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AI Partnerships and Competition: Much Ado About Nothing?

TOTM Competition policymakers around the world have been expressing concerns about competition in emerging artificial-intelligence (AI) industries, with some taking steps to investigate them further. These . . .

Competition policymakers around the world have been expressing concerns about competition in emerging artificial-intelligence (AI) industries, with some taking steps to investigate them further. These fears are notably fueled by a sense that incumbent (albeit, in adjacent markets) digital platforms may use strategic partnerships with AI firms to stave off competition from this fast-growing field.

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Antitrust & Consumer Protection

ICLE Comments to UK Competition and Markets Authority on AI Partnerships

Regulatory Comments Executive Summary We thank the Competition and Markets Authority (CMA) for this invitation to comment (ITC) on partnerships and other arrangements involving artificial intelligence (AI).[1] . . .

Executive Summary

We thank the Competition and Markets Authority (CMA) for this invitation to comment (ITC) on partnerships and other arrangements involving artificial intelligence (AI).[1] The International Center for Law & Economics (ICLE) is a nonprofit, nonpartisan global research and policy center founded with the goal of building the intellectual foundations for sensible, economically grounded policy. ICLE promotes the use of law & economics methodologies to inform public-policy debates and has longstanding expertise in the evaluation of competition law and policy. ICLE’s interest is to ensure that competition law remains grounded in clear rules, established precedent, a record of evidence, and sound economic analysis.

In our comments, we express concern that policymakers’ current concerns about competition in AI industries may be unwarranted. This is particularly true of the notion that incumbent digital platforms may use strategic partnerships with AI firms to insulate themselves from competition, including the three transactions that are central to the current ITC:

  1. Amazon’s partnership with Anthropic;
  2. Microsoft’s partnership with Mistral AI; and,
  3. Microsoft’s hiring of former Inflection AI employees (including, notably, founder Mustafa Suleyman) and related arrangements with the company.

Indeed, publicly available information suggests these transactions may not warrant merger-control investigation, let alone the heightened scrutiny that comes with potential Phase II proceedings. At the very least, given the AI industry’s competitive landscape, there is little to suggest these transactions merit closer scrutiny than similar deals in other sectors.

Overenforcement in the field of generative AI paradoxically could engender the very harms that policymakers currently seek to avert. As we explain in greater detail below, preventing so-called “big tech” firms from competing in these markets (for example, by threatening competition intervention as soon as they build strategic relationships with AI startups) may thwart an important source of competition needed to keep today’s leading generative-AI firms in check. In short, competition in AI markets is important[2], but trying naïvely to hold incumbent (in adjacent markets) tech firms back out of misguided fears they will come to dominate this space is likely to do more harm than good.

At a more granular level, there are important reasons to believe these agreements will have no negative impact on competition and they may, in fact, benefit consumers—e.g., by enabling those startups to raise capital and deploy their services at an even larger scale. In other words, they do not bear any of the prima facie traits of “killer acquisitions” or even of the acquisition of “nascent potential competitors”.[3]

Most importantly, these partnerships all involve the acquisition of minority stakes that do not entail any change of control over the target companies. Amazon, for instance, will not have “ownership control” of Anthropic. The precise amount of shares acquired has not been made public, but a reported investment of $4 billion in a company valued at $18.4 billion does not give Amazon a majority stake or sufficient voting rights to control the company or its competitive strategy. [4] It has also been reported that the deal will not give Amazon any seats on the Anthropic board or special voting rights (such as the power to veto some decisions).[5] There is thus little reason to believe Amazon has acquired indirect or de facto control over Anthropic.

Microsoft’s investment in Mistral AI is even smaller, in both absolute and relative terms. Microsoft is reportedly investing only $16 million in a company valued at $2.1 billion.[6] This represents less than 1% of Mistral’s equity, making it all but impossible for Microsoft to exert any significant control or influence over Mistral AI’s competitive strategy. Likewise, there have been no reports of Microsoft acquiring seats on Mistral AI’s board or special voting rights. We can therefore be confident that the deal will not affect competition in AI markets.

Much of the same applies to Microsoft’s dealings with Inflection AI. Microsoft hired two of the company’s three founders (which currently does not fall under the scope of merger laws), and also paid $620 million for nonexclusive rights to sell access to the Inflection AI model through its Azure Cloud.[7] Admittedly, the latter could entail (depending on deal’s specifics) some limited control over Inflection AI’s competitive strategy, but there is currently no evidence to suggest this will be the case.

Finally, none of these deals entails any competitively significant behavioral commitments from the target companies. There are no reports of exclusivity agreements or other commitments that would restrict third parties’ access to these firms’ underlying AI models. Again, this means the deals are extremely unlikely to negatively impact the competitive landscape in these markets.

At a more macro level, how the CMA deals with these proposed partnerships could have important ramifications for the UK economy. On the one hand, competition authorities (including the CMA) may be tempted to avoid the mistakes they arguably made during the formative years of what have become today’s largest online platforms.[8] The argument is that tougher enforcement may have reduced the high levels of concentration we see in these markets (the counterpoint is that these markets present features that naturally lead to relatively high levels of concentration and that this concentration benefits consumers in several ways[9]).

Unfortunately, this urge to curtail false negatives may come at the expense of judicial errors that hobble the UK economy. Discussing the EU’s AI Act during a recent interview, French President Emmanuel Macron implicitly suggested the UK is in a unique position to attract AI (and other tech) investments away from the European Union. In his words:

We can decide to regulate much faster and much stronger than our major competitors. But we will regulate things that we will no longer produce or invent. This is never a good idea…

When I look at France, it is probably the first country in terms of artificial intelligence in continental Europe. We are neck and neck with the British. They will not have this regulation on foundational models. But above all, we are all very far behind the Chinese and the Americans. [10]

To capitalise on this opportunity, however, the UK must foster a fertile environment for startup activity. The CMA’s approach to merger review in the AI industry is a small, but important, part of this picture. Looking at AI partnerships in an even-handed manner would signal a commitment to evidence-based policymaking that creates legal certainty for startups. For instance, sound merger-review principles would assure founders that corporate acquisition will remain a viable exit strategy in all but exceptional circumstances.

Of course, none of this is to say that established competition-law principles should play second fiddle to broader geopolitical ambitions. It does, however, suggest that the cost of false positives is particularly high in key industries like AI.

In short, how the CMA approaches these AI partnerships is of pivotal importance for both UK competition policy and the country’s broader economic ambitions. The CMA should therefore look at these partnerships with an open mind, despite the important political and reputational pressure to be seen as “doing something” in this cutting-edge industry. Generative AI is already changing the ways that many firms do business and improving employee productivity in many industries.[11] The technology is also increasingly useful in the field of scientific research, where it has enabled new complex models that expand scientists’ reach.[12] And while sensible enforcement is of vital importance to maintain competition and consumer welfare, it must be grounded in empirical evidence.

In the remainder of these comments, we will discuss the assumptions that underpin calls for heightened competition scrutiny in AI industries, and explain why they are unfounded. The big picture is that AI markets have grown rapidly, and new players are thriving. This would suggest that competition is intense. If incumbent firms could easily leverage their dominance into burgeoning generative-AI markets, we would not have seen the growth of such AI “unicorns” as OpenAI, Midjourney, and Anthropic, to name but a few. Furthermore, AI platforms developed by incumbent data collectors—such as Meta’s Llama or Google’s Bard, recently relaunched as Gemini—have struggled to gain traction. Of course, this is not to say that competition enforcers shouldn’t care about generative AI markets, but rather that there is currently no apparent need for increased competition scrutiny in these markets.

The comments proceed as follows. Section I summarises recent calls for competition intervention in generative-AI markets. Section II argues that many of these calls are underpinned by fears of data-related incumbency advantages (often referred to as “data-network effects”), including in the context of mergers. Section III explains why these effects are unlikely to play a meaningful role in generative-AI markets. Section IV concludes by offering five key takeaways to help policymakers better weigh the tradeoffs inherent to competition intervention (including merger control) in generative-AI markets.

I. Calls for Intervention in AI Markets

It was once (and frequently) said that Google’s “data monopoly” was unassailable: “If ‘big data’ is the oil of the information economy, Google has Standard Oil-like monopoly dominance—and uses that control to maintain its dominant position”.[13] Similar claims of data dominance have been attached to nearly all large online platforms, including Facebook (Meta), Amazon, and Uber.[14]

While some of these claims continue even today (for example, “big data” is a key component of the U.S. Justice Department’s (DOJ) Google Search and adtech antitrust suits),[15] a shiny new data target has emerged in the form of generative artificial intelligence (AI). The launch of ChatGPT in November 2022, as well as the advent of AI image-generation services like Midjourney and Dall-E, have dramatically expanded the public’s conception of what is—and what might be—possible to achieve with generative-AI technologies built on massive datasets.

While these services remain both in the early stages of mainstream adoption and in the throes of rapid, unpredictable technological evolution, they nevertheless already appear to be on the radar of competition policymakers around the world. Several antitrust enforcers appear to believe that, by acting now, they can avoid the “mistakes” that were purportedly made during the formative years of Web 2.0.[16] These mistakes, critics assert, include failing to appreciate the centrality of data in online markets, as well as letting mergers go unchecked and allowing early movers to entrench their market positions.[17] As Lina Khan, chair of the U.S. Federal Trade Commission (FTC), put it: “we are still reeling from the concentration that resulted from Web 2.0, and we don’t want to repeat the mis-steps of the past with AI”.[18]

This response from the competition-policy world is deeply troubling. Rather than engage in critical self-assessment and adopt an appropriately restrained stance, the enforcement community appears to be champing at the bit. Rather than assessing their prior assumptions based on the current technological moment, enforcers’ top priority appears to be figuring out how to rapidly and almost reflexively deploy existing competition tools to address the presumed competitive failures presented by generative AI.[19]

It is increasingly common for competition enforcers to argue that so-called “data-network effects” serve not only to entrench incumbents in those markets where the data is collected, but also to confer similar, self-reinforcing benefits in adjacent markets. Several enforcers have, for example, prevented large online platforms from acquiring smaller firms in adjacent markets, citing the risk that they could use their vast access to data to extend their dominance into these new markets.[20]

They have also launched consultations to ascertain the role that data plays in AI competition. For instance, in an ongoing consultation, the European Commission asks: “What is the role of data and what are its relevant characteristics for the provision of generative AI systems and/or components, including AI models?”[21] Unsurprisingly, the FTC has likewise been bullish about the risks posed by incumbents’ access to data. In comments submitted to the U.S. Copyright Office, for example, the FTC argued that:

The rapid development and deployment of AI also poses potential risks to competition. The rising importance of AI to the economy may further lock in the market dominance of large incumbent technology firms. These powerful, vertically integrated incumbents control many of the inputs necessary for the effective development and deployment of AI tools, including cloud-based or local computing power and access to large stores of training data. These dominant technology companies may have the incentive to use their control over these inputs to unlawfully entrench their market positions in AI and related markets, including digital content markets.[22]

Certainly, it stands to reason that the largest online platforms—including Alphabet, Meta, Apple, and Amazon—should have a meaningful advantage in the burgeoning markets for generative-AI services. After all, it is widely recognised that data is an essential input for generative AI.[23] This competitive advantage should be all the more significant, given that these firms have been at the forefront of AI technology for more than a decade. Over this period, Google’s DeepMind and AlphaGo and Meta’s NLLB-200 have routinely made headlines.[24] Apple and Amazon also have vast experience with AI assistants, and all of these firms use AI technology throughout their platforms.[25]

Contrary to what one might expect, however, the tech giants have, to date, been largely unable to leverage their vast data troves of data to outcompete startups like OpenAI and Midjourney. At the time of writing, OpenAI’s ChatGPT appears to be, by far, the most successful chatbot,[26] despite the large tech platforms’ apparent access to far more (and more up-to-date) data.

In these comments, we suggest that there are important lessons to glean from these developments, if only enforcers would stop to reflect. The meteoric rise of consumer-facing AI services should offer competition enforcers and policymakers an opportunity for introspection. As we explain, the rapid emergence of generative-AI technology may undercut many core assumptions of today’s competition-policy debates, which have focused largely on the rueful after-effects of the purported failure of 20th-century antitrust to address the allegedly manifest harms of 21st-century technology. These include the notions that data advantages constitute barriers to entry and can be leveraged to project dominance into adjacent markets; that scale itself is a market failure to be addressed by enforcers; and that the use of consumer data is inherently harmful to those consumers.

II. Data-Network Effects Theory and Enforcement

Proponents of more extensive intervention by competition enforcers into digital markets often cite data-network effects as a source of competitive advantage and barrier to entry (though terms like “economies of scale and scope” may offer more precision).[27] The crux of the argument is that “the collection and use of data creates a feedback loop of more data, which ultimately insulates incumbent platforms from entrants who, but for their data disadvantage, might offer a better product”.[28] This self-reinforcing cycle purportedly leads to market domination by a single firm. Thus, it is argued, e.g., that Google’s “ever-expanding control of user personal data, and that data’s critical value to online advertisers, creates an insurmountable barrier to entry for new competition”.[29]

But it is important to note the conceptual problems these claims face. Because data can be used to improve products’ quality and/or to subsidise their use, treating the possession of data as an entry barrier suggests that any product improvement or price reduction made by an incumbent could be a problematic entry barrier to any new entrant. This is tantamount to an argument that competition itself is a cognizable barrier to entry. Of course, it would be a curious approach to antitrust if competition were treated as a problem, as it would imply that firms should under-compete—i.e., should forego consumer-welfare enhancements—in order to inculcate a greater number of firms in a given market, simply for its own sake.[30]

Meanwhile, actual economic studies of data-network effects have been few and far between, with scant empirical evidence to support the theory.[31] Andrei Hagiu and Julian Wright’s theoretical paper offers perhaps the most comprehensive treatment of the topic to date.[32] The authors ultimately conclude that data-network effects can be of differing magnitudes and have varying effects on firms’ incumbency advantage.[33] They cite Grammarly (an AI writing-assistance tool) as a potential example: “As users make corrections to the suggestions offered by Grammarly, its language experts and artificial intelligence can use this feedback to continue to improve its future recommendations for all users”.[34]

This is echoed by other economists who contend that “[t]he algorithmic analysis of user data and information might increase incumbency advantages, creating lock-in effects among users and making them more reluctant to join an entrant platform”.[35] Crucially, some scholars take this logic a step further, arguing that platforms may use data from their “origin markets” in order to enter and dominate adjacent ones:

First, as we already mentioned, data collected in the origin market can be used, once the enveloper has entered the target market, to provide products more efficiently in the target market. Second, data collected in the origin market can be used to reduce the asymmetric information to which an entrant is typically subject when deciding to invest (for example, in R&D) to enter a new market. For instance, a search engine could be able to predict new trends from consumer searches and therefore face less uncertainty in product design.[36]

This possibility is also implicit in Hagiu and Wright’s paper.[37] Indeed, the authors’ theoretical model rests on an important distinction between within-user data advantages (that is, having access to more data about a given user) and across-user data advantages (information gleaned from having access to a wider user base). In both cases, there is an implicit assumption that platforms may use data from one service to gain an advantage in another market (because what matters is information about aggregate or individual user preferences, regardless of its origin).

Our review of the economic evidence suggests that several scholars have, with varying degrees of certainty, raised the possibility that incumbents may leverage data advantages to stifle competitors in their primary market or in adjacent ones (be it via merger or organic growth). As we explain below, however, there is ultimately little evidence to support such claims. Policymakers have, however, been keenly receptive to these limited theoretical findings, basing multiple decisions on these theories, often with little consideration given to the caveats that accompany them.[38]

Indeed, it is remarkable that, in its section on “[t]he data advantage for incumbents”, the “Furman Report” created for the UK government cited only two empirical economic studies, and they offer directly contradictory conclusions with respect to the question of the strength of data advantages.[39] Nevertheless, the Furman Report concludes that data “may confer a form of unmatchable advantage on the incumbent business, making successful rivalry less likely”,[40] and adopts without reservation “convincing” evidence from non-economists that have no apparent empirical basis.[41]

In the Google/Fitbit merger proceedings, the European Commission found that the combination of data from Google services with that of Fitbit devices would reduce competition in advertising markets:

Giving [sic] the large amount of data already used for advertising purposes that Google holds, the increase in Google’s data collection capabilities, which goes beyond the mere number of active users for which Fitbit has been collecting data so far, the Transaction is likely to have a negative impact on the development of an unfettered competition in the markets for online advertising.[42]

As a result, the Commission cleared the merger on the condition that Google refrain from using data from Fitbit devices for its advertising platform.[43] The Commission also appears likely to focus on similar issues in its ongoing investigation of Microsoft’s investment into OpenAI.[44]

Along similar lines, in its complaint to enjoin Meta’s purchase of Within Unlimited—makers of the virtual-reality (VR) fitness app Supernatural—the FTC relied on, among other things, the fact that Meta could leverage its data about VR-user behavior to inform its decisions and potentially outcompete rival VR-fitness apps: “Meta’s control over the Quest platform also gives it unique access to VR user data, which it uses to inform strategic decisions”.[45]

The DOJ’s twin cases against Google also implicate data leveraging and data barriers to entry. The agency’s adtech complaint charges that “Google intentionally exploited its massive trove of user data to further entrench its monopoly across the digital advertising industry”.[46] Similarly, in its Google Search complaint, the agency argues that:

Google’s anticompetitive practices are especially pernicious because they deny rivals scale to compete effectively. General search services, search advertising, and general search text advertising require complex algorithms that are constantly learning which organic results and ads best respond to user queries; the volume, variety, and velocity of data accelerates the automated learning of search and search advertising algorithms.[47]

Finally, updated merger guidelines published in recent years by several competition enforcers cite the acquisition of data as a potential source of competition concerns. For instance, the FTC and DOJ’s 2023 guidelines state that “acquiring data that helps facilitate matching, sorting, or prediction services may enable the platform to weaken rival platforms by denying them that data”.[48] Likewise, the CMA itself warns against incumbents acquiring firms in order to obtain their data and foreclose other rivals:

Incentive to foreclose rivals…

7.19(e) Particularly in complex and dynamic markets, firms may not focus on short term margins but may pursue other objectives to maximise their long-run profitability, which the CMA may consider. This may include… obtaining access to customer data….[49]

In short, competition authorities around the globe have been taking an increasingly aggressive stance on data-network effects. Among the ways this has manifested is in enforcement decisions based on fears that data collected by one platform might confer a decisive competitive advantage in adjacent markets. Unfortunately, these concerns rest on little to no empirical evidence, either in the economic literature or the underlying case records.

III. Data-Incumbency Advantages in Generative-AI Markets

Given the assertions detailed in the previous section, it would be reasonable to assume that firms such as Google, Meta, and Amazon should be in pole position to dominate the burgeoning market for generative AI. After all, these firms have not only been at the forefront of the field for the better part of a decade, but they also have access to vast troves of data, the likes of which their rivals could only dream when they launched their own services. Thus, the authors of the Furman Report caution that “to the degree that the next technological revolution centres around artificial intelligence and machine learning, then the companies most able to take advantage of it may well be the existing large companies because of the importance of data for the successful use of these tools”.[50]

To date, however, this is not how things have unfolded—although it bears noting these markets remain in flux and the competitive landscape is susceptible to change. The first significantly successful generative-AI service was arguably not from either Meta—which had been working on chatbots for years and had access to, arguably, the world’s largest database of actual chats—or Google. Instead, the breakthrough came from a previously unknown firm called OpenAI.

OpenAI’s ChatGPT service currently holds an estimated 60% of the market (though reliable numbers are somewhat elusive).[51] It broke the record for the fastest online service to reach 100 million users (in only a couple of months), more than four times faster than previous record holder TikTok.[52] Based on Google Trends data, ChatGPT is nine times more popular worldwide than Google’s own Bard service, and 14 times more popular in the United States.[53] In April 2023, ChatGPT reportedly registered 206.7 million unique visitors, compared to 19.5 million for Google’s Bard.[54] In short, at the time we are writing, ChatGPT appears to be the most popular chatbot. The entry of large players such as Google Bard or Meta AI appear to have had little effect thus far on its market position.[55]

The picture is similar in the field of AI-image generation. As of August 2023, Midjourney, Dall-E, and Stable Diffusion appear to be the three market leaders in terms of user visits.[56] This is despite competition from the likes of Google and Meta, who arguably have access to unparalleled image and video databases by virtue of their primary platform activities.[57]

This raises several crucial questions: how have these AI upstarts managed to be so successful, and is their success just a flash in the pan before Web 2.0 giants catch up and overthrow them? While we cannot answer either of these questions dispositively, we offer what we believe to be some relevant observations concerning the role and value of data in digital markets.

A first important observation is that empirical studies suggest that data exhibits diminishing marginal returns. In other words, past a certain point, acquiring more data does not confer a meaningful edge to the acquiring firm. As Catherine Tucker put it following a review of the literature: “Empirically there is little evidence of economies of scale and scope in digital data in the instances where one would expect to find them”.[58]

Likewise, following a survey of the empirical literature on this topic, Geoffrey Manne and Dirk Auer conclude that:

Available evidence suggests that claims of “extreme” returns to scale in the tech sector are greatly overblown. Not only are the largest expenditures of digital platforms unlikely to become proportionally less important as output increases, but empirical research strongly suggests that even data does not give rise to increasing returns to scale, despite routinely being cited as the source of this effect.[59]

In other words, being the firm with the most data appears to be far less important than having enough data. Moreover, this lower bar may be accessible to far more firms than one might initially think possible. Furthermore, obtaining sufficient data could become easier still—that is, the volume of required data could become even smaller—with technological progress. For instance, synthetic data may provide an adequate substitute to real-world data,[60] or may even outperform real-world data.[61] As Thibault Schrepel and Alex Pentland surmise:

[A]dvances in computer science and analytics are making the amount of data less relevant every day. In recent months, important technological advances have allowed companies with small data sets to compete with larger ones.[62]

Indeed, past a certain threshold, acquiring more data might not meaningfully improve a service, where other improvements (such as better training methods or data curation) could have a large impact. In fact, there is some evidence that excessive data impedes a service’s ability to generate results appropriate for a given query: “[S]uperior model performance can often be achieved with smaller, high-quality datasets than massive, uncurated ones. Data curation ensures that training datasets are devoid of noise, irrelevant instances, and duplications, thus maximizing the efficiency of every training iteration”.[63]

Consider, for instance, a user who wants to generate an image of a basketball. Using a model trained on an indiscriminate range and number of public photos in which a basketball appears surrounded by copious other image data, the user may end up with an inordinately noisy result. By contrast, a model trained with a better method on fewer, more carefully selected images could readily yield far superior results.[64] In one important example:

[t]he model’s performance is particularly remarkable, given its small size. “This is not a large language model trained on the whole Internet; this is a relatively small transformer trained for these tasks,” says Armando Solar-Lezama, a computer scientist at the Massachusetts Institute of Technology, who was not involved in the new study…. The finding implies that instead of just shoving ever more training data into machine-learning models, a complementary strategy might be to offer AI algorithms the equivalent of a focused linguistics or algebra class.[65]

Platforms’ current efforts are thus focused on improving the mathematical and logical reasoning of large language models (LLMs), rather than maximizing training datasets.[66]

Two points stand out. The first is that firms like OpenAI rely largely on publicly available datasets—such as GSM8K—to train their LLMs.[67] Second, the real challenge to create cutting-edge AI is not so much in collecting data, but rather in creating innovative AI-training processes and architectures:

[B]uilding a truly general reasoning engine will require a more fundamental architectural innovation. What’s needed is a way for language models to learn new abstractions that go beyond their training data and have these evolving abstractions influence the model’s choices as it explores the space of possible solutions.

We know this is possible because the human brain does it. But it might be a while before OpenAI, DeepMind, or anyone else figures out how to do it in silicon.[68]

Furthermore, it is worth noting that the data most relevant to startups in a given market may not be those held by large incumbent platforms in other markets, but rather data specific to the market in which the startup is active or, even better, to the given problem it is attempting to solve:

As Andres Lerner has argued, if you wanted to start a travel business, the data from Kayak or Priceline would be far more relevant. Or if you wanted to start a ride-sharing business, data from cab companies would be more useful than the broad, market-cross-cutting profiles Google and Facebook have. Consider companies like Uber, Lyft and Sidecar that had no customer data when they began to challenge established cab companies that did possess such data. If data were really so significant, they could never have competed successfully. But Uber, Lyft and Sidecar have been able to effectively compete because they built products that users wanted to use—they came up with an idea for a better mousetrap. The data they have accrued came after they innovated, entered the market and mounted their successful challenges—not before.[69]

The bottom line is that data is not the be-all and end-all that many in competition circles make it out to be. While data often may confer marginal benefits, there is little sense that these benefits are ultimately decisive.[70] As a result, incumbent platforms’ access to vast numbers of users and troves of data in their primary markets might only marginally affect their competitiveness in AI markets.

A related observation is that firms’ capabilities and other features of their products arguably play a more important role than the data they own.[71] Examples of this abound in digital markets. Google overthrew Yahoo in search, despite initially having access to far fewer users and far less data; Google and Apple overcame Microsoft in the smartphone operating system market, despite having comparatively tiny ecosystems (at the time) to leverage; and TikTok rose to prominence despite intense competition from incumbents like Instagram, which had much larger user bases. In each of these cases, important product-design decisions (such as the PageRank algorithm, recognizing the specific needs of mobile users,[72] and TikTok’s clever algorithm) appear to have played a far more significant role than initial user and data endowments (or lack thereof).

All of this suggests that the early success of OpenAI likely has more to do with its engineering decisions than with what data it did or did not possess. Going forward, OpenAI and its rivals’ ability to offer and monetise compelling stores offering custom versions of their generative-AI technology will arguably play a much larger role than (and contribute to) their ownership of data.[73] In other words, the ultimate challenge is arguably to create a valuable platform, of which data ownership is a consequence, but not a cause.

It is also important to note that, in those instances where it is valuable, data does not just fall from the sky. Instead, it is through smart business and engineering decisions that firms can generate valuable information (which does not necessarily correlate with owning more data).

For instance, OpenAI’s success with ChatGPT is often attributed to its more efficient algorithms and training models, which arguably have enabled the service to improve more rapidly than its rivals.[74] Likewise, the ability of firms like Meta and Google to generate valuable data for advertising arguably depends more on design decisions that elicit the right data from users, rather than the raw number of users in their networks.

Put differently, setting up a business so as to extract and organise the right information is more important than simply owning vast troves of data.[75] Even in those instances where high-quality data is an essential parameter of competition, it does not follow that having vaster databases or more users on a platform necessarily leads to better information for the platform.

Indeed, if data ownership consistently conferred a significant competitive advantage, these new firms would not be where they are today. This does not, of course, mean that data is worthless. Rather, it means that competition authorities should not assume that the mere possession of data is a dispositive competitive advantage, absent compelling empirical evidence to support such a finding. In this light, the current wave of decisions and competition-policy pronouncements that rely on data-related theories of harm are premature.

IV. Five Key Takeaways: Reconceptualizing the Role of Data in Generative-AI Competition

As we explain above, data network effects are not the source of barriers to entry that they are sometimes made out to be. The picture is far more nuanced. Indeed, as economist Andres Lerner demonstrated almost a decade ago (and the assessment is only truer today):

Although the collection of user data is generally valuable for online providers, the conclusion that such benefits of user data lead to significant returns to scale and to the entrenchment of dominant online platforms is based on unsupported assumptions. Although, in theory, control of an “essential” input can lead to the exclusion of rivals, a careful analysis of real-world evidence indicates that such concerns are unwarranted for many online businesses that have been the focus of the “big data” debate.[76]

While data can be an important part of the competitive landscape, incumbents’ data advantages are far less pronounced than today’s policymakers commonly assume. In that respect, five main lessons emerge:

  1. Data can be (very) valuable, but beyond a certain threshold, those benefits tend to diminish. In other words, having the most data is less important than having enough;
  2. The ability to generate valuable information does not depend on the number of users or the amount of data a platform has previously acquired;
  3. The most important datasets are not always proprietary;
  4. Technological advances and platforms’ engineering decisions affect their ability to generate valuable information, and this effect swamps effects stemming from the amount of data they own; and
  5. How platforms use data is arguably more important than what data or how much data they own.

These lessons have important ramifications for policy debates over the competitive implications of data in technologically evolving areas.

First, it is not surprising that startups, rather than incumbents, have taken an early lead in generative AI (and in Web 2.0 before it). After all, if data-incumbency advantages are small or even nonexistent, then smaller and more nimble players may have an edge over established tech platforms. This is all the more likely given that, despite significant efforts, the biggest tech platforms were unable to offer compelling generative-AI chatbots and image-generation services before the emergence of ChatGPT, Dall-E, Midjourney, etc.

This failure suggests that, in a process akin to Clayton Christensen’s “innovator’s dilemma”,[77] something about the incumbent platforms’ existing services and capabilities was holding them back in those markets. Of course, this does not necessarily mean that those same services or capabilities could not become an advantage when the generative-AI market starts addressing issues of monetisation and scale.[78] But it does mean that assumptions about a firm’s market power based on its possession of data are off the mark.

