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The dystopian novel is a powerful literary genre. It has given us such masterpieces as Nineteen Eighty-Four, Brave New World, and Fahrenheit 451. Though these novels often shed light on the risks of contemporary society and the zeitgeist of the era in which they were written, they also almost always systematically overshoot the mark (intentionally or not) and severely underestimate the radical improvements that stem from the technologies (or other causes) that they fear.

But dystopias are not just a literary phenomenon; they are also a powerful force in policy circles. This is epitomized by influential publications such as The Club of Rome’s 1972 report The Limits of Growth, whose dire predictions of Malthusian catastrophe have largely failed to materialize.

In an article recently published in the George Mason Law Review, we argue that contemporary antitrust scholarship and commentary is similarly afflicted by dystopian thinking. In that respect, today’s antitrust pessimists have set their sights predominantly on the digital economy—”Big Tech” and “Big Data”—in the process of alleging a vast array of potential harms.

Scholars have notably argued that the data created and employed by the digital economy produces network effects that inevitably lead to tipping and to more concentrated markets (e.g., here and here). In other words, firms will allegedly accumulate insurmountable data advantages and thus thwart competitors for extended periods of time.

Some have gone so far as to argue that this threatens the very fabric of western democracy. For instance, parallels between the novel Nineteen Eighty-Four and the power of large digital platforms were plain to see when Epic Games launched an antitrust suit against Apple and its App Store in August 2020. The gaming company released a short video clip parodying Apple’s famous “1984” ad (which, upon its release, was itself widely seen as a critique of the tech incumbents of the time). Similarly, a piece in the New Statesman—titled “Slouching Towards Dystopia: The Rise of Surveillance Capitalism and the Death of Privacy”—concluded that:

Our lives and behaviour have been turned into profit for the Big Tech giants—and we meekly click ‘Accept.’ How did we sleepwalk into a world without privacy?

In our article, we argue that these fears are symptomatic of two different but complementary phenomena, which we refer to as “Antitrust Dystopia” and “Antitrust Nostalgia.”

Antitrust Dystopia is the pessimistic tendency among competition scholars and enforcers to assert that novel business conduct will cause technological advances to have unprecedented, anticompetitive consequences. This is almost always grounded in the belief that “this time is different”—that, despite the benign or positive consequences of previous, similar technological advances, this time those advances will have dire, adverse consequences absent enforcement to stave off abuse.

Antitrust Nostalgia is the biased assumption—often built into antitrust doctrine itself—that change is bad. Antitrust Nostalgia holds that, because a business practice has seemingly benefited competition before, changing it will harm competition going forward. Thus, antitrust enforcement is often skeptical of, and triggered by, various deviations from status quo conduct and relationships (i.e., “nonstandard” business arrangements) when change is, to a first approximation, the hallmark of competition itself.

Our article argues that these two worldviews are premised on particularly questionable assumptions about the way competition unfolds, in this case, in data-intensive markets.

The Case of Big Data Competition

The notion that digital markets are inherently more problematic than their brick-and-mortar counterparts—if there even is a meaningful distinction—is advanced routinely by policymakers, journalists, and other observers. The fear is that, left to their own devices, today’s dominant digital platforms will become all-powerful, protected by an impregnable “data barrier to entry.” Against this alarmist backdrop, nostalgic antitrust scholars have argued for aggressive antitrust intervention against the nonstandard business models and contractual arrangements that characterize these markets.

But as our paper demonstrates, a proper assessment of the attributes of data-intensive digital markets does not support either the dire claims or the proposed interventions.

  1. Data is information

One of the most salient features of the data created and consumed by online firms is that, jargon aside, it is just information. As with other types of information, it thus tends to have at least some traits usually associated with public goods (i.e., goods that are non-rivalrous in consumption and not readily excludable). As the National Bureau of Economic Research’s Catherine Tucker argues, data “has near-zero marginal cost of production and distribution even over long distances,” making it very difficult to exclude others from accessing it. Meanwhile, multiple economic agents can simultaneously use the same data, making it non-rivalrous in consumption.

As we explain in our paper, these features make the nature of modern data almost irreconcilable with the alleged hoarding and dominance that critics routinely associate with the tech industry.

