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In current discussions of technology markets, few words are heard more often than “platform.” Initial public offering (IPO) prospectuses use “platform” to describe a service that is bound to dominate a digital market. Antitrust regulators use “platform” to describe a service that dominates a digital market or threatens to do so. In either case, “platform” denotes power over price. For investors, that implies exceptional profits; for regulators, that implies competitive harm.

Conventional wisdom holds that platforms enjoy high market shares, protected by high barriers to entry, which yield high returns. This simple logic drives the market’s attribution of dramatically high valuations to dramatically unprofitable businesses and regulators’ eagerness to intervene in digital platform markets characterized by declining prices, increased convenience, and expanded variety, often at zero out-of-pocket cost. In both cases, “burning cash” today is understood as the path to market dominance and the ability to extract a premium from consumers in the future.

This logic is usually wrong. 

The Overlooked Basics of Platform Economics

To appreciate this perhaps surprising point, it is necessary to go back to the increasingly overlooked basics of platform economics. A platform can refer to any service that matches two complementary populations. A search engine matches advertisers with consumers, an online music service matches performers and labels with listeners, and a food-delivery service matches restaurants with home diners. A platform benefits everyone by facilitating transactions that otherwise might never have occurred.

A platform’s economic value derives from its ability to lower transaction costs by funneling a multitude of individual transactions into a single convenient hub.  In pursuit of minimum costs and maximum gains, users on one side of the platform will tend to favor the most popular platforms that offer the largest number of users on the other side of the platform. (There are partial exceptions to this rule when users value being matched with certain typesof other users, rather than just with more users.) These “network effects” mean that any successful platform market will always converge toward a handful of winners. This positive feedback effect drives investors’ exuberance and regulators’ concerns.

There is a critical point, however, that often seems to be overlooked.

Market share only translates into market power to the extent the incumbent is protected against entry within some reasonable time horizon.  If Warren Buffett’s moat requirement is not met, market share is immaterial. If owns 100% of the online pet food delivery market but entry costs are asymptotic, then market power is negligible. There is another important limiting principle. In platform markets, the depth of the moat depends not only on competitors’ costs to enter the market, but users’ costs in switching from one platform to another or alternating between multiple platforms. If users can easily hop across platforms, then market share cannot confer market power given the continuous threat of user defection. Put differently: churn limits power over price.

Contrary to natural intuitions, this is why a platform market consisting of only a few leaders can still be intensely competitive, keeping prices low (down to and including $0) even if the number of competitors is low. It is often asserted, however, that users are typically locked into the dominant platform and therefore face high switching costs, which therefore implicitly satisfies the moat requirement. If that is true, then the “high churn” scenario is a theoretical curiosity and a leading platform’s high market share would be a reliable signal of market power. In fact, this common assumption likely describes the atypical case. 

AWS and the Cloud Data-Storage Market

This point can be illustrated by considering the cloud data-storage market. This would appear to be an easy case where high switching costs (due to the difficulty in shifting data among storage providers) insulate the market leader against entry threats. Yet the real world does not conform to these expectations. 

While Amazon Web Services pioneered the $100 billion-plus market and is still the clear market leader, it now faces vigorous competition from Microsoft Azure, Google Cloud, and other data-storage or other cloud-related services. This may reflect the fact that the data storage market is far from saturated, so new users are up for grabs and existing customers can mitigate lock-in by diversifying across multiple storage providers. Or it may reflect the fact that the market’s structure is fluid as a function of technological changes, enabling entry at formerly bundled portions of the cloud data-services package. While it is not always technologically feasible, the cloud storage market suggests that users’ resistance to platform capture can represent a competitive opportunity for entrants to challenge dominant vendors on price, quality, and innovation parameters.

The Surprising Instability of Platform Dominance

The instability of leadership positions in the cloud storage market is not exceptional. 

Consider a handful of once-powerful platforms that were rapidly dethroned once challenged by a more efficient or innovative rival: Yahoo and Alta Vista in the search-engine market (displaced by Google); Netscape in the browser market (displaced by Microsoft’s Internet Explorer, then displaced by Google Chrome); Nokia and then BlackBerry in the mobile wireless-device market (displaced by Apple and Samsung); and Friendster in the social-networking market (displaced by Myspace, then displaced by Facebook). AOL was once thought to be indomitable; now it is mostly referenced as a vintage email address. The list could go on.

Overestimating platform dominance—or more precisely, assuming platform dominance without close factual inquiry—matters because it promotes overestimates of market power. That, in turn, cultivates both market and regulatory bubbles: investors inflate stock valuations while regulators inflate the risk of competitive harm. 

DoorDash and the Food-Delivery Services Market

Consider the DoorDash IPO that launched in early December 2020. The market’s current approximately $50 billion valuation of a business that has been almost consistently unprofitable implicitly assumes that DoorDash will maintain and expand its position as the largest U.S. food-delivery platform, which will then yield power over price and exceptional returns for investors. 

There are reasons to be skeptical. Even where DoorDash captures and holds a dominant market share in certain metropolitan areas, it still faces actual and potential competition from other food-delivery services, in-house delivery services (especially by well-resourced national chains), and grocery and other delivery services already offered by regional and national providers. There is already evidence of these expected responses to DoorDash’s perceived high delivery fees, a classic illustration of the disciplinary effect of competitive forces on the pricing choices of an apparently dominant market leader. These “supply-side” constraints imposed by competitors are compounded by “demand-side” constraints imposed by customers. Home diners incur no more than minimal costs when swiping across food-delivery icons on a smartphone interface, casting doubt that high market share is likely to translate in this context into market power.

Deliveroo and the Costs of Regulatory Autopilot

Just as the stock market can suffer from delusions of platform grandeur, so too some competition regulators appear to have fallen prey to the same malady. 

A vivid illustration is provided by the 2019 decision by the Competition Markets Authority (CMA), the British competition regulator, to challenge Amazon’s purchase of a 16% stake in Deliveroo, one of three major competitors in the British food-delivery services market. This intervention provides perhaps the clearest illustration of policy action based on a reflexive assumption of market power, even in the face of little to no indication that the predicate conditions for that assumption could plausibly be satisfied.

Far from being a dominant platform, Deliveroo was (and is) a money-losing venture lagging behind money-losing Just Eat (now Just Eat Takeaway) and Uber Eats in the U.K. food-delivery services market. Even Amazon had previously closed its own food-delivery service in the U.K. due to lack of profitability. Despite Deliveroo’s distressed economic circumstances and the implausibility of any market power arising from Amazon’s investment, the CMA nonetheless elected to pursue the fullest level of investigation. While the transaction was ultimately approved in August 2020, this intervention imposed a 15-month delay and associated costs in connection with an investment that almost certainly bolstered competition in a concentrated market by funding a firm reportedly at risk of insolvency.  This is the equivalent of a competition regulator driving in reverse.

Concluding Thoughts

There seems to be an increasingly common assumption in commentary by the press, policymakers, and even some scholars that apparently dominant platforms usually face little competition and can set, at will, the terms of exchange. For investors, this is a reason to buy; for regulators, this is a reason to intervene. This assumption is sometimes realized, and, in that case, antitrust intervention is appropriate whenever there is reasonable evidence that market power is being secured through something other than “competition on the merits.” However, several conditions must be met to support the market power assumption without which any such inquiry would be imprudent. Contrary to conventional wisdom, the economics and history of platform markets suggest that those conditions are infrequently satisfied.

Without closer scrutiny, reflexively equating market share with market power is prone to lead both investors and regulators astray.  

[TOTM: The following is part of a digital symposium by TOTM guests and authors on the law, economics, and policy of the antitrust lawsuits against Google. The entire series of posts is available here.]

Google is facing a series of lawsuits in 2020 and 2021 that challenge some of the most fundamental parts of its business, and of the internet itself — Search, Android, Chrome, Google’s digital-advertising business, and potentially other services as well. 

The U.S. Justice Department (DOJ) has brought a case alleging that Google’s deals with Android smartphone manufacturers, Apple, and third-party browsers to make Google Search their default general search engine are anticompetitive (ICLE’s tl;dr on the case is here), and the State of Texas has brought a suit against Google’s display advertising business. These follow a market study by the United K’s Competition and Markets Authority that recommended an ex ante regulator and code of conduct for Google and Facebook. At least one more suit is expected to follow.

These lawsuits will test ideas that are at the heart of modern antitrust debates: the roles of defaults and exclusivity deals in competition; the costs of self-preferencing and its benefits to competition; the role of data in improving software and advertising, and its role as a potential barrier to entry; and potential remedies in these markets and their limitations.

This Truth on the Market symposium asks contributors with wide-ranging viewpoints to comment on some of these issues as they arise in the lawsuits being brought—starting with the U.S. Justice Department’s case against Google for alleged anticompetitive practices in search distribution and search-advertising markets—and continuing throughout the duration of the lawsuits.

Rolled by Rewheel, Redux

Eric Fruits —  15 December 2020

The Finnish consultancy Rewheel periodically issues reports using mobile wireless pricing information to make claims about which countries’ markets are competitive and which are not. For example, Rewheel claims Canada and Greece have the “least competitive monthly prices” while the United Kingdom and Finland have the most competitive.

Rewheel often claims that the number of carriers operating in a country is the key determinant of wireless pricing. 

Their pricing studies attract a great deal of attention. For example, in February 2019 testimony before the U.S. House Energy and Commerce Committee, Phillip Berenbroick of Public Knowledge asserted: “Rewheel found that consumers in markets with three facilities-based providers paid twice as much per gigabyte as consumers in four firm markets.” So, what’s wrong with Rewheel? An earlier post highlights some of the flaws in Rewheel’s methodology. But there’s more.

Rewheel creates fictional market baskets of mobile plans for each provider in a county. Country-by-country comparisons are made by evaluating the lowest-priced basket for each country and the basket with the median price.

Rewheel’s market baskets are hypothetical packages that say nothing about which plans are actually chosen by consumers or what the actual prices paid by those consumers were. This is not a new criticism. In 2014, Pauline Affeldt and Rainer Nitsche called these measures “meaningless”:

Such approaches are taken by Rewheel (2013) and also the Austrian regulator rtr … Such studies face the following problems: They may pick tariffs that are relatively meaningless in the country. They will have to assume one or more consumption baskets (voice minutes, data volume etc.) in order to compare tariffs. This may drive results. Apart from these difficulties such comparisons require very careful tracking of tariffs and their changes. Even if one assumes studying a sample of tariffs is potentially meaningful, a comparison across countries (or over time) would still require taking into account key differences across countries (or over time) like differences in demand, costs, network quality etc.

For example, reporting that the average price of a certain T-Mobile USA smartphone, tablet and home Internet plan is $125 is about as useless as knowing that the average price of a Kroger shopping cart containing a six-pack of Budweiser, a dozen eggs, and a pound of oranges is $10. Is Safeway less “competitive” if the price of the same cart of goods is $12? What could you say about pricing at a store that doesn’t sell Budweiser (e.g., Trader Joe’s)?

Rewheel solves that last problem by doing something bonkers. If a carrier doesn’t offer a plan in one of Rewheel’s baskets, they “assign” the HIGHEST monthly price in the world. 

For example, Rewheel notes that Vodafone India does not offer a fixed wireless broadband plan with at least 1,000GB of data and download speeds of 100 Mbps or faster. So, Rewheel “assigns” Vodafone India the highest price in its dataset. That price belongs to a plan that’s sold in the United Kingdom. It simply makes no sense. 

To return to the supermarket analogy, it would be akin to saying that, if a Trader Joe’s in the United States doesn’t sell six-packs of Budweiser, we should assume the price of Budweiser at Trader Joe’s is equal to the world’s most expensive six-pack of the beer. In reality, Trader Joe’s is known for having relatively low prices. But using the Rewheel approach, the store would be assessed to have some of the highest prices.

