It will have some positive effects on economic welfare, to the extent it succeeds in lifting artificial barriers to competition that harm consumers and workers—such as allowing direct sales of hearing aids in drug stores—and helping to eliminate unnecessary occupational licensing restrictions, to name just two of several examples.
But it will likely have substantial negative effects on economic welfare as well. Many aspects of the order appear to emphasize new regulation—such as Net Neutrality requirements that may reduce investment in broadband by internet service providers—and imposing new regulatory requirements on airlines, pharmaceutical companies, digital platforms, banks, railways, shipping, and meat packers, among others. Arbitrarily imposing new rules in these areas, without a cost-beneficial appraisal and a showing of a market failure, threatens to reduce innovation and slow economic growth, hurting producers and consumer. (A careful review of specific regulatory proposals may shed greater light on the justifications for particular regulations.)
Antitrust-related proposals to challenge previously cleared mergers, and to impose new antitrust rulemaking, are likely to raise costly business uncertainty, to the detriment of businesses and consumers. They are a recipe for slower economic growth, not for vibrant competition.
An underlying problem with the order is that it is based on the false premise that competition has diminished significantly in recent decades and that “big is bad.” Economic analysis found in the February 2020 Economic Report of the President, and in other economic studies, debunks this flawed assumption.
In short, the order commits the fundamental mistake of proposing intrusive regulatory solutions for a largely nonexistent problem. Competitive issues are best handled through traditional well-accepted antitrust analysis, which centers on promoting consumer welfare and on weighing procompetitive efficiencies against anticompetitive harm on a case-by-case basis. This approach:
Deals effectively with serious competitive problems; while at the same time
Cabining error costs by taking into account all economically relevant considerations on a case-specific basis.
Rather than using an executive order to direct very specific regulatory approaches without a strong economic and factual basis, the Biden administration would have been better served by raising a host of competitive issues that merit possible study and investigation by expert agencies. Such an approach would have avoided imposing the costs of unwarranted regulation that unfortunately are likely to stem from the new order.
Finally, the order’s call for new regulations and the elimination of various existing legal policies will spawn matter-specific legal challenges, and may, in many cases, not succeed in court. This will impose unnecessary business uncertainty in addition to public and private resources wasted on litigation.
From Sen. Elizabeth Warren (D-Mass.) to Sen. Josh Hawley (R-Mo.), populist calls to “fix” our antitrust laws and the underlying Consumer Welfare Standard have found a foothold on Capitol Hill. At the same time, there are calls to “fix” the Supreme Court by packing it with new justices. The court’s unanimous decision in NCAA v. Alston demonstrates that neither needs repair. To the contrary, clearly anti-competitive conduct—like the NCAA’s compensation rules—is proscribed under the Consumer Welfare Standard, and every justice from Samuel Alito to Sonia Sotomayor can agree on that.
In 1984, the court in NCAA v. Board of Regents suggested that “courts should take care when assessing the NCAA’s restraints on student-athlete compensation.” After all, joint ventures like sports leagues are entitled to rule-of-reason treatment. But while times change, the Consumer Welfare Standard is sufficiently flexible to meet those changes.
Where a competitive restraint exists primarily to ensure that “enormous sums of money flow to seemingly everyone except the student athletes,” the court rightly calls it out for what it is. As Associate Justice Brett Kavanaugh wrote in his concurrence:
Nowhere else in America can businesses get away with agreeing not to pay their workers a fair market rate on the theory that their product is defined by not paying their workers a fair market rate. And under ordinary principles of antitrust law, it is not evident why college sports should be any different. The NCAA is not above the law.
Disturbing these “ordinary principles”—whether through legislation, administrative rulemaking, or the common law—is simply unnecessary. For example, the Open Markets Institute filed an amicus brief arguing that the rule of reason should be “bounded” and willfully blind to the pro-competitive benefits some joint ventures can create (an argument that has been used, unsuccessfully, to attack ridesharing services like Uber and Lyft). Sen. Amy Klobuchar (D-Minn.) has proposed shifting the burden of proof so that merging parties are guilty until proven innocent. Sen. Warren would go further, deeming Amazon’s acquisition of Whole Foods anti-competitive simply because the company is “big,” and ignoring the merger’s myriad pro-competitive benefits. Sen. Hawley has gone further still: calling on Amazon to be investigated criminally for the crime of being innovative and successful.
Several of the current proposals, including those from Sens. Klobuchar and Hawley (and those recently introduced in the House that essentially single out firms for disfavored treatment), would replace the Consumer Welfare Standard that has underpinned antitrust law for decades with a policy that effectively punishes firms for being politically unpopular.
These examples demonstrate we should be wary when those in power assert that things are so irreparably broken that they need a complete overhaul. The “solutions” peddled usually increase politicians’ power by enabling them to pick winners and losers through top-down approaches that stifle the bottom-up innovations that make consumers’ lives better.
Are antitrust law and the Supreme Court perfect? Hardly. But in a 9-0 decision, the court proved this week that there’s nothing broken about either.
Interrogations concerning the role that economic theory should play in policy decisions are nothing new. Milton Friedman famously drew a distinction between “positive” and “normative” economics, notably arguing that theoretical models were valuable, despite their unrealistic assumptions. Kenneth Arrow and Gerard Debreu’s highly theoretical work on General Equilibrium Theory is widely acknowledged as one of the most important achievements of modern economics.
But for all their intellectual value and academic merit, the use of models to inform policy decisions is not uncontroversial. There is indeed a long and unfortunate history of influential economic models turning out to be poor depictions (and predictors) of real-world outcomes.
This raises a key question: should policymakers use economic models to inform their decisions and, if so, how? This post uses the economics of externalities to illustrate both the virtues and pitfalls of economic modeling. Throughout economic history, externalities have routinely been cited to support claims of market failure and calls for government intervention. However, as explained below, these fears have frequently failed to withstand empirical scrutiny.
Today, similar models are touted to support government intervention in digital industries. Externalities are notably said to prevent consumers from switching between platforms, allegedly leading to unassailable barriers to entry and deficient venture-capital investment. Unfortunately, as explained below, the models that underpin these fears are highly abstracted and far removed from underlying market realities.
Ultimately, this post argues that, while models provide a powerful way of thinking about the world, naïvely transposing them to real-world settings is misguided. This is not to say that models are useless—quite the contrary. Indeed, “falsified” models can shed powerful light on economic behavior that would otherwise prove hard to understand.
Fears surrounding economic externalities are as old as modern economics. For example, in the 1950s, economists routinely cited bee pollination as a source of externalities and, ultimately, market failure.
The basic argument was straightforward: Bees and orchards provide each other with positive externalities. Bees cross-pollinate flowers and orchards contain vast amounts of nectar upon which bees feed, thus improving honey yields. Accordingly, several famous economists argued that there was a market failure; bees fly where they please and farmers cannot prevent bees from feeding on their blossoming flowers—allegedly causing underinvestment in both. This led James Meade to conclude:
[T]he apple-farmer provides to the beekeeper some of his factors free of charge. The apple-farmer is paid less than the value of his marginal social net product, and the beekeeper receives more than the value of his marginal social net product.
If, then, apple producers are unable to protect their equity in apple-nectar and markets do not impute to apple blossoms their correct shadow value, profit-maximizing decisions will fail correctly to allocate resources at the margin. There will be failure “by enforcement.” This is what I would call an ownership externality. It is essentially Meade’s “unpaid factor” case.
It took more than 20 years and painstaking research by Steven Cheung to conclusively debunk these assertions. So how did economic agents overcome this “insurmountable” market failure?
The answer, it turns out, was extremely simple. While bees do fly where they please, the relative placement of beehives and orchards has a tremendous impact on both fruit and honey yields. This is partly because bees have a very limited mean foraging range (roughly 2-3km). This left economic agents with ample scope to prevent free-riding.
Using these natural sources of excludability, they built a web of complex agreements that internalize the symbiotic virtues of beehives and fruit orchards. To cite Steven Cheung’s research:
Pollination contracts usually include stipulations regarding the number and strength of the colonies, the rental fee per hive, the time of delivery and removal of hives, the protection of bees from pesticide sprays, and the strategic placing of hives. Apiary lease contracts differ from pollination contracts in two essential aspects. One is, predictably, that the amount of apiary rent seldom depends on the number of colonies, since the farmer is interested only in obtaining the rent per apiary offered by the highest bidder. Second, the amount of apiary rent is not necessarily fixed. Paid mostly in honey, it may vary according to either the current honey yield or the honey yield of the preceding year.
But what of neighboring orchards? Wouldn’t these entail a more complex externality (i.e., could one orchard free-ride on agreements concluded between other orchards and neighboring apiaries)? Apparently not:
Acknowledging the complication, beekeepers and farmers are quick to point out that a social rule, or custom of the orchards, takes the place of explicit contracting: during the pollination period the owner of an orchard either keeps bees himself or hires as many hives per area as are employed in neighboring orchards of the same type. One failing to comply would be rated as a “bad neighbor,” it is said, and could expect a number of inconveniences imposed on him by other orchard owners. This customary matching of hive densities involves the exchange of gifts of the same kind, which apparently entails lower transaction costs than would be incurred under explicit contracting, where farmers would have to negotiate and make money payments to one another for the bee spillover.