2. Data is not scarce; expertise is

Another important feature of data is that it is ubiquitous. The predominant challenge for firms is not so much in obtaining data but, rather, in drawing useful insights from it. This has two important implications for antitrust policy.

First, although data does not have the self-reinforcing characteristics of network effects, there is a sense that acquiring a certain amount of data and expertise is necessary to compete in data-heavy industries. It is (or should be) equally apparent, however, that this “learning by doing” advantage rapidly reaches a point of diminishing returns.

This is supported by significant empirical evidence. As our survey of the empirical literature shows, data generally entails diminishing marginal returns:

Second, it is firms’ capabilities, rather than the data they own, that lead to success in the marketplace. Critics who argue that firms such as Amazon, Google, and Facebook are successful because of their superior access to data might, in fact, have the causality in reverse. Arguably, it is because these firms have come up with successful industry-defining paradigms that they have amassed so much data, and not the other way around.

This dynamic can be seen at play in the early days of the search-engine market. In 2013, The Atlantic ran a piece titled “What the Web Looked Like Before Google.” By comparing the websites of Google and its rivals in 1998 (when Google Search was launched), the article shows how the current champion of search marked a radical departure from the status quo.

Even if it stumbled upon it by chance, Google immediately identified a winning formula for the search-engine market. It ditched the complicated classification schemes favored by its rivals and opted, instead, for a clean page with a single search box. This ensured that users could access the information they desired in the shortest possible amount of time—thanks, in part, to Google’s PageRank algorithm.

It is hardly surprising that Google’s rivals struggled to keep up with this shift in the search-engine industry. The theory of dynamic capabilities tells us that firms that have achieved success by indexing the web will struggle when the market rapidly moves toward a new paradigm (in this case, Google’s single search box and ten blue links). During the time it took these rivals to identify their weaknesses and repurpose their assets, Google kept on making successful decisions: notably, the introduction of Gmail, its acquisitions of YouTube and Android, and the introduction of Google Maps, among others.

Seen from this evolutionary perspective, Google thrived because its capabilities were perfect for the market at that time, while rivals were ill-adapted.

3.    Data as a byproduct of, and path to, platform monetization

Policymakers should also bear in mind that platforms often must go to great lengths in order to create data about their users—data that these same users often do not know about themselves. Under this framing, data is a byproduct of firms’ activity, rather than an input necessary for rivals to launch a business.

This is especially clear when one looks at the formative years of numerous online platforms. Most of the time, these businesses were started by entrepreneurs who did not own much data but, instead, had a brilliant idea for a service that consumers would value. Even if data ultimately played a role in the monetization of these platforms, it does not appear that it was necessary for their creation.

Data often becomes significant only at a relatively late stage in these businesses’ development. A quick glance at the digital economy is particularly revealing in this regard. Google and Facebook, in particular, both launched their platforms under the assumption that building a successful product would eventually lead to significant revenues.

It took five years from its launch for Facebook to start making a profit. Even at that point, when the platform had 300 million users, it still was not entirely clear whether it would generate most of its income from app sales or online advertisements. It was another three years before Facebook started to cement its position as one of the world’s leading providers of online ads. During this eight-year timespan, Facebook prioritized user growth over the monetization of its platform. The company appears to have concluded (correctly, it turns out) that once its platform attracted enough users, it would surely find a way to make itself highly profitable.

This might explain how Facebook managed to build a highly successful platform despite a large data disadvantage when compared to rivals like MySpace. And Facebook is no outlier. The list of companies that prevailed despite starting with little to no data (and initially lacking a data-dependent monetization strategy) is lengthy. Other examples include TikTok, Airbnb, Amazon, Twitter, PayPal, Snapchat, and Uber.

Those who complain about the unassailable competitive advantages enjoyed by companies with troves of data have it exactly backward. Companies need to innovate to attract consumer data or else consumers will switch to competitors, including both new entrants and established incumbents. As a result, the desire to make use of more and better data drives competitive innovation, with manifestly impressive results. The continued explosion of new products, services, and apps is evidence that data is not a bottleneck to competition, but a spur to drive it.