Because of Rewheel’s “assignment” of highest monthly prices to many plans, it’s irrelevant whether their analysis is based on a country’s median price or lowest price. The median is skewed and the lowest actual may be missing from the dataset.

Rewheel publishes these reports to support its argument that mobile prices are lower in markets with four carriers than in those with three carriers. But even if we accept Rewheel’s price data as reliable, which it isn’t, their own data show no relationship between the number of carriers and average price.

Notice the huge overlap of observations among markets with three and four carriers. 

Rewheel’s latest report provides a redacted dataset, reporting only data usage and weighted average price for each provider. So, we have to work with what we have. 

A simple regression analysis shows there is no statistically significant difference in the intercept or the slopes for markets with three, four or five carriers (the default is three carriers in the regression). Based on the data Rewheel provides to the public, the number of carriers in a country has no relationship to wireless prices.

Rewheel seems to have a rich dataset of pricing information that could be useful to inform policy. It’s a shame that their topline summaries seem designed to support a predetermined conclusion.

Congressman Buck’s “Third Way” report offers a compromise between the House Judiciary Committee’s majority report, which proposes sweeping new regulation of tech companies, and the status quo, which Buck argues is unfair and insufficient. But though Buck rejects many of the majority’s reports proposals, what he proposes instead would lead to virtually the same outcome via a slightly longer process. 

The most significant majority proposals that Buck rejects are the structural separation to prevent a company that runs a platform from operating on that platform “in competition with the firms dependent on its infrastructure”, and line-of-business restrictions that would confine tech companies to a small number of markets, to prevent them from preferencing their other products to the detriment of competitors.

Buck rules these out, saying that they are “regulatory in nature [and] invite unforeseen consequences and divert attention away from public interest antitrust enforcement by our antitrust agencies.” He goes on to say that “this proposal is a thinly veiled call to break up Big Tech firms.”

Instead, Buck endorses, either fully or provisionally, measures including revitalising the essential facilities doctrine, imposing data interoperability mandates on platforms, and changing antitrust law to prevent “monopoly leveraging and predatory pricing”. 

Put together, though, these would amount to the same thing that the Democratic majority report proposes: a world where platforms are basically just conduits, regulated to be neutral and open, and where the companies that run them require a regulator’s go-ahead for important decisions — a process that would be just as influenced lobbying and political considerations, and insulated from market price signals, as any other regulator’s decisions are.

Revitalizing the essential facilities doctrine

Buck describes proposals to “revitalize the essential facilities doctrine” as “common ground” that warrant further consideration. This would mean that platforms deemed to be “essential facilities” would be required to offer access to their platform to third parties at a “reasonable” price, except in exceptional circumstances. The presumption would be that these platforms were anticompetitively foreclosing third party developers and merchants by either denying them access to their platforms or by charging them “too high” prices. 

This would require the kind of regulatory oversight that Buck says he wants to avoid. He says that “conservatives should be wary of handing additional regulatory authority to agencies in an attempt to micromanage platforms’ access rules.” But there’s no way to avoid this when the “facility” — and hence its pricing and access rules — changes as frequently as any digital platform does. In practice, digital platforms would have to justify their pricing rules and decisions about exclusion of third parties to courts or a regulator as often as they make those decisions.

If Apple’s App Store were deemed an essential facility such that it is presumed to be foreclosing third party developers any time it rejected their submissions, it would have to submit to regulatory scrutiny of the “reasonableness” of its commercial decisions on, literally, a daily basis.

That would likely require price controls to prevent platforms from using pricing to de facto exclude third parties they did not want to deal with. Adjudication of “fair” pricing by courts is unlikely to be a sustainable solution. Justice Breyer, in Town of Concord v. Boston Edison Co., considered this to be outside the courts’ purview:

[H]ow is a judge or jury to determine a ‘fair price?’ Is it the price charged by other suppliers of the primary product? None exist. Is it the price that competition ‘would have set’ were the primary level not monopolized? How can the court determine this price without examining costs and demands, indeed without acting like a rate-setting regulatory agency, the rate-setting proceedings of which often last for several years? Further, how is the court to decide the proper size of the price ‘gap?’ Must it be large enough for all independent competing firms to make a ‘living profit,’ no matter how inefficient they may be? . . . And how should the court respond when costs or demands change over time, as they inevitably will?

In practice, infrastructure treated as an essential facility is usually subject to pricing control by a regulator. This has its own difficulties. The UK’s energy and water infrastructure is an example. In determining optimal access pricing, regulators must determine the price that weighs competing needs to maximise short-term output, incentivise investment by the infrastructure owner, incentivise innovation and entry by competitors (e.g., local energy grids) and, of course, avoid “excessive” pricing. 

This is a near-impossible task, and the process is often drawn out and subject to challenges even in markets where the infrastructure is relatively simple. It is even less likely that these considerations would be objectively tractable in digital markets.

Treating a service as an essential facility is based on the premise that, absent mandated access, it is impossible to compete with it. But mandating access does not, on its own, prevent it from extracting monopoly rents from consumers; it just means that other companies selling inputs can have their share of the rents. 

So you may end up with two different sets of price controls: on the consumer side, to determine how much monopoly rent can be extracted from consumers, and on the access side, to determine how the monopoly rents are divided.

The UK’s energy market has both, for example. In the case of something like an electricity network, where it may simply not be physically or economically feasible to construct a second, competing network, this might be the least-bad course of action. In such circumstances, consumer-side price regulation might make sense. 

But if a service could, in fact, be competed with by others, treating it as an essential facility may be affirmatively harmful to competition and consumers if it diverts investment and time away from that potential competitor by allowing other companies to acquire some of the incumbent’s rents themselves.

The HJC report assumes that Apple is a monopolist, because, among people who own iPhones, the App Store is the only way to install third-party software. Treating the App Store as an essential facility may mean a ban on Apple charging “excessive prices” to companies like Spotify or Epic that would like to use it, or on Apple blocking them for offering users alternative in-app ways of buying their services.

If it were impossible for users to switch from iPhones, or for app developers to earn revenue through other mechanisms, this logic might be sound. But it would still not change the fact that the App Store platform was able to charge users monopoly prices; it would just mean that Epic and Spotify could capture some of those monopoly rents for themselves. Nice for them, but not for consumers. And since both companies have already grown to be pretty big and profitable with the constraints they object to in place, it seems difficult to argue that they cannot compete with these in place and sounds more like they’d just like a bigger share of the pie.

And, in fact, it is possible to switch away from the iPhone to Android. I have personally switched back and forth several times over the past few years, for example. And so have many others — despite what some claim, it’s really not that hard, especially now that most important data is stored on cloud-based services, and both companies offer an app to switch from the other. Apple also does not act like a monopolist — its Bionic chips are vastly better than any competitor’s and it continues to invest in and develop them.

So in practice, users switching from iPhone to Android if Epic’s games and Spotify’s music are not available constrains Apple, to some extent. If Apple did drive those services permanently off their platform, it would make Android relatively more attractive, and some users would move away — Apple would bear some of the costs of its ecosystem becoming worse. 

Assuming away this kind of competition, as Buck and the majority report do, is implausible. Not only that, but Buck and the majority believe that competition in this market is impossible — no policy or antitrust action could change things, and all that’s left is to regulate the market like it’s an electricity grid. 

And it means that platforms could often face situations where they could not expect to make themselves profitable after building their markets, since they could not control the supply side in order to earn revenues. That would make it harder to build platforms, and weaken competition, especially competition faced by incumbents.

Mandating interoperability

Interoperability mandates, which Buck supports, require platforms to make their products open and interoperable with third party software. If Twitter were required to be interoperable, for example, it would have to provide a mechanism (probably a set of open APIs) by which third party software could tweet and read its feeds, upload photos, send and receive DMs, and so on. 

Obviously, what interoperability actually involves differs from service to service, and involves decisions about design that are specific to each service. These variations are relevant because they mean interoperability requires discretionary regulation, including about product design, and can’t just be covered by a simple piece of legislation or a court order. 

To give an example: interoperability means a heightened security risk, perhaps from people unwittingly authorising a bad actor to access their private messages. How much is it appropriate to warn users about this, and how tight should your security controls be? It is probably excessive to require that users provide a sworn affidavit with witnesses, and even some written warnings about the risks may be so over the top as to scare off virtually any interested user. But some level of warning and user authentication is appropriate. So how much? 

Similarly, a company that has been required to offer its customers’ data through an API, but doesn’t really want to, can make life miserable for third party services that want to use it. Changing the API without warning, or letting its service drop or slow down, can break other services, and few users will be likely to want to use a third-party service that is unreliable. But some outages are inevitable, and some changes to the API and service are desirable. How do you decide how much?

These are not abstract examples. Open Banking in the UK, which requires interoperability of personal and small business current accounts, is the most developed example of interoperability in the world. It has been cited by former Chair of the Council of Economic Advisors, Jason Furman, among others, as a model for interoperability in tech. It has faced all of these questions: one bank, for instance, required that customers pass through twelve warning screens to approve a third party app to access their banking details.

To address problems like this, Open Banking has needed an “implementation entity” to design many of its most important elements. This is a de facto regulator, and it has taken years of difficult design decisions to arrive at Open Banking’s current form. 

Having helped write the UK’s industry review into Open Banking, I am cautiously optimistic about what it might be able to do for banking in Britain, not least because that market is already heavily regulated and lacking in competition. But it has been a huge undertaking, and has related to a relatively narrow set of data (its core is just two different things — the ability to read an account’s balance and transaction history, and the ability to initiate payments) in a sector that is not known for rapidly changing technology. Here, the costs of regulation may be outweighed by the benefits.

I am deeply sceptical that the same would be the case in most digital markets, where products do change rapidly, where new entrants frequently attempt to enter the market (and often succeed), where the security trade-offs are even more difficult to adjudicate, and where the economics are less straightforward, given that many services are provided at least in part because of the access to customer data they provide. 

Even if I am wrong, it is unavoidable that interoperability in digital markets would require an equivalent body to make and implement decisions when trade-offs are involved. This, again, would require a regulator like the UK’s implementation entity, and one that was enormous, given the number and diversity of services that it would have to oversee. And it would likely have to make important and difficult design decisions to which there is no clear answer. 

Banning self-preferencing

Buck’s Third Way would also ban digital platforms from self-preferencing. This typically involves an incumbent that can provide a good more cheaply than its third-party competitors — whether it’s through use of data that those third parties do not have access to, reputational advantages that mean customers will be more likely to use their products, or through scale efficiencies that allow it to provide goods to a larger customer base for a cheaper price. 

Although many people criticise self-preferencing as being unfair on competitors, “self-preferencing” is an inherent part of almost every business. When a company employs its own in-house accountants, cleaners or lawyers, instead of contracting out for them, it is engaged in internal self-preferencing. Any firm that is vertically integrated to any extent, instead of contracting externally for every single ancillary service other than the one it sells in the market, is self-preferencing. Coase’s theory of the firm is all about why this kind of behaviour happens, instead of every worker contracting on the open market for everything they do. His answer is that transaction costs make it cheaper to bring certain business relationships in-house than to contract externally for them. Virtually everyone agrees that this is desirable to some extent.

Nor does it somehow become a problem when the self-preferencing takes place on the consumer product side. Any firm that offers any bundle of products — like a smartphone that can run only the manufacturer’s operating system — is engaged in self-preferencing, because users cannot construct their own bundle with that company’s hardware and another’s operating system. But the efficiency benefits often outweigh the lack of choice.

Self-preferencing in digital platforms occurs, for example, when Google includes relevant Shopping or Maps results at the top of its general Search results, or when Amazon gives its own store-brand products (like the AmazonBasics range) a prominent place in the results listing.

