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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.

The European Commission this week published its proposed Artificial Intelligence Regulation, setting out new rules for  “artificial intelligence systems” used within the European Union. The regulation—the commission’s attempt to limit pernicious uses of AI without discouraging its adoption in beneficial cases—casts a wide net in defining AI to include essentially any software developed using machine learning. As a result, a host of software may fall under the regulation’s purview.

The regulation categorizes AIs by the kind and extent of risk they may pose to health, safety, and fundamental rights, with the overarching goal to:

  • Prohibit “unacceptable risk” AIs outright;
  • Place strict restrictions on “high-risk” AIs;
  • Place minor restrictions on “limited-risk” AIs;
  • Create voluntary “codes of conduct” for “minimal-risk” AIs;
  • Establish a regulatory sandbox regime for AI systems; 
  • Set up a European Artificial Intelligence Board to oversee regulatory implementation; and
  • Set fines for noncompliance at up to 30 million euros, or 6% of worldwide turnover, whichever is greater.

AIs That Are Prohibited Outright

The regulation prohibits AI that are used to exploit people’s vulnerabilities or that use subliminal techniques to distort behavior in a way likely to cause physical or psychological harm. Also prohibited are AIs used by public authorities to give people a trustworthiness score, if that score would then be used to treat a person unfavorably in a separate context or in a way that is disproportionate. The regulation also bans the use of “real-time” remote biometric identification (such as facial-recognition technology) in public spaces by law enforcement, with exceptions for specific and limited uses, such as searching for a missing child.

The first prohibition raises some interesting questions. The regulation says that an “exploited vulnerability” must relate to age or disability. In its announcement, the commission says this is targeted toward AIs such as toys that might induce a child to engage in dangerous behavior.

The ban on AIs using “subliminal techniques” is more opaque. The regulation doesn’t give a clear definition of what constitutes a “subliminal technique,” other than that it must be something “beyond a person’s consciousness.” Would this include TikTok’s algorithm, which imperceptibly adjusts the videos shown to the user to keep them engaged on the platform? The notion that this might cause harm is not fanciful, but it’s unclear whether the provision would be interpreted to be that expansive, whatever the commission’s intent might be. There is at least a risk that this provision would discourage innovative new uses of AI, causing businesses to err on the side of caution to avoid the huge penalties that breaking the rules would incur.

The prohibition on AIs used for social scoring is limited to public authorities. That leaves space for socially useful expansions of scoring systems, such as consumers using their Uber rating to show a record of previous good behavior to a potential Airbnb host. The ban is clearly oriented toward more expansive and dystopian uses of social credit systems, which some fear may be used to arbitrarily lock people out of society.

The ban on remote biometric identification AI is similarly limited to its use by law enforcement in public spaces. The limited exceptions (preventing an imminent terrorist attack, searching for a missing child, etc.) would be subject to judicial authorization except in cases of emergency, where ex-post authorization can be sought. The prohibition leaves room for private enterprises to innovate, but all non-prohibited uses of remote biometric identification would be subject to the requirements for high-risk AIs.

Restrictions on ‘High-Risk’ AIs

Some AI uses are not prohibited outright, but instead categorized as “high-risk” and subject to strict rules before they can be used or put to market. AI systems considered to be high-risk include those used for:

  • Safety components for certain types of products;
  • Remote biometric identification, except those uses that are banned outright;
  • Safety components in the management and operation of critical infrastructure, such as gas and electricity networks;
  • Dispatching emergency services;
  • Educational admissions and assessments;
  • Employment, workers management, and access to self-employment;
  • Evaluating credit-worthiness;
  • Assessing eligibility to receive social security benefits or services;
  • A range of law-enforcement purposes (e.g., detecting deepfakes or predicting the occurrence of criminal offenses);
  • Migration, asylum, and border-control management; and
  • Administration of justice.

While the commission considers these AIs to be those most likely to cause individual or social harm, it may not have appropriately balanced those perceived harms with the onerous regulatory burdens placed upon their use.

As Mikołaj Barczentewicz at the Surrey Law and Technology Hub has pointed out, the regulation would discourage even simple uses of logic or machine-learning systems in such settings as education or workplaces. This would mean that any workplace that develops machine-learning tools to enhance productivity—through, for example, monitoring or task allocation—would be subject to stringent requirements. These include requirements to have risk-management systems in place, to use only “high quality” datasets, and to allow human oversight of the AI, as well as other requirements around transparency and documentation.

The obligations would apply to any companies or government agencies that develop an AI (or for whom an AI is developed) with a view toward marketing it or putting it into service under their own name. The obligations could even attach to distributors, importers, users, or other third parties if they make a “substantial modification” to the high-risk AI, market it under their own name, or change its intended purpose—all of which could potentially discourage adaptive use.

Without going into unnecessary detail regarding each requirement, some are likely to have competition- and innovation-distorting effects that are worth discussing.

The rule that data used to train, validate, or test a high-risk AI has to be high quality (“relevant, representative, and free of errors”) assumes that perfect, error-free data sets exist, or can easily be detected. Not only is this not necessarily the case, but the requirement could impose an impossible standard on some activities. Given this high bar, high-risk AIs that use data of merely “good” quality could be precluded. It also would cut against the frontiers of research in artificial intelligence, where sometimes only small and lower-quality datasets are available to train AI. A predictable effect is that the rule would benefit large companies that are more likely to have access to large, high-quality datasets, while rules like the GDPR make it difficult for smaller companies to acquire that data.

High-risk AIs also must submit technical and user documentation that detail voluminous information about the AI system, including descriptions of the AI’s elements, its development, monitoring, functioning, and control. These must demonstrate the AI complies with all the requirements for high-risk AIs, in addition to documenting its characteristics, capabilities, and limitations. The requirement to produce vast amounts of information represents another potentially significant compliance cost that will be particularly felt by startups and other small and medium-sized enterprises (SMEs). This could further discourage AI adoption within the EU, as European enterprises already consider liability for potential damages and regulatory obstacles as impediments to AI adoption.

The requirement that the AI be subject to human oversight entails that the AI can be overseen and understood by a human being and that the AI can never override a human user. While it may be important that an AI used in, say, the criminal justice system must be understood by humans, this requirement could inhibit sophisticated uses beyond the reasoning of a human brain, such as how to safely operate a national electricity grid. Providers of high-risk AI systems also must establish a post-market monitoring system to evaluate continuous compliance with the regulation, representing another potentially significant ongoing cost for the use of high-risk AIs.

The regulation also places certain restrictions on “limited-risk” AIs, notably deepfakes and chatbots. Such AIs must be labeled to make a user aware they are looking at or listening to manipulated images, video, or audio. AIs must also be labeled to ensure humans are aware when they are speaking to an artificial intelligence, where this is not already obvious.

Taken together, these regulatory burdens may be greater than the benefits they generate, and could chill innovation and competition. The impact on smaller EU firms, which already are likely to struggle to compete with the American and Chinese tech giants, could prompt them to move outside the European jurisdiction altogether.

Regulatory Support for Innovation and Competition

To reduce the costs of these rules, the regulation also includes a new regulatory “sandbox” scheme. The sandboxes would putatively offer environments to develop and test AIs under the supervision of competent authorities, although exposure to liability would remain for harms caused to third parties and AIs would still have to comply with the requirements of the regulation.

SMEs and startups would have priority access to the regulatory sandboxes, although they must meet the same eligibility conditions as larger competitors. There would also be awareness-raising activities to help SMEs and startups to understand the rules; a “support channel” for SMEs within the national regulator; and adjusted fees for SMEs and startups to establish that their AIs conform with requirements.

These measures are intended to prevent the sort of chilling effect that was seen as a result of the GDPR, which led to a 17% increase in market concentration after it was introduced. But it’s unclear that they would accomplish this goal. (Notably, the GDPR contained similar provisions offering awareness-raising activities and derogations from specific duties for SMEs.) Firms operating in the “sandboxes” would still be exposed to liability, and the only significant difference to market conditions appears to be the “supervision” of competent authorities. It remains to be seen how this arrangement would sufficiently promote innovation as to overcome the burdens placed on AI by the significant new regulatory and compliance costs.

Governance and Enforcement

Each EU member state would be expected to appoint a “national competent authority” to implement and apply the regulation, as well as bodies to ensure high-risk systems conform with rules that require third party-assessments, such as remote biometric identification AIs.

The regulation establishes the European Artificial Intelligence Board to act as the union-wide regulatory body for AI. The board would be responsible for sharing best practices with member states, harmonizing practices among them, and issuing opinions on matters related to implementation.

As mentioned earlier, maximum penalties for marketing or using a prohibited AI (as well as for failing to use high-quality datasets) would be a steep 30 million euros or 6% of worldwide turnover, whichever is greater. Breaking other requirements for high-risk AIs carries maximum penalties of 20 million euros or 4% of worldwide turnover, while maximums of 10 million euros or 2% of worldwide turnover would be imposed for supplying incorrect, incomplete, or misleading information to the nationally appointed regulator.

Is the Commission Overplaying its Hand?

While the regulation only restricts AIs seen as creating risk to society, it defines that risk so broadly and vaguely that benign applications of AI may be included in its scope, intentionally or unintentionally. Moreover, the commission also proposes voluntary codes of conduct that would apply similar requirements to “minimal” risk AIs. These codes—optional for now—may signal the commission’s intent eventually to further broaden the regulation’s scope and application.

The commission clearly hopes it can rely on the “Brussels Effect” to steer the rest of the world toward tighter AI regulation, but it is also possible that other countries will seek to attract AI startups and investment by introducing less stringent regimes.

For the EU itself, more regulation must be balanced against the need to foster AI innovation. Without European tech giants of its own, the commission must be careful not to stifle the SMEs that form the backbone of the European market, particularly if global competitors are able to innovate more freely in the American or Chinese markets. If the commission has got the balance wrong, it may find that AI development simply goes elsewhere, with the EU fighting the battle for the future of AI with one hand tied behind its back.

Politico has released a cache of confidential Federal Trade Commission (FTC) documents in connection with a series of articles on the commission’s antitrust probe into Google Search a decade ago. The headline of the first piece in the series argues the FTC “fumbled the future” by failing to follow through on staff recommendations to pursue antitrust intervention against the company. 

But while the leaked documents shed interesting light on the inner workings of the FTC, they do very little to substantiate the case that the FTC dropped the ball when the commissioners voted unanimously not to bring an action against Google.

Drawn primarily from memos by the FTC’s lawyers, the Politico report purports to uncover key revelations that undermine the FTC’s decision not to sue Google. None of the revelations, however, provide evidence that Google’s behavior actually harmed consumers.

The report’s overriding claim—and the one most consistently forwarded by antitrust activists on Twitter—is that FTC commissioners wrongly sided with the agency’s economists (who cautioned against intervention) rather than its lawyers (who tenuously recommended very limited intervention). 

Indeed, the overarching narrative is that the lawyers knew what was coming and the economists took wildly inaccurate positions that turned out to be completely off the mark:

But the FTC’s economists successfully argued against suing the company, and the agency’s staff experts made a series of predictions that would fail to match where the online world was headed:

— They saw only “limited potential for growth” in ads that track users across the web — now the backbone of Google parent company Alphabet’s $182.5 billion in annual revenue.

— They expected consumers to continue relying mainly on computers to search for information. Today, about 62 percent of those queries take place on mobile phones and tablets, nearly all of which use Google’s search engine as the default.

— They thought rivals like Microsoft, Mozilla or Amazon would offer viable competition to Google in the market for the software that runs smartphones. Instead, nearly all U.S. smartphones run on Google’s Android and Apple’s iOS.

— They underestimated Google’s market share, a heft that gave it power over advertisers as well as companies like Yelp and Tripadvisor that rely on search results for traffic.

The report thus asserts that:

The agency ultimately voted against taking action, saying changes Google made to its search algorithm gave consumers better results and therefore didn’t unfairly harm competitors.

That conclusion underplays what the FTC’s staff found during the probe. In 312 pages of documents, the vast majority never publicly released, staffers outlined evidence that Google had taken numerous steps to ensure it would continue to dominate the market — including emerging arenas such as mobile search and targeted advertising. [EMPHASIS ADDED]

What really emerges from the leaked memos, however, is analysis by both the FTC’s lawyers and economists infused with a healthy dose of humility. There were strong political incentives to bring a case. As one of us noted upon the FTC’s closing of the investigation: “It’s hard to imagine an agency under more pressure, from more quarters (including the Hill), to bring a case around search.” Yet FTC staff and commissioners resisted that pressure, because prediction is hard. 