Another important implication is that, paradoxically, policymakers’ efforts to prevent Web 2.0 platforms from competing freely in generative-AI markets may ultimately backfire and lead to less, not more, competition. Indeed, OpenAI is currently acquiring a sizeable lead in generative AI. While competition authorities might like to think that other startups will emerge and thrive in this space, it is important not to confuse desires with reality. While there currently exists a vibrant AI-startup ecosystem, there is at least a case to be made that the most significant competition for today’s AI leaders will come from incumbent Web 2.0 platforms—although nothing is certain at this stage.

Policymakers should beware not to stifle that competition on the misguided assumption that competitive pressure from large incumbents is somehow less valuable to consumers than that which originates from smaller firms. This is particularly relevant in the context of merger control. An acquisition (or an “acqui-hire”) by a “big tech” company does not only, in principle, entail a minor risk to harm competition (it is not a horizontal merger[79]) but could create a stronger competitor to the current market leaders.

Finally, even if there were a competition-related market failure to be addressed in the field of generative AI (which is anything but clear), the remedies under contemplation may do more harm than good. Some of the solutions that have been put forward have highly ambiguous effects on consumer welfare. Scholars have shown that, e.g., mandated data sharing—a solution championed by EU policymakers, among others—may sometimes dampen competition in generative-AI markets.[80] This is also true of legislation like the General Data Protection Regulation (GDPR), which makes it harder for firms to acquire more data about consumers—assuming such data is, indeed, useful to generative-AI services.[81]

In sum, it is a flawed understanding of the economics and practical consequences of large agglomerations of data that lead competition authorities to believe that data-incumbency advantages are likely to harm competition in generative AI markets—or even in the data-intensive Web 2.0 markets that preceded them. Indeed, competition or regulatory intervention to “correct” data barriers and data network and scale effects is liable to do more harm than good.


[1] CMA Seeks Views on AI Partnerships and Other Arrangements, Competition and Markets Authority (24 Apr. 2024), https://www.gov.uk/government/news/cma-seeks-views-on-ai-partnerships-and-other-arrangements.

[2] AI, of course, is not a market (at least not a relevant antitrust market). Within the realm of what is being called “AI”, companies can offer myriad products and services, and specific relevant markets would need to be defined before assessing harm to competition in specific cases.

[3] OECD, Start-ups, Killer Acquisitions and Merger Control (2020), available at https://web-archive.oecd.org/2020-10-16/566931-start-ups-killer-acquisitions-and-merger-control-2020.pdf.

[4] Kate Rooney & Hayden Field, Amazon Spends $2.75 Billion on AI Startup Anthropic in Its Largest Venture Investment Yet, CNBC (27 Mar. 2024), https://www.cnbc.com/2024/03/27/amazon-spends-2point7b-on-startup-anthropic-in-largest-venture-investment.html.

[5] Id.

[6] Tom Warren, Microsoft Partners with Mistral in Second AI Deal Beyond OpenAI, The Verge (26 Feb. 2024), https://www.theverge.com/2024/2/26/24083510/microsoft-mistral-partnership-deal-azure-ai.

[7] Mark Sullivan, Microsoft’s Inflection AI Grab Likely Cost More Than $1 Billion, Says An Insider (Exclusive), Fast Company  (26 Mar. 2024), https://www.fastcompany.com/91069182/microsoft-inflection-ai-exclusive; see also, Mustafa Suleyman, DeepMind and Inflection Co-Founder, Joins Microsoft to Lead Copilot, Microsoft Corporate Blogs (19 Mar. 2024), https://blogs.microsoft.com/blog/2024/03/19/mustafa-suleyman-deepmind-and-inflection-co-founder-joins-microsoft-to-lead-copilot; Krystal Hu & Harshita Mary Varghese, Microsoft Pays Inflection $ 650 Mln in Licensing Deal While Poaching Top Talent, Source Says, Reuters (21 Mar. 2024), https://www.reuters.com/technology/microsoft-agreed-pay-inflection-650-mln-while-hiring-its-staff-information-2024-03-21; The New Inflection: An Important Change to How We’ll Work, Inflection (Mar. 19, 2024), https://inflection.ai/the-new-inflection; Julie Bort, Here’s How Microsoft Is Providing a ‘Good Outcome’ for Inflection AI VCs, as Reid Hoffman Promised, Tech Crunch (21 Mar. 2024), https://techcrunch.com/2024/03/21/microsoft-inflection-ai-investors-reid-hoffman-bill-gates.

[8] See Rana Foroohar, The Great US-Europe Antitrust Divide, Financial Times (5 Feb. 2024), https://www.ft.com/content/065a2f93-dc1e-410c-ba9d-73c930cedc14 (quoting FTC Chair Lina Khan “we are still reeling from the concentration that resulted from Web 2.0, and we don’t want to repeat the mis-steps of the past with AI”).

[9] See, e.g., Geoffrey Manne & Dirk Auer, Antitrust Dystopia and Antitrust Nostalgia: Alarmist Theories of Harm in Digital Markets and Their Origins, 28 Geo. Mason L. Rev. 1279, 1294 (2021). (“But while these increasing returns can cause markets to become more concentrated, they also imply that it is often more efficient to have a single firm serve the entire market. For instance, to a first approximation, network effects, which are one potential source of increasing returns, imply that it is more valuable-not just to the platform, but to the users themselves-for all users to be present on the same network or platform. In other words, fragmentation—de-concentration—may be more of a problem than monopoly in markets that exhibit network effects and increasing returns to scale. Given this, it is far from clear that antitrust authorities should try to prevent consolidation in markets that exhibit such characteristics, nor is it self-evident that these markets somehow produce less consumer surplus than markets that do not exhibit such increasing returns”.)

[10] Javier Espinoza & Leila Abboud, EU’s New AI Act Risks Hampering Innovation, Warns Emmanuel Macron, Financial Times (11 Dec. 2023), https://www.ft.com/content/9339d104-7b0c-42b8-9316-72226dd4e4c0.

[11] See, e.g., Michael Chui, et al., The Economic Potential of Generative AI: The Next Productivity Frontier, McKinsey (14 Jun. 2023), https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/the-economic-potential-of-generative-AI-the-next-productivity-frontier.

[12] See, e.g., Zhuoran Qiao, Weili Nie, Arash Vahdat, Thomas F. Miller III, & Animashree Anandkumar, State-Specific Protein–Ligand Complex Structure Prediction with a Multiscale Deep Generative Model, 6 Nature Machine Intelligence, 195-208 (2024); see also Jaemin Seo, Sang Kyeun Kim, Azarakhsh Jalalvand, Rory Conlin, Andrew Rothstein, Joseph Abbate, Keith Erickson, Josiah Wai, Ricardo Shousha, & Egemen Kolemen, Avoiding Fusion Plasma Tearing Instability with Deep Reinforcement Learning, 626 Nature, 746-751 (2024).

[13] Nathan Newman, Taking on Google’s Monopoly Means Regulating Its Control of User Data, Huffington Post (24 Sep. 2013), http://www.huffingtonpost.com/nathan-newman/taking-on-googlesmonopol_b_3980799.html.

[14] See, e.g., Lina Khan & K. Sabeel Rahman, Restoring Competition in the U.S. Economy, in Untamed: How to Check Corporate, Financial, and Monopoly Power (Nell Abernathy, Mike Konczal, & Kathryn Milani, eds., 2016), at 23 (“From Amazon to Google to Uber, there is a new form of economic power on display, distinct from conventional monopolies and oligopolies…, leverag[ing] data, algorithms, and internet-based technologies… in ways that could operate invisibly and anticompetitively”.); Mark Weinstein, I Changed My Mind—Facebook Is a Monopoly, Wall St. J. (1 Oct. 2021), https://www.wsj.com/articles/facebook-is-monopoly-metaverse-users-advertising-platforms-competition-mewe-big-tech-11633104247 (“[T]he glue that holds it all together is Facebook’s monopoly over data…. Facebook’s data troves give it unrivaled knowledge about people, governments—and its competitors”.).

[15] See, generally, Abigail Slater, Why “Big Data” Is a Big Deal, The Reg. Rev. (6 Nov. 2023), https://www.theregreview.org/2023/11/06/slater-why-big-data-is-a-big-deal; Amended Complaint at ¶36, United States v. Google, 1:20-cv-03010- (D.D.C. 2020); Complaint at ¶37, United States v. Google, 1:23-cv-00108 (E.D. Va. 2023), https://www.justice.gov/opa/pr/justice-department-sues-google-monopolizing-digital-advertising-technologies (“Google intentionally exploited its massive trove of user data to further entrench its monopoly across the digital advertising industry”.).

[16] See, e.g., Press Release, Commission Launches Calls for Contributions on Competition in Virtual Worlds and Generative AI, European Commission (9 Jan. 2024), https://ec.europa.eu/commission/presscorner/detail/en/IP_24_85; Krysten Crawford, FTC’s Lina Khan Warns Big Tech over AI, SIEPR (3 Nov. 2020), https://siepr.stanford.edu/news/ftcs-lina-khan-warns-big-tech-over-ai (“Federal Trade Commission Chair Lina Khan delivered a sharp warning to the technology industry in a speech at Stanford on Thursday: Antitrust enforcers are watching what you do in the race to profit from artificial intelligence”.) (emphasis added).

[17] See, e.g., John M. Newman, Antitrust in Digital Markets, 72 Vand. L. Rev. 1497, 1501 (2019) (“[T]he status quo has frequently failed in this vital area, and it continues to do so with alarming regularity. The laissez-faire approach advocated for by scholars and adopted by courts and enforcers has allowed potentially massive harms to go unchecked”.); Bertin Martins, Are New EU Data Market Regulations Coherent and Efficient?, Bruegel Working Paper 21/23 (2023), https://www.bruegel.org/working-paper/are-new-eu-data-market-regulations-coherent-and-efficient (“Technical restrictions on access to and re-use of data may result in failures in data markets and data-driven services markets”.); Valéria Faure-Muntian, Competitive Dysfunction: Why Competition Law Is Failing in a Digital World, The Forum Network (24 Feb. 2021), https://www.oecd-forum.org/posts/competitive-dysfunction-why-competition-law-is-failing-in-a-digital-world.

[18] See Foroohar, supra note 8.

[19] See, e.g., Press Release, European Commission, supra note 16.

[20] See infra, Section II. Commentators have also made similar claims; see, e.g., Ganesh Sitaram & Tejas N. Narechania, It’s Time for the Government to Regulate AI. Here’s How, Politico (15 Jan. 2024) (“All that cloud computing power is used to train foundation models by having them “learn” from incomprehensibly huge quantities of data. Unsurprisingly, the entities that own these massive computing resources are also the companies that dominate model development. Google has Bard, Meta has LLaMa. Amazon recently invested $4 billion into one of OpenAI’s leading competitors, Anthropic. And Microsoft has a 49 percent ownership stake in OpenAI — giving it extraordinary influence, as the recent board struggles over Sam Altman’s role as CEO showed”.).

[21] Press Release, European Commission, supra note 16.

[22] Comment of U.S. Federal Trade Commission to the U.S. Copyright Office, Artificial Intelligence and Copyright, Docket No. 2023-6 (30 Oct. 2023), at 4, https://www.ftc.gov/legal-library/browse/advocacy-filings/comment-federal-trade-commission-artificial-intelligence-copyright (emphasis added).

[23] See, e.g., Joe Caserta, Holger Harreis, Kayvaun Rowshankish, Nikhil Srinidhi, & Asin Tavakoli, The Data Dividend: Fueling Generative AI, McKinsey Digital (15 Sep. 2023), https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/the-data-dividend-fueling-generative-ai (“Your data and its underlying foundations are the determining factors to what’s possible with generative AI”.).

[24] See, e.g., Tim Keary, Google DeepMind’s Achievements and Breakthroughs in AI Research, Techopedia (11 Aug. 2023), https://www.techopedia.com/google-deepminds-achievements-and-breakthroughs-in-ai-research; See, e.g., Will Douglas Heaven, Google DeepMind Used a Large Language Model to Solve an Unsolved Math Problem, MIT Technology Review (14 Dec. 2023), https://www.technologyreview.com/2023/12/14/1085318/google-deepmind-large-language-model-solve-unsolvable-math-problem-cap-set; see also, A Decade of Advancing the State-of-the-Art in AI Through Open Research, Meta (30 Nov. 2023), https://about.fb.com/news/2023/11/decade-of-advancing-ai-through-open-research; see also, 200 Languages Within a Single AI Model: A Breakthrough in High-Quality Machine Translation, Meta, https://ai.meta.com/blog/nllb-200-high-quality-machine-translation (last visited 18 Jan. 2023).

[25] See, e.g., Jennifer Allen, 10 Years of Siri: The History of Apple’s Voice Assistant, Tech Radar (4 Oct. 2021), https://www.techradar.com/news/siri-10-year-anniversary; see also Evan Selleck, How Apple Is Already Using Machine Learning and AI in iOS, Apple Insider (20 Nov. 2023), https://appleinsider.com/articles/23/09/02/how-apple-is-already-using-machine-learning-and-ai-in-ios; see also, Kathleen Walch, The Twenty Year History Of AI At Amazon, Forbes (19 July 2019), https://www.forbes.com/sites/cognitiveworld/2019/07/19/the-twenty-year-history-of-ai-at-amazon.

[26] See infra Section III.

[27] See, e.g., Cédric Argenton & Jens Prüfer, Search Engine Competition with Network Externalities, 8 J. Comp. L. & Econ. 73, 74 (2012).

[28] John M. Yun, The Role of Big Data in Antitrust, in The Global Antitrust Institute Report on the Digital Economy (Joshua D. Wright & Douglas H. Ginsburg, eds., 11 Nov. 2020) at 233, https://gaidigitalreport.com/2020/08/25/big-data-and-barriers-to-entry/#_ftnref50; see also, e.g., Robert Wayne Gregory, Ola Henfridsson, Evgeny Kaganer, & Harris Kyriakou, The Role of Artificial Intelligence and Data Network Effects for Creating User Value, 46 Acad. of Mgmt. Rev. 534 (2020), final pre-print version at 4, http://wrap.warwick.ac.uk/134220) (“A platform exhibits data network effects if, the more that the platform learns from the data it collects on users, the more valuable the platform becomes to each user”.); see also, Karl Schmedders, José Parra-Moyano, & Michael Wade, Why Data Aggregation Laws Could be the Answer to Big Tech Dominance, Silicon Republic (6 Feb. 2024), https://www.siliconrepublic.com/enterprise/data-ai-aggregation-laws-regulation-big-tech-dominance-competition-antitrust-imd.

[29] Nathan Newman, Search, Antitrust, and the Economics of the Control of User Data, 31 Yale J. Reg. 401, 409 (2014) (emphasis added); see also id. at 420 & 423 (“While there are a number of network effects that come into play with Google, [“its intimate knowledge of its users contained in its vast databases of user personal data”] is likely the most important one in terms of entrenching the company’s monopoly in search advertising…. Google’s overwhelming control of user data… might make its dominance nearly unchallengeable”.).

[30] See also Yun, supra note 28 at 229 (“[I]nvestments in big data can create competitive distance between a firm and its rivals, including potential entrants, but this distance is the result of a competitive desire to improve one’s product”.).

[31] For a review of the literature on increasing returns to scale in data (this topic is broader than data-network effects) see Geoffrey Manne & Dirk Auer, Antitrust Dystopia and Antitrust Nostalgia: Alarmist Theories of Harm in Digital Markets and Their Origins, 28 Geo Mason L. Rev. 1281, 1344 (2021).

[32] Andrei Hagiu & Julian Wright, Data-Enabled Learning, Network Effects, and Competitive Advantage, 54 RAND J. Econ. 638 (2023).

[33] Id. at 639. The authors conclude that “Data-enabled learning would seem to give incumbent firms a competitive advantage. But how strong is this advantage and how does it differ from that obtained from more traditional mechanisms…”.

[34] Id.

[35] Bruno Jullien & Wilfried Sand-Zantman, The Economics of Platforms: A Theory Guide for Competition Policy, 54 Info. Econ. & Pol’y 10080, 101031 (2021).

[36] Daniele Condorelli & Jorge Padilla, Harnessing Platform Envelopment in the Digital World, 16 J. Comp. L. & Pol’y 143, 167 (2020).

[37] See Hagiu & Wright, supra note 32.

[38] For a summary of these limitations, see generally Catherine Tucker, Network Effects and Market Power: What Have We Learned in the Last Decade?, Antitrust (2018) at 72, available at https://sites.bu.edu/tpri/files/2018/07/tucker-network-effects-antitrust2018.pdf; see also Manne & Auer, supra note 31, at 1330.

[39] See Jason Furman, Diane Coyle, Amelia Fletcher, Derek McAuley, & Philip Marsden (Dig. Competition Expert Panel), Unlocking Digital Competition (2019) at 32-35 (“Furman Report”), available at https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/785547/unlocking_digital_competition_furman_review_web.pdf.

[40] Id. at 34.

[41] Id. at 35. To its credit, it should be noted, the Furman Report does counsel caution before mandating access to data as a remedy to promote competition. See id. at 75. That said, the Furman Report maintains that such a remedy should certainly be on the table because “the evidence suggests that large data holdings are at the heart of the potential for some platform markets to be dominated by single players and for that dominance to be entrenched in a way that lessens the potential for competition for the market”. Id. In fact, the evidence does not show this.

[42] Case COMP/M.9660 — Google/Fitbit, Commission Decision (17 Dec. 2020) (Summary at O.J. (C 194) 7), available at https://ec.europa.eu/competition/mergers/cases1/202120/m9660_3314_3.pdf at 455.

[43] Id. at 896.

[44] See Natasha Lomas, EU Checking if Microsoft’s OpenAI Investment Falls Under Merger Rules, TechCrunch (9 Jan. 2024), https://techcrunch.com/2024/01/09/openai-microsoft-eu-merger-rules.

[45] Amended Complaint at 11, Meta/Zuckerberg/Within, Fed. Trade Comm’n. (2022) (No. 605837), available at https://www.ftc.gov/system/files/ftc_gov/pdf/D09411%20-%20AMENDED%20COMPLAINT%20FILED%20BY%20COUNSEL%20SUPPORTING%20THE%20COMPLAINT%20-%20PUBLIC%20%281%29_0.pdf.

[46] Amended Complaint (D.D.C), supra note 15 at ¶37.

[47] Amended Complaint (E.D. Va), supra note 15 at ¶8.

[48] Merger Guidelines, US Dep’t of Justice & Fed. Trade Comm’n (2023) at 25, available at https://www.ftc.gov/system/files/ftc_gov/pdf/2023_merger_guidelines_final_12.18.2023.pdf.

[49] Merger Assessment Guidelines, Competition and Mkts. Auth (2021) at  ¶7.19(e), available at https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/1051823/MAGs_for_publication_2021_–_.pdf.

[50] Furman Report, supra note 39, at ¶4.

[51] See, e.g., Chris Westfall, New Research Shows ChatGPT Reigns Supreme in AI Tool Sector, Forbes (16 Nov. 2023), https://www.forbes.com/sites/chriswestfall/2023/11/16/new-research-shows-chatgpt-reigns-supreme-in-ai-tool-sector/?sh=7de5de250e9c.

[52] See Krystal Hu, ChatGPT Sets Record for Fastest-Growing User Base, Reuters (2 Feb. 2023), https://www.reuters.com/technology/chatgpt-sets-record-fastest-growing-user-base-analyst-note-2023-02-01; Google: The AI Race Is On, App Economy Insights (7 Feb. 2023), https://www.appeconomyinsights.com/p/google-the-ai-race-is-on.

[53] See Google Trends, https://trends.google.com/trends/explore?date=today%205-y&q=%2Fg%2F11khcfz0y2,%2Fg%2F11ts49p01g&hl=en (last visited 12 Jan. 2024) and https://trends.google.com/trends/explore?date=today%205-y&geo=US&q=%2Fg%2F11khcfz0y2,%2Fg%2F11ts49p01g&hl=en (last visited 12 Jan. 2024).

[54] See David F. Carr, As ChatGPT Growth Flattened in May, Google Bard Rose 187%, Similarweb Blog (5 Jun. 2023), https://www.similarweb.com/blog/insights/ai-news/chatgpt-bard.

[55] See Press Release, Introducing New AI Experiences Across Our Family of Apps and Devices, Meta (27 Sep. 2023), https://about.fb.com/news/2023/09/introducing-ai-powered-assistants-characters-and-creative-tools; Sundar Pichai, An Important Next Step on Our AI Journey, Google Keyword Blog (Feb. 6, 2023), https://blog.google/technology/ai/bard-google-ai-search-updates.

[56] See Ion Prodan, 14 Million Users: Midjourney’s Statistical Success, Yon (Aug. 19, 2023), https://yon.fun/midjourney-statistics; see also Andrew Wilson, Midjourney Statistics: Users, Polls, & Growth [Oct 2023], ApproachableAI (13 Oct. 2023), https://approachableai.com/midjourney-statistics.

[57] See Hema Budaraju, New Ways to Get Inspired with Generative AI in Search, Google Keyword Blog (12 Oct. 2023), https://blog.google/products/search/google-search-generative-ai-october-update; Imagine with Meta AI, Meta (last visited Jan. 12, 2024), https://imagine.meta.com.

[58] Catherine Tucker, Digital Data, Platforms and the Usual [Antitrust] Suspects: Network Effects, Switching Costs, Essential Facility, 54 Rev. Indus. Org. 683, 686 (2019).

[59] Manne & Auer, supra note 31, at 1345.

[60] See, e.g., Stefanie Koperniak, Artificial Data Give the Same Results as Real Data—Without Compromising Privacy, MIT News (3 Mar. 2017), https://news.mit.edu/2017/artificial-data-give-same-results-as-real-data-0303 (“[Authors] describe a machine learning system that automatically creates synthetic data—with the goal of enabling data science efforts that, due to a lack of access to real data, may have otherwise not left the ground. While the use of authentic data can cause significant privacy concerns, this synthetic data is completely different from that produced by real users—but can still be used to develop and test data science algorithms and models”.).

[61] See, e.g., Rachel Gordon, Synthetic Imagery Sets New Bar in AI Training Efficiency, MIT News (20 Nov. 2023), https://news.mit.edu/2023/synthetic-imagery-sets-new-bar-ai-training-efficiency-1120 (“By using synthetic images to train machine learning models, a team of scientists recently surpassed results obtained from traditional ‘real-image’ training methods.).

[62] Thibault Schrepel & Alex ‘Sandy’ Pentland, Competition Between AI Foundation Models: Dynamics and Policy Recommendations, MIT Connection Science Working Paper (Jun. 2023), at 8.

[63] Igor Susmelj, Optimizing Generative AI: The Role of Data Curation, Lightly (last visited 15 Jan. 2024), https://www.lightly.ai/post/optimizing-generative-ai-the-role-of-data-curation.

[64] See, e.g., Xiaoliang Dai, et al., Emu: Enhancing Image Generation Models Using Photogenic Needles in a Haystack, ArXiv (27 Sep. 2023) at 1, https://ar5iv.labs.arxiv.org/html/2309.15807 (“[S]upervised fine-tuning with a set of surprisingly small but extremely visually appealing images can significantly improve the generation quality”.); see also, Hu Xu, et al., Demystifying CLIP Data, ArXiv (28 Sep. 2023), https://arxiv.org/abs/2309.16671.

[65] Lauren Leffer, New Training Method Helps AI Generalize like People Do, Sci. Am. (26 Oct. 2023), https://www.scientificamerican.com/article/new-training-method-helps-ai-generalize-like-people-do (discussing Brendan M. Lake & Marco Baroni, Human-Like Systematic Generalization Through a Meta-Learning Neural Network, 623 Nature 115 (2023)).

[66] Timothy B. Lee, The Real Research Behind the Wild Rumors about OpenAI’s Q* Project, Ars Technica (Dec. 8, 2023), https://arstechnica.com/ai/2023/12/the-real-research-behind-the-wild-rumors-about-openais-q-project.

[67] Id.; see also GSM8K, Papers with Code (last visited 18 Jan. 2023), available at https://paperswithcode.com/dataset/gsm8k; MATH Dataset, GitHub (last visited 18 Jan. 2024), available at https://github.com/hendrycks/math.

[68] Lee, supra note 66.

[69] Geoffrey Manne & Ben Sperry, Debunking the Myth of a Data Barrier to Entry for Online Services, Truth on the Market (26 Mar. 2015), https://truthonthemarket.com/2015/03/26/debunking-the-myth-of-a-data-barrier-to-entry-for-online-services (citing Andres V. Lerner, The Role of ‘Big Data’ in Online Platform Competition (26 Aug. 2014), https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2482780.).

[70] See Catherine Tucker, Digital Data as an Essential Facility: Control, CPI Antitrust Chron. (Feb. 2020), at 11 (“[U]ltimately the value of data is not the raw manifestation of the data itself, but the ability of a firm to use this data as an input to insight”.).

[71] Or, as John Yun put it, data is only a small component of digital firms’ production function. See Yun, supra note 28, at 235 (“Second, while no one would seriously dispute that having more data is better than having less, the idea of a data-driven network effect is focused too narrowly on a single factor improving quality. As mentioned in supra Section I.A, there are a variety of factors that enter a firm’s production function to improve quality”.).

[72] Luxia Le, The Real Reason Windows Phone Failed Spectacularly, History–Computer (8 Aug. 2023), https://history-computer.com/the-real-reason-windows-phone-failed-spectacularly.

[73] Introducing the GPT Store, Open AI (Jan. 10, 2024), https://openai.com/blog/introducing-the-gpt-store.

[74] See Michael Schade, How ChatGPT and Our Language Models are Developed, OpenAI, https://help.openai.com/en/articles/7842364-how-chatgpt-and-our-language-models-are-developed; Sreejani Bhattacharyya, Interesting Innovations from OpenAI in 2021, AIM (1 Jan. 2022), https://analyticsindiamag.com/interesting-innovations-from-openai-in-2021; Danny Hernadez & Tom B. Brown, Measuring the Algorithmic Efficiency of Neural Networks, ArXiv (8 May 2020), https://arxiv.org/abs/2005.04305.

[75] See Yun, supra note 28 at 235 (“Even if data is primarily responsible for a platform’s quality improvements, these improvements do not simply materialize with the presence of more data—which differentiates the idea of data-driven network effects from direct network effects. A firm needs to intentionally transform raw, collected data into something that provides analytical insights. This transformation involves costs including those associated with data storage, organization, and analytics, which moves the idea of collecting more data away from a strict network effect to more of a ‘data opportunity.’”).

[76] Lerner, supra note 69, at 4-5 (emphasis added).

[77] See Clayton M. Christensen, The Innovator’s Dilemma: When New Technologies Cause Great Firms to Fail (2013).

[78] See David J. Teece, Dynamic Capabilities and Strategic Management: Organizing for Innovation and Growth (2009).

[79] Antitrust merger enforcement has long assumed that horizontal mergers are more likely to cause problems for consumers than the latter. See: Geoffrey A. Manne, Dirk Auer, Brian Albrecht, Eric Fruits, Daniel J. Gilman, & Lazar Radic, Comments of the International Center for Law and Economics on the FTC & DOJ Draft Merger Guidelines, (18 Sep. 2023), https://laweconcenter.org/resources/comments-of-the-international-center-for-law-and-economics-on-the-ftc-doj-draft-merger-guidelines.

[80] See Hagiu & Wright, supra note 32, at 32 (“We use our dynamic framework to explore how data sharing works: we find that it in-creases consumer surplus when one firm is sufficiently far ahead of the other by making the laggard more competitive, but it decreases consumer surplus when the firms are sufficiently evenly matched by making firms compete less aggressively, which in our model means subsidizing consumers less”.); see also Lerner, supra note 69.

[81] See, e.g., Hagiu & Wright, id. (“We also use our model to highlight an unintended consequence of privacy policies. If such policies reduce the rate at which firms can extract useful data from consumers, they will tend to increase the incumbent’s competitive advantage, reflecting that the entrant has more scope for new learning and so is affected more by such a policy”.); Jian Jia, Ginger Zhe Jin, & Liad Wagman, The Short-Run Effects of the General Data Protection Regulation on Technology Venture Investment, 40 Marketing Sci. 593 (2021) (finding GDPR reduced investment in new and emerging technology firms, particularly in data-related ventures); James Campbell, Avi Goldfarb, & Catherine Tucker, Privacy Regulation and Market Structure, 24 J. Econ. & Mgmt. Strat. 47 (2015) (“Consequently, rather than increasing competition, the nature of transaction costs implied by privacy regulation suggests that privacy regulation may be anti-competitive”.).

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Antitrust & Consumer Protection

ICLE Comments to the Brazilian Ministry of Finance on Competition in Digital Markets

Regulatory Comments Executive Summary We are thankful for the opportunity to submit comments to the secretariat of economic reforms of the Ministry of Finance’s Public Consultation regarding . . .

Executive Summary

We are thankful for the opportunity to submit comments to the secretariat of economic reforms of the Ministry of Finance’s Public Consultation regarding competition in digital markets. The International Center for Law & Economics (“ICLE”) is a nonprofit, nonpartisan global research and policy center founded with the goal of building the intellectual foundations for sensible, economically grounded policy. ICLE promotes the use of law & economics methodologies to inform public-policy debates and has longstanding expertise in the evaluation of competition law and policy. ICLE’s interest is to ensure that competition law remains grounded in clear rules, established precedent, a record of evidence, and sound economic analysis.