There are good reasons to think that both of these are good for competition and consumer welfare. Google making Shopping results easily visible makes it a stronger competitor to Amazon, and including Maps results when you search for a restaurant just makes it more convenient to get the information you’re looking for.

Amazon sells its own private label products partially because doing so is profitable (even when undercutting rivals), partially to fill holes in product lines (like clothing, where 11% of listings were Amazon private label as of November 2018), and partially because it increases users’ likelihood to use Amazon if they expect to find a reliable product from a brand they trust. According to Amazon, they account for less than 1% of its annual retail sales, in contrast to the 19% of revenues ($54 billion) Amazon makes from third party seller services, which includes Marketplace commissions. Any analysis that ignores that Amazon has to balance those sources of revenue, and so has to tread carefully, is deficient. 

With “commodity” products (like, say, batteries and USB cables), where multiple sellers are offering very similar or identical versions of the same thing, private label competition works well for both Amazon and consumers. By Amazon’s own rules it can enter this market using aggregated data, but this doesn’t give it a significant advantage, because that data is easily obtainable from multiple sources, including Amazon itself, which makes detailed aggregated sales data freely available to third-party retailers

Amazon does profit from sales of these products, of course. And other merchants suffer by having to cut their prices to compete. That’s precisely what competition involves — competition is incompatible with a quiet life for businesses. But consumers benefit, and the biggest benefit to Amazon is that it assures its potential customers that when they visit they will be able to find a product that is cheap and reliable, so they keep coming back.

It is even hard to argue that in aggregate this practice is damaging to third-party sellers: many, like Anker, have built successful businesses on Amazon despite private-label competition precisely because the value of the platform increases for all parties as user trust and confidence in it does.

In these cases and in others, platforms act to solve market failures on the markets they host, as Andrei Hagiu has argued. To maximize profits, digital platforms need to strike a balance between being an attractive place for third-party merchants to sell their goods and being attractive to consumers by offering low prices. The latter will frequently clash with the former — and that’s the difficulty of managing a platform. 

To mistake this pro-competitive behaviour with an absence of competition is misguided. But that is a key conclusion of Buck’s Third Way: that the damage to competitors makes this behaviour harmful overall, and that it should be curtailed with “non-discrimination” rules. 

Treating below-cost selling as “predatory pricing”

Buck’s report equates below-cost selling with predatory pricing (“predatory pricing, also known as below-cost selling”). This is mistaken. Predatory pricing refers to a particular scenario where your price cut is temporary and designed to drive a competitor out of business, so that you can raise prices later and recoup your losses. 

It is easy to see that this does not describe the vast majority of below-cost selling. Buck’s formulation would describe all of the following as “predatory pricing”:

  • A restaurants that gives away ketchup for free;
  • An online retailer that offers free shipping and returns;
  • A grocery store that sells tins of beans for 3p a can. (This really happened when I was a child.)

The rationale for offering below-cost prices differs in each of these cases. Sometimes it’s a marketing ploy — Tesco sells those beans to get some free media, and to entice people into their stores, hoping they’ll decide to do the rest of their weekly shop there at the same time. Sometimes it’s about reducing frictions — the marginal cost of ketchup is so low that it’s simpler to just give it away. Sometimes it’s about reducing the fixed costs of transactions so more take place — allowing customers who buy your products to return them easily may mean more are willing to buy them overall, because there’s less risk for them if they don’t like what they buy. 

Obviously, none of these is “predatory”: none is done in the expectation that the below-cost selling will drive those businesses’ competitors out of business, allowing them to make monopoly profits later.

True predatory pricing is theoretically possible, but very difficult. As David Henderson describes, to successfully engage in predatory pricing means taking enormous and rising losses that grow for the “predatory” firm as customers switch to it from its competitor. And once the rival firm has exited the market, if the predatory firm raises prices above average cost (i.e., to recoup its losses), there is no guarantee that a new competitor will not enter the market selling at the previously competitive price. And the competing firm can either shut down temporarily or, in some cases, just buy up the “predatory” firm’s discounted goods to resell later. It is debatable whether the canonical predatory pricing case, Standard Oil, is itself even an example of that behaviour.

Offering a product below cost in a multi-sided market (like a digital platform) can be a way of building a customer base in order to incentivise entry on the other side of the market. When network effects exist, so additional users make the service more valuable to existing users, it can be worthwhile to subsidise the initial users until the service reaches a certain size. 

Uber subsidising drivers and riders in a new city is an example of this — riders want enough drivers on the road that they know they’ll be picked up fairly quickly if they order one, and drivers want enough riders that they know they’ll be able to earn a decent night’s fares if they use the app. This requires a certain volume of users on both sides — to get there, it can be in everyone’s interest for the platform to subsidise one or both sides of the market to reach that critical mass.

The slightly longer road to regulation

That is another reason for below-cost pricing: someone other than the user may be part-paying for a product, to build a market they hope to profit from later. Platforms must adjust pricing and their offerings to each side of their market to manage supply and demand. Epic, for example, is trying to build a desktop computer game store to rival the largest incumbent, Steam. To win over customers, it has been giving away games for free to users, who can own them on that store forever. 

That is clearly pro-competitive — Epic is hoping to get users over the habit of using Steam for all their games, in the hope that they will recoup the costs of doing so later in increased sales. And it is good for consumers to get free stuff. This kind of behaviour is very common. As well as Uber and Epic, smaller platforms do it too. 

Buck’s proposals would make this kind of behaviour much more difficult, and permitted only if a regulator or court allows it, instead of if the market can bear it. On both sides of the coin, Buck’s proposals would prevent platforms from the behaviour that allows them to grow in the first place — enticing suppliers and consumers and subsidising either side until critical mass has been reached that allows the platform to exist by itself, and the platform owner to recoup its investments. Fundamentally, both Buck and the majority take the existence of platforms as a given, ignoring the incentives to create new ones and compete with incumbents. 

In doing so, they give up on competition altogether. As described, Buck’s provisions would necessitate ongoing rule-making, including price controls, to work. It is unlikely that a court could do this, since the relevant costs would change too often for one-shot rule-making of the kind a court could do. To be effective at all, Buck’s proposals would require an extensive, active regulator, just as the majority report’s would. 

Buck nominally argues against this sort of outcome — “Conservatives should be wary of handing additional regulatory authority to agencies in an attempt to micromanage platforms’ access rules” — but it is probably unavoidable, given the changes he proposes. And because the rule changes he proposes would apply to the whole economy, not just tech, his proposals may, perversely, end up being even more extensive and interventionist than the majority’s.

Other than this, the differences in practice between Buck’s proposals and the Democrats’ proposals would be trivial. At best, Buck’s Third Way is just a longer route to the same destination.

What is a search engine?

Dirk Auer —  21 October 2020

What is a search engine? This might seem like an innocuous question, but it lies at the heart of the US Department of Justice and state Attorneys’ General antitrust complaint against Google, as well as the European Commission’s Google Search and Android decisions. It is also central to a report published by the UK’s Competition & Markets Authority (“CMA”). To varying degrees, all of these proceedings are premised on the assumption that Google enjoys a monopoly/dominant position over online search. But things are not quite this simple. 

Despite years of competition decisions and policy discussions, there are still many unanswered questions concerning the operation of search markets. For example, it is still unclear exactly which services compete against Google Search, and how this might evolve in the near future. Likewise, there has only been limited scholarly discussion as to how a search engine monopoly would exert its market power. In other words, what does a restriction of output look like on a search platform — particularly on the user side

Answering these questions will be essential if authorities wish to successfully bring an antitrust suit against Google for conduct involving search. Indeed, as things stand, these uncertainties greatly complicate efforts (i) to rigorously define the relevant market(s) in which Google Search operates, (ii) to identify potential anticompetitive effects, and (iii) to apply the quantitative tools that usually underpin antitrust proceedings.

In short, as explained below, antitrust authorities and other plaintiffs have their work cut out if they are to prevail in court.

Consumers demand information 

For a start, identifying the competitive constraints faced by Google presents authorities and plaintiffs with an important challenge.

Even proponents of antitrust intervention recognize that the market for search is complex. For instance, the DOJ and state AGs argue that Google dominates a narrow market for “general search services” — as opposed to specialized search services, content sites, social networks, and online marketplaces, etc. The EU Commission reached the same conclusion in its Google Search decision. Finally, commenting on the CMA’s online advertising report, Fiona Scott Morton and David Dinielli argue that: 

General search is a relevant market […]

In this way, an individual specialized search engine competes with a small fraction of what the Google search engine does, because a user could employ either for one specific type of search. The CMA concludes that, from the consumer standpoint, a specialized search engine exerts only a limited competitive constraint on Google.

(Note that the CMA stressed that it did not perform a market definition exercise: “We have not carried out a formal market definition assessment, but have instead looked at competitive constraints across the sector…”).

In other words, the above critics recognize that search engines are merely tools that can serve multiple functions, and that competitive constraints may be different for some of these. But this has wider ramifications that policymakers have so far overlooked. 

When quizzed about his involvement with Neuralink (a company working on implantable brain–machine interfaces), Elon Musk famously argued that human beings already share a near-symbiotic relationship with machines (a point already made by others):

The purpose of Neuralink [is] to create a high-bandwidth interface to the brain such that we can be symbiotic with AI. […] Because we have a bandwidth problem. You just can’t communicate through your fingers. It’s just too slow.

Commentators were quick to spot this implications of this technology for the search industry:

Imagine a world when humans would no longer require a device to search for answers on the internet, you just have to think of something and you get the answer straight in your head from the internet.

As things stand, this example still belongs to the realm of sci-fi. But it neatly illustrates a critical feature of the search industry. 

Search engines are just the latest iteration (but certainly not the last) of technology that enables human beings to access specific pieces of information more rapidly. Before the advent of online search, consumers used phone directories, paper maps, encyclopedias, and other tools to find the information they were looking for. They would read newspapers and watch television to know the weather forecast. They went to public libraries to undertake research projects (some still do), etc.

And, in some respects, the search engine is already obsolete for many of these uses. For instance, virtual assistants like Alexa, Siri, Cortana and Google’s own Google Assistant offering can perform many functions that were previously the preserve of search engines: checking the weather, finding addresses and asking for directions, looking up recipes, answering general knowledge questions, finding goods online, etc. Granted, these virtual assistants partly rely on existing search engines to complete tasks. However, Google is much less dominant in this space, and search engines are not the sole source on which virtual assistants rely to generate results. Amazon’s Alexa provides a fitting example (here and here).

Along similar lines, it has been widely reported that 60% of online shoppers start their search on Amazon, while only 26% opt for Google Search. In other words, Amazon’s ability to rapidly show users the product they are looking for somewhat alleviates the need for a general search engine. In turn, this certainly constrains Google’s behavior to some extent. And much of the same applies to other websites that provide a specific type of content (think of Twitter, LinkedIn, Tripadvisor,, etc.)

Finally, it is also revealing that the most common searches on Google are, in all likelihood, made to reach other websites — a function for which competition is literally endless:

The upshot is that Google Search and other search engines perform a bundle of functions. Most of these can be done via alternative means, and this will increasingly be the case as technology continues to advance. 

This is all the more important given that the vast majority of search engine revenue derives from roughly 30 percent of search terms (notably those that are linked to product searches). The remaining search terms are effectively a loss leader. And these profitable searches also happen to be those where competition from alternative means is, in all likelihood, the strongest (this includes competition from online retail platforms, and online travel agents like or Kayak, but also from referral sites, direct marketing, and offline sources). In turn, this undermines US plaintiffs’ claims that Google faces little competition from rivals like Amazon, because they don’t compete for the entirety of Google’s search results (in other words, Google might face strong competition for the most valuable ads):

108. […] This market share understates Google’s market power in search advertising because many search-advertising competitors offer only specialized search ads and thus compete with Google only in a limited portion of the market. 

