Digital monopolies: Privacy protection or price regulation?

Highlights

• Data give digital monopolies market power from improved matching & reduced privacy.

• While data improve matches, they allow extraction of consumer surplus.

• Thus, consumer surplus is maximized for intermediate privacy.

• With regulated prices, consumer surplus is maximized without privacy protection.

• Price regulation may allow data’s social benefits without consumer harm.

Abstract

Increasing returns to scale in data gathering and processing give rise to a new form of monopoly, referred to here as digital monopoly. Digital monopolies create new challenges for regulators and antitrust authorities. We address two in this paper: market power arising from improved match values and from reduced privacy. The digital monopoly’s profit and social surplus always increase as privacy decreases. However, consumer surplus is non-monotone in privacy. Without privacy, the match value is perfect but completely extracted by the digital monopoly. In contrast, as privacy goes to infinity, match values and social surplus go to zero. With regulated prices, consumer surplus is maximized without privacy protection. As with natural monopolies, price regulation thus remains an appropriate tool in the digital age to capture the social benefits from increasing returns to scale without harming consumers.

Introduction

Larger markets are better, all else equal, because they can execute the same trades as smaller, standalone markets, and sometimes execute more or more valuable trades. Consistent with this, Internet-based matchmakers that realize powerful data-driven increasing returns to scale, such as Amazon, Google, and Spotify, have come to prominence in the digital age. Firms that operate in environments for which efficiency dictates that a single firm is optimal are naturally referred to as digital monopolies.¹ Just as was the case with natural monopolies, digital monopolies call for antitrust scrutiny and possibly regulation. Indeed, recently digital monopolies have received intense scrutiny from antitrust authorities around the world.²

Traditionally, regulation and policy intervention have worked best when they were guided by well-defined objectives such as consumer or social surplus. In this tradition, we analyze the pros and cons of interventions in an environment in which a digital monopoly can use data to either improve matching only or, instead, to improve matching and to adjust pricing. Although this distinction has typically not been formulated explicitly, it is a key issue in ongoing antitrust debates. As a case in point, it makes a difference to advertisers whether Google uses its data only to better match advertisers to consumers or, alternatively, to improve matching and to adjust the (reserve) prices that it charges advertisers.

Based on a parsimonious model in which more data improves the distribution from which the consumer draws its value, with the improvement being in the sense of hazard rate dominance, we show that the distinction has striking implications for the consumer surplus effects of privacy protection. If data are used exclusively to improve match values, then consumer surplus increases monotonically in the data to which the digital monopoly has access. Put differently, in this case privacy protection unambiguously harms the consumer. In sharp contrast, when the monopoly also uses the information about the consumer’s preferences for pricing purposes, the consumer surplus consequences of privacy protection are less clear cut. To a lesser or greater extent, the monopoly extracts part of the additional surplus generated by improvements in matching. In the limit, as the matching becomes perfect, consumers have no private information left and hence lose their entire information rent, while the monopoly captures the entire social surplus. In both cases, social surplus is maximized when all information is revealed to the digital monopoly. However, when data are also used for pricing, the monopoly is not only able to perfectly match the product to the consumer, but also to match the price to the consumer’s value, thereby, in the limit, depriving the consumer of all surplus.

As an example, consider the online firm Ziprecruiter, which matches potential employers to jobseekers. The data collected by Ziprecruiter regarding the characteristics of a potential employer both improves match values, to the benefit of the employer, and allows Ziprecruiter to more precisely estimate the employer’s willingness to pay for the service, to the detriment of the employer.³

From a consumer surplus perspective, the central issue is not the protection of privacy but rather the protection of information rents. In our model, fixing the level of data held by the digital monopoly, the protection of information rents can be achieved by regulating prices.⁴ If the price is fixed, then data can only be used to improve match values, and improving match values is in the digital monopoly’s best interest because it increases the probability of a trade, and, of course, is in the consumer’s best interest.

The obvious flip side to the dire implications for consumer surplus when privacy vanishes completely and the digital monopoly’s pricing is not restricted is that producer surplus increases and becomes identical to social surplus. Digital monopolies can thus be expected to resist attempts to regulate their pricing. Apart from this natural, and in many ways inevitable, conflict about the division of social surplus, a potential drawback to price regulation is that it may decrease the digital monopoly’s incentives to invest in data analytics and product quality. If price regulation decreases equilibrium investments substantively, then there is a tradeoff between the social surplus and consumer surplus that can be achieved via regulating pricing.

That being said, this paper is exclusively concerned with issues pertaining to what are sensibly called private values settings, in which matching individuals’ preferences as closely as possible is what a benevolent social planner would do.

The remainder of this paper is organized as follows. In Section 2, we describe the model. In Section 3, we derive results and discuss price regulation. In Section 4, we extend the model to allow for investments into data analytics and product quality. In Section 5, we provide an extension to allow competition. In Section 6, we discuss related literature and provide additional discussion of the implications of our results for policy debates surrounding property rights. Section 7 contains conclusions.