In short, not only did the bee/orchard externality model fail, but it failed to account for extremely obvious counter-evidence. Even a rapid flip through the Yellow Pages (or, today, a search on Google) would have revealed a vibrant market for bee pollination. In short, the bee externalities, at least as presented in economic textbooks, were merely an economic “fable.” Unfortunately, they would not be the last.
Lighthouses provide another cautionary tale. Indeed, Henry Sidgwick, A.C. Pigou, John Stuart Mill, and Paul Samuelson all cited the externalities involved in the provision of lighthouse services as a source of market failure.
Here, too, the problem was allegedly straightforward. A lighthouse cannot prevent ships from free-riding on its services when they sail by it (i.e., it is mostly impossible to determine whether a ship has paid fees and to turn off the lighthouse if that is not the case). Hence there can be no efficient market for light dues (lighthouses were seen as a “public good”). As Paul Samuelson famously put it:
Take our earlier case of a lighthouse to warn against rocks. Its beam helps everyone in sight. A businessman could not build it for a profit, since he cannot claim a price from each user. This certainly is the kind of activity that governments would naturally undertake.
He added that:
[E]ven if the operators were able—say, by radar reconnaissance—to claim a toll from every nearby user, that fact would not necessarily make it socially optimal for this service to be provided like a private good at a market-determined individual price. Why not? Because it costs society zero extra cost to let one extra ship use the service; hence any ships discouraged from those waters by the requirement to pay a positive price will represent a social economic loss—even if the price charged to all is no more than enough to pay the long-run expenses of the lighthouse.
More than a century after it was first mentioned in economics textbooks, Ronald Coase finally laid the lighthouse myth to rest—rebutting Samuelson’s second claim in the process.
What piece of evidence had eluded economists for all those years? As Coase observed, contemporary economists had somehow overlooked the fact that large parts of the British lighthouse system were privately operated, and had been for centuries:
[T]he right to operate a lighthouse and to levy tolls was granted to individuals by Acts of Parliament. The tolls were collected at the ports by agents (who might act for several lighthouses), who might be private individuals but were commonly customs officials. The toll varied with the lighthouse and ships paid a toll, varying with the size of the vessel, for each lighthouse passed. It was normally a rate per ton (say 1/4d or 1/2d) for each voyage. Later, books were published setting out the lighthouses passed on different voyages and the charges that would be made.
In other words, lighthouses used a simple physical feature to create “excludability” and prevent free-riding. The main reason ships require lighthouses is to avoid hitting rocks when they make their way to a port. By tying port fees and light dues, lighthouse owners—aided by mild government-enforced property rights—could easily earn a return on their investments, thus disproving the lighthouse free-riding myth.
Ultimately, this meant that a large share of the British lighthouse system was privately operated throughout the 19th century, and this share would presumably have been more pronounced if government-run “Trinity House” lighthouses had not crowded out private investment:
The position in 1820 was that there were 24 lighthouses operated by Trinity House and 22 by private individuals or organizations. But many of the Trinity House lighthouses had not been built originally by them but had been acquired by purchase or as the result of the expiration of a lease.
Of course, this system was not perfect. Some ships (notably foreign ones that did not dock in the United Kingdom) might free-ride on this arrangement. It also entailed some level of market power. The ability to charge light dues meant that prices were higher than the “socially optimal” baseline of zero (the marginal cost of providing light is close to zero). Though it is worth noting that tying port fees and light dues might also have decreased double marginalization, to the benefit of sailors.
Samuelson was particularly weary of this market power that went hand in hand with the private provision of public goods, including lighthouses:
Being able to limit a public good’s consumption does not make it a true-blue private good. For what, after all, are the true marginal costs of having one extra family tune in on the program? They are literally zero. Why then prevent any family which would receive positive pleasure from tuning in on the program from doing so?
However, as Coase explained, light fees represented only a tiny fraction of a ship’s costs. In practice, they were thus unlikely to affect market output meaningfully:
[W]hat is the gain which Samuelson sees as coming from this change in the way in which the lighthouse service is financed? It is that some ships which are now discouraged from making a voyage to Britain because of the light dues would in future do so. As it happens, the form of the toll and the exemptions mean that for most ships the number of voyages will not be affected by the fact that light dues are paid. There may be some ships somewhere which are laid up or broken up because of the light dues, but the number cannot be great, if indeed there are any ships in this category.
Samuelson’s critique also falls prey to the Nirvana Fallacy pointed out by Harold Demsetz: markets might not be perfect, but neither is government intervention. Market power and imperfect appropriability are the two (paradoxical) pitfalls of the first; “white elephants,” underinvestment, and lack of competition (and the information it generates) tend to stem from the latter.
Which of these solutions is superior, in each case, is an empirical question that early economists had simply failed to consider—assuming instead that market failure was systematic in markets that present prima facie externalities. In other words, models were taken as gospel without any circumspection about their relevance to real-world settings.
The Tragedy of the Commons
Externalities were also said to undermine the efficient use of “common pool resources,” such grazing lands, common irrigation systems, and fisheries—resources where one agent’s use diminishes that of others, and where exclusion is either difficult or impossible.
The most famous formulation of this problem is Garret Hardin’s highly influential (over 47,000 cites) “tragedy of the commons.” Hardin cited the example of multiple herdsmen occupying the same grazing ground:
The rational herdsman concludes that the only sensible course for him to pursue is to add another animal to his herd. And another; and another … But this is the conclusion reached by each and every rational herdsman sharing a commons. Therein is the tragedy. Each man is locked into a system that compels him to increase his herd without limit—in a world that is limited. Ruin is the destination toward which all men rush, each pursuing his own best interest in a society that believes in the freedom of the commons.
In more technical terms, each economic agent purportedly exerts an unpriced negative externality on the others, thus leading to the premature depletion of common pool resources. Hardin extended this reasoning to other problems, such as pollution and allegations of global overpopulation.
Although Hardin hardly documented any real-world occurrences of this so-called tragedy, his policy prescriptions were unequivocal:
The most important aspect of necessity that we must now recognize, is the necessity of abandoning the commons in breeding. No technical solution can rescue us from the misery of overpopulation. Freedom to breed will bring ruin to all.
As with many other theoretical externalities, empirical scrutiny revealed that these fears were greatly overblown. In her Nobel-winning work, Elinor Ostrom showed that economic agents often found ways to mitigate these potential externalities markedly. For example, mountain villages often implement rules and norms that limit the use of grazing grounds and wooded areas. Likewise, landowners across the world often set up “irrigation communities” that prevent agents from overusing water.
Along similar lines, Julian Morris and I conjecture that informal arrangements and reputational effects might mitigate opportunistic behavior in the standard essential patent industry.
These bottom-up solutions are certainly not perfect. Many common institutions fail—for example, Elinor Ostrom documents several problematic fisheries, groundwater basins and forests, although it is worth noting that government intervention was sometimes behind these failures. To cite but one example:
Several scholars have documented what occurred when the Government of Nepal passed the “Private Forest Nationalization Act” […]. Whereas the law was officially proclaimed to “protect, manage and conserve the forest for the benefit of the entire country”, it actually disrupted previously established communal control over the local forests. Messerschmidt (1986, p.458) reports what happened immediately after the law came into effect:
Nepalese villagers began freeriding — systematically overexploiting their forest resources on a large scale.
In any case, the question is not so much whether private institutions fail, but whether they do so more often than government intervention. be it regulation or property rights. In short, the “tragedy of the commons” is ultimately an empirical question: what works better in each case, government intervention, propertization, or emergent rules and norms?
More broadly, the key lesson is that it is wrong to blindly apply models while ignoring real-world outcomes. As Elinor Ostrom herself put it:
The intellectual trap in relying entirely on models to provide the foundation for policy analysis is that scholars then presume that they are omniscient observers able to comprehend the essentials of how complex, dynamic systems work by creating stylized descriptions of some aspects of those systems.
In 1985, Paul David published an influential paper arguing that market failures undermined competition between the QWERTY and Dvorak keyboard layouts. This version of history then became a dominant narrative in the field of network economics, including works by Joseph Farrell & Garth Saloner, and Jean Tirole.
The basic claim was that QWERTY users’ reluctance to switch toward the putatively superior Dvorak layout exerted a negative externality on the rest of the ecosystem (and a positive externality on other QWERTY users), thus preventing the adoption of a more efficient standard. As Paul David put it:
Although the initial lead acquired by QWERTY through its association with the Remington was quantitatively very slender, when magnified by expectations it may well have been quite sufficient to guarantee that the industry eventually would lock in to a de facto QWERTY standard. […]
Competition in the absence of perfect futures markets drove the industry prematurely into standardization on the wrong system — where decentralized decision making subsequently has sufficed to hold it.
Unfortunately, many of the above papers paid little to no attention to actual market conditions in the typewriter and keyboard layout industries. Years later, Stan Liebowitz and Stephen Margolis undertook a detailed analysis of the keyboard layout market. They almost entirely rejected any notion that QWERTY prevailed despite it being the inferior standard:
Yet there are many aspects of the QWERTY-versus-Dvorak fable that do not survive scrutiny. First, the claim that Dvorak is a better keyboard is supported only by evidence that is both scant and suspect. Second, studies in the ergonomics literature find no significant advantage for Dvorak that can be deemed scientifically reliable. Third, the competition among producers of typewriters, out of which the standard emerged, was far more vigorous than is commonly reported. Fourth, there were far more typing contests than just the single Cincinnati contest. These contests provided ample opportunity to demonstrate the superiority of alternative keyboard arrangements. That QWERTY survived significant challenges early in the history of typewriting demonstrates that it is at least among the reasonably fit, even if not the fittest that can be imagined.