Critics might mistakenly take the above for an argument that Google has no market power because competition is “just a click away”. But the point is more subtle, and has important implications as far as market definition is concerned.

Authorities should not define the search market by arguing that no other rival is quite like Google (or one if its rivals) — as the DOJ and state AGs did in their complaint:

90. Other search tools, platforms, and sources of information are not reasonable substitutes for general search services. Offline and online resources, such as books, publisher websites, social media platforms, and specialized search providers such as Amazon, Expedia, or Yelp, do not offer consumers the same breadth of information or convenience. These resources are not “one-stop shops” and cannot respond to all types of consumer queries, particularly navigational queries. Few consumers would find alternative sources a suitable substitute for general search services. Thus, there are no reasonable substitutes for general search services, and a general search service monopolist would be able to maintain quality below the level that would prevail in a competitive market. 

And as the EU Commission did in the Google Search decision:

(162) For the reasons set out below, there is, however, limited demand side substitutability between general search services and other online services. […]

(163) There is limited substitutability between general search services and content sites. […]

(166) There is also limited substitutability between general search services and specialised search services. […]

(178) There is also limited substitutability between general search services and social networking sites.

Ad absurdum, if consumers suddenly decided to access information via other means, Google could be the only firm to provide general search results and yet have absolutely no market power. 

Take the example of Yahoo: Despite arguably remaining the most successful “web directory”, it likely lost any market power that it had when Google launched a superior — and significantly more successful — type of search engine. Google Search may not have provided a complete, literal directory of the web (as did Yahoo), but it offered users faster access to the information they wanted. In short, the Yahoo example shows that being unique is not equivalent to having market power. Accordingly, any market definition exercise that merely focuses on the idiosyncrasies of firms is likely to overstate their actual market power. 

Given what precedes, the question that authorities should ask is thus whether Google Search (or another search engine) performs so many unique functions that it may be in a position to restrict output. So far, no one appears to have convincingly answered this question.

Similar uncertainties surround the question of how a search engine might restrict output, especially on the user side of the search market. Accordingly, authorities will struggle to produce evidence (i) the Google has market power, especially on the user side of the market, and (ii) that its behavior has anticompetitive effects.

Consider the following:

The SSNIP test (which is the standard method of defining markets in antitrust proceedings) is inapplicable to the consumer side of search platforms. Indeed, it is simply impossible to apply a hypothetical 10% price increase to goods that are given away for free.

This raises a deeper question: how would a search engine exercise its market power? 

For a start, it seems unlikely that it would start charging fees to its users. For instance, empirical research pertaining to the magazine industry (also an ad-based two-sided market) suggests that increased concentration does not lead to higher magazine prices. Minjae Song notably finds that:

Taking the advantage of having structural models for both sides, I calculate equilibrium outcomes for hypothetical ownership structures. Results show that when the market becomes more concentrated, copy prices do not necessarily increase as magazines try to attract more readers.

It is also far from certain that a dominant search engine would necessarily increase the amount of adverts it displays. To the contrary, market power on the advertising side of the platform might lead search engines to decrease the number of advertising slots that are available (i.e. reducing advertising output), thus showing less adverts to users. 

Finally, it is not obvious that market power would lead search engines to significantly degrade their product (as this could ultimately hurt ad revenue). For example, empirical research by Avi Goldfarb and Catherine Tucker suggests that there is some limit to the type of adverts that search engines could profitably impose upon consumers. They notably find that ads that are both obtrusive and targeted decrease subsequent purchases:

Ads that match both website content and are obtrusive do worse at increasing purchase intent than ads that do only one or the other. This failure appears to be related to privacy concerns: the negative effect of combining targeting with obtrusiveness is strongest for people who refuse to give their income and for categories where privacy matters most.

The preceding paragraphs find some support in the theoretical literature on two-sided markets literature, which suggests that competition on the user side of search engines is likely to be particularly intense and beneficial to consumers (because they are more likely to single-home than advertisers, and because each additional user creates a positive externality on the advertising side of the market). For instance, Jean Charles Rochet and Jean Tirole find that:

The single-homing side receives a large share of the joint surplus, while the multi-homing one receives a small share.

This is just a restatement of Mark Armstrong’s “competitive bottlenecks” theory:

Here, if it wishes to interact with an agent on the single-homing side, the multi-homing side has no choice but to deal with that agent’s chosen platform. Thus, platforms have monopoly power over providing access to their single-homing customers for the multi-homing side. This monopoly power naturally leads to high prices being charged to the multi-homing side, and there will be too few agents on this side being served from a social point of view (Proposition 4). By contrast, platforms do have to compete for the single-homing agents, and high profits generated from the multi-homing side are to a large extent passed on to the single-homing side in the form of low prices (or even zero prices).

All of this is not to suggest that Google Search has no market power, or that monopoly is necessarily less problematic in the search engine industry than in other markets. 

Instead, the argument is that analyzing competition on the user side of search platforms is unlikely to yield dispositive evidence of market power or anticompetitive effects. This is because market power is hard to measure on this side of the market, and because even a monopoly platform might not significantly restrict user output. 

That might explain why the DOJ and state AGs analysis of anticompetitive effects is so limited. Take the following paragraph (provided without further supporting evidence):

167. By restricting competition in general search services, Google’s conduct has harmed consumers by reducing the quality of general search services (including dimensions such as privacy, data protection, and use of consumer data), lessening choice in general search services, and impeding innovation. 

Given these inherent difficulties, antitrust investigators would do better to focus on the side of those platforms where mainstream IO tools are much easier to apply and where a dominant search engine would likely restrict output: the advertising market. Not only is it the market where search engines are most likely to exert their market power (thus creating a deadweight loss), but — because it involves monetary transactions — this side of the market lends itself to the application of traditional antitrust tools.  

Looking at the right side of the market

Finally, and unfortunately for Google’s critics, available evidence suggests that its position on the (online) advertising market might not meet the requirements necessary to bring a monopolization case (at least in the US).

For a start, online advertising appears to exhibit the prima facie signs of a competitive market. As Geoffrey Manne, Sam Bowman and Eric Fruits have argued:

Over the past decade, the price of advertising has fallen steadily while output has risen. Spending on digital advertising in the US grew from $26 billion in 2010 to nearly $130 billion in 2019, an average increase of 20% a year. Over the same period the Producer Price Index for Internet advertising sales declined by nearly 40%. The rising spending in the face of falling prices indicates the number of ads bought and sold increased by approximately 27% a year. Since 2000, advertising spending has been falling as a share of GDP, with online advertising growing as a share of that. The combination of increasing quantity, decreasing cost, and increasing total revenues are consistent with a growing and increasingly competitive market.

Second, empirical research suggests that the market might need to be widened to include offline advertising. For instance, Avi Goldfarb and Catherine Tucker show that there can be important substitution effects between online and offline advertising channels:

Using data on the advertising prices paid by lawyers for 139 Google search terms in 195 locations, we exploit a natural experiment in “ambulance-chaser” regulations across states. When lawyers cannot contact clients by mail, advertising prices per click for search engine advertisements are 5%–7% higher. Therefore, online advertising substitutes for offline advertising.

Of course, a careful examination of the advertising industry could also lead authorities to define a narrower relevant market. For example, the DOJ and state AG complaint argued that Google dominated the “search advertising” market:

97. Search advertising in the United States is a relevant antitrust market. The search advertising market consists of all types of ads generated in response to online search queries, including general search text ads (offered by general search engines such as Google and Bing) […] and other, specialized search ads (offered by general search engines and specialized search providers such as Amazon, Expedia, or Yelp). 

Likewise, the European Commission concluded that Google dominated the market for “online search advertising” in the AdSense case (though the full decision has not yet been made public). Finally, the CMA’s online platforms report found that display and search advertising belonged to separate markets. 

But these are empirical questions that could dispositively be answered by applying traditional antitrust tools, such as the SSNIP test. And yet, there is no indication that the authorities behind the US complaint undertook this type of empirical analysis (and until its AdSense decision is made public, it is not clear that the EU Commission did so either). Accordingly, there is no guarantee that US courts will go along with the DOJ and state AGs’ findings.

In short, it is far from certain that Google currently enjoys an advertising monopoly, especially if the market is defined more broadly than that for “search advertising” (or the even narrower market for “General Search Text Advertising”). 

Concluding remarks

The preceding paragraphs have argued that a successful antitrust case against Google is anything but a foregone conclusion. In order to successfully bring a suit, authorities would notably need to figure out just what market it is that Google is monopolizing. In turn, that would require a finer understanding of what competition, and monopoly, look like in the search and advertising industries.

Apple’s legal team will be relieved that “you reap what you sow” is just a proverb. After a long-running antitrust battle against Qualcomm unsurprisingly ended in failure, Apple now faces antitrust accusations of its own (most notably from Epic Games). Somewhat paradoxically, this turn of events might cause Apple to see its previous defeat in a new light. Indeed, the well-established antitrust principles that scuppered Apple’s challenge against Qualcomm will now be the rock upon which it builds its legal defense.

But while Apple’s reversal of fortunes might seem anecdotal, it neatly illustrates a fundamental – and often overlooked – principle of antitrust policy: Antitrust law is about maximizing consumer welfare. Accordingly, the allocation of surplus between two companies is only incidentally relevant to antitrust proceedings, and it certainly is not a goal in and of itself. In other words, antitrust law is not about protecting David from Goliath.

Jockeying over the distribution of surplus

Or at least that is the theory. In practice, however, most antitrust cases are but small parts of much wider battles where corporations use courts and regulators in order to jockey for market position and/or tilt the distribution of surplus in their favor. The Microsoft competition suits brought by the DOJ and the European commission (in the EU and US) partly originated from complaints, and lobbying, by Sun Microsystems, Novell, and Netscape. Likewise, the European Commission’s case against Google was prompted by accusations from Microsoft and Oracle, among others. The European Intel case was initiated following a complaint by AMD. The list goes on.

The last couple of years have witnessed a proliferation of antitrust suits that are emblematic of this type of power tussle. For instance, Apple has been notoriously industrious in using the court system to lower the royalties that it pays to Qualcomm for LTE chips. One of the focal points of Apple’s discontent was Qualcomm’s policy of basing royalties on the end-price of devices (Qualcomm charged iPhone manufacturers a 5% royalty rate on their handset sales – and Apple received further rebates):

“The whole idea of a percentage of the cost of the phone didn’t make sense to us,” [Apple COO Jeff Williams] said. “It struck at our very core of fairness. At the time we were making something really really different.”

This pricing dispute not only gave rise to high-profile court cases, it also led Apple to lobby Standard Developing Organizations (“SDOs”) in a partly successful attempt to make them amend their patent policies, so as to prevent this type of pricing. 

However, in a highly ironic turn of events, Apple now finds itself on the receiving end of strikingly similar allegations. At issue is the 30% commission that Apple charges for in app purchases on the iPhone and iPad. These “high” commissions led several companies to lodge complaints with competition authorities (Spotify and Facebook, in the EU) and file antitrust suits against Apple (Epic Games, in the US).

Of course, these complaints are couched in more sophisticated, and antitrust-relevant, reasoning. But that doesn’t alter the fact that these disputes are ultimately driven by firms trying to tilt the allocation of surplus in their favor (for a more detailed explanation, see Apple and Qualcomm).

Pushback from courts: The Qualcomm case

Against this backdrop, a string of recent cases sends a clear message to would-be plaintiffs: antitrust courts will not be drawn into rent allocation disputes that have no bearing on consumer welfare. 

The best example of this judicial trend is Qualcomm’s victory before the United States Court of Appeal for the 9th Circuit. The case centered on the royalties that Qualcomm charged to OEMs for its Standard Essential Patents (SEPs). Both the district court and the FTC found that Qualcomm had deployed a series of tactics (rebates, refusals to deal, etc) that enabled it to circumvent its FRAND pledges. 

