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The patent system is too often caricatured as involving the grant of “monopolies” that may be used to delay entry and retard competition in key sectors of the economy. The accumulation of allegedly “poor-quality” patents into thickets and portfolios held by “patent trolls” is said by critics to spawn excessive royalty-licensing demands and threatened “holdups” of firms that produce innovative products and services. These alleged patent abuses have been characterized as a wasteful “tax” on high-tech implementers of patented technologies, which inefficiently raises price and harms consumer welfare.

Fortunately, solid scholarship has debunked these stories and instead pointed to the key role patents play in enhancing competition and driving innovation. See, for example, here, here, here, here, here, here, and here.

Nevertheless, early indications are that the Biden administration may be adopting a patent-skeptical attitude. Such an attitude was revealed, for example, in the president’s July 9 Executive Order on Competition (which suggested an openness to undermining the Bayh-Dole Act by using march-in rights to set prices; to weakening pharmaceutical patent rights; and to weakening standard essential patents) and in the administration’s inexplicable decision to waive patent protection for COVID-19 vaccines (see here and here).

Before it takes further steps that would undermine patent protections, the administration should consider new research that underscores how patents help to spawn dynamic market growth through “design around” competition and through licensing that promotes new technologies and product markets.

Patents Spawn Welfare-Enhancing ‘Design Around’ Competition

Critics sometimes bemoan the fact that patents covering a new product or technology allegedly retard competition by preventing new firms from entering a market. (Never mind the fact that the market might not have existed but for the patent.) This thinking, which confuses a patent with a product-market monopoly, is badly mistaken. It is belied by the fact that the publicly available patented technology itself (1) provides valuable information to third parties; and (2) thereby incentivizes them to innovate and compete by refining technologies that fall outside the scope of the patent. In short, patents on important new technologies stimulate, rather than retard, competition. They do this by leading third parties to “design around” the patented technology and thus generate competition that features a richer set of technological options realized in new products.

The importance of design around is revealed, for example, in the development of the incandescent light bulb market in the late 19th century, in reaction to Edison’s patent on a long-lived light bulb. In a 2021 article in the Journal of Competition Law and Economics, Ron D. Katznelson and John Howells did an empirical study of this important example of product innovation. The article’s synopsis explains:

Designing around patents is prevalent but not often appreciated as a means by which patents promote economic development through competition. We provide a novel empirical study of the extent and timing of designing around patent claims. We study the filing rate of incandescent lamp-related patents during 1878–1898 and find that the enforcement of Edison’s incandescent lamp patent in 1891–1894 stimulated a surge of patenting. We studied the specific design features of the lamps described in these lamp patents and compared them with Edison’s claimed invention to create a count of noninfringing designs by filing date. Most of these noninfringing designs circumvented Edison’s patent claims by creating substitute technologies to enable participation in the market. Our forward citation analysis of these patents shows that some had introduced pioneering prior art for new fields. This indicates that invention around patents is not duplicative research and contributes to dynamic economic efficiency. We show that the Edison lamp patent did not suppress advance in electric lighting and the market power of the Edison patent owner weakened during this patent’s enforcement. We propose that investigation of the effects of design around patents is essential for establishing the degree of market power conferred by patents.

In a recent commentary, Katznelson highlights the procompetitive consumer welfare benefits of the Edison light bulb design around:

GE’s enforcement of the Edison patent by injunctions did not stifle competition nor did it endow GE with undue market power, let alone a “monopoly.” Instead, it resulted in clear and tangible consumer welfare benefits. Investments in design-arounds resulted in tangible and measurable dynamic economic efficiencies by (a) increased competition, (b) lamp price reductions, (c) larger choice of suppliers, (d) acceleration of downstream development of new electric illumination technologies, and (e) collateral creation of new technologies that would not have been developed for some time but for the need to design around Edison’s patent claims. These are all imparted benefits attributable to patent enforcement.

Katznelson further explains that “the mythical harm to innovation inflicted by enforcers of pioneer patents is not unique to the Edison case.” He cites additional research debunking claims that the Wright brothers’ pioneer airplane patent seriously retarded progress in aviation (“[a]ircraft manufacturing and investments grew at an even faster pace after the assertion of the Wright Brothers’ patent than before”) and debunking similar claims made about the early radio industry and the early automobile industry. He also notes strong research refuting the patent holdup conjecture regarding standard essential patents. He concludes by bemoaning “infringers’ rhetoric” that “suppresses information on the positive aspects of patent enforcement, such as the design-around effects that we study in this article.”

The Bayh-Dole Act: Licensing that Promotes New Technologies and Product Markets

The Bayh-Dole Act of 1980 has played an enormously important role in accelerating American technological innovation by creating a property rights-based incentive to use government labs. As this good summary from the Biotechnology Innovation Organization puts it, it “[e]mpowers universities, small businesses and non-profit institutions to take ownership [through patent rights] of inventions made during federally-funded research, so they can license these basic inventions for further applied research and development and broader public use.”

The act has continued to generate many new welfare-enhancing technologies and related high-tech business opportunities even during the “COVID slowdown year” of 2020, according to a newly released survey by a nonprofit organization representing the technology management community (see here):  

° The number of startup companies launched around academic inventions rose from 1,040 in 2019 to 1,117 in 2020. Almost 70% of these companies locate in the same state as the research institution that licensed them—making Bayh-Dole a critical driver of state and regional economic development;
° Invention disclosures went from 25,392 to 27,112 in 2020;
° New patent applications increased from 15,972 to 17,738;
° Licenses and options went from 9,751 in ’19 to 10,050 in ’20, with 60% of licenses going to small companies; and
° Most impressive of all—new products introduced to the market based on academic inventions jumped from 711 in 2019 to 933 in 2020.

Despite this continued record of success, the Biden Administration has taken actions that create uncertainty about the government’s support for Bayh-Dole.  

As explained by the Congressional Research Service, “march-in rights allow the government, in specified circumstances, to require the contractor or successors in title to the patent to grant a ‘nonexclusive, partially exclusive, or exclusive license’ to a ‘responsible applicant or applicants.’ If the patent owner refuses to do so, the government may grant the license itself.” Government march-in rights thus far have not been invoked, but a serious threat of their routine invocation would greatly disincentivize future use of Bayh-Dole, thereby undermining patent-backed innovation.

Despite this, the president’s July 9 Executive Order on Competition (noted above) instructed the U.S. Commerce Department to defer finalizing a regulation (see here) “that would have ensured that march-in rights under Bayh Dole would not be misused to allow the government to set prices, but utilized for its statutory intent of providing oversight so good faith efforts are being made to turn government-funded innovations into products. But that’s all up in the air now.”

What’s more, a new U.S. Energy Department policy that would more closely scrutinize Bayh-Dole patentees’ licensing transactions and acquisitions (apparently to encourage more domestic manufacturing) has raised questions in the Bayh-Dole community and may discourage licensing transactions (see here and here). Added to this is the fact that “prominent Members of Congress are pressing the Biden Administration to misconstrue the march-in rights clause to control prices of products arising from National Institutes of Health and Department of Defense funding.” All told, therefore, the outlook for continued patent-inspired innovation through Bayh-Dole processes appears to be worse than it has been in many years.

Conclusion

The patent system does far more than provide potential rewards to enhance incentives for particular individuals to invent. The system also creates a means to enhance welfare by facilitating the diffusion of technology through market processes (see here).

