[TOTM: The following is part of a digital symposium by TOTM guests and authors on Antitrust’s Uncertain Future: Visions of Competition in the New Regulatory Landscape. Information on the authors and the entire series of posts is available here.]
In Free to Choose, Milton Friedman famously noted that there are four ways to spend money[1]:
Spending your own money on yourself. For example, buying groceries or lunch. There is a strong incentive to economize and to get full value.
Spending your own money on someone else. For example, buying a gift for another. There is a strong incentive to economize, but perhaps less to achieve full value from the other person’s point of view. Altruism is admirable, but it differs from value maximization, since—strictly speaking—giving cash would maximize the other’s value. Perhaps the point of a gift is that it does not amount to cash and the maximization of the other person’s welfare from their point of view.
Spending someone else’s money on yourself. For example, an expensed business lunch. “Pass me the filet mignon and Chateau Lafite! Do you have one of those menus without any prices?” There is a strong incentive to get maximum utility, but there is little incentive to economize.
Spending someone else’s money on someone else. For example, applying the proceeds of taxes or donations. There may be an indirect desire to see utility, but incentives for quality and cost management are often diminished.
This framework can be criticized. Altruism has a role. Not all motives are selfish. There is an important role for action to help those less fortunate, which might mean, for instance, that a charity gains more utility from category (4) (assisting the needy) than from category (3) (the charity’s holiday party). It always depends on the facts and the context. However, there is certainly a grain of truth in the observation that charity begins at home and that, in the final analysis, people are best at managing their own affairs.
How would this insight apply to data interoperability? The difficult cases of assisting the needy do not arise here: there is no serious sense in which data interoperability does, or does not, result in destitution. Thus, Friedman’s observations seem to ring true: when spending data, those whose data it is seem most likely to maximize its value. This is especially so where collection of data responds to incentives—that is, the amount of data collected and processed responds to how much control over the data is possible.
The obvious exception to this would be a case of market power. If there is a monopoly with persistent barriers to entry, then the incentive may not be to maximize total utility, and therefore to limit data handling to the extent that a higher price can be charged for the lesser amount of data that does remain available. This has arguably been seen with some data-handling rules: the “Jedi Blue” agreement on advertising bidding, Apple’s Intelligent Tracking Prevention and App Tracking Transparency, and Google’s proposed Privacy Sandbox, all restrict the ability of others to handle data. Indeed, they may fail Friedman’s framework, since they amount to the platform deciding how to spend others’ data—in this case, by not allowing them to collect and process it at all.
It should be emphasized, though, that this is a special case. It depends on market power, and existing antitrust and competition laws speak to it. The courts will decide whether cases like Daily Mail v Google and Texas et al. v Google show illegal monopolization of data flows, so as to fall within this special case of market power. Outside the United States, cases like the U.K. Competition and Markets Authority’s Google Privacy Sandbox commitments and the European Union’s proposed commitments with Amazon seek to allow others to continue to handle their data and to prevent exclusivity from arising from platform dynamics, which could happen if a large platform prevents others from deciding how to account for data they are collecting. It will be recalled that even Robert Bork thought that there was risk of market power harms from the large Microsoft Windows platform a generation ago.[2] Where market power risks are proven, there is a strong case that data exclusivity raises concerns because of an artificial barrier to entry. It would only be if the benefits of centralized data control were to outweigh the deadweight loss from data restrictions that this would be untrue (though query how well the legal processes verify this).
Yet the latest proposals go well beyond this. A broad interoperability right amounts to “open season” for spending others’ data. This makes perfect sense in the European Union, where there is no large domestic technology platform, meaning that the data is essentially owned via foreign entities (mostly, the shareholders of successful U.S. and Chinese companies). It must be very tempting to run an industrial policy on the basis that “we’ll never be Google” and thus to embrace “sharing is caring” as to others’ data.
But this would transgress the warning from Friedman: would people optimize data collection if it is open to mandatory sharing even without proof of market power? It is deeply concerning that the EU’s DATA Act is accompanied by an infographic that suggests that coffee-machine data might be subject to mandatory sharing, to allow competition in services related to the data (e.g., sales of pods; spare-parts automation). There being no monopoly in coffee machines, this simply forces vertical disintegration of data collection and handling. Why put a data-collection system into a coffee maker at all, if it is to be a common resource? Friedman’s category (4) would apply: the data is taken and spent by another. There is no guarantee that there would be sensible decision making surrounding the resource.
It will be interesting to see how common-law jurisdictions approach this issue. At the risk of stating the obvious, the polity in continental Europe differs from that in the English-speaking democracies when it comes to whether the collective, or the individual, should be in the driving seat. A close read of the UK CMA’s Google commitments is interesting, in that paragraph 30 requires no self-preferencing in data collection and requires future data-handling systems to be designed with impacts on competition in mind. No doubt the CMA is seeking to prevent data-handling exclusivity on the basis that this prevents companies from using their data collection to compete. This is far from the EU DATA Act’s position in that it is certainly not a right to handle Google’s data: it is simply a right to continue to process one’s own data.
