wait times did not increase “substantially.” The study could not adjust for drivers who already planned to make themselves available only after the concert’s completion. The authors did not explain why surge pricing was only in place for 35 of the 75 minute surge, but my guess is that a non-price related increase in supply might at least be part of the explanation.
This is all fine and good but even better with a point of comparison like a control. Uber cannot recreate an Ariana Grande concert at the Madison Square Garden for an exact comparison. However, some high demand period of similar scale without surge pricing can provide a sufficient substitute. Such an event occurred during last New Year’s Eve in New York City. For a 26 minute period, a technical glitch prevented the surge pricing algorithm from working. Surge pricing was in effect before the outage. This period is a great natural experiment to study because:
“New Year’s Eve represents one of the busiest days of the year for Uber and illustrates why surge pricing is necessary in inducing driverpartner response. At the same time that demand is unusually high, driverpartners are simultaneously reluctant to work because the value of their leisure time (e.g., their own celebrations of New Year’s Eve) is high. Put bluntly, people do not want to drive on NYE, and, in the absence of surge pricing, we might expect the gap between supply and demand to be large.”
During the outage of the price surge, completion rates plunged severely. Prices fell from 2.7x normal prices to 1.0x the standard fare. The artificially low fares caused a surge in demand that sent completion rates hurtling downward. At its worst, the completion rate dropped below 25%. Sure, a few people got a good deal, but the vast majority of people wanting a ride could not get one. This kind of poor service level is very bad business for Uber. Without proper pricing, Uber could quickly get a reputation as a system that does not provide good service and has few actual rides to offer at the moment users strongly desire one. Drivers were also not properly compensated for providing such a valuable service during this time (the authors do not mention whether Uber “made good” with those drivers).
This valuable Economics 101 lesson from Uber reminds us of the power of pricing to allocate scarce resources in an efficient manner. It also demonstrates how proper pricing provides strong incentives to bring supply and demand into balance to maintain good service levels for those people who participate in the market. The study does NOT cover what happens to those people who withdraw from the surge pricing period. For example, do they return after pricing returns to normal? This kind of loyalty would be good for Uber longer-term. Or do consumers priced out during surge pricing pay for alternative transportation options which are presumably cheaper but not quite as convenient? Studying retention after such defection would be key for Uber to understand how to price even more efficiently in the future.