Ironically, the very prediction errors that the agency’s staff cautioned against are now being held against them. Yet the claims that these errors (especially the economists’) systematically cut in one direction (i.e., against enforcement) and that all of their predictions were wrong are both wide of the mark. 

Decisions Under Uncertainty

In seeking to make an example out of the FTC economists’ inaccurate predictions, critics ignore that antitrust investigations in dynamic markets always involve a tremendous amount of uncertainty; false predictions are the norm. Accordingly, the key challenge for policymakers is not so much to predict correctly, but to minimize the impact of incorrect predictions.

Seen in this light, the FTC economists’ memo is far from the laissez-faire manifesto that critics make it out to be. Instead, it shows agency officials wrestling with uncertain market outcomes, and choosing a course of action under the assumption the predictions they make might indeed be wrong. 

Consider the following passage from FTC economist Ken Heyer’s memo:

The great American philosopher Yogi Berra once famously remarked “Predicting is difficult, especially about the future.” How right he was. And yet predicting, and making decisions based on those predictions, is what we are charged with doing. Ignoring the potential problem is not an option. So I will be reasonably clear about my own tentative conclusions and recommendation, recognizing that reasonable people, perhaps applying a somewhat different standard, may disagree. My recommendation derives from my read of the available evidence, combined with the standard I personally find appropriate to apply to Commission intervention. [EMPHASIS ADDED]

In other words, contrary to what many critics have claimed, it simply is not the case that the FTC’s economists based their recommendations on bullish predictions about the future that ultimately failed to transpire. Instead, they merely recognized that, in a dynamic and unpredictable environment, antitrust intervention requires both a clear-cut theory of anticompetitive harm and a reasonable probability that remedies can improve consumer welfare. According to the economists, those conditions were absent with respect to Google Search.

Perhaps more importantly, it is worth asking why the economists’ erroneous predictions matter at all. Do critics believe that developments the economists missed warrant a different normative stance today?

In that respect, it is worth noting that the economists’ skepticism appeared to have rested first and foremost on the speculative nature of the harms alleged and the difficulty associated with designing appropriate remedies. And yet, if anything, these two concerns appear even more salient today. 

Indeed, the remedies imposed against Google in the EU have not delivered the outcomes that enforcers expected (here and here). This could either be because the remedies were insufficient or because Google’s market position was not due to anticompetitive conduct. Similarly, there is still no convincing economic theory or empirical research to support the notion that exclusive pre-installation and self-preferencing by incumbents harm consumers, and a great deal of reason to think they benefit them (see, e.g., our discussions of the issue here and here). 

Against this backdrop, criticism of the FTC economists appears to be driven more by a prior assumption that intervention is necessary—and that it was and is disingenuous to think otherwise—than evidence that erroneous predictions materially affected the outcome of the proceedings.

To take one example, the fact that ad tracking grew faster than the FTC economists believed it would is no less consistent with vigorous competition—and Google providing a superior product—than with anticompetitive conduct on Google’s part. The same applies to the growth of mobile operating systems. Ditto the fact that no rival has managed to dislodge Google in its most important markets. 

In short, not only were the economist memos informed by the very prediction difficulties that critics are now pointing to, but critics have not shown that any of the staff’s (inevitably) faulty predictions warranted a different normative outcome.

Putting Erroneous Predictions in Context

So what were these faulty predictions, and how important were they? Politico asserts that “the FTC’s economists successfully argued against suing the company, and the agency’s staff experts made a series of predictions that would fail to match where the online world was headed,” tying this to the FTC’s failure to intervene against Google over “tactics that European regulators and the U.S. Justice Department would later label antitrust violations.” The clear message is that the current actions are presumptively valid, and that the FTC’s economists thwarted earlier intervention based on faulty analysis.

But it is far from clear that these faulty predictions would have justified taking a tougher stance against Google. One key question for antitrust authorities is whether they can be reasonably certain that more efficient competitors will be unable to dislodge an incumbent. This assessment is necessarily forward-looking. Framed this way, greater market uncertainty (for instance, because policymakers are dealing with dynamic markets) usually cuts against antitrust intervention.

This does not entirely absolve the FTC economists who made the faulty predictions. But it does suggest the right question is not whether the economists made mistakes, but whether virtually everyone did so. The latter would be evidence of uncertainty, and thus weigh against antitrust intervention.

In that respect, it is worth noting that the staff who recommended that the FTC intervene also misjudged the future of digital markets.For example, while Politico surmises that the FTC “underestimated Google’s market share, a heft that gave it power over advertisers as well as companies like Yelp and Tripadvisor that rely on search results for traffic,” there is a case to be made that the FTC overestimated this power. If anything, Google’s continued growth has opened new niches in the online advertising space.

Pinterest provides a fitting example; despite relying heavily on Google for traffic, its ad-funded service has witnessed significant growth. The same is true of other vertical search engines like Airbnb, Booking.com, and Zillow. While we cannot know the counterfactual, the vertical search industry has certainly not been decimated by Google’s “monopoly”; quite the opposite. Unsurprisingly, this has coincided with a significant decrease in the cost of online advertising, and the growth of online advertising relative to other forms.

Politico asserts not only that the economists’ market share and market power calculations were wrong, but that the lawyers knew better:

The economists, relying on data from the market analytics firm Comscore, found that Google had only limited impact. They estimated that between 10 and 20 percent of traffic to those types of sites generally came from the search engine.

FTC attorneys, though, used numbers provided by Yelp and found that 92 percent of users visited local review sites from Google. For shopping sites like eBay and TheFind, the referral rate from Google was between 67 and 73 percent.

This compares apples and oranges, or maybe oranges and grapefruit. The economists’ data, from Comscore, applied to vertical search overall. They explicitly noted that shares for particular sites could be much higher or lower: for comparison shopping, for example, “ranging from 56% to less than 10%.” This, of course, highlights a problem with the data provided by Yelp, et al.: it concerns only the websites of companies complaining about Google, not the overall flow of traffic for vertical search.

But the more important point is that none of the data discussed in the memos represents the overall flow of traffic for vertical search. Take Yelp, for example. According to the lawyers’ memo, 92 percent of Yelp searches were referred from Google. Only, that’s not true. We know it’s not true because, as Yelp CEO Jerry Stoppelman pointed out around this time in Yelp’s 2012 Q2 earnings call: 

When you consider that 40% of our searches come from mobile apps, there is quite a bit of un-monetized mobile traffic that we expect to unlock in the near future.

The numbers being analyzed by the FTC staff were apparently limited to referrals to Yelp’s website from browsers. But is there any reason to think that is the relevant market, or the relevant measure of customer access? Certainly there is nothing in the staff memos to suggest they considered the full scope of the market very carefully here. Indeed, the footnote in the lawyers’ memo presenting the traffic data is offered in support of this claim:

Vertical websites, such as comparison shopping and local websites, are heavily dependent on Google’s web search results to reach users. Thus, Google is in the unique position of being able to “make or break any web-based business.”

It’s plausible that vertical search traffic is “heavily dependent” on Google Search, but the numbers offered in support of that simply ignore the (then) 40 percent of traffic that Yelp acquired through its own mobile app, with no Google involvement at all. In any case, it is also notable that, while there are still somewhat fewer app users than web users (although the number has consistently increased), Yelp’s app users view significantly more pages than its website users do — 10 times as many in 2015, for example.

Also noteworthy is that, for whatever speculative harm Google might be able to visit on the company, at the time of the FTC’s analysis Yelp’s local ad revenue was consistently increasing — by 89% in Q3 2012. And that was without any ad revenue coming from its app (display ads arrived on Yelp’s mobile app in Q1 2013, a few months after the staff memos were written and just after the FTC closed its Google Search investigation). 

In short, the search-engine industry is extremely dynamic and unpredictable. Contrary to what many have surmised from the FTC staff memo leaks, this cuts against antitrust intervention, not in favor of it.

The FTC Lawyers’ Weak Case for Prosecuting Google

At the same time, although not discussed by Politico, the lawyers’ memo also contains errors, suggesting that arguments for intervention were also (inevitably) subject to erroneous prediction.

Among other things, the FTC attorneys’ memo argued the large upfront investments were required to develop cutting-edge algorithms, and that these effectively shielded Google from competition. The memo cites the following as a barrier to entry:

A search engine requires algorithmic technology that enables it to search the Internet, retrieve and organize information, index billions of regularly changing web pages, and return relevant results instantaneously that satisfy the consumer’s inquiry. Developing such algorithms requires highly specialized personnel with high levels of training and knowledge in engineering, economics, mathematics, sciences, and statistical analysis.

If there are barriers to entry in the search-engine industry, algorithms do not seem to be the source. While their market shares may be smaller than Google’s, rival search engines like DuckDuckGo and Bing have been able to enter and gain traction; it is difficult to say that algorithmic technology has proven a barrier to entry. It may be hard to do well, but it certainly has not proved an impediment to new firms entering and developing workable and successful products. Indeed, some extremely successful companies have entered into similar advertising markets on the backs of complex algorithms, notably Instagram, Snapchat, and TikTok. All of these compete with Google for advertising dollars.

The FTC’s legal staff also failed to see that Google would face serious competition in the rapidly growing voice assistant market. In other words, even its search-engine “moat” is far less impregnable than it might at first appear.

Moreover, as Ben Thompson argues in his Stratechery newsletter: 

The Staff memo is completely wrong too, at least in terms of the potential for their proposed remedies to lead to any real change in today’s market. This gets back to why the fundamental premise of the Politico article, along with much of the antitrust chatter in Washington, misses the point: Google is dominant because consumers like it.

This difficulty was deftly highlighted by Heyer’s memo:

If the perceived problems here can be solved only through a draconian remedy of this sort, or perhaps through a remedy that eliminates Google’s legitimately obtained market power (and thus its ability to “do evil”), I believe the remedy would be disproportionate to the violation and that its costs would likely exceed its benefits. Conversely, if a remedy well short of this seems likely to prove ineffective, a remedy would be undesirable for that reason. In brief, I do not see a feasible remedy for the vertical conduct that would be both appropriate and effective, and which would not also be very costly to implement and to police. [EMPHASIS ADDED]

Of course, we now know that this turned out to be a huge issue with the EU’s competition cases against Google. The remedies in both the EU’s Google Shopping and Android decisions were severely criticized by rival firms and consumer-defense organizations (here and here), but were ultimately upheld, in part because even the European Commission likely saw more forceful alternatives as disproportionate.

And in the few places where the legal staff concluded that Google’s conduct may have caused harm, there is good reason to think that their analysis was flawed.

Google’s ‘revenue-sharing’ agreements

It should be noted that neither the lawyers nor the economists at the FTC were particularly bullish on bringing suit against Google. In most areas of the investigation, neither recommended that the commission pursue a case. But one of the most interesting revelations from the recent leaks is that FTC lawyers did advise the commission’s leadership to sue Google over revenue-sharing agreements that called for it to pay Apple and other carriers and manufacturers to pre-install its search bar on mobile devices:

FTC staff urged the agency’s five commissioners to sue Google for signing exclusive contracts with Apple and the major wireless carriers that made sure the company’s search engine came pre-installed on smartphones.

The lawyers’ stance is surprising, and, despite actions subsequently brought by the EU and DOJ on similar claims, a difficult one to countenance. 

To a first approximation, this behavior is precisely what antitrust law seeks to promote: we want companies to compete aggressively to attract consumers. This conclusion is in no way altered when competition is “for the market” (in this case, firms bidding for exclusive placement of their search engines) rather than “in the market” (i.e., equally placed search engines competing for eyeballs).

Competition for exclusive placement has several important benefits. For a start, revenue-sharing agreements effectively subsidize consumers’ mobile device purchases. As Brian Albrecht aptly puts it:

This payment from Google means that Apple can lower its price to better compete for consumers. This is standard; some of the payment from Google to Apple will be passed through to consumers in the form of lower prices.