U.S. proposals are at an earlier stage. It would seem important, as a matter of principle, not to make arbitrary decisions about vertical integration in data systems, and to identify specific market-power concerns instead, in line with common-law approaches to antitrust.
It might be very attractive to the EU to spend others’ data on their behalf, but that does not make it right. Those working on the U.S. proposals would do well to ensure that there is a meaningful market-power gate to avoid unintended consequences.
Disclaimer: The author was engaged for expert advice relating to the UK CMA’s Privacy Sandbox case on behalf of the complainant Marketers for an Open Web.
[1] Milton Friedman, Free to Choose, 1980, pp.115-119
[2] Comments at the Yale Law School conference, Robert H. Bork’s influence on Antitrust Law, Sep. 27-28, 2013.
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.
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.
[TOTM: The following is part of a digital symposium by TOTM guests and authors on the legal and regulatory issues that arose during Ajit Pai’s tenure as chairman of the Federal Communications Commission. The entire series of posts is available here.
Jerry Ellig was a research professor at The George Washington University Regulatory Studies Center and served as chief economist at the Federal Communications Commission from 2017 to 2018. Tragically, he passed away Jan. 20, 2021. TOTM is honored to publish his contribution to this symposium.]
One significant aspect of Chairman Ajit Pai’s legacy is not a policy change, but an organizational one: establishment of the Federal Communications Commission’s (FCC’s) Office of Economics and Analytics (OEA) in 2018.
Prior to OEA, most of the FCC’s economists were assigned to the various policy bureaus, such as Wireless, Wireline Competition, Public Safety, Media, and International. Each of these bureaus had its own chief economist, but the rank-and-file economists reported to the managers who ran the bureaus – usually attorneys who also developed policy and wrote regulations. In the words of former FCC Chief Economist Thomas Hazlett, the FCC had “no location anywhere in the organizational structure devoted primarily to economic analysis.”
Establishment of OEA involved four significant changes. First, most of the FCC’s economists (along with data strategists and auction specialists) are now grouped together into an organization separate from the policy bureaus, and they are managed by other economists. Second, the FCC rules establishing the new office tasked OEA with reviewing every rulemaking, reviewing every other item with economic content that comes before the commission for a vote, and preparing a full benefit-cost analysis for any regulation with $100 million or more in annual economic impact. Third, a joint memo from the FCC’s Office of General Counsel and OEA specifies that economists are to be involved in the early stages of all rulemakings. Fourth, the memo also indicates that FCC regulatory analysis should follow the principles articulated in Executive Order 12866 and Office of Management and Budget Circular A-4 (while specifying that the FCC, as an independent agency, is not bound by the executive order).
While this structure for managing economists was new for the FCC, it is hardly uncommon in federal regulatory agencies. Numerous independent agencies that deal with economic regulation house their economists in a separate bureau or office, including the Securities and Exchange Commission, the Commodity Futures Trading Commission, the Surface Transportation Board, the Office of Comptroller of the Currency, and the Federal Trade Commission. The SEC displays even more parallels with the FCC. A guidance memo adopted in 2012 by the SEC’s Office of General Counsel and Division of Risk, Strategy and Financial Innovation (the name of the division where economists and other analysts were located) specifies that economists are to be involved in the early stages of all rulemakings and articulates best analytical practices based on Executive Order 12866 and Circular A-4.
A separate economics office offers several advantages over the FCC’s prior approach. It gives the economists greater freedom to offer frank advice, enables them to conduct higher-quality analysis more consistent with the norms of their profession, and may ultimately make it easier to uphold FCC rules that are challenged in court.
Independence. When I served as chief economist at the FCC in 2017-2018, I gathered from conversations that the most common practice in the past was for attorneys who wrote rules to turn to economists for supporting analysis after key decisions had already been made. This was not always the process, but it often occurred. The internal working group of senior FCC career staff who drafted the plan for OEA reached similar conclusions. After the establishment of OEA, an FCC economist I interviewed noted how his role had changed: “My job used to be to support the policy decisions made in the chairman’s office. Now I’m much freer to speak my own mind.”
Ensuring economists’ independence is not a problem unique to the FCC. In a 2017 study, Stuart Shapiro found that most of the high-level economists he interviewed who worked on regulatory impact analyses in federal agencies perceive that economists can be more objective if they are located outside the program office that develops the regulations they are analyzing. As one put it, “It’s very difficult to conduct a BCA [benefit-cost analysis] if our boss wrote what you are analyzing.” Interviews with senior economists and non-economists who work on regulation that I conducted for an Administrative Conference of the United States project in 2019 revealed similar conclusions across federal agencies. Economists located in organizations separate from the program office said that structure gave them greater independence and ability to develop better analytical methodologies. On the other hand, economists located in program offices said they experienced or knew of instances where they were pressured or told to produce an analysis with the results decision-makers wanted.