In short, there was little to no evidence supporting the view that QWERTY inefficiently prevailed because of network effects. The falsification of this narrative also weakens broader claims that network effects systematically lead to either excess momentum or excess inertia in standardization. Indeed, it is tempting to characterize all network industries with heavily skewed market shares as resulting from market failure. Yet the QWERTY/Dvorak story suggests that such a conclusion would be premature.
Killzones, Zoom, and TikTok
If you are still reading at this point, you might think that contemporary scholars would know better than to base calls for policy intervention on theoretical externalities. Alas, nothing could be further from the truth.
For instance, a recent paper by Sai Kamepalli, Raghuram Rajan and Luigi Zingales conjectures that the interplay between mergers and network externalities discourages the adoption of superior independent platforms:
If techies expect two platforms to merge, they will be reluctant to pay the switching costs and adopt the new platform early on, unless the new platform significantly outperforms the incumbent one. After all, they know that if the entering platform’s technology is a net improvement over the existing technology, it will be adopted by the incumbent after merger, with new features melded with old features so that the techies’ adjustment costs are minimized. Thus, the prospect of a merger will dissuade many techies from trying the new technology.
Although this key behavioral assumption drives the results of the theoretical model, the paper presents no evidence to support the contention that it occurs in real-world settings. Admittedly, the paper does present evidence of reduced venture capital investments after mergers involving large tech firms. But even on their own terms, this data simply does not support the authors’ behavioral assumption.
And this is no isolated example. Over the past couple of years, several scholars have called for more muscular antitrust intervention in networked industries. A common theme is that network externalities, switching costs, and data-related increasing returns to scale lead to inefficient consumer lock-in, thus raising barriers to entry for potential rivals (here, here, here).
But there are also countless counterexamples, where firms have easily overcome potential barriers to entry and network externalities, ultimately disrupting incumbents.
Zoom is one of the most salient instances. As I have written previously:
To get to where it is today, Zoom had to compete against long-established firms with vast client bases and far deeper pockets. These include the likes of Microsoft, Cisco, and Google. Further complicating matters, the video communications market exhibits some prima facie traits that are typically associated with the existence of network effects.
Along similar lines, Geoffrey Manne and Alec Stapp have put forward a multitude of other examples. These include: The demise of Yahoo; the disruption of early instant-messaging applications and websites; MySpace’s rapid decline; etc. In all these cases, outcomes do not match the predictions of theoretical models.
More recently, TikTok’s rapid rise offers perhaps the greatest example of a potentially superior social-networking platform taking significant market share away from incumbents. According to the Financial Times, TikTok’s video-sharing capabilities and its powerful algorithm are the most likely explanations for its success.
While these developments certainly do not disprove network effects theory, they eviscerate the common belief in antitrust circles that superior rivals are unable to overthrow incumbents in digital markets. Of course, this will not always be the case. As in the previous examples, the question is ultimately one of comparing institutions—i.e., do markets lead to more or fewer error costs than government intervention? Yet this question is systematically omitted from most policy discussions.
My argument is not that models are without value. To the contrary, framing problems in economic terms—and simplifying them in ways that make them cognizable—enables scholars and policymakers to better understand where market failures might arise, and how these problems can be anticipated and solved by private actors. In other words, models alone cannot tell us that markets will fail, but they can direct inquiries and help us to understand why firms behave the way they do, and why markets (including digital ones) are organized in a given way.
In that respect, both the theoretical and empirical research cited throughout this post offer valuable insights for today’s policymakers.
For a start, as Ronald Coase famously argued in what is perhaps his most famous work, externalities (and market failure more generally) are a function of transaction costs. When these are low (relative to the value of a good), market failures are unlikely. This is perhaps clearest in the “Fable of the Bees” example. Given bees’ short foraging range, there were ultimately few real-world obstacles to writing contracts that internalized the mutual benefits of bees and orchards.
Perhaps more importantly, economic research sheds light on behavior that might otherwise be seen as anticompetitive. The rules and norms that bind farming/beekeeping communities, as well as users of common pool resources, could easily be analyzed as a cartel by naïve antitrust authorities. Yet externality theory suggests they play a key role in preventing market failure.
Along similar lines, mergers and acquisitions (as well as vertical integration, more generally) can reduce opportunism and other externalities that might otherwise undermine collaboration between firms (here, here and here). And much of the same is true for certain types of unilateral behavior. Tying video games to consoles (and pricing the console below cost) can help entrants overcome network externalities that might otherwise shield incumbents. Likewise, Google tying its proprietary apps to the open source Android operating system arguably enabled it to earn a return on its investments, thus overcoming the externality problem that plagues open source software.
All of this raises a tantalizing prospect that deserves far more attention than it is currently given in policy circles: authorities around the world are seeking to regulate the tech space. Draft legislation has notably been tabled in the United States, European Union and the United Kingdom. These draft bills would all make it harder for large tech firms to implement various economic hierarchies, including mergers and certain contractual arrangements.
This is highly paradoxical. If digital markets are indeed plagued by network externalities and high transaction costs, as critics allege, then preventing firms from adopting complex hierarchies—which have traditionally been seen as a way to solve externalities—is just as likely to exacerbate problems. In other words, like the economists of old cited above, today’s policymakers appear to be focusing too heavily on simple models that predict market failure, and far too little on the mechanisms that firms have put in place to thrive within this complex environment.
The bigger picture is that far more circumspection is required when using theoretical models in real-world policy settings. Indeed, as Harold Demsetz famously put it, the purpose of normative economics is not so much to identify market failures, but to help policymakers determine which of several alternative institutions will deliver the best outcomes for consumers:
This nirvana approach differs considerably from a comparative institution approach in which the relevant choice is between alternative real institutional arrangements. In practice, those who adopt the nirvana viewpoint seek to discover discrepancies between the ideal and the real and if discrepancies are found, they deduce that the real is inefficient. Users of the comparative institution approach attempt to assess which alternative real institutional arrangement seems best able to cope with the economic problem […].
Lina Khan’s appointment as chair of the Federal Trade Commission (FTC) is a remarkable accomplishment. At 32 years old, she is the youngest chair ever. Her longstanding criticisms of the Consumer Welfare Standard and alignment with the neo-Brandeisean school of thought make her appointment a significant achievement for proponents of those viewpoints.
Her appointment also comes as House Democrats are preparing to mark up five bills designed to regulate Big Tech and, in the process, vastly expand the FTC’s powers. This expansion may combine with Khan’s appointment in ways that lawmakers considering the bills have not yet considered.
As things stand, the FTC under Khan’s leadership is likely to push for more extensive regulatory powers, akin to those held by the Federal Communications Commission (FCC). But these expansions would be trivial compared to what is proposed by many of the bills currently being prepared for a June 23 mark-up in the House Judiciary Committee.
The flagship bill—Rep. David Cicilline’s (D-R.I.) American Innovation and Choice Online Act—is described as a platform “non-discrimination” bill. I have already discussed what the real-world effects of this bill would likely be. Briefly, it would restrict platforms’ ability to offer richer, more integrated services at all, since those integrations could be challenged as “discrimination” at the cost of would-be competitors’ offerings. Things like free shipping on Amazon Prime, pre-installed apps on iPhones, or even including links to Gmail and Google Calendar at the top of a Google Search page could be precluded under the bill’s terms; in each case, there is a potential competitor being undermined.
But this shifts the focus to the FTC itself, and implies that it would have potentially enormous discretionary power under these proposals to enforce the law selectively.
Companies found guilty of breaching the bill’s terms would be liable for civil penalties of up to 15 percent of annual U.S. revenue, a potentially significant sum. And though the Supreme Court recently ruled unanimously against the FTC’s powers to levy civil fines unilaterally—which the FTC opposed vociferously, and may get restored by other means—there are two scenarios through which it could end up getting extraordinarily extensive control over the platforms covered by the bill.
The first course is through selective enforcement. What Singer above describes as a positive—the fact that enforcers would just let “benign” violations of the law be—would mean that the FTC itself would have tremendous scope to choose which cases it brings, and might do so for idiosyncratic, politicized reasons.
The second path would be to use these powers as leverage to get broad consent decrees to govern the conduct of covered platforms. These occur when a lawsuit is settled, with the defendant company agreeing to change its business practices under supervision of the plaintiff agency (in this case, the FTC). The Cambridge Analytica lawsuit ended this way, with Facebook agreeing to change its data-sharing practices under the supervision of the FTC.
This path would mean the FTC creating bespoke, open-ended regulation for each covered platform. Like the first path, this could create significant scope for discretionary decision-making by the FTC and potentially allow FTC officials to impose their own, non-economic goals on these firms. And it would require costly monitoring of each firm subject to bespoke regulation to ensure that no breaches of that regulation occurred.
