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Georgetown University’s Newspaper of Record since 1920

The Hoya

Georgetown University’s Newspaper of Record since 1920

The Hoya

Economics 101 Proved Right, Thanks to Uber


If you walk by Uncommon Grounds between the hours of 9 a.m. and 5 p.m., chances are you will overhear a Georgetown undergrad and a company recruiter talking about thought leadership, company culture, work-life balance or gamification. That last one means the application of game-design thinking to non-game contexts to increase user engagement. While all of these buzzwords are commonplace in the Sellinger Lounge coffee chat arena, there is one concept that is becoming almost ubiquitous in the business vernacular, regardless of industry — and that is big data.

As a refresher, big data is generally defined as the compilation of enormous data sets that, when analyzed, reveal patterns, trends and associations, particularly related to consumer behavior.

Companies are ceaselessly trying to find more innovative ways to collect and analyze this information in order to help them better understand their consumers. And their efforts pay off: When synthesized properly, big data allows companies to capitalize on latent opportunities in their respective markets and provide better service to their customers.

Uber is a case in point, as co-author of the New York Times bestselling book “Freakonomics,” Steven Levitt ’s work with the company has shown. The ride-sharing giant’s surge-pricing algorithm is no exception to this rule. From its onset, surge-pricing was a method to tailor Uber’s pricing to meet the dynamic demand constraints of a given market at peak times. The idea is fundamentally for Uber to increase price as the demand for rides increases in order to avoid “stock outs,” or the inability to match you with a driver when you’re trying to get a ride to the airport — perhaps as it starts to rain.

If this is starting to give you flashbacks to your “Principles of Economics” class, your professors will be happy to know that you did actually learn something .

Uber’s surge-pricing strategy is perhaps the first real-life proof of the theoretical demand curve that is found at the root of any economics course. In other words, Uber has provided the first tangible example of a demand curve. By mapping a precise demand curve, Uber has become the first company to know exactly how much a price increase — or decrease — will impact demand.

In addition to mapping its demand curve, the depth and variety of data that Uber has collected over the years has allowed for consumer surplus to be accurately quantified for the first time. Consumer surplus is the economic term used to describe the difference between how much total utility or value you place on getting a good or service versus the price you actually paid for it. In other words, if you have ever purchased something and thought:, “I would have paid a lot more for that,” the difference between the maximum price you would have paid and the price you were charged is your consumer surplus.

Consumer surplus, like the demand curve, is a definition that appears in every economic textbook but is one that has not been able to be accurately quantified — until now. Since Uber has been able to trace how much people are willing to pay for their rides, they have also been able to determine how much they have “saved” consumers.

The results of these consumer surplus calculations are telling. When Levitt worked with Uber to determine that the company has generated $7 billion in consumer surplus in New York, Los Angeles, San Francisco and Chicago alone. This illustrates that the benefit Uber offers to consumers far outweighs the losses incurred by taxi cab drivers in those cities.

If you’re not an econ-junkie you might be wondering, “So what?”

What Uber has been able to accomplish in terms of mapping its demand curve and precisely quantifying consumer surplus demonstrate the myriad of possibilities analyzing big data presents. It is only through recording and analyzing the countless data sets of Uber-user rides that Uber and a team of economists have been able to prove the long-accepted theory of supply and demand.

While big data has often been valued for its ability give companies a greater understanding of their consumers, it is now evolving into a method of proving and quantifying theories that have long been considered “unsolvable.” Big data is becoming valuable not only for its profit potential but also for its ability to further academic discussion. Additionally, big data is now allowing consumers to better understand markets,and vice versa.

Although all the possibilities and opportunities associated with big data are still unknown, for today, economists are joining in the growing number of big data supporters.

For that, you can thank Uber.

Bianca DiSanto is a senior in the McDonough School of Business. Think Tech appears every Friday.

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    Bryan AtkinsOct 14, 2016 at 8:44 am

    Greets Ms. DiSanto,

    This piece makes sense … as far as it goes.
    My problem with it is the same problem I have with the field of economics.

    I contend the field is terminally myopic because its philosophic orientation is far too limited.

    For context, citing this: “We need scarcely add that the contemplation in natural science of a wider domain than the actual leads to a far better understanding of the actual.” Sir Arthur Eddington — “The Nature of the Physical World”

    Think economists (pols, voters, etc.) fail to consider code, including monetary code, in a physics / evolution / complexity context.

    The idea that world culture’s dominant information-processing mechanism for calibrating and ordering complex relationships — humans using monetary code — could possibly generate selectable relationship hierarchies in-and-across geo eco bio cultural & tech networks, and importantly, across time … no, fail.
    (Yes, legal, religious, moral, and other codes are in play, but again, monetary code is clearly dominant.)

    Per the patterns revealed by the big data of evolutionary history, I contend that exponentially accelerating complexity has crushed the information-processing efficacy of humans using monetary code. The mechanism lacks reach, speed, accuracy & power.

    We’re in Anthropocene.
    Part of that: Human cultural selection increasingly drives natural selection.
    Part of that: We’re increasingly doing natural selection with world culture’s dominant code for relationship / reality interface: monetary code.
    FAIL. Exhibit A: Sky. Exhibit B: Ocean.

    If your culture’s relationship with the sky and ocean are deadly, your cultural genome sucks.

    If interested, see 6 Concepts 4 Survival
    The Price Is Wrong

    Bryan Atkins