Market Clearing Without Consternation?:
The Case of Uber’s “Surge” Pricing

The on-demand car service Uber has a “surge” pricing policy: during periods of peak demand, such as during snowstorms and on New Year’s Eve, prices have increased as much as 8x. Uber’s stated goal of the policy is to increase the supply of drivers and ensure that cars are still available. Although would-be riders know about the policy (and even have to confirm the fare multiplier on their iPhones), it has generated a great deal of mostly negative media attention. To many, dynamic pricing is just price gouging and immoral.

Uber’s CEO has argued that other companies practice dynamic pricing (e.g., hotels, airlines, clubs, etc.) and that Uber’s core, unwavering goal is to ensure that a car is always available. These justifications sound reasonable, but are they persuasive?

An MTurk experiment

I decided to test whether different “elaborations” such as the CEO’s can alter opinions about the morality of surge pricing. To do this, I created a series of HITs (human intelligence tasks) on Amazon Mechanical Turk. In each HIT:

  1. I first described how surge pricing worked and asked respondents to rate the policy on a five-point scale from “Very Wrong” to “Very Right.”
  2. I then had respondents read one of 18 “elaborations” that they were instructed to regard as true. Some of these elaborations were similar to claims made by the Uber CEO or his critics. Each elaboration was a separate HIT and respondents only considered one at at time.
  3. In response to the elaborations, respondents were asked to rate how this elaboration changed their baseline view, on five-point scale from “Much More Wrong” to “Much Less Wrong.”

Baseline views on the morality of surge pricing

As expected, a large chunk of respondents don’t like surge pricing:
Notes: For each bin, a 95% CI is shown for that proportion. The first response of each worker was used.

Do “elaborations” change views?

Price setting. Uber has been a bit opaque about how surge prices are actually arrived at. Given people’s taste for procedural justice and their tendency to view acts of omission and comission differently, I suspected that details about how the surge price was obtained would matter. I created three HITs related to price-setting:

  • “The actual surge price is set by an algorithm designed to make sure there is always a car available within a city within 20 minutes.”
  • “Uber lowers prices during periods of slack demand.”
  • “The actual surge price is set by Uber based on their guess about how much more demand there will be.”

In the figure below, we can see that the (tautological) statement that Uber lowers prices during periods of slack demand improves opinions substantially. Further, stating that the price was determined by an algorithm working against a liquidity contraint also strongly improved views. In contrast, stating that the price was made on the basis of a “guess” by Uber about likely demand made views much more negative.

Tighter parallelism between questions would be ideal, but I suspect that three factors made the “algorithm” perspective more persuasive: (1) with the algorithm, high prices were a byproduct rather than a goal, (2) moral agency is transferred from Uber employees to “the algorithm” and (3) the phrasing “guess” implies a lack of care and diligence.

Notes:For each bin, a 95% CI is shown for that proportion. The red line shows the predicted proportions from a fitted ordered logistic regression model.

Competitive landscape. The CEO of Uber has repeatedly discussed how dynamic pricing is commonplace in other industries. I created three HITs about the strategies used by firms—two about Uber’s competitors and one about an industry that practices dynamic pricing:

  • “Hotels in the area also use “surge” pricing to meet increases in demand.”
  • “Uber has a number of competitors, and they also use surge pricing.”
  • “Uber has a number of competitors, and they do not use surge pricing.”

In the figure below, we can see that neither hotels nor competitors practicing dynamic pricing does much to improve opinions. However, the elaboration that Uber practices surge pricing when its competitors do not has a strongly negative effect: the distrbution of responses is very left-shifted toward even dimmer views. It would seem Uber’s best bet would be to hope Lyft starts their own surge pricing (oh look, they did).


Uber’s revenue. Other work looking at how individuals judge the morality of market actions has found that people view actions to increase already-positive profits differently from actions to “save” a company (Kahneman, Knetsch & Thaler, or KKT). The two revenue related elaborations were:

  • “Uber as a whole is losing money.”
  • “Uber as a whole has high profits.”

In the figure below, we can see that KKT result is strongly replicated: views are much more negative when Uber is profitable than when it is making money.

Miscellaneous. I tried a few other elaborations that do not fit neatly into a bucket:

  • “People needing cars had other alternatives during surge pricing, such as public transportation.”
  • “Uber has told users about the policy up-front and it is very clear to them what the price will be.”
  • “A majority of economists view this surge pricing as an efficient way to allocate scarce goods.”

We can see that all of these elaborations “work” in improving views. It seems Uber is wise to point out that transactions are entered into freely. This appears to be better than showing that alternatives existed (which also had a positive effect). The respondents were also surprisingly (to me, at least) open to the idea that expert opinion on pricing might change their views. Maybe this is a good idea…



There are several complexities that I elided over in this post. For example, there is the question of how repondent’s prior beliefs affect their willingness to change views (in short, people with negative initial views are less likely to be persuaded, but they have the same directional effects as people with positive views). If there’s interest, I’ll follow-up with some more details in a longer post. I’ll also share a repository with the code, data and experimental materials.