“economic power as inextricably political. Power in industry is the power to steer outcomes. It grants outsized control to a few, subjecting the public to unaccountable private power—and thereby threatening democratic order. The account also offers a positive vision of how economic power should be organized (decentralized and dispersed), a recognition that forms of economic power are not inevitable and instead can be restructured.” [italics added]
Though I have focused on Cicilline’s flagship bill, others grant significant new powers to the FTC, as well. The data portability and interoperability bill doesn’t actually define what “data” is; it leaves it to the FTC to “define the term ‘data’ for the purpose of implementing and enforcing this Act.” And, as I’ve written elsewhere, data interoperability needs significant ongoing regulatory oversight to work at all, a responsibility that this bill also hands to the FTC. Even a move as apparently narrow as data portability will involve a significant expansion of the FTC’s powers and give it a greater role as an ongoing economic regulator.
Democratic leadership of the House Judiciary Committee have leaked the approach they plan to take to revise U.S. antitrust law and enforcement, with a particular focus on digital platforms.
Broadly speaking, the bills would: raise fees for larger mergers and increase appropriations to the FTC and DOJ; require data portability and interoperability; declare that large platforms can’t own businesses that compete with other businesses that use the platform; effectively ban large platforms from making any acquisitions; and generally declare that large platforms cannot preference their own products or services.
All of these are ideas that have been discussed before. They are very much in line with the EU’s approach to competition, which places more regulation-like burdens on big businesses, and which is introducing a Digital Markets Act that mirrors the Democrats’ proposals. Some Republicans are reportedly supportive of the proposals, which is surprising since they mean giving broad, discretionary powers to antitrust authorities that are controlled by Democrats who take an expansive view of antitrust enforcement as a way to achieve their other social and political goals. The proposals may also be unpopular with consumers if, for example, they would mean that popular features like integrating Maps into relevant Google Search results becomes prohibited.
The multi-bill approach here suggests that the committee is trying to throw as much at the wall as possible to see what sticks. It may reflect a lack of confidence among the proposers in their ability to get their proposals through wholesale, especially given that Amy Klobuchar’s CALERA bill in the Senate creates an alternative that, while still highly interventionist, does not create ex ante regulation of the Internet the same way these proposals do.
In general, the bills are misguided for three main reasons.
One, they seek to make digital platforms into narrow conduits for other firms to operate on, ignoring the value created by platforms curating their own services by, for example, creating quality controls on entry (as Apple does on its App Store) or by integrating their services with related products (like, say, Google adding events from Gmail to users’ Google Calendars).
Two, they ignore the procompetitive effects of digital platforms extending into each other’s markets and competing with each other there, in ways that often lead to far more intense competition—and better outcomes for consumers—than if the only firms that could compete with the incumbent platform were small startups.
Three, they ignore the importance of incentives for innovation. Platforms invest in new and better products when they can make money from doing so, and limiting their ability to do that means weakened incentives to innovate. Startups and their founders and investors are driven, in part, by the prospect of being acquired, often by the platforms themselves. Making those acquisitions more difficult, or even impossible, means removing one of the key ways startup founders can exit their firms, and hence one of the key rewards and incentives for starting an innovative new business.
The flagship bill, introduced by Antitrust Subcommittee Chairman David Cicilline (D-R.I.), establishes a definition of “covered platform” used by several of the other bills. The measures would apply to platforms with at least 500,000 U.S.-based users, a market capitalization of more than $600 billion, and that is deemed a “critical trading partner” with the ability to restrict or impede the access that a “dependent business” has to its users or customers.
Cicilline’s bill would bar these covered platforms from being able to promote their own products and services over the products and services of competitors who use the platform. It also defines a number of other practices that would be regarded as discriminatory, including:
Restricting or impeding “dependent businesses” from being able to access the platform or its software on the same terms as the platform’s own lines of business;
Conditioning access or status on purchasing other products or services from the platform;
Using user data to support the platform’s own products in ways not extended to competitors;
Restricting the platform’s commercial users from using or accessing data generated on the platform from their own customers;
Restricting platform users from uninstalling software pre-installed on the platform;
Restricting platform users from providing links to facilitate business off of the platform;
Preferencing the platform’s own products or services in search results or rankings;
Interfering with how a dependent business prices its products;
Impeding a dependent business’ users from connecting to services or products that compete with those offered by the platform; and
Retaliating against users who raise concerns with law enforcement about potential violations of the act.
On a basic level, these would prohibit lots of behavior that is benign and that can improve the quality of digital services for users. Apple pre-installing a Weather app on the iPhone would, for example, run afoul of these rules, and the rules as proposed could prohibit iPhones from coming with pre-installed apps at all. Instead, users would have to manually download each app themselves, if indeed Apple was allowed to include the App Store itself pre-installed on the iPhone, given that this competes with other would-be app stores.
Apart from the obvious reduction in the quality of services and convenience for users that this would involve, this kind of conduct (known as “self-preferencing”) is usually procompetitive. For example, self-preferencing allows platforms to compete with one another by using their strength in one market to enter a different one; Google’s Shopping results in the Search page increase the competition that Amazon faces, because it presents consumers with a convenient alternative when they’re shopping online for products. Similarly, Amazon’s purchase of the video-game streaming service Twitch, and the self-preferencing it does to encourage Amazon customers to use Twitch and support content creators on that platform, strengthens the competition that rivals like YouTube face.
It also helps innovation, because it gives firms a reason to invest in services that would otherwise be unprofitable for them. Google invests in Android, and gives much of it away for free, because it can bundle Google Search into the OS, and make money from that. If Google could not self-preference Google Search on Android, the open source business model simply wouldn’t work—it wouldn’t be able to make money from Android, and would have to charge for it in other ways that may be less profitable and hence give it less reason to invest in the operating system.
This behavior can also increase innovation by the competitors of these companies, both by prompting them to improve their products (as, for example, Google Android did with Microsoft’s mobile operating system offerings) and by growing the size of the customer base for products of this kind. For example, video games published by console manufacturers (like Nintendo’s Zelda and Mario games) are often blockbusters that grow the overall size of the user base for the consoles, increasing demand for third-party titles as well.
Sponsored by Rep. Pramila Jayapal (D-Wash.), this bill would make it illegal for covered platforms to control lines of business that pose “irreconcilable conflicts of interest,” enforced through civil litigation powers granted to the Federal Trade Commission (FTC) and the U.S. Justice Department (DOJ).
Specifically, the bill targets lines of business that create “a substantial incentive” for the platform to advantage its own products or services over those of competitors that use the platform, or to exclude or disadvantage competing businesses from using the platform. The FTC and DOJ could potentially order that platforms divest lines of business that violate the act.
This targets similar conduct as the previous bill, but involves the forced separation of different lines of business. It also appears to go even further, seemingly implying that companies like Google could not even develop services like Google Maps or Chrome because their existence would create such “substantial incentives” to self-preference them over the products of their competitors.
Apart from the straightforward loss of innovation and product developments this would involve, requiring every tech company to be narrowly focused on a single line of business would substantially entrench Big Tech incumbents, because it would make it impossible for them to extend into adjacent markets to compete with one another. For example, Apple could not develop a search engine to compete with Google under these rules, and Amazon would be forced to sell its video-streaming services that compete with Netflix and Youtube.
Introduced by Rep. Hakeem Jeffries (D-N.Y.), this bill would bar covered platforms from making essentially any acquisitions at all. To be excluded from the ban on acquisitions, the platform would have to present “clear and convincing evidence” that the acquired business does not compete with the platform for any product or service, does not pose a potential competitive threat to the platform, and would not in any way enhance or help maintain the acquiring platform’s market position.
So this proposal would probably reduce investment in U.S. startups, since it makes it more difficult for them to be acquired. It would therefore reduce innovation as a result. It would also reduce inter-platform competition by banning deals that allow firms to move into new markets, like the acquisition of Beats that helped Apple to build a Spotify competitor, or the deals that helped Google, Microsoft, and Amazon build cloud-computing services that all compete with each other. It could also reduce competition faced by old industries, by preventing tech companies from buying firms that enable it to move into new markets—like Amazon’s acquisitions of health-care companies that it has used to build a health-care offering. Even Walmart’s acquisition of Jet.com, which it has used to build an Amazon competitor, could have been banned under this law if Walmart had had a higher market cap at the time.
Under terms of the legislation, covered platforms would be required to allow third parties to transfer data to their users or, with the user’s consent, to a competing business. It also would require platforms to facilitate compatible and interoperable communications with competing businesses. The law directs the FTC to establish technical committees to promulgate the standards for portability and interoperability.
It can also make digital services more buggy and unreliable, by requiring that they are built in a more “open” way that may be more prone to unanticipated software mismatches. A good example is that of Windows vs iOS; Windows is far more interoperable with third-party software than iOS is, but tends to be less stable as a result, and users often prefer the closed, stable system.
Interoperability requirements also entail ongoing regulatory oversight, to make sure data is being provided to third parties reliably. It’s difficult to build an app around another company’s data without assurance that the data will be available when users want it. For a requirement as broad as this bill’s, that could mean setting up quite a large new de facto regulator.
In the UK, Open Banking (an interoperability requirement imposed on British retail banks) has suffered from significant service outages, and targets a level of uptime that many developers complain is too low for them to build products around. Nor has Open Banking yet led to any obvious competition benefits.