Our comments respectfully suggest careful consideration before approving any sectoral regulation of digital markets in Brazil.

Digital markets are generally dynamic, competitive, and beneficial to consumers. Those benefits derive from increased productivity and relatively cheap access to information. Whereas there are always possible competition issues and anticompetitive behavior, these are neither pervasive nor sufficiently unique to justify strict, sui generis preemptive rules. Instead, existing antitrust laws (Act No. 12,529/2011) are sufficient to address potential anticompetitive practices in digital markets. Furthermore, and as demonstrated by recent case law, the Conselho Administrativo de Defesa Econômica (CADE)—the Brazilian competition authority—has the necessary expertise to handle these cases.

There are, of course, challenges in applying antitrust laws to digital markets. For example, defining relevant markets and dominant positions in multisided platform cases, and in the fast-changing digital landscape, can be difficult. The contours of the relevant market are not always clear, and the boundaries between the digital and nondigital world are sometimes overstated. Those challenges can, however, be properly addressed through the existing legal framework and with some institutional measures, such as equipping CADE with more resources to incorporate advanced, state-of-the-art technical expertise.

Finally, ex-ante regulations like the European Union’s Digital Markets Act (DMA) can have unintended consequences, such as stifling innovation, reducing consumer welfare, and increasing compliance costs. They can also lead to increased risks of regulatory capture and rent seeking, as the verdict on whether a gatekeeper has complied with the law often comes down to the degree to which rivals are satisfied. Of course, rivals have a clear personal stake in never being satisfied. By tethering intervention to a comparatively clear public-benefit standard—consumer welfare—competition laws minimize the potential for error costs and decrease the chances that the law will be coopted for private gain.

I. Objectives and Regulatory Rationale

1.1 What economic and competitive reasons would justify the regulation of digital platforms in Brazil?

In general terms, we believe Brazil does not need sectoral regulations for digital platforms, given that the markets for such services are reasonably competitive. According to economic theory and long-tested economic principles, ex-ante regulation[1] is justified only in the presence of market failures[2]. Digital markets, however, do not present the kind of market failures that warrant ex-ante regulation. For example, digital markets do not present natural monopolies, significant externalities, public goods, or informational asymmetries.

To be sure, one can find some levels of informational asymmetries or externalities, but not to such a  magnitude that they could not be addressed through market competition (actual or potential) or through general rules, such as data-protection or consumer-protection laws. A more plausible argument can be made regarding the presence of “network effects” in online platforms. If a firm moves fast and is the first to attract customers, that customer base will, in turn, attract more customers and sellers. This network growth could, so the story goes, result in a single firm monopolizing the market. However, as Evans and Schmalensee, have pointed out, that result is far from inevitable:

Systematic research on online platforms by several authors, including one of us, shows considerable churn in leadership for online platforms over periods shorter than a decade. Then there is the collection of dead or withered platforms that dot this sector, including Blackberry and Windows in smartphone operating systems, AOL in messaging, Orkut in social networking, and Yahoo in mass online media.[3]

Some regulations and proposals—namely, the European Union’s Digital Markets Act (DMA) or the proposed American Innovation and Choice Online Act (AICOA) in the United States—mention the alleged failures of antitrust law (i.e., “too slow” and “too hard for plaintiffs”) as the primary rationale to regulate digital markets. As Giuseppe Colangelo has explained:

Against this background, the regulatory approaches recently advanced do not seem to reflect the distinctive features of digital markets, but rather the need to design enforcement short-cuts to cope with growing concerns that antitrust law is unable to address potential anticompetitive practices by large online platforms. Hence, in most of the mentioned reports, the revival of regulation seems supported more by an alleged antitrust enforcement failure rather than true a market failure. The goal is indeed to fill alleged enforcement gaps in the current antitrust rules by introducing tools aimed at lowering legal standards and evidentiary burdens in order to address anti-competitive practices that standard antitrust analysis would struggle to tackle.[4]

This could be a plausible justification for regulation. Antitrust cases could be more expedited. Competition agencies and courts should generally have more resources and faster procedures to adjudicate cases before market structures or markets in general change, rendering any potential intervention useless.

The fact that cases are “hard to win”, however, is not a valid justification. This might actually be an advantage, not a shortcoming, of antitrust law—especially in the context of “abuse of dominance” or monopolization cases[5]. Regulations like the DMA replace the concepts of “relevant markets” and “market power” or “dominant position” with others like “core platforms services” or “gatekeeper”, with the express intent of providing shortcuts to condemn business models and practices. But these “shortcuts” have a cost: they can easily lead to condemnation of business models and practices that provide benefits for consumers, such as lower prices and a safer user experience, among others.

Even those open to considering digital-markets regulation acknowledge that there are considerable challenges, especially if the intent is to regulate digital platforms like “essential facilities”:

In the tech industry, the first challenge is to identify a stable essential facility. It must be stable because divestitures take a while to perform, and the cost of implementing them would not be worth its while if the location of the essential facility kept migrating. This condition may not be met, though. While the technology and market segments of electricity, railroads and (up to the 1980s) telecoms had not changed much since the early 20th century, digital markets are fast? moving. This makes it difficult for regulators to identify, collect data on, and regulate essential facilities, if the corresponding technologies and demands keep morphing.[6]

Moreover, even if warranted, regulations create barriers to entry and regulatory risks, and they restrict the monetization of business assets. They also tend to make markets less attractive and could deter potential competitors from entering them. It is possible that the DMA is already producing such consequences. As Alba Ribera has explained:

One of the greatest examples of the dichotomy that arises between the different types of consequences that can be generated by the regulatory capture of digital ecosystems can be found in Meta’s recent decision not to launch its new service Threads in the European Economic Space. To the extent that its service could be interpreted as falling within the definition of a “core platform service” belonging to the category of “online social networks” (listed by the DMA), Meta decided to refrain from entering the European market, due to the disproportionate burden that the demanding obligations imposed by the DMA would entail. It should be noted that Threads is still an entrant service in the online social networking market, in contrast to the predominant position occupied by X (previously known as Twitter). In this way, we observe that the categorization as a core platform service unifies and eliminates all the nuances that free competition entails with respect to incoming services in the markets.[7]

In addition, DMA-like regulation could have additional costs for a developing economy like Brazil, where digital markets are not yet as mature as in the EU. As we have explained, while ex-ante regulation of digital markets is not warranted even when a market is mature, bigger and more developed economies may at least be able to afford the costs generated by such regulation.[8]

Some of these unintended consequences were already observable in the EU even before the DMA fully entered into force. From the perspective of users, regulation can serve to make services and products more expensive. Facebook is already trying a new business model in the EU where the consumer would see no ads (thus, there would be no data collection, or less collection of data for marketing purposes, at any rate), but would have to pay for subscriptions. Some American and European privacy-minded users may prefer this model, and would probably be able to afford it. But that is hardly the case for Latin American consumers, who on average have less than a third of the income of their European counterparts. In fact, it is arguably consumers in developing countries who have benefitted the most from digital platforms with zero-price or otherwise affordable products, such as Whatsapp and Facebook.

From the perspective of the companies that own and operate digital platforms and services, if regulations like the DMA make their platforms less profitable, some could choose not to enter or, indeed, to leave such markets. As Geoffrey Manne and Dirk Auer have explained, “to regulate competition, you first need to attract competition”:

Perhaps the biggest factor cautioning emerging markets against adoption of DMA-inspired regulations is that such rules would impose heavy compliance costs to doing business in markets that are often anything but mature. It is probably fair to say that, in many (maybe most) emerging markets, the most pressing challenge is to attract investment from international tech firms in the first place, not how to regulate their conduct.

The most salient example comes from South Africa, which has sketched out plans to regulate digital markets. The Competition Commission has announced that Amazon, which is not yet available in the country, would fall under these new rules should it decide to enter—essentially on the presumption that Amazon would overthrow South Africa’s incumbent firms.

It goes without saying that, at the margin, such plans reduce either the likelihood that Amazon will enter the South African market at all, or the extent of its entry should it choose to do so. South African consumers thus risk losing the vast benefits such entry would bring—benefits that dwarf those from whatever marginal increase in competition might be gained from subjecting Amazon to onerous digital-market regulations.[9]

FIGURE 1: US Search Results for ‘Crepes in Paris’

SOURCE: Chamber of Progress[10]

The DMA entered into effect in full force in March 2024, and while it may be too early to reach definitive conclusions about its impact, consumers are already experiencing a degraded user experience. For example, the French newspaper Liberation has detailed how Google Maps’ map results are not showing directly in search-results pages in the same ways they once did (See Figures 1 and 2).

Presumably, this is happening because a direct link to Google Maps would constitute “self-preferencing” (See our answer to question 4, below) wherein Google, the search engine, would be “unfairly” directing traffic to its own digital-navigation service. Such conduct is prohibited by Art.6(5) of the DMA. But this kind of integration is very convenient for consumers, who can search for a restaurant and then quickly find the directions to walk or commute to it (and sometimes even book a table).

FIGURE II: French VPN Search Results for ‘Crepes in Paris’

SOURCE: Chamber of Progress[11]

While removing some features, Google is also adding more results to its results pages, because it assumes that it is required under the DMA to provide “fair” links to competing sites like Yelp and TripAdvisor.[12] In theory, the consequence of such requirements is “more options” for consumers. In practice, what consumers have is a more cluttered results page.

Apple highlights another quality-degrading consequence of the DMA: the obligation it imposes that platforms like iOS allow competing app stores and to allow apps to be downloaded directly from their websites (“sideloading”).[13] This “openness”, however, would allow that third-party applications to bypass controls and protections implemented to safeguard users’ security and privacy.[14]

Finally, it is worth mentioning that the DMA’s unintended consequences affect not only consumers, but also business users. Since Google began to implement the DMA on 19 January, 2024, early estimates suggest that clicks from Google ads to hotel websites decreased by 17.6%.[15]  Presumably, this is a failure even by the DMA’s own (uncertain) standards.

1.2 Are there different reasons for regulating or not regulating different types of platforms?

This is a truly relevant question. As we have explained in our previous answer, we do not believe that digital markets generally need to be regulated. But there is an important preceding question: are these markets sufficiently similar to one another to be covered by a single body of regulation?

The terms “digital platforms” and “digital markets” are extremely broad. As was explained at a recent OECD Competition Committee meeting:

The digital economy spans from online retail to real estate listings to concert tickets to travel booking to social media. Consequently, there is not a universally defined digital market. While digital markets are dynamic and evolving, as many markets are, digital market innovations in some segments are not as groundbreaking as they once were. In a similar manner, prominent digital market characteristics are not unique to digital markets. Print newspapers are multi-sided markets. Broadcast radio is zero-price[16]” (emphasis added).

In that same vein, Herbert Hovenkamp concludes that:

… broad regulation is ill-suited for digital platforms because they are so disparate. By contrast, regulation in industries such as air travel, electric power, and telecommunications targets firms with common technologies and similar market relationships. This is not the case, however, with the four major digital platforms that have drawn so much media and political attention—namely, Amazon, Apple, Facebook, and Google. These platforms have different inputs. They sell different products, albeit with some overlap, and only some of these products are digital. They deal with customers and diverse sets of third parties in different ways. What they have in common is that they are very large and that a sizeable portion of their operating technology is digital.[17]

When dealing with platforms so different from one another—such as, e.g., Google and Nubank, or Spotify and Ebanx—it is highly unlikely that a single body of strict ex-ante rules would appropriate for them all. In some of these markets, there are clear market leaders with significant market share and few competitors. Others are more fragmented, with more evenly distributed market shares. Some markets present strong “network effects” (e.g., payment systems); while, in others, any “network effects” are much milder (e.g., streaming audio and video). Some products and platforms rely on extremely specific user data, while others work with more general data, etc.

Thus, some rules will be useless in certain markets. To the extent that they must be enforced across the board, however, they will nevertheless generate compliance costs that could be passed on to consumers, despite generating little or no benefits. For example, a data-sharing mandate like the one contained in Art.6 DMA could force gatekeepers to share data that is of little use to other platforms or “business users”. Even when the rules achieve their intended goal of helping business users, they could still negatively impact consumers. The DMA, however, does not allow for any consumer welfare or efficiency exemptions from the conduct it mandates.

1.3 To what extent does the Brazilian context approach or differ from the context of other jurisdictions that have adopted or are considering new regulations for digital platforms? Which cases, studies, or concrete examples in Brazil would indicate the need to review the Brazilian legal-regulatory framework?

The Brazilian context presents several differences from that of other jurisdictions that have adopted or are considering digital-platform regulations. These differences stem from the overall economic context, digital-market characteristics, institutional context, and previous enforcement of antitrust law in each of these divergent marketplaces.

Brazil is, of course, an important economy with tremendous potential, but it remains a developing one. Its GDP growth is projected to slow in 2024. According to the OECD, “(r)ecent reforms have reduced unnecessary bureaucracy and regulations, but further efforts are needed to reduce administrative burdens on markets for goods and services that hamper competition and productivity growth”[18]. In that vein, Brazil should be wary of rushing to pass new regulations that could discourage both local and foreign investment.

Regarding the Brazilian legal and regulatory framework, we should bear in mind that jurisdictions like the EU experimented with the use of antitrust law in digital markets for years before passing the DMA. In fact, most—if not all—of the DMA’s prohibitions and obligations stem from prior competition-law cases[19]. The EU eventually decided that it preferred to pass blanket ex-ante rules against certain practices, rather than having to litigate each through competition law. Whether or not this was the right decision is up for debate (our position is that it was not), but one thing is certain: The EU deployed its competition toolkit against digital platforms extensively before learning from those outcomes and deciding that it needed to be complemented with a new and broader set of enforcer-friendly bright-line rules.

By contrast, Brazil has initiated only a handful of antitrust cases against digital platforms. According to numbers published by CADE[20], it has reviewed 233 merger cases related to digital-platform markets between 1995 and 2023. Regarding unilateral conduct (monopolization cases)—those most relevant for the discussion of digital-market regulation, like Bill 2768/2020 already being discussed in the Brazilian Congress (hereinafter, Bill 2768)[21]—CADE opened 23 conduct cases. Of those 23 cases, nine are still under investigation, 11 were dismissed, and only three were settled via a cease-and-desist agreement. In this sense, only three cases (CDAs) out of 23 were “condemned”. It is highly questionable whether these cases provide sufficient evidence of intrinsic competition problems in digital markets.

In fact, the recent entry of companies into many of those markets suggests that the opposite is closer to the truth. There are numerous examples of entry in a variety of digital services, including the likes of TikTok, Shein, Shopee, and Daki, to name just a few.

II. Sufficiency and Adequacy of the Current Model of Economic Regulation and Defense of Competition

2.1 Is the existing legal and institutional framework for the defense of competition—notably, Law No. 12,529/2011—sufficient to deal with the dynamics of digital platforms? Are there competition and economic problems that are not satisfactorily addressed by the current legislation? What improvements would be desirable to the Brazilian System for the Defense of Competition (SBDC) to deal more effectively with digital platforms?

Yes. To be sure, as in any market, competition problems can emerge in digital markets (e.g., there may be incentives to behave anticompetitively, and some conduct could have an anticompetitive impact), but any possible anticompetitive conduct can and should be addressed by applying antitrust law (Law No. 12,529/2011).

As Colangelo and Borgogno have argued:

… recent and ongoing antitrust investigations demonstrate that standard competition law still provides a flexible framework to scrutinize several practices sometimes described as new and peculiar to app stores.

This is particularly true in Europe, where the antitrust framework grants significant leeway to antitrust enforcers relative to the U.S. scenario, as illustrated by the recent Google Shopping decision.[22]

Indeed, the European Commission has initiated procedures and even imposed fines against Google,[23] while the UK Competition and Markets Authority has settled cases with negotiated remedies against Amazon.[24] In the United States, both the Federal Trade Commission and the U.S. Justice Department (and several states) have initiated cases against Google,[25] Facebook,[26] and Amazon.[27]

In the same way, we think that CADE should be able to address any potential competition issues. CADE has already initiated investigations and cases related to alleged refusals to deal, self-preferencing, and discrimination against companies like Google, Apple, Meta, Uber, Booking.com, Decolar.com, and Expedia—i.e., precisely the firms that would presumably be covered by a new digital-markets regulation.

A review conducted by the OECD in 2019 concluded that “(w)hile competition law regimes in many emerging economies may still struggle to achieve enforcement goals, the Brazilian regime has largely been considered a success”[28] and that:

CADE is well-regarded within the competition practitioner community both nationally and internationally, the business community, and within the Government administration due to its technical capabilities. It is considered one of the most efficient public agencies in Brazil and its international standing as a leading competition authority both regionally and globally reinforces this domestic view that it is a model public agency.[29]

There should therefore be no doubt in that regard that CADE has the institutional tools and the technical expertise to properly deal with cases in digital markets.

Moreover, based on the EU experience, there is a risk of double jeopardy at the intersection of traditional competition law and ex-ante digital regulation. As Giuseppe Colangelo has written, the DMA is grounded explicitly on the notion that competition law alone is insufficient to effectively address the challenges and systemic problems posed by the digital-platform economy[30]. Indeed, the scope of antitrust is limited to certain instances of market power (e.g., dominance on specific markets) and of anticompetitive behavior. Further, its enforcement occurs ex post and requires extensive investigation on a case-by-case basis of what are often extraordinarily complex sets of facts. Proponents of ex-ante digital-markets regulation argue that competition law therefore may not effectively address the challenges to well-functioning markets posed by the conduct of gatekeepers, who are not necessarily dominant in competition-law terms. As a result, regimes like the DMA invoke regulatory intervention to complement traditional antitrust rules by introducing a set of ex-ante obligations for online platforms designated as gatekeepers. This also allows enforcers to dispense with the laborious process of defining relevant markets, proving dominance, and measuring market effects.

But despite claims that the DMA is not an instrument of competition law, and thus would not affect how antitrust rules apply in digital markets, the regime does appear to blur the line between regulation and antitrust by mixing their respective features and goals. Indeed, the DMA shares the same aims and protects the same legal interests as competition law.

Further, its list of prohibitions is effectively a synopsis of past and ongoing antitrust cases, such as Google Shopping (Case T-612/17), Apple (AT.40437) and Amazon (Cases AT.40462 and AT.40703). Acknowledging the continuum between competition law and the DMA, the European Competition Network (ECN) and some EU member states (self-anointed “friends of an effective DMA”) initially proposed empowering national competition authorities (NCAs) to enforce DMA obligations[31].

Similarly, the prohibitions and obligations often contemplated in proposed digital-markets regulations could, in theory, all be imposed by CADE. In fact, CADE has investigated, and is still investigating, several large companies that would likely fall within the purview of a digital-markets regulation, including Google, Apple, Meta, (still under investigation) Uber, Booking.com, Decolar.com, Expedia and iFood (settled through case-and-desist agreements). CADE’s past and current investigations against these companies already covered conduct targeted by the DMA—such as, e.g., refusal to deal, self-preferencing, and discrimination[32].[16] Existing competition law under Act 12.529/11, the Brazilian competition law, thus clearly already captures these forms of conduct.

The difference between the two regimes is that, while general antitrust law requires a showing of harm and exempts conduct that benefits consumers, sector-specific regulation would, in principle, not.

There is one additional complication. Specific regulation of digital markets (such as Bill 2768) pursues many (though not all) of the same objectives as Act 12.529/11. Insofar as these objectives are shared, it could lead to double jeopardy—i.e., the same conduct being punished twice under slightly different regimes. It could also produce contradictory results because, as pointed out above, the objectives pursued by the two bills are not identical. Act 12.529/11 is guided by the goals of “free competition, freedom of initiative, social role of property, consumer protection and prevention of the abuse of economic power” (Art. 1). To these objectives, Bill 2768 adds “reduction of regional and social inequalities” and “increase of social participation in matters of public interest”. While it is true that these principles derive from Art. 170 of the Brazilian Constitution (“economic order”), the mismatch between the goals of Act 12.529/11 and Bill 2768 may be sufficient to lead to situations in which conduct that is allowed or even encouraged under Act 12.529/11 is prohibited under Bill 2768.

For instance, procompetitive conduct by a covered platform could nevertheless exacerbate “regional or social inequalities”, because it invests heavily in one region but not others. In a similar vein, safety, privacy, and security measures implemented by, e.g., an app-store operator that typically would be considered beneficial for consumers under antitrust law[33] could feasibly lead to less participation in discussions of public interest (assuming one could easily define the meaning of such a term).

Accordingly, sector-specific regulation for digital markets could fragment Brazil’s legal framework due to overlaps with competition law, stifle procompetitive conduct, and lead to contradictory results. This, in turn, is likely to impact legal certainty and the rule of law in Brazil, which could adversely influence foreign direct investment[34].

III. Sufficiency and Adequacy of the Current Model of Economic Regulation and Defense of Competition

3. Law No. 12,529/2011 establishes, in paragraph 2 of article 36 that: “A dominant position is presumed whenever a company or group of companies is capable of unilaterally or coordinated changes in market conditions or when it controls 20% (twenty percent) or more of the relevant market, and this percentage may be changed by CADE for specific sectors of the economy”. Are the definitions of Law 12,529/2011 related to market power and abuse of dominant position sufficient and adequate, as they are applied, to identify market power of digital platforms? If not, what are the limitations?

The existence of a rule like the one contained in paragraph 2 of article 36 of Law No. 12,259/2011 is yet another reason to question any proposal to enact sector-specific regulation of digital markets. The article’s legal presumption is one of the “shortcuts” that regulations like the DMA equip competition agencies or regulators with, allegedly to avoid the administrative costs involved in defining relevant markets. This is one of the purported “benefits” of ex-ante regulation of digital markets.

But a presumption of dominance where market shares exceed 20% is not sufficient to identify digital platforms’ market power, as it would lead to too many “false positives”. It is important to note that market share alone is a misleading indicator of market power. A firm with a large market share could have little market power if it faces market substitution, potential competition, or competitors with able to increase production capacity[35].

To be sure, some competition laws around the world include dominance presumptions based on market share, but in those cases, the thresholds tend to be higher (40% or more).[36]

4. Some behaviors with potential competitive risks have become relevant in discussions about digital platforms, including: (i) economic discrimination by algorithms; (ii) lack of interoperability between competing platforms in certain circumstances; (iii) the excessive use of personal data collected, associated with possible discriminatory conduct; and (iv) the leverage effect of a platform’s own product to the detriment of other competitors in adjacent markets; among others. To what extent does the antitrust law offer provisions to mitigate competition concerns that arise from vertical or complementarity relationships on digital platforms? Which conducts with anticompetitive potential would not be identified or corrected through the application of traditional antitrust tools?

As we have explained in our answer to Question 2, any possible anticompetitive conduct in digital platforms can and should be addressed with the application of antitrust law.

There are certain types of behavior in digital markets that have been targeted by ex-ante regulations that are nevertheless capable of—or even central to—delivering significant procompetitive benefits. It would be unjustified and harmful to subject such conduct to per se prohibitions, or to reverse the burden of proof. Instead, this type of conduct should be approached neutrally, and examined on a case-by-case basis[37].

1. Self-preferencing

Self-preferencing refers to when a company gives preferential treatment to one of its own products (presumably, this type of behavior could already be caught by Art. 10, paragraph II of Bill 2768). An example would be Google displaying its shopping service at the top of search results, ahead of alternative shopping services. Critics of this practice argue that it puts dominant firms in competition with other firms that depend on their services, and that this allows companies to leverage their power in one market to gain a foothold in an adjacent market, thus expanding and consolidating their dominance. But this behavior can also be procompetitive and beneficial to users.

Over the past several years, a growing number of critics have argued that big-tech platforms harm competition by favoring their own content over that of their complementors. Over time, this argument against self-preferencing has become one of the most prominent among those seeking to impose novel regulatory restrictions on these platforms.

According to this line of argument, complementors are “at the mercy” of tech platforms. By discriminating in favor of their own content and against independent “edge providers,” tech platforms cause “the rewards for edge innovation [to be] dampened by runaway appropriation,” leading to “dismal” prospects “for independents in the internet economy—and edge innovation generally.”[38]

The problem, however, is that the claims of presumptive consumer harm from self-preferencing (also known as “vertical discrimination”) are based neither on sound economics nor evidence.

The notion that a platform’s entry into competition with edge providers is harmful to innovation is entirely speculative. Moreover, it is flatly contradicted by a range of studies that show the opposite is likely to be true. In reality, platform competition is more complicated than simple theories of vertical discrimination would have it,[39] and the literature establishes that there is certainly no basis for a presumption of harm.[40]

The notion that platforms should be forced to allow complementors to compete on their own terms—free of constraints or competition from platforms—is a flavor of the idea that platforms are most socially valuable when they are most “open.” But mandating openness is not without costs, most importantly in terms of the platform’s effective operation and its incentives for innovation.

“Open” and “closed” platforms are simply different ways to supply similar services, and there is scope for competition among these divergent approaches. By prohibiting self-preferencing, a regulator might therefore foreclose competition to consumers’ detriment. As we have noted elsewhere:

For Apple (and its users), the touchstone of a good platform is not ‘openness’, but carefully curated selection and security, understood broadly as encompassing the removal of objectionable content, protection of privacy, and protection from ‘social engineering’ and the like. By contrast, Android’s bet is on the open platform model, which sacrifices some degree of security for the greater variety and customization associated with more open distribution. These are legitimate differences in product design and business philosophy.[41]

Moreover, it is important to note that the appropriation of edge innovation and its incorporation into a platform (a commonly decried form of platform self-preferencing) greatly enhances the innovation’s value by sharing it more broadly, ensuring its coherence with the platform, providing incentivizes for optimal marketing and promotion, and the like. In other words, even if there is a cost in terms of reduced edge innovation, the immediate consumer-welfare gains from platform appropriation may well outweigh those (speculative) losses.

Crucially, platforms have an incentive to optimize openness, and to assure complementors of sufficient returns on their platform-specific investments. This does not, however, mean that maximum openness is always optimal. In fact, a well-managed platform typically will exert top-down control where doing so is most important, and openness where control is least meaningful.[42] But this means that it is impossible to know whether any particular platform constraint (including self-prioritization) on edge-provider conduct is deleterious, and similarly whether any move from more to less openness (or the reverse) is harmful.

This state of affairs contributes to the indeterminate and complex structure of platform enterprises. Consider, for example, the large online platforms like Google and Facebook. These entities elicit participation from users and complementors by making access freely available for a wide range of uses, exerting control over that access only in such limited ways as to ensure high quality and performance. At the same time, however, these platform operators also offer proprietary services in competition with complementors, or offer portions of the platform for sale or use only under more restrictive terms that facilitate a financial return to the platform. Thus, for example, Google makes Android freely available, but imposes contractual terms that require installation of certain Google services in order to ensure sufficient return.

The key is understanding that, while constraints on complementors’ access and use may look restrictive relative to an imaginary world without any restrictions, the platform would not be built in such a world the first place. Moreover, compared to the other extreme of full appropriation, such constraints are relatively minor and represent far less than full appropriation of value or restriction on access. As Jonathan Barnett aptly sums it up:

The [platform] therefore faces a basic trade-off. On the one hand, it must forfeit control over a portion of the platform in order to elicit user adoption. On the other hand, it must exert control over some other portion of the platform, or some set of complementary goods or services, in order to accrue revenues to cover development and maintenance costs (and, in the case of a for-profit entity, in order to capture any remaining profits).[43]

For instance, companies may choose to favor their own products or services because they are better able to guarantee their quality or quick delivery.[44][ Amazon, for instance, may be better placed to ensure that products provided by the Fulfilled by Amazon (FBA) logistics service are delivered in a timely manner, relative to other services. Consumers also may benefit from self-preferencing in other ways. If, for instance, Google were prevented from prioritizing Google Maps or YouTube videos in its search queries, it could be harder for users to find optimal and relevant results. If Amazon is prohibited from preferencing its own line of products on Amazon Marketplace, it might instead opt not to sell competitors’ products at all.

The power to prohibit platforms from requiring or encouraging customers of one product to also use another would limit or prevent self-preferencing and other similar behavior. Granted, traditional competition law has sought to restrict the “bundling” of products by requiring they be purchased together, but to prohibit incentivizes, as well, goes much further.

2. Interoperability

Another mot du jour is interoperability, which might fall under Art. 10, paragraph IV of Bill 2768. In the context of digital ex-ante regulation, “interoperability” means that covered companies could be forced to ensure that their products integrate with those of other firms—e.g., requiring a social network be open to integration with other services and apps, a mobile-operating system be open to third-party app stores, or a messaging service be compatible with other messaging services.

Without regulation, firms may or may not choose to make their software interoperable. But both the DMA and the UK’s proposed Digital Markets, Competition and Consumer Bill (“DMCC”)[45] would empower authorities to require it. Another example is data “portability”, under which customers are permitted to move their data from one supplier to another, in much the same way that a telephone number can be retained when one changes networks.