The FTC provides an informative case study. From 1955-1961, many of the FTC’s economists reported to the attorneys who conducted antitrust cases; in 1961, they were moved into a separate Bureau of Economics. Fritz Mueller, the FTC chief economist responsible for moving the antitrust economists back into the Bureau of Economics, noted that they were originally placed under the antitrust attorneys because the attorneys wanted more control over the economic analysis. A 2015 evaluation by the FTC’s Inspector General concluded that the Bureau of Economics’ existence as a separate organization improves its ability to offer “unbiased and sound economic analysis to support decision-making.”
Higher-quality analysis. An issue closely related to economists’ independence is the quality of the economic analysis. Executive branch regulatory economists interviewed by Richard Williams expressed concern that the economic analysis was more likely to be changed to support decisions when the economists are located in the program office that writes the regulations. More generally, a study that Catherine Konieczny and I conducted while we were at the FCC found that executive branch agencies are more likely to produce higher-quality regulatory impact analyses if the economists responsible for the analysis are in an independent economics office rather than the program office.
Upholding regulations in court. In Michigan v. EPA, the Supreme Court held that it is unreasonable for agencies to refuse to consider regulatory costs if the authorizing statute does not prohibit them from doing so. This precedent will likely increase judicial expectations that agencies will consider economic issues when they issue regulations. The FCC’s OGC-OEA memo cites examples of cases where the quality of the FCC’s economic analysis either helped or harmed the commission’s ability to survive legal challenge under the Administrative Procedure Act’s “arbitrary and capricious” standard. More systematically, a recent Regulatory Studies Center working paper finds that a higher-quality economic analysis accompanying a regulation reduces the likelihood that courts will strike down the regulation, provided that the agency explains how it used the analysis in decisions.
Two potential disadvantages of a separate economics office are that it may make the economists easier to ignore (what former FCC Chief Economist Tim Brennan calls the “Siberia effect”) and may lead the economists to produce research that is less relevant to the practical policy concerns of the policymaking bureaus. The FCC’s reorganization plan took these disadvantages seriously.
To ensure that the ultimate decision-makers—the commissioners—have access to the economists’ analysis and recommendations, the rules establishing the office give OEA explicit responsibility for reviewing all items with economic content that come before the commission. Each item is accompanied by a cover memo that indicates whether OEA believes there are any significant issues, and whether they have been dealt with adequately. To ensure that economists and policy bureaus work together from the outset of regulatory initiatives, the OGC-OEA memo instructs:
Bureaus and Offices should, to the extent practicable, coordinate with OEA in the early stages of all Commission-level and major Bureau-level proceedings that are likely to draw scrutiny due to their economic impact. Such coordination will help promote productive communication and avoid delays from the need to incorporate additional analysis or other content late in the drafting process. In the earliest stages of the rulemaking process, economists and related staff will work with programmatic staff to help frame key questions, which may include drafting options memos with the lead Bureau or Office.
While presiding over his final commission meeting on Jan. 13, Pai commented, “It’s second nature now for all of us to ask, ‘What do the economists think?’” The real test of this institutional innovation will be whether that practice continues under a new chair in the next administration.
The Wall Street Journal reports that Amazon employees have been using data from individual sellers to identify products to compete with with its own ‘private label’ (or own-brand) products, such as AmazonBasics, Presto!, and Pinzon.
However, it is interesting to think about the underlying incentives facing Amazon here, and in particular why Amazon’s company policy is not to use individual seller data to develop products (rogue employees violating this policy, notwithstanding). One possibility is that it is a way for Amazon to balance its competition with some third parties with protections for others that it sees as valuable to its platform overall.
Amazon does use aggregated seller data to develop and market its products. If two or more merchants are selling a product, Amazon’s employees can see how popular it is. This might seem like a trivial distinction, but it might exist for good reason. It could be because sellers of unique products actually do have the bargaining power to demand that Amazon does not use their data to compete with them, or for public relations reasons, although it’s not clear how successful that has been.
But another possibility is that it may be a self-imposed restraint. Amazon sells its own private label products partially because doing so is profitable (even when undercutting rivals), partially to fill holes in product lines (like clothing, where 11% of listings were Amazon private label as of November 2018), and partially because it increases users’ likelihood to use Amazon if they expect to find a reliable product from a brand they trust. According to the Journal, they account for less than 1% of Amazon’s product sales, in contrast to the 19% of revenues ($54 billion) Amazon makes from third party seller services, which includes Marketplace commissions. Any analysis that ignores that Amazon has to balance those sources of revenue, and so has to tread carefully, is deficient.