However, the Court of Appeal was not convinced. It failed to find any consumer harm, or recognizable antitrust infringement. Instead, it held that the dispute at hand was essentially a matter of contract law:

To the extent Qualcomm has breached any of its FRAND commitments, a conclusion we need not and do not reach, the remedy for such a breach lies in contract and patent law. 

This is not surprising. From the outset, numerous critics pointed that the case lied well beyond the narrow confines of antitrust law. The scathing dissenting statement written by Commissioner Maureen Olhaussen is revealing:

[I]n the Commission’s 2-1 decision to sue Qualcomm, I face an extraordinary situation: an enforcement action based on a flawed legal theory (including a standalone Section 5 count) that lacks economic and evidentiary support, that was brought on the eve of a new presidential administration, and that, by its mere issuance, will undermine U.S. intellectual property rights in Asia and worldwide. These extreme circumstances compel me to voice my objections. 

In reaching its conclusion, the Court notably rejected the notion that SEP royalties should be systematically based upon the “Smallest Saleable Patent Practicing Unit” (or SSPPU):

Even if we accept that the modem chip in a cellphone is the cellphone’s SSPPU, the district court’s analysis is still fundamentally flawed. No court has held that the SSPPU concept is a per se rule for “reasonable royalty” calculations; instead, the concept is used as a tool in jury cases to minimize potential jury confusion when the jury is weighing complex expert testimony about patent damages.

Similarly, it saw no objection to Qualcomm licensing its technology at the OEM level (rather than the component level):

Qualcomm’s rationale for “switching” to OEM-level licensing was not “to sacrifice short-term benefits in order to obtain higher profits in the long run from the exclusion of competition,” the second element of the Aspen Skiing exception. Aerotec Int’l, 836 F.3d at 1184 (internal quotation marks and citation omitted). Instead, Qualcomm responded to the change in patent-exhaustion law by choosing the path that was “far more lucrative,” both in the short term and the long term, regardless of any impacts on competition. 

Finally, the Court concluded that a firm breaching its FRAND pledges did not automatically amount to anticompetitive conduct: 

We decline to adopt a theory of antitrust liability that would presume anticompetitive conduct any time a company could not prove that the “fair value” of its SEP portfolios corresponds to the prices the market appears willing to pay for those SEPs in the form of licensing royalty rates.

Taken together, these findings paint a very clear picture. The Qualcomm Court repeatedly rejected the radical idea that US antitrust law should concern itself with the prices charged by monopolists — as opposed to practices that allow firms to illegally acquire or maintain a monopoly position. The words of Learned Hand and those of Antonin Scalia (respectively, below) loom large:

The successful competitor, having been urged to compete, must not be turned upon when he wins. 


To safeguard the incentive to innovate, the possession of monopoly power will not be found unlawful unless it is accompanied by an element of anticompetitive conduct.

Other courts (both in the US and abroad) have reached similar conclusions

For instance, a district court in Texas dismissed a suit brought by Continental Automotive Systems (which supplies electronic systems to the automotive industry) against a group of SEP holders. 

Continental challenged the patent holders’ decision to license their technology at the vehicle rather than component level (the allegation is very similar to the FTC’s complaint that Qualcomm licensed its SEPs at the OEM, rather than chipset level). However, following a forceful intervention by the DOJ, the Court ultimately held that the facts alleged by Continental were not indicative of antitrust injury. It thus dismissed the case.

Likewise, within weeks of the Qualcomm and Continental decisions, the UK Supreme court also ruled in favor of SEP holders. In its Unwired Planet ruling, the Court concluded that discriminatory licenses did not automatically infringe competition law (even though they might breach a firm’s contractual obligations):

[I]t cannot be said that there is any general presumption that differential pricing for licensees is problematic in terms of the public or private interests at stake.

In reaching this conclusion, the UK Supreme Court emphasized that the determination of whether licenses were FRAND, or not, was first and foremost a matter of contract law. In the case at hand, the most important guide to making this determination were the internal rules of the relevant SDO (as opposed to competition case law):

Since price discrimination is the norm as a matter of licensing practice and may promote objectives which the ETSI regime is intended to promote (such as innovation and consumer welfare), it would have required far clearer language in the ETSI FRAND undertaking to indicate an intention to impose the more strict, “hard-edged” non-discrimination obligation for which Huawei contends. Further, in view of the prevalence of competition laws in the major economies around the world, it is to be expected that any anti-competitive effects from differential pricing would be most appropriately addressed by those laws

All of this ultimately led the Court to rule in favor of Unwired Planet, thus dismissing Huawei’s claims that it had infringed competition law by breaching its FRAND pledges. 

In short, courts and antitrust authorities on both sides of the Atlantic have repeatedly, and unambiguously, concluded that pricing disputes (albeit in the specific context of technological standards) are generally a matter of contract law. Antitrust/competition law intercedes only when unfair/excessive/discriminatory prices are both caused by anticompetitive behavior and result in anticompetitive injury.

Apple’s Loss is… Apple’s gain.

Readers might wonder how the above cases relate to Apple’s app store. But, on closer inspection the parallels are numerous. As explained above, courts have repeatedly stressed that antitrust enforcement should not concern itself with the allocation of surplus between commercial partners. Yet that is precisely what Epic Game’s suit against Apple is all about.

Indeed, Epic’s central claim is not that it is somehow foreclosed from Apple’s App Store (for example, because Apple might have agreed to exclusively distribute the games of one of Epic’s rivals). Instead, all of its objections are down to the fact that it would like to access Apple’s store under more favorable terms:

Apple’s conduct denies developers the choice of how best to distribute their apps. Developers are barred from reaching over one billion iOS users unless they go through Apple’s App Store, and on Apple’s terms. […]

Thus, developers are dependent on Apple’s noblesse oblige, as Apple may deny access to the App Store, change the terms of access, or alter the tax it imposes on developers, all in its sole discretion and on the commercially devastating threat of the developer losing access to the entire iOS userbase. […]

By imposing its 30% tax, Apple necessarily forces developers to suffer lower profits, reduce the quantity or quality of their apps, raise prices to consumers, or some combination of the three.

And the parallels with the Qualcomm litigation do not stop there. Epic is effectively asking courts to make Apple monetize its platform at a different level than the one that it chose to maximize its profits (no more monetization at the app store level). Similarly, Epic Games omits any suggestion of profit sacrifice on the part of Apple — even though it is a critical element of most unilateral conduct theories of harm. Finally, Epic is challenging conduct that is both the industry norm and emerged in a highly competitive setting.

In short, all of Epic’s allegations are about monopoly prices, not monopoly maintenance or monopolization. Accordingly, just as the SEP cases discussed above were plainly beyond the outer bounds of antitrust enforcement (something that the DOJ repeatedly stressed with regard to the Qualcomm case), so too is the current wave of antitrust litigation against Apple. When all is said and done, Apple might thus be relieved that Qualcomm was victorious in their antitrust confrontation. Indeed, the legal principles that caused its demise against Qualcomm are precisely the ones that will, likely, enable it to prevail against Epic Games.

This week the Senate will hold a hearing into potential anticompetitive conduct by Google in its display advertising business—the “stack” of products that it offers to advertisers seeking to place display ads on third-party websites. It is also widely reported that the Department of Justice is preparing a lawsuit against Google that will likely include allegations of anticompetitive behavior in this market, and is likely to be joined by a number of state attorneys general in that lawsuit. Meanwhile, several papers have been published detailing these allegations

This aspect of digital advertising can be incredibly complex and difficult to understand. Here we explain how display advertising fits in the broader digital advertising market, describe how display advertising works, consider the main allegations against Google, and explain why Google’s critics are misguided to focus on antitrust as a solution to alleged problems in the market (even if those allegations turn out to be correct).

Display advertising in context

Over the past decade, the price of advertising has fallen steadily while output has risen. Spending on digital advertising in the US grew from $26 billion in 2010 to nearly $130 billion in 2019, an average increase of 20% a year. Over the same period the Producer Price Index for Internet advertising sales declined by nearly 40%. The rising spending in the face of falling prices indicates the number of ads bought and sold increased by approximately 27% a year. Since 2000, advertising spending has been falling as a share of GDP, with online advertising growing as a share of that. The combination of increasing quantity, decreasing cost, and increasing total revenues are consistent with a growing and increasingly competitive market.

Display advertising on third-party websites is only a small subsection of the digital advertising market, comprising approximately 15-20% of digital advertising spending in the US. The rest of the digital advertising market is made up of ads on search results pages on sites like Google, Amazon and Kayak, on people’s Instagram and Facebook feeds, listings on sites like Zillow (for houses) or Craigslist, referral fees paid to price comparison websites for things like health insurance, audio and visual ads on services like Spotify and Hulu, and sponsored content from influencers and bloggers who will promote products to their fans. 

And digital advertising itself is only one of many channels through which companies can market their products. About 53% of total advertising spending in the United States goes on digital channels, with 30% going on TV advertising and the rest on things like radio ads, billboards and other more traditional forms of advertising. A few people still even read physical newspapers and the ads they contain, although physical newspapers’ bigger money makers have traditionally been classified ads, which have been replaced by less costly and more effective internet classifieds, such as those offered by Craigslist, or targeted ads on Google Maps or Facebook.

Indeed, it should be noted that advertising itself is only part of the larger marketing market of which non-advertising marketing communication—e.g., events, sales promotion, direct marketing, telemarketing, product placement—is as big a part as is advertising (each is roughly $500bn globally); it just hasn’t been as thoroughly disrupted by the Internet yet. But it is a mistake to assume that digital advertising is not a part of this broader market. And of that $1tr global market, Internet advertising in total occupies only about 18%—and thus display advertising only about 3%.

Ad placement is only one part of the cost of digital advertising. An advertiser trying to persuade people to buy its product must also do market research and analytics to find out who its target market is and what they want. Moreover, there are the costs of designing and managing a marketing campaign and additional costs to analyze and evaluate the effectiveness of the campaign. 

Nevertheless, one of the most straightforward ways to earn money from a website is to show ads to readers alongside the publisher’s content. To satisfy publishers’ demand for advertising revenues, many services have arisen to automate and simplify the placement of and payment for ad space on publishers’ websites. Google plays a large role in providing these services—what is referred to as “open display” advertising. And it is Google’s substantial role in this space that has sparked speculation and concern among antitrust watchdogs and enforcement authorities.

Before delving into the open display advertising market, a quick note about terms. In these discussions, “advertisers” are businesses that are trying to sell people stuff. Advertisers include large firms such as Best Buy and Disney and small businesses like the local plumber or financial adviser. “Publishers” are websites that carry those ads, and publish content that users want to read. Note that the term “publisher” refers to all websites regardless of the things they’re carrying: a blog about the best way to clean stains out of household appliances is a “publisher” just as much as the New York Times is. 

Under this broad definition, Facebook, Instagram, and YouTube are also considered publishers. In their role as publishers, they have a common goal: to provide content that attracts users to their pages who will act on the advertising displayed. “Users” are you and me—the people who want to read publishers’ content, and to whom advertisers want to show ads. Finally, “intermediaries” are the digital businesses, like Google, that sit in between the advertisers and the publishers, allowing them to do business with each other without ever meeting or speaking.

The display advertising market

If you’re an advertiser, display advertising works like this: your company—one that sells shoes, let’s say—wants to reach a certain kind of person and tell her about the company’s shoes. These shoes are comfortable, stylish, and inexpensive. You use a tool like Google Ads (or, if it’s a big company and you want a more expansive campaign over which you have more control, Google Marketing Platform) to design and upload an ad, and tell Google about the people you want to read—their age and location, say, and/or characterizations of their past browsing and searching habits (“interested in sports”). 