The usual argument is that the power to require interoperability might be necessary to overcome network effects and barriers to entry/expansion. Clearly, portability similarly makes it easier for users to switch from one provider to another and, to that extent, intensifies competition or makes entry easier. The Brazilian government should not, however, overlook that both come with costs to consumer choice—in particular, by raising security and privacy concerns, while generating uncertain benefits for competition. It is not as though competition disappears when customers cannot switch services as easily as they can turn on a light. Companies compete upfront to attract such consumers through tactics like penetration pricing, introductory offers, and price wars.[46]

A closed system—that is, one with relatively limited interoperability—may help to limit security and privacy risks. This could encourage platform usage and enhance the user experience. For example, by remaining relatively closed and curated, Apple’s App Store grants users assurances that apps meet certain standards of security and trustworthiness. “Open” and “closed” ecosystems are not synonymous with “good” and “bad”, but instead represent differing product-design philosophies, either of which might be preferred by consumers. By forcing companies to operate “open” platforms, interoperability obligations could undermine this kind of inter-brand competition and override consumer choices.

Apart from potentially damaging the user experience, it is also doubtful whether some interoperability mandates—such as those between social-media or messaging services—can achieve their stated objective of lowering barriers to entry and promoting greater competition. Consumers are not necessarily more likely to switch platforms simply because they are interoperable. An argument can even be made that making messaging apps interoperable, in fact, reduces the incentive to download competing apps, as users can already interact from the incumbent messaging app with competitors.

3. Choice screens

Some ex-ante rules seek to address firms’ ability to influence user choice of apps through pre-installation, defaults and the design of app stores. This has sometimes resulted in “choice screen” mandates—e.g., requiring users to choose which search engine or mapping service is installed on their phone. But it is important to understand the tradeoffs at play here: choice screens may facilitate competition, but they do so at the expense of the user experience, in terms of the time taken to make such choices. There is a risk, without evidence of consumer demand for “choice screens”, that such rules merely impose legislators’ preference for greater optionality over what users find most convenient. Unless there is explicit public demand in Brazil for such measures, it would be ill-advised to implement a choice-screen obligation.

4. Size and market power

Many of the prohibitions and obligations contemplated in ex-ante digital-regulation regimes target incumbents’ size, scalability, and “strategic significance”. It is widely claimed that, because of network effects, digital markets are prone to “tipping”, wherein once a producer gains sufficient market share, it quickly becomes a complete or near-complete monopolist. Although they may begin as very competitive, these markets therefore exhibit a marked “winner-takes-all” characteristic. Ex-ante rules often try to avert or revert this outcome by targeting a company’s size, or by targeting companies with market power.

But many investments and innovations that would benefit consumers—either immediately or over the long term—may also serve to enhance a company’s market power, size, or strategic significance. Indeed, improving a firm’s products and thereby increasing its sales will often lead to increased market power.

Accordingly, targeting size or conduct that bolsters market power, without any accompanying evidence of harm, creates a serious danger of broad inhibition of research, innovation, and investment—all to the detriment of consumers. Insofar as such rules prevent the growth and development of incumbent firms, they may also harm competition, since it may well be these firms that are most likely to challenge the market power of firms in adjacent markets. The case of Meta’s introduction of Threads as a challenge to Twitter (or X) appears to be just such an example. Here, per-se rules adopted to prohibit bolstering a firm’s size or market power in one market may, in fact, prevent that firm’s entry into a market dominated by another. In that case, policymaker action protects monopoly power. Therefore, a much subtler approach to regulation is required.

We do not think it appropriate to reverse the burden of proof in the context of alleged competition harms in digital platforms. Without substantive evidence that such conduct causes widespread harm to a well-defined public interest (e.g., similar to cartels in the context of antitrust law), there is no justification for reversing the burden of proof, and any such reversals risk undermining consumer benefits and innovation, and discouraging investment in the Brazilian economy, out of a justified fear that procompetitive conduct will result in fines and remedies. By the same token, where the appointed enforcer makes a prima facie case of harm—whether in the context of antitrust law or ex-ante digital regulation—it should also be prepared to address arguments related to efficiencies.

5. Regarding the control of structures, is there a need for some type of adaptation in the parameters of submission and analysis of merger acts that seeks to make the detection of potential harm to competition in digital markets more effective? For example: mechanisms for reviewing acquisitions below the notification thresholds, burden of proof, and elements for analysis – such as the role of data, among others – that contribute to a holistic approach to the topic.

No, no change is needed regarding notification thresholds or analysis criteria for merger operations in digital markets. In line with our answer to Question 4 above (see 4.4, on “size and market power”), we do not think it is appropriate to reverse the burden of proof in the context of digital platforms.

As Bowman and Dumitriu show in a paper[47] analyzing a United Kingdom proposal to create special (more stringent) rules for mergers in the digital sector, mergers and acquisitions can actually enhance competition in digital markets, because:

  1. They are a profitable exit strategy for entrepreneurs;
  2. They enable an efficient “market for corporate control”;
  3. They can reduce transaction costs among complementary products; and
  4. They can support inter-platform competition.

Therefore, Bowman and Dumitriu recommend that “the government should consider a more moderate approach thar retains the balance of probabilities approach” and that, rather than reform competition laws, it should work to increase the availability of growth capital to small firms (tax breaks, financial support, etc.)[48].

There may, of course, be some challenges in applying antitrust laws to digital markets. It is often mentioned that defining relevant markets is harder in the digital context, due to their complexity and multi-sidedness, and the fact that competition is often not price-based. The rapid evolution of digital markets and the presence of network effects are also mentioned as reasons to create new rules.

Methodological difficulties do not, however, justify a major revamp of antitrust rules. Antitrust law and economics are sufficiently flexible and versatile to adapt to new markets. Modernization of the analysis and methodologies, of course, is always welcome, but that can be done within the current set of rules. Rather, it would be valuable to encourage the use of the same general analyses and tools in a wide scope of markets, so that the authority has a common benchmark and more general lessons to extract from specific cases.

IV. Design of a Possible Regulatory Model for Procompetitive Economic Regulation

5. Should Brazil adopt specific rules of a preventive nature (ex ante character) to deal with digital platforms, in order to avoid conduct that is harmful to competition or consumers? Would antitrust law—with or without amendments to deal specifically with digital markets—be sufficient to identify and remedy competition problems effectively, after the occurrence of anticompetitive conduct (ex post model) or by the analysis of merger acts?

No, there should not be absolute prohibitions on these sorts of conduct, especially without substantive experience to suggest that such conduct is always or almost always harmful and largely irredeemable (NB: Here, we answer the question in general terms; please see our answer to Question 4 for a discussion of why particular conduct (e.g., self-preferencing) should not be per-se prohibited).

Regardless of the harm to the targeted companies, overly broad prohibitions (or mandates) can harm consumers by chilling procompetitive conduct and discouraging innovation and investment. This is particularly true when no showing of harm is required and the law is not amenable to efficiencies arguments, as in the case of the DMA. The fact that such prohibitions apply to vastly different markets (for example, cloud services have little to do with search engines) regardless of context is also a sure sign that they are overly broad and poorly designed.

In fact, there are indications that, where DMA-style regulations have been introduced, it has delayed the advance of technology. For example, Google’s Bard artificial intelligence (AI) was rolled out later in Europe due to the EU’s uncertain and strict AI and privacy regulations.[49] Similarly, Meta’s Threads was not initially available in the EU, because of the constraints imposed by both the DMA and the EU’s data-privacy regulation (GDPR).[50] Twitter/X CEO Elon Musk has indicated that the cost of complying with EU digital regulations, such as the Digital Services Act, could prompt the company to exit the European market.[51]

Apart from foreclosing procompetitive conduct that benefits consumers and freezing technology in time (which would ultimately exacerbate the technological chasm between more and less advanced countries), rigid per-se rules could also apply to many budding companies that cannot be considered “gatekeepers” by any stretch of the imagination. This risk is particularly notable in the context of Brazil, given the extremely low threshold for what constitutes a “gatekeeper” enshrined in Article 9 (R$70 million, or approximately USD$14 million). Thus, many Brazilian “unicorns” could—either immediately or in the near future—be captured by these new, restrictive rules, which could in turn stunt their growth and chill innovative products. Ultimately, this would imperil Brazil’s emerging status as “[Latin America’s] most established startup hub,” and cast a shadow on what The Economist has referred to as the bright future of Latin American startups.[52][33]

The list of harmed companies could include some of Brazil’s most promising startups, such as:

  • 99 (transport app)
  • Neon Bank (digital bank)
  • C6 Bank (digital bank)
  • CloudWalk (payment method)
  • Creditas (lending platform)
  • Ebanx ((payment solutions)
  • Facily (social commerce)
  • Frete.com (road freight)
  • Gympass (from corporate benefits)
  • Hotmart (platform for selling digital products)
  • iFood (delivery)
  • Loft (rental platform)
  • Loggi (logistics)
  • Bitcoin Market (cryptocurrency broker)
  • Merama (e-commerce)
  • Madeira Madeira (home and decoration products store)
  • Nubank (bank)
  • Olist (e-commerce)
  • Wildlife (game developer)
  • Quinto Andar (rental platform)
  • Vtex (technology and digital commerce)
  • Unico (biometrics)
  • Dock (infrastructure)
  • Pismo (technology for payments and banking services)[53][34]

6.1. What is the possible combination of these two regulatory techniques (ex ante and ex post) for the case of digital platforms? Which approach would be advisable for the Brazilian context, also considering the different degrees of flexibility necessary to adequately identify the economic agents that should be the focus of any regulatory action and the corresponding obligations?

As mentioned in our answers to questions 1, 4, and 6, we don’t think there is a valid justification to regulate digital markets at the sectoral level. Therefore, there is not an “ideal” combination of ex ante and ex post intervention in such markets. Digital competition and the “rule of reason” used to analyze unilateral conduct already provide the flexibility needed to adequately identify the economic agents that should be the focus of intervention (after the fact, with actual information about the impact of specific conducts in the market) and the corresponding obligations (remedies).

7. Jurisdictions that have adopted or are considering the adoption of pro-competitive regulatory models – such as the new European Union rules, the Japanese legislation and the United Kingdom’s regulatory proposal, among others – have opted for an asymmetric model of regulation, differentiating the impact of digital platforms based on their segment of operation and according to their size, as is the case with gatekeepers in the European DMA.

7.1. Should Brazilian legislation that introduces parameters for the economic regulation of digital platforms be symmetrical, covering all agents in this market or, on the contrary, asymmetric, establishing obligations only for some economic agents?

Regulations like the DMA or Brazil’s proposed Bill 2768 contemplate thresholds (usually based on sales or the number of users) that trigger application of its prohibitions and mandates. In theory, these thresholds make said regulations more “reasonable”, in the sense they would be enforced only against digital platform that are “too big” or “too powerful”. Sales and quantity of users, however, are not reliable proxies for market power. In that sense, as we have explained in our previous answers, ex-ante regulation of digital markets would enforce “blind” rules that will ban conduct or business models that are beneficial for consumers.

Moreover, asymmetric regulation (especially absent evidence of market power by any specific economic agent) could “distort market signals and create opportunities for strategic and inefficient uses of regulatory authority by competitors”[54].

7.2. If the answer is to adopt asymmetric regulation, what parameters or references should be used for this type of differentiation? What would be the criteria (quantitative or qualitative) that should be adopted to identify the economic agents that should be subject to platform regulation in the Brazilian case?

As mentioned in our answers to questions 1, 4, and 6, we do not think there is a valid justification to regulate digital markets, much less in an asymmetric way. If, however, a regulation were to be adopted and designed to apply to only some specific market actors, it should be applied only after a finding of a large degree of market power (that is, “monopoly power” or a “dominant position”).

8. Are there risks for Brazil arising from the non-adoption of a new pro-competitive regulatory model, especially considering the scenario in which other jurisdictions have already adopted or are in the process of adopting specific rules aimed at digital platforms, taking into account the global performance of the largest platforms? What benefits could be obtained by adopting a similar regulation in Brazil?

Every approach entails risks. The question is whether adopting ex-ante rules is riskier than not adopting them, an assessment that ultimately comes down to an evaluation of error costs. In our view, there are not any significant risks (if any) of not adopting a specific regulation for digital markets and, in any case, those risks that do exist are far outweighed by the benefits. Countries that take their time to study markets, perform proper regulatory-impact analysis, and enact a serious notice-and comment-process, will be most able to learn from the experience of other regulators and markets[55]. The recent deployment of the DMA in Europe will be useful case study. South Korea, for instance, recently hit the “pause button” on its proposal to regulate digital markets—citing, among other reasons “exploring methods to regulate platforms efficiently while reducing the industry’s load”.[56]

The other side of the coin is that promptly approving regulation has costs: inefficiency, regulatory burden, and unintended consequences like less competition and inferior products delivered to consumers, as explained above. Furthermore, once ex-ante rules are passed, any ensuing costs and unintended consequences will be exceedingly difficult to reverse.

8.1. How would Brazil, in the case of the adoption of an eventual pro-competition regulation, integrate itself into this global context?

Brazil, its policymakers, regulators, and competition agencies can perfectly integrate into a global context of digitalization of markets without adopting ex-ante regulation of digital markets. Brazil can collaborate and exchange information with other policymakers and enforcement agencies under existing competition laws and forums like the OECD and the International Competition Network. With these interactions, Brazil can assure that its legal and institutional framework is up to date and that its regulations are based on evidence and solid economic theory.

Finally, only a handful of countries have adopted comprehensive ex-ante digital competition rules; namely, the EU and Germany. Others are considering their adoption, but have not done so yet (e.g., Turkey, South Africa, Australia, and South Korea). The extent to which the global context is currently defined by these new, experimental rules is thus often overstated. As argued above, Brazil should wait and see. If the new rules prove not to be what their proponents claim—as we have argued here—Brazil would derive a competitive advantage from not following suit.

V. Institutional Arrangement for Regulation and Supervision

9. Is it necessary to have a specific regulator for the supervision and regulation of large digital platforms in Brazil, considering only the economic-competitive dimension?

9.1. If so, would it be appropriate to set up a specific regulatory body or to assign new powers to existing bodies? What institutional coordination mechanisms would be necessary, both in a scenario involving existing bodies and institutions, and in the hypothesis of the creation of a new regulator?

In line with our previous answers, we do not think it is necessary to set up a new regulator or assign regulatory functions to existing agencies. Bill 2768, for instance, proposes to give ANATEL the function to oversee digital markets, building on its expertise in telecommunications regulation. Most of the proposals to regulate digital markets, however, appear to be competition-based, or at least declare the pursuit of goals similar to competition law. Therefore, the agency best-positioned to enforce such a regulation would, in principle, be CADE. Conversely, there is a palpable risk that, in discharging its duties under Bill 2768, ANATEL would transpose the logic and principles of telecommunications regulation to “digital” markets. That would be misguided, as these are two very different markets.

Not only are “digital” markets substantively different from telecommunications markets, but there is really no such thing as a clearly demarcated concept of a “digital market”. For example, the digital platforms described in Art. 6, paragraph II of Bill 2768 are not homogenous, and cover a range of different business models. In addition, virtually every market today incorporates “digital” elements, such as data. Indeed, companies operating in sectors as divergent as retail, insurance, health care, pharmaceuticals, production, and distribution have all been “digitalized.” What appears to be needed is an enforcer with a nuanced understanding of the dynamics of digitalization and, especially, the idiosyncrasies of digital platforms as two-sided markets. While CADE arguably lacks substantive experience with digital platforms, it is better-placed to enforce Bill 2768 than ANATEL because of its deep experience with the enforcement of competition policy.

Moreover, having the regulation applied by CADE would reduce the risk or “regulatory capture”. As Jean Tirole has explained:

… regulatory capture, which is one of the reasons why multi?industry regulators and competition authorities were created in the past. This raises the issue of where the new agency should be located. It could be part of the Competition authority, part of another agency (…), or a stand?alone entity. Making it part of the Competition Authority would reduce a bit the risk of capture and would also avoid the lengthy debates about which companies are really digital, which might arise if the unit is located within a sectoral regulator[57].

[1] By ex-ante regulation, we mean specific rules and duties that are sector specific (“digital markets”), whose application would not be based on the effects of the conduct regulated and where fines would apply in case of noncompliance. See Bruce H. Kobayashi & Joshua D. Wright, Antitrust and Ex-Ante Sector Regulation, The Glob. Antitrust Inst. Report on the Dig. Econ 25. (2020); See Table 1, at 869.

[2] See Robert Cooter & Tomas Ulen, Law and Economics (2000), at 40-43; W. Kip Viscusi, Joseph E. Harrington, Jr. and John M. Vernon, Economics of Regulation and Antitrust (2005), at 376-379.

[3] David S. Evans & Richard Schmalensee. Debunking The “Network Effects” Bogeyman, Regulation 39 (Winter 2017-2018) available at https://www.cato.org/sites/cato.org/files/serials/files/regulation/2017/12/regulation-v40n4-1.pdf.

[4] Giuseppe Colangelo, Evaluating the Case for Regulation of Digital Platforms, The Glob. Antitrust Inst. Report on the Dig. Econ 26, 930 (2020) https://gaidigitalreport.com/2020/10/04/evaluating-the-case-for-ex-ante-regulation-of-digital-platforms.

[5] We often run the risk of condemning business practices and models we don’t fully understand. Sometimes, even the businesses that implement them don’t fully know or understand the impact of such practices. See Frank H. Easterbrook, Limits of Antitrust, 63 Tex. L. Rev. 1 (1984).

[6] Jean Tirole, Competition and the Industrial Challenge for the Digital Age, 6 (2020), available at https://www.tse-fr.eu/sites/default/files/TSE/documents/doc/by/tirole/competition_and_the_industrial_challenge_april_3_2020.pdf.

[7] Alba Ribera, La Regulación de los Ecosistemas Digitales Frente a las Relaciones Complejas se los Operadores Económicos, Centro Competencia (18 Oct. 2023), https://centrocompetencia.com/regulacion-ecosistemas-digitales-relaciones-complejas-operadores-economicos. Free translation of the following text in Spanish: “Uno de los mayores ejemplos de la dicotomía que se erige entre los distintos tipos de consecuencias que se pueden generar por la captura regulatoria de los ecosistemas digitales lo podemos encontrar en la reciente decisión de Meta, de no lanzar su nuevo servicio Threads en el Espacio Económico Europeo. En la medida en que su servicio podría interpretarse de forma que cayera dentro de la definición de un “servicio básico de plataforma” perteneciente a la categoría de redes sociales en línea” (listada por la LMD), Meta decidió abstenerse de entrar en el mercado europeo, por la carga desproporcionada que le supondría las exigentes obligaciones impuestas por la LMD. Cabe notar que Threads es aún un servicio entrante en el mercado de redes sociales en línea, en contraste con la posición predominante ocupada por la actual X (anteriormente conocida como Twitter). De esta forma, observamos que la categorización como servicio básico de plataforma unifica y elimina todos los matices que el propio juego de la libre competencia opera respecto de servicios entrantes en los mercados”.

[8] Lazar Radic, Digital-Market Regulation: One Size Does Not Fit All, Truth on the Market (17 Apr. 2023), https://truthonthemarket.com/2023/04/17/digital-market-regulation-one-size-does-not-fit-all. “While perhaps the EU—the world’s third largest economy—can afford to impose costly and burdensome regulation on digital companies because it has considerable leverage to ensure (with some, though as we have seen, by no means absolute, certainty) that they will not desert the European market, smaller economies that are unlikely to be seen by GAMA as essential markets are playing a different game”.

[9] The argument presented in the article is about South Africa, but it is relevant to Brazil. See Geoffrey Manne & Dirk Auer, Brussels Effect or Brussels Defect: Digital Regulation in Emerging Markets, Truth on the Market (20 Dec. 2022), https://truthonthemarket.com/2022/12/20/brussels-effect-or-brussels-defect-digital-regulation-in-emerging-markets.

[10] Adam Kovacevich, Europe’s Digital Market Act Fails Consumers, Chamber of Progress (4 Mar. 2024), https://medium.com/chamber-of-progress/europes-digital-market-act-fails-consumers-dcaf70cc548c.

[11] Id.

[12] Id.

[13] Jon Porter & David Pierce, Apple Is Bringing Sideloading and Alternate App Stores to the iPhone, The Verge (25 Jan. 2024), https://www.theverge.com/2024/1/25/24050200/apple-third-party-app-stores-allowed-iphone-ios-europe-digital-markets-act.

[14] See Apple, Complying with the Digital Markets Act (2024), available at https://developer.apple.com/security/complying-with-the-dma.pdf.

[15] Mirai, LinkedIn (Feb. 2024), https://www.linkedin.com/feed/update/urn:li:activity:7161330551709138945.

[16] See, The Evolving Concept of Market Power in the Digital Economy – Summaries of Contributions 6, OECD, (22 June 2022), available at https://one.oecd.org/document/DAF/COMP/WD(2022)63/en/pdf.

[17] Herbert Hovenkamp, Antitrust and Platform Monopoly. 130 Yale L. J. 1952, 1956 (2021).

[18] Brazil Should Boost Productivity And Infrastructure Investment To Drive Growth, OECD (18 Dec. 2023), https://www.oecd.org/newsroom/brazil-should-boost-productivity-and-infrastructure-investment-to-drive-growth.htm.

[19] See Giuseppe Colangelo, The Digital Markets Act and EU Antitrust Enforcement: Double & Triple Jeopardy, Int’l Ctr. For L. and Econ. (23 Mar. 2022), https://laweconcenter.org/resources/the-digital-markets-act-and-eu-antitrust-enforcement-double-triple-jeopardy.

[20] CADE, Mercados de Plataformas Digitais, SEPN 515 Conjunto D, Lote 4, Ed. Carlos Taurisano CEP: 70.770-504 – Brasília/DF, available at https://cdn.cade.gov.br/Portal/centrais-de-conteudo/publicacoes/estudos-economicos/cadernos-do-cade/Caderno_Plataformas-Digitais_Atualizado_29.08.pdf.

[21] See https://www.camara.leg.br/proposicoesWeb/fichadetramitacao?idProposicao=2337417.

[22] Giuseppe Colangelo & Oscar Borgogno, App Stores as Public Utilities?, Truth on the Market (19 Jan. 2022), https://truthonthemarket.com/2022/01/19/app-stores-as-public-utilities.

[23] See a list here https://en.wikipedia.org/wiki/Antitrust_cases_against_Google_by_the_European_Union.

[24] See https://www.gov.uk/cma-cases/amazon-online-retailer-investigation-into-anti-competitive-practices.

[25] See https://www.justice.gov/opa/pr/justice-department-sues-google-monopolizing-digital-advertising-technologies.

[26] See https://www.ftc.gov/legal-library/browse/cases-proceedings/191-0134-facebook-inc-ftc-v.

[27] See https://www.ftc.gov/news-events/news/press-releases/2023/09/ftc-sues-amazon-illegally-maintaining-monopoly-power.

[28] OECD, OECD Peer Reviews of Competition Law and Policy: Brazil 18 (2019), www.oecd.org/daf/competition/oecd-peer-reviews-of-competition-law-and-policy-brazil-2019.htm.

[29] Id. at 24.

[30] Colangelo, supra note 20.

[31] How National Competition Agencies Can Strengthen the DMA, European Competition Network (22 Jun. 2021), available at https://ec.europa.eu/competition/ecn/DMA_joint_EU_NCAs_paper_21.06.2021.pdf.

[32] For a detailed overview of CADE’s decisions in digital platforms and payments services, see CADE, Mercados de Plataformas Digitais, Cadernos de Cade (Aug. 2023), available at https://cdn.cade.gov.br/Portal/centrais-de-conteudo/publicacoes/estudos-economicos/cadernos-do-cade/Caderno_Plataformas-Digitais_Atualizado_29.08.pdf.

[33] See, e.g., Epic Games, Inc. v. Apple Inc. 20-cv-05640-YGR.

[34] Joseph Staats & Glen Biglaiser, Foreign Direct Investment in Latin America: The Importance of Judicial Strength and Rule of Law, Int’l Studies Quarterly, 56(1), 193–202 (2012).

[35] Richard A. Posner & William M. Landes, Market Power in Antitrust Cases, 94 Harv. L. Rev. 937 (1980), 947-950.

[36] See, e.g., Roundtable of Safe Harbours and Legal Presumptions in Competition Law – Note by Germany 5, OECD (Dec. 2017), available at https://one.oecd.org/document/DAF/COMP/WD(2017)88/en/pdf.

[37] The following is adapted from Geoffrey Manne, Against the Vertical Discrimination Presumption, Concurrences N° 2-2020, Art. N° 94267 (May 2020), https://www.concurrences.com/en/review/numeros/no-2-2020/editorial/foreword and our comments on the UK’s proposed Digital Markets, Competition and Consumers (“DMCC”) Bill: Dirk Auer, Matthew Lesh, & Lazar Radic, Digital Overload: How the Digital Markets, Competition and Consumers Bill’s sweeping new powers threaten Britain’s economy, 4 IEA Perspectives 16-21 (2023), available at https://iea.org.uk/wp-content/uploads/2023/09/Perspectives_4_Digital-overload_web.pdf.

[38] Hal Singer, How Big Tech Threatens Economic Liberty, The Am. Conserv. (7 May 2019), https://www.theamericanconservative.com/articles/how-big-tech-threatens-economic-liberty.

[39] Most of these theories, it must be noted, ignore the relevant and copious strategy literature on the complexity of platform dynamics. See, e.g., Jonathan M. Barnett, The Host’s Dilemma: Strategic Forfeiture in Platform Markets for Informational Goods, 124 Harv. L. Rev. 1861 (2011); David J. Teece, Profiting from Technological Innovation: Implications for Integration, Collaboration, Licensing and Public Policy, 15 Res. Pol’y 285 (1986); Andrei Hagiu & Kevin Boudreau, Platform Rules: Multi-Sided Platforms as Regulators, in Platforms, Markets and Innovation, (Andrei Gawer ed., 2009); Kevin Boudreau, Open Platform Strategies and Innovation: Granting Access vs. Devolving Control, 56 Mgmt. Sci. 1849 (2010).

[40] For examples of this literature and a brief discussion of its findings, see Manne, supra note 37.

[41] Brief for the International Center for law and Economics as Amicus Curiae, Epic Games v. Apple, No. 21-16506, 21-16695 (2022).

[42] See generally, Hagiu & Boudreau, supra note 30; Barnett, supra note 30.

[43] Barnett, id.

[44] See Lazar Radic & Geoffrey Manne, Amazon Italy’s Efficiency Offense. Truth on the Market (11 Jan. 2022), https://truthonthemarket.com/2022/01/11/amazon-italys-efficiency-offense.

[45] Introduced as Bill 294 (2022-23), currently HL Bill 12 (2023-24), Digital Markets, Competition and Consumers Bill, https://bills.parliament.uk/bills/3453.

[46] Joseph Farrell & Paul Klemperer, Coordination and Lock-In: Competition with Switching Costs and Network Effects, 3 Handbook of Indus. Org. 3, 1967-2072 (2007).

[47] Sam Bowman & Sam Dimitriu, Better Together: The Procompetitive Effects of Mergers In Tech 9-15 (2021) The Entrepreneurs Net. & The Int’l Ctr. for L. & Econ. (2021), available at https://laweconcenter.org/wp-content/uploads/2021/10/BetterTogether.pdf.

[48] Id. at 23.

[49] Clothilde Goujard, Google Forced to Postpone Bard Chatbot’s EU Launch over Privacy Concerns, Politico (13 Jun. 2023), https://www.politico.eu/article/google-postpone-bard-chatbot-eu-launch-privacy-concern.

[50] Makena Kelly, Here’s Why Threads Is Delayed in Europe, The Verge (10 Jul. 2023), https://www.theverge.com/23789754/threads-meta-twitter-eu-dma-digital-markets.

[51] Musk Considers Removing X Platform from Europe over EU Law, EurActiv (19 Oct. 2023), https://www.euractiv.com/section/platforms/news/musk-considers-removing-x-platform-from-europe-over-eu-law.

[52] The Future Is Bright for Latin American Startups, The Economist (13 Nov. 2023), https://www.economist.com/the-world-ahead/2023/11/13/the-future-is-bright-for-latin-american-startups.

[53] See Distrito, Panorama Tech América Latina (2023), available at https://static.poder360.com.br/2023/09/latam-report-1.pdf.

[54] David L. Kaserman & John W. Mayo, Competition and Asymmetric Regulation in Long-Distance Telecommunications: An Assessment of the Evidence, 4 CommLaw Conspectus 1, 4 (1996).

[55] See Mario Zúñiga, From Europe, with Love: Lessons in Regulatory Humility Following the DMA Implementation, Truth on the Market (22 Feb. 2024), https://truthonthemarket.com/2024/02/22/from-europe-with-love-lessons-in-regulatory-humility-following-the-dma-implementation.

[56] Kwon Soon-Wan & Yeom Hyun-a, South Korea Hits Pause on Anti-Monopoly Platform Act Targeting Google, Apple, The Chosun Daily (8 Feb. 2024), https://www.chosun.com/english/national-en/2024/02/08/A4U4X6TWEFFOXF7ITCS5K6SZN4.

[57] Jean Tirole, Competition and the Industrial Challenge for the Digital Age, Inst. Fiscal. Studies (2022), at 7, available at https://ifs.org.uk/inequality/wp-content/uploads/2022/03/Competition-and-the-industrial-challenge-IFS-Deaton-Review.pdf.