This finding is not new. For instance, Ronald Coase famously argued that the Federal Communications Commission (FCC) was wrong to ban the broadcasting industry’s equivalent of revenue-sharing agreements, so-called payola:

[I]f the playing of a record by a radio station increases the sales of that record, it is both natural and desirable that there should be a charge for this. If this is not done by the station and payola is not allowed, it is inevitable that more resources will be employed in the production and distribution of records, without any gain to consumers, with the result that the real income of the community will tend to decline. In addition, the prohibition of payola may result in worse record programs, will tend to lessen competition, and will involve additional expenditures for regulation. The gain which the ban is thought to bring is to make the purchasing decisions of record buyers more efficient by eliminating “deception.” It seems improbable to me that this problematical gain will offset the undoubted losses which flow from the ban on Payola.

Applying this logic to Google Search, it is clear that a ban on revenue-sharing agreements would merely lead both Google and its competitors to attract consumers via alternative means. For Google, this might involve “complete” vertical integration into the mobile phone market, rather than the open-licensing model that underpins the Android ecosystem. Valuable specialization may be lost in the process.

Moreover, from Apple’s standpoint, Google’s revenue-sharing agreements are profitable only to the extent that consumers actually like Google’s products. If it turns out they don’t, Google’s payments to Apple may be outweighed by lower iPhone sales. It is thus unlikely that these agreements significantly undermined users’ experience. To the contrary, Apple’s testimony before the European Commission suggests that “exclusive” placement of Google’s search engine was mostly driven by consumer preferences (as the FTC economists’ memo points out):

Apple would not offer simultaneous installation of competing search or mapping applications. Apple’s focus is offering its customers the best products out of the box while allowing them to make choices after purchase. In many countries, Google offers the best product or service … Apple believes that offering additional search boxes on its web browsing software would confuse users and detract from Safari’s aesthetic. Too many choices lead to consumer confusion and greatly affect the ‘out of the box’ experience of Apple products.

Similarly, Kevin Murphy and Benjamin Klein have shown that exclusive contracts intensify competition for distribution. In other words, absent theories of platform envelopment that are arguably inapplicable here, competition for exclusive placement would lead competing search engines to up their bids, ultimately lowering the price of mobile devices for consumers.

Indeed, this revenue-sharing model was likely essential to spur the development of Android in the first place. Without this prominent placement of Google Search on Android devices (notably thanks to revenue-sharing agreements with original equipment manufacturers), Google would likely have been unable to monetize the investment it made in the open source—and thus freely distributed—Android operating system. 

In short, Politico and the FTC legal staff do little to show that Google’s revenue-sharing payments excluded rivals that were, in fact, as efficient. In other words, Bing and Yahoo’s failure to gain traction may simply be the result of inferior products and cost structures. Critics thus fail to show that Google’s behavior harmed consumers, which is the touchstone of antitrust enforcement.

Self-preferencing

Another finding critics claim as important is that FTC leadership declined to bring suit against Google for preferencing its own vertical search services (this information had already been partially leaked by the Wall Street Journal in 2015). Politico’s framing implies this was a mistake:

When Google adopted one algorithm change in 2011, rival sites saw significant drops in traffic. Amazon told the FTC that it saw a 35 percent drop in traffic from the comparison-shopping sites that used to send it customers

The focus on this claim is somewhat surprising. Even the leaked FTC legal staff memo found this theory of harm had little chance of standing up in court:

Staff has investigated whether Google has unlawfully preferenced its own content over that of rivals, while simultaneously demoting rival websites…. 

…Although it is a close call, we do not recommend that the Commission proceed on this cause of action because the case law is not favorable to our theory, which is premised on anticompetitive product design, and in any event, Google’s efficiency justifications are strong. Most importantly, Google can legitimately claim that at least part of the conduct at issue improves its product and benefits users. [EMPHASIS ADDED]

More importantly, as one of us has argued elsewhere, the underlying problem lies not with Google, but with a standard asset-specificity trap:

A content provider that makes itself dependent upon another company for distribution (or vice versa, of course) takes a significant risk. Although it may benefit from greater access to users, it places itself at the mercy of the other — or at least faces great difficulty (and great cost) adapting to unanticipated, crucial changes in distribution over which it has no control…. 

…It was entirely predictable, and should have been expected, that Google’s algorithm would evolve. It was also entirely predictable that it would evolve in ways that could diminish or even tank Foundem’s traffic. As one online marketing/SEO expert puts it: On average, Google makes about 500 algorithm changes per year. 500!….

…In the absence of an explicit agreement, should Google be required to make decisions that protect a dependent company’s “asset-specific” investments, thus encouraging others to take the same, excessive risk? 

Even if consumers happily visited rival websites when they were higher-ranked and traffic subsequently plummeted when Google updated its algorithm, that drop in traffic does not amount to evidence of misconduct. To hold otherwise would be to grant these rivals a virtual entitlement to the state of affairs that exists at any given point in time. 

Indeed, there is good reason to believe Google’s decision to favor its own content over that of other sites is procompetitive. Beyond determining and ensuring relevance, Google surely has the prerogative to compete vigorously and decide how to design its products to keep up with a changing market. In this case, that means designing, developing, and offering its own content in ways that partially displace the original “ten blue links” design of its search results page and instead offer its own answers to users’ queries.

Competitor Harm Is Not an Indicator of the Need for Intervention

Some of the other information revealed by the leak is even more tangential, such as that the FTC ignored complaints from Google’s rivals:

Amazon and Facebook privately complained to the FTC about Google’s conduct, saying their business suffered because of the company’s search bias, scraping of content from rival sites and restrictions on advertisers’ use of competing search engines. 

Amazon said it was so concerned about the prospect of Google monopolizing the search advertising business that it willingly sacrificed revenue by making ad deals aimed at keeping Microsoft’s Bing and Yahoo’s search engine afloat.

But complaints from rivals are at least as likely to stem from vigorous competition as from anticompetitive exclusion. This goes to a core principle of antitrust enforcement: antitrust law seeks to protect competition and consumer welfare, not rivals. Competition will always lead to winners and losers. Antitrust law protects this process and (at least theoretically) ensures that rivals cannot manipulate enforcers to safeguard their economic rents. 

This explains why Frank Easterbrook—in his seminal work on “The Limits of Antitrust”—argued that enforcers should be highly suspicious of complaints lodged by rivals:

Antitrust litigation is attractive as a method of raising rivals’ costs because of the asymmetrical structure of incentives…. 

…One line worth drawing is between suits by rivals and suits by consumers. Business rivals have an interest in higher prices, while consumers seek lower prices. Business rivals seek to raise the costs of production, while consumers have the opposite interest…. 

…They [antitrust enforcers] therefore should treat suits by horizontal competitors with the utmost suspicion. They should dismiss outright some categories of litigation between rivals and subject all such suits to additional scrutiny.

Google’s competitors spent millions pressuring the FTC to bring a case against the company. But why should it be a failing for the FTC to resist such pressure? Indeed, as then-commissioner Tom Rosch admonished in an interview following the closing of the case:

They [Google’s competitors] can darn well bring [a case] as a private antitrust action if they think their ox is being gored instead of free-riding on the government to achieve the same result.

Not that they would likely win such a case. Google’s introduction of specialized shopping results (via the Google Shopping box) likely enabled several retailers to bypass the Amazon platform, thus increasing competition in the retail industry. Although this may have temporarily reduced Amazon’s traffic and revenue (Amazon’s sales have grown dramatically since then), it is exactly the outcome that antitrust laws are designed to protect.

Conclusion

When all is said and done, Politico’s revelations provide a rarely glimpsed look into the complex dynamics within the FTC, which many wrongly imagine to be a monolithic agency. Put simply, the FTC’s commissioners, lawyers, and economists often disagree vehemently about the appropriate course of conduct. This is a good thing. As in many other walks of life, having a market for ideas is a sure way to foster sound decision making.

But in the final analysis, what the revelations do not show is that the FTC’s market for ideas failed consumers a decade ago when it declined to bring an antitrust suit against Google. They thus do little to cement the case for antitrust intervention—whether a decade ago, or today.

[TOTM: The following is part of a blog series by TOTM guests and authors on the law, economics, and policy of the ongoing COVID-19 pandemic. The entire series of posts is available here.

This post is authored by Julian Morris, (Director of Innovation Policy, ICLE).]

Governments are beginning to lift the lockdowns they imposed to slow the spread of COVID-19. That is a good thing. But simply lifting the restrictions won’t immediately take us back to normality. For that to happen requires a massive investment in mechanisms that will rebuild trust.

Prior to COVID-19, people implicitly trusted that travelling on public transit, working in an office, attending a ball game, or going to a shopping mall would not subject them to the risk of infection by a potentially deadly virus (or any other terrible eventuality). In the wake of the pandemic, this implicit trust is gone. Many people are afraid of COVID-19 and will require reassurance. While governments likely contributed significantly to the loss of trust, they are likely not in the best position to rebuild that trust. The onus is thus on businesses and civic organizations to provide reassurance and rebuild trust. This post outlines two ways businesses can contribute to this effort.

Lockdowns and the Trust Deficit

As the incidence of COVID-19 began to rise dramatically in March, governments across the world imposed “lockdowns.” These curfew-like arrangements have gone well beyond the limits on public gatherings and other “social distancing” strategies deployed during previous major pandemics such as the Spanish ‘flu of 1918-19. Indeed, they are among the most far-reaching restrictions ever imposed on human activity during peacetime. Hundreds of millions of people have been cooped up at home for nearly two months, allowed out only briefly each day for exercise or to buy groceries. Millions of those now at home have also lost their main source of income.

Governments are now finally beginning to remove some of the most severe of these restrictions, allowing more businesses to operate. As they do so, businesses are trying to figure out what the post-lockdown economy is going to look like: Will employees come back to work in offices? Will customers shop in stores, eat at restaurants, visit movie theatres, and use rideshares, taxis, planes, and public transit?

Many people are fearful about the consequences of going back to work. A recent IPSOS-MORI poll for the Washington Post found that 74 percent of American adults want policymakers to, “keep trying to slow the spread of the coronavirus, even if that means keeping many businesses closed,” while just 25 percent prefer to, “open up businesses and get the economy going again, even if that means more people would get the coronavirus.” Meanwhile, in a recent survey in the UK, the TUC union found that 40% of workers were worried about the prospects of returning to crowded workplaces.  

The loss of trust is likely in part be due to conditioning: for the past two months we have been told by all and sundry to avoid other people (except over Zoom). Governments likely contributed to this through their promotion of scary predictions that millions could die if people didn’t “stay home, stay safe.” Partly, however, it is a natural reaction to the perceived threat posed by COVID-19.

For the elderly and those with underlying conditions more likely to be adversely affected by COVID-19, such anxiety is understandable. But even many people less likely to become seriously ill or die from COVID-19 are worried. This is also not surprising: They may have heard horror stories of young, otherwise healthy people who ended up on a ventilator and either died or suffered permanent lung damage. Or perhaps they read about the mysterious effects COVID-19 can have on other organs, ranging from the intestines to the brain. Or they may have a more vulnerable person in our household and are worried about the possibility that we might infect them. Or, as I am sure is the case with many, they just don’t know—and this is their reaction to uncertainty (fueled, in part by the now-discredited predictions of doom).

Regardless of why a person fears COVID-19, the fact is that many do. And one thing common to all of them is a trust deficit. Given widespread uncertainty regarding who has the virus, how can one trust that the business one works, shops, or dines at provides a safe environment free of COVID-19? This even extends to friends and colleagues: how can one individual trust another individual they might encounter while at work or at play? And it applies also to the use of taxis and rideshares; how can riders and drivers trust one another?

It might be argued that since governments were in no small part responsible for generating the trust deficit, through their well-intentioned but probably misguided efforts to lock down the economy and constant exhortations to avoid all human contact, they should now be trying to do what they can to rebuild trust. Unfortunately, however, they may not be in a very good position to do that. While governments are quite good at scaring people (“I’m from the government and I’m here to help”), they are less good at providing reassurance (“I’m from the government and I’m here to help”), or even data. In other words, governments aren’t much good at engaging in the kinds of “costly signalling” necessary to build trust between individuals and businesses. As a result, much of the responsibility for rebuilding trust will fall on businesses and civic organizations.