But it does even more than that. It actually drives new forms of dynamic competition by inducing third parties to design around new patents, to the benefit of consumers and the overall economy. As revealed by the Bayh-Dole Act, it also has facilitated the more efficient use of federal labs to generate innovation and new products and processes that would not otherwise have seen the light of day. Let us hope that the Biden administration pays heed to these benefits to the American economy and thinks again before taking steps that would further weaken our patent system.     

Over the past decade and a half, virtually every branch of the federal government has taken steps to weaken the patent system. As reflected in President Joe Biden’s July 2021 executive order, these restraints on patent enforcement are now being coupled with antitrust policies that, in large part, adopt a “big is bad” approach in place of decades of economically grounded case law and agency guidelines.

This policy bundle is nothing new. It largely replicates the innovation policies pursued during the late New Deal and the postwar decades. That historical experience suggests that a “weak-patent/strong-antitrust” approach is likely to encourage neither innovation nor competition.

The Overlooked Shortfalls of New Deal Innovation Policy

Starting in the early 1930s, the U.S. Supreme Court issued a sequence of decisions that raised obstacles to patent enforcement. The Franklin Roosevelt administration sought to take this policy a step further, advocating compulsory licensing for all patents. While Congress did not adopt this proposal, it was partially implemented as a de facto matter through antitrust enforcement. Starting in the early 1940s and continuing throughout the postwar decades, the antitrust agencies secured judicial precedents that treated a broad range of licensing practices as per se illegal. Perhaps most dramatically, the U.S. Justice Department (DOJ) secured more than 100 compulsory licensing orders against some of the nation’s largest companies. 

The rationale behind these policies was straightforward. By compelling access to incumbents’ patented technologies, courts and regulators would lower barriers to entry and competition would intensify. The postwar economy declined to comply with policymakers’ expectations. Implementation of a weak-IP/strong-antitrust innovation policy over the course of four decades yielded the opposite of its intended outcome. 

Market concentration did not diminish, turnover in market leadership was slow, and private research and development (R&D) was confined mostly to the research labs of the largest corporations (who often relied on generous infusions of federal defense funding). These tendencies are illustrated by the dramatically unequal allocation of innovation capital in the postwar economy.  As of the late 1950s, small firms represented approximately 7% of all private U.S. R&D expenditures.  Two decades later, that figure had fallen even further. By the late 1970s, patenting rates had plunged, and entrepreneurship and innovation were in a state of widely lamented decline.

Why Weak IP Raises Entry Costs and Promotes Concentration

The decline in entrepreneurial innovation under a weak-IP regime was not accidental. Rather, this outcome can be derived logically from the economics of information markets.

Without secure IP rights to establish exclusivity, engage securely with business partners, and deter imitators, potential innovator-entrepreneurs had little hope to obtain funding from investors. In contrast, incumbents could fund R&D internally (or with federal funds that flowed mostly to the largest computing, communications, and aerospace firms) and, even under a weak-IP regime, were protected by difficult-to-match production and distribution efficiencies. As a result, R&D mostly took place inside the closed ecosystems maintained by incumbents such as AT&T, IBM, and GE.

Paradoxically, the antitrust campaign against patent “monopolies” most likely raised entry barriers and promoted industry concentration by removing a critical tool that smaller firms might have used to challenge incumbents that could outperform on every competitive parameter except innovation. While the large corporate labs of the postwar era are rightly credited with technological breakthroughs, incumbents such as AT&T were often slow in transforming breakthroughs in basic research into commercially viable products and services for consumers. Without an immediate competitive threat, there was no rush to do so. 

Back to the Future: Innovation Policy in the New New Deal

Policymakers are now at work reassembling almost the exact same policy bundle that ended in the innovation malaise of the 1970s, accompanied by a similar reliance on public R&D funding disbursed through administrative processes. However well-intentioned, these processes are inherently exposed to political distortions that are absent in an innovation environment that relies mostly on private R&D funding governed by price signals. 

This policy bundle has emerged incrementally since approximately the mid-2000s, through a sequence of complementary actions by every branch of the federal government.

  • In 2011, Congress enacted the America Invents Act, which enables any party to challenge the validity of an issued patent through the U.S. Patent and Trademark Office’s (USPTO) Patent Trial and Appeals Board (PTAB). Since PTAB’s establishment, large information-technology companies that advocated for the act have been among the leading challengers.
  • In May 2021, the Office of the U.S. Trade Representative (USTR) declared its support for a worldwide suspension of IP protections over Covid-19-related innovations (rather than adopting the more nuanced approach of preserving patent protections and expanding funding to accelerate vaccine distribution).  
  • President Biden’s July 2021 executive order states that “the Attorney General and the Secretary of Commerce are encouraged to consider whether to revise their position on the intersection of the intellectual property and antitrust laws, including by considering whether to revise the Policy Statement on Remedies for Standard-Essential Patents Subject to Voluntary F/RAND Commitments.” This suggests that the administration has already determined to retract or significantly modify the 2019 joint policy statement in which the DOJ, USPTO, and the National Institutes of Standards and Technology (NIST) had rejected the view that standard-essential patent owners posed a high risk of patent holdup, which would therefore justify special limitations on enforcement and licensing activities.

The history of U.S. technology markets and policies casts great doubt on the wisdom of this weak-IP policy trajectory. The repeated devaluation of IP rights is likely to be a “lose-lose” approach that does little to promote competition, while endangering the incentive and transactional structures that sustain robust innovation ecosystems. A weak-IP regime is particularly likely to disadvantage smaller firms in biotech, medical devices, and certain information-technology segments that rely on patents to secure funding from venture capital and to partner with larger firms that can accelerate progress toward market release. The BioNTech/Pfizer alliance in the production and distribution of a Covid-19 vaccine illustrates how patents can enable such partnerships to accelerate market release.  

The innovative contribution of BioNTech is hardly a one-off occurrence. The restoration of robust patent protection in the early 1980s was followed by a sharp increase in the percentage of private R&D expenditures attributable to small firms, which jumped from about 5% as of 1980 to 21% by 1992. This contrasts sharply with the unequal allocation of R&D activities during the postwar period.

Remarkably, the resurgence of small-firm innovation following the strong-IP policy shift, starting in the late 20th century, mimics tendencies observed during the late 19th and early-20th centuries, when U.S. courts provided a hospitable venue for patent enforcement; there were few antitrust constraints on licensing activities; and innovation was often led by small firms in partnership with outside investors. This historical pattern, encompassing more than a century of U.S. technology markets, strongly suggests that strengthening IP rights tends to yield a policy “win-win” that bolsters both innovative and competitive intensity. 

An Alternate Path: ‘Bottom-Up’ Innovation Policy

To be clear, the alternative to the policy bundle of weak-IP/strong antitrust does not consist of a simple reversion to blind enforcement of patents and lax administration of the antitrust laws. A nuanced innovation policy would couple modern antitrust’s commitment to evidence-based enforcement—which, in particular cases, supports vigorous intervention—with a renewed commitment to protecting IP rights for innovator-entrepreneurs. That would promote competition from the “bottom up” by bolstering maverick innovators who are well-positioned to challenge (or sometimes partner with) incumbents and maintaining the self-starting engine of creative disruption that has repeatedly driven entrepreneurial innovation environments. Tellingly, technology incumbents have often been among the leading advocates for limiting patent and copyright protections.  