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Antitrust & Consumer Protection

Labor Monopsony and Antitrust Enforcement: A Cautionary Tale

ICLE White Paper Executive Summary In recent years, there has been growing interest among economists, lawyers, and policymakers in the concept of monopsony power, particularly in labor markets. . . .

Executive Summary

In recent years, there has been growing interest among economists, lawyers, and policymakers in the concept of monopsony power, particularly in labor markets. This interest has been spurred partially by academic research suggesting that labor-market concentration may be more prevalent than previously thought, as well as policy developments signaling a more aggressive approach by antitrust authorities to labor-monopsony issues. Despite this momentum, however, significant empirical and conceptual challenges remain in the use of antitrust law to address labor monopsony.

A. Economics Challenges

On the empirical front, the evidence on the extent and impact of labor monopsony is mixed. While some studies have found evidence of labor-market concentration and its effects on wages, these studies often rely on indirect measures that have limited applicability to antitrust cases. More direct estimates of monopsony power are rare, and often rely on stylized economic models that may not capture the complexities of real-world labor markets. Moreover, the economics literature has not reached a clear consensus on the appropriate framework to assess labor-market power in antitrust contexts.

Conceptually, there are important differences between monopoly and monopsony that complicate the application of traditional antitrust tools and standards to labor markets. One key difference is that monopsony and monopoly markets do not sit at the same place in the supply chain. This matters because all supply chains end with final consumers, and antitrust policy must grapple with how to balance effects at different levels of the distribution chain. In evaluating monopsony, authorities must consider the “pass through” to final product markets, a complication that does not arise in the mirror-image case of monopoly.

Another conceptual challenge is how to handle merger efficiencies in labor-market cases. In input markets, traditional efficiencies and increased buyer power are often two sides of the same coin, presenting difficult tradeoffs for authorities. Additionally, market definition—a cornerstone of modern antitrust policy—becomes more complex in labor markets, where the boundaries between different occupations, industries, and geographic areas can be blurry.

B. Policymakers’ Response

Despite these challenges, antitrust authorities have recently signaled a more aggressive approach to labor-monopsony issues. The Federal Trade Commission’s (FTC) noncompete ban, challenge to the Kroger/Albertsons merger, and the 2023 Merger Guidelines’ discussion of labor-market effects are all prominent examples of this trend. But these enforcement actions and policy statements often gloss over the unsettled state of the economics literature and the legal difficulties of proving labor-market harms under existing antitrust standards.

For example, the 2023 Merger Guidelines assert that labor markets have unique features that may exacerbate the competitive effects of mergers, but do not fully grapple with the limitations of the economic models and empirical evidence underlying these claims. Similarly, while the FTC’s Kroger/Albertsons complaint advances a novel “union grocery labor” market definition, it is unclear whether this approach aligns with economic realities or legal precedent.

C. Legal Difficulties

More broadly, it remains uncertain whether demonstrating and remedying monopsony power is feasible under existing legal standards. While harms to workers can theoretically be cognizable under the antitrust laws, proving such harms is challenging, especially under the prevailing consumer-welfare standard. Recent criminal cases targeting wage fixing and no-poach agreements have faced difficulties, and civil cases require showing harm to downstream consumers, not just workers.

Addressing these issues may require rethinking the goals and methods of antitrust enforcement. The consumer-welfare standard becomes difficult to apply when a merger may harm workers but benefit consumers downstream. Weighing these cross-market effects raises unresolved questions about the proper balance between consumer and producer surplus. While the 2023 Merger Guidelines assert that harms to upstream competition cannot be offset by benefits to downstream consumers, the basis for this stance in case law is questionable.

There are also important differences between monopoly and monopsony that complicate the mirror-image application of antitrust tools to labor markets. Most fundamentally, authorities must grapple with how to balance effects at different levels of the supply chain—an issue that does not arise in the standard monopoly context.

Moreover, the unique features of labor markets—such as the importance of firm-specific investments in human capital—pose challenges for market definition and the assessment of competitive effects. Traditional concentration measures and econometric tools used in product markets may not readily translate to the labor context. And the potential for countervailing effects on workers and consumers creates difficult tradeoffs in merger review.

Given these complexities, this paper urges caution and further study before radically expanding labor-antitrust enforcement. Advocates of reform should engage seriously with the empirical and conceptual issues highlighted here, rather than assuming that current law and economics support their policy prescriptions. Courts and enforcers should carefully consider the limitations of existing approaches and develop more robust analytical frameworks suited to the realities of labor markets.

D. The Road to Antitrust Enforcement in Labor Markets

This does not mean that antitrust has no role to play in addressing labor-market power. But it does counsel against a rush to condemn mergers and practices based on simplistic models or tenuous evidence. A more gradual, case-by-case approach focused on building legal precedent and economic consensus may be warranted. In the meantime, further dialogue between labor economists, antitrust experts, and policymakers is essential to aligning theory, evidence, and doctrine.

Such an agenda might include:

  • Developing more direct, antitrust-relevant measures of labor-market power beyond concentration ratios.
  • Studying the effects of specific mergers and practices on labor-market outcomes, rather than simply correlating concentration with wages.
  • Refining models of dynamic competition and firm-specific investments in labor markets and considering their implications for antitrust enforcement.
  • Clarifying the goals of antitrust in labor markets and how to weigh effects on different stakeholders under the consumer-welfare standard (or alternative frameworks).

The paper concludes by noting that, while the road ahead is challenging, the growing interest in labor antitrust presents an opportunity for interdisciplinary research and policy innovation. By carefully building on existing knowledge and legal frameworks, academics and practitioners can help craft an antitrust regime that promotes competition and welfare in labor markets without unduly chilling procompetitive conduct. The key is to remain grounded in sound economics and committed to empirical rigor, while adapting to the unique features of labor markets. With such an approach, antitrust can play a valuable role in ensuring that workers share in the benefits of a well-functioning economy.

I. Introduction

Market power—traditionally discussed in terms of monopoly power on the sell side—has faced increasing scrutiny from the buy-side perspective. This is especially true regarding labor monopsony, where employers may exert undue control over employees, thereby influencing wages and working conditions. This shift in focus reflects a growing concern among economists, lawyers, and policymakers about the implications of such power dynamics in the labor market. The growing discourse around monopsony power in labor markets has been further marked by a keen interest in applying antitrust laws to combat these concerns.

Recent policy initiatives and enforcement decisions indicate a burgeoning will to leverage antitrust law against perceived labor-market power abuses. In the first half of 2024 alone, the Federal Trade Commission (FTC) has enacted a rule banning noncompete agreements for nearly all workers in the United States, justified on grounds that such agreements amount to “unfair methods of competition.”[1] The FTC has also brought an enforcement action challenging the proposed Kroger/Albertsons merger, in part predicated on concerns about the combination’s potential to diminish labor competition and exacerbate monopsony power in local labor markets.[2] At year-end 2023, meanwhile, the FTC and the U.S. Justice Department (DOJ) Antitrust Division published updated merger guidelines that, for the first time, included an expanded discussion of monopsony issues.[3] While the noncompete ban, the Kroger/Albertsons merger challenge, and the 2023 Merger Guidelines are the most prominent examples, they are far from the only ones.[4]

This paper argues that, despite growing interest in the use of antitrust law to address labor monopsony, such efforts are not supported by empirical and theoretical foundations sufficient to bear the weight of these galvanized efforts. While policy proceeds apace, the debate is far from settled on the economic evidence, analytical tools, and legal standards appropriate for understanding and addressing monopsony power in labor markets as an antitrust concern. In fact, the current state of economic research and antitrust jurisprudence raises more questions than answers about the appropriate framework for assessing labor-market power.

Examples of this disconnect are legion. Empirical data concerning the magnitude and impact of labor monopsonies is inconsistent. Evidence on the extent of labor-market power is mixed, with studies reaching divergent conclusions depending on the data, methodology, and markets analyzed. While the Biden administration has been quick to cite economic research on labor-market concentration and earnings as motivating factors,[5] the referenced studies provide only indirect evidence of monopsony power and have limited applicability to antitrust cases, while direct estimates of monopsony power are rare and often rely on economic models that have not yet been accepted within antitrust. A more complete analysis of the literature on concentration in labor markets, meanwhile, does not support the narrative that labor markets are extremely concentrated across wide swathes of the economy. From a theoretical standpoint, the economics literature has not reached a clear consensus on the appropriate antitrust framework for labor markets. Moreover, the distinct economics of monopsony contrast with those of monopoly, introducing unresolved complexities into customary modes of antitrust analysis, such as market definition, assessment of efficiencies, and the consumer-welfare standard.

The antitrust authorities have ignored these complications in their recent actions. For example, Guideline 10 of the 2023 Merger Guidelines states that labor markets frequently have unique characteristics that may exacerbate the competitive effects of mergers:

[L]abor markets often exhibit high switching costs and search frictions due to the process of finding, applying, interviewing for, and acclimating to a new job. Switching costs can also arise from investments specific to a type of job or a particular geographic location. Moreover, the individual needs of workers may limit the geographical and work scope of the jobs that are competitive substitutes.[6]

This implies that market attributes like switching costs, search costs, and transportation costs are unique to labor markets. Of course, this is not true. Nor is there any reason to think labor markets are even relatively more susceptible to such costs. At the same time, the guidelines’ statement implies that these labor-market costs are borne only by workers, rather than employers. But there is no reason why that should be the case. Indeed, switching costs do not always make markets less competitive.[7]

The guidelines further assert that relevant labor markets “can be relatively narrow,” and that “the level of concentration at which competition concerns arise may be lower in labor markets than in product markets, given the unique features of certain labor markets.”[8] Because these are the merger guidelines and are meant to cover a wide variety of situations, one could read “may” as implying something more than a possibility. Indeed, the guidelines clearly appear to indicate that, following mergers, anticompetitive effects are more of a concern in labor markets than in product markets.

Unfortunately, the models commonly employed in labor economics to support these claims rely on assumptions about worker mobility, employer conduct, and market structure that likely oversimplify real-world dynamics. All models are simplifications, but how important are those simplifications for antitrust? The economic models commonly used to study labor markets have not been subjected to the same level of antitrust scrutiny as those employed in industrial-organization (IO) economics to analyze product markets. Over the past several decades, IO models of imperfect competition have been rigorously adapted and applied to assess the competitive effects of mergers, collusive agreements, and exclusionary practices in antitrust matters. Empirical IO research has frequently focused on questions of direct relevance to antitrust enforcement, and IO economists have often played an active role in developing the analytical tools used by agencies and courts.

In contrast, most labor-economics research has been conducted without an explicit focus on antitrust policy and, until recently, labor economists were rarely involved in antitrust matters. As a result, the key assumptions and implications of labor-economics models have not been fully stress tested against the evidentiary burdens and legal standards of antitrust cases—at least, not in the same ways as their IO counterparts. This disconnect poses challenges to the effective application of labor economics to antitrust enforcement, as the models and empirical techniques most familiar to labor economists may not align well with the demands of antitrust law.

Moreover, it’s not just the economics that is more unsettled than the current administration would like to claim; the law is unsettled, too. It is unclear whether demonstrating and remedying monopsony power is feasible under existing legal standards, for example. It is true that harms to labor can be cognizable under the antitrust laws, which prohibit certain exercises of monopsony power, and not just monopoly power. There are, however, ambiguities in accurately defining the boundaries of relevant labor markets. And establishing tangible anticompetitive effects on workers as “consumers” of jobs also poses challenges.

Wage-fixing agreements are per se illegal, but the decisions in recent criminal no-poach and wage-fixing cases suggest difficulties in proving that such agreements amount to meaningful market allocation, rather than insignificant job-posting-policy changes, that would be inconsistent with a per se rule. For example, in United States v. DaVita Inc., the judge ruled that no-poach agreements could be an illegal market-allocation agreement.[9] But the jury acquitted the defendants of criminal no-poach charges, finding that the DOJ had failed to prove that the agreements at-issue were made with the purpose of allocating the market and ending meaningful competition for employees. The government has faced similar difficulties in other cases.[10]

Outside of per se cases, antitrust becomes even more complicated. Addressing labor-market power requires tradeoffs under established antitrust standards, raising unresolved questions about the goals of antitrust enforcement. As Herbert Hovenkamp notes, “it has been explicit from the start that antitrust’s concern is protection from reduced market output and, concurrently, higher prices.”[11] This focus on output and price effects in downstream product markets sits uneasily with concerns about labor market harms, which may not always manifest in higher consumer prices or reduced output in the downstream product market.

For example, the consumer-welfare standard becomes difficult to apply when a merger may harm workers, but benefit consumers downstream, as when wage reductions for workers accompany consumer benefits (such as lower prices) in downstream product and service markets. Do all mergers that reduce wages for one market of workers “substantially lessen competition” in a “line of commerce”?[12] In practice, weighing these cross-market effects raises unresolved questions about the goals of antitrust enforcement. Is the sole focus on final-product consumers, or should producer surplus also be considered? If so, how should we value and compare producer versus consumer harms?

The 2023 Merger Guidelines acknowledge these issues, but sidestep them, by asserting that:

If the merger may substantially lessen competition or tend to create a monopoly in upstream markets, that loss of competition is not offset by purported benefits in a separate downstream product market. Because the Clayton Act prohibits mergers that may substantially lessen competition or tend to create a monopoly in any line of commerce and in any section of the country, a merger’s harm to competition among buyers is not saved by benefits to competition among sellers.[13]

As we explain below, however, the issue is not so simple, and its resolution cannot be assumed simply by quoting the Clayton Act.[14]

While the guidelines propose treating labor markets similarly to product markets for analytical purposes, the Kroger/Albertsons complaint suggests that, in practice, the agency believes that labor markets should be defined more narrowly—for example, unionized workers in very narrow geographic areas.[15] This approach raises further conceptual issues in market definition, as labor markets may transcend traditional industry and geographic boundaries in complex ways. More work is needed to align labor economics with the realities of antitrust enforcement. Answering these questions may require revisiting foundational assumptions that currently guide antitrust policy. Caution is thus warranted before concluding that antitrust can or should seek to remedy monopsony, absent harm to consumers of final goods.

Therefore, while monopsony concerns are becoming more prevalent in academic and policy discussions, the agencies should be extremely hesitant as they move forward. Some have argued that “[m]ergers affecting the labor market require some rethinking of merger policy, although not any altering of its fundamentals.”[16] As we discuss below, however, while the economic “fundamentals” undergirding merger policy may not change for labor-market mergers, the “rethinking” required to properly assess such mergers entails fundamental changes that have not yet been adequately studied or addressed. As many have pointed out, there is only a scant history of merger enforcement in input markets in general, and even less in labor markets.[17] It is premature to offer guidelines or impose nationwide bans on labor practices, while purporting to synthesize past practice and the state of knowledge, when neither is well-established.

The following sections illustrate some of the significant disconnects between labor economics and antitrust enforcement, highlighting the need for further research and dialogue between the two fields. In short, while interest is growing, labor economics cannot yet be readily plugged into antitrust enforcement in the same way that IO theory and empirics have been.

II. The Contemporary Relationship Between Labor and Antitrust

As discussed in the previous section, the 2023 Merger Guidelines, Kroger/Albertsons complaint, and the FTC’s noncompete rule evidence an invigorated policy effort to address competition concerns in labor markets. The merger guidelines discuss the potential labor-market implications of mergers in multiple sections, and adopt a guideline specifically related to labor-market considerations that calls out the purportedly unique features of labor-monopsony markets “that can exacerbate the competitive effects of a merger.”[18] While the noncompete ban contains an extensive discussion of the labor-economics literature on noncompetes,[19] the sweeping nature of the ban suggests that policymakers view monopsony power as a pervasive issue affecting most workers, despite the nuances and ambiguity of the literature.[20] And the FTC’s complaint in the Kroger/Albertsons case argues that the merger would eliminate labor-market competition between Kroger and Albertsons and would increase their leverage in negotiations with local unions over wages, benefits, and working conditions in an asserted “union grocery labor” market—introducing a novel and remarkably narrow market definition and an untested, contentious theory of harm (reduction in bargaining leverage) particular to labor markets.[21]

While these efforts may signal a newly heightened attention to labor-market concerns, the antitrust focus on labor monopsony did not originate with them. In recent years, there has been growing interest in using the tools of antitrust to address labor issues, with both academic literature and enforcement actions paving the way for a more labor-centric approach to antitrust. This section provides an overview of some of the key developments in this area, illustrating the growing attention given to labor-market power by antitrust authorities and scholars.

Conceptually, the relationship between labor economics and antitrust law has also been a subject of growing academic attention in recent years. A number of law-review articles have highlighted the historical disconnect between the two fields, noting that labor markets have often been overlooked in antitrust analysis.[22] They also point, however, to some areas where labor economics has begun to make inroads into antitrust enforcement.

On the policy front, President Joe Biden explicitly called for greater scrutiny of “monopsony power” in labor markets in his 2021 executive order on competition.[23] The U.S. antitrust agencies have similarly been ramping up enforcement and other policy work at the intersection of labor and competition policy. For instance, the DOJ sued to block Penguin Random House’s acquisition of Simon & Schuster, in part based on monopsony concerns regarding the market for top-selling book authors.[24] Under the current leadership, the FTC has brought and settled several enforcement actions alleging that certain noncompete agreements violated the FTC Act’s prohibition on “unfair methods of competition.”[25] The day after announcing the first three of those settlements, the FTC first proposed a nationwide ban on the use of noncompetes via a notice of proposed rulemaking.[26]

As noted above, the DOJ has brought several recent wage-fixing cases, albeit with limited success.[27] Previously, during the Obama administration, the DOJ and FTC jointly issued antitrust guidance for human-resource professionals that warned that agreements among competing employers to fix terms of employment may violate the antitrust laws.[28] The DOJ also brought suits against major Silicon Valley employers for entering into anticompetitive “no-poach” agreements to restrict hiring of engineers and programmers from competitor firms.[29] The department alleged in those suits that the agreements amounted to unlawful allocation of the relevant labor market among horizontal competitors. The DOJ also challenged a hospital association’s members agreement to set uniform billing rates for certain nurses as an improper exertion of buyer power.[30] Although both the “no-poach” and nurse wage-setting actions ultimately settled, these cases demonstrated an increasing willingness to extend antitrust scrutiny to labor-market effects and to discipline allegedly monopsonistic practices by dominant buyers of labor.

Finally, in 2022, the FTC signed a memorandum of understanding with the National Labor Relations Board (NLRB) “regarding information sharing, cross-agency training, and outreach in areas of common regulatory interest.”[31] In 2023, the FTC signed a similar memorandum of understanding with the U.S. Labor Department.[32]

While these recent developments reflect growing interest in the application of antitrust law to labor-monopsony concerns, the linkage between labor economics and antitrust is not yet as developed as the one between antitrust law and IO and antitrust economics for output markets. Over the 20th century, the fields of IO economics and antitrust law evolved considerably. While the two fields are not co-extensive, the mutual influence has been considerable and ongoing, as strong connections have developed between economic theory, empirical study, and legal doctrine. Models of imperfect competition were incorporated into analyses of mergers, collusion, and exclusionary practices.[33] Notably, even the Chicago School, despite some scholars’ claims to the contrary,[34] made extensive use of models beyond perfect competition as a central part of its approach to antitrust.[35] Empirical IO research also frequently studied topics directly relevant to antitrust inquiries.[36] This close, co-evolutionary relationship does not yet exist—at least, not to the same extent—between labor economics and antitrust.[37]

While some scholars have worked to integrate labor and antitrust economics more closely, most empirical research remains focused on indirect concentration measures, rather than pricing conduct directly relevant to antitrust enforcement. Labor economics does not yet have IO’s established track record of successful application to assessing the competitive impact of mergers, restraints, or exclusionary practices. Before that sort of track record can be built, certain limitations must be overcome—not least that labor research has largely developed without a focus on, or involvement in, antitrust policy.

III. The Newly Developing Economic Literature on Labor-Market Power

Labor markets have become an increasingly popular topic in antitrust-policy debates. These debates have, at least in part, been spurred by academic research that purports to find widespread market power in labor markets, thus warranting the need for antitrust scrutiny.[38] For example, the U.S. Treasury Department’s report on “The State of Labor Market Competition” connects the economics research to “a description of Biden Administration actions to improve competition.”[39] Unfortunately, conclusions that the labor-market-power literature supports tougher antitrust enforcement often rely on indirect measures of market power, such as concentration figures, that are sometimes far-removed from the needs of antitrust enforcement, which usually requires more direct measures and more antitrust-relevant markets.[40]

Against this backdrop, this section reviews the scholarly evidence on labor-market power. Subsection A reviews economic papers that attempt to measure firms’ labor-market power directly, while Subsection B reviews papers that rely on such proxies as industry-concentration measures (i.e., indirect evidence of labor-market power). Ultimately, we find that these bodies of research say little about the need for tougher antitrust enforcement, largely because their measures of market power fail to indicate that there is an antitrust-relevant problem that is currently unaddressed in labor markets.

A. Direct Evidence: Do Employers Have Significant Labor-Market Power?

How do we measure labor-market power? While the bulk of the evidence on labor markets is only indirectly related to market power (if related at all), there have been a few explicit attempts to quantify the extent of labor-market power within U.S. markets.

The most popular way to directly estimate labor-market power is through the residual labor-supply elasticity that a firm faces. A labor-supply elasticity measures how responsive the supply of labor is to a change in wages. In the simplest model, a more elastic labor supply means workers have more outside options and employers have less wage-setting power. In the extreme, a perfectly competitive firm faces a perfectly elastic residual supply curve; in the baseline (two-firm) model, if one firm pays $0.01 less than the other employer, all the employees will leave for the other employer.

Outside of the perfectly competitive case, a firm may have some degree of labor-market power, which can be measured by the difference between the wage and the marginal revenue product, known as the wage “markdown.”[41] In the case of perfect competition (i.e., no market power), the firm is unable to pay wages below the marginal product of labor (the revenue generated for the firm by an additional worker), and thus the labor markdown of wages is zero. By contrast, the presence of a larger wage markdown (because of a lower labor elasticity) indicates greater labor-market power.[42]

Naidu, Posner, and Weyl summarize estimates of labor-supply elasticity from several studies, finding evidence of substantial market power in some labor markets, but by no means all.[43] Indeed, the underlying papers find residual labor elasticities ranging from 0.1 to 4.2, which would mean that workers are receiving between 9% and 81% of their marginal product, depending on the particular paper’s estimate.[44] While the list of papers estimating labor elasticity is too lengthy to detail in this paper, the upshot for antitrust policy is that low elasticity (and thus large labor-market power) is not universal (nor should we expect it to be; even if average market power is large, not every market is average).[45]

But even if the empirical labor-economics literature unanimously identified a large degree of labor-market power, which it does not, it would remain unclear what the implications are for antitrust policy. The crux of the problem is that the literature’s estimates of labor elasticities generally rely on assumptions that may not mirror those typically used in antitrust analysis. Applying these estimates to a simple antitrust model of monopsony generates implications that go against the data. For example, a labor-supply elasticity of 0.1 would imply a labor share of income of just 8% in the model described in Naidu, Posner, & Weyl.[46] That is far lower than the actual labor share observed in most countries, which has fallen, but is still closer to 60%, not 8%.[47] This suggests that the connection between the estimate and the model may not be appropriate. Thus, while labor-supply elasticities can provide valuable information about the degree of labor-market competition, antitrust practitioners should be wary of applying them mechanically to standard models of product-market competition without considering the unique features and dynamics of labor markets.

There can also be discrepancies between the tools employed to estimate labor-supply elasticities, on the one hand, and the needs of antitrust enforcement, on the other. For instance, a study by Ransom and Sims employs a search model—a standard tool in labor economics, but not a model generally seen in antitrust. The model is based on the idea of “search frictions,” which refers to the time and effort required for workers to find jobs and for employers to fill vacancies.[48] Because of these frictions, workers may accept lower-paying jobs while continuing to search for better opportunities.

This model assumes that, in the long run, the number of workers leaving a job is equal to the number of workers taking a new job. While this “steady state” assumption may hold in many contexts, it is not one typically seen in antitrust analysis of product markets. If the assumption is violated, estimates of labor-market power derived from the model could be biased in either direction, depending on the specific imbalance of worker flows. In the realm of antitrust enforcement, this could lead to both false positives and false negatives. It remains to be seen what courts would do when confronted with these new models.

Conversely, other papers attempt to apply the standard Cournot model from antitrust product-market analysis to labor markets.[49] In this approach, the authors take the median Herfindahl-Hirschman Index (HHI), a common measure of market concentration, and divide it by the aggregate labor-supply elasticity to estimate labor-market power. But there may be a mismatch here, as well. Indeed, it is unclear whether the Cournot model, where firms commit to hiring a certain number of workers each period, is a realistic representation of labor markets for antitrust purposes, because it relies on critical assumptions that may not be present in real-world markets, such as simple wage-posting, monopsony models. In fact, this may explain why search models, despite their flaws, remain the most common approach to assessing labor markets.

Recognizing these limitations, a burgeoning literature attempts to design labor-market competition models that better align with the needs and realities of antitrust analysis. But as yet, there is no silver bullet. Azar, Berry, and Marinescu, for example, combine elements of a static model of imperfect competition (commonly used in IO economics) with a labor-market model.[50] This approach aims to capture the dynamics of labor-market competition more accurately by considering the differentiation among jobs and workers’ preferences.

The authors use data on job vacancies from CareerBuilder.com (a popular online job board) to estimate a model of differentiated jobs and workers’ preferences for those jobs. Because of data limitations, however, they only have information on the elasticity of vacancy demand—i.e., the intensity of responses to posted job vacancies—not on actual wages. To overcome this, they assume a simple model where employers post wages and workers choose whether to accept those offers, similar to how firms post prices in the Cournot model of product-market competition. Using this approach, the authors estimate that workers are paid 21% less than their marginal product, suggesting significant labor-market power.[51] But their model relies on the same long-run-equilibrium assumption discussed earlier, where the number of workers leaving a job equals the number of workers taking a new job.

One final approach uses wage markdowns to estimate labor-market power, but this, too, is far from perfect. Yeh, Macaluso, and Hershbein, for example, use data from the U.S. Census Bureau to estimate markdowns in the manufacturing sector.[52] They find that, on average, workers earn about 65 cents for every dollar of value they generate for their employer.[53] This would imply a significant degree of labor-market power. The researchers also find that markdowns tend to be larger for bigger companies, suggesting that these firms have more power to set wages.[54] Interestingly, they find that markdowns decreased from the late 1970s to the early 2000s, but have increased sharply over the past 20 years.[55] This recent increase in markdowns could indicate a growing problem of labor-market power.

Unfortunately, interpreting markdowns as a clear sign of labor-market power is not always straightforward, and there are reasons to be skeptical of these results. To see why, imagine two hair salons: Salon A is a basic salon that charges $20 for a haircut, while Salon B is a luxury salon that charges $40 for a haircut that the econometrician believes is the same quality. If both salons hire hairdressers who can do one haircut per hour, Salon B might pay only slightly more than Salon A—say $21 per hour—to attract hairdressers. This means that the hairdressers at Salon B are receiving a wage that is far less than the $40 value of their marginal product. Superficially, this might look like a sign of labor-market power.

But where the price difference is attributable to non-labor factors—such as the salon’s luxury branding, posh environment, and free drinks—the apparent markdown might, in fact, reflect the salon owner’s return on investment, rather than its power to set wages. This is why some economists view markdowns as a “residual”—the leftover value after accounting for other factors.[56] In the real world, we do not know whether an apparent markdown comes from labor-market power due to weak competition, or whether it is a return to something the owner contributes that the economist does not see.

In fact, some evidence suggests that a significant portion of markdowns may be just that: a return to some technology the firm has rather than labor-market power. Kirov and Traina look at markdowns in U.S. manufacturing over time and find that workers received the full value of their output in 1972, but only about half in 2014.[57] They argue that this increase in markdowns was driven largely by rapid productivity growth due to technological advancements, not by slower wage growth. The authors find that markdowns were strongly correlated with measures of information technology, management practices, and automation. This suggests that the growing gap between worker pay and productivity might be more about technological change than about employers’ bargaining power—a very different issue than the monopsony problem that antitrust law could (potentially) address.

All of this is not to say that labor-economics tools are unsuitable for antitrust policy or enforcement. Rather, it highlights the need for further research and legal precedent to establish how these tools can be effectively adapted to meet the evidentiary standards and analytical frameworks of antitrust law. While proponents of increased labor-antitrust enforcement may be eager to apply insights from labor economics to antitrust cases, it is crucial to recognize that this translation is not always straightforward and may require careful consideration of the underlying assumptions and their implications for antitrust analysis.

In short, there is a gap between existing direct evidence on labor-market power and the needs of antitrust policy and enforcement. Labor economics generally relies on models that are not germane to antitrust enforcers, while the models that are common in antitrust enforcement might not fully capture the dynamics of labor markets. Further research and dialogue between labor economists and antitrust experts is needed to develop a consistent and reliable framework to analyze labor-market power in antitrust cases. Until then, the inapt assumptions and limitations of the models presented to antitrust authorities and courts call their predictive value into question.