Businesses can do several things that would likely reduce this trust deficit and allay the fears of employees and customers. First, they can establish, communicate, and implement clear standards for employees and customers regarding the practices to be adopted to reduce infection risk. Second, and relatedly, where employees are likely to be working in close quarters with one another or with customers or suppliers, they can adopt mechanisms to establish the COVID-19 status of those employees, suppliers and customers (somewhat along the lines of the system implemented by Taiwan in February and subsequently elaborated by Hal Singer in his post in this series here). 

The following sections briefly consider how such systems might work.

CV19 Standards

Companies that have not been locked down are already implementing processes to limit the exposure of employees to potentially infected customers, suppliers, and other employees. For example, many supermarkets require staff to use masks and/or protective screens and gloves. Some stores also require customers to wear masks, limit how many people can be in the store, and impose distancing rules. Some have even built seemingly permanent screens in front of check-out clerks and imposed seemingly permanent rules for in-store movement.  Other stores and restaurants are currently limiting service to take-out and delivery.

At present, the approaches taken by businesses vary considerably. There is nothing inherently wrong with this; indeed, it is a healthy part of a market process in which companies develop different solutions to the same problem and allow consumers to pick and choose the ones that work best for them. Consumers can be aided in this process by reading reviews and ratings provided by other consumers; that model has worked well for goods and services purchased online. As Paul Seabright has noted, these systems are designed to enable users to build trusting relationships with suppliers. Survey data suggest that consumers find such systems more trustworthy than government regulations.

But when consumers are not well placed to evaluate the most effective solution, for example because it is difficult to observe the effectiveness of the solution directly, it can be helpful for third parties to evaluate the various solutions and either rank them or set out voluntary pass-fail standards. COVID-19 is just such a case: individual consumers and employees are unlikely to be in a good position to evaluate the relative effectiveness of different processes and technologies designed to limit the transmission of SARS-CoV-2. As such, pass-fail standards developed and/or validated by credible, independent third parties are likely to be the most effective way to help rebuild trust.

Standards will vary depending on the type of establishment and activity. For some businesses, such as theatres, gyms, and mass transit systems, the standards will likely be more onerous than others. Plausibly, such establishments could reduce transmission through such things as: mandatory masks, mandatory use of antiviral hand sanitizer on entry, regular cleaning, the use of HEPA filters (which remove the droplets on which the virus is spread), and other technologies. But given the very close proximity of people in such systems, often for extended periods (half an hour or more), the risk of significant viral load being transferred from one person to another, even if wearing basic masks, remains.

For standards to be effective as a means of regaining the trust of employees, suppliers, and consumers, it is important that they are communicated effectively through marketing campaigns, likely including advertising and signage. Standards will also likely change over time as understanding of the way the virus is transmitted, technologies that can prevent transmission, and hence best practices improve. The need for such standards will also likely change over time and once the virus is no longer a major threat there should be no need for such standards. For these reasons, standards should be both voluntary and developed privately. However, governments can play a role in encouraging the adoption of such standards by legislating that organizations that are compliant with a recognized standard will not be liable if an infection occurs on their property or through the actions of their employees.

In addition to other practices designed to reduce transmission of the SARS-CoV-2 virus, some businesses have begun testing employees for the virus, to determine who is and who is not currently infected, so that infected individuals can be isolated until they are no longer infectious (employees who are required to isolate continue to receive their salary). Some businesses are also considering testing for antibodies to the virus, to determine who has had the virus and likely has some immunity. By doing such testing, businesses are probably reducing transmission both among employees and between employees and customers to a greater extent than by merely implementing technologies, hygiene and distancing rules. But the tests are not perfect and given the potential for infection outside work, it is possible that an employee who tests negative on one day could then become infected and be infective a few days later. While daily testing might be an option for some firms, it is unrealistic for most—and will not solve the trust problem for most individuals.

CV19 Status Verification

This brings us to the second major thing that business can do to reduce the trust gap: status verification. The idea here is to enable parties to ascertain one another’s current COVID-19 status without the need to resort to constant testing. One possible approach is to use a smartphone-based app that combines various pieces of information (time stamped virus tests and antibody tests, anonymized information about contacts with people who subsequently tested positive, and self-reported health-relevant data) to offer the most accurate and up-to-date status of an individual.

In principle, such a status app could be used by employers to minimize the likelihood that their staff have COVID (and to require those that may be infected to self-isolate and obtain a test). But their potential application is far wider:

·       Universities, churches, theatres, restaurants, bars, and events might utilize the status app not only for employees but also to determine who may participate and/or what forms of PPE they should utilize and/or where participants may congregate.

·       Airlines might utilize status apps to determine who might fly and where passengers should be seated.

·       Jurisdictions might utilize status apps as a means of facilitating more rapid immigration – and to enable those who most likely do not have COVID-19 to avoid most quarantine requirements.

·       Public transit systems might utilize status apps to determine who can use the system.

·       Taxis and ridesharing services, such as Uber and Lyft, might utilize data from the status app to help match riders and drivers.

·       Personal services facilitators such as Thumbtack might utilize the app to help match service providers and customers.

·       Hotels, AirBnB and vacation rental facilitators such as vrbo might use status apps for both hosts (and their employees and contractors) and guests in order to minimize infection risk during a visit.

·       Online dating and matchmaking services such as Match and Tinder might utilize status apps to help facilitate virus-compatible matches. (While SARS-CoV-2/COVID-19 is not really comparable to HIV/AIDS, it is noteworthy that sites already exist that seek to match people who are HIV positive.)

How a CV19 Status App might Work

A basic schema for a CV19 status app would be:

·       Red = Has COVID-19 (e.g. recently tested positive for virus)

·       Red-Amber = May have COVID-19 (e.g. recently tested negative for virus but either has COVID-19 related symptoms or has been in contact with someone who tested positive).

·       Amber = Is susceptible: Has not had COVID-19 and likely does not have COVID-19 (e.g. recently tested negative for COVID-19, has no COVID-19 symptoms, and has had no recent known contact with someone who tested positive).

·       Green = Has had COVID-19 and is now presumed to be immune (either tested positive for CV19 and then tested negative for CV19, or tested negative for CV19 and also tested positive for Antibodies) (See below regarding immunity concerns.)

This schema is shown in the decision tree below

There are numerous technical issues relating to the operation of an app designed to establish a person’s CV19 status that must be addressed for it to function effectively. First, it will be necessary to ensure that the person using the app is the person whose status is being asserted. It should be possible to address this by storing the information from tests, contacts with infected people, and self-reported symptoms on an immutable digital ledger and use biometric identification both to record and to share status information. (Storing the status information on a person’s phone in this way also avoids the risk of hacking that plagues centralized databases.)

Next there is the question of authenticating test data recorded by the app. Ideally, this would be done by having a trusted third party—such as a doctor, nurse, or pharmacist—verify the data. If that is not feasible—for example because the test was carried out at home—then some other mechanism will be required to ensure the data is input correctly, such as rewards for accurate self-reports and/or penalties for inaccurate self-reports. (Self-reported data could also be treated within the system as less reliable, or simply as tentative—requiring verified test data to be added within a specified period.)

Beyond these verification issues, there remain problems with the specificity and sensitivity of tests—implying a likelihood of both false positive and false negatives. Although there are now both PCR and antibody tests that achieve very high levels of accuracy, even small numbers of false negative PCR tests and false positive antibody tests would clearly create problems for the effective functioning of the status app system. To address these problems, it may be necessary to undertake secondary testing for some portion of the tests.

The more challenging problem is that of infection after tests are conducted. As noted above, this can in principle be mitigated—but not eliminated—by incorporating contact tracing and/or self-reporting of symptoms. Related to this is the possibility that having COVID-19 confers only limited immunity (as has been suggested in relation to some people who have seemingly become reinfected). This obviously poses problems for the notion of a “Green” status; if reinfection is possible, then Green clearly would not be a permanent designation and would require regular testing. The evidence remains ambiguous, with news of five US sailors who had COVID then tested negative twice subsequently having new symptoms and testing positive again; on the other hand, a recent study suggests that people who test positive after recovery do not have a live (infectious) version of the virus.

Contact tracing apps have been used successfully in several locations as part of a strategy for containing COVID-19. However, the only really successful implementations so far have been those in China, South Korea and Hong Kong, which had a mandatory component and were highly centralized. By contrast, apps that required voluntary uptake have generally been less successful.

One reason for the lack of success of voluntary contact tracing apps is heightened concern regarding privacy (for example, the app used in Hong Kong enables anyone to find the gender, age, and precise locations of every person in the city who currently has COVID-19). Of course it is worth repeating Jane Bambauer’s observation in an earlier post that “Objections to surveillance lose their moral and logical bearings when the alternatives are out-of-control disease or mass lockdowns. Compared to those, mass surveillance is the most liberty-preserving option.” But assuming imprisonment is not the only alternative, concerns over privacy are not necessarily unmoored from logic or ethics (pace Christine Wilson’s earlier post). And to address these concerns, several groups have developed privacy-protecting systems. For example, the TCN coalition developed a system that shares anonymized tokens with other nearby phones over Bluetooth Low Energy. That system has now been adopted by Google and Apple in an API that is being made available to government health authorities (but not to other private app developers).

Another reason voluntary contact tracing apps have not been successful is the lack of incentives to adopt them. The main benefit of a contact tracing app is that it notifies the user when they have been in close contact with someone who subsequently tested positive. Logically, the people most likely voluntarily to adopt a contact tracing app are those who are most risk averse. But those people would also presumably be taking strong measures to avoid contracting COVID-19, so they would be less likely to become infected. By contrast, the people most likely to become infected are those who are least risk averse. But those people are least likely to be motivated to use the contact tracing app. In other words, even if there is relatively wide uptake of the app (say, 40% of the population, as in Iceland), it is likely to miss many of the people most likely to be spreading COVID-19 and so would not actually be very useful as a means of identifying and containing clusters.

Tying the contact tracing app to a CV19 Status App potentially overcomes this incentive compatibility problem, since anyone who wants to engage in an activity that requires use of the app would automatically participate in the contact tracing system. It could thus be quite effective at identifying instances of transmission that occur during activities that require the app to be used, which would also presumably be activities that put users at higher risk.

Nonetheless, for the app to be useful as a means of identifying clusters of COVID-19, either a significant proportion of common activities would have to require use of the app (e.g. public transit, rideshares, gyms, and shopping malls) or it would have to be used by at least some proportion of those not required to use it for access to activities.  

Adding a symptom monitoring component can help in two ways. First, by offering users a way to self-assess for early symptoms of COVID-19, it encourages more people to download and use the app.  More important, symptom monitoring can help identify additional potential COVID-19 infections, both among the individuals reporting symptoms and among their contacts. Thus, the combination of test data, symptom data and contact tracing become the information determining a person’s current status in a manner that is more reliable than relying on any one datum.

It should be noted that even combining these data will not make the status app 100% accurate. Some people with COVID-19 will likely slip through as Green or Orange and others will likely inadvertently be infected as a result. But the number of such instances is likely to be small and certainly much lower than would be the case without the use of the app. Moreover, widespread use of the app should dramatically reduce the infection rate throughout the population, with benefits to all.     

Conclusions

Both CV19 standards and CV19 status verification offer potential means by which to address the trust deficit that has emerged in the context of the continuing COVID-19 pandemic. A company that adopts both solutions would likely dramatically reduce the chances of their employees, suppliers and customers contracting the virus on their premises. That would also likely reduce the company’s liability, which could be rewarded by insurance providers offering discounts. Indeed, one could envisage a greater role for insurance companies in designing or certifying the standards and the status app.

However, the real benefits of these systems come not from one or a few companies adopting them but from widespread adoption, which has the potential dramatically to reduce the transmission of the virus both now and in the future (should there be a second wave). This leads to something of a paradox: Governments could mandate adoption, but such an approach may be counterproductive for two reasons. First, much knowledge is dispersed and tacit, so it is generally better to allow private actors to determine which standards to adopt (lest an inferior standard be the subject of a mandate). Second, if companies are genuinely concerned to address the trust deficit, then they will be willing to invest in standards and to limit access though status apps — both of which entail costs. By contrast, if governments mandate the use of standards and apps, they would effectively prevent firms from engaging in such costly signalling, so would undermine at least part of the effectiveness of such tools as trust-generative.

A spate of recent newspaper investigations and commentary have focused on Apple allegedly discriminating against rivals in the App Store. The underlying assumption is that Apple, as a vertically integrated entity that operates both a platform for third-party apps and also makes it own apps, is acting nefariously whenever it “discriminates” against rival apps through prioritization, enters into popular app markets, or charges a “tax” or “surcharge” on rival apps. 