Advocates of a weak-patent/strong-antitrust policy believe it will enhance competitive and innovative intensity in technology markets. History suggests that this combination is likely to produce the opposite outcome.  

Jonathan M. Barnett is the Torrey H. Webb Professor of Law at the University of Southern California, Gould School of Law. This post is based on the author’s recent publications, Innovators, Firms, and Markets: The Organizational Logic of Intellectual Property (Oxford University Press 2021) and “The Great Patent Grab,” in Battles Over Patents: History and the Politics of Innovation (eds. Stephen H. Haber and Naomi R. Lamoreaux, Oxford University Press 2021).

President Joe Biden named his post-COVID-19 agenda “Build Back Better,” but his proposals to prioritize support for government-run broadband service “with less pressure to turn profits” and to “reduce Internet prices for all Americans” will slow broadband deployment and leave taxpayers with an enormous bill.

Policymakers should pay particular heed to this danger, amid news that the Senate is moving forward with considering a $1.2 trillion bipartisan infrastructure package, and that the Federal Communications Commission, the U.S. Commerce Department’s National Telecommunications and Information Administration, and the U.S. Agriculture Department’s Rural Utilities Service will coordinate on spending broadband subsidy dollars.

In order to ensure that broadband subsidies lead to greater buildout and adoption, policymakers must correctly understand the state of competition in broadband and not assume that increasing the number of firms in a market will necessarily lead to better outcomes for consumers or the public.

A recent white paper published by us here at the International Center for Law & Economics makes the case that concentration is a poor predictor of competitiveness, while offering alternative policies for reaching Americans who don’t have access to high-speed Internet service.

The data show that the state of competition in broadband is generally healthy. ISPs routinely invest billions of dollars per year in building, maintaining, and upgrading their networks to be faster, more reliable, and more available to consumers. FCC data show that average speeds available to consumers, as well as the number of competitors providing higher-speed tiers, have increased each year. And prices for broadband, as measured by price-per-Mbps, have fallen precipitously, dropping 98% over the last 20 years. None of this makes sense if the facile narrative about the absence of competition were true.

In our paper, we argue that the real public policy issue for broadband isn’t curbing the pursuit of profits or adopting price controls, but making sure Americans have broadband access and encouraging adoption. In areas where it is very costly to build out broadband networks, like rural areas, there tend to be fewer firms in the market. But having only one or two ISPs available is far less of a problem than having none at all. Understanding the underlying market conditions and how subsidies can both help and hurt the availability and adoption of broadband is an important prerequisite to good policy.

The basic problem is that those who have decried the lack of competition in broadband often look at the number of ISPs in a given market to determine whether a market is competitive. But this is not how economists think of competition. Instead, economists look at competition as a dynamic process where changes in supply and demand factors are constantly pushing the market toward new equilibria.

In general, where a market is “contestable”—that is, where existing firms face potential competition from the threat of new entry—even just a single existing firm may have to act as if it faces vigorous competition. Such markets often have characteristics (e.g., price, quality, and level of innovation) similar or even identical to those with multiple existing competitors. This dynamic competition, driven by changes in technology or consumer preferences, ensures that such markets are regularly disrupted by innovative products and services—a process that does not always favor incumbents.

Proposals focused on increasing the number of firms providing broadband can actually reduce consumer welfare. Whether through overbuilding—by allowing new private entrants to free-ride on the initial investment by incumbent companies—or by going into the Internet business itself through municipal broadband, government subsidies can increase the number of firms providing broadband. But it can’t do so without costs―which include not just the cost of the subsidies themselves, which ultimately come from taxpayers, but also the reduced incentives for unsubsidized private firms to build out broadband in the first place.

If underlying supply and demand conditions in rural areas lead to a situation where only one provider can profitably exist, artificially adding another completely reliant on subsidies will likely just lead to the exit of the unsubsidized provider. Or, where a community already has municipal broadband, it is unlikely that a private ISP will want to enter and compete with a firm that doesn’t have to turn a profit.

A much better alternative for policymakers is to increase the demand for buildout through targeted user subsidies, while reducing regulatory barriers to entry that limit supply.

For instance, policymakers should consider offering connectivity vouchers to unserved households in order to stimulate broadband deployment and consumption. Current subsidy programs rely largely on subsidizing the supply side, but this requires the government to determine the who and where of entry. Connectivity vouchers would put the choice in the hands of consumers, while encouraging more buildout to areas that may currently be uneconomic to reach due to low population density or insufficient demand due to low adoption rates.

Local governments could also facilitate broadband buildout by reducing unnecessary regulatory barriers. Local building codes could adopt more connection-friendly standards. Local governments could also reduce the cost of access to existing poles and other infrastructure. Eligible Telecommunications Carrier (ETC) requirements could also be eliminated, because they deter potential providers from seeking funds for buildout (and don’t offer countervailing benefits).

Albert Einstein once said: “if I were given one hour to save the planet, I would spend 59 minutes defining the problem, and one minute resolving it.” When it comes to encouraging broadband buildout, policymakers should make sure they are solving the right problem. The problem is that the cost of building out broadband to unserved areas is too high or the demand too low—not that there are too few competitors.

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.

Bees

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.

A finding echoed by Francis Bator:

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.

The Lighthouse

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.

Dvorak Keyboards

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.

In Conclusion

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 […].

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. 

For more, our “Joint Submission of Antitrust Economists, Legal Scholars, and Practitioners” set out why many of the House Democrats’ assumptions about the state of the economy and antitrust enforcement were mistaken. And my post, “Buck’s “Third Way”: A Different Road to the Same Destination”, argued that House Republicans like Ken Buck were misguided in believing they could support some of the proposals and avoid the massive regulatory oversight that they said they rejected.

Platform Anti-Monopoly Act 

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.

For more, check out “Against the Vertical Discrimination Presumption” by Geoffrey Manne and Dirk Auer’s piece “On the Origin of Platforms: An Evolutionary Perspective”.

Ending Platform Monopolies Act 

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.

For more, check out Geoffrey Manne’s written testimony to the House Antitrust Subcommittee and “Platform Self-Preferencing Can Be Good for Consumers and Even Competitors” by Geoffrey and me. 

Platform Competition and Opportunity Act

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. 

The two main ways that founders and investors can make a return on a successful startup are to float the company at IPO or to be acquired by another business. The latter of these, acquisitions, is extremely important. Between 2008 and 2019, 90 percent of U.S. start-up exits happened through acquisition. In a recent survey, half of current startup executives said they aimed to be acquired. One study found that countries that made it easier for firms to be taken over saw a 40-50 percent increase in VC activity, and that U.S. states that made acquisitions harder saw a 27 percent decrease in VC investment deals

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.

For more, check out Dirk Auer’s piece “Facebook and the Pros and Cons of Ex Post Merger Reviews” and my piece “Cracking down on mergers would leave us all worse off”. 

ACCESS Act

The Augmenting Compatibility and Competition by Enabling Service Switching (ACCESS) Act, sponsored by Rep. Mary Gay Scanlon (D-Pa.), would establish data portability and interoperability requirements for platforms. 

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. 