We’ve Been Here Before: The Microsoft Antitrust Saga

Dystopian and nostalgic discussions concerning the power of successful technology firms are nothing new. Throughout recent history, there have been repeated calls for antitrust authorities to reign in these large companies. These calls for regulation have often led to increased antitrust scrutiny of some form. The Microsoft antitrust cases—which ran from the 1990s to the early 2010s on both sides of the Atlantic—offer a good illustration of the misguided “Antitrust Dystopia.”

In the mid-1990s, Microsoft was one of the most successful and vilified companies in America. After it obtained a commanding position in the desktop operating system market, the company sought to establish a foothold in the burgeoning markets that were developing around the Windows platform (many of which were driven by the emergence of the Internet). These included the Internet browser and media-player markets.

The business tactics employed by Microsoft to execute this transition quickly drew the ire of the press and rival firms, ultimately landing Microsoft in hot water with antitrust authorities on both sides of the Atlantic.

However, as we show in our article, though there were numerous calls for authorities to adopt a precautionary principle-type approach to dealing with Microsoft—and antitrust enforcers were more than receptive to these calls—critics’ worst fears never came to be.

This positive outcome is unlikely to be the result of the antitrust cases that were brought against Microsoft. In other words, the markets in which Microsoft operated seem to have self-corrected (or were misapprehended as competitively constrained) and, today, are generally seen as being unproblematic.

This is not to say that antitrust interventions against Microsoft were necessarily misguided. Instead, our critical point is that commentators and antitrust decisionmakers routinely overlooked or misinterpreted the existing and nonstandard market dynamics that ultimately prevented the worst anticompetitive outcomes from materializing. This is supported by several key factors.

First, the remedies that were imposed against Microsoft by antitrust authorities on both sides of the Atlantic were ultimately quite weak. It is thus unlikely that these remedies, by themselves, prevented Microsoft from dominating its competitors in adjacent markets.

Note that, if this assertion is wrong, and antitrust enforcement did indeed prevent Microsoft from dominating online markets, then there is arguably no need to reform the antitrust laws on either side of the Atlantic, nor even to adopt a particularly aggressive enforcement position. The remedies that were imposed on Microsoft were relatively localized. Accordingly, if antitrust enforcement did indeed prevent Microsoft from dominating other online markets, then it is antitrust enforcement’s deterrent effect that is to thank, and not the remedies actually imposed.

Second, Microsoft lost its bottleneck position. One of the biggest changes that took place in the digital space was the emergence of alternative platforms through which consumers could access the Internet. Indeed, as recently as January 2009, roughly 94% of all Internet traffic came from Windows-based computers. Just over a decade later, this number has fallen to about 31%. Android, iOS, and OS X have shares of roughly 41%, 16%, and 7%, respectively. Consumers can thus access the web via numerous platforms. The emergence of these alternatives reduced the extent to which Microsoft could use its bottleneck position to force its services on consumers in online markets.

Third, it is possible that Microsoft’s own behavior ultimately sowed the seeds of its relative demise. In particular, the alleged barriers to entry (rooted in nostalgic market definitions and skeptical analysis of “ununderstandable” conduct) that were essential to establishing the antitrust case against the company may have been pathways to entry as much as barriers.

Consider this error in the Microsoft court’s analysis of entry barriers: the court pointed out that new entrants faced a barrier that Microsoft didn’t face, in that Microsoft didn’t have to contend with a powerful incumbent impeding its entry by tying up application developers.

But while this may be true, Microsoft did face the absence of any developers at all, and had to essentially create (or encourage the creation of) businesses that didn’t previously exist. Microsoft thus created a huge positive externality for new entrants: existing knowledge and organizations devoted to software development, industry knowledge, reputation, awareness, and incentives for schools to offer courses. It could well be that new entrants, in fact, faced lower barriers with respect to app developers than did Microsoft when it entered.

In short, new entrants may face even more welcoming environments because of incumbents. This enabled Microsoft’s rivals to thrive.

Conclusion

Dystopian antitrust prophecies are generally doomed to fail, just like those belonging to the literary world. The reason is simple. While it is easy to identify what makes dominant firms successful in the present (i.e., what enables them to hold off competitors in the short term), it is almost impossible to conceive of the myriad ways in which the market could adapt. Indeed, it is today’s supra-competitive profits that spur the efforts of competitors.