With “commodity” products (like, say, batteries and USB cables), where multiple sellers are offering very similar or identical versions of the same thing, private label competition works well for both Amazon and consumers. By Amazon’s own rules it can enter this market using aggregated data, but this doesn’t give it a significant advantage, since that data is easily obtainable from multiple sources, including Amazon itself, which makes detailed aggregated sales data freely available to third-party retailers.
But to the extent that Amazon competes against innovative third-party sellers (typically manufacturers doing direct sales, as opposed to pure retailers simply re-selling others’ products), there is a possibility that the prospect of having to compete with Amazon may diminish their incentive to develop new products and sell them on Amazon’s platform.
This is the strongest argument that is made against private label offerings in general. When they involve some level of copying an innovative product, where the innovator has been collecting above-normal profits and those profits are what spur the innovation in the first place, a private label product that comes along and copies the product effectively free rides on the innovation and captures some of its return. That may get us less innovation than society—or a platform trying to host as many innovative products as possible—would like.
While the Journal conflates these two kinds of products, Amazon’s own policies may be tailored specifically to take account of the distinction, and maximise the total value of its marketplace to consumers.
This is nominally the focus of the Journal story: a car trunk organiser company with an (apparently) innovative product says that Amazon moving in to compete with its own AmazonBasics version competed away many of its sales. In this sort of situation, the free-rider problem described above might apply where future innovation is discouraged. Why bother to invent things like this if you’re just going to have your invention ripped off?
Of course, many such innovations are protected by patents. But there may be valuable innovations that are not, and even patented innovations are not perfectly protected given the costs of enforcement. But a platform like Amazon can adopt rules that fine-tune the protections offered by the legal system in an effort to increase the value of the platform for both innovators and consumers alike.
And that may be why Amazon has its rule against using individual seller data to compete: to allow creators of new products to collect more rents from their inventions, with a promise that, unless and until their product is commodified by other means (as indicated by the product being available from multiple other sellers), Amazon won’t compete against such sellers using any special insights it might have from that seller using Amazon’s Marketplace.
This doesn’t mean Amazon refuses to compete (or refuses to allow others to compete); it has other rules that sometimes determine that boundary, as when it enters into agreements with certain brands to permit sales of the brand on the platform only by sellers authorized by the brand owner. Rather, this rule is a more limited—but perhaps no less important—one that should entice innovators to use Amazon’s platform to sell their products without concern that doing so will create a special risk that Amazon can compete away their returns using information uniquely available to it. In effect, it’s a promise that innovators won’t lose more by choosing to sell on Amazon rather than through other retail channels..
Like other platforms, to maximise its profits Amazon needs to strike a balance between being an attractive place for third party merchants to sell their goods, and being attractive to consumers by offering as many inexpensive, innovative, and reliable products as possible. Striking that balance is challenging, but a rule that restrains the platform from using its unique position to expropriate value from innovative sellers helps to protect the income of genuinely innovative third parties, and induces them to sell products consumers want on Amazon, while still allowing Amazon (and third-party sellers) to compete with commodity products.
The fact that Amazon has strong competition online and offline certainly acts as an important constraint here, too: if Amazon behaved too badly, third parties might not sell on it at all, and Amazon would have none of the seller data that is allegedly so valuable to it.
But even in a world where Amazon had a huge, sticky customer base that meant it was not an option to sell elsewhere—which the Journal article somewhat improbably implies—Amazon would still need third parties to innovate and sell things on its platform.
What the Journal story really seems to demonstrate is the sort of genuine principal-agent problem that all large businesses face: the company as a whole needs to restrain its private label section in various respects but its agents in the private label section want to break those rules to maximise their personal performance (in this case, by launching a successful new AmazonBasics product). It’s like a rogue trader at a bank who breaks the rules to make herself look good by, she hopes, getting good results.This is just one of many rules that a platform like Amazon has to preserve the value of its platform. It’s probably not the most important one. But understanding why it exists may help us to understand why simple stories of platform predation don’t add up, and help to demonstrate the mechanisms that companies like Amazon use to maximise the total value of their platform, not just one part of it.
“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.
Next week the FCC is slated to vote on the second iteration of Chairman Wheeler’s proposed broadband privacy rules. Of course, as has become all too common, none of us outside the Commission has actually seen the proposal. But earlier this month Chairman Wheeler released a Fact Sheet that suggests some of the ways it would update the rules he initially proposed.
According to the Fact Sheet, the new proposed rules are
designed to evolve with changing technologies and encourage innovation, and are in harmony with other key privacy frameworks and principles — including those outlined by the Federal Trade Commission and the Administration’s Consumer Privacy Bill of Rights.
Unfortunately, the Chairman’s proposal appears to fall short of the mark on both counts.