Data portability and interoperability involve trade-offs in terms of security and usability, and overseeing them can be extremely costly and difficult. In security terms, interoperability requirements prevent companies from using closed systems to protect users from hostile third parties. Mandatory openness means increasing—sometimes, substantially so—the risk of data breaches and leaks. In practice, that could mean users’ private messages or photos being leaked more frequently, or activity on a social media page that a user considers to be “their” private data, but that “belongs” to another user under the terms of use, can be exported and publicized as such. 

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.

For more, check out Gus Hurwitz’s piece “Portable Social Media Aren’t Like Portable Phone Numbers” and my piece “Why Data Interoperability Is Harder Than It Looks: The Open Banking Experience”.

Merger Filing Fee Modernization Act

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. 

For more, check out my post “Buck’s “Third Way”: A Different Road to the Same Destination” and Thom Lambert’s post “Bad Blood at the FTC”.

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

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

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

AIs That Are Prohibited Outright

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

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

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

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

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

Restrictions on ‘High-Risk’ AIs

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

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

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

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

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

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

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

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

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

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

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

Regulatory Support for Innovation and Competition

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

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

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

Governance and Enforcement

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

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

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

Is the Commission Overplaying its Hand?

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

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

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

[TOTM: The following is part of a digital symposium by TOTM guests and authors on the legal and regulatory issues that arose during Ajit Pai’s tenure as chairman of the Federal Communications Commission. The entire series of posts is available here.

Thomas B. Nachbar is a professor of law at the University of Virginia School of Law and a senior fellow at the Center for National Security Law.]

It would be impossible to describe Ajit Pai’s tenure as chair of the Federal Communications Commission as ordinary. Whether or not you thought his regulatory style or his policies were innovative, his relationship with the public has been singular for an FCC chair. His Reese’s mug, alone, has occupied more space in the American media landscape than practically any past FCC chair. From his first day, he has attracted consistent, highly visible criticism from a variety of media outlets, although at least John Oliver didn’t describe him as a dingo. Just today, I read that Ajit Pai single handedly ruined the internet, which when I got up this morning seemed to be working pretty much the same way it was four years ago.

I might be biased in my view of Ajit. I’ve known him since we were law school classmates, when he displayed the same zeal and good-humored delight in confronting hard problems that I’ve seen in him at the commission. So I offer my comments not as an academic and student of FCC regulation, but rather as an observer of the communications regulatory ecosystem that Ajit has dominated since his appointment. And while I do not agree with everything he’s done at the commission, I have admired his single-minded determination to pursue policies that he believes will expand access to advanced telecommunications services. One can disagree with how he’s pursued that goal—and many have—but characterizing his time as chair in any other way simply misses the point. Ajit has kept his eye on expanding access, and he has been unwavering in pursuit of that objective, even when doing so has opened him to criticism, which is the definition of taking political risk.

Thus, while I don’t think it’s going to be the most notable policy he’s participated in at the commission, I would like to look at Ajit’s tenure through the lens of one small part of one fairly specific proceeding: the commission’s decision to include SpaceX as a low-latency provider in the Rural Digital Opportunity Fund (RDOF) Auction.

The decision to include SpaceX is at one level unremarkable. SpaceX proposes to offer broadband internet access through low-Earth-orbit satellites, which is the kind of thing that is completely amazing but is becoming increasingly un-amazing as communications technology advances. SpaceX’s decision to use satellites is particularly valuable for initiatives like the RDOF, which specifically seek to provide services where previous (largely terrestrial) services have not. That is, in fact, the whole point of the RDOF, a point that sparked fiery debate over the FCC’s decision to focus the first phase of the RDOF on areas with no service rather than areas with some service. Indeed, if anything typifies the current tenor of the debate (at the center of which Ajit Pai has resided since his confirmation as chair), it is that a policy decision over which kind of under-served areas should receive more than $16 billion in federal funding should spark such strongly held views. In the end, SpaceX was awarded $885.5 million to participate in the RDOF, almost 10% of the first-round funds awarded.

But on a different level, the decision to include SpaceX is extremely remarkable. Elon Musk, SpaceX’s pot-smoking CEO, does not exactly fit regulatory stereotypes. (Disclaimer: I personally trust Elon Musk enough to drive my children around in one of his cars.) Even more significantly, SpaceX’s Starlink broadband service doesn’t actually exist as a commercial product. If you go to Starlink’s website, you won’t find a set of splashy webpages featuring products, services, testimonials, and a variety of service plans eager for a monthly assignation with your credit card or bank account. You will be greeted with a page asking for your email and service address in case you’d like to participate in Starlink’s beta program. In the case of my address, which is approximately 100 miles from the building where the FCC awarded SpaceX over $885 million to participate in the RDOF, Starlink is not yet available. I will, however, “be notified via email when service becomes available in your area,” which is reassuring but doesn’t get me any closer to watching cat videos.

That is perhaps why Chairman Pai was initially opposed to including SpaceX in the low-latency portion of the RDOF. SpaceX was offering unproven technology and previous satellite offerings had been high-latency, which is good for some uses but not others.

But then, an even more remarkable thing happened, at least in Washington: a regulator at the center of a controversial issue changed his mind and—even more remarkably—admitted his decision might not work out. When the final order was released, SpaceX was allowed to bid for low-latency RDOF funds even though the commission was “skeptical” of SpaceX’s ability to deliver on its low-latency promise. Many doubted that SpaceX would be able to effectively compete for funds, but as we now know, that decision led to SpaceX receiving a large share of the Phase I funds. Of course, that means that if SpaceX doesn’t deliver on its latency promises, a substantial part of the RDOF Phase I funds will fail to achieve their purpose, and the FCC will have backed the wrong horse.

I think we are unlikely to see such regulatory risk-taking, both technically and politically, in what will almost certainly be a more politically attuned commission in the coming years. Even less likely will be acknowledgments of uncertainty in the commission’s policies. Given the political climate and the popular attention policies like network neutrality have attracted, I would expect the next chair’s views about topics like network neutrality to exhibit more unwavering certainty than curiosity and more resolve than risk-taking. The most defining characteristic of modern communications technology and markets is change. We are all better off with a commission in which the other things that can change are minds.

With the COVID-19 vaccine made by Moderna joining the one from Pfizer and BioNTech in gaining approval from the U.S. Food and Drug Administration, it should be time to celebrate the U.S. system of pharmaceutical development. The system’s incentives—notably granting patent rights to firms that invest in new and novel discoveries—have worked to an astonishing degree, producing not just one but as many as three or four effective approaches to end a viral pandemic that, just a year ago, was completely unknown.

Alas, it appears not all observers agree. Now that we have the vaccines, some advocate suspending or limiting patent rights—for example, by imposing a compulsory licensing scheme—with the argument that this is the only way for the vaccines to be produced in mass quantities worldwide. Some critics even assert that abolishing or diminishing property rights in pharmaceuticals is needed to end the pandemic.

In truth, we can effectively and efficiently distribute the vaccines while still maintaining the integrity of our patent system. 

What the false framing ignores are the important commercialization and distribution functions that patents provide, as well as the deep, long-term incentives the patent system provides to create medical innovations and develop a robust pharmaceutical supply chain. Unless we are sure this is the last pandemic we will ever face, repealing intellectual property rights now would be a catastrophic mistake.