Using that information, Google finds ad space on websites whose audiences match the people you want to target. This ad space is auctioned off to the highest bidder among the range of companies vying, with your shoe company, to reach users matching the characteristics of the website’s users. Thanks to tracking data, it doesn’t just have to be sports-relevant websites: as a user browses sports-related sites on the web, her browser picks up files (cookies) that will tag her as someone potentially interested in sports apparel for targeting later.

So a user might look at a sports website and then later go to a recipe blog, and there receive the shoes ad on the basis of her earlier browsing. You, the shoe seller, hope that she will either click through and buy (or at least consider buying) the shoes when she sees those ads, but one of the benefits of display advertising over search advertising is that—as with TV ads or billboard ads—just seeing the ad will make her aware of the product and potentially more likely to buy it later. Advertisers thus sometimes pay on the basis of clicks, sometimes on the basis of views, and sometimes on the basis of conversion (when a consumer takes an action of some sort, such as making a purchase or filling out a form).

That’s the advertiser’s perspective. From the publisher’s perspective—the owner of that recipe blog, let’s say—you want to auction ad space off to advertisers like that shoe company. In that case, you go to an ad server—Google’s product is called AdSense—give them a little bit of information about your site, and add some html code to your website. These ad servers gather information about your content (e.g., by looking at keywords you use) and your readers (e.g., by looking at what websites they’ve used in the past to make guesses about what they’ll be interested in) and places relevant ads next to and among your content. If they click, lucky you—you’ll get paid a few cents or dollars. 

Apart from privacy concerns about the tracking of users, the really tricky and controversial part here concerns the way scarce advertising space is allocated. Most of the time, it’s done through auctions that happen in real time: each time a user loads a website, an auction is held in a fraction of a second to decide which advertiser gets to display an ad. The longer this process takes, the slower pages load and the more likely users are to get frustrated and go somewhere else.

As well as the service hosting the auction, there are lots of little functions that different companies perform that make the auction and placement process smoother. Some fear that by offering a very popular product integrated end to end, Google’s “stack” of advertising products can bias auctions in favour of its own products. There’s also speculation that Google’s product is so tightly integrated and so effective at using data to match users and advertisers that it is not viable for smaller rivals to compete.

We’ll discuss this speculation and fear in more detail below. But it’s worth bearing in mind that this kind of real-time bidding for ad placement was not always the norm, and is not the only way that websites display ads to their users even today. Big advertisers and websites often deal with each other directly. As with, say, TV advertising, large companies advertising often have a good idea about the people they want to reach. And big publishers (like popular news websites) often have a good idea about who their readers are. For example, big brands often want to push a message to a large number of people across different customer types as part of a broader ad campaign. 

Of these kinds of direct sales, sometimes the space is bought outright, in advance, and reserved for those advertisers. In most cases, direct sales are run through limited, intermediated auction services that are not open to the general market. Put together, these kinds of direct ad buys account for close to 70% of total US display advertising spending. The remainder—the stuff that’s left over after these kinds of sales have been done—is typically sold through the real-time, open display auctions described above.

Different adtech products compete on their ability to target customers effectively, to serve ads quickly (since any delay in the auction and ad placement process slows down page load times for users), and to do so inexpensively. All else equal (including the effectiveness of the ad placement), advertisers want to pay the lowest possible price to place an ad. Similarly, publishers want to receive the highest possible price to display an ad. As a result, both advertisers and publishers have a keen interest in reducing the intermediary’s “take” of the ad spending.

This is all a simplification of how the market works. There is not one single auction house for ad space—in practice, many advertisers and publishers end up having to use lots of different auctions to find the best price. As the market evolved to reach this state from the early days of direct ad buys, new functions that added efficiency to the market emerged. 

In the early years of ad display auctions, individual processes in the stack were performed by numerous competing companies. Through a process of “vertical integration” some companies, such as Google, brought these different processes under the same roof, with the expectation that integration would streamline the stack and make the selling and placement of ads more efficient and effective. The process of vertical integration in pursuit of efficiency has led to a more consolidated market in which Google is the largest player, offering simple, integrated ad buying products to advertisers and ad selling products to publishers. 

Google is by no means the only integrated adtech service provider, however: Facebook, Amazon, Verizon, AT&T/Xandr, theTradeDesk, LumenAd, Taboola and others also provide end-to-end adtech services. But, in the market for open auction placement on third-party websites, Google is the biggest.

The cases against Google

The UK’s Competition and Markets Authority (CMA) carried out a formal study into the digital advertising market between 2019 and 2020, issuing its final report in July of this year. Although also encompassing Google’s Search advertising business and Facebook’s display advertising business (both of which relate to ads on those companies “owned and operated” websites and apps), the CMA study involved the most detailed independent review of Google’s open display advertising business to date. 

That study did not lead to any competition enforcement proceedings, but it did conclude that Google’s vertically integrated products led to conflicts of interest that could lead it to behaving in ways that did not benefit the advertisers and publishers that use it. One example was Google’s withholding of certain data from publishers that would make it easier for them to use other ad selling products; another was the practice of setting price floors that allegedly led advertisers to pay more than they would otherwise.

Instead the CMA recommended the setting up of a “Digital Markets Unit” (DMU) that could regulate digital markets in general, and a code of conduct for Google and Facebook (and perhaps other large tech platforms) intended to govern their dealings with smaller customers.

The CMA’s analysis is flawed, however. For instance, it makes big assumptions about the dependency of advertisers on display advertising, largely assuming that they would not switch to other forms of advertising if prices rose, and it is light on economics. But factually it is the most comprehensively researched investigation into digital advertising yet published.

Piggybacking on the CMA’s research, and mounting perhaps the strongest attack on Google’s adtech offerings to date, was a paper released just prior to the CMA’s final report called “Roadmap for a Digital Advertising Monopolization Case Against Google”, by Yale economist Fiona Scott Morton and Omidyar Network lawyer David Dinielli. Dinielli will testify before the Senate committee.

While the Scott Morton and Dinielli paper is extremely broad, it also suffers from a number of problems. 

One, because it was released before the CMA’s final report, it is largely based on the interim report released months earlier by the CMA, halfway through the market study in December 2019. This means that several of its claims are out of date. For example, it makes much of the possibility raised by the CMA in its interim report that Google may take a larger cut of advertising spending than its competitors, and claims made in another report that Google introduces “hidden” fees that increases the overall cut it takes from ad auctions. 

But in the final report, after further investigation, the CMA concludes that this is not the case. In the final report, the CMA describes its analysis of all Google Ad Manager open auctions related to UK web traffic during the period between 8–14 March 2020 (involving billions of auctions). This, according to the CMA, allowed it to observe any possible “hidden” fees as well. The CMA concludes:

Our analysis found that, in transactions where both Google Ads and Ad Manager (AdX) are used, Google’s overall take rate is approximately 30% of advertisers’ spend. This is broadly in line with (or slightly lower than) our aggregate market-wide fee estimate outlined above. We also calculated the margin between the winning bid and the second highest bid in AdX for Google and non-Google DSPs, to test whether Google was systematically able to win with a lower margin over the second highest bid (which might have indicated that they were able to use their data advantage to extract additional hidden fees). We found that Google’s average winning margin was similar to that of non-Google DSPs. Overall, this evidence does not indicate that Google is currently extracting significant hidden fees. As noted below, however, it retains the ability and incentive to do so. (p. 275, emphasis added)

Scott Morton and Dinielli also misquote and/or misunderstand important sections of the CMA interim report as relating to display advertising when, in fact, they relate to search. For example, Scott Morton and Dinielli write that the “CMA concluded that Google has nearly insurmountable advantages in access to location data, due to the location information [uniquely available to it from other sources].” (p. 15). The CMA never makes any claim of “insurmountable advantage,” however. Rather, to support the claim, Scott Morton and Dinielli cite to a portion of the CMA interim report recounting a suggestion made by Microsoft regarding the “critical” value of location data in providing relevant advertising. 

But that portion of the report, as well as the suggestion made by Microsoft, is about search advertising. While location data may also be valuable for display advertising, it is not clear that the GPS-level data that is so valuable in providing mobile search ad listings (for a nearby cafe or restaurant, say) is particularly useful for display advertising, which may be just as well-targeted by less granular, city- or county-level location data, which is readily available from a number of sources. In any case, Scott Morton and Dinielli are simply wrong to use a suggestion offered by Microsoft relating to search advertising to demonstrate the veracity of an assertion about a conclusion drawn by the CMA regarding display advertising. 

Scott Morton and Dinielli also confusingly word their own judgements about Google’s conduct in ways that could be misinterpreted as conclusions by the CMA:

The CMA reports that Google has implemented an anticompetitive sales strategy on the publisher ad server end of the intermediation chain. Specifically, after purchasing DoubleClick, which became its publisher ad server, Google apparently lowered its prices to publishers by a factor of ten, at least according to one publisher’s account related to the CMA. (p. 20)

In fact, the CMA does not conclude that Google lowering its prices was an “anticompetitive sales strategy”—it does not use these words at all—and what Scott Morton and Dinielli are referring to is a claim by a rival ad server business, Smart, that Google cutting its prices after acquiring Doubleclick led to Google expanding its market share. Apart from the misleading wording, it is unclear why a competition authority should consider it to be “anticompetitive” when prices are falling and kept low, and—as Smart reported to the CMA—its competitor’s response is to enhance its own offering. 

The case that remains

Stripping away the elements of Scott Morton and Dinielli’s case that seem unsubstantiated by a more careful reading of the CMA reports, and with the benefit of the findings in the CMA’s final report, we are left with a case that argues that Google self-preferences to an unreasonable extent, giving itself a product that is as successful as it is in display advertising only because of Google’s unique ability to gain advantage from its other products that have little to do with display advertising. Because of this self-preferencing, they might argue, innovative new entrants cannot compete on an equal footing, so the market loses out on incremental competition because of the advantages Google gets from being the world’s biggest search company, owning YouTube, running Google Maps and Google Cloud, and so on. 

The most significant examples of this are Google’s use of data from other products—like location data from Maps or viewing history from YouTube—to target ads more effectively; its ability to enable advertisers placing search ads to easily place display ads through the same interface; its introduction of faster and more efficient auction processes that sidestep the existing tools developed by other third-party ad exchanges; and its design of its own tool (“open bidding”) for aggregating auction bids for advertising space to compete with (rather than incorporate) an alternative tool (“header bidding”) that is arguably faster, but costs more money to use.

These allegations require detailed consideration, and in a future paper we will attempt to assess them in detail. But in thinking about them now it may be useful to consider the remedies that could be imposed to address them, assuming they do diminish the ability of rivals to compete with Google: what possible interventions we could make in order to make the market work better for advertisers, publishers, and users. 

We can think of remedies as falling into two broad buckets: remedies that stop Google from doing things that improve the quality of its own offerings, thus making it harder for others to keep up; and remedies that require it to help rivals improve their products in ways otherwise accessible only to Google (e.g., by making Google’s products interoperable with third-party services) without inherently diminishing the quality of Google’s own products.

The first camp of these, what we might call “status quo minus,” includes rules banning Google from using data from its other products or offering single order forms for advertisers, or, in the extreme, a structural remedy that “breaks up” Google by either forcing it to sell off its display ad business altogether or to sell off elements of it. 

What is striking about these kinds of interventions is that all of them “work” by making Google worse for those that use it. Restrictions on Google’s ability to use data from other products, for example, will make its service more expensive and less effective for those who use it. Ads will be less well-targeted and therefore less effective. This will lead to lower bids from advertisers. Lower ad prices will be transmitted through the auction process to produce lower payments for publishers. Reduced publisher revenues will mean some content providers exit. Users will thus be confronted with less available content and ads that are less relevant to them and thus, presumably, more annoying. In other words: No one will be better off, and most likely everyone will be worse off.