A bill that mirrors language in the Endless Frontier Act recently passed by the U.S. Senate, would significantly raise filing fees for the largest mergers. Rather than the current cap of $280,000 for mergers valued at more than $500 million, the bill—sponsored by Rep. Joe Neguse (D-Colo.)–the new schedule would assess fees of $2.25 million for mergers valued at more than $5 billion; $800,000 for those valued at between $2 billion and $5 billion; and $400,000 for those between $1 billion and $2 billion.
Smaller mergers would actually see their filing fees cut: from $280,000 to $250,000 for those between $500 million and $1 billion; from $125,000 to $100,000 for those between $161.5 million and $500 million; and from $45,000 to $30,000 for those less than $161.5 million.
In addition, the bill would appropriate $418 million to the FTC and $252 million to the DOJ’s Antitrust Division for Fiscal Year 2022. Most people in the antitrust world are generally supportive of more funding for the FTC and DOJ, although whether this is actually good or not depends both on how it’s spent at those places.
It’s hard to object if it goes towards deepening the agencies’ capacities and knowledge, by hiring and retaining higher quality staff with salaries that are more competitive with those offered by the private sector, and on greater efforts to study the effects of the antitrust laws and past cases on the economy. If it goes toward broadening the activities of the agencies, by doing more and enabling them to pursue a more aggressive enforcement agenda, and supporting whatever of the above proposals make it into law, then it could be very harmful.
Despite calls fromsomeNGOs to mandate radical interoperability, the EU’s draft Digital Markets Act (DMA) adopted a more measured approach, requiring full interoperability only in “ancillary” services like identification or payment systems. There remains the possibility, however, that the DMA proposal will be amended to include stronger interoperability mandates, or that such amendments will be introduced in the Digital Services Act. Without the right checks and balances, this could pose grave threats to Europeans’ privacy and security.
At the most basic level, interoperability means a capacity to exchange information between computer systems. Email is an example of an interoperable standard that most of us use today. Expanded interoperability could offer promising solutions to some of today’s difficult problems. For example, it might allow third-party developers to offer different “flavors” of social media news feed, with varying approaches to content ranking and moderation (see Daphne Keller, Mike Masnick, and Stephen Wolfram for more on that idea). After all, in a pluralistic society, someone will always be unhappy with what some others consider appropriate content. Why not let smaller groups decide what they want to see?
But to achieve that goal using currently available technology, third-party developers would have to be able to access all of a platform’s content that is potentially available to a user. This would include not just content produced by users who explicitly agrees for their data to be shared with third parties, but also content—e.g., posts, comments, likes—created by others who may have strong objections to such sharing. It doesn’t require much imagination to see how, without adequate safeguards, mandating this kind of information exchange would inevitably result in something akin to the 2018 Cambridge Analytica data scandal.
It is telling that supporters of this kind of interoperability use services like email as their model examples. Email (more precisely, the SMTP protocol) originally was designed in a notoriously insecure way. It is a perfect example of the opposite of privacy by design. A good analogy for the levels of privacy and security provided by email, as originally conceived, is that of a postcard message sent without an envelope that passes through many hands before reaching the addressee. Even today, email continues to be a source of security concerns due to its prioritization of interoperability.
It also is telling that supporters of interoperability tend to point to what are small-scale platforms (e.g., Mastodon) or protocols with unacceptably poor usability for most of today’s Internet users (e.g., Usenet). When proposing solutions to potential privacy problems—e.g., that users will adequately monitor how various platforms use their data—they often assume unrealistic levels of user interest or technical acumen.
Interoperability in the DMA
The current draft of the DMA contains several provisions that broadly construe interoperability as applying only to “gatekeepers”—i.e., the largest online platforms:
Mandated interoperability of “ancillary services” (Art 6(1)(f));
Real-time data portability (Art 6(1)(h)); and
Business-user access to their own and end-user data (Art 6(1)(i)).
The first provision, (Art 6(1)(f)), is meant to force gatekeepers to allow e.g., third-party payment or identification services—for example, to allow people to create social media accounts without providing an email address, which is possible using services like “Sign in with Apple.” This kind of interoperability doesn’t pose as big of a privacy risk as mandated interoperability of “core” services (e.g., messaging on a platform like WhatsApp or Signal), partially due to a more limited scope of data that needs to be exchanged.
However, even here, there may be some risks. For example, users may choose poorly secured identification services and thus become victims of attacks. Therefore, it is important that gatekeepers not be prevented from protecting their users adequately. Of course,there are likely trade-offs between those protections and the interoperability that some want. Proponents of stronger interoperability want this provision amended to cover all “core” services, not just “ancillary” ones, which would constitute precisely the kind of radical interoperability that cannot be safely mandated today.
The other two provisions do not mandate full two-way interoperability, where a third party could both read data from a service like Facebook and modify content on that service. Instead, they provide for one-way “continuous and real-time” access to data—read-only.
The second provision (Art 6(1)(h)) mandates that gatekeepers give users effective “continuous and real-time” access to data “generated through” their activity. It’s not entirely clear whether this provision would be satisfied by, e.g., Facebook’s Graph API, but it likely would not be satisfied simply by being able to download one’s Facebook data, as that is not “continuous and real-time.”
Importantly, the proposed provision explicitly references the General Data Protection Regulation (GDPR), which suggests that—at least as regards personal data—the scope of this portability mandate is not meant to be broader than that from Article 20 GDPR. Given the GDPR reference and the qualification that it applies to data “generated through” the user’s activity, this mandate would not include data generated by other users—which is welcome, but likely will not satisfy the proponents of stronger interoperability.
The third provision from Art 6(1)(i) mandates only “continuous and real-time” data access and only as regards data “provided for or generated in the context of the use of the relevant core platform services” by business users and by “the end users engaging with the products or services provided by those business users.” This provision is also explicitly qualified with respect to personal data, which are to be shared after GDPR-like user consent and “only where directly connected with the use effectuated by the end user in respect of” the business user’s service. The provision should thus not be a tool for a new Cambridge Analytica to siphon data on users who interact with some Facebook page or app and their unwitting contacts. However, for the same reasons, it will also not be sufficient for the kinds of uses that proponents of stronger interoperability envisage.
Why can’t stronger interoperability be safely mandated today?
Let’s imagine that Art 6(1)(f) is amended to cover all “core” services, so gatekeepers like Facebook end up with a legal duty to allow third parties to read data from and write data to Facebook via APIs. This would go beyond what is currently possible using Facebook’s Graph API, and would lack the current safety valve of Facebook cutting off access because of the legal duty to deal created by the interoperability mandate. As Cory Doctorow and Bennett Cyphers note, there are at least three categories of privacy and security risks in this situation:
1. Data sharing and mining via new APIs;
2. New opportunities for phishing and sock puppetry in a federated ecosystem; and
3. More friction for platforms trying to maintain a secure system.
Unlike some other proponents of strong interoperability, Doctorow and Cyphers are open about the scale of the risk: “[w]ithout new legal safeguards to protect the privacy of user data, this kind of interoperable ecosystem could make Cambridge Analytica-style attacks more common.”
There are bound to be attempts to misuse interoperability through clearly criminal activity. But there also are likely to be more legally ambiguous attempts that are harder to proscribe ex ante. Proposals for strong interoperability mandates need to address this kind of problem.
So, what could be done to make strong interoperability reasonably safe? Doctorow and Cyphers argue that there is a “need for better privacy law,” but don’t say whether they think the GDPR’s rules fit the bill. This may be a matter of reasonable disagreement.
What isn’t up for serious debate is that the current framework and practice of privacy enforcement offers little confidence that misuses of strong interoperability would be detected and prosecuted, much less that they would be prevented (see here and here on GDPR enforcement). This is especially true for smaller and “judgment-proof” rule-breakers, including those from outside the European Union. Addressing the problems of privacy law enforcement is a herculean task, in and of itself.
The day may come when radical interoperability will, thanks to advances in technology and/or privacy enforcement, become acceptably safe. But it would be utterly irresponsible to mandate radical interoperability in the DMA and/or DSA, and simply hope the obvious privacy and security problems will somehow be solved before the law takes force. Instituting such a mandate would likely discredit the very idea of interoperability.
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;
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.
We can expect a decision very soon from the High Court of Ireland on last summer’s Irish Data Protection Commission (“IDPC”) decision that placed serious impediments in the transfer data across the Atlantic. That decision, coupled with the July 2020 Court of Justice of the European Union (“CJEU”) decision to invalidate the Privacy Shield agreement between the European Union and the United States, has placed the future of transatlantic trade in jeopardy.
In 2015, the EU Schrems decision invalidated the previously longstanding “safe harbor” agreement between the EU and U.S. to ensure data transfers between the two zones complied with EU privacy requirements. The CJEU later invalidated the Privacy Shield agreement that was created in response to Schrems. In its decision, the court reasoned that U.S. foreign intelligence laws like FISA Section 702 and Executive Order 12333—which give the U.S. government broad latitude to surveil data and offer foreign persons few rights to challenge such surveillance—rendered U.S. firms unable to guarantee the privacy protections of EU citizens’ data.
The IDPC’s decision employed the same logic: if U.S. surveillance laws give the government unreviewable power to spy on foreign citizens’ data, then standard contractual clauses—an alternative mechanism for firms for transferring data—are incapable of satisfying the requirements of EU law.