The supply chains necessary to adequately scale drug production are incredibly complex, and do not appear overnight. The coordination and technical expertise needed to support worldwide distribution of medicines depends on an ongoing pipeline of a wide variety of pharmaceuticals to keep the entire operation viable. Public-spirited officials may in some cases be able to piece together facilities sufficient to produce and distribute a single medicine in the short term, but over the long term, global health depends on profit motives to guarantee the commercialization pipeline remains healthy. 

But the real challenge is in maintaining proper incentives to develop new drugs. It has long been understood that information goods like intellectual property will be undersupplied without sufficient legal protections. Innovators and those that commercialize innovations—like researchers and pharmaceutical companies—have less incentive to discover and market new medicines as the likelihood that they will be able to realize a return for their efforts diminishes. Without those returns, it’s far less certain the COVID vaccines would have been produced so quickly, or at all. The same holds for the vaccines we will need for the next crisis or badly needed treatments for other deadly diseases.

Patents are not the only way to structure incentives, as can be seen with the current vaccines. Pharmaceutical companies also took financial incentives from various governments in the form of direct payment or in purchase guarantees. But this enhances, rather than diminishes, the larger argument. There needs to be adequate returns for those who engage in large, risky undertakings like creating a new drug. 

Some critics would prefer to limit pharmaceutical companies’ returns solely to those early government investments, but there are problems with this approach. It is difficult for governments to know beforehand what level of profit is needed to properly incentivize firms to engage in producing these innovations.  To the extent that direct government investment is useful, it often will be as an additional inducement that encourages new entry by multiple firms who might each pursue different technologies. 

Thus, in the case of coronavirus vaccines, government subsidies may have enticed more competitors to enter more quickly, or not to drop out as quickly, in hopes that they would still realize a profit, notwithstanding the risks. Where there might have been only one or two vaccines produced in the United States, it appears likely we will see as many as four.

But there will always be necessary trade-offs. Governments cannot know how to set proper incentives to encourage development of every possible medicine for every possible condition by every possible producer.  Not only do we not know which diseases and which firms to prioritize, but we have no idea how to determine which treatment approaches to encourage. 

The COVID-19 vaccines provide a clear illustration of this problem. We have seen development of both traditional vaccines and experimental mRNA treatments to combat the virus. Thankfully, both have shown positive results, but there was no way to know that in March. In this perennial state of ignorance,t markets generally have provided the best—though still imperfect—way to make decisions. 

The patent system’s critics sometimes claim that prizes would offer a better way to encourage discovery. But if we relied solely on government-directed prizes, we might never have had the needed research into the technology that underlies mRNA. As one recent report put it, “before messenger RNA was a multibillion-dollar idea, it was a scientific backwater.” Simply put, without patent rights as the backstop to purely academic or government-led innovation and commercialization, it is far less likely that we would have seen successful COVID vaccines developed as quickly.

It is difficult for governments to be prepared for the unknown. Abolishing or diminishing pharmaceutical patents would leave us even less prepared for the next medical crisis. That would only add to the lasting damage that the COVID-19 pandemic has already wrought on the world.

Big Tech but Bigger Ideas

Bowman Heiden —  30 November 2020
[TOTM: The following is part of a symposium by TOTM guests and authors marking the release of Nicolas Petit’s “Big Tech and the Digital Economy: The Moligopoly Scenario.” The entire series of posts is available here.

This post is authored by Bowman Heiden (Co-director of the Center for Intellectual Property (CIP), a joint center between University of Gothenburg (Sweden), Chalmers University of Technology (Sweden), and the Norwegian University for Science and Technology).
]

As an academic working at the intersection of economics, law, and innovation, I was excited to see Nicolas Petit apply an interdisciplinary approach to investigate big tech in the digital economy. Working across law, business, and engineering has taught me the importance of bringing together different theoretical perspectives and mindsets to address complex issues. [RL1] Below is a short discussion of a few interdisciplinary areas where Petit helps us to bridge the theoretical gap so as to unveil the complexity and provide new insights for policymakers.

Competition Strategy vs. Competition Law

Business schools do not typically teach competition law and law schools do not teach competition strategy. This creates a theoretical disconnect that spills over into professional life. Business schools tell students to create a sustainable competitive advantage, which often means building a dominant market position. Law schools, on the other hand, treat dominant positions as socially undesirable and likely illegal.

As Petit points out, one of the main competitive strategy frameworks—the Porter’s Five Forces model, named for Michael E. Porter of Harvard Business School—provides a much broader perspective on rivalry than legal regimes like the OECD’s Competition Assessment Toolkit, which use a product-centric “more restrictive method of competitive assessment.” The progression of technology and new creative business models, such as multisided platforms, thus continuously push the frontier. Over time, this fundamentally alters the nature of competition within product markets as a unit of analysis.

If one seeks answers within “the law,” there will always be a lag between commercial reality (i.e., market norms) and legal doctrine (i.e., statutory norms). Petit reminds us that big tech is a different kind of competition whose holistic nature may require a new analytic framework to understand its welfare effects. If the fundamental principles of market competition are changing, we will not likely find the answer in historical precedents.

Innovation and Entrepreneurship vs. Economics

Given the focus on innovation as the key source of economic development, it is strange that innovation plays such a small role in mainstream economics. Schumpeter tried to convince us some 80 years ago that the market power generated from innovation and entrepreneurship had a positive economic impact, even for larger firms. But his concept of “creative destruction” has never really been able to gain much ground over the “invisible hand.” One cannot help but conclude this is because the static world is easier to understand and model mathematically than the dynamic world.

But the world is not static. Even if we agree that we are concerned primarily with welfare, we still need to decide whether we want to be better off now or later. A static analysis of welfare promotes a world without innovation. It is almost as if economics doesn’t appreciate “time” as a variable. If Schumpeter is right and it is disequilibrium, not equilibrium, that is the most relevant economic phenomenon, then we have the cart in front of the horse. Does innovation breed competition or does competition breed innovation? If the market power associated with innovation produces greater welfare than perfect competition, then how useful is competition as a proxy for welfare? In other words, if perfect competition inhibits innovation and dynamic efficiency, then more competition cannot be a societal goal in of itself.

Having time as a core variable forces us to think about the future and how we get from here to there. It is not enough to model evolution in the short term as a sea full of fish, and in the long term, as a city full of people. How the world changes and how quickly it changes are also important. Even with the long-term benefits of creative destruction, it is important to remember that a significant group of voting-age citizens will likely suffer in the short term.

Petit’s discussion of big tech’s exploration, change and pivot flexibility implicitly reminds us that time matters. Time is the carrier of innovation and uncertainty. This has fundamental impacts on the nature of competition and competition’s impact on welfare, which cannot be properly understood only through comparative statics. Though he claims his goal is “not to formulate a new Schumpeterian theory of monopoly efficiency,” he is rightly Schumpeterian in moving innovation and uncertainty closer to the center stage of analysis in law and economics.

Certainly, in a dynamic, global market, there is credence to Petit’s supposition that market power in digital markets can be welfare enhancing in the short term and can potentially instigate innovation, particularly disruptive innovation, in the long term. Both constraints and uncertainty typically spur innovation.

Politics vs. Economics

If ignorance was our only challenge in pursuing enlightened public policy, there would be little to worry about. We would constantly generate hypotheses and test them empirically to adapt to a changing world. Unfortunately, we have two more formidable adversaries: ideology and self-interest.