The reason a “single order form” helps Google is that it is useful to advertisers, the same way it’s useful to be able to buy all your groceries at one store instead of lots of different ones. Similarly, vertical integration in the “ad stack” allows for a faster, cheaper, and simpler product for users on all sides of the market. A different kind of integration that has been criticized by others, where third-party intermediaries can bid more quickly if they host on Google Cloud, benefits publishers and users because it speeds up auction time, allowing websites to load faster. So does Google’s unified alternative to “header bidding,” giving a speed boost that is apparently valuable enough to publishers that they will pay for it.

So who would benefit from stopping Google from doing these things, or even forcing Google to sell its operations in this area? Not advertisers or publishers. Maybe Google’s rival ad intermediaries would; presumably, artificially hamstringing Google’s products would make it easier for them to compete with Google. But if so, it’s difficult to see how this would be an overall improvement. It is even harder to see how this would improve the competitive process—the very goal of antitrust. Rather, any increase in the competitiveness of rivals would result not from making their products better, but from making Google’s product worse. That is a weakening of competition, not its promotion. 

On the other hand, interventions that aim to make Google’s products more interoperable at least do not fall prey to this problem. Such “status quo plus” interventions would aim to take the benefits of Google’s products and innovations and allow more companies to use them to improve their own competing products. Not surprisingly, such interventions would be more in line with the conclusions the CMA came to than the divestitures and operating restrictions proposed by Scott Morton and Dinielli, as well as (reportedly) state attorneys general considering a case against Google.

But mandated interoperability raises a host of different concerns: extensive and uncertain rulemaking, ongoing regulatory oversight, and, likely, price controls, all of which would limit Google’s ability to experiment with and improve its products. The history of such mandated duties to deal or compulsory licenses is a troubled one, at best. But even if, for the sake of argument, we concluded that these kinds of remedies were desirable, they are difficult to impose via an antitrust lawsuit of the kind that the Department of Justice is expected to launch. Most importantly, if the conclusion of Google’s critics is that Google’s main offense is offering a product that is just too good to compete with without regulating it like a utility, with all the costs to innovation that that would entail, maybe we ought to think twice about whether an antitrust intervention is really worth it at all.

Earlier this year the UK government announced it was adopting the main recommendations of the Furman Report into competition in digital markets and setting up a “Digital Markets Taskforce” to oversee those recommendations being put into practice. The Competition and Markets Authority’s digital advertising market study largely came to similar conclusions (indeed, in places it reads as if the CMA worked backwards from those conclusions).

The Furman Report recommended that the UK should overhaul its competition regime with some quite significant changes to regulate the conduct of large digital platforms and make it harder for them to acquire other companies. But, while the Report’s panel is accomplished and its tone is sober and even-handed, the evidence on which it is based does not justify the recommendations it makes.

Most of the citations in the Report are of news reports or simple reporting of data with no analysis, and there is very little discussion of the relevant academic literature in each area, even to give a summary of it. In some cases, evidence and logic are misused to justify intuitions that are just not supported by the facts.

Killer acquisitions

One particularly bad example is the report’s discussion of mergers in digital markets. The Report provides a single citation to support its proposals on the question of so-called “killer acquisitions” — acquisitions where incumbent firms acquire innovative startups to kill their rival product and avoid competing on the merits. The concern is that these mergers slip under the radar of current merger control either because the transaction is too small, or because the purchased firm is not yet in competition with the incumbent. But the paper the Report cites, by Colleen Cunningham, Florian Ederer and Song Ma, looks only at the pharmaceutical industry. 

The Furman Report says that “in the absence of any detailed analysis of the digital sector, these results can be roughly informative”. But there are several important differences between the drug markets the paper considers and the digital markets the Furman Report is focused on. 

The scenario described in the Cunningham, et al. paper is of a patent holder buying a direct competitor that has come up with a drug that emulates the patent holder’s drug without infringing on the patent. As the Cunningham, et al. paper demonstrates, decreases in development rates are a feature of acquisitions where the acquiring company holds a patent for a similar product that is far from expiry. The closer a patent is to expiry, the less likely an associated “killer” acquisition is. 

But tech typically doesn’t have the clear and predictable IP protections that would make such strategies reliable. The long and uncertain development and approval process involved in bringing a drug to market may also be a factor.

There are many more differences between tech acquisitions and the “killer acquisitions” in pharma that the Cunningham, et al. paper describes. SO-called “acqui-hires,” where a company is acquired in order to hire its workforce en masse, are common in tech and explicitly ruled out of being “killers” by this paper, for example: it is not harmful to overall innovation or output overall if a team is moved to a more productive project after an acquisition. And network effects, although sometimes troubling from a competition perspective, can also make mergers of platforms beneficial for users by growing the size of that platform (because, of course, one of the points of a network is its size).

The Cunningham, et al. paper estimates that 5.3% of pharma acquisitions are “killers”. While that may seem low, some might say it’s still 5.3% too much. However, it’s not obvious that a merger review authority could bring that number closer to zero without also rejecting more mergers that are good for consumers, making people worse off overall. Given the number of factors that are specific to pharma and that do not apply to tech, it is dubious whether the findings of this paper are useful to the Furman Report’s subject at all. Given how few acquisitions are found to be “killers” in pharma with all of these conditions present, it seems reasonable to assume that, even if this phenomenon does apply in some tech mergers, it is significantly rarer than the ~5.3% of mergers Cunningham, et al. find in pharma. As a result, the likelihood of erroneous condemnation of procompetitive mergers is significantly higher. 

In any case, there’s a fundamental disconnect between the “killer acquisitions” in the Cunningham, et al. paper and the tech acquisitions described as “killers” in the popular media. Neither Facebook’s acquisition of Instagram nor Google’s acquisition of Youtube, which FTC Commissioner Rohit Chopra recently highlighted, would count, because in neither case was the acquired company “killed.” Nor were any of the other commonly derided tech acquisitions — e.g., Facebook/Whatsapp, Google/Waze, Microsoft.LinkedIn, or Amazon/Whole Foods — “killers,” either. 

In all these high-profile cases the acquiring companies expanded the service and invested more in them. One may object that these services would have competed with their acquirers had they remained independent, but this is a totally different argument to the scenarios described in the Cunningham, et al. paper, where development of a new drug is shut down by the acquirer ostensibly to protect their existing product. It is thus extremely difficult to see how the Cunningham, et al. paper is even relevant to the digital platform context, let alone how it could justify a wholesale revision of the merger regime as applied to digital platforms.

A recent paper (published after the Furman Report) does attempt to survey acquisitions by Google, Amazon, Facebook, Microsoft, and Apple. Out of 175 acquisitions in the 2015-17 period the paper surveys, only one satisfies the Cunningham, et al. paper’s criteria for being a potentially “killer” acquisition — Facebook’s acquisition of a photo sharing app called Masquerade, which had raised just $1 million in funding before being acquired.

In lieu of any actual analysis of mergers in digital markets, the Report falls back on a puzzling logic:

To date, there have been no false positives in mergers involving the major digital platforms, for the simple reason that all of them have been permitted. Meanwhile, it is likely that some false negatives will have occurred during this time. This suggests that there has been underenforcement of digital mergers, both in the UK and globally. Remedying this underenforcement is not just a matter of greater focus by the enforcer, as it will also need to be assisted by legislative change.

This is very poor reasoning. It does not logically follow that the (presumed) existence of false negatives implies that there has been underenforcement, because overenforcement carries costs as well. Moreover, there are strong reasons to think that false positives in these markets are more costly than false negatives. A well-run court system might still fail to convict a few criminals because the cost of accidentally convicting an innocent person was so high.

The UK’s competition authority did commission an ex post review of six historical mergers in digital markets, including Facebook/Instagram and Google/Waze, two of the most controversial in the UK. Although it did suggest that the review process could have been done differently, it also highlighted efficiencies that arose from each, and did not conclude that any has led to consumer detriment.


The Report is vague about which mergers it considers to have been uncompetitive, and apart from the aforementioned text it does not really attempt to justify its recommendations around merger control. 

Despite this, the Report recommends a shift to a ‘balance of harms’ approach. Under the current regime, merger review focuses on the likelihood that a merger would reduce competition which, at least, gives clarity about the factors to be considered. A ‘balance of harms’ approach would require the potential scale (size) of the merged company to be considered as well. 

This could give basis for blocking any merger at all on ‘scale’ grounds. After all, if a photo editing app with a sharing timeline can grow into the world’s second largest social network, how could a competition authority say with any confidence that some other acquisition might not prevent the emergence of a new platform on a similar scale, however unlikely? This could provide a basis for blocking almost any acquisition by an incumbent firm, and make merger review an even more opaque and uncertain process than it currently is, potentially deterring efficiency-raising mergers or leading startups that would like to be acquired to set up and operate overseas instead (or not to be started up in the first place).

The treatment of mergers is just one example of the shallowness of the Report. In many other cases — the discussions of concentration and barriers to entry in digital markets, for example — big changes are recommended on the basis of a handful of papers or less. Intuition repeatedly trumps evidence and academic research.

The Report’s subject is incredibly broad, of course, and one might argue that such a limited, casual approach is inevitable. In this sense the Report may function perfectly well as an opening brief introducing the potential range of problems in the digital economy that a rational competition authority might consider addressing. But the complexity and uncertainty of the issues is no reason to eschew rigorous, detailed analysis before determining that a compelling case has been made. Adopting the Report’s assumptions — and in many cases that is the very most one can say of them — of harm and remedial recommendations on the limited bases it offers is sure to lead to erroneous enforcement of competition law in a way that would reduce, rather than enhance, consumer welfare.

Last month the EU General Court annulled the EU Commission’s decision to block the proposed merger of Telefónica UK by Hutchison 3G UK. 

It what could be seen as a rebuke of the Directorate-General for Competition (DG COMP), the court clarified the proof required to block a merger, which could have a significant effect on future merger enforcement:

In the context of an analysis of a significant impediment to effective competition the existence of which is inferred from a body of evidence and indicia, and which is based on several theories of harm, the Commission is required to produce sufficient evidence to demonstrate with a strong probability the existence of significant impediments following the concentration. Thus, the standard of proof applicable in the present case is therefore stricter than that under which a significant impediment to effective competition is “more likely than not,” on the basis of a “balance of probabilities,” as the Commission maintains. By contrast, it is less strict than a standard of proof based on “being beyond all reasonable doubt.”

Over the relevant time period, there were four retail mobile network operators in the United Kingdom: (1) EE Ltd, (2) O2, (3) Hutchison 3G UK Ltd (“Three”), and (4) Vodafone. The merger would have combined O2 and Three, which would account for 30-40% of the retail market. 

The Commission argued that Three’s growth in market share over time and its classification as a “maverick” demonstrated that Three was an “important competitive force” that would be eliminated with the merger. The court was not convinced: 

The mere growth in gross add shares over several consecutive years of the smallest mobile network operator in an oligopolistic market, namely Three, which has in the past been classified as a “maverick” by the Commission (Case COMP/M.5650 — T-Mobile/Orange) and in the Statement of Objections in the present case, does not in itself constitute sufficient evidence of that operator’s power on the market or of the elimination of the important competitive constraints that the parties to the concentration exert upon each other.

While the Commission classified Three as a maverick, it also claimed that maverick status was not necessary to be an important competitive force. Nevertheless, the Commission pointed to Three’s history of maverick-y behavior by launching its “One Plan” as well as free international roaming and offering 4G at no additional cost. The court, however, noted that those initiatives were “historical in nature,” and provided no evidence of future conduct: 

The Commission’s reasoning in that regard seems to imply that an undertaking which has historically played a disruptive role will necessarily play the same role in the future and cannot reposition itself on the market by adopting a different pricing policy.

The EU General Court appears to express the same frustration with mavericks as the court in in H&R Block/TaxACT: “The arguments over whether TaxACT is or is not a ‘maverick’ — or whether perhaps it once was a maverick but has not been a maverick recently — have not been particularly helpful to the Court’s analysis.”

