For decades, economists have measured economic activity largely in terms of a few simple, concrete variables. According to the standard models, capital, labor and technology come together to produce goods and services that consumers purchase. These days, however, there’s another variable at play that’s just as important to the economy: data.
Data is so valuable that many companies offer goods or services at a steep discount, if they can collect it. The problem for economists is that consumer data—unlike capital or labor—is abstract and hard to measure. Because of this, useful models of the data economy have been hard to come by.
New research by Professors Laura Veldkamp and Maryam Farboodi set out to change that. “We wanted to make data a part of the conversation about how economies fluctuate and evolve,” says Veldkamp, the Leon G. Cooperman Professor of Finance & Economics at Columbia Business School. “We can’t just stick our heads in the sand and pretend it’s not there.”
A model produced by Veldkamp and Farboodi shows that data behaves a lot like a new form of capital. Companies accumulate it over time, using it to become more productive and gain advantages over rivals. This insight helps explain how data is reshaping competition, market power and consumer welfare. It has important implications for how companies and consumers should think about data, and it sets the stage for economic analysis that’s better suited to today’s realities.
Data as the new capital
The paper’s biggest gambit is to treat data as an economic asset that builds up through everyday business activity. Here’s what that looks like in the real world: Every time you search for something, click on a product, stream a song or buy groceries with a loyalty card, you generate information. Companies collect that information, store it and use it to refine their offerings.
Unlike oil or steel, data doesn’t get used up. It can be analyzed repeatedly at very low cost. That makes it durable. And the more of it a company has, the better its predictions become—about what customers want, how much they’re willing to pay, or which ad they’re most likely to click.
In the authors’ model, this accumulation creates a feedback loop. More customers generate more data. More data improves the product or service. A better product attracts more customers. And around the cycle goes—even faster as AI becomes more powerful.
Over time, this loop can produce enormous advantages for firms that get big quickly. That helps explain a familiar pattern in the digital economy: Once a platform reaches a certain scale, it becomes very hard to catch. The paper suggests that the success of today’s megabrands shouldn’t be attributed just to brand recognition or network effects. In reality, it often has a lot to do with the compounding value of accumulated data.
Advantages and tradeoffs for consumers
For consumers, the widespread circulation of personal data offers two big advantages. First, when companies understand preferences more clearly, they’re better at predicting products and services you actually want. Streaming platforms suggest shows you’ll probably enjoy. Online stores recommend items in your size and style. The list goes on.
The second—and less recognized—advantage is that we’re often paying less for products and services because companies that value our data effectively offer us a hidden discount to collect it. Another way to look at this is that every time we make a purchase, we’re really doing two transactions: We’re buying a good or service and we’re selling our data. This explains why some platforms are “free.” They may not cost money, but they cost information.
Where this kind of consumption becomes problematic is that the data part of the equation is often invisible to consumers. We may not realize how valuable our data is to companies, and we may not be given the choice to opt out of sharing it. What’s more, the same data that allows companies to serve us better can also allow them to extract more value from us. With detailed knowledge of consumer behavior, firms can target advertising more effectively or design pricing strategies that capture more of what we’re willing to pay.
Major policy implications
One of the paper’s key insights is that data creates spillover effects that firms don’t fully take into account. A company collecting data gains private benefits, but the broader social consequences—positive or negative—may be larger.
That’s where federal policy could have a big impact. “I would love for the Trade Commission to use existing anti-bundling laws to force firms to offer an option to consumers,” Veldkamp says. There could be one price for a good or service if we don’t give up our data, she suggests, and another price for if we do.
More broadly, Veldkamp says, data should be acknowledged as an asset by both governments and companies. This move has a precedent: In 2024, China became the first country to allow organizations to classify data as intangible assets on their balance sheets, and the country’s newly created National Data Administration coordinates data sharing and integration across multiple entities.
“We all know that data is one of the most valuable assets in the world today,” Veldkamp says. “Why are we not putting it on our balance sheets?”