Ideology is cognitive closure, a type of self-inflicted ignorance, where someone internalizes a set of beliefs that defines who they are. All new information that contradicts the chosen ideology will likely be rejected as a starting point (e.g., the notion that big is always bad). This is what Max Planck meant when he said “a new scientific truth does not triumph by convincing its opponents and making them see the light, but rather because its opponents eventually die, and a new generation grows up that is familiar with it.” The world is full of ideologues; nowadays, the objective centrist is the radical, which is where I see that Petit has positioned himself.

Policy is also highly influenced by self-interest, which in a capitalist society is entirely rational. Self-interest is the cornerstone of the concept of the ”invisible hand” and basic price theory when applied to the commerical market. But what are the implications when it is applied to policy? Is lobbying simply part of a free market for policies, where self-interested actors compete, not in the game itself, but to change the rules of the game in their favor? The idea that those who control the economic base control the infrastructure of society is not new. From a policy perspective, is the invisible hand leading us to prosperity or is it giving us the finger?[RL2] 

Just as economist Robert Solow helped us to understand the extent of our ignorance about economic growth, so has this work by Nicolas Petit. Hopefully, it will ignite a new conversation about the role of innovation and uncertainty, not only in antitrust, but also in mainstream economic thought, all without the need for Planck’s funeral procession.

The Limits of Rivalry

Kelly Fayne —  2 November 2020
[TOTM: The following is part of a symposium by TOTM guests and authors marking the release of Nicolas Petit’s “Big Tech and the Digital Economy: The Moligopoly Scenario.” The entire series of posts is available here.

This post is authored by Kelly Fayne (Antitrust Associate, Latham & Watkins).
]

Nicholas Petit, with Big Tech and the Digital Economy: The Moligopoly Scenario, enters the fray at this moment of peak consternation about big tech platforms to reexamine antitrust’s role as referee.  Amongst calls on the one hand like those in the Majority Staff Report and Recommendation from the Subcommittee on Antitrust (“these firms have too much power, and that power must be reined in and subject to appropriate oversight and enforcement”) and, on the other hand, understandably strong disagreement from the firms targeted, Petit offers a diagnosis.  A focus on the protection of rivalry for rivalry’s sake is insufficiently adaptive to the “distinctive features of digital industries, firms, and markets.”

I am left wondering, however, if he’s misdiagnosed the problem – or at least whether the cure he offers would be seen as sufficient by those most vocally asserting that antitrust is failing.  And, of course, I recognize that his objective in writing this book is not to bring harmony to a deeply divided debate, but to offer an improved antitrust framework for navigating big tech.

Petit, in Chapter 5 (“Antitrust in Moligopoly Markets”), says: “So the real question is this: should we abandon, or at least radically alter traditional antitrust principals modeled on rivalry in digital markets? The answer is yes.”  He argues that “protecting rivalry is not perforce socially beneficial in industries with increasing returns to adoption.”  But it is his tethering to the notion of what is “socially beneficial” that creates a challenge.

Petit argues that the function of the current antitrust legal regimes – most significantly the US and EU – is to protect rivalry.   He observes several issues with rivalry when applied as both a test and a remedy for market power.  One of the most valuable insights Petit offers in his impressive work in this book, is that tipped markets may not be all that bad.  In fact, when markets exhibit increasing returns to adoption, allowing the winner to take it all (or most) may be more welfare enhancing than trying to do the antitrust equivalent of forcing two magnets to remain apart.  And, assuming all the Schumpeterian dynamics align, he’s right.  Or rather, he’s right if you agree that welfare is the standard by which what is socially beneficial should be measured.  

Spoiler alert: My own view is that antitrust requires an underlying system of measurement, and the best available system is welfare-based. More on this below. 

When it comes to evaluating horizontal mergers, Petit suggests an alternative regime calibrated to handle the unique circumstances that arise in tech deals.  But his new framework remains largely tethered to (or at least based in the intuitions of) a variation of the welfare standard that, for the most part, still underlies modern applications of antitrust laws. So the question becomes, if you alter the means, but leave the ends unchanged, do you get different results?  At least in the  merger context, I’m not so sure.  And if the results are for the most part the same, do we really need an alternative path to achieving them?  Probably not. 

The Petit horizontal merger test (1) applies a non-rebuttable (OMG!) presumption of prohibition on mergers to monopoly by the dominant platform in “tipped markets,” and (2) permits some acquisitions in untipped markets without undue regard to whether the acquiring firm is dominant in another market.  A non-rebuttable presumption, admittedly, elicited heavy-pressure red pen in the margins upon my first read.  Upon further reflection … I still don’t like it. I am, however, somewhat comforted because I suspect that its practical application would land us largely in the same place as current applications of antitrust for at least the vast majority of tech transactions.  And that is because Petit’s presumptive prohibition on mergers in tipped markets doesn’t cancel the fight, it changes the venue.  

The exercise of determining whether or not the market is tipped in effect replicates the exercise of assessing whether the dominant firm has a significant degree of market power, and concludes in the affirmative.  Enforcers around the world already look skeptically at firms with perceived market power when they make horizontal acquisitions (among an already rare group of cases in which such deals are attempted).  I recognize that there is theoretical daylight between Petit’s proposed test and one in which the merging parties are permitted an efficiencies defense, but in practice, the number of deals cleared solely on the basis of countervailing procompetitive efficiencies has historically been small. Thus, the universe of deals swept up in the per se prohibition could easily end up a null set.  (Or at least, I think it should be a null set given how quickly the tech industry evolves and transforms). 

As for the untipped markets, Petit argues that it is “unwarranted to treat firms with monopoly positions in tipped markets more strictly than others when they make indirect entry in untipped markets.”  He further argues that there is “no economic basis to prefer indirect entry by an incumbent firm from a tipped market over entry from (i) a new firm or (ii) an established firm from an untipped market.  Firm type is not determinative of the weight of social welfare brought by a unit of innovation.”  His position is closely aligned with the existing guidance on vertical and conglomerate mergers, including in the recently issued FTC and DOJ Vertical Merger Guidelines, although his discussion contains a far more nuanced perspective on how network effects and the leveraging of market power from one market to another overlay into the vertical merger math.  In the end, however, whether one applies the existing vertical merger approach or the Petit proposal, I hypothesize little divergence in outcomes.  

All of the above notwithstanding, Petit’s endeavor to devise a framework more closely calibrated to the unique features of tech platforms is admirable, as is the care and thoughtfulness he’s taken to the task.  If the audience for this book takes the view that the core principals of economic welfare should underlie antitrust laws and their application, Petit is likely to find it receptive.  While many (me included) may not think a new regime is necessary, the way that he articulates the challenges presented by platforms and evolving technologies is enlightening even for those who think an old approach can learn new tricks.  And, of course, the existing approach, but has the added benefit of being adaptable to applications outside of tech platforms. 

Still, the purpose of antitrust law is where the far more difficult debate is taking place.  And this is where, as I mentioned above, I think Petit may have misdiagnosed the shortcomings of neo-structuralism (or the neo-Brandeisian school, or Antitrust 2.0, or Hipster Antitrust, and so on). In short, these are frameworks that focus first on the number and size of players in an industry and guard against concentration, even in the absence of a causal link between these structural elements and adverse impact on consumer, and/or total welfare. Petit describes neo-structuralism as focusing on rivalry without having an “an evaluative premise” (i.e., an explanation for why big = bad).  I’m less sure that it lacks an evaluative premise, rather, I think it might have several (potentially competing) evaluative premises.  