For most people, the word discrimination has a pejorative connotation of animus based upon prejudice: racism, sexism, homophobia. One of the definitions you will find in the dictionary reflects this. But another definition is a lot less charged: the act of making or perceiving a difference. (This is what people mean when they say that a person has a discriminating palate, or a discriminating taste in music, for example.)

In economics, discrimination can be a positive attribute. For instance, effective price discrimination can result in wealthier consumers paying a higher price than less well off consumers for the same product or service, and it can ensure that products and services are in fact available for less-wealthy consumers in the first place. That would seem to be a socially desirable outcome (although under some circumstances, perfect price discrimination can be socially undesirable). 

Antitrust law rightly condemns conduct only when it harms competition and not simply when it harms a competitor. This is because it is competition that enhances consumer welfare, not the presence or absence of a competitor — or, indeed, the profitability of competitors. The difficult task for antitrust enforcers is to determine when a vertically integrated firm with “market power” in an upstream market is able to effectively discriminate against rivals in a downstream market in a way that harms consumers

Even assuming the claims of critics are true, alleged discrimination by Apple against competitor apps in the App Store may harm those competitors, but it doesn’t necessarily harm either competition or consumer welfare.

The three potential antitrust issues facing Apple can be summarized as:

There is nothing new here economically. All three issues are analogous to claims against other tech companies. But, as I detail below, the evidence to establish any of these claims at best represents harm to competitors, and fails to establish any harm to the competitive process or to consumer welfare.

Prioritization

Antitrust enforcers have rejected similar prioritization claims against Google. For instance, rivals like Microsoft and Yelp have funded attacks against Google, arguing the search engine is harming competition by prioritizing its own services in its product search results over competitors. As ICLE and affiliated scholars have pointed out, though, there is nothing inherently harmful to consumers about such prioritization. There are also numerous benefits in platforms directly answering queries, even if it ends up directing users to platform-owned products or services.

As Geoffrey Manne has observed:

there is good reason to believe that Google’s decision to favor its own content over that of other sites is procompetitive. Beyond determining and ensuring relevance, Google surely has the prerogative to vigorously compete and to decide how to design its products to keep up with a changing market. In this case, that means designing, developing, and offering its own content to partially displace the original “ten blue links” design of its search results page and offer its own answers to users’ queries in its stead. 

Here, the antitrust case against Apple for prioritization is similarly flawed. For example, as noted in a recent article in the WSJ, users often use the App Store search in order to find apps they already have installed:

“Apple customers have a very strong connection to our products and many of them use search as a way to find and open their apps,” Apple said in a statement. “This customer usage is the reason Apple has strong rankings in search, and it’s the same reason Uber, Microsoft and so many others often have high rankings as well.” 

If a substantial portion of searches within the App Store are for apps already on the iPhone, then showing the Apple app near the top of the search results could easily be consumer welfare-enhancing. 

Apple is also theoretically leaving money on the table by prioritizing its (already pre-loaded) apps over third party apps. If its algorithm promotes its own apps over those that may earn it a 30% fee — additional revenue — the prioritization couldn’t plausibly be characterized as a “benefit” to Apple. Apple is ultimately in the business of selling hardware. Losing customers of the iPhone or iPad by prioritizing apps consumers want less would not be a winning business strategy.

Further, it stands to reason that those who use an iPhone may have a preference for Apple apps. Such consumers would be naturally better served by seeing Apple’s apps prioritized over third-party developer apps. And if consumers do not prefer Apple’s apps, rival apps are merely seconds of scrolling away.

Moreover, all of the above assumes that Apple is engaging in sufficiently pervasive discrimination through prioritzation to have a major impact on the app ecosystem. But substantial evidence exists that the universe of searches for which Apple’s algorithm prioritizes Apple apps is small. For instance, most searches are for branded apps already known by the searcher:

Keywords: how many are brands?

  • Top 500: 58.4%
  • Top 400: 60.75%
  • Top 300: 68.33%
  • Top 200: 80.5%
  • Top 100: 86%
  • Top 50: 90%
  • Top 25: 92%
  • Top 10: 100%

This is corroborated by data from the NYT’s own study, which suggests Apple prioritized its own apps first in only roughly 1% of the overall keywords queried: 

Whatever the precise extent of increase in prioritization, it seems like any claims of harm are undermined by the reality that almost 99% of App Store results don’t list Apple apps first. 

The fact is, very few keyword searches are even allegedly affected by prioritization. And the algorithm is often adjusting to searches for apps already pre-loaded on the device. Under these circumstances, it is very difficult to conclude consumers are being harmed by prioritization in search results of the App Store.

Entry

The issue of Apple building apps to compete with popular apps in its marketplace is similar to complaints about Amazon creating its own brands to compete with what is sold by third parties on its platform. For instance, as reported multiple times in the Washington Post:

Clue, a popular app that women use to track their periods, recently rocketed to the top of the App Store charts. But the app’s future is now in jeopardy as Apple incorporates period and fertility tracking features into its own free Health app, which comes preinstalled on every device. Clue makes money by selling subscriptions and services in its free app. 

However, there is nothing inherently anticompetitive about retailers selling their own brands. If anything, entry into the market is normally procompetitive. As Randy Picker recently noted with respect to similar claims against Amazon: 

The heart of this dynamic isn’t new. Sears started its catalogue business in 1888 and then started using the Craftsman and Kenmore brands as in-house brands in 1927. Sears was acquiring inventory from third parties and obviously knew exactly which ones were selling well and presumably made decisions about which markets to enter and which to stay out of based on that information. Walmart, the nation’s largest retailer, has a number of well-known private brands and firms negotiating with Walmart know full well that Walmart can enter their markets, subject of course to otherwise applicable restraints on entry such as intellectual property laws… I think that is possible to tease out advantages that a platform has regarding inventory experimentation. It can outsource some of those costs to third parties, though sophisticated third parties should understand where they can and cannot have a sustainable advantage given Amazon’s ability to move to build-or-bought first-party inventory. We have entire bodies of law— copyright, patent, trademark and more—that limit the ability of competitors to appropriate works, inventions and symbols. Those legal systems draw very carefully considered lines regarding permitted and forbidden uses. And antitrust law generally favors entry into markets and doesn’t look to create barriers that block firms, large or small, from entering new markets.

If anything, Apple is in an even better position than Amazon. Apple invests revenue in app development, not because the apps themselves generate revenue, but because it wants people to use the hardware, i.e. the iPhones, iPads, and Apple Watches. The reason Apple created an App Store in the first place is because this allows Apple to make more money from selling devices. In order to promote security on those devices, Apple institutes rules for the App Store, but it ultimately decides whether to create its own apps and provide access to other apps based upon its desire to maximize the value of the device. If Apple chooses to create free apps in order to improve iOS for users and sell more hardware, it is not a harm to competition.

Apple’s ability to enter into popular app markets should not be constrained unless it can be shown that by giving consumers another choice, consumers are harmed. As noted above, most searches in the App Store are for branded apps to begin with. If consumers already know what they want in an app, it hardly seems harmful for Apple to offer — and promote — its own, additional version as well. 

In the case of Clue, if Apple creates a free health app, it may hurt sales for Clue. But it doesn’t hurt consumers who want the functionality and would prefer to get it from Apple for free. This sort of product evolution is not harming competition, but enhancing it. And, it must be noted, Apple doesn’t exclude Clue from its devices. If, indeed, Clue offers a better product, or one that some users prefer, they remain able to find it and use it.

The so-called App Store “Tax”

The argument that Apple has an unfair competitive advantage over rival apps which have to pay commissions to Apple to be on the App Store (a “tax” or “surcharge”) has similarly produced no evidence of harm to consumers. 

Apple invested a lot into building the iPhone and the App Store. This infrastructure has created an incredibly lucrative marketplace for app developers to exploit. And, lest we forget a point fundamental to our legal system, Apple’s App Store is its property

The WSJ and NYT stories give the impression that Apple uses its commissions on third party apps to reduce competition for its own apps. However, this is inconsistent with how Apple charges its commission

For instance, Apple doesn’t charge commissions on free apps, which make up 84% of the App Store. Apple also doesn’t charge commissions for apps that are free to download but are supported by advertising — including hugely popular apps like Yelp, Buzzfeed, Instagram, Pinterest, Twitter, and Facebook. Even apps which are “readers” where users purchase or subscribe to content outside the app but use the app to access that content are not subject to commissions, like Spotify, Netflix, Amazon Kindle, and Audible. Apps for “physical goods and services” — like Amazon, Airbnb, Lyft, Target, and Uber — are also free to download and are not subject to commissions. The class of apps which are subject to a 30% commission include:

  • paid apps (like many games),
  • free apps that then have in-app purchases (other games and services like Skype and TikTok), 
  • and free apps with digital subscriptions (Pandora, Hulu, which have 30% commission first year and then 15% in subsequent years), and
  • cross-platform apps (Dropbox, Hulu, and Minecraft) which allow for digital goods and services to be purchased in-app and Apple collects commission on in-app sales, but not sales from other platforms. 

Despite protestations to the contrary, these costs are hardly unreasonable: third party apps receive the benefit not only of being in Apple’s App Store (without which they wouldn’t have any opportunity to earn revenue from sales on Apple’s platform), but also of the features and other investments Apple continues to pour into its platform — investments that make the ecosystem better for consumers and app developers alike. There is enormous value to the platform Apple has invested in, and a great deal of it is willingly shared with developers and consumers.  It does not make it anticompetitive to ask those who use the platform to pay for it. 

In fact, these benefits are probably even more important for smaller developers rather than bigger ones who can invest in the necessary back end to reach consumers without the App Store, like Netflix, Spotify, and Amazon Kindle. For apps without brand reputation (and giant marketing budgets), the ability for consumers to trust that downloading the app will not lead to the installation of malware (as often occurs when downloading from the web) is surely essential to small developers’ ability to compete. The App Store offers this.

Despite the claims made in Spotify’s complaint against Apple, Apple doesn’t have a duty to deal with app developers. Indeed, Apple could theoretically fill the App Store with only apps that it developed itself, like Apple Music. Instead, Apple has opted for a platform business model, which entails the creation of a new outlet for others’ innovation and offerings. This is pro-consumer in that it created an entire marketplace that consumers probably didn’t even know they wanted — and certainly had no means to obtain — until it existed. Spotify, which out-competed iTunes to the point that Apple had to go back to the drawing board and create Apple Music, cannot realistically complain that Apple’s entry into music streaming is harmful to competition. Rather, it is precisely what vigorous competition looks like: the creation of more product innovation, lower prices, and arguably (at least for some) higher quality.

Interestingly, Spotify is not even subject to the App Store commission. Instead, Spotify offers a work-around to iPhone users to obtain its premium version without ads on iOS. What Spotify actually desires is the ability to sell premium subscriptions to Apple device users without paying anything above the de minimis up-front cost to Apple for the creation and maintenance of the App Store. It is unclear how many potential Spotify users are affected by the inability to directly buy the ad-free version since Spotify discontinued offering it within the App Store. But, whatever the potential harm to Spotify itself, there’s little reason to think consumers or competition bear any of it. 

Conclusion

There is no evidence that Apple’s alleged “discrimination” against rival apps harms consumers. Indeed, the opposite would seem to be the case. The regulatory discrimination against successful tech platforms like Apple and the App Store is far more harmful to consumers.

Ours is not an age of nuance.  It’s an age of tribalism, of teams—“Yer either fer us or agin’ us!”  Perhaps I should have been less surprised, then, when I read the unfavorable review of my book How to Regulate in, of all places, the Federalist Society Review.

I had expected some positive feedback from reviewer J. Kennerly Davis, a contributor to the Federalist Society’s Regulatory Transparency Project.  The “About” section of the Project’s website states:

In the ultra-complex and interconnected digital age in which we live, government must issue and enforce regulations to protect public health and safety.  However, despite the best of intentions, government regulation can fail, stifle innovation, foreclose opportunity, and harm the most vulnerable among us.  It is for precisely these reasons that we must be diligent in reviewing how our policies either succeed or fail us, and think about how we might improve them.