Surmising that the economy will come to be dominated by a small number of successful firms is thus the same as believing that all market participants can be outsmarted by a few successful ones. This might occur in some cases or for some period of time, but as our article argues, it is bound to happen far less often than pessimists fear.

In short, dystopian scholars have not successfully made the case for precautionary antitrust. Indeed, the economic features of data make it highly unlikely that today’s tech giants could anticompetitively maintain their advantage for an indefinite amount of time, much less leverage this advantage in adjacent markets.

With this in mind, there is one dystopian novel that offers a fitting metaphor to end this Article. The Man in the High Castle tells the story of an alternate present, where Axis forces triumphed over the Allies during the second World War. This turns the dystopia genre on its head: rather than argue that the world is inevitably sliding towards a dark future, The Man in the High Castle posits that the present could be far worse than it is.

In other words, we should not take any of the luxuries we currently enjoy for granted. In the world of antitrust, critics routinely overlook that the emergence of today’s tech industry might have occurred thanks to, and not in spite of, existing antitrust doctrine. Changes to existing antitrust law should thus be dictated by a rigorous assessment of the various costs and benefits they would entail, rather than a litany of hypothetical concerns. The most recent wave of calls for antitrust reform have so far failed to clear this low bar.

Why Data Is Not the New Oil

Alec Stapp —  8 October 2019

“Data is the new oil,” said Jaron Lanier in a recent op-ed for The New York Times. Lanier’s use of this metaphor is only the latest instance of what has become the dumbest meme in tech policy. As the digital economy becomes more prominent in our lives, it is not unreasonable to seek to understand one of its most important inputs. But this analogy to the physical economy is fundamentally flawed. Worse, introducing regulations premised upon faulty assumptions like this will likely do far more harm than good. Here are seven reasons why “data is the new oil” misses the mark:

1. Oil is rivalrous; data is non-rivalrous

If someone uses a barrel of oil, it can’t be consumed again. But, as Alan McQuinn, a senior policy analyst at the Information Technology and Innovation Foundation, noted, “when consumers ‘pay with data’ to access a website, they still have the same amount of data after the transaction as before. As a result, users have an infinite resource available to them to access free online services.” Imposing restrictions on data collection makes this infinite resource finite. 

2. Oil is excludable; data is non-excludable

Oil is highly excludable because, as a physical commodity, it can be stored in ways that prevent use by non-authorized parties. However, as my colleagues pointed out in a recent comment to the FTC: “While databases may be proprietary, the underlying data usually is not.” They go on to argue that this can lead to under-investment in data collection:

[C]ompanies that have acquired a valuable piece of data will struggle both to prevent their rivals from obtaining the same data as well as to derive competitive advantage from the data. For these reasons, it also  means that firms may well be more reluctant to invest in data generation than is socially optimal. In fact, to the extent this is true there is arguably more risk of companies under-investing in data  generation than of firms over-investing in order to create data troves with which to monopolize a market. This contrasts with oil, where complete excludability is the norm.

3. Oil is fungible; data is non-fungible

Oil is a commodity, so, by definition, one barrel of oil of a given grade is equivalent to any other barrel of that grade. Data, on the other hand, is heterogeneous. Each person’s data is unique and may consist of a practically unlimited number of different attributes that can be collected into a profile. This means that oil will follow the law of one price, while a dataset’s value will be highly contingent on its particular properties and commercialization potential.

4. Oil has positive marginal costs; data has zero marginal costs

There is a significant expense to producing and distributing an additional barrel of oil (as low as $5.49 per barrel in Saudi Arabia; as high as $21.66 in the U.K.). Data is merely encoded information (bits of 1s and 0s), so gathering, storing, and transferring it is nearly costless (though, to be clear, setting up systems for collecting and processing can be a large fixed cost). Under perfect competition, the market clearing price is equal to the marginal cost of production (hence why data is traded for free services and oil still requires cold, hard cash).