As I discuss in detail in a letter filed with the Commission yesterday, despite the Chairman’s rhetoric, the rules described in the Fact Sheet fail to align with the FTC’s approach to privacy regulation embodied in its 2012 Privacy Report in at least two key ways:
First, the Fact Sheet significantly expands the scope of information that would be considered “sensitive” beyond that contemplated by the FTC. That, in turn, would impose onerous and unnecessary consumer consent obligations on commonplace uses of data, undermining consumer welfare, depriving consumers of information and access to new products and services, and restricting competition.
Second, unlike the FTC’s framework, the proposal described by the Fact Sheet ignores the crucial role of “context” in determining the appropriate level of consumer choice before affected companies may use consumer data. Instead, the Fact Sheet takes a rigid, acontextual approach that would stifle innovation and harm consumers.
The Chairman’s proposal moves far beyond the FTC’s definition of “sensitive” information requiring “opt-in” consent
The FTC’s privacy guidance is, in its design at least, appropriately flexible, aimed at balancing the immense benefits of information flows with sensible consumer protections. Thus it eschews an “inflexible list of specific practices” that would automatically trigger onerous consent obligations and “risk[] undermining companies’ incentives to innovate and develop new products and services….”
Under the FTC’s regime, depending on the context in which it is used (on which see the next section, below), the sensitivity of data delineates the difference between data uses that require “express affirmative” (opt-in) consent and those that do not (requiring only “other protections” short of opt-in consent — e.g., opt-out).
Because the distinction is so important — because opt-in consent is much more likely to staunch data flows — the FTC endeavors to provide guidance as to what data should be considered sensitive, and to cabin the scope of activities requiring opt-in consent. Thus, the FTC explains that “information about children, financial and health information, Social Security numbers, and precise geolocation data [should be treated as] sensitive.” But beyond those instances, the FTC doesn’t consider any other type of data as inherently sensitive.
By contrast, and without explanation, Chairman Wheeler’s Fact Sheet significantly expands what constitutes “sensitive” information requiring “opt-in” consent by adding “web browsing history,” “app usage history,” and “the content of communications” to the list of categories of data deemed sensitive in all cases.
By treating some of the most common and important categories of data as always “sensitive,” and by making the sensitivity of data the sole determinant for opt-in consent, the Chairman’s proposal would make it almost impossible for ISPs to make routine (to say nothing of innovative), appropriate, and productive uses of data comparable to those undertaken by virtually every major Internet company. This goes well beyond anything contemplated by the FTC — with no evidence of any corresponding benefit to consumers and with obvious harm to competition, innovation, and the overall economy online.
And because the Chairman’s proposal would impose these inappropriate and costly restrictions only on ISPs, it would create a barrier to competition by ISPs in other platform markets, without offering a defensible consumer protection rationale to justify either the disparate treatment or the restriction on competition.
“Opt-in” offers no greater privacy protection than allowing consumers to “opt-out”…, yet it imposes significantly higher costs on consumers, businesses, and the economy.
Not surprisingly, these costs fall disproportionately on the relatively poor and the less technology-literate. In the former case, opt-in requirements may deter companies from offering services at all, even to people who would make a very different trade-off between privacy and monetary price. In the latter case, because an initial decision to opt-in must be taken in relative ignorance, users without much experience to guide their decisions will face effectively higher decision-making costs than more knowledgeable users.
The Chairman’s proposal ignores the central role of context in the FTC’s privacy framework
In part for these reasons, central to the FTC’s more flexible framework is the establishment of a sort of “safe harbor” for data uses where the benefits clearly exceed the costs and consumer consent may be inferred:
Companies do not need to provide choice before collecting and using consumer data for practices that are consistent with the context of the transaction or the company’s relationship with the consumer….
Thus for many straightforward uses of data, the “context of the transaction,” not the asserted “sensitivity” of the underlying data, is the threshold question in evaluating the need for consumer choice in the FTC’s framework.
Chairman Wheeler’s Fact Sheet, by contrast, ignores this central role of context in its analysis. Instead, it focuses solely on data sensitivity, claiming that doing so is “in line with customer expectations.”
But this is inconsistent with the FTC’s approach.
In fact, the FTC’s framework explicitly rejects a pure “consumer expectations” standard:
Rather than relying solely upon the inherently subjective test of consumer expectations, the… standard focuses on more objective factors related to the consumer’s relationship with a business.
And while everyone agrees that sensitivity is a key part of pegging privacy regulation to actual consumer and corporate relationships, the FTC also recognizes that the importance of the sensitivity of the underlying data varies with the context in which it is used. Or, in the words of the White House’s 2012 Consumer Data Privacy in a Networked World Report (introducing its Consumer Privacy Bill of Rights), “[c]ontext should shape the balance and relative emphasis of particular principles” guiding the regulation of privacy.