I might not have expressed these sentiments in such pro-regulation terms.  For example, I don’t think government should regulate, even “to protect public health and safety,” absent (1) a market failure and (2) confidence that systematic governmental failures won’t cause the cure to be worse than the disease.  I agree, though, that regulation is sometimes appropriate, that government interventions often fail (in systematic ways), and that regulatory policies should regularly be reviewed with an eye toward reducing the combined costs of market and government failures.

Those are, in fact, the central themes of How to Regulate.  The book sets forth an overarching goal for regulation (minimize the sum of error and decision costs) and then catalogues, for six oft-cited bases for regulating, what regulatory tools are available to policymakers and how each may misfire.  For every possible intervention, the book considers the potential for failure from two sources—the knowledge problem identified by F.A. Hayek and public choice concerns (rent-seeking, regulatory capture, etc.).  It ends up arguing:

  • for property rights-based approaches to environmental protection (versus the command-and-control status quo);
  • for increased reliance on the private sector to produce public goods;
  • that recognizing property rights, rather than allocating usage, is the best way to address the tragedy of the commons;
  • that market-based mechanisms, not shareholder suits and mandatory structural rules like those imposed by Sarbanes-Oxley and Dodd-Frank, are the best way to constrain agency costs in the corporate context;
  • that insider trading restrictions should be left to corporations themselves;
  • that antitrust law should continue to evolve in the consumer welfare-focused direction Robert Bork recommended;
  • against the FCC’s recently abrogated net neutrality rules;
  • that occupational licensure is primarily about rent-seeking and should be avoided;
  • that incentives for voluntary disclosure will usually obviate the need for mandatory disclosure to correct information asymmetry;
  • that the claims of behavioral economics do not justify paternalistic policies to protect people from themselves; and
  • that “libertarian-paternalism” is largely a ruse that tends to morph into hard paternalism.

Given the congruence of my book’s prescriptions with the purported aims of the Regulatory Transparency Project—not to mention the laundry list of specific market-oriented policies the book advocates—I had expected a generally positive review from Mr. Davis (whom I sincerely thank for reading and reviewing the book; book reviews are a ton of work).

I didn’t get what I’d expected.  Instead, Mr. Davis denounced my book for perpetuating “progressive assumptions about state and society” (“wrongheaded” assumptions, the editor’s introduction notes).  He responded to my proposed methodology with a “meh,” noting that it “is not clearly better than the status quo.”  His one compliment, which I’ll gladly accept, was that my discussion of economic theory was “generally accessible.”

Following are a few thoughts on Mr. Davis’s critiques.

Are My Assumptions Progressive?

According to Mr. Davis, my book endorses three progressive concepts:

(i) the idea that market based arrangements among private parties routinely misallocate resources, (ii) the idea that government policymakers are capable of formulating executive directives that can correct private ordering market failures and optimize the allocation of resources, and (iii) the idea that the welfare of society is actually something that exists separate and apart from the individual welfare of each of the members of society.

I agree with Mr. Davis that these are progressive ideas.  If my book embraced them, it might be fair to label it “progressive.”  But it doesn’t.  Not one of them.

  1. Market Failure

Nothing in my book suggests that “market based arrangements among private parties routinely misallocate resources.”  I do say that “markets sometimes fail to work well,” and I explain how, in narrow sets of circumstances, market failures may emerge.  Understanding exactly what may happen in those narrow sets of circumstances helps to identify the least restrictive option for addressing problems and would thus would seem a pre-requisite to effective policymaking for a conservative or libertarian.  My mere invocation of the term “market failure,” however, was enough for Mr. Davis to kick me off the team.

Mr. Davis ignored altogether the many points where I explain how private ordering fixes situations that could lead to poor market performance.  At the end of the information asymmetry chapter, for example, I write,

This chapter has described information asymmetry as a problem, and indeed it is one.  But it can also present an opportunity for profit.  Entrepreneurs have long sought to make money—and create social value—by developing ways to correct informational imbalances and thereby facilitate transactions that wouldn’t otherwise occur.

I then describe the advent of companies like Carfax, AirBnb, and Uber, all of which offer privately ordered solutions to instances of information asymmetry that might otherwise create lemons problems.  I conclude:

These businesses thrive precisely because of information asymmetry.  By offering privately ordered solutions to the problem, they allow previously under-utilized assets to generate heretofore unrealized value.  And they enrich the people who created and financed them.  It’s a marvelous thing.

That theme—that potential market failures invite privately ordered solutions that often obviate the need for any governmental fix—permeates the book.  In the public goods chapter, I spend a great deal of time explaining how privately ordered devices like assurance contracts facilitate the production of amenities that are non-rivalrous and non-excludable.  In discussing the tragedy of the commons, I highlight Elinor Ostrom’s work showing how “groups of individuals have displayed a remarkable ability to manage commons goods effectively without either privatizing them or relying on government intervention.”  In the chapter on externalities, I spend a full seven pages explaining why Coasean bargains are more likely than most people think to prevent inefficiencies from negative externalities.  In the chapter on agency costs, I explain why privately ordered solutions like the market for corporate control would, if not precluded by some ill-conceived regulations, constrain agency costs better than structural rules from the government.

Disregarding all this, Mr. Davis chides me for assuming that “markets routinely fail.”  And, for good measure, he explains that government interventions are often a bigger source of failure, a point I repeatedly acknowledge, as it is a—perhaps the—central theme of the book.

  1. Trust in Experts

In what may be the strangest (and certainly the most misleading) part of his review, Mr. Davis criticizes me for placing too much confidence in experts by giving short shrift to the Hayekian knowledge problem and the insights of public choice.

          a.  The Knowledge Problem

According to Mr. Davis, the approach I advocate “is centered around fully functioning experts.”  He continues:

This progressive trust in experts is misplaced.  It is simply false to suppose that government policymakers are capable of formulating executive directives that effectively improve upon private arrangements and optimize the allocation of resources.  Friedrich Hayek and other classical liberals have persuasively argued, and everyday experience has repeatedly confirmed, that the information needed to allocate resources efficiently is voluminous and complex and widely dispersed.  So much so that government experts acting through top down directives can never hope to match the efficiency of resource allocation made through countless voluntary market transactions among private parties who actually possess the information needed to allocate the resources most efficiently.

Amen and hallelujah!  I couldn’t agree more!  Indeed, I said something similar when I came to the first regulatory tool my book examines (and criticizes), command-and-control pollution rules.  I wrote:

The difficulty here is an instance of a problem that afflicts regulation generally.  At the end of the day, regulating involves centralized economic planning:  A regulating “planner” mandates that productive resources be allocated away from some uses and toward others.  That requires the planner to know the relative value of different resource uses.  But such information, in the words of Nobel laureate F.A. Hayek, “is not given to anyone in its totality.”  The personal preferences of thousands or millions of individuals—preferences only they know—determine whether there should be more widgets and fewer gidgets, or vice-versa.  As Hayek observed, voluntary trading among resource owners in a free market generates prices that signal how resources should be allocated (i.e., toward the uses for which resource owners may command the highest prices).  But centralized economic planners—including regulators—don’t allocate resources on the basis of relative prices.  Regulators, in fact, generally assume that prices are wrong due to the market failure the regulators are seeking to address.  Thus, the so-called knowledge problem that afflicts regulation generally is particularly acute for command-and-control approaches that require regulators to make refined judgments on the basis of information about relative costs and benefits.

That was just the first of many times I invoked the knowledge problem to argue against top-down directives and in favor of market-oriented policies that would enable individuals to harness local knowledge to which regulators would not be privy.  The index to the book includes a “knowledge problem” entry with no fewer than nine sub-entries (e.g., “with licensure regimes,” “with Pigouvian taxes,” “with mandatory disclosure regimes”).  There are undoubtedly more mentions of the knowledge problem than those listed in the index, for the book assesses the degree to which the knowledge problem creates difficulties for every regulatory approach it considers.

Mr. Davis does mention one time where I “acknowledge[] the work of Hayek” and “recognize[] that context specific information is vitally important,” but he says I miss the point:

Having conceded these critical points [about the importance of context-specific information], Professor Lambert fails to follow them to the logical conclusion that private ordering arrangements are best for regulating resources efficiently.  Instead, he stops one step short, suggesting that policymakers defer to the regulator most familiar with the regulated party when they need context-specific information for their analysis.  Professor Lambert is mistaken.  The best information for resource allocation is not to be found in the regional office of the regulator.  It resides with the persons who have long been controlled and directed by the progressive regulatory system.  These are the ones to whom policymakers should defer.

I was initially puzzled by Mr. Davis’s description of how my approach would address the knowledge problem.  It’s inconsistent with the way I described the problem (the “regional office of the regulator” wouldn’t know people’s personal preferences, etc.), and I couldn’t remember ever suggesting that regulatory devolution—delegating decisions down toward local regulators—was the solution to the knowledge problem.

When I checked the citation in the sentences just quoted, I realized that Mr. Davis had misunderstood the point I was making in the passage he cited (my own fault, no doubt, not his).  The cited passage was at the very end of the book, where I was summarizing the book’s contributions.  I claimed to have set forth a plan for selecting regulatory approaches that would minimize the sum of error and decision costs.  I wanted to acknowledge, though, the irony of promulgating a generally applicable plan for regulating in a book that, time and again, decries top-down imposition of one-size-fits-all rules.  Thus, I wrote:

A central theme of this book is that Hayek’s knowledge problem—the fact that no central planner can possess and process all the information needed to allocate resources so as to unlock their greatest possible value—applies to regulation, which is ultimately a set of centralized decisions about resource allocation.  The very knowledge problem besetting regulators’ decisions about what others should do similarly afflicts pointy-headed academics’ efforts to set forth ex ante rules about what regulators should do.  Context-specific information to which only the “regulator on the spot” is privy may call for occasional departures from the regulatory plan proposed here.

As should be obvious, my point was not that the knowledge problem can generally be fixed by regulatory devolution.  Rather, I was acknowledging that the general regulatory approach I had set forth—i.e., the rules policymakers should follow in selecting among regulatory approaches—may occasionally misfire and should thus be implemented flexibly.

           b.  Public Choice Concerns

A second problem with my purported trust in experts, Mr. Davis explains, stems from the insights of public choice:

Actual policymakers simply don’t live up to [Woodrow] Wilson’s ideal of the disinterested, objective, apolitical, expert technocrat.  To the contrary, a vast amount of research related to public choice theory has convincingly demonstrated that decisions of regulatory agencies are frequently shaped by politics, institutional self-interest and the influence of the entities the agencies regulate.

Again, huzzah!  Those words could have been lifted straight out of the three full pages of discussion I devoted to public choice concerns with the very first regulatory intervention the book considered.  A snippet from that discussion:

While one might initially expect regulators pursuing the public interest to resist efforts to manipulate regulation for private gain, that assumes that government officials are not themselves rational, self-interest maximizers.  As scholars associated with the “public choice” economic tradition have demonstrated, government officials do not shed their self-interested nature when they step into the public square.  They are often receptive to lobbying in favor of questionable rules, especially since they benefit from regulatory expansions, which tend to enhance their job status and often their incomes.  They also tend to become “captured” by powerful regulatees who may shower them with personal benefits and potentially employ them after their stints in government have ended.

That’s just a slice.  Elsewhere in those three pages, I explain (1) how the dynamic of concentrated benefits and diffuse costs allows inefficient protectionist policies to persist, (2) how firms that benefit from protectionist regulation are often assisted by “pro-social” groups that will make a public interest case for the rules (Bruce Yandle’s Bootleggers and Baptists syndrome), and (3) the “[t]wo types of losses [that] result from the sort of interest-group manipulation public choice predicts.”  And that’s just the book’s initial foray into public choice.  The entry for “public choice concerns” in the book’s index includes eight sub-entries.  As with the knowledge problem, I addressed the public choice issues that could arise from every major regulatory approach the book considered.

For Mr. Davis, though, that was not enough to keep me out of the camp of Wilsonian progressives.  He explains:

Professor Lambert devotes a good deal of attention to the problem of “agency capture” by regulated entities.  However, he fails to acknowledge that a symbiotic relationship between regulators and regulated is not a bug in the regulatory system, but an inherent feature of a system defined by extensive and continuing government involvement in the allocation of resources.