5. Oil is a search good; data is an experience good

Oil is a search good, meaning its value can be assessed prior to purchasing. By contrast, data tends to be an experience good because companies don’t know how much a new dataset is worth until it has been combined with pre-existing datasets and deployed using algorithms (from which value is derived). This is one reason why purpose limitation rules can have unintended consequences. If firms are unable to predict what data they will need in order to develop new products, then restricting what data they’re allowed to collect is per se anti-innovation.

6. Oil has constant returns to scale; data has rapidly diminishing returns

As an energy input into a mechanical process, oil has relatively constant returns to scale (e.g., when oil is used as the fuel source to power a machine). When data is used as an input for an algorithm, it shows rapidly diminishing returns, as the charts collected in a presentation by Google’s Hal Varian demonstrate. The initial training data is hugely valuable for increasing an algorithm’s accuracy. But as you increase the dataset by a fixed amount each time, the improvements steadily decline (because new data is only helpful in so far as it’s differentiated from the existing dataset).

7. Oil is valuable; data is worthless

The features detailed above — rivalrousness, fungibility, marginal cost, returns to scale — all lead to perhaps the most important distinction between oil and data: The average barrel of oil is valuable (currently $56.49) and the average dataset is worthless (on the open market). As Will Rinehart showed, putting a price on data is a difficult task. But when data brokers and other intermediaries in the digital economy do try to value data, the prices are almost uniformly low. The Financial Times had the most detailed numbers on what personal data is sold for in the market:

  • “General information about a person, such as their age, gender and location is worth a mere $0.0005 per person, or $0.50 per 1,000 people.”
  • “A person who is shopping for a car, a financial product or a vacation is more valuable to companies eager to pitch those goods. Auto buyers, for instance, are worth about $0.0021 a pop, or $2.11 per 1,000 people.”
  • “Knowing that a woman is expecting a baby and is in her second trimester of pregnancy, for instance, sends the price tag for that information about her to $0.11.”
  • “For $0.26 per person, buyers can access lists of people with specific health conditions or taking certain prescriptions.”
  • “The company estimates that the value of a relatively high Klout score adds up to more than $3 in word-of-mouth marketing value.”
  • [T]he sum total for most individuals often is less than a dollar.

Data is a specific asset, meaning it has “a significantly higher value within a particular transacting relationship than outside the relationship.” We only think data is so valuable because tech companies are so valuable. In reality, it is the combination of high-skilled labor, large capital expenditures, and cutting-edge technologies (e.g., machine learning) that makes those companies so valuable. Yes, data is an important component of these production functions. But to claim that data is responsible for all the value created by these businesses, as Lanier does in his NYT op-ed, is farcical (and reminiscent of the labor theory of value). 

Conclusion

People who analogize data to oil or gold may merely be trying to convey that data is as valuable in the 21st century as those commodities were in the 20th century (though, as argued, a dubious proposition). If the comparison stopped there, it would be relatively harmless. But there is a real risk that policymakers might take the analogy literally and regulate data in the same way they regulate commodities. As this article shows, data has many unique properties that are simply incompatible with 20th-century modes of regulation.

A better — though imperfect — analogy, as author Bernard Marr suggests, would be renewable energy. The sources of renewable energy are all around us — solar, wind, hydroelectric — and there is more available than we could ever use. We just need the right incentives and technology to capture it. The same is true for data. We leave our digital fingerprints everywhere — we just need to dust for them.

The CPI Antitrust Chronicle published Geoffrey Manne’s and my recent paperThe Problems and Perils of Bootstrapping Privacy and Data into an Antitrust Framework as part of a symposium on Big Data in the May 2015 issue. All of the papers are worth reading and pondering, but of course ours is the best ;).

In it, we analyze two of the most prominent theories of antitrust harm arising from data collection: privacy as a factor of non-price competition, and price discrimination facilitated by data collection. We also analyze whether data is serving as a barrier to entry and effectively preventing competition. We argue that, in the current marketplace, there are no plausible harms to competition arising from either non-price effects or price discrimination due to data collection online and that there is no data barrier to entry preventing effective competition.

The issues of how to regulate privacy issues and what role competition authorities should in that, are only likely to increase in importance as the Internet marketplace continues to grow and evolve. The European Commission and the FTC have been called on by scholars and advocates to take greater consideration of privacy concerns during merger review and encouraged to even bring monopolization claims based upon data dominance. These calls should be rejected unless these theories can satisfy the rigorous economic review of antitrust law. In our humble opinion, they cannot do so at this time.