Ultimately, the direct evidence from labor-elasticity estimates and other measures of labor-market power remains limited in scope and varies widely across studies. While these studies provide valuable insights, they are far from conclusive, and do not yet approach the level of evidence and analysis typically relied upon in the IO literature to assess product-market competition. Courts and policymakers are likely to expect a more robust and consistent body of evidence before making significant changes to antitrust enforcement in labor markets. The disputes over direct evidence on labor-market power underscore the need for further research and highlight the challenges of applying antitrust tools to labor markets based on the current state of knowledge. Antitrust enforcers should take policy insights gleaned from labor-economics studies with a grain of salt, as they may be of limited use when informing antitrust policy decisions.

B. Indirect Evidence: Are Labor Markets ‘Relatively Narrow’?

The 2023 Merger Guidelines assert that labor markets can be “relatively narrow” and that “the level of concentration at which competition concerns arise may be lower in labor markets than in product markets, given the unique features of certain labor markets.”[58] The academic literature, however, presents a more nuanced picture that casts doubt on some of these claims. This section provides an abbreviated review of that literature. A more thorough explanation is provided in the Appendix.[59]

Given the limited direct evidence discussed in the previous section, as well as the difficulties entailed in collecting and applying it, it is not surprising that many scholars have turned to indirect measures of market power to fill the evidentiary gap. There are, however, significant issues with these indirect measures, as they often rely on concentration metrics, such as the Herfindahl-Hirschman Index (HHI), which are more readily available, but considerably less reliable than direct estimates of market power.[60]

While all indirect data sources have limitations, some are more comprehensive and reliable than others. The most comprehensive data is administrative data. While these differ on the levels of concentration, depending on how narrowly the market is defined, they consistently document falling concentration levels in local labor markets, where most job search and hiring occurs. [61] These studies have the advantage of comprehensive coverage of employers and workers, but often define labor markets based on industry codes, rather than occupations, which may not fully capture the relevant competitors for specific types of labor.

On the other hand, the administrative data concern all employer establishments.[62] The administrative data directly measure employment levels and shares, instead of being restricted to online vacancies as a proxy for employment.[63] This distinction matters, because employment shares are the natural counterpart of market shares—a cornerstone of antitrust enforcement. Concentration measures based on vacancies will be systematically higher than those based on employment, because not all firms will hire in any particular period (in addition to any other issues with the data sample). Using the most direct comparison available, the governmental microdata finds an average HHI roughly one-tenth as large as that found using vacancy data.[64]

Unfortunately, no dataset is perfect, even the administrative data. For example, many rely on employment data organized by North American Industry Classification System (NAICS) codes for market definition, which are organized by establishment, not by occupation. For example, all Wal-Mart employees at a store are labeled as NAICS 4521 (Department Stores), instead of being broken out by different occupations (Standard Occupational Classifications or “SOC”) for different vacancies.[65] That makes their results better interpreted as local industrial-concentration measures, instead of true labor-market concentration measures.

For pure concentration measures, this may not matter too much. Berger, Herkenhoff, and Mongey argue that “there is little practical difference in defining a market at the occupation-city level rather than the industry-city level as these two measures are highly correlated.”[66] But at the more granular level of antitrust enforcement, the difference between measures may be significant. In particular, many workers may be able to easily substitute between employers located in different industries. An accountant, for instance, might be just as qualified to work for a bank as for a hotel or a tech company. This cross-industry substitution is obscured by market definition undertaken at the NAICS level.

With these caveats about market definition, what does the administrative data show about concentration? Rinz uses the Longitudinal Business Database, covering nearly all private-sector employers, to estimate labor-market concentration from 1976 to 2015.[67] At the beginning and end of the time period studied, unsurprisingly, Rinz finds rural labor markets to be more concentrated than urban markets.[68] He finds that the average local HHI, defined by commuting zones and four-digit NAICS industries, decreased from 0.16 in 1976 to 0.12 in 2015, indicating a shift toward less-concentrated local markets. Local concentration fell in all population quintiles.[69]

By contrast, national HHI increased modestly over the same period, driven by large firms entering more local markets.[70] Similarly, Lipsius documents falling local concentration from 1976 to 2015, using alternative market definitions based on five-digit NAICS codes and urban areas, rather than commuting zones.[71] Despite these definitional differences, the average local HHI remains consistently low, ranging from 0.14 to 0.17 depending on the year and market definition. Berger, Herkenhoff, & Mongey further corroborate these findings with a different way of averaging HHI measures across markets.[72] They estimate an average local HHI of 0.17 for the year 2014, with even lower concentration levels when analyzing individual sectors like manufacturing and services. The average local HHI levels documented in these studies are below the 1,800 (or 0.18) threshold associated with highly concentrated markets in the 2023 Merger Guidelines.[73]

Studies using job vacancies, rather than employment data, tend to find higher market concentration, but this may partly be driven by their omission of job openings that are not published online (or at all). Indeed, the most well-cited papers on labor-market concentration use online job postings to measure concentration.[74] These studies can define labor markets more granularly, but they may not capture all employers and job openings, particularly those that are not advertised online. This focus on vacancies rather than employment may not always reflect the actual options available to workers, as not all job vacancies are advertised (online).[75]

While the 2023 Merger Guidelines suggest that labor markets warrant a lower concentration threshold for competition concerns, they do not provide a clear basis for this assertion or specify what that threshold should be. The indirect evidence from local labor-market concentration metrics does not support the notion that labor markets are inherently more problematic than product markets, from a concentration perspective. Instead, these low and falling concentration levels suggest that many local labor markets are relatively competitive and do not necessarily require a lower concentration threshold for merger analysis. While the guidelines’ recognition of labor markets’ unique features is important, this acknowledgment should be coupled with a more precise and empirically grounded approach to defining concentration thresholds.

More fundamentally, regardless of the data source used, market-definition issues remain. The variety of concentration estimates stemming from different geographic units and shifting occupational groupings demonstrates the lack of clarity around reasonable market boundaries. Worker mobility also introduces questions about appropriate geographic scope. While some labor markets may be highly concentrated, it does not follow that relevant antitrust labor markets are often relatively narrow. Establishing narrowness, in the antitrust sense, requires specific proof that additional employer options do not provide meaningful competitive discipline against potential wage reductions—something these papers do not do.

The upshot is that antitrust enforcers will need to rely on case-specific evidence, rather than broad claims of high concentration levels and narrow labor markets. Concentration measures have long been considered imperfect indicators of market power in antitrust policy and IO debates.[76] While high concentration may be suggestive of market power, it is not conclusive evidence. Many factors other than concentration can affect wages, such as differences in firm productivity, local labor-market conditions (e.g., urban vs. rural), and institutional factors like unionization rates.

Moreover, there is good evidence that employer concentration does not lead to depressed wages.[77] For example, Kirov and Traina find that rising markdowns (the gap between worker productivity and wages) are more strongly associated with technology-related factors, such as automation and managerial practices, than with employer concentration.[78] Moreover, they caution that:

These results suggest the workhorse assumptions behind some of the labor-market power literature might need reevaluation, particularly work that uses cross-sectional variation to infer trends in labor-market power. Concentration is likely an inappropriate measure of labor-market power in this case.[79]

Their critique underscores the limitations of relying heavily on concentration metrics to assess labor-market competition, especially when making claims about trends over time. As Berry, Gaynor, and Scott Morton write:

A main difficulty in [the monopsony power literature] is that most of the existing studies of monopsony and wages follow the structure-conduct-performance paradigm; that is, they argue that greater concentration of employers can be applied to labor markets and then proceed to estimate regressions of wages on measures of concentration. For the same reasons we discussed above, studies like this may provide some interesting descriptions of concentration and wages but are not ultimately informative about whether monopsony power has grown and is depressing wages.[80]

This is not to say that indirect evidence of market power is entirely without value. These studies can provide useful background information to guide antitrust policy. Moreover, antitrust law itself often relies on indirect measures of market power, such as concentration ratios and HHIs. In the case of antitrust enforcement, however, these measures are typically derived from carefully defined relevant markets. Defining the relevant market for labor is a complex task that requires considering such factors as job characteristics, worker skills, worker mobility, and geographic scope. There is currently little consensus among labor economists about the best way to define labor markets for antitrust purposes.

Ultimately, the indirect evidence from concentration metrics does not support the merger guidelines’ strong claims about ubiquitous labor-market narrowness or the need for a lower concentration threshold in merger analysis. While concentration trends are not uniform across all markets and data sources, the weight of the evidence points toward falling local concentration and increasing labor-market competition over time (if concentration is a proxy for competition). Antitrust authorities should engage with this evidence and provide a stronger empirical basis for their policy recommendations, rather than relying on unsubstantiated assumptions about the inherent narrowness of labor markets.

IV. The Problems of Addressing Labor-Market Power Under Antitrust Law

The empirical literature that attempts to measure labor-market power remains unsettled and limited, and provides, at best, only indirect evidence of economy-wide monopsony power. But even if robust measures of labor monopsony were available, applying antitrust laws to remedy monopsony power would still face conceptual hurdles. Economic theory indicates important differences between monopoly and monopsony power that complicate simple policy translation.

While antitrust statutes technically apply equally both upstream and downstream,[81] the economics of monopoly versus monopsony raise thorny theoretical issues regarding dynamic efficiency, merger efficiencies, market definition, and more that may differ between the two. Just as the empirical questions remain far from settled, the theory provides little straightforward guidance on how to address these concerns.

U.S. antitrust agencies have nevertheless long sought to reinvigorate anti-monopsony enforcement. Before concluding that labor-monopsony enforcement should be a priority for antitrust enforcers, both the evidentiary limitations and conceptual challenges warrant careful consideration by enforcers, scholars, and the courts.

On the surface, it may appear that monopsony is simply the “mirror image” of monopoly.”[82] There are, however, several important differences between monopoly and monopsony, as well as several complications that monopsony analysis raises that significantly distinguishes it from monopoly analysis. Most fundamental among these, monopsony and monopoly markets do not sit at the same place in the supply chain.[83] This matters, because all supply chains end with final consumers. Accordingly, from a policy standpoint, it is essential to decide whether antitrust ultimately seeks to maximize output and welfare at that (final) level of the distribution chain (albeit indirectly); whether intermediate levels of the distribution chain (e.g., an input market) should be analyzed in isolation; or whether effects in both must be somehow aggregated and balanced.

This has important ramifications for antitrust enforcement against monopsonies. As we explain below, competitive conditions of input markets have salient impacts on prices and output in product markets. Given this, any evaluation of monopsony must consider the “pass through” to the final product market. There is, however, no such “mirror image” complication in the consideration of final-product monopoly markets. Along similar lines, treating the assessment of mergers in input markets as the simple mirror image of product-market mergers presents important problems for how authorities address merger efficiencies, as traditional efficiencies and increased buyer power are often two sides of the same coin. Finally, it is unclear how authorities should think about market definition—a cornerstone of modern antitrust policy—in labor markets, in particular.

The upshot is that, while monopsony concerns have become more prevalent in academic and policy discussions, the agencies should be extremely hesitant as they move forward. Some have argued that “[m]ergers affecting the labor market require some rethinking of merger policy, although not any altering of its fundamentals.”[84] As we discuss below, however, while the economic “fundamentals” undergirding merger policy may not change for labor-market mergers, the “rethinking” required to properly assess such mergers does entail fundamental changes that have not yet been adequately studied or addressed. As many have pointed out, there is only a scant history of merger enforcement in input markets in general, and even less in labor markets.[85] It is premature to offer guidelines that purport to synthesize past practice and the state of knowledge, when neither is well-established.

A. Theoretical Differences Between Monopoly and Monopsony

Before getting to the practical differences of a monopoly case versus a monopsony case, consider the theoretical differences between identifying monopsony power and monopoly power.[86] Suppose, for now, that a merger either generates efficiency gains or market power, but not both. In a monopoly case, if there are efficiency gains from a merger, the quantity sold in the output market will increase. With sufficient data, the agencies will be able to see (or estimate) the efficiencies directly in the output market. Efficiency gains result in either greater output at lower unit cost, or else product-quality improvements that increase consumer demand. In contrast, if the merger simply enhances monopoly power without efficiency gains, the quantity sold will decrease, either because the merging parties raise prices or quality declines. The empirical implication of the merger is seen directly in the market in question.

The monopsony case is, however, rather more complicated. Ultimately, we can be certain of the effects of monopsony only by looking at the output market, not the input market where the monopsony power is claimed. To see this, consider again a merger that generates either efficiency gains or market (now monopsony) power. A merger that creates monopsony power will necessarily reduce the prices and quantity purchased of inputs like labor and materials. But this same effect (reduced prices and quantities for inputs) would also be observed if the merger is efficiency enhancing. If there are efficiency gains, the merged entity may purchase fewer of one or more inputs than the parties did pre-merger. For example, if the efficiency gain arises from the elimination of redundancies in a hospital merger, the hospital will buy fewer inputs, hire fewer technicians, or purchase fewer medical supplies.

We have seen there are scale efficiencies associated with a hospital merger. As work from the FTC’s Bureau of Economics explains, there can be scale efficiencies associated with “surgical procedures that exhibit a volume-outcome relationship.”[87] Typically, these are high-risk, complex procedures. “By consolidating such procedures at fewer hospitals, or by sending experienced personnel from one hospital to another, a system potentially can reap the benefits of increased scale.”[88] That is, reassignment of personnel and/or consolidation of procedures (and attendant personnel) at fewer hospitals can facilitate more efficient, and higher quality, provision of services, even as it may decrease labor demand in certain geographic markets. This may even reduce the wages of technicians or the price of medical supplies, even if the newly merged hospitals do not exercise any market power to suppress wages.[89]

Decisionmakers cannot simply look at the quantity of inputs purchased in the monopsony case as the flip side of the quantity sold in the monopoly case, because the efficiency-enhancing merger can look like the monopsony merger in terms of the level of inputs purchased. The court can only differentiate a merger that generates monopsony power from a merger that increases productive efficiencies by looking at the output market. Once we look at the output market, as in a monopoly case, if the merger is efficiency-enhancing, there will be an increase in output-market quantity. If the merger increases monopsony power, by contrast, the firm perceives its marginal cost as higher than before the merger and will reduce output.[90]

In short, the assumption that monopsony analysis is simply the mirror image of monopoly analysis does not hold.[91] In both types of mergers—those that possibly generate monopoly and those that possibly generate monopsony—agencies and courts cannot look solely at the input market to differentiate them from efficiency-enhancing mergers; they must also look at the output market. Therefore, it is impossible to discuss monopsony power coherently without considering the output market.

This crucial conceptual difference in the theoretical understanding of monopsony versus monopoly has important implications for antitrust enforcement in labor markets. The need to look at output markets to distinguish efficiency-enhancing mergers from monopsonistic ones complicates the analysis and may require a different approach than traditional monopoly cases. Antitrust authorities and courts must carefully consider how a merger affects both output and input markets, and weigh potential efficiencies against anticompetitive effects.

This is particularly challenging under the consumer-welfare standard, which focuses on output-market effects. The potential for countervailing effects on output and input markets creates difficult tradeoffs for enforcers and courts, who must balance the interests of consumers, workers, and overall economic efficiency.

B. Monopsony and Merger Efficiencies

In real-world cases, mergers will not necessarily be either solely efficiency-enhancing or solely monopsony-generating, but a blend of the two. Any rigorous consideration of merger effects must account for both and make some tradeoff between them. It’s true that, in some cases, there will be output increases alongside labor-market increases and, in such scenarios, we can look simply at output.[92] In the standard monopsony models in economics, there is no offsetting effect; harm to sellers of inputs (workers) hurts consumers, as well.[93] This was the case in the recent successful action to block Penguin-Random House from merging with Simon & Schuster.[94] The parties agreed that, if there was harm to the authors, there would be fewer books, thereby harming consumers.[95] There was no need to think about offsetting harms. That’s the easy case.

But what about other cases where the effects are not so clearcut? The question of how guidelines should address monopsony power is inextricably tied to consideration of merger efficiencies—particularly given the point above that identifying and evaluating monopsony power will often depend on its effects in downstream markets.

This reality raises some thorny problems for monopsony-merger review that have not been well-studied to date:

Admitting the existence of efficiencies gives rise to a subsequent set of difficult questions central to which is “what counts as an efficiency?.” A good example of why the economics of this is difficult is considering the case in which a horizontal merger leads to increased bargaining power with upstream suppliers. The merger may lead to the merging parties being able to extract necessary inputs at a lower price than they otherwise would be able to. If so, does this merger enhance competition in a possible upstream market? Perhaps not. However, to the extent that the ability to obtain inputs at a lower price leads to an increase in the total output of the industry, then downstream consumers may in fact benefit. Whether the possible increase in the total surplus created by such a scenario should be regarded as off-setting any perceived loss in competition in a more narrowly defined upstream market is a question that warrants more attention than it has attracted to.[96]

With monopoly mergers, plaintiffs must show that a transaction will reduce competition, leading to an output reduction and increased consumer prices. This finding can be rebutted by demonstrating cost-saving or quality-improving efficiencies that lead to lower prices or other forms of increased consumer welfare. In evaluating such mergers, agencies and courts must weigh the upward pricing pressure from reduced competition against the downward pricing pressure associated with increased efficiencies and the potential for improved quality.

As we have explained above, this analysis becomes more complicated when a merger raises monopsony concerns. In a simple model, the monopsony merger would increase market power in the input market (e.g., labor), leading to a lower price paid for the input and a smaller quantity used of the input relative to pre-merger levels. Assuming no change in market power in the final product market, these cost savings would result in lower prices paid by consumers. Should such efficiency effects “count” in evaluating mergers alleged to lessen competition in input markets? It is surely too facile a response to assert that such efficiency effects would be “out of market” and thus irrelevant. If that were the case, the legality of a merger would turn arbitrarily on the choice of input or output market, while flatly ignoring evident and quantifiable effects in an equally affected market. No sensible approach to antitrust would countenance this arbitrariness.[97]

Some would argue these are the types of efficiencies that merger policy is meant to encourage. Others may counter that policy should encourage technological efficiencies, while discouraging efficiencies stemming from the exercise of monopsony power.

But this raises another complication: How do agencies and courts distinguish “good” efficiencies from “bad?” Is reducing the number of executives pro- or anticompetitive? Is shutting down a factory or health-care facility made redundant post-merger pro- or anticompetitive? Trying to answer these questions places agencies and courts in the position of second guessing not just the effects of business decisions, but also the intent of those decisions (to a first approximation, the observed outcomes are identical). But intent is far from dispositive in determining the competitive effects of business conduct, and it may be misleading.[98] Even worse, it can create a Catch-22 where an efficiencies defense in the product market is turned into an efficiencies offense in the input market—e.g., a hyper-efficient merged entity may outcompete rivals in the product market, possibly leading to monopsony in the input market. In ambiguous cases, this means the outcome may depend on whether it is challenged on the input or output side of the market. It even implies that overcoming a challenge by successfully identifying efficiencies in one case creates the predicate for a challenge based on effects on the other side of the market.

Hemphill and Rose argue that “harm to input markets suffices to establish an antitrust violation.”[99] But surely, this cannot be a general principle, at least not if markdowns are seen as a form of anticompetitive harm. To see why, consider a merger that has no effect on either monopoly or monopoly power; it solely improves the merging parties’ technology by removing redundancies. For example, suppose the merged firms require fewer janitors. By assumption, this merger lowers consumer prices and increases consumer and total welfare. But proponents of the Hemphill and Rose view would likely call it an antitrust violation, because it harms the input market for janitors. Fewer janitors will be hired, and janitors’ wages may fall (even though, by assumption, there is no monopsony power pushing down wages).

This likely explains why Marinescu and Hovenkamp recognize that assessing a monopsony claim requires looking at both input and output markets:

To have a chance of succeeding, an efficiency case for a merger affecting a labor market must show that post-merger reorganization will decrease the need for workers and will not lower total production. Both of these requirements are essential. A merger that decreases the need for workers may represent nothing more than an exercise of monopsony power, but in that case, ceteris paribus, it will also reduce production. By contrast, a merger that eliminates duplication can also reduce the need for workers, but production will not go down. Indeed, it should go up to the extent that the post-merger firm has lower costs.[100]

The complications only multiply once we move beyond a classical, wage-posting monopsony. For example, many labor-market models include some form of wage bargaining.[101] Labor economists believe this captures important aspects of labor markets that are not purely about wage-posting.[102] With bargaining—as compared to classical monopsony—when firms achieve more product-market power, they generate higher profits and, therefore, more potential surplus to be split between employers and employees.[103] Workers (at least those who keep their jobs), may welcome greater monopoly power, as they are able to extract higher wage rents, which would not be the case for a firm earning thin or no margins in an extremely competitive product market. Consequently, this generates the opposite implication at the firm level: more product market power puts upward, not downward, pressure on wages. Yet, presumably, no one would argue that courts should allow mergers simply because they raises wages. But then the reverse should also be true: courts should not block mergers simply because they lower wages.

Far from being a theoretical curiosity, bargaining is of first-order importance when we are thinking about unions and labor markets. In its Kroger/Albertsons complaint, for example, the FTC defines the relevant labor market as “union grocery labor” and alleges that the merger would harm competition specifically for these workers.[104] But through their collective-bargaining agreements, unions exercise monopoly power in labor negotiations that likely counterbalances any attempted exercise of monopsony power by the merged firm.[105] If there is no increase in monopsony power, but there is an increase in monopoly power, the union will bargain to split that profit and increase wages.

How likely is this outcome? One local union endorsed the merger and divestiture package, arguing that “[e]mployees of Kroger and C&S will be better off than employees of other potential buyers.”[106] Of course, it is possible that most unions do not believe wages will increase; after all, delegates of the UFCW unanimously voted to oppose the merger.[107] And yet, rather than citing concern over monopsony power or lower wages, the union delegates’ stated reason for their opposition was lack of transparency.[108] The point is not to draw a conclusion about this particular merger’s likely effects on wages; it is to point out the complex tradeoffs inherent in applying antitrust to labor markets.

And there are further complications. When dynamic effects are taken into account, for example, even apparent harms confined to the seller side of an input market may turn into benefits:

[T]he presence of larger buyers can make it more profitable for a supplier to reduce marginal cost (or, likewise, to increase quality). This result stands in stark contrast to an often expressed view whereby the exercise of buyer power would stifle suppliers’ investment incentives. In a model with bilateral negotiations, a supplier can extract more of the profits from an investment if it faces more powerful buyers, though the supplier’s total profits decline. Furthermore, the presence of more powerful buyers creates additional incentives to lower marginal cost as this reduces the value of buyers’ alternative supply options.[109]

Of course, none of this is to say that creation of monopsony power should categorically be excluded from the scope of antitrust enforcement. But it is quite apparent that this sort of enforcement raises complicated tradeoffs that are elided or underappreciated in the current discourse, and manifestly underexplored in the law.[110]

C. Determining the Relevant Market for Labor

Even in the most basic monopoly cases, agencies and courts face enormous challenges in accurately identifying relevant markets. These challenges are multiplied in input markets—especially labor markets—in which monopsony is alleged. Many inputs are highly substitutable across a wide range of industries, firms, and geographies. For example, changes in technology—such as the development of PEX tubing and quick-connect fittings—allow laborers and carpenters to perform work previously done exclusively by plumbers. Technological changes have also expanded the relevant market in skilled labor. Remote work during the COVID-19 pandemic, for example, demonstrates that many skilled workers are not bound by geography and compete in national—if not international—labor markets.

When Whole Foods attempted to acquire Wild Oats, the FTC defined (and the court accepted) the relevant market as “premium natural and organic supermarkets,” as a way to exclude larger firms, such as Walmart and Kroger, from the relevant product market.[111] But even if one were to accept the FTC’s product-market definition, it is unlikely that anyone would consider employment at a “premium natural and organic supermarket” as a distinct input market.[112] Even the narrowest industries considered in the economics literature would never be defined that narrowly. This is because the skillset required to work at Whole Foods overlaps considerably with the skillset demanded by myriad other retailers and other employers, and virtually completely overlaps with the skillset needed to work at Kroger or another grocer.

As noted above, the FTC’s complaint in Kroger/Albertsons defines the relevant labor market as “union grocery labor” in “local CBA areas” (i.e., the geographic areas covered by each collective-bargaining agreement’s jurisdiction).[113] While the alleged product-market definition aligns with the FTC’s approach in past supermarket mergers, the labor-market definition is novel and does not appear to have a direct precedent in prior cases.[114] By focusing on unionized workers in specific localized areas, the FTC is implicitly arguing that the merger’s potential anticompetitive effects on labor are limited to these narrow categories of workers.

This approach to labor-market definition diverges from much of the economic literature on labor monopsony, which often defines markets based on industry or occupation codes that may not capture the full scope of competition for workers.[115] The FTC’s narrow market definition may reflect the practical challenges of bringing a labor-monopsony case under existing antitrust frameworks. But it also risks overlooking the fluid and dynamic nature of labor markets, where workers may have employment options across different industries, occupations, and geographies.[116]

We can see the difficulty with pursuing a labor-monopsony case by recognizing that the usual antitrust tools—such as merger simulation—cannot be easily applied to the labor market. Unlike the DOJ’s recent success in blocking Penguin-Random House from merging with Simon & Schuster on grounds that the merger would hurt authors with advances above $250,000,[117] the labor market for most employees is much larger than the two merging companies. This fact alone likely renders the DOJ’s successful challenge in that case more of an aberration than a model for future labor-market enforcement actions, as is sometimes claimed.[118]

Indeed, the relevant market often cannot be narrowed down to even a handful of readily identifiable companies. For the vast majority of workers, a great number of potential employers would remain following a merger. This “potential competition”—the range of feasible employers that present an outside option to the merged companies’ present employees—limits the merged firm’s ability to exercise monopsony power in its labor negotiations. While we are not aware of publicly available data that would more comprehensively illustrate worker flows among different companies (and industries), such flows of retail workers into and out of roughly adjacent labor markets make intuitive sense. As economist Kevin Murphy has explained:

If you look at where people go when they leave a firm or where people come from when they go to the firm, often very diffuse. People go many, many different places. If you look at employer data and you ask where do people go when they leave, often you’ll find no more than five percent of them go to any one firm, that they go all over the place. And some go in the same industry. Some go in other industries. Some change occupations. Some don’t. You look at plant closings, where people go. Again, not so often a big concentration of where they go to. If you look at data on where people are hired from, you see much the same patterns. That’s kind of a much more diffuse nature.[119]

In any particular merger—such as between Kroger and Albertsons, for example—an overwhelming majority of Kroger workers’ next best option (i.e., what they would do if a store closed) will not be at an Albertsons store, but something completely outside of the market for grocery-store labor (or even outside the retail-food industry more broadly). Where that is the case, the merger would not take away those workers’ next best option, and the merger cannot be said to increase labor-monopsony power to the extent necessary to justify blocking it.[120]

Fundamentally, the labor-economics literature has offered little guidance to date on how to define markets in labor cases. As explained above, concentration varies greatly, depending on the exact definition of the relevant market, especially the geographic market.[121] It is virtually impossible to know what outside options to include in the relevant market, and it may not always be possible to identify even where such potential employers are located (e.g., are commuting zones, for example, better proxies for the relevant geographic labor market than metropolitan areas?). These market-definition issues are far more acute in monopsony cases than in traditional monopoly cases, both because the intrinsic question of substitutes is more complicated and because there is far less precedent to guide parties and enforcers.

D. Labor Markets Are Not Spot Markets

The merger guidelines stress that labor markets are not simple spot markets where each side calls out a price and the two make an exchange when bid/ask prices align. As the guidelines state, “labor markets often exhibit high switching costs and search frictions due to the process of finding, applying, interviewing for, and acclimating to a new job.”[122] Moreover, “finding a job requires the worker and the employer to agree to the match. Even within a given salary and skill range, employers often have specific demands for the experience, skills, availability, and other attributes they desire in their employees.”[123]

The typical employment contract is often more complicated than the typical end-user purchase agreement. Employment contracts are, indeed, not spot contracts, and thus contain a temporal dimension often absent from the product markets at-issue in monopoly cases. The terms of employment contracts are also rarely purely monetary, and the value of any given employment contract (and especially of aggregated “employment data”) may not be reflected in the nominal “price” (i.e., wage) of the agreement. Various benefits, deferred compensation, location, start date, moving costs and the like can dramatically complicate identifying the value of employment contracts. Complicating matters further is that the value of these terms to any given employee may vary widely, as people’s preferences for employment terms are significantly idiosyncratic. All of which makes the analysis of observable employment terms inordinately complicated and assessments of market power fraught with error.

There are, however, additional relevant aspects of labor markets that distinguish them from spot markets and that warrant consideration in antitrust analysis. One crucial factor is that employment relationships frequently involve mutual investments by both parties that develop over time. Employers often make substantial investments to build workers’ firm-specific skills through training, knowledge sharing, and opportunities to form client relationships.[124] Some of these skills are general and portable across firms, while others are firm specific and have limited value to other employers.

Firm-specific investments can increase workers’ productivity at their current firms, but also make it more costly for them to switch jobs, potentially giving employers some labor-market power. This “lock in” effect exists because the worker’s current role is more valuable due to firm-specific investments and, in some cases, this increased value cannot be ported to a new employer.