To be honest, I’m not sure what that last sentence means.  Apparently, I didn’t recite some talismanic incantation that would indicate that I really do believe public choice concerns are a big problem for regulation.  I did say this in one of the book’s many discussions of public choice:

A regulator that has both regular contact with its regulatees and significant discretionary authority over them is particularly susceptible to capture.  The regulator’s discretionary authority provides regulatees with a strong motive to win over the regulator, which has the power to hobble the regulatee’s potential rivals and protect its revenue stream.  The regular contact between the regulator and the regulatee provides the regulatee with better access to those in power than that available to parties with opposing interests.  Moreover, the regulatee’s preferred course of action is likely (1) to create concentrated benefits (to the regulatee) and diffuse costs (to consumers generally), and (2) to involve an expansion of the regulator’s authority.  The upshot is that that those who bear the cost of the preferred policy are less likely to organize against it, and regulators, who benefit from turf expansion, are more likely to prefer it.  Rate-of-return regulation thus involves the precise combination that leads to regulatory expansion at consumer expense: broad and discretionary government power, close contact between regulators and regulatees, decisions that generally involve concentrated benefits and diffuse costs, and regular opportunities to expand regulators’ power and prestige.

In light of this combination of features, it should come as no surprise that the history of rate-of-return regulation is littered with instances of agency capture and regulatory expansion.

Even that was not enough to convince Mr. Davis that I reject the Wilsonian assumption of “disinterested, objective, apolitical, expert technocrat[s].”  I don’t know what more I could have said.

  1. Social Welfare

Mr. Davis is right when he says, “Professor Lambert’s ultimate goal for his book is to provide policymakers with a resource that will enable them to make regulatory decisions that produce greater social welfare.”  But nowhere in my book do I suggest, as he says I do, “that the welfare of society is actually something that exists separate and apart from the individual welfare of each of the members of society.”  What I mean by “social welfare” is the aggregate welfare of all the individuals in a society.  And I’m careful to point out that only they know what makes them better off.  (At one point, for example, I write that “[g]overnment planners have no way of knowing how much pleasure regulatees derive from banned activities…or how much displeasure they experience when they must comply with an affirmative command…. [W]ith many paternalistic policies and proposals…government planners are really just guessing about welfare effects.”)

I agree with Mr. Davis that “[t]here is no single generally accepted methodology that anyone can use to determine objectively how and to what extent the welfare of society will be affected by a particular regulatory directive.”  For that reason, nowhere in the book do I suggest any sort of “metes and bounds” measurement of social welfare.  (I certainly do not endorse the use of GDP, which Mr. Davis rightly criticizes; that term appears nowhere in the book.)

Rather than prescribing any sort of precise measurement of social welfare, my book operates at the level of general principles:  We have reasons to believe that inefficiencies may arise when conditions are thus; there is a range of potential government responses to this situation—from doing nothing, to facilitating a privately ordered solution, to mandating various actions; based on our experience with these different interventions, the likely downsides of each (stemming from, for example, the knowledge problem and public choice concerns) are so-and-so; all things considered, the aggregate welfare of the individuals within this group will probably be greatest with policy x.

It is true that the thrust of the book is consequentialist, not deontological.  But it’s a book about policy, not ethics.  And its version of consequentialism is rule, not act, utilitarianism.  Is a consequentialist approach to policymaking enough to render one a progressive?  Should we excise John Stuart Mill’s On Liberty from the classical liberal canon?  I surely hope not.

Is My Proposed Approach an Improvement?

Mr. Davis’s second major criticism of my book—that what it proposes is “just the status quo”—has more bite.  By that, I mean two things.  First, it’s a more painful criticism to receive.  It’s easier for an author to hear “you’re saying something wrong” than “you’re not saying anything new.”

Second, there may be more merit to this criticism.  As Mr. Davis observes, I noted in the book’s introduction that “[a]t times during the drafting, I … wondered whether th[e] book was ‘original’ enough.”  I ultimately concluded that it was because it “br[ought] together insights of legal theorists and economists of various stripes…and systematize[d] their ideas into a unified, practical approach to regulating.”  Mr. Davis thinks I’ve overstated the book’s value, and he may be right.

The current regulatory landscape would suggest, though, that my book’s approach to selecting among potential regulatory policies isn’t “just the status quo.”  The approach I recommend would generate the specific policies catalogued at the outset of this response (in the bullet points).  The fact that those policies haven’t been implemented under the existing regulatory approach suggests that what I’m recommending must be something different than the status quo.

Mr. Davis observes—and I acknowledge—that my recommended approach resembles the review required of major executive agency regulations under Executive Order 12866, President Clinton’s revised version of President Reagan’s Executive Order 12291.  But that order is quite limited in its scope.  It doesn’t cover “minor” executive agency rules (those with expected costs of less than $100 million) or rules from independent agencies or from Congress or from courts or at the state or local level.  Moreover, I understand from talking to a former administrator of the Office of Information and Regulatory Affairs, which is charged with implementing the order, that it has actually generated little serious consideration of less restrictive alternatives, something my approach emphasizes.

What my book proposes is not some sort of governmental procedure; indeed, I emphasize in the conclusion that the book “has not addressed … how existing regulatory institutions should be reformed to encourage the sort of analysis th[e] book recommends.”  Instead, I propose a way to think through specific areas of regulation, one that is informed by a great deal of learning about both market and government failures.  The best audience for the book is probably law students who will someday find themselves influencing public policy as lawyers, legislators, regulators, or judges.  I am thus heartened that the book is being used as a text at several law schools.  My guess is that few law students receive significant exposure to Hayek, public choice, etc.

So, who knows?  Perhaps the book will make a difference at the margin.  Or perhaps it will amount to sound and fury, signifying nothing.  But I don’t think a classical liberal could fairly say that the analysis it counsels “is not clearly better than the status quo.”

A Truly Better Approach to Regulating

Mr. Davis ends his review with a stirring call to revamp the administrative state to bring it “in complete and consistent compliance with the fundamental law of our republic embodied in the Constitution, with its provisions interpreted to faithfully conform to their original public meaning.”  Among other things, he calls for restoring the separation of powers, which has been erased in agencies that combine legislative, executive, and judicial functions, and for eliminating unchecked government power, which results when the legislature delegates broad rulemaking and adjudicatory authority to politically unaccountable bureaucrats.

Once again, I concur.  There are major problems—constitutional and otherwise—with the current state of administrative law and procedure.  I’d be happy to tear down the existing administrative state and begin again on a constitutionally constrained tabula rasa.

But that’s not what my book was about.  I deliberately set out to write a book about the substance of regulation, not the process by which rules should be imposed.  I took that tack for two reasons.  First, there are numerous articles and books, by scholars far more expert than I, on the structure of the administrative state.  I could add little value on administrative process.

Second, the less-addressed substantive question—what, as a substantive matter, should a policy addressing x do?—would exist even if Mr. Davis’s constitutionally constrained regulatory process were implemented.  Suppose that we got rid of independent agencies, curtailed delegations of rulemaking authority to the executive branch, and returned to a system in which Congress wrote all rules, the executive branch enforced them, and the courts resolved any disputes.  Someone would still have to write the rule, and that someone (or group of people) should have some sense of the pros and cons of one approach over another.  That is what my book seeks to provide.

A hard core Hayekian—one who had immersed himself in Law, Legislation, and Liberty—might respond that no one should design regulation (purposive rules that Hayek would call thesis) and that efficient, “purpose-independent” laws (what Hayek called nomos) will just emerge as disputes arise.  But that is not Mr. Davis’s view.  He writes:

A system of governance or regulation based on the rule of law attains its policy objectives by proscribing actions that are inconsistent with those objectives.  For example, this type of regulation would prohibit a regulated party from discharging a pollutant in any amount greater than the limiting amount specified in the regulation.  Under this proscriptive approach to regulation, any and all actions not specifically prohibited are permitted.

Mr. Davis has thus contemplated a purposive rule, crafted by someone.  That someone should know the various policy options and the upsides and downsides of each.  How to Regulate could help.

Conclusion

I’m not sure why Mr. Davis viewed my book as no more than dressed-up progressivism.  Maybe he was triggered by the book’s cover art, which he says “is faithful to the progressive tradition,” resembling “the walls of public buildings from San Francisco to Stalingrad.”  Maybe it was a case of Sunstein Derangement Syndrome.  (Progressive legal scholar Cass Sunstein had nice things to say about the book, despite its criticisms of a number of his ideas.)  Or perhaps it was that I used the term “market failure.”  Many conservatives and libertarians fear, with good reason, that conceding the existence of market failures invites all sorts of government meddling.

At the end of the day, though, I believe we classical liberals should stop pretending that market outcomes are always perfect, that pure private ordering is always and everywhere the best policy.  We should certainly sing markets’ praises; they usually work so well that people don’t even notice them, and we should point that out.  We should continually remind people that government interventions also fail—and in systematic ways (e.g., the knowledge problem and public choice concerns).  We should insist that a market failure is never a sufficient condition for a governmental fix; one must always consider whether the cure will be worse than the disease.  In short, we should take and promote the view that government should operate “under a presumption of error.”

That view, economist Aaron Director famously observed, is the essence of laissez faire.  It’s implicit in the purpose statement of the Federalist Society’s Regulatory Transparency Project.  And it’s the central point of How to Regulate.

So let’s go easy on the friendly fire.

So I’ve just finished writing a book (hence my long hiatus from Truth on the Market).  Now that the draft is out of my hands and with the publisher (Cambridge University Press), I figured it’s a good time to rejoin my colleagues here at TOTM.  To get back into the swing of things, I’m planning to produce a series of posts describing my new book, which may be of interest to a number of TOTM readers.  I’ll get things started today with a brief overview of the project.

The book is titled How to Regulate: A Guide for Policy Makers.  A topic of that enormity could obviously fill many volumes.  I sought to address the matter in a single, non-technical book because I think law schools often do a poor job teaching their students, many of whom are future regulators, the substance of sound regulation.  Law schools regularly teach administrative law, the procedures that must be followed to ensure that rules have the force of law.  Rarely, however, do law schools teach students how to craft the substance of a policy to address a new perceived problem (e.g., What tools are available? What are the pros and cons of each?).

Economists study that matter, of course.  But economists are often naïve about the difficulty of transforming their textbook models into concrete rules that can be easily administered by business planners and adjudicators.  Many economists also pay little attention to the high information requirements of the policies they propose (i.e., the Hayekian knowledge problem) and the susceptibility of those policies to political manipulation by well-organized interest groups (i.e., public choice concerns).

How to Regulate endeavors to provide both economic training to lawyers and law students and a sense of the “limits of law” to the economists and other policy wonks who tend to be involved in crafting regulations.  Below the fold, I’ll give a brief overview of the book.  In later posts, I’ll describe some of the book’s specific chapters. Continue Reading…

As the late Nobel Laureate James Buchanan and other economists have long pointed out, even in the case of market failure, regulation is only potentially justified if economic welfare under regulation is likely to be higher than under an unregulated market – not an easy test to meet, in light of rampant government failure.  Nevertheless, as the costs of government regulation mount (see here), federal agencies continue to plow ahead undeterred in the endless search for regulatory “fixes” to non-existent problems.   Government interference in the emerging market for recreational drones is a recent and particularly egregious case in point.

Heritage Foundation Policy Analyst Jason Snead and Visiting Legal Fellow John Michael Seibler recently documented the many problems with the Federal Aviation Administration’s (FAA) recent mandate that recreational drones be federally registered, accompanied by potential criminal penalties applicable to those who fail to comply.  As the authors explain in some detail (footnotes omitted), these FAA actions rest on dubious (to say the least) legal authority, represent a grossly inappropriate criminalization of legitimate conduct, fail to meet any sort of reasonableness test for regulation, and undermine innovation:

With passage of the 2012 FAA Modernization and Reform Act, Congress explicitly told the Federal Aviation Administration to leave recreational drones alone, but the FAA has charged ahead anyway. In just two months, with no input from Congress or the public, unelected and unaccountable bureaucrats have devised a way to apply the pre-existing aircraft registration penalties to create a federal felony offense that can result in up to three years in prison and up to $277,500 in fines for failing to register as the owner of a qualifying drone—essentially a toy.

As bad as this is for unwary drone owners, the real legacy of the FAA’s drone registry may be much broader. To justify its rushed regulatory action, the FAA, relying on trumped-up claims about the risk and harms associated with drone use, has asserted its regulatory muscle to protect society from these as yet unrealized dangers. Such thinking has important ramifications for the regulation of innovation and may be only a foretaste of future regulatory actions that deter or dissuade adoption of some new and innovative technologies. . . .