Excerpts:

PRIVACY AS AN ELEMENT OF NON-PRICE COMPETITION

The Horizontal Merger Guidelines have long recognized that anticompetitive effects may “be manifested in non-price terms and conditions that adversely affect customers.” But this notion, while largely unobjectionable in the abstract, still presents significant problems in actual application.

First, product quality effects can be extremely difficult to distinguish from price effects. Quality-adjusted price is usually the touchstone by which antitrust regulators assess prices for competitive effects analysis. Disentangling (allegedly) anticompetitive quality effects from simultaneous (neutral or pro-competitive) price effects is an imprecise exercise, at best. For this reason, proving a product-quality case alone is very difficult and requires connecting the degradation of a particular element of product quality to a net gain in advantage for the monopolist.

Second, invariably product quality can be measured on more than one dimension. For instance, product quality could include both function and aesthetics: A watch’s quality lies in both its ability to tell time as well as how nice it looks on your wrist. A non-price effects analysis involving product quality across multiple dimensions becomes exceedingly difficult if there is a tradeoff in consumer welfare between the dimensions. Thus, for example, a smaller watch battery may improve its aesthetics, but also reduce its reliability. Any such analysis would necessarily involve a complex and imprecise comparison of the relative magnitudes of harm/benefit to consumers who prefer one type of quality to another.

PRICE DISCRIMINATION AS A PRIVACY HARM

If non-price effects cannot be relied upon to establish competitive injury (as explained above), then what can be the basis for incorporating privacy concerns into antitrust? One argument is that major data collectors (e.g., Google and Facebook) facilitate price discrimination.

The argument can be summed up as follows: Price discrimination could be a harm to consumers that antitrust law takes into consideration. Because companies like Google and Facebook are able to collect a great deal of data about their users for analysis, businesses could segment groups based on certain characteristics and offer them different deals. The resulting price discrimination could lead to many consumers paying more than they would in the absence of the data collection. Therefore, the data collection by these major online companies facilitates price discrimination that harms consumer welfare.

This argument misses a large part of the story, however. The flip side is that price discrimination could have benefits to those who receive lower prices from the scheme than they would have in the absence of the data collection, a possibility explored by the recent White House Report on Big Data and Differential Pricing.

While privacy advocates have focused on the possible negative effects of price discrimination to one subset of consumers, they generally ignore the positive effects of businesses being able to expand output by serving previously underserved consumers. It is inconsistent with basic economic logic to suggest that a business relying on metrics would want to serve only those who can pay more by charging them a lower price, while charging those who cannot afford it a larger one. If anything, price discrimination would likely promote more egalitarian outcomes by allowing companies to offer lower prices to poorer segments of the population—segments that can be identified by data collection and analysis.

If this group favored by “personalized pricing” is as big as—or bigger than—the group that pays higher prices, then it is difficult to state that the practice leads to a reduction in consumer welfare, even if this can be divorced from total welfare. Again, the question becomes one of magnitudes that has yet to be considered in detail by privacy advocates.

DATA BARRIER TO ENTRY

Either of these theories of harm is predicated on the inability or difficulty of competitors to develop alternative products in the marketplace—the so-called “data barrier to entry.” The argument is that upstarts do not have sufficient data to compete with established players like Google and Facebook, which in turn employ their data to both attract online advertisers as well as foreclose their competitors from this crucial source of revenue. There are at least four reasons to be dubious of such arguments:

  1. Data is useful to all industries, not just online companies;
  2. It’s not the amount of data, but how you use it;
  3. Competition online is one click or swipe away; and
  4. Access to data is not exclusive

CONCLUSION

Privacy advocates have thus far failed to make their case. Even in their most plausible forms, the arguments for incorporating privacy and data concerns into antitrust analysis do not survive legal and economic scrutiny. In the absence of strong arguments suggesting likely anticompetitive effects, and in the face of enormous analytical problems (and thus a high risk of error cost), privacy should remain a matter of consumer protection, not of antitrust.