The implications that flow from this are troubling, to say the least. In the worst case, laws like the CLOUD Act could leave a wide swath of U.S. firms practically incapable doing business in the EU. In the slightly less bad case, firms could be forced to completely localize their data and disrupt the economies of scale that flow from being able to process global data in a unified manner. In any case, the costs for compliance will be massive.
But even if the Irish court upholds the IDPC’s decision, there could still be a path forward for the U.S. and EU to preserve transatlantic digital trade. EU Commissioner for Justice Didier Reynders and U.S. Commerce Secretary Gina Raimondo recently issued a joint statement asserting they are “intensifying” negotiations to develop an enhanced successor to the EU-US Privacy Shield agreement. One can hope the talks are both fast and intense.
It seems unlikely that the Irish High Court would simply overturn the IDPC’s ruling. Instead, the IDCP’s decision will likely be upheld, possibly with recommended modifications. But even in that case, there is a process that buys the U.S. and EU a bit more time before any transatlantic trade involving consumer data grinds to a halt.
After considering replies to its draft decision, the IDPC would issue final recommendations on the extent of the data-transfer suspensions it deems necessary. It would then need to harmonize its recommendations with the other EU data-protection authorities. Theoretically, that could occur in a matter of days, but practically speaking, it would more likely occur over weeks or months. Assuming we get a decision from the Irish High Court before the end of April, it puts the likely deadline for suspension of transatlantic data transfers somewhere between June and September.
That’s not great, but it is not an impossible hurdle to overcome and there are temporary fixes the Biden administration could put in place. Two major concerns need to be addressed.
U.S. data collection on EU citizens needs to be proportional to the necessities of intelligence gathering. Currently, the U.S. intelligence agencies have wide latitude to collect a large amount of data.
The ombudsperson the Privacy Shield agreement created to be responsible for administering foreign citizen data requests was not sufficiently insulated from the political process, creating the need for adequate redress by EU citizens.
As Alex Joel recently noted, the Biden administration has ample powers to effect many of these changes through executive action. After all, EO 12333 was itself a creation of the executive branch. Other changes necessary to shape foreign surveillance to be in accord with EU requirements could likewise arise from the executive branch.
Nonetheless, Congress should not take that as a cue for complacency. It is possible that even if the Biden administration acts, the CJEU could find some or all of the measures insufficient. As the Biden team works to put changes in place through executive order, Congress should pursue surveillance reform through legislation.
Theoretically, the above fixes should be possible; there is not much partisan rancor about transatlantic trade as a general matter. But time is short, and this should be a top priority on policymakers’ radars.
(note: edited to clarify that the Irish High Court is not reviewing SCC’s directly and that the CLOUD Act would not impose legal barriers for firms, but practical ones).
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.
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:
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.
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 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.
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.
Policy discussions about the use of personal data often have “less is more” as a background assumption; that data is overconsumed relative to some hypothetical optimal baseline. This overriding skepticism has been the backdrop for sweeping new privacy regulations, such as the California Consumer Privacy Act (CCPA) and the EU’s General Data Protection Regulation (GDPR).
More recently, as part of the broad pushback against data collection by online firms, some have begun to call for creating property rights in consumers’ personal data or for data to be treated as labor. Prominent backers of the idea include New York City mayoral candidate Andrew Yang and computer scientist Jaron Lanier.
The discussion has escaped the halls of academia and made its way into popular media. During a recent discussion with Tesla founder Elon Musk, comedian and podcast host Joe Rogan argued that Facebook is “one gigantic information-gathering business that’s decided to take all of the data that people didn’t know was valuable and sell it and make f***ing billions of dollars.” Musk appeared to agree.
The animosity exhibited toward data collection might come as a surprise to anyone who has taken Econ 101. Goods ideally end up with those who value them most. A firm finding profitable ways to repurpose unwanted scraps is just the efficient reallocation of resources. This applies as much to personal data as to literal trash.
Unfortunately, in the policy sphere, few are willing to recognize the inherent trade-off between the value of privacy, on the one hand, and the value of various goods and services that rely on consumer data, on the other. Ideally, policymakers would look to markets to find the right balance, which they often can. When the transfer of data is hardwired into an underlying transaction, parties have ample room to bargain.
But this is not always possible. In some cases, transaction costs will prevent parties from bargaining over the use of data. The question is whether such situations are so widespread as to justify the creation of data property rights, with all of the allocative inefficiencies they entail. Critics wrongly assume the solution is both to create data property rights and to allocate them to consumers. But there is no evidence to suggest that, at the margin, heightened user privacy necessarily outweighs the social benefits that new data-reliant goods and services would generate. Recent experience in the worlds of personalized medicine and the fight against COVID-19 help to illustrate this point.
Data Property Rights and Personalized Medicine
The world is on the cusp of a revolution in personalized medicine. Advances such as the improved identification of biomarkers, CRISPR genome editing, and machine learning, could usher a new wave of treatments to markedly improve health outcomes.
Personalized medicine uses information about a person’s own genes or proteins to prevent, diagnose, or treat disease. Genetic-testing companies like 23andMe or Family Tree DNA, with the large troves of genetic information they collect, could play a significant role in helping the scientific community to further medical progress in this area.
However, despite the obvious potential of personalized medicine, many of its real-world applications are still very much hypothetical. While governments could act in any number of ways to accelerate the movement’s progress, recent policy debates have instead focused more on whether to create a system of property rights covering personal genetic data.
Some raise concerns that it is pharmaceutical companies, not consumers, who will reap the monetary benefits of the personalized medicine revolution, and that advances are achieved at the expense of consumers’ and patients’ privacy. They contend that data property rights would ensure that patients earn their “fair” share of personalized medicine’s future profits.
But it’s worth examining the other side of the coin. There are few things people value more than their health. U.S. governmental agencies place the value of a single life at somewhere between $1 million and $10 million. The commonly used quality-adjusted life year metric offers valuations that range from $50,000 to upward of $300,000 per incremental year of life.
It therefore follows that the trivial sums users of genetic-testing kits might derive from a system of data property rights would likely be dwarfed by the value they would enjoy from improved medical treatments. A strong case can be made that policymakers should prioritize advancing the emergence of new treatments, rather than attempting to ensure that consumers share in the profits generated by those potential advances.
These debates drew increased attention last year, when 23andMe signed a strategic agreement with the pharmaceutical company Almirall to license the rights related to an antibody Almirall had developed. Critics pointed out that 23andMe’s customers, whose data had presumably been used to discover the potential treatment, received no monetary benefits from the deal. Journalist Laura Spinney wrote in The Guardian newspaper:
23andMe, for example, asks its customers to waive all claims to a share of the profits arising from such research. But given those profits could be substantial—as evidenced by the interest of big pharma—shouldn’t the company be paying us for our data, rather than charging us to be tested?
In the deal’s wake, some argued that personal health data should be covered by property rights. A cardiologist quoted in Fortune magazine opined: “I strongly believe that everyone should own their medical data—and they have a right to that.” But this strong belief, however widely shared, ignores important lessons that law and economics has to teach about property rights and the role of contractual freedom.
Why Do We Have Property Rights?
Among the many important features of property rights is that they create “excludability,” the ability of economic agents to prevent third parties from using a given item. In the words of law professor Richard Epstein:
[P]roperty is not an individual conception, but is at root a social conception. The social conception is fairly and accurately portrayed, not by what it is I can do with the thing in question, but by who it is that I am entitled to exclude by virtue of my right. Possession becomes exclusive possession against the rest of the world…
Excludability helps to facilitate the trade of goods, offers incentives to create those goods in the first place, and promotes specialization throughout the economy. In short, property rights create a system of exclusion that supports creating and maintaining valuable goods, services, and ideas.
But property rights are not without drawbacks. Physical or intellectual property can lead to a suboptimal allocation of resources, namely market power (though this effect is often outweighed by increased ex ante incentives to create and innovate). Similarly, property rights can give rise to thickets that significantly increase the cost of amassing complementary pieces of property. Often cited are the historic (but contested) examples of tolling on the Rhine River or the airplane patent thicket of the early 20th century. Finally, strong property rights might also lead to holdout behavior, which can be addressed through top-down tools, like eminent domain, or private mechanisms, like contingent contracts.
In short, though property rights—whether they cover physical or information goods—can offer vast benefits, there are cases where they might be counterproductive. This is probably why, throughout history, property laws have evolved to achieve a reasonable balance between incentives to create goods and to ensure their efficient allocation and use.
Personal Health Data: What Are We Trying to Incentivize?
There are at least three critical questions we should ask about proposals to create property rights over personal health data.
What goods or behaviors would these rights incentivize or disincentivize that are currently over- or undersupplied by the market?
Are goods over- or undersupplied because of insufficient excludability?
Could these rights undermine the efficient use of personal health data?
Much of the current debate centers on data obtained from direct-to-consumer genetic-testing kits. In this context, almost by definition, firms only obtain consumers’ genetic data with their consent. In western democracies, the rights to bodily integrity and to privacy generally make it illegal to administer genetic tests against a consumer or patient’s will. This makes genetic information naturally excludable, so consumers already benefit from what is effectively a property right.