In creating its new drone-owners’ registry, the FAA claimed . . . [an] exemption [from Administrative Procedure Act [APA] notice-and-comment rulemaking], owing to the immediate dangers that the agency has alleged stem from the proliferation of drones in the national airspace. According to the FAA, “it is critical that the Department be able to link the expected number of new unmanned aircraft to their owners and educate these new owners prior to commencing operations.” But there are reasons to doubt the FAA’s claims that drones have suddenly become a problem and that it could therefore not countenance any delay.

The rapid growth of small, recreational drones is not new; in fact, Congress legislated on the subject of drone policy in 2012, fully three years before the FAA claimed a sudden exigency to justify rushing its registry into effect.

Claims of immediate danger are greatly exaggerated. There is no documented instance of a drone colliding with another aircraft, and it is unclear how dangerous such a collision would be.

The number of incidents—interference with emergency services, near-collisions, and other criminal misdeeds—is insignificant compared to the number of drones in circulation. For example, the FAA reported 764 unconfirmed drone sightings near airports or aircraft over an 11-month period at a time when there were possibly as many as a million registry-eligible drones in the hands of consumers.

A full analysis of the FAA’s claimed APA exemption is beyond the scope of this paper, but it is clear that there is reason to doubt the validity of the agency’s claims. In the process of rushing its registry, the FAA exposed hundreds of thousands of drone owners to steep civil and criminal penalties for conduct that is not inherently wrongful and that was not unlawful before the rule went into effect. . . .

[H]ere it seems clear that the FAA was not empowered either to criminalize the failure to register a recreational drone or to require its registration in the first place. While agencies get deference . . . to interpret vague and ambiguous statutes, the statute in this instance is not ambiguous . . . .

In addition to the fact that the FAA acted unlawfully here, the FAA drone registry merits reconsideration because it needlessly and hastily resorted to criminal penalties when civil fines would have sufficed to satisfy the government’s interests. . . .

Treating such relatively trivial conduct as failing to register a child’s toy the same way we treat murder, robbery, or theft ignores the profound difference between the two classes of offenses and puts parties engaged in entirely legitimate activities without any intent to break the law at risk of criminal punishment. This problem is only compounded by the fact that by the FAA’s own estimates, there may be as many as a million registry-eligible drone owners, and this population grows daily.

Yet the FAA cannot guarantee that all—or even most—of this group is aware of the registration requirement or that they face draconian criminal penalties for failing to comply. Since most people do not think to check with a federal agency before using their latest toy or gadget, this leaves a significant and growing segment of the population needlessly exposed to criminal liability. The explosive growth of federal criminal law and the dramatic expansion of the administrative state have gone hand-in-hand. Regulations like the FAA drone registration requirement generally make it all but impossible for individuals to know which of their toys—or any other things considered potentially “dangerous”—are permissible today but will make them felons tomorrow.

The significance of the FAA’s registry extends beyond its immediate impact on drone owners: It sets a precedent for criminalizing other innovations utilizing “emergency” rulemaking procedures premised on overblown claims of harm. While this is a particularly egregious abuse of the criminal law, government has a history of criminalizing or threatening to criminalize innovation under the “precautionary principle,” the belief that because a new idea or technology could pose some theoretical danger or risk in the future, public policies should control or limit the development of such innovations until their creators can prove that they won’t cause any harms.

Innovations affected by precautionary government action include commercial use of the Internet (until 1989); an at-home 99 genetic analysis kit; 3-D printing; Caller ID; Uber and Lyft, transportation services offered as an alternative to traditional taxi cabs; Airbnb and other short-term home rental companies offering alternative vacation rentals; driverless cars; and FWD (“Skype before Skype was Skype”), which eventually shut down in part because U.S. attorneys put the reigns on FWD to seek FCC approvals while foreign founders of Skype proceeded apace with no regard for U.S. regulatory approvals.

Criminalizing or otherwise restraining technologies like e-mail sounds laughable today, but e-mails were new and strange once, and like the driverless and Internet-connected cars just beginning to emerge in the market today, people felt that “the more we learn about [them]…the more we’re learning to fear them.” Telephones, too, were new and strange once, but “people quickly adjusted to the new device. ‘Ultimately, the telephone proved too useful to abandon for the sake of social discomfort.’” When the telephone morphed into the cellular phone, the public once again became alarmed over the possibility of cell phone radiation causing cancer. That fear eventually proved to be unfounded, but imagine the consequences and the cost, both social and economic, if the government had banned cell phones until that risk was definitively disproven.

This thinking is antithetical to the core premise of a bottom-up, market-based economy and threatens technological progress, entrepreneurship, and prosperity. Precautionary rulemaking also (ironically for a theory premised on protecting society from unknown harms) leaves society exposed to existing hazards that new technologies might otherwise remedy. Drones, for example, might be useful tools in fighting wildfires and providing environmental disaster relief, or detecting threats to community safety, or performing tasks that would otherwise place a human being in danger. Public policies that, based on unproven potential risks, prevent or slow the development of those capabilities force society to forego the opportunity to benefit from social adaptation and repeated trial and error.

Legislators and policymakers are standing by to capitalize on irrational fears or discomforts by introducing new legislation and regulations and claiming that such measures are necessary to protect the public from dangerous unknown technologies when, in fact, those fears are overblown. Often, these claims are hyped to distract from other motives, whether it be protecting an entrenched and politically connected interest, enhancing one’s notoriety, or establishing regulatory purview over an expansive new sector. The public would be better served by policies that allow innovative technologies to be brought to market and that let the market and society sort out the winners and losers.

The FAA’s ill-considered decision to create a recreational drone register illustrates one of the most troublesome aspects of much recent American regulation – the tendency to apply criminal sanctions to violations of myriad pettifogging rules, part of the broader problem of overcriminalization.  Many of the new regulatory crimes stigmatize routine actions carried out by individuals who had no idea they were engaging in illegal conduct (a problem recently elaborated upon by Heritage Foundation Senior Legal Fellow Paul Larkin).  The damage caused by such penalties extends far beyond the direct imposition of economic costs – it involves serious reputational harm and sharply constrains individual freedom.  As Heritage Senior Legal Fellow John Malcolm has pointed out:

There is a unique stigma that goes with being branded a criminal. Not only can you lose your liberty and certain civil rights, but you lose your reputation—an intangible yet invaluable commodity, precious to entities and individuals alike, that once damaged can be nearly impossible to repair. In addition to standard penalties that are imposed on those who are convicted of crimes, a series of burdensome collateral consequences that are often imposed by state or federal laws can follow an individual for life.

In order to preserve the moral authority of our legal system and engender respect for the rule of law, we should be especially careful before enacting laws or promulgating regulations that can cause an individual to be unfairly branded as a criminal. . . .

The mere existence of criminal regulations dramatically alters the relationship between the regulatory agency and the regulated power. All an agency has to do is suggest that a regulated person or entity might face criminal prosecution and penalties for failure to follow an agency directive, and the regulated person or entity will likely fall quickly into line without questioning the agency’s authority.

In short, advocates of reducing the burden of regulation may wish to emphasize the threat that it often poses to individual liberties, as well as its economic harm.

by Thomas W. Hazlett, H.H. Macaulay Endowed Chair in Economics at Clemson University

Josh Wright is a tour de force. He has broken the mold for a Washington regulator — and created a new one. As a scholar, he carefully crafts his analyses of public policy. As a strategic thinker, he tackles the issues that redound to the greatest social benefit. And as a champion of competitive markets, he forcefully advances rules to encourage innovation and consumer welfare. Nearly as important as his diligence within the regulatory process, he is transparent in his objectives and takes every opportunity to enunciate his principles for action. The public knows what he is doing and why it is important. 

As a sample of Commissioner Wright’s dedication to improving regulatory law, I am delighted to suggest the talk he gave April 2, 2015 at Clemson University, hosted by the Information Economy Project. His title: Regulation in High-Tech Markets: Public Choice, Regulatory Capture, and the FTC. He was particularly concerned in describing the harm produced by state and local barriers blocking competitive forces with respect to emerging, disruptive innovations such as Uber and AirBnB, offering remedies available via competition policy. The talk is posted here.

UPDATE: I’ve been reliably informed that Vint Cerf coined the term “permissionless innovation,” and, thus, that he did so with the sorts of private impediments discussed below in mind rather than government regulation. So consider the title of this post changed to “Permissionless innovation SHOULD not mean ‘no contracts required,'” and I’ll happily accept that my version is the “bastardized” version of the term. Which just means that the original conception was wrong and thank god for disruptive innovation in policy memes!

Can we dispense with the bastardization of the “permissionless innovation” concept (best developed by Adam Thierer) to mean “no contracts required”? I’ve been seeing this more and more, but it’s been around for a while. Some examples from among the innumerable ones out there:

Vint Cerf on net neutrality in 2009:

We believe that the vast numbers of innovative Internet applications over the last decade are a direct consequence of an open and freely accessible Internet. Many now-successful companies have deployed their services on the Internet without the need to negotiate special arrangements with Internet Service Providers, and it’s crucial that future innovators have the same opportunity. We are advocates for “permissionless innovation” that does not impede entrepreneurial enterprise.

Net neutrality is replete with this sort of idea — that any impediment to edge providers (not networks, of course) doing whatever they want to do at a zero price is a threat to innovation.

Chet Kanojia (Aereo CEO) following the Aereo decision:

It is troubling that the Court states in its decision that, ‘to the extent commercial actors or other interested entities may be concerned with the relationship between the development and use of such technologies and the Copyright Act, they are of course free to seek action from Congress.’ (Majority, page 17)That begs the question: Are we moving towards a permission-based system for technology innovation?

At least he puts it in the context of the Court’s suggestion that Congress pass a law, but what he really wants is to not have to ask “permission” of content providers to use their content.

Mike Masnick on copyright in 2010:

But, of course, the problem with all of this is that it goes back to creating permission culture, rather than a culture where people freely create. You won’t be able to use these popular or useful tools to build on the works of others — which, contrary to the claims of today’s copyright defenders, is a key component in almost all creativity you see out there — without first getting permission.

Fair use is, by definition, supposed to be “permissionless.” But the concept is hardly limited to fair use, is used to justify unlimited expansion of fair use, and is extended by advocates to nearly all of copyright (see, e.g., Mike Masnick again), which otherwise requires those pernicious licenses (i.e., permission) from others.

The point is, when we talk about permissionless innovation for Tesla, Uber, Airbnb, commercial drones, online data and the like, we’re talking (or should be) about ex ante government restrictions on these things — the “permission” at issue is permission from the government, it’s the “permission” required to get around regulatory roadblocks imposed via rent-seeking and baseless paternalism. As Gordon Crovitz writes, quoting Thierer:

“The central fault line in technology policy debates today can be thought of as ‘the permission question,'” Mr. Thierer writes. “Must the creators of new technologies seek the blessing of public officials before they develop and deploy their innovations?”

But it isn’t (or shouldn’t be) about private contracts.

Just about all human (commercial) activity requires interaction with others, and that means contracts and licenses. You don’t see anyone complaining about the “permission” required to rent space from a landlord. But that some form of “permission” may be required to use someone else’s creative works or other property (including broadband networks) is no different. And, in fact, it is these sorts of contracts (and, yes, the revenue that may come with them) that facilitates people engaging with other commercial actors to produce things of value in the first place. The same can’t be said of government permission.

Don’t get me wrong – there may be some net welfare-enhancing regulatory limits that might require forms of government permission. But the real concern is the pervasive abuse of these limits, imposed without anything approaching a rigorous welfare determination. There might even be instances where private permission, imposed, say, by a true monopolist, might be problematic.

But this idea that any contractual obligation amounts to a problematic impediment to innovation is absurd, and, in fact, precisely backward. Which is why net neutrality is so misguided. Instead of identifying actual, problematic impediments to innovation, it simply assumes that networks threaten edge innovation, without any corresponding benefit and with such certainty (although no actual evidence) that ex ante common carrier regulations are required.

“Permissionless innovation” is a great phrase and, well developed (as Adam Thierer has done), a useful concept. But its bastardization to justify interference with private contracts is unsupported and pernicious.