By contrast, Chairman Wheeler’s “sensitivity-determines-consumer-expectations” framing is a transparent attempt to claim fealty to the FTC’s (and the Administration’s) privacy standards while actually implementing a privacy regime that is flatly inconsistent with them.
The FTC’s approach isn’t perfect, but that’s no excuse to double down on its failings
The FTC’s privacy guidance, and even more so its privacy enforcement practices under Section 5, are far from perfect. The FTC should be commended for its acknowledgement that consumers’ privacy preferences and companies’ uses of data will change over time, and that there are trade-offs inherent in imposing any constraints on the flow of information. But even the FTC fails to actually assess the magnitude of the costs and benefits of, and the deep complexities involved in, the trade-off, and puts an unjustified thumb on the scale in favor of limiting data use.
But that’s no excuse for Chairman Wheeler to ignore what the FTC gets right, and to double down on its failings. Based on the Fact Sheet (and the initial NPRM), it’s a virtual certainty that the Chairman’s proposal doesn’t heed the FTC’s refreshing call for humility and flexibility regarding the application of privacy rules to ISPs (and other Internet platforms):
These are complex and rapidly evolving areas, and more work should be done to learn about the practices of all large platform providers, their technical capabilities with respect to consumer data, and their current and expected uses of such data.
The rhetoric of the Chairman’s Fact Sheet is correct: the FCC should in fact conform its approach to privacy to the framework established by the FTC. Unfortunately, the reality of the Fact Sheet simply doesn’t comport with its rhetoric.
As the FCC’s vote on the Chairman’s proposal rapidly nears, and in light of its significant defects, we can only hope that the rest of the Commission refrains from reflexively adopting the proposed regime, and works to ensure that these problematic deviations from the FTC’s framework are addressed before moving forward.
The CPI Antitrust Chronicle published Geoffrey Manne’s and my recent paper, The 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.
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:
Data is useful to all industries, not just online companies;
It’s not the amount of data, but how you use it;
Competition online is one click or swipe away; and
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.
Recent years have seen an increasing interest in incorporating privacy into antitrust analysis. The FTC and regulators in Europe have rejected these calls so far, but certain scholars and activists continue their attempts to breathe life into this novel concept. Elsewhere we have written at length on the scholarship addressing the issue and found the case for incorporation wanting. Among the errors proponents make is a persistent (and woefully unsubstantiated) assertion that online data can amount to a barrier to entry, insulating incumbent services from competition and ensuring that only the largest providers thrive. This data barrier to entry, it is alleged, can then allow firms with monopoly power to harm consumers, either directly through “bad acts” like price discrimination, or indirectly by raising the costs of advertising, which then get passed on to consumers.
A case in point was on display at last week’s George Mason Law & Economics Center Briefing on Big Data, Privacy, and Antitrust. Building on their growing body of advocacy work, Nathan Newman and Allen Grunes argued that this hypothesized data barrier to entry actually exists, and that it prevents effective competition from search engines and social networks that are interested in offering services with heightened privacy protections.
According to Newman and Grunes, network effects and economies of scale ensure that dominant companies in search and social networking (they specifically named Google and Facebook — implying that they are in separate markets) operate without effective competition. This results in antitrust harm, they assert, because it precludes competition on the non-price factor of privacy protection.
In other words, according to Newman and Grunes, even though Google and Facebook offer their services for a price of $0 and constantly innovate and upgrade their products, consumers are nevertheless harmed because the business models of less-privacy-invasive alternatives are foreclosed by insufficient access to data (an almost self-contradicting and silly narrative for many reasons, including the big question of whether consumers prefer greater privacy protection to free stuff). Without access to, and use of, copious amounts of data, Newman and Grunes argue, the algorithms underlying search and targeted advertising are necessarily less effective and thus the search product without such access is less useful to consumers. And even more importantly to Newman, the value to advertisers of the resulting consumer profiles is diminished.
Newman has put forth a number of other possible antitrust harms that purportedly result from this alleged data barrier to entry, as well. Among these is the increased cost of advertising to those who wish to reach consumers. Presumably this would harm end users who have to pay more for goods and services because the costs of advertising are passed on to them. On top of that, Newman argues that ad networks inherently facilitate price discrimination, an outcome that he asserts amounts to antitrust harm.
FTC Commissioner Maureen Ohlhausen (who also spoke at the George Mason event) recently made the case that antitrust law is not well-suited to handling privacy problems. She argues — convincingly — that competition policy and consumer protection should be kept separate to preserve doctrinal stability. Antitrust law deals with harms to competition through the lens of economic analysis. Consumer protection law is tailored to deal with broader societal harms and aims at protecting the “sanctity” of consumer transactions. Antitrust law can, in theory, deal with privacy as a non-price factor of competition, but this is an uneasy fit because of the difficulties of balancing quality over two dimensions: Privacy may be something some consumers want, but others would prefer a better algorithm for search and social networks, and targeted ads with free content, for instance.