When consumers decide to use a genetic-testing kit, the terms set by the testing firm generally stipulate how their personal data will be used. 23andMe has a detailed policy to this effect, as does Family Tree DNA. In the case of 23andMe, consumers can decide whether their personal information can be used for the purpose of scientific research:
You have the choice to participate in 23andMe Research by providing your consent. … 23andMe Research may study a specific group or population, identify potential areas or targets for therapeutics development, conduct or support the development of drugs, diagnostics or devices to diagnose, predict or treat medical or other health conditions, work with public, private and/or nonprofit entities on genetic research initiatives, or otherwise create, commercialize, and apply this new knowledge to improve health care.
Because this transfer of personal information is hardwired into the provision of genetic-testing services, there is space for contractual bargaining over the allocation of this information. The right to use personal health data will go toward the party that values it most, especially if information asymmetries are weeded out by existing regulations or business practices.
Regardless of data property rights, consumers have a choice: they can purchase genetic-testing services and agree to the provider’s data policy, or they can forgo the services. The service provider cannot obtain the data without entering into an agreement with the consumer. While competition between providers will affect parties’ bargaining positions, and thus the price and terms on which these services are provided, data property rights likely will not.
So, why do consumers transfer control over their genetic data? The main reason is that genetic information is inaccessible and worthless without the addition of genetic-testing services. Consumers must pass through the bottleneck of genetic testing for their genetic data to be revealed and transformed into usable information. It therefore makes sense to transfer the information to the service provider, who is in a much stronger position to draw insights from it. From the consumer’s perspective, the data is not even truly “transferred,” as the consumer had no access to it before the genetic-testing service revealed it. The value of this genetic information is then netted out in the price consumers pay for testing kits.
If personal health data were undersupplied by consumers and patients, testing firms could sweeten the deal and offer them more in return for their data. U.S. copyright law covers original compilations of data, while EU law gives 15 years of exclusive protection to the creators of original databases. Legal protections for trade secrets could also play some role. Thus, firms have some incentives to amass valuable health datasets.
But some critics argue that health data is, in fact, oversupplied. Generally, such arguments assert that agents do not account for the negative privacy externalities suffered by third-parties, such as adverse-selection problems in insurance markets. For example, Jay Pil Choi, Doh Shin Jeon, and Byung Cheol Kim argue:
Genetic tests are another example of privacy concerns due to informational externalities. Researchers have found that some subjects’ genetic information can be used to make predictions of others’ genetic disposition among the same racial or ethnic category. … Because of practical concerns about privacy and/or invidious discrimination based on genetic information, the U.S. federal government has prohibited insurance companies and employers from any misuse of information from genetic tests under the Genetic Information Nondiscrimination Act (GINA).
But if these externalities exist (most of the examples cited by scholars are hypothetical), they are likely dwarfed by the tremendous benefits that could flow from the use of personal health data. Put differently, the assertion that “excessive” data collection may create privacy harms should be weighed against the possibility that the same collection may also lead to socially valuable goods and services that produce positive externalities.
In any case, data property rights would do little to limit these potential negative externalities. Consumers and patients are already free to agree to terms that allow or prevent their data from being resold to insurers. It is not clear how data property rights would alter the picture.
Proponents of data property rights often claim they should be associated with some form of collective bargaining. The idea is that consumers might otherwise fail to receive their “fair share” of genetic-testing firms’ revenue. But what critics portray as asymmetric bargaining power might simply be the market signaling that genetic-testing services are in high demand, with room for competitors to enter the market. Shifting rents from genetic-testing services to consumers would undermine this valuable price signal and, ultimately, diminish the quality of the services.
Perhaps more importantly, to the extent that they limit the supply of genetic information—for example, because firms are forced to pay higher prices for data and thus acquire less of it—data property rights might hinder the emergence of new treatments. If genetic data is a key input to develop personalized medicines, adopting policies that, in effect, ration the supply of that data is likely misguided.
Even if policymakers do not directly put their thumb on the scale, data property rights could still harm pharmaceutical innovation. If existing privacy regulations are any guide—notably, thepreviously mentioned GDPR and CCPA, as well as the federal Health Insurance Portability and Accountability Act (HIPAA)—such rights might increase red tape for pharmaceutical innovators. Privacy regulations routinely limit firms’ ability to put collected data to new and previously unforeseen uses. They also limit parties’ contractual freedom when it comes to gathering consumers’ consent.
At the margin, data property rights would make it more costly for firms to amass socially valuable datasets. This would effectively move the personalized medicine space further away from a world of permissionless innovation, thus slowing down medical progress.
In short, there is little reason to believe health-care data is misallocated. Proposals to reallocate rights to such data based on idiosyncratic distributional preferences threaten to stifle innovation in the name of privacy harms that remain mostly hypothetical.
Data Property Rights and COVID-19
The trade-off between users’ privacy and the efficient use of data also has important implications for the fight against COVID-19. Since the beginning of the pandemic, several promising initiatives have been thwarted by privacy regulations and concerns about the use of personal data. This has potentially prevented policymakers, firms, and consumers from putting information to its optimal social use. High-profile issues have included:
Each of these cases may involve genuine privacy risks. But to the extent that they do, those risks must be balanced against the potential benefits to society. If privacy concerns prevent us from deploying contact tracing or green passes at scale, we should question whether the privacy benefits are worth the cost. The same is true for rules that prohibit amassing more data than is strictly necessary, as is required by data-minimization obligations included in regulations such as the GDPR.
If our initial question was instead whether the benefits of a given data-collection scheme outweighed its potential costs to privacy, incentives could be set such that competition between firms would reduce the amount of data collected—at least, where minimized data collection is, indeed, valuable to users. Yet these considerations are almost completely absent in the COVID-19-related privacy debates, as they are in the broader privacy debate. Against this backdrop, the case for personal data property rights is dubious.
The key question is whether policymakers should make it easier or harder for firms and public bodies to amass large sets of personal data. This requires asking whether personal data is currently under- or over-provided, and whether the additional excludability that would be created by data property rights would offset their detrimental effect on innovation.
Swaths of personal data currently lie untapped. With the proper incentive mechanisms in place, this idle data could be mobilized to develop personalized medicines and to fight the COVID-19 outbreak, among many other valuable uses. By making such data more onerous to acquire, property rights in personal data might stifle the assembly of novel datasets that could be used to build innovative products and services.
On the other hand, when dealing with diffuse and complementary data sources, transaction costs become a real issue and the initial allocation of rightscan matter a great deal. In such cases, unlike the genetic-testing kits example, it is not certain that users will be able to bargain with firms, especially where their personal information is exchanged by third parties.
If optimal reallocation is unlikely, should property rights go to the person covered by the data or to the collectors (potentially subject to user opt-outs)? Proponents of data property rights assume the first option is superior. But if the goal is to produce groundbreaking new goods and services, granting rights to data collectors might be a superior solution. Ultimately, this is an empirical question.
As Richard Epstein puts it, the goal is to “minimize the sum of errors that arise from expropriation and undercompensation, where the two are inversely related.” Rather than approach the problem with the preconceived notion that initial rights should go to users, policymakers should ensure that data flows to those economic agents who can best extract information and knowledge from it.
As things stand, there is little to suggest that the trade-offs favor creating data property rights. This is not an argument for requisitioning personal information or preventing parties from transferring data as they see fit, but simply for letting markets function, unfettered by misguided public policies.
The antitrust exemption in question, embodied in the Journalism Competition and Preservation Act of 2021, was introduced March 10 simultaneously in the U.S. House and Senate. The press release announcing the bill’s introduction portrayed it as a “good government” effort to help struggling newspapers in their negotiations with large digital platforms, and thereby strengthen American democracy:
We must enable news organizations to negotiate on a level playing field with the big tech companies if we want to preserve a strong and independent press[.] …
A strong, diverse, free press is critical for any successful democracy. …
Nearly 90 percent of Americans now get news while on a smartphone, computer, or tablet, according to a Pew Research Center survey conducted last year, dwarfing the number of Americans who get news via television, radio, or print media. Facebook and Google now account for the vast majority of online referrals to news sources, with the two companies also enjoying control of a majority of the online advertising market. This digital ad duopoly has directly contributed to layoffs and consolidation in the news industry, particularly for local news.
This legislation would address this imbalance by providing a safe harbor from antitrust laws so publishers can band together to negotiate with large platforms. It provides a 48-month window for companies to negotiate fair terms that would flow subscription and advertising dollars back to publishers, while protecting and preserving Americans’ right to access quality news. These negotiations would strictly benefit Americans and news publishers at-large; not just one or a few publishers.
The Journalism Competition and Preservation Act only allows coordination by news publishers if it (1) directly relates to the quality, accuracy, attribution or branding, and interoperability of news; (2) benefits the entire industry, rather than just a few publishers, and are non-discriminatory to other news publishers; and (3) is directly related to and reasonably necessary for these negotiations.
Lurking behind this public-spirited rhetoric, however, is the specter of special interest rent seeking by powerful media groups, as discussed in an insightful article by Thom Lambert. The newspaper industry is indeed struggling, but that is true overseas as well as in the United States. Competition from internet websites has greatly reduced revenues from classified and non-classified advertising. As Lambert notes, in “light of the challenges the internet has created for their advertising-focused funding model, newspapers have sought to employ the government’s coercive power to increase their revenues.”