Rivalry indeed has no inherent value, it is good – or perceived as good – as a means to an end.  If that end is consumer welfare, then the limiting principle on when rivalry is achieving its end is whether welfare is enhanced or not.  But many have argued that rivalry could have other potential benefits.  For instance, the Antitrust Subcommittee House Report, identifies several potential objectives for competition law: driving innovation and entrepreneurship, privacy, the protection of political and economic liberties, and controlling influence of private firms over the policymaking process.  Even if we grant that competition could be a means to achieving these ends, the measure of success for competition laws would have to be the degree to which the ends are achieved.  For example, if one argues that competition law should be used to promote privacy, we would measure the success of those laws by whether they do in fact promote privacy, not whether they maintain a certain number of players in an industry.  Although, we should also consider whether competition law really is the most efficient and effective means to those ends. 

Returning again to merger control, in the existing US regime, and under the Petit proposal, a dominant tech platform might be permitted to acquire a large player in an unrelated market assuming there is no augmentation of market power as a result of the interplay between the two and if the deal is, on net, efficiency enhancing.  In simpler terms, if consumers are made better off through lower prices, better services, increased innovation etc. the deal is permitted to proceed.  Yet, if antitrust were calibrated, e.g., for a primary purpose of disaggregating corporate control over capital to minimize political influence by large firms, you could see the same transition failing to achieve approval.  If privacy were the primary goal, perhaps certain deals would be blocked if the merging parties are both in possession of detailed consumer data without regard to their size or existence of other players in the same space.  

The failure of neo-structuralism (etc.) is, in my view, also likely the basis for its growing popularity.  Petit argues that the flaw is that it promotes rivalry as an end in itself.  I posit instead that neo-structuralism is flawed because it promotes rivalry as a means and is agnostic to the ends.  As a result, people with strongly differing views on the optimal ends of competition law can appear to agree with one another by agreeing on the means and in doing so, promote a competition law framework that risks being untethered and undisciplined.  In the absence of a clearly articulated policy goal – whether it is privacy, or economic equality, or diluting political influence, or even consumer welfare – there is no basis on which to evaluate whether any given competition law is structured or applied optimally.  If rivalry is to be the means by which we implement our policy goals, how do we know when we have enough rivalry, or too little?  We can’t.  

It is on this point that I think there is more work to undertake in a complete critique of the failings of neo-structuralism (and any other neo-isms to come).  In addition to other merits, welfare maximization gives us a framework to hold the construct and application of competition law accountable.  It is irresponsible to replace a system that has, as Petit puts it, an “evaluative premise” with one possesses no ends-based framework for evaluation, leaving the law rudderless and susceptible to arbitrary or even selective enforcement.

[TOTM: The following is part of a symposium by TOTM guests and authors marking the release of Nicolas Petit’s “Big Tech and the Digital Economy: The Moligopoly Scenario.” The entire series of posts is available here.

This post is authored by Peter Klein (Professor of Entrepreneurship, Baylor University).
]

Nicolas Petit’s insightful and provocative book ends with a chapter on “Big Tech’s Novel Harms,” asking whether antitrust is the appropriate remedy for popular (and academic) concerns about privacy, fake news, and hate speech. In each case, he asks whether the alleged harms are caused by a lack of competition among platforms – which could support a case for breaking them up – or by the nature of the underlying technologies and business models. He concludes that these problems are not alleviated (and may even be exacerbated) by applying competition policy and suggests that regulation, not antitrust, is the more appropriate tool for protecting privacy and truth.

What kind of regulation? Treating digital platforms like public utilities won’t work, Petit argues, because the product is multidimensional and competition takes place on multiple margins (the larger theme of the book): “there is a plausible chance that increased competition in digital markets will lead to a race to the bottom, in which price competition (e.g., on ad markets) will be the winner, and non-price competition (e.g., on privacy) will be the loser.” Utilities regulation also provides incentives for rent-seeking by less efficient rivals. Retail regulation, aimed at protecting small firms, may end up helping incumbents instead by raising rivals’ costs.

Petit concludes that consumer protection regulation (such as Europe’s GDPR) is a better tool for guarding privacy and truth, though it poses challenges as well. More generally, he highlights the vast gulf between the economic analysis of privacy and speech and the increasingly loud calls for breaking up the big tech platforms, which would do little to alleviate these problems.

As in the rest of the book, Petit’s treatment of these complex issues is thoughtful, careful, and systematic. I have more fundamental problems with conventional antitrust remedies and think that consumer protection is problematic when applied to data services (even more so than in other cases). Inspired by this chapter, let me offer some additional thoughts on privacy and the nature of data which speak to regulation of digital platforms and services.

First, privacy, like information, is not an economic good. Just as we don’t buy and sell information per se but information goods (books, movies, communications infrastructure, consultants, training programs, etc.), we likewise don’t produce and consume privacy but what we might call privacy goods: sunglasses, disguises, locks, window shades, land, fences and, in the digital realm, encryption software, cookie blockers, data scramblers, and so on.

Privacy goods and services can be analyzed just like other economic goods. Entrepreneurs offer bundled services that come with varying degrees of privacy protection: encrypted or regular emails, chats, voice and video calls; browsers that block cookies or don’t; social media sites, search engines, etc. that store information or not; and so on. Most consumers seem unwilling to sacrifice other functionality for increased privacy, as suggested by the small market shares held by DuckDuckGo, Telegram, Tor, and the like suggest. Moreover, while privacy per se is appealing, there are huge efficiency gains from matching on buyer and seller characteristics on sharing platforms, digital marketplaces, and dating sites. There are also substantial cost savings from electronic storage and sharing of private information such as medical records and credit histories. And there is little evidence of sellers exploiting such information to engage in price discrimination. (Aquisti, Taylor, and Wagman, 2016 provide a detailed discussion of many of these issues.)

Regulating markets for privacy goods via bans on third-party access to customer data, mandatory data portability, and stiff penalties for data breaches is tricky. Such policies could make digital services more valuable, but it is not obvious why the market cannot figure this out. If consumers are willing to pay for additional privacy, entrepreneurs will be eager to supply it. Of course, bans on third-party access and other forms of sharing would require a fundamental change in the ad-based revenue model that makes free or low-cost access possible, so platforms would have to devise other means of monetizing their services. (Again, many platforms already offer ad-free subscriptions, so it’s unclear why those who prefer ad-based, free usage should be prevented from doing so.)

What about the idea that I own “my” data and that, therefore, I should have full control over how it is used? Some of the utilities-based regulatory models treat platforms as neutral storage places or conduits for information belonging to users. Proposals for data portability suggest that users of technology platforms should be able to move their data from platform to platform, downloading all their personal information from one platform then uploading it to another, then enjoying the same functionality on the new platform as longtime users.