In fact, there is general agreement with Commissioner Ohlhausen on her basic points, even among critics like Newman and Grunes. But, as mentioned above, views diverge over whether there are some privacy harms that should nevertheless factor into competition analysis, and on whether there is in fact a data barrier to entry that makes these harms possible.
As we explain below, however, the notion of data as an antitrust-relevant barrier to entry is simply a myth. And, because all of the theories of “privacy as an antitrust harm” are essentially predicated on this, they are meritless.
First, data is useful to all industries — this is not some new phenomenon particular to online companies
It bears repeating (because critics seem to forget it in their rush to embrace “online exceptionalism”) that offline retailers also receive substantial benefit from, and greatly benefit consumers by, knowing more about what consumers want and when they want it. Through devices like coupons and loyalty cards (to say nothing of targeted mailing lists and the age-old practice of data mining check-out receipts), brick-and-mortar retailers can track purchase data and better serve consumers. Not only do consumers receive better deals for using them, but retailers know what products to stock and advertise and when and on what products to run sales. For instance:
Following its acquisition of Kosmix in 2011, Walmart established @WalmartLabs, which created its own product search engine for online shoppers. In the first year of its use alone, the number of customers buying a product on Walmart.com after researching a purchase increased by 20 percent. According to Ron Bensen, the vice president of engineering at @WalmartLabs, the combination of in-store and online data could give brick-and-mortar retailers like Walmart an advantage over strictly online stores.
Panera and a whole host of restaurants, grocery stores, drug stores and retailers use loyalty cards to advertise and learn about consumer preferences.
And of course there is a host of others uses for data, as well, including security, fraud prevention, product optimization, risk reduction to the insured, knowing what content is most interesting to readers, etc. The importance of data stretches far beyond the online world, and far beyond mere retail uses more generally. To describe even online giants like Amazon, Apple, Microsoft, Facebook and Google as having a monopoly on data is silly.
Second, it’s not the amount of data that leads to success but building a better mousetrap
The value of knowing someone’s birthday, for example, is not in that tidbit itself, but in the fact that you know this is a good day to give that person a present. Most of the data that supports the advertising networks underlying the Internet ecosphere is of this sort: Information is important to companies because of the value that can be drawn from it, not for the inherent value of the data itself. Companies don’t collect information about you to stalk you, but to better provide goods and services to you.
Moreover, data itself is not only less important than what can be drawn from it, but data is also less important than the underlying product it informs. For instance, Snapchat created a challenger to Facebook so successfully (and in such short time) that Facebook attempted to buy it for $3 billion (Google offered $4 billion). But Facebook’s interest in Snapchat wasn’t about its data. Instead, Snapchat was valuable — and a competitive challenge to Facebook — because it cleverly incorporated the (apparently novel) insight that many people wanted to share information in a more private way.
Relatedly, Twitter, Instagram, LinkedIn, Yelp, Pinterest (and Facebook itself) all started with little (or no) data and they have had a lot of success. Meanwhile, despite its supposed data advantages, Google’s attempts at social networking — Google+ — have never caught up to Facebook in terms of popularity to users (and thus not to advertisers either). And scrappy social network Ello is starting to build a significant base without data collection for advertising at all.
At the same time it’s simply not the case that the alleged data giants — the ones supposedly insulating themselves behind data barriers to entry — actually have the type of data most relevant to startups anyway. As Andres Lerner has argued, if you wanted to start a travel business, the data from Kayak or Priceline would be far more relevant. Or if you wanted to start a ride-sharing business, data from cab companies would be more useful than the broad, market-cross-cutting profiles Google and Facebook have. Consider companies like Uber, Lyft and Sidecar that had no customer data when they began to challenge established cab companies that did possess such data. If data were really so significant, they could never have competed successfully. But Uber, Lyft and Sidecar have been able to effectively compete because they built products that users wanted to use — they came up with an idea for a better mousetrap.The data they have accrued came after they innovated, entered the market and mounted their successful challenges — not before.
In reality, those who complain about data facilitating unassailable competitive advantages have it exactly backwards. Companies need to innovate to attract consumer data, otherwise 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 other apps is evidence that data is not a bottleneck to competition but a spur to drive it.
Third, competition online is one click or thumb swipe away; that is, barriers to entry and switching costs are low
Somehow, in the face of alleged data barriers to entry, competition online continues to soar, with newcomers constantly emerging and triumphing. This suggests that the barriers to entry are not so high as to prevent robust competition.
Again, despite the supposed data-based monopolies of Facebook, Google, Amazon, Apple and others, there exist powerful competitors in the marketplaces they compete in:
Google flight search has failed to seriously challenge — let alone displace — its competitors, as critics feared. Kayak, Expedia and the like remain the most prominent travel search sites — despite Google having literally purchased ITA’s trove of flight data and data-processing acumen.