In particular, media groups have successfully lobbied various foreign governments to impose rules requiring that Google and Facebook pay newspapers licensing fees to display content. The Australian government went even further by mandating that digital platforms share their advertising revenue with news publishers and give the publishers advance notice of any algorithm changes that could affect page rankings and displays. Media rent-seeking efforts took a different form in the United States, as Lambert explains (citations omitted):
In the United States, news publishers have sought to extract rents from digital platforms by lobbying for an exemption from the antitrust laws. Their efforts culminated in the introduction of the Journalism Competition and Preservation Act of 2018. According to a press release announcing the bill, it would allow “small publishers to band together to negotiate with dominant online platforms to improve the access to and the quality of news online.” In reality, the bill would create a four-year safe harbor for “any print or digital news organization” to jointly negotiate terms of trade with Google and Facebook. It would not apply merely to “small publishers” but would instead immunize collusive conduct by such major conglomerates as Murdoch’s News Corporation, the Walt Disney Corporation, the New York Times, Gannet Company, Bloomberg, Viacom, AT&T, and the Fox Corporation. The bill would permit news organizations to fix prices charged to digital platforms as long as negotiations with the platforms were not limited to price, were not discriminatory toward similarly situated news organizations, and somehow related to “the quality, accuracy, attribution or branding, and interoperability of news.” Given the ease of meeting that test—since news organizations could always claim that higher payments were necessary to ensure journalistic quality—the bill would enable news publishers in the United States to extract rents via collusion rather than via direct government coercion, as in Australia.
The 2021 version of the JCPA is nearly identical to the 2018 version discussed by Thom. The only substantive change is that the 2021 version strengthens the pro-cartel coalition by adding broadcasters (it applies to “any print, broadcast, or news organization”). While the JCPA plainly targets Facebook and Google (“online content distributors” with “not fewer than 1,000,000,000 monthly active users, in the aggregate, on its website”), Microsoft President Brad Smith noted in a March 12 House Antitrust Subcommittee Hearing on the bill that his company would also come under its collective-bargaining terms. Other online distributors could eventually become subject to the proposed law as well.
Purported justifications for the proposal were skillfully skewered by John Yun in a 2019 article on the substantively identical 2018 JCPA. Yun makes several salient points. First, the bill clearly shields price fixing. Second, the claim that all news organizations (in particular, small newspapers) would receive the same benefit from the bill rings hollow. The bill’s requirement that negotiations be “nondiscriminatory as to similarly situated news content creators” (emphasis added) would allow the cartel to negotiate different terms of trade for different “tiers” of organizations. Thus The New York Times and The Washington Post, say, might be part of a top tier getting the most favorable terms of trade. Third, the evidence does not support the assertion that Facebook and Google are monopolistic gateways for news outlets.
Yun concludes by summarizing the case against this legislation (citations omitted):
Put simply, the impact of the bill is to legalize a media cartel. The bill expressly allows the cartel to fix the price and set the terms of trade for all market participants. The clear goal is to transfer surplus from online platforms to news organizations, which will likely result in higher content costs for these platforms, as well as provisions that will stifle the ability to innovate. In turn, this could negatively impact quality for the users of these platforms.
Furthermore, a stated goal of the bill is to promote “quality” news and to “highlight trusted brands.” These are usually antitrust code words for favoring one group, e.g., those that are part of the News Media Alliance, while foreclosing others who are not “similarly situated.” What about the non-discrimination clause? Will it protect non-members from foreclosure? Again, a careful reading of the bill raises serious questions as to whether it will actually offer protection. The bill only ensures that the terms of the negotiations are available to all “similarly situated” news organizations. It is very easy to carve out provisions that would favor top tier members of the media cartel.
Additionally, an unintended consequence of antitrust exemptions can be that it makes the beneficiaries lax by insulating them from market competition and, ultimately, can harm the industry by delaying inevitable and difficult, but necessary, choices. There is evidence that this is what occurred with the Newspaper Preservation Act of 1970, which provided antitrust exemption to geographically proximate newspapers for joint operations.
There are very good reasons why antitrust jurisprudence reserves per se condemnation to the most egregious anticompetitive acts including the formation of cartels. Legislative attempts to circumvent the federal antitrust laws should be reserved solely for the most compelling justifications. There is little evidence that this level of justification has been met in this present circumstance.
Statutory exemptions to the antitrust laws have long been disfavored, and with good reason. As I explained in my 2005 testimony before the Antitrust Modernization Commission, such exemptions tend to foster welfare-reducing output restrictions. Also, empirical research suggests that industries sheltered from competition perform less well than those subject to competitive forces. In short, both economic theory and real-world data support a standard that requires proponents of an exemption to bear the burden of demonstrating that the exemption will benefit consumers.
This conclusion applies most strongly when an exemption would specifically authorize hard-core price fixing, as in the case with the JCPA. What’s more, the bill’s proponents have not borne the burden of justifying their pro-cartel proposal in economic welfare terms—quite the opposite. Lambert’s analysis exposes this legislation as the product of special interest rent seeking that has nothing to do with consumer welfare. And Yun’s evaluation of the bill clarifies that, not only would the JCPA foster harmful collusive pricing, but it would also harm its beneficiaries by allowing them to avoid taking steps to modernize and render themselves more efficient competitors.
In sum, though the JCPA claims to fly a “public interest” flag, it is just another private interest bill promoted by well-organized rent seekers would harm consumer welfare and undermine innovation.
Critics of big tech companies like Google and Amazon are increasingly focused on the supposed evils of “self-preferencing.” This refers to when digital platforms like Amazon Marketplace or Google Search, which connect competing services with potential customers or users, also offer (and sometimes prioritize) their own in-house products and services.
The objection, raised by several members and witnesses during a Feb. 25 hearing of the House Judiciary Committee’s antitrust subcommittee, is that it is unfair to third parties that use those sites to allow the site’s owner special competitive advantages. Is it fair, for example, for Amazon to use the data it gathers from its service to design new products if third-party merchants can’t access the same data? This seemingly intuitive complaint was the basis for the European Commission’s landmark case against Google.
But we cannot assume that something is bad for competition just because it is bad for certain competitors. A lot of unambiguously procompetitive behavior, like cutting prices, also tends to make life difficult for competitors. The same is true when a digital platform provides a service that is better than alternatives provided by the site’s third-party sellers.
It’s probably true that Amazon’s access to customer search and purchase data can help it spot products it can undercut with its own versions, driving down prices. But that’s not unusual; most retailers do this, many to a much greater extent than Amazon. For example, you can buy AmazonBasics batteries for less than half the price of branded alternatives, and they’re pretty good.
There’s no doubt this is unpleasant for merchants that have to compete with these offerings. But it is also no different from having to compete with more efficient rivals who have lower costs or better insight into consumer demand. Copying products and seeking ways to offer them with better features or at a lower price, which critics of self-preferencing highlight as a particular concern, has always been a fundamental part of market competition—indeed, it is the primary way competition occurs in most markets.
Store-branded versions of iPhone cables and Nespresso pods are certainly inconvenient for those companies, but they offer consumers cheaper alternatives. Where such copying may be problematic (say, by deterring investments in product innovations), the law awards and enforces patents and copyrights to reward novel discoveries and creative works, and trademarks to protect brand identity. But in the absence of those cases where a company has intellectual property, this is simply how competition works.
The fundamental question is “what benefits consumers?” Services like Yelp object that they cannot compete with Google when Google embeds its Google Maps box in Google Search results, while Yelp cannot do the same. But for users, the Maps box adds valuable information to the results page, making it easier to get what they want. Google is not making Yelp worse by making its own product better. Should it have to refrain from offering services that benefit its users because doing so might make competing products comparatively less attractive?
Self-preferencing also enables platforms to promote their offerings in other markets, which is often how large tech companies compete with each other. Amazon has a photo-hosting app that competes with Google Photos and Apple’s iCloud. It recently emailed its customers to promote it. That is undoubtedly self-preferencing, since other services cannot market themselves to Amazon’s customers like this, but if it makes customers aware of an alternative they might not have otherwise considered, that is good for competition.
This kind of behavior also allows companies to invest in offering services inexpensively, or for free, that they intend to monetize by preferencing their other, more profitable products. For example, Google invests in Android’s operating system and gives much of it away for free precisely because it can encourage Android customers to use the profitable Google Search service. Despite claims to the contrary, it is difficult to see this sort of cross-subsidy as harmful to consumers.
All platforms are open or closed to varying degrees. Retail “platforms,” for example, exist on a spectrum on which Craigslist is more open and neutral than eBay, which is more so than Amazon, which is itself relatively more so than, say, Walmart.com. Each position on this spectrum offers its own benefits and trade-offs for consumers. Indeed, some customers’ biggest complaint against Amazon is that it is too open, filled with third parties who leave fake reviews, offer counterfeit products, or have shoddy returns policies. Part of the role of the site is to try to correct those problems by making better rules, excluding certain sellers, or just by offering similar options directly.
Regulators and legislators often act as if the more open and neutral, the better, but customers have repeatedly shown that they often prefer less open, less neutral options. And critics of self-preferencing frequently find themselves arguing against behavior that improves consumer outcomes, because it hurts competitors. But that is the nature of competition: what’s good for consumers is frequently bad for competitors. If we have to choose, it’s consumers who should always come first.