With the General Court’s recent decision raising the bar of proof required to block a merger, it also provided a “strong probability” that the days of maverick madness may soon be over.  

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

This post is authored by Dirk Auer, (Senior Researcher, Liege Competition & Innovation Institute; Senior Fellow, ICLE).]

Privacy absolutism is the misguided belief that protecting citizens’ privacy supersedes all other policy goals, especially economic ones. This is a mistake. Privacy is one value among many, not an end in itself. Unfortunately, the absolutist worldview has filtered into policymaking and is beginning to have very real consequences. Readers need look no further than contact tracing applications and the fight against Covid-19.

Covid-19 has presented the world with a privacy conundrum worthy of the big screen. In fact, it’s a plotline we’ve seen before. Moviegoers will recall that, in the wildly popular film “The Dark Knight”, Batman has to decide between preserving the privacy of Gotham’s citizens or resorting to mass surveillance in order to defeat the Joker. Ultimately, the caped crusader begrudgingly chooses the latter. Before the Covid-19 outbreak, this might have seemed like an unrealistic plot twist. Fast forward a couple of months, and it neatly illustrates the difficult decision that most western societies urgently need to make as they consider the use of contract tracing apps to fight Covid-19.

Contact tracing is often cited as one of the most promising tools to safely reopen Covid-19-hit economies. Unfortunately, its adoption has been severely undermined by a barrage of overblown privacy fears.

Take the contact tracing API and App co-developed by Apple and Google. While these firms’ efforts to rapidly introduce contact tracing tools are laudable, it is hard to shake the feeling that they have been holding back slightly. 

In an overt attempt to protect users’ privacy, Apple and Google’s joint offering does not collect any location data (a move that has irked some states). Similarly, both firms have repeatedly stressed that users will have to opt-in to their contact tracing solution (as opposed to the API functioning by default). And, of course, all the data will be anonymous – even for healthcare authorities. 

This is a missed opportunity. Google and Apple’s networks include billions of devices. That puts them in a unique position to rapidly achieve the scale required to successfully enable the tracing of Covid-19 infections. Contact tracing applications need to reach a critical mass of users to be effective. For instance, some experts have argued that an adoption rate of at least 60% is necessary. Unfortunately, existing apps – notably in Singapore, Australia, Norway and Iceland – have struggled to get anywhere near this number. Forcing users to opt-out of Google and Apple’s services could go a long way towards inverting this trend. Businesses could also boost these numbers by making them mandatory for their employees and consumers.

However, it is hard to blame Google or Apple for not pushing the envelope a little bit further. For the best part of a decade, they and other firms have repeatedly faced specious accusations of “surveillance capitalism”. This has notably resulted in heavy-handed regulation (including the GDPR, in the EU, and the CCPA, in California), as well as significant fines and settlements

Those chickens have now come home to roost. The firms that are probably best-placed to implement an effective contact tracing solution simply cannot afford the privacy-related risks. This includes the risk associated with violating existing privacy law, but also potential reputational consequences. 

Matters have also been exacerbated by the overly cautious stance of many western governments, as well as their citizens: 

  • The European Data Protection Board cautioned governments and private sector actors to anonymize location data collected via contact tracing apps. The European Parliament made similar pronouncements.
  • A group of Democratic Senators pushed back against Apple and Google’s contact tracing solution, notably due to privacy considerations.
  • And public support for contact tracing is also critically low. Surveys in the US show that contact tracing is significantly less popular than more restrictive policies, such as business and school closures. Similarly, polls in the UK suggest that between 52% and 62% of Britons would consider using contact tracing applications.
  • Belgium’s initial plans for a contact tracing application were struck down by its data protection authority on account that they did not comply with the GDPR.
  • Finally, across the globe, there has been pushback against so-called “centralized” tracing apps, notably due to privacy fears.

In short, the West’s insistence on maximizing privacy protection is holding back its efforts to combat the joint threats posed by Covid-19 and the unfolding economic recession. 

But contrary to the mass surveillance portrayed in the Dark Knight, the privacy risks entailed by contact tracing are for the most part negligible. State surveillance is hardly a prospect in western democracies. And the risk of data breaches is no greater here than with many other apps and services that we all use daily. To wit, password, email, and identity theft are still, by far, the most common targets for cyber attackers. Put differently, cyber criminals appear to be more interested in stealing assets that can be readily monetized, rather than location data that is almost worthless. This suggests that contact tracing applications, whether centralized or not, are unlikely to be an important target for cyberattackers.

The meagre risks entailed by contact tracing – regardless of how it is ultimately implemented – are thus a tiny price to pay if they enable some return to normalcy. At the time of writing, at least 5,8 million human beings have been infected with Covid-19, causing an estimated 358,000 deaths worldwide. Both Covid-19 and the measures destined to combat it have resulted in a collapse of the global economy – what the IMF has called “the worst economic downturn since the great depression”. Freedoms that the west had taken for granted have suddenly evaporated: the freedom to work, to travel, to see loved ones, etc. Can anyone honestly claim that is not worth temporarily sacrificing some privacy to partially regain these liberties?

More generally, it is not just contact tracing applications and the fight against Covid-19 that have suffered because of excessive privacy fears. The European GDPR offers another salient example. Whatever one thinks about the merits of privacy regulation, it is becoming increasingly clear that the EU overstepped the mark. For instance, an early empirical study found that the entry into force of the GDPR markedly decreased venture capital investments in Europe. Michal Gal aptly summarizes the implications of this emerging body of literature:

The price of data protection through the GDPR is much higher than previously recognized. The GDPR creates two main harmful effects on competition and innovation: it limits competition in data markets, creating more concentrated market structures and entrenching the market power of those who are already strong; and it limits data sharing between different data collectors, thereby preventing the realization of some data synergies which may lead to better data-based knowledge. […] The effects on competition and innovation identified may justify a reevaluation of the balance reached to ensure that overall welfare is increased. 

In short, just like the Dark Knight, policymakers, firms and citizens around the world need to think carefully about the tradeoff that exists between protecting privacy and other objectives, such as saving lives, promoting competition, and increasing innovation. As things stand, however, it seems that many have veered too far on the privacy end of the scale.

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

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

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

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

Lockdowns and the Trust Deficit

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

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

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

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

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

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

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

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

The following sections briefly consider how such systems might work.

CV19 Standards

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

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

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

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

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

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

CV19 Status Verification

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

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

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

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

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

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

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

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

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

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

How a CV19 Status App might Work

A basic schema for a CV19 status app would be:

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

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

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

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

This schema is shown in the decision tree below

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

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

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

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

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

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

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

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

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

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

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


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

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

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

This post is authored by Will Rinehart, (Senior Research Fellow, Center for Growth and Opportunity).]

Nellie Bowles, a longtime critic of tech, recently had a change of heart about tech, which she relayed in the New York Times:

Before the coronavirus, there was something I used to worry about. It was called screen time. Perhaps you remember it.

I thought about it. I wrote about it. A lot. I would try different digital detoxes as if they were fad diets, each working for a week or two before I’d be back on that smooth glowing glass.

Now I have thrown off the shackles of screen-time guilt. My television is on. My computer is open. My phone is unlocked, glittering. I want to be covered in screens. If I had a virtual reality headset nearby, I would strap it on.

Bowles isn’t alone. The Washington Post recently documented how social distancing has caused people to “rethink of one of the great villains of modern technology: screens.” Matthew Yglesias of Vox has been critical of tech in the past as well, but recently admitted that these tools are “making our lives much better.” Cal Newport might have called for Twitter to be shut down, but now thinks the service can be useful. These anecdotes speak to a larger trend. According to one national poll, some 88 percent of Americans now have a better appreciation for technology since this pandemic has forced them to rely upon it. 

Before COVID-19, catchy headlines like “Heavy Social Media Use Linked With Mental Health Issues In Teens” and “Have Smartphones Destroyed a Generation?” were met with nods and approvals. These concerns found backing in legislation like Senator Josh Hawley’s “Social Media Addiction Reduction Technology Act” or SMART Act. The opening lines of the SMART Act make it clear the legislation would “prohibit social media companies from using practices that exploit human psychology or brain physiology to substantially impede freedom of choice, [and] to require social media companies to take measures to mitigate the risks of internet addiction and psychological exploitation.”  

Most psychologists steer clear of using the term addiction because it means a person engages in hazardous use, shows tolerance, and neglects social roles. Because social media, gaming, and cell phone use don’t meet this threshold, the profession tends to describe those who experience negative impacts as engaging in problematic use of the tech, which is only applied to a small minority. According to one estimate, for example, only half of a percent of gamers have patterns of problematic use. 

Even though tech use doesn’t meet the criteria for addiction, the term addiction finds purchase in policy discussions and media outlets because it suggests a healthier norm. Computer games have prosocial benefits, yet it is common to hear that the activity is no match for going outside to play. The same kind of argument exists with social media and phone use; face-to-face communication is preferred to tech-enabled communication. 

But the coronavirus has inverted the normal conditions. Social distancing doesn’t allow us to connect in person or play outside with friends. Faced with no other alternative, technology has been embraced. Videoconferencing is up, as is social media use. This new norm has  brought with it a needed rethink of critiques of tech. Even before this moment, however, the research on tech effects has had its problems.    

To begin, even though it has been researched extensively, screen time and social media use aren’t shown to clearly cause harm. Earlier this year, psychologists Candice Odgers and Michaeline Jensen conducted a massive literature review and summarized the research as “a mix of often conflicting small positive, negative and null associations.” The researchers also point out that studies finding a negative relationship between well-being and tech use tend to be correlational, not causational, and thus are “unlikely to be of clinical or practical significance” to parents or therapists.  

Through no fault of their own, researchers tend to focus a limited number of relationships when it comes to tech use. But professors Amy Orben and Andrew Przybylski were able to sidestep these problems by getting computers to test every theoretically defensible hypothesis. In a writeup appropriately titled “Beyond Cherry-Picking,” the duo explained why this method is important to policy makers:

Although statistical significance is often used as an indicator that findings are practically significant, the paper moves beyond this surrogate to put its findings in a real-world context.  In one dataset, for example, the negative effect of wearing glasses on adolescent well-being is significantly higher than that of social media use. Yet policymakers are currently not contemplating pumping billions into interventions that aim to decrease the use of glasses.

Their academic paper throws cold water on the screen time and tech use debate. Since social media explains only 0.4% of the variation in well-being, much greater welfare gains can be made by concentrating on other policy issues. For example, regularly eating breakfast, getting enough sleep, and avoiding marijuana use play much larger roles in the well-being of adolescents. Social media is only a tiny portion of what determines well-being as the chart below helps to illustrate. 

Second, most social media research relies on self-reporting methods, which are systematically biased and often unreliable. Communication professor Michael Scharkow, for example, compared self-reports of Internet use with the computer log files, which show everything that a computer has done and when, and found that “survey data are only moderately correlated with log file data.” A quartet of psychology professors in the UK discovered that self-reported smartphone use and social media addiction scales face similar problems in that they don’t correctly capture reality. Patrick Markey, Professor and Director of the IR Laboratory at Villanova University, summarized the work, “the fear of smartphones and social media was built on a castle made of sand.”  

Expert bodies have also been changing their tune as well. The American Academy of Pediatrics took a hardline stance for years, preaching digital abstinence. But the organization has since backpedaled and now says that screens are fine in moderation. The organization now suggests that parents and children should work together to create boundaries. 

Once this pandemic is behind us, policymakers and experts should reconsider the screen time debate. We need to move from loaded terms like addiction and embrace a more realistic model of the world. The truth is that everyone’s relationship with technology is complicated. Instead of paternalistic legislation, leaders should place the onus on parents and individuals to figure out what is right for them.