This is a Brookings Center on Regulation and Markets working paper.
Executive Summary
The emergence of the AI-driven digital economy has raised concerns among economists and policymakers regarding the market power of firms that sell data. The unique characteristics of data, such as its large fixed cost and ability to be replicated at zero marginal cost, suggest the potential for natural monopolies in data markets. However, little is known about how data markets function and how data is priced. This paper documents characteristics of data markets, explores the potential market power of data monopolists through theoretical analysis and uses modeling and data together to explore how consumers fare under different data pricing models.
The authors develop a dynamic model of a monopolist data seller with two crucial features: Data that many other buyers also have loses value, and data sellers cannot commit not to sell the same data to more buyers in the future. In such circumstances, even data monopolists have limited power to extract profits. Customers who anticipate more future sales of the data they buy will discount the value of the data. Customers’ willingness to pay for something that many others will know tomorrow is low. Thus, the concern shifts from excessive profits to potential under-provision of data.
Data subscriptions offer a means for firms to recapture lost revenue due to the lack of commitment. The model predicts that firms should choose subscriptions if financially unconstrained, if data has a potentially broad audience, and if it depreciates slowly. Empirical evidence from data markets supports these predictions and validates the model.
Finally, the authors use the evidence from online data markets to quantify the model and measure welfare. They find that data subscriptions are better for consumers. Even though firms extract more profits from subscriptions, consumers are better off because these profits ensure that firms invest in high-quality data. Regulation to eliminate data market power may backfire because without rents the incentive to invest in high-quality data disappears.
Download the full working paper here.
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