People looking for local reviews go to Yelp and TripAdvisor (and, increasingly, Facebook) as often as Google.
With its recent acquisition of the shopping search engine, TheFind, and test-run of a “buy” button, Facebook is also gearing up to become a major competitor in the realm of e-commerce, challenging Amazon.
Likewise, Amazon recently launched its own ad network, “Amazon Sponsored Links,” to challenge other advertising players.
Even assuming for the sake of argument that data creates a barrier to entry, there is little evidence that consumers cannot easily switch to a competitor. While there are sometimes network effects online, like with social networking, history still shows that people will switch. MySpace was considered a dominant network until it made a series of bad business decisions and everyone ended up on Facebook instead. Similarly, Internet users can and do use Bing, DuckDuckGo, Yahoo, and a plethora of more specialized search engines on top of and instead of Google. And don’t forget that Google itself was once an upstart new entrant that replaced once-household names like Yahoo and AltaVista.
Fourth, access to data is not exclusive
Critics like Newman have compared Google to Standard Oil and argued that government authorities need to step in to limit Google’s control over data. But to say data is like oil is a complete misnomer. If Exxon drills and extracts oil from the ground, that oil is no longer available to BP. Data is not finite in the same way. To use an earlier example, Google knowing my birthday doesn’t limit the ability of Facebook to know my birthday, as well. While databases may be proprietary, the underlying data is not. And what matters more than the data itself is how well it is analyzed.
This is especially important when discussing data online, where multi-homing is ubiquitous, meaning many competitors end up voluntarily sharing access to data. For instance, I can use the friend-finder feature on WordPress to find Facebook friends, Google connections, and people I’m following on Twitter who also use the site for blogging. Using this feature allows WordPress to access your contact list on these major online players.
Further, it is not apparent that Google’s competitors have less data available to them. Microsoft, for instance, has admitted that it may actually have more data. And, importantly for this discussion, Microsoft may have actually garnered some of its data for Bing from Google.
If Google has a high cost per click, then perhaps it’s because it is worth it to advertisers: There are more eyes on Google because of its superior search product. Contra Newman and Grunes, Google may just be more popular for consumers and advertisers alike because the algorithm makes it more useful, not because it has more data than everyone else.
Fifth, the data barrier to entry argument does not have workable antitrust remedies
The misguided logic of data barrier to entry arguments leaves a lot of questions unanswered. Perhaps most important among these is the question of remedies. What remedy would apply to a company found guilty of leveraging its market power with data?
It’s actually quite difficult to conceive of a practical means for a competition authority to craft remedies that would address the stated concerns without imposing enormous social costs. In the unilateral conduct context, the most obvious remedy would involve the forced sharing of data.
On the one hand, as we’ve noted, it’s not clear this would actually accomplish much. If competitors can’t actually make good use of data, simply having more of it isn’t going to change things. At the same time, such a result would reduce the incentive to build data networks to begin with. In their startup stage, companies like Uber and Facebook required several months and hundreds of thousands, if not millions, of dollars to design and develop just the first iteration of the products consumers love. Would any of them have done it if they had to share their insights? In fact, it may well be that access to these free insights is what competitors actually want; it’s not the data they’re lacking, but the vision or engineering acumen to use it.
Other remedies limiting collection and use of data are not only outside of the normal scope of antitrust remedies, they would also involve extremely costly court supervision and may entail problematic “collisions between new technologies and privacy rights,” as the last year’s White House Report on Big Data and Privacy put it.
It is equally unclear what an antitrust enforcer could do in the merger context. As Commissioner Ohlhausen has argued, blocking specific transactions does not necessarily stop data transfer or promote privacy interests. Parties could simply house data in a standalone entity and enter into licensing arrangements. And conditioning transactions with forced data sharing requirements would lead to the same problems described above.
If antitrust doesn’t provide a remedy, then it is not clear why it should apply at all. The absence of workable remedies is in fact a strong indication that data and privacy issues are not suitable for antitrust. Instead, such concerns would be better dealt with under consumer protection law or by targeted legislation.
In short, all of this hand-wringing over privacy is largely a tempest in a teapot — especially when one considers the extent to which the White House and other government bodies have studiously ignored the real threat: government misuse of data à la the NSA. It’s almost as if the White House is deliberately shifting the public’s gaze from the reality of extensive government spying by directing it toward a fantasy world of nefarious corporations abusing private information….
The White House’s proposed bill is emblematic of many government “fixes” to largely non-existent privacy issues, and it exhibits the same core defects that undermine both its claims and its proposed solutions. As a result, the proposed bill vastly overemphasizes regulation to the dangerous detriment of the innovative benefits of Big Data for consumers and society at large.
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