Of course, there are substantial technical obstacles to such proposals. Data would have to be stored in a universal format – not just the text or media users upload to platforms, but also records of all interactions (likes, shares, comments), the search and usage patterns of users, and any other data generated as a result of the user’s actions and interactions with other users, advertisers, and the platform itself. It is unlikely that any universal format could capture this information in a form that could be transferred from one platform to another without a substantial loss of functionality, particularly for platforms that use algorithms to determine how information is presented to users based on past use. (The extreme case is a platform like TikTok which uses usage patterns as a substitute for follows, likes, and shares, portability to construct a “feed.”)

Moreover, as each platform sets its own rules for what information is allowed, the import functionality would have to screen the data for information allowed on the original platform but not the new (and the reverse would be impossible – a user switching from Twitter to Gab, for instance, would have no way to add the content that would have been permitted on Gab but was never created in the first place because it would have violated Twitter rules).

There is a deeper, philosophical issue at stake, however. Portability and neutrality proposals take for granted that users own “their” data. Users create data, either by themselves or with their friends and contacts, and the platform stores and displays the data, just as a safe deposit box holds documents or jewelry and a display case shows of an art collection. I should be able to remove my items from the safe deposit box and take them home or to another bank, and a “neutral” display case operator should not prevent me from showing off my preferred art (perhaps subject to some general rules about obscenity or harmful personal information).

These analogies do not hold for user-generated information on internet platforms, however. “My data” is a record of all my interactions with platforms, with other users on those platforms, with contractual partners of those platforms, and so on. It is co-created by these interactions. I don’t own these records any more than I “own” the fact that someone saw me in the grocery store yesterday buying apples. Of course, if I have a contract with the grocer that says he will keep my purchase records private, and he shares them with someone else, then I can sue him for breach of contract. But this isn’t theft. He hasn’t “stolen” anything; there is nothing for him to steal. If a grocer — or an owner of a tech platform — wants to attract my business by monetizing the records of our interactions and giving me a cut, he should go for it. I still might prefer another store. In any case, I don’t have the legal right to demand this revenue stream.

Likewise, “privacy” refers to what other people know about me – it is knowledge in their heads, not mine. Information isn’t property. If I know something about you, that knowledge is in my head; it’s not something I took from you. Of course, if I obtained or used that info in violation of a prior agreement, then I’m guilty of breach, and I use that information to threaten or harass you, I may be guilty of other crimes. But the popular idea that tech companies are stealing and profiting from something that’s “ours” isn’t right.

The concept of co-creation is important, because these digital records, like other co-created assets, can be more or less relationship specific. The late Oliver Williamson devoted his career to exploring the rich variety of contractual relationships devised by market participants to solve complex contracting problems, particularly in the face of asset specificity. Relationship-specific investments can be difficult for trading parties to manage, but they typically create more value. A legal regime in which only general-purpose, easily redeployable technologies were permitted would alleviate the holdup problem, but at the cost of a huge loss in efficiency. Likewise, a world in which all digital records must be fully portable reduces switching costs, but results in technologies for creating, storing, and sharing information that are less valuable. Why would platform operators invest in efficiency improvements if they cannot capture some of that value by means of proprietary formats, interfaces, sharing rules, and other arrangements?  

In short, we should not be quick to assume “market failure” in the market for privacy goods (or “true” news, whatever that is). Entrepreneurs operating in a competitive environment – not the static, partial-equilibrium notion of competition from intermediate micro texts but the rich, dynamic, complex, and multimarket kind of competition described in Petit’s book – can provide the levels of privacy and truthiness that consumers prefer.

[TOTM: The following is part of a symposium by TOTM guests and authors marking the release of Nicolas Petit’s “Big Tech and the Digital Economy: The Moligopoly Scenario.” The entire series of posts is available here.

This post is authored by Richard N. Langlois
(Professor of Economics, University of Connecticut).]

Market share has long been the talisman of antitrust economics.  Once we properly define what “the product” is, all we have to do is look at shares in the relevant market.  In such an exercise, today’s high-tech firms come off badly.  Each of them has a large share of the market for some “product.” What I appreciate about Nicolas Petit’s notion of “moligopoly” is that it recognizes that genuine competition is a far more complex and interesting phenomenon, one that goes beyond the category of “the product.”

In his chapter 4, Petit lays out how this works with six of today’s large high-tech companies, adding Netflix to the usual Big Five of Amazon, Apple, Facebook, Google, and Microsoft.  If I understand properly, what he means by “moligopoly” is that these large firms have their hands in many different relevant markets.  Because they seem to be selling different “products,” they don’t seem to be competing with one another.  Yet, in a fundamental sense, they are very much competing with one another, and perhaps with firms that do not yet exist.  

In this view, diversification is at the heart of competition.  Indeed, Petit wonders at one point whether we are in a new era of “conglomeralism.”  I would argue that the diversified high-tech firms we see today are actually very unlike the conglomerates of the late twentieth century.  In my view, the earlier conglomerates were not equilibrium phenomena but rather short-lived vehicles for the radical restructuring of the American economy in the post- Bretton Woods era of globalization.  A defining characteristic of those firms was that their diversification was unrelated, not just in terms of the SIC codes of their products but also in terms of their underlying capabilities.  If we look only at the products on the demand side, today’s high-tech firms might also seem to reflect unrelated diversification.  In fact, however, unlike in the twentieth-century conglomerates, the activities of present-day high-tech firms are connected on the supply side by a common set of capabilities involving the deployment of digital technology. 

Thus the boundaries of markets can shift and morph unexpectedly.  Enterprises that may seem entirely different actually harbor the potential to invade one other’s territory (or invade new territory – “competing against non-consumption”).  What Amazon can do, Google can do; and so can Microsoft.  The arena is competitive not because firms have a small share of relevant markets but because all of them sit beneath four or five damocletian swords, suspended by the thinnest of horsehairs.  No wonder the executives of high-tech firms sound paranoid.

Petit speculates that today’s high-tech companies have diversified (among other reasons) because of complementarities.  That may be part of the story.  But as Carliss Baldwin argues (and as Petit mentions in passing), we can think about the investments high-tech firms seem to be making as options – experiments that may or may not pay off.  The more uncertain the environment, the more valuable it is to have many diverse options.  A decade or so after the breakup of AT&T, the “baby Bells” were buying into landline, cellular, cable, satellite, and many other things, not because, as many thought at the time, that these were complementary, but because no one had any idea what would be important in the future (including whether there would be any complementarities).  As uncertainty resolved, these lines of business became more specialized, and the babies unbundled.  (As I write, AT&T, the baby Bell that snagged the original company name, is probably about to sell off DirectTV at a loss.)  From this perspective, the high degree of diversification we observe today implies not control of markets but the opposite – existential uncertainty about the future.

I wonder whether this kind of competition is unique to the age of the Internet.  There is an entire genre of business-school case built around an epiphany of the form: “we thought we were in the X business, but we were really in the Y business all along!”  I have recently read (listened to, technically) Marc Levinson’s wonderful history of containerized shipping.  Here the real competition occurred across modes of transport, not within existing well-defined markets.  The innovators came to realize that they were in the logistics business, not in the trucking business or the railroad business or the ocean-shipping business.  (Some of the most interesting parts of the story were about how entrepreneurship happens in a heavily regulated environment.  At one point early in the story, Malcolm McLean, the most important of these entrepreneurs, had to buy up other trucking firms just to obtain the ICC permits necessary to redesign routes efficiently.)  Of course, containerized shipping is also a modular system that some economists have accused of being a general-purpose technology like the Internet.