In other cases, however, employers can and do invest in training that provides workers with general—and thus transferable—skills.[125] In such examples, there is a risk that those workers will leave for a competitor before the employer can fully recoup its investment. A higher wage may be justified for a subsequent employer, as the employee comes with the added value provided by the former employer (e.g., training, knowledge of competitively valuable information, relationships with potential customers). This “holdup” problem can lead firms to underinvest in worker training, even when such training would be socially beneficial.

To mitigate this risk, firms and workers may seek contractual solutions that incentivize workers to stay long enough for the firm to earn a return on its investment. These arrangements could include promises of future wage increases, promotions, or other benefits that are contingent on the worker remaining with the firm. In turn, these contractual mechanisms create a new problem: once the investment is made and the worker has acquired valuable skills, they may be “locked in” to their current employer through the promise (implicit or explicit) of future wage gains or other benefits.

Of course, to the extent these arrangements give firms some ex-post market power, they are accompanied by terms implicitly or explicitly sharing the benefits with employees. But if a merger enhances employers’ ability to make such productivity-enhancing investments, it could simultaneously increase labor-market power while generating efficiencies, which may be shared with employees in ways that are difficult to identify or to value. Assessing the competitive effects of such a merger requires identifying and weighing these competing effects, which may be extremely difficult.

The FTC’s complaint against the proposed Kroger/Albertsons merger provides a concrete example of how antitrust enforcers must grapple with these issues in practice.[126] In defining the relevant labor markets, the FTC focuses on “union grocery labor” in “local CBA areas” (i.e., the geographic areas covered by each collective-bargaining agreement’s jurisdiction).[127] By narrowing the market to unionized workers covered by specific CBAs, the FTC appears to be making a form of lock-in argument. The complaint alleges that “[u]nion grocery workers can move between grocery employers covered by their union while retaining their pension and healthcare benefits, as well as other valuable workplace benefits and protections provided by the CBAs. If a union grocery worker leaves for a non-union employer, however, the worker will lose any non-vested CBA benefits and protections.”[128] In other words, the CBA-specific benefits function similarly to firm-specific investments in tying workers to a particular set of employers, or a contractual solution to the holdup problem involving promised future benefits, potentially giving those employers monopsony power.

From an antitrust perspective, assessing such a merger’s effect on firm-specific investments is complex. Will the merger increase or decrease employers’ incentive to provide worker training? How should antitrust balance potential productivity gains against increased labor-market power over workers? Efficiency arguments by merging parties should be met with appropriate skepticism, but such investments may be more than a rounding error in calculating overall effects. Indeed, the concept of firms investing in building worker skills is more than just a theoretical curiosity; there is clear empirical evidence that these investments occur, affect human capital, and have effects on wages.[129] These dynamic investment effects are first-order factors in labor markets, but are not easily captured in a static monopsony framework. Further study on these tradeoffs within merger analysis is essential.

The complications caused by the importance of investment in workers show up in antitrust contexts beyond merger enforcement, such as the FTC’s noncompete rulemaking.[130] The FTC recognized as much, noting that “[t]here is some empirical evidence that non-competes increase investment in human capital of workers, capital investment, and R&D investment,”[131] and citing numerous studies indicating such effects.[132] Of course, the commission nevertheless adopted a rule banning all noncompete agreements outright, despite this recognition.

All of this makes the simple monopsony model difficult to apply and map to the actual competition that occurs in the market. For example, to estimate labor-supply elasticities, many papers take a traditional monopsony model that assumes a spot market where the buyer sets a price and lets as many people buy as are willing.[133] Such analysis can be informative, but it may say little about the competitive effects of various practices in real-world antitrust markets.

The point is not to establish the proper model of human-capital formation. Instead, it is simply to point out that human-capital development is of first-order importance in labor markets. How should antitrust treat it? Contrary to the impression from the merger guidelines (and the short shrift given this point in the proposed NCA rules), not every feature of the labor market simply points toward a need for more enforcement.

V. Monopsony and the Consumer-Welfare Standard

As discussed in the previous sections, using antitrust enforcement to thwart potential monopsony harms is a task full of evidentiary difficulties and complex, poorly understood tradeoffs. Perhaps more problematically, it is also unclear whether (and, if so, how) such an endeavor is consistent with the consumer-welfare standard—the lodestar of antitrust enforcement, at least as it is currently understood and implemented by courts.[134]

Marinescu & Hovenkamp assert that:

Properly defined, the consumer welfare standard applies in exactly the same way to monopsony. Its goal is high output, which comes from the elimination of monopoly power in the purchasing market.… [W]hen consumer welfare is properly defined as targeting monopolistic restrictions on output, it is well suited to address anticompetitive consequences on both the selling and the buying side of markets, and those that affect labor as well as the ones that affect products. In cases where output does not decrease, the anticompetitive harm to trading partners can also be invoked.[135]

And Hemphill & Rose state that:

Overall, then, a trading partner welfare approach accords well with the case law and economic reasoning, and under this approach, a merger that results in increased classical monopsony power may be condemned on account of harm to the input market.[136]

But this is far from self-evident. There are at least two problems with this reasoning.

To start, the assertion that harm to input providers that does not result in reduced product output is actionable is based on a tenuous assertion that a mere pecuniary transfer is sufficient to establish anticompetitive harm.[137] This is problematic, because such “harms” actually benefit consumers in the baseline model. In the extreme example, all of the benefits of a better negotiating position are passed on to consumers, and the firm is more of a direct intermediary trading on behalf of consumers, rather than a monopolistic reseller.[138]

The main justification for ignoring these cross-market effects (as with all market-definition exercises) is primarily a pragmatic one (although it is rather weakened in light of modern analytical methods).[139] But particularly in the context of inputs to a specific output market, these cross-market effects are inextricably linked and hardly beyond calculation. As the enforcement agencies have previously recognized, “[i]nextricably linked out-of-market efficiencies, however, can cause the Agencies, in their discretion, not to challenge mergers that would be challenged absent the efficiencies.”[140]

The assertion that pecuniary transfers of bargaining power are actionable is also inconsistent with the fundamental basis for antitrust enforcement, which seeks to mitigate deadweight loss, but not mere pecuniary transfers that do not result in anticompetitive effects.[141]

Second, it is unclear whether the consumer-welfare standard applies to input markets. At its heart, the consumer-welfare standard focuses on the effects that a(n) (incipient) monopolist’s behavior may have on consumers. And courts have extended this welfare calculation to all direct purchasers affected by anticompetitive behavior. Less clear is whether courts have consistently extended (or would extend) this notion of anticompetitive harm to all “trading partners” in input markets.[142] This goes to the very heart of the consumer-welfare standard:

[I]f only consumers matter, then a buying cartel should be perfectly legal and indeed should be encouraged. Monopsony power would not matter in antitrust cases, because the fact that sellers are harmed is irrelevant under a consumer surplus standard. I know of no proponent of the consumer surplus standard who endorses buyer cartels, or who believes that monopsony is not harmful. Instead, proponents of a consumer surplus rule tend to argue that buyer cartels and monopsony are exceptions to the otherwise sensible rule of maximizing consumer surplus. However, the need for these exceptions illustrates the lack of a coherent logic for the consumer surplus standard.[143]

Other scholars appear too ready to accept that there is a “coherent logic” of the consumer-welfare standard that unquestionably contemplates upstream trading-partner welfare because their interests align with those of consumers:

A useful definition of “consumer welfare” is that antitrust should be driven by concerns for trading partners, including intermediate and final purchasers, and also sellers, including sellers of their labor. These all benefit from high output, high quality, competitive prices, and unrestrained innovation. Higher output and lower prices are good indicators of competitive benefit, and there is little practical difference between the way courts talk about antitrust harm and the idea of “consumer welfare.”[144]

As we explain above, however, this coincidence of interest is far from complete, and lower wages could be consistent with both efficiency and monopsony.[145] As the FTC summarized in closing the investigation of a merger between two pharmacy benefit managers, “[a]s a general matter, transactions that allow firms to reduce the costs of input products have a high likelihood of benefitting consumers, since lower costs create incentives to lower prices.”[146] “Higher output and lower prices [may be] good indicators of competitive benefit,” but it seems problematic to assume they reflect a clear benefit to workers if they result from lower wages. Indeed:

Larger buyers may also be able to reduce their purchasing costs at the expense of suppliers…. The concept of buyer power as an efficiency defence rests squarely on such a presumption. What is more, the argument also posits that the exercise of buyer power will not only have distributional consequences, but also increase welfare and consumer surplus by reducing deadweight loss. As we spell out in detail below, welfare gains may arise both at the upstream level, i.e., in the transactions between the more powerful merged firm and its suppliers, as well as at the downstream level, where the creation of buyer power may translate into increased rivalry and lower prices. The extent to which final consumers ultimately benefit is of particular importance if antitrust authorities rely more on a consumer standard when assessing mergers. If total welfare is the standard, however, distributional issues are not directly relevant and any pass-on to consumers is thus only relevant in as much as it contributes to total welfare.[147]

This raises an obvious question: can the consumer-welfare standard (and thus antitrust authorities and courts) reach a finding of anticompetitive harm if consumers (at least, in the narrow market under investigation) are ultimately charged lower prices?

Consider Judge Breyer’s Kartell opinion. As Steve Salop explains:

The famous Kartell opinion written by Judge (now Justice) Stephen Breyer provides an analysis of a buyer-side “cartel” (comprised of final consumers and their “agent” insurance provider, Blue Cross) that also is consistent with the true consumer welfare standard.… Buyer-side cartels generally are inefficient and reduce aggregate economic welfare because they reduce output below the competitive level…. However, a buyer-side cartel. comprised of final consumers generally would raise true consumer welfare (i.e., consumer surplus) because gains accrued from the lower prices would outweigh the losses from the associated output reduction, even though the conduct inherently reduces total welfare (i.e., total surplus).…

… Judge Breyer treated Blue Cross essentially as an agent for the customers it insured, rather than as an intermediary firm that purchased inputs and sold outputs as a monopolistic reseller. The court apparently assumed (perhaps wrongfully) that Blue Cross would pass on its lower input costs to its customers in the form of lower insurance premiums….

… In permitting Blue Cross to achieve and exercise monopsony power by aggregating the underlying consumer demands for medical care—i.e., permitting Blue Cross to act as the agent for final consumers—the Kartell court implicitly opted for the true consumer welfare standard. Blue Cross’s assumed monopsony conduct on behalf of its subscribers would thus lead to higher welfare for its subscribers despite reduced efficiency and lower aggregate economic welfare. Thus, this result represents a clear (if only implicit) judicial preference for the true consumer welfare standard rather than the aggregate economic welfare standard.[148]

By this logic, it seems, the relevant “consumer” welfare in antitrust analysis—including that of mergers that increase either monopoly or monopsony power—is that of the literal consumer: the final product’s end-user. But this contrasts quite sharply with the standard mode of analysis in monopsony cases as the mirror image of monopoly, in which the merging parties’ trading partner (whether upstream or downstream) is the relevant locus of welfare analysis.

Indeed, extended to other current potential cases, this mode of analysis raises a distinct problem for the agencies. Consider, for example, a hypothetical case against Kroger-Albertsons that did not mention the product market and in which the merger was alleged to increase monopsony power, but not monopoly power. Should such a challenge fail regardless of the effect on input providers because Kroger can be considered “an agent for the customers it [sells to]”? There is, as Salop seems to suggest,[149] some merit in such an approach, but it is certainly not how similar cases have been evaluated in the past.

Indeed, the rule of reason arguably contemplates some sort of balancing of effects across markets.

Critically, the balancing required by the rule of reason is neither quantitative nor precise. In California Dental Association, the Supreme Court described a court’s task as reaching a “conclusion about the principal tendency of a restriction” on competition. If a restraint suppresses competition in one market and promotes competition in a related market, the Chicago Board of Trade and Sylvania statements of the rule of reason can be read to hold that legality turns on which effect predominates in a qualitative sense.[150]

The U.S. Supreme Court’s Alston case highlights this dynamic, and in a case involving labor-market monopsony, no less. Despite the NCAA’s undisputed monopsony power in the “market for athletic services” (an upstream labor market), the Court considered its proferred procompetitive justification of preserving amateurism in college sports—an effect avowedly in the downstream, output market.[151] As the Court described the proceedings below:

The NCAA’s only remaining defense was that its rules preserve amateurism, which in turn widens consumer choice by providing a unique product—amateur college sports as distinct from professional sports. Admittedly, this asserted benefit accrues to consumers in the NCAA’s seller-side consumer market rather than to student-athletes whose compensation the NCAA fixes in its buyer-side labor market. But, the NCAA argued, the district court needed to assess its restraints in the labor market in light of their procompetitive benefits in the consumer market—and the district court agreed to do so.[152]

Tellingly, the district court’s rejection of the NCAA’s procompetitive justification turned on the lack of connection between it and the challenged conduct in the input market. “As the court put it, the evidence failed ‘to establish that the challenged compensation rules, in and of themselves, have any direct connection to consumer demand.’”[153] The plain implication is that, where restraints in one market are sufficiently connected to benefits in another market, those benefits will be considered—and may turn out to justify—the challenged restraints.[154]

There is perhaps no easy answer to the difficulty of assessing harm in upstream markets when downstream markets benefit. At first blush, excluding deadweight losses that stem from monopsony power (or, at least, forcing plaintiffs to show that downstream purchasers are also harmed) seems like legalistic reasoning largely incompatible with the welfarist ancestry of the consumer-welfare standard.[155] Indeed, the consumer-welfare standard is largely premised on the assumption that increased output is desirable, and deadweight losses are harmful to society, regardless of their second-order effects.

There is no tension here when output and labor both benefit from an action; sometimes, output reduction goes directly with labor harms.[156] But what about the cases that are not so neat? It seems odd to depart from this focus on output as the lodestar of antitrust just because a supplier, rather than a consumer, is being harmed.

Faced with what may potentially be intractable economic questions, antitrust courts have, for the sake of administrability, often decided to limit antitrust analysis to what economics generally refer to as partial-equilibrium analysis.[157] This largely explains, e.g., why only direct purchasers can claim antitrust damages.[158] Perhaps it also explains why the Court in Ohio v. American Express chose to simply ignore potential harm to cash purchasers in limiting the market in that case to the “market for credit-card transactions,” even though the district court found that Amex’s conduct would increase retail prices for cash consumers [159]

But much to some commentators’ chagrin,[160] the Court in Amex did take account of cross-market effects—in that case, by combining both sides of a two-sided market into a single market—and noted that failing to do so would lead to error.[161] While the Court limited its holding to two-sided, “simultaneous transaction” markets,[162] it is difficult to escape the realization that the logic of the holding—and the arbitrariness of considering effects on one side in isolation—would apply as well to the analysis of upstream and downstream trading partners:

Absent consideration of both sides of a platform, the analysis will arbitrarily include and exclude various sets of users and transactions, and incorrectly assess the extent and consequences of market power. Indeed, evidence of a price effect on only one side of a two-sided platform can be consistent with either neutral, anticompetitive, or procompetitive conduct. Only when output is defined to incorporate the two-sidedness of the product, and where price and quality are assessed on both sides of a sufficiently interrelated two-sided platform, is it even possible to distinguish between procompetitive and anticompetitive effects.[163]

The upshot is that, with some notable exceptions (such as the case of two-sided markets in Amex), antitrust courts have been reluctant to analyze competitive effects in adjacent markets. Alas, it is unclear where that line is appropriately drawn, or whether it has been drawn somewhat arbitrarily in the past.

What might seem like an arbitrary decision appears more reasonable, of course, when one considers the sheer complexity of the task at-hand. Economic behavior will often have second-order effects that run in an opposite direction to its first-order or “partial equilibrium” ones. A coal monopoly may cause buyers to opt for cleaner energy sources; a conservation cartel may maximize the long-term value of scarce resources.[164] Yet surely there are cases where out-of-market effects are “inextricably linked” to in-market effects, and where extending the analysis would not create insurmountable burdens. A practical approach—and one consistent with the broad scope of the rule of reason—would at least consider out-of-market effects when they are a direct and identifiable consequence of conduct challenged in a separate market.

The question is further complicated in merger cases where the Clayton Act’s “any line of commerce” language seems to limit merger analysis to a single market, and where the Court’s holding in Philadelphia National Bank clearly reiterates this apparent constraint.[165] But those legal rules do not address the economic propriety of so limiting merger analysis, and neither is predicated on the complexity of undertaking the requisite economic analysis. Indeed, whatever the merits of such an approach at the time Philadelphia National Bank was decided, both the law and the economics have moved past them:

Despite the incorporation of efficiencies analysis into modern merger evaluation, and the advances in economics that allow efficiencies to be identified and calculated more accurately than at the time of Philadelphia National Bank, antitrust doctrine in the United States still supports a regime that fails to take into account efficiencies arising outside of the relevant market. Only a handful of federal court cases since Philadelphia National Bank raise the issue of out-of-market efficiencies, and those that address the merits quickly dispatch such efficiencies as being precluded by the Supreme Court precedent. In light of the advances in the ability to identify and measure efficiency benefits, the federal courts should update antitrust doctrine to support a serious and committed treatment of out-of-market efficiencies in merger analysis.[166]

In part reflecting this change in approach, the Court in Baker Hughes held that “[t]he Supreme Court has adopted a totality-of-the-circumstances approach to the statute [Section 7], weighing a variety of factors to determine the effects of particular transactions on competition.”[167] And lower courts have been increasingly willing to consider efficiencies in evaluating the application of Section 7 to proposed mergers.[168] It is even arguable that the district court in New York v. Deutsche Telekom (reviewing the T-Mobile/Spring merger) credited out-of-market efficiencies in approving the merger.[169]

Moreover, as with virtually all legislative language, the Clayton Act’s language is not as clear as some make it out to be. The phrase “in any line of commerce” need not be interpreted to constrain the permissible zone of analysis, or to condemn effects in a single “line of commerce” regardless of its effects in another. Rather, the phrase’s most obvious meaning is to indicate that no area of commercial activity is exempted from the Clayton Act. Indeed, the use of the word “line” to refer to the indicated area rather than “market” seems clearly to indicate general categories of business that are to be included in the law’s prescriptions, rather than specific markets for identifying effects.

In other words, “it is plain that Section 7 does not limit the range of ‘lines of commerce’ that can trigger a merger’s prohibition.”[170] But it is by no means clear that Section 7 proscribes liability when a merger “lessen[s] competition” in a single market, regardless of whether it may enhance competition elsewhere in the same “line of commerce.”[171] As the Court suggested in Amex, the relevant “line of commerce” may incorporate distinct markets that need not exist on the same side of a given transaction. Indeed, modern “business ecosystem” theories suggest that conglomerate businesses with widely different “markets,” interrelated by an overarching business model that “inextricably links” them, may constitute something like a single “line of commerce,” despite the superficial distinctions between the components that comprise them.[172]

The question remains whether antitrust law has a comparative advantage in dealing with more “systemic” issues (like worker welfare, environmental effects, or even the “amateurism” offered by the NCAA in Alston), or whether other legal frameworks are better adapted. Put differently, antitrust law’s main strength might be that it is mostly a consumer-oriented body of law that focuses on a single tractable problem: the prices consumers and other direct purchasers pay for goods. If that is true, then other bodies of law (such as, e.g., labor and environmental laws) may be better suited to deal with broader harms. Indeed, in the case of each of these fields, there exists a massive regulatory apparatus specifically designed to implement government standards. Under the law as it stands, where antitrust law and a regulatory regime conflict, antitrust must give way.[173]

We do not purport to have a satisfactory answer to this complicated question. In fact, it is probably fair to say that one does not exist. Antitrust law can either depart from its welfarist underpinnings—a large loss for its economic consistency—or it can follow those principles toward difficult problems that may ultimately impair its administrability. At this juncture, it is not clear there is a compromise that would enable enforcers to thread the needle to solve this complex conundrum. And if such as solution exists, it has yet to be articulated in a convincing manner that may lead to actionable insights for enforcers or courts. But it is crucial to note that some cross-market analysis may be unavoidable under a welfarist approach if antitrust is going to continue to attempt to address potential harms in upstream markets, including labor markets.

Given all of this, the FTC and DOJ’s update of their merger guidelines to address monopsony harms, while clearly important, also appears to be premature, compared to the state of the economic literature, and potentially unactionable (or, at least, incoherent as stated) under the consumer-welfare standard. This is not to say the antitrust-policy world should simply ignore monopsony harms, but rather that more research, discussion, and case law are needed before definitive guidelines can be written. Ultimately, it may well be that legislative change is needed before any such guidelines will be enforceable before the courts.

VI. A Path Forward: An Agenda for Antitrust and Labor Markets

The previous sections have highlighted the empirical and conceptual challenges that complicate the application of antitrust law to labor monopsony. While the growing interest in this area presents opportunities for research and policy innovation, it is important to approach these issues with a mix of enthusiasm and skepticism. The current state of economic knowledge and antitrust doctrine suggests that we are not yet ready for a major expansion of enforcement in labor markets. This, however, does not mean that antitrust has no role to play or that the status quo is optimal. Rather, it suggests the need for a thoughtful and incremental approach that prioritizes the development of better analytical tools, evidence-based policymaking, and inter-disciplinary collaboration.

The recent FTC complaint against the proposed Kroger/Albertsons merger underscores the importance of the issues raised in this paper, as well as the ongoing challenges that antitrust authorities face when assessing labor-market effects in merger cases.[174] While the complaint reflects an increased focus on labor issues in merger enforcement, it also highlights the complexities of defining markets, assessing competitive effects, and weighing efficiency claims in this context. The Kroger/Albertsons case provides a real-world example of how the FTC is grappling with these issues in practice, but also raises questions about the rigor of its proposed market definitions, the sufficiency of evidence required, and the theories of harm proposed.

Perhaps most notably, although the complaint proposes two distinct markets, one on either side of the supermarket business (“union grocery labor” on the one hand, and “the retail sale of food and other grocery products,” on the other), it fails to note that both are simultaneously intrinsic to the operation of supermarkets. It also fails to offer any suggestion for how a court should respond if, for example, harm is found in one market but not the other. Of course, as noted, the complaint does not even contemplate the possibility that its alleged theory of harm in the labor market could result in procompetitive effects in the retail market.[175]

As labor-market concerns continue to arise in antitrust cases, it will be critical for the FTC and other enforcers to develop more robust analytical frameworks and evidentiary standards to support their claims, and for courts and policymakers to provide clearer guidance on how labor-market harms should be assessed under existing legal standards. While the FTC’s increased focus on labor issues is noteworthy, the Kroger/Albertsons complaint also demonstrates that the agency’s approach needs to be further refined and clarified.

One key priority should be to develop more direct, antitrust-relevant measures of labor-market power. While some recent studies have proposed measures such as labor-supply elasticity[176] and wage markdowns,[177] these tools have not been widely validated in antitrust contexts. Moreover, as discussed earlier, these measures may be sensitive to assumptions about the nature of competition.[178] Further refinement and testing of these measures, with a focus on their robustness and applicability to antitrust cases, is needed.

In addition, scholars should continue to study the effects of specific mergers and practices on labor-market outcomes, using more sophisticated research designs that can isolate causal impacts. While some recent studies have taken steps in this direction,[179] much more work is needed to build a body of evidence that can inform antitrust enforcement. In particular, studies that can disentangle the effects of labor-market concentration from other factors, such as firm-specific investments and productivity differences, would be valuable.

Scholars and policymakers should also continue to refine models of dynamic competition and firm-specific investments in labor markets, with an eye toward their implications for antitrust enforcement. As discussed earlier, standard static models of monopsony may not fully capture the complexities of labor-market competition, such as the role of search frictions, bargaining, and human-capital investments. Some recent papers have started to incorporate these features,[180] but more work is needed to develop tractable models that can guide enforcement decisions. It remains to be seen to what extent the FTC’s lock-in argument in the Kroger/Albertsons complaint will be supported with such models.[181]

Another key priority should be to clarify the goals and legal standards for antitrust enforcement in labor markets. The consumer-welfare standard, which has long guided antitrust policy, becomes difficult to apply when a merger or practice may harm workers but benefit consumers.[182] While some have argued for a “worker-welfare standard” that would prioritize the interests of workers,[183] it is not clear whether this would be consistent with the goals of antitrust law, nor how it would be reconciled with simultaneous findings of countervailing consumer effects.[184] Policymakers, courts, and scholars should continue to grapple with these normative questions and work toward developing a coherent and administrable framework for weighing labor-market effects in antitrust cases.

Finally, it is important to foster dialogue and collaboration between antitrust and labor experts to develop a shared understanding of the issues at-stake. Economists, lawyers, and policymakers approaching these issues from different perspectives must find common ground and a common language to assess concerns about labor-market power.

While these challenges are significant, there are reasons for cautious optimism. The increased attention to labor-market power from scholars, policymakers, and the public has created a unique opportunity to reexamine long-held assumptions and explore new approaches. By pursuing an agenda that emphasizes empirical rigor, legal clarity, and interdisciplinary collaboration, we can make progress toward more competitive labor markets. This will not happen overnight, just as the development of the consumer-welfare standard and the integration of antitrust with economic theory did not happen overnight. By staying focused on the ultimate goal of promoting the welfare of both workers and consumers, and being willing to adapt to new evidence and insights, we can move closer to an antitrust regime that is suited to the realities of the modern labor market.

Given that these complex tradeoffs still lack anything approaching definitive resolution in research or precedent, antitrust authorities would best serve the integrity of enforcement standards by exercising restraint. The disregard of difficult tradeoffs and the premature or overzealous application of questionable theories both risk distorting competition and innovation incentives more than protecting them. This is not an argument against addressing labor-market power entirely through uncertain means, as further co-evolution of economic and legal understanding may resolve some quandaries. It is, however, an argument that threading the needle to expand prohibitions into input markets requires a cautious, studious approach—especially when they conflict with the consumer interests that antitrust ultimately aims to safeguard.

Appendix: Detailed Discussion of Labor-Market Concentration Research and Its Implications for Antitrust

The 2023 Merger Guidelines assert that labor markets can be “relatively narrow” and that “the level of concentration at which competition concerns arise may be lower in labor markets than in product markets, given the unique features of certain labor markets.”[185] The academic literature presents a more nuanced picture, however, and casts doubt on these claims. This section provides a more thorough review of the literature discussed in Section III.B, infra.

By examining the strengths and limitations of each approach, we aim to provide a balanced assessment of what the current evidence can (and cannot) tell us about the extent of labor-market power in the U.S. economy. Our review suggests that, while some labor markets may indeed be highly concentrated, the evidence does not support a blanket characterization of labor markets as “narrow.” Antitrust authorities should carefully consider the specific contours of the relevant labor market in each case, drawing on multiple data sources and methodologies. The broad pattern does not support general presumptions that mergers systematically make already-narrow labor markets dramatically more concentrated over time. If anything, concentration data indicate that labor markets are growing more competitive.

I. Administrative Data

The narrative of rising employer dominance and increasing labor-market concentration has been challenged by recent research using comprehensive administrative data. These studies generally find that, while national labor-market concentration has been rising, local concentration levels have declined or remained stable over recent decades.

Papers leveraging datasets like the Longitudinal Business Database, which covers nearly all private-sector employers, point to falling concentration within local labor markets, such as commuting zones and urban areas. Rinz[186] and Lipsius[187] both used this data and estimated decreasing local concentration from 1976-2015, even as national measures increased. Their explanation is the entry of large firms into more local markets over time.

Autor, Patterson, and Van Reenen reinforce these findings using Economic Census data across major sectors. They estimated local-employment concentration fell from 0.35 in 1992 to 0.30 in 2017, contrary to rising national concentration.[188] This divergence was partly driven by employment shifts away from the highly concentrated manufacturing sector toward more competitive services sectors.

Focusing on just manufacturing, Benmelech, Bergman, and Kim found relatively stable average local concentration from 1978-2016 in the Longitudinal Business Database.[189] Importantly, their wage data allowed them to examine concentration’s direct earnings impact, suggesting a 3% wage decrease when moving from a low to high concentration market, or 9-14% using mergers as an instrument. This correlation, even with an instrument, should be interpreted with caution.

Modeling by Berger, Herkenhoff, and Mongey highlighted weighting concentration by payroll, rather than employment.[190] Though producing lower estimates, their approach still showed the diverging national/local trends.

While mixed, this literature consistently finds declining or stable local-labor market concentration when leveraging government-collected microdata. This casts doubt on claims of pervasive local-monopsony power and suggests national trends may be more relevant for assessing competitiveness. These findings have antitrust-policy implications regarding employer concentration and merger effects.

The papers that use administrative data find a trend that contradicts the popular narrative. They generally find a decline in local-labor market concentration, alongside a rise at the national level. Such findings suggest that employer dominance in the labor market may not be as pervasive or detrimental at the local level as it is nationally, complicating the narrative of widespread monopsony power in labor markets.

A. Rinz (2022) and Lipsius (2018)

First, let us consider papers that use administrative data, generally considered to be the best when available. Rinz uses administrative data from the Longitudinal Business Data and finds that local labor-market concentration has been declining, while national concentration has been increasing.[191] Lipsius uses the same dataset and finds the same result, but focuses on connecting labor-market concentration to changes in labor share of income.[192] Both papers have data on employment at the firm level for the years 1976-2015, so they are able to study the evolution over time. The data cover the near universe of non-farm, private establishments with employees.

The two papers use different levels of aggregation. Rinz uses four-digit NAICS for the job description and commuting zones for the location. Lipsius used 5-digit NAICS codes and urban areas, which are smaller than commuting zones but based on economic integration instead of political lines, such as counties.

Rinz assesses concentration using HHI measures. He finds that, at the national level, HHI declined roughly 40 percent from 1976 to 1983, stayed flat through the 1980s and has risen since. When divided into commuting zones, however, he finds a falling trend in concentration. The difference in trends has various explanations, but the simplest is that large firms are entering more and more labor markets. For example, when Wal-Mart enters a small town with one retail store, national concentration may rise, even though the town’s concentration falls.

Source: Rinz (2022)[193]

B. Autor, Patterson, & Van Reenen (2023)

Recent work by Autor, Patterson, and Van Reenen provides additional evidence on trends in local labor-market concentration using establishment-level data from the Economic Census.[194] Autor, et al. analyze six broad sectors—manufacturing, retail trade, wholesale trade, services, utilities/transportation, and finance—that comprise roughly 80% of U.S. employment and GDP. The authors have data covering the period from 1982-2017 for manufacturing, retail, wholesale, and services, and going back to 1992 for the others. They define markets by county and by six-digit NAICS industry, and find that employment-based HHI fell from 0.35 in 1992 to 0.30 in 2017.[195] Similar results hold for three- and four-digit NAICS.[196] This contrasts with the rise in national employment concentration over the same period, which rose by 1.7 points for employment (from 0.025 in 1992 to 0.042 in 2017).[197] The authors also show substantial divergence between national and local concentration trends over the longer 1982 to 2017 period for the four sectors with available data. Moreover, the local-employment HHI exhibits a consistent downward trend over most five-year intervals between 1992 and 2017. Overall, the results point to a robust fall in local employment concentration that runs counter to the rise in national concentration.

Some of this trend is structural. A key element of Autor et al.’s analysis is distinguishing between changes occurring within industries, versus those across industries. The divergence between national and local employment-concentration trends is largely attributable to the reallocation of economic activity from more-concentrated manufacturing industries to less-concentrated service industries. In fact, the authors show that, holding industry structure fixed at 1992 levels, local employment concentration would have risen by about 9%, rather than falling by 5%.[198] This between-industry reallocation had a smaller dampening effect on sales concentration, since the shift from manufacturing to services was greater for employment than sales. At the same time, Autor et al. find that concentration has risen within detailed industries and localities for both employment and sales.

C. Benmelech, Bergman, & Kim (2022)

Diving into manufacturing, specifically. Benmelech, Bergman, and Kim uses administrative, micro-level data on manufacturing establishments (“plants”), covering the period 1978-2016.[199] To calculate concentration measures, they use the Longitudinal Business Database (as did Rinz and Lipsius).[200] They use four-digit standard industry-classification codes (the predecessor of NAICS codes). For concentration measures, their data shares all the costs and benefits of the Longitudinal Business Database discussed above.

For manufacturing, they find the average levels of concentration have remained relatively stable, with employment-weighted HHI being 0.569 for the period 1978-1987 and 0.587 for 2008-2016.[201] One should be careful when extrapolating from manufacturing to the whole U.S. economy, given that manufacturing has been declining and the forces changing manufacturing may not apply to the rest of the economy. According to the U.S. Bureau of Labor Statistics, the percentage of employment in manufacturing sector dropped from roughly 22% in 1980 to slightly more than 10% in 2012 (Lipsius 2018, p. 4).

They supplement the concentration measures with two data sets: the Census of Manufacturers, which covers all plants in years ending in 2 and 7, and the Annual Survey of Manufacturers, which covers about 50,000 plants with a threshold of 250-1000 employees for the non-Census years. Other smaller firms are sampled randomly. The Annual Survey of Manufacturers is mandatory reporting, subject to fines for misreporting. They collected data on many things, such as value of shipments. For our discussion, the important thing is that they collect data on actual wages and labor hours, compared to simply posted wages. Moreover, since they are looking at manufacturing, they have better estimates of productivity of firms, as they have better data on inputs and outputs at the plant level. In their baseline regression, moving from a market that is one standard deviation below the median to one standard deviation above is associated with a 3% decline in wages.

Moreover, they are able to use mergers and acquisitions to instrument for concentration to potentially estimate a causal effect of concentration on wages. Using their instrumental-variable approach, they estimate that moving from a market that is one standard deviation below the median to one standard deviation above is associated with a decline in wages of between 9% and 14%.

D.  Berger, Herkenhoff, & Mongey (2022)

Berger, Herkenhoff, and Mongey estimate a general-equilibrium model to measure labor-market power.[202] In the process, their model suggests a certain way to average HHI across markets. They start with LBD at the 3-digit industry level within commuting zones, but they are still left with the problem of how to weight different markets. Instead of weighting by employment level or vacancies level, they weight by market-level payroll, which lowers concentrations slightly, although the trend remains the same.

They find that local concentration is declining over the full period, while national-concentration measures are more complicated. For tradeable sectors, national concentration is falling. For non-tradeable sectors, after falling in the early 1980s, it has slowly risen. But non-tradeables are larger, so the overall national concentration measure has also been rising since the mid 1980s.

In the data (model) weighted average concentration measured in terms of employment is 0.15 (0.16) and in terms of payroll is 0.17 (0.17). In the data (model) unweighted average concentration measured in terms of employment is 0.45 (0.32) and in terms of payroll is 0.48 (0.33).

Source: Berger, Herkenhoff, & Mongey (2022)[203]

E. Handwerker & Dey (2023)

Handwerker and Dey use microdata from the Occupational Employment and Wage Statistics, mapped to the Quarterly Census of Employment and Wages, which records quarterly employment levels for each establishment in the United States that reports to state-level Unemployment Insurance departments.[204] They define markets by 6-digit SOC by metropolitan area. They also look by industry, instead of occupation. They focus on the case where they weight markets by payroll shares, following the theory of Berger, Herkenhoff, and Mongey.[205]

They find an average HHI that is relatively stable and low. They also only look at the private sector and weight by employment, so their results are more directly comparable to some other papers. For example, they directly compare the concentration measures in their data to the 26 occupations of Azar, Marinescu, and Steinbaum.[206] Handwerker and Dey find an HHI in the private sector of one-tenth that found in Azar, Marinescu, and Steinbaum (0.0383 vs. 0.3157).[207] This is the clearest example of how the different data sources matter for concentration numbers.

Source: Handwerker & Dey (2023)[208]

II. Online Job Vacancies

While the above papers use administrative data, other papers on labor-market concentration use online job vacancies (postings) to measure concentration.

A. Azar, Marinescu, Steinbaum, & Taska (2020)

Azar, Marinescu, Steinbaum, and Taska use data on job openings from Burning Glass Technologies (BGT), which collects online job-posting data from 40,000 websites.[209] They restrict their analysis to calendar year 2016, which was the most recent year with available data when the paper was first written. They claim the years 2007-2015 show similar concentration measures (footnote 4).

The papers that use job openings, compared to measures of employment levels, claim openings are a better way to gauge how easy it is for searching workers to find a new job.[210] The nearest government-data product to BGT’s is the Job Opening and Labor Turnover Survey (JOLTS), which is a nationally representative sample of employers. When comparing BGT’s collected job postings to the job postings in JOLTS, the authors estimate that they captured roughly 85% of the job openings in the United States during 2016.

BGT cleans the data to remove double postings and consolidate different spellings for the same employer; i.e., “Bausch and Lomb”, “Bausch Lomb”, and “Bausch & Lomb” are marked as the same employer. After cleaning, 35.9% of employer names are missing, especially if staffing companies do not want to disclose the employer. They assume that all of these with missing employer names are different employers. This means that they have a lower bound on market-concentration measures.

For job description, the BGT dataset uses the Standard Occupational Code (SOC). In the baseline, they consider 200 occupations, which capture 90% of the vacancies in their dataset.[211] For occupations, the authors use six-digit SOC codes for their baseline, but argue that is likely too broad.[212] For location, they use commuting zones, which are geographic definitions based on groups of counties and were developed by the U.S. Department of Agriculture (USDA) to capture local economies and labor markets.[213]

In the SOC-6 occupation by commuting zone by quarter, they find an average HHI of 0.44. For reference, the 2010 Horizontal Merger Guidelines defined markets with post-merger HHIs exceeding 2,500 or 0.25 as “highly concentrated,” and held that mergers in such markets that also increase the HHI level by at least 100 points “raise significant competitive concerns and often warrant scrutiny.”[214] Using the 2010 thresholds, they find that 60% of markets were considered “highly concentrated.”[215] They calculate many other measures of concentration, including at different percentiles and how they vary across the country.[216]

B. Schubert, Stansbury, & Taska (2024)

Schubert, Stansbury, and Taska also use BGT data on vacancies, but with data from 2011 through 2019.[217] They define markets by SOC-6, but use metropolitan area as the location. They do not focus on trends in concentration but on the distribution of concentration and its relationship to wages through outside options to other markets. While the median market has an HHI of 0.0882, the 75th percentile market has an HHI of 0.2143 and the 95th percentile market has an HHI of over 0.8025.[218]

If, however, you weight by level of employment—since many markets have low levels of employment but high levels of concentration—the 50th percentile worker works in a market with an HHI of 0.0137; the 75th percentile worker in a market with an HHI of 0.0404; and the 95th percentile worker in a market with an HHI of 0.1845.[219] That means that under their data and definition of markets, around 5% of workers are in markets that cross the merger-guidelines threshold for a structural presumption (an HHI greater than 1,800 or 0.18, along with an increase of HHI of 100 or 0.01).[220]

When weighting each labor market equally, instead of by size, they find around 25% of markets are over the new threshold.[221] In contrast, using the same data source (BGT) but defining markets differently, Azar, Marinescu, Steinbaum, and Taska find 60% of markets were above the 2,500 threshold.[222]

Source: Schubert, Stansbury, & Taska (2024)[223]

C. Azar, Marinescu, & Steinbaum (2022)

Azar, Marinescu, and Steinbaum use data from CareerBuilder.com, which is a large online job board.[224] The total number of vacancies on CareerBuilder.com represents 35% of the total vacancies in the US in January 2011, as counted by JOLTS. They consider the SOC-5 definition and pick the 13 most frequent occupations over the 2009 to 2012 window, plus the three most frequent occupations in manufacturing and construction. They then consider the SOC-6 definition, which further splits the SOC-5, and end up with 26 occupations in total.[225]

Like Azar, Marinescu, Steinbaum, and Taska,[226] they use commuting zones. They also have data on the number of applicants, which allows measures of “tightness” as (number of vacancies)/(number of applications). They calculate an average HHI for vacancies of 0.3157. When they look at the average based on applications, they find a higher HHI of 0.3480.[227] Again, this is significantly higher than the HHI measure found for the same occupations but using the administrative microdata.[228]

[1] Non-Compete Clause Rule, Final Rule (RIN 3084-AB74, adopted Apr. 23, 2024) (to be codified at 16 C.F.R. Part 910), available at https://www.ftc.gov/system/files/ftc_gov/pdf/noncompete-rule.pdf.

[2] Complaint, In the Matter of the Kroger Company and Albertsons Companies, Inc., FTC Docket No. D-9428 (Feb. 26, 2024), https://www.ftc.gov/legal-library/browse/cases-proceedings/kroger-companyalbertsons-companies-inc-matter.

[3] U.S. Dep’t. of Just. & Fed. Trade Comm’n, Merger Guidelines 27 (2023), available at https://www.justice.gov/d9/2023-12/2023%20Merger%20Guidelines.pdf.

[4] See infra Part II.

[5] See generally U.S. Dep’t of the Treas., The State of Labor Market Competition (Mar. 7, 2022), available at https://home.treasury.gov/system/files/136/State-of-Labor-Market-Competition-2022.pdf.

[6] Merger Guidelines, supra note 3, at 27.

[7] See Jean-Pierre Dubé, Günter J. Hitsch, &Peter E. Rossi, Do Switching Costs Make Markets Less Competitive?, 46 J. Marketing Rsrch. 435, 435 (2009) (“In the simulations, prices are as much as 18% lower with than without switching costs. More important, equilibrium prices do not increase even in the presence of switching costs that are of the same order of magnitude as product price.”).

[8] Merger Guidelines, supra note 3, at 27.

[9] United States v. DaVita Inc., et al., Case No. 21-cr-00229 (D. Colo. 2021).

[10] See, e.g., United States v. Patel, et al., Case No. 21-cr-00220 (D. Conn. 2021) (acquitting all defendants and holding that the evidence did not permit a reasonable jury to conclude there was an agreement to meaningfully allocate the labor market for engineers); United States v. Manahe, et al., Case No. 22-cr-00013 (D. Me. 2022) (acquitting all defendants of charges of a wage-fixing conspiracy among home-healthcare agencies); United States v. Surgical Care Affiliates LLC, et al., Case No. 21-cr-00011 (N.D. Tex. 2021) (DOJ voluntarily dismissed its indictment of a no-poach conspiracy of senior-level surgical facility employees).

[11] Herbert Hovenkamp, The Slogans and Goals of Antitrust Law, 25 Leg. & Pub. Pol’y 705, 705 (2023).

[12] See 15 U.S.C. § 18 (2018) (“No person… shall acquire… the whole or any part of the stock… of another person…, where in any line of commerce…, the effect of such acquisition may be substantially to lessen competition….”).

[13] Merger Guidelines, supra note 3, at 27 (bold/italics emphasis added; italics-only emphasis in original).

[14] See infra Sections IV.B and V.

[15] Complaint, In the Matter of Kroger/Albertsons, supra note 2.

[16] Ioana Marinescu & Herbert J. Hovenkamp, Anticompetitive Mergers in Labor Markets, 94 Indiana L.J. 1031, 1034 (2019).

[17] See, e.g., id. (“While the use of section 7 to pursue mergers among buyers is well established, there is relatively little case law.”).

[18] Merger Guidelines, supra note 3, at 26-27.

[19] See Non-Compete Clause Rule, Final Rule, supra note 1.

[20] For an extensive review of the noncompete literature relied upon by the FTC and a discussion of the nuances and limitations of that literature, see Alden Abbott, et al., Comments of Scholars of Law & Economics and ICLE in the Matter of Non-Compete Clause Rulemaking, FTC Matter No. P201200 (Apr. 19, 2023), https://laweconcenter.org/resources/comments-of-scholars-of-law-economics-and-icle-in-the-matter-of-non-compete-clause-rulemaking.

[21] Complaint, In the Matter of Kroger/Albertsons, supra note 2, at ¶¶ 63 & 70.

[22] See, e.g., Suresh Naidu, Eric A. Posner, & Glen Weyl, Antitrust Remedies for Labor Market Power, 132 Harv. L. Rev. 536 (2018), (“As far as we know, the DOJ and FTC have never challenged a merger because of its possible anticompetitive effects on labor markets, or even rigorously analyzed the labor market effects of mergers as they do for product market effects. Nor have we found a reported case in which a court found that a merger resulted in illegal labor market concentration.”). Ioana Marinescu & Eric A. Posner, Why Has Antitrust Law Failed Workers?, 105 CORNELL L. REV. 1343 (2020)

[23] Exec. Order No.14036, 86 FR 36987 (2021).

[24] See United States v. Bertelsmann SE & Co. KGaA, et al., 646 F. Supp. 3d 1 (D.D.C. 2022).

[25] See Press Release, FTC Cracks Down on Companies That Impose Harmful Noncompete Restrictions on Thousands of Workers, Fed. Trade. Comm’n, (Jan. 4, 2023), https://www.ftc.gov/news-events/news/press-releases/2023/01/ftc-cracks-down-companies-impose-harmful-noncompete-restrictions-thousands-workers. See also, e.g., Complaint and Decision and Order, In the Matter of Anchor Glass Container Corp., et al., Fed. Trade. Comm’n (Jun. 2, 2023), https://www.ftc.gov/news-events/news/press-releases/2023/01/ftc-cracks-down-companies-impose-harmful-noncompete-restrictions-thousands-workers https://www.ftc.gov/legal-library/browse/cases-proceedings/2110182-anchor-glass.

[26] See Non-Compete Clause Rule, Notice of Proposed Rulemaking, 88 Fed. Reg. 3482 (RIN 3084, proposed Jan. 19, 2023) (to be codified at 16 C.F.R. Part 910).

[27] See cases referenced supra note 10.

[28] Dept of Just. & Fed. Trade Comm’n, Antitrust Guidance For Human Resource Professionals (2016), https://www.justice.gov/atr/file/903511/download.

[29] Press Release, Justice Department Requires Six High Tech Companies to Stop Entering into Anticompetitive Employee Solicitation Agreements, U.S Dept. of Just. (Sep. 24, 2010), https://www.justice.gov/opa/pr/justice-department-requires-six-high-tech-companies-stop-entering-anticompetitive-employee.

[30] United States v. Arizona Hosp & Healthcare Ass’n & AzHHA Service Corp., No. CV07-1030-PHX (D. Az. May 22, 2007).

[31] Memorandum of Understanding Between the Fed. Trade Comm’n and the Nat’l Labor Relations Bd. Regarding Information Sharing, Cross-Agency Training, and Outreach in Areas of Common Regulatory Interest (Jul. 19, 2022), available at https://www.ftc.gov/system/files/ftc_gov/pdf/ftcnlrb%20mou%2071922.pdf.

[32] Memorandum of Understanding Between the U.S. Dep’t of Labor and the Fed. Trade Comm’n (Aug. 30, 2023), available at https://www.ftc.gov/system/files/ftc_gov/pdf/23-mou-146_oasp_and_ftc_mou_final_signed.pdf.

[33] See generally Herbert Hovenkamp, The Antitrust Enterprise: Principle and Execution 38-9 (2005) (citing examples and noting that “post-Chicago theory typically models strategic behavior by use of game theory, with alternatives that reach far beyond the conventional Cournot oligopoly analysis”). See also, e.g., Edward J. Green & Robert H. Porter, Noncooperative Collusion under Imperfect Price Information, 52 Econometrica 87 (1984); Thomas G. Krattenmaker & Steven C. Salop, Anticompetitive Exclusion: Raising Rivals’ Costs to Achieve Power over Price, 96 Yale L.J. 209 (1986).

[34] Herbert J. Hovenkamp & Fiona Scott Morton, Framing the Chicago School of Antitrust Analysis, 168 U. Pa. L. Rev. 1843, 1847 (2020) (“Built into Chicago School doctrine was a strong presumption that markets work themselves pure without any assistance from government. By contrast, imperfect competition models gave more equal weight to competitive and noncompetitive explanations for economic behavior….”).

[35] See, e.g., Lester G. Telser, Why Should Manufacturers Want Fair Trade?, 3 J.L. & Econ. 86 (1960); George J. Stigler, A Theory of Oligopoly, 72 J. Pol. Econ. 44 (1964); Howard Marvel, Exclusive Dealing, 25 J. L. Econ. 1 (1982).

[36] See, e.g., Gregory J. Werden & Luke M. Froeb, The Effects of Mergers in Differentiated Products Industries: Logit Demand and Merger Policy, 10 J.L. Econ. & Org. 407 (1994); Jonathan B. Baker & Timothy F. Bresnahan, The Gains from Merger or Collusion in Product-Differentiated Industries, 33 J. Indus. Econ. 427 (1985).

[37] To be clear, this is merely a descriptive claim about the present state of the relationship between labor economics and antitrust, not a normative claim that the two fields should not develop stronger connections.

[38] See, e.g., Jose Azar, Iona Marinescu, & Marshall Steinbaum, Labor Market Concentration, 57 J. Hum. Res. S167, S197 (Supp. 2022) (“The type of analysis we provide could be used to incorporate labor market concentration concerns as a factor in antitrust analysis.”).

[39] See U.S. Dep’t of the Treas., supra note 5.

[40] See, e.g., Azar, Marinescu, & Steinbaum, supra note 38, at S174 (“Our baseline measure of market power in a labor market is the Herfindahl–Hirschman index (HHI)….”); Carl Shapiro, Protecting Competition in the American Economy: Merger Control, Tech Titans, Labor Markets, 33 J. Econ. Persp. 69, 75-76 (2019). (“Measures of industry concentration based on data from the US Economic Census are simply not very informative for merger analysis because these data are available only at an aggregated level. The modest increases in concentration observed when using these data confirm that the largest firms are responsible for a greater portion of economic activity in many industries, but they tell us very little about concentration in properly defined relevant antitrust markets… Furthermore, it is important to remember that an increase in concentration in a properly defined relevant market does not prove that competition in that market has declined.”).

[41] This is effectively the labor-market equivalent of markups that measure whether firms enjoy market power in the market for goods or services. See, e.g., Naidu, Posner, & Weyl, supra note 22, at 556 (“The firm’s absolute markup is the gap between this price and the firm’s cost. The markup equals the difference between the monopoly price and the competitive price, and thus serves as a natural gauge of market power… As in the monopoly case, a monopsonist will not internalize this effect on workers and will choose an “absolute markdown” of wages below the marginal revenue product.”).

[42] As we will discuss later, this connection between labor-supply elasticities, marginal products, and wages is more complicated. For example, the markdown could be a mismeasured return to technology, not traditional market power. See, e.g., Ivan Kirov & James Traina, Labor Market Power and Technological Change in US Manufacturing, conference paper for Institute for Labor Economics (Oct 2022), at 42, available at https://conference.iza.org/conference_files/Macro_2022/traina_j33031.pdf (“The labor [markdown] therefore increases because “productivity” rises, and not because pay falls. This suggests that technological change plays a large role in the rise of the labor [markdown].”).

[43] See Naidu, Posner, & Weyl, supra note 22.

[44] Id. at 567. See also Douglas O. Staiger, Joanne Spetz, & Ciaran S. Phibbs, Is There Monopsony in the Labor Market? Evidence from a Natural Experiment, 28 J. LAB. ECON. 211 (2010); Arindrajit Dube, Laura Giuliano, & Jonathan Leonard, Fairness and Frictions: The Impact of Unequal Raises on Quit Behavior, 109 AM. ECON. REV. 620 (2019).

[45] For one example, Matsudaira uses a natural experiment around the introduction of state minimum-nurse-staffing laws and evidence consistent with perfect competition and zero market power for nurse-aides. High and low market power can exist at the same time. See Jordan D. Matsudaira, Monopsony in the Low-Wage Labor Market? Evidence from Minimum Nurse Staffing Regulations, 96 Rev. Econ. & Stat. 92 (2014).

[46] See Naidu, Posner, & Weyl, supra note 22, at 564-566.

[47] Loukas Karabarbounis & Brent Neiman, The Global Decline of the Labor Share, 129 Quarterly J. of Econ. 61, 71 (2013).

[48] Michael R. Ransom & David P. Sims, Estimating the Firm’s Labor Supply Curve in a “New Monopsony” Framework: Schoolteachers in Missouri, 28 J. LAB. ECON. 331 (2010).

[49] See, e.g., Efraim Benmelech, Nittai K. Bergman, & Hyunseob Kim, Strong Employers and Weak Employees, How Does Employer Concentration Affect Wages?, 57 J. Hum. Res. S200 (Supp. 2022). See also David Berger, Kyle Herkenhoff, & Simon Mongey, Labor Market Power, 112 Am. Econ. Rev. 1147 (2022).

[50] José A. Azar, Steven T. Berry, & Ioana Marinescu, Estimating Labor Market Power (Nat’l Bureau of Econ. Rsch., Working Paper No. 30365, 2022).

[51] Id. at 35.

[52] Chen Yeh, Claudia Macaluso, & Brad Hershbein, Monopsony in the US Labor Market, 112 Am. Econ Rev. 2099 (2022).

[53] Id. at 2099.

[54] Id. at 2114.

[55] Id. at 2099.

[56] See Steven Berry, Market Structure and Competition Redux, Presentation at Fed. Trade. Comm’n Micro Conference (Nov. 2017), available at https://www.ftc.gov/system/files/documents/public_events/1208143/22_-_steven_berry_keynote.pdf; See also Brian Albrecht, Markups as Residuals, Economic Forces (Nov. 17, 2022), www.economicforces.xyz/p/markups-as-residuals.

[57] See Kirov & Traina, supra note 42.

[58] 2023 Merger Guidelines, supra note 3.

[59] See infra Appendix.

[60] In order to evaluate concentration, the relevant market must be defined. For labor markets, the relevant market is usually defined as both the job description (e.g., nurse) and the location of the job (e.g., Portland area). Using this, one can calculate some measure of concentration, such as the HHI. Economics papers tend to report HHI as a percentage, instead of as a cardinal number out of 10,000, as used in the merger guidelines. For example, an HHI of 1,800 would be written as “0.18.”

[61] See, e.g., Kevin Rinz, Labor Market Concentration, Earnings, and Inequality, 57 J. Hum. Res. S251 (Supp. 2022); David Autor, Christina Patterson, & John Van Reenen, Local and National Concentration Trends in Jobs and Sales: The Role of Structural Transformation, 5 (Nat’l Bureau of Econ. Rsch., Working Paper No. 31130, 2023) at 7 (“The employment-based HHI fell by 2.3 points, from 33.3 in 1992 to 31.0 in 2017, which stands in contrast to the 3.4 point rise in the sales HHI. Our estimates for local employment concentration echo those of Rinz (2022), who uses the LBD.”) (emphasis in original).

[62] Rinz, id. at S256.

[63] See Azar, Marinescu, & Steinbaum, supra note 38.

[64] Handwerker & Dey directly compare the concentration measures in their data to the 26 occupations studied by Azar, Marinescu, & Steinbaum. They find an HHI in the private sector of 0.0383, compared to 0.3157 in Azar, Marinescu, & Steinbaum. See Elizabeth Weber Handwerker & Matthew Dey, Some Facts About Concentrated Labor Markets in the United States, 63 Indus. Rel. 132, 135 (2023); Azar, Marinescu, & Steinbaum, supra note 38.

[65] A firm may have multiple establishments, and the data allow different NAICS codes for each establishment, so, in some cases and to some extent, different types of workers can be separated out if they work in different locations.

[66] Berger, Herkenhoff, & Mongey, supra note 49, at 1169 (citing Elizabeth Handwerker & Matthew Dey, Megafirms and Monopsonists: Not the Same Employers, Not the Same Workers (Unpublished)).

[67] Rinz, supra note 61.

[68] Id. at S264 (“In both years, the areas that are most concentrated tend to be rural. In particular, the Great Plains region has a relatively large number of highly concentrated commuting zones in both 1976 and 2015. The least concentrated markets tend to be in urban areas.”).

[69] Kevin Rinz, Labor Market Concentration, Earnings Inequality, and Earnings Mobility, National Bureau of Economic Research Summer Institute (Jul. 23, 2019) (slides obtained from author).

[70] Rinz, supra note 61 at S253.

[71] See Ben Lipsius, Labor Market Concentration Does Not Explain the Falling Labor Share, Working Paper (2018), available at https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3279007.

[72] See Berger, Herkenhoff, & Mongey, supra note 49.

[73] See 2023 Merger Guidelines, supra note 3.

[74] See, e.g., Azar, Marinescu, and Steinbaum, supra note 38; Jose Azar, Iona Marinescu, Marshall Steinbaum, & Bledi Taska, Concentration in US Labor Markets: Evidence from Online Vacancy Data, 66 Labor Econ. 101886 (2020).

[75] For a more detailed discussion of these papers and their limitations, see Appendix Section II, infra.

[76] See Harold Demsetz, Industry Structure, Market Rivalry, and Public Policy, 16 J. L. & Econ. 1 (1973). See also, e.g., Richard Schmalensee, Inter-Industry Studies of Structure and Performance, in 2 Handbook of Industrial Organization 951 (Richard Schmalensee & Robert Willig, eds., 1989); William N. Evans, Luke M. Froeb, & Gregory J. Werden, Endogeneity in the Concentration-Price Relationship: Causes, Consequences, and Cures, 41 J. Indus. Econ. 431 (1993); Berry, supra note 56; Nathan Miller, et al., On the Misuse of Regressions of Price on the HHI in Merger Review, 10 J. Antitrust Enf. 248 (2022).

[77] Some papers find lower wages in markets with higher employer concentration, but do not differentiate rural from urban labor markets. Rural and urban labor markets can differ significantly in terms of their economic structures, job opportunities, and wage levels. Any regression of wages on concentration is likely picking up something unrelated to concentration directly. See Benmelech, Bergman, & Kim, supra note 49.

[78] Kirov & Traina, supra note 42.

[79] Id. at 46 (emphasis added).

[80] Steven Berry, Martin Gaynor, & Fiona Scott Morton, Do Increasing Markups Matter? Lessons from Empirical Industrial Organization, 33 J. Econ. Persp. 44, 57 (2019) (emphasis added).

[81] The antitrust statutes do not distinguish buy-side and sell-side behavior, besides the partial exception in Section 6 of the Clayton Act, which provides that workers do not violate antitrust laws when they organize unions. See 15 U.S.C. § 17 (“The labor of a human being is not a commodity or article of commerce. Nothing contained in the antitrust laws shall be construed to forbid the existence and operation of labor… organizations, instituted for the purposes of mutual help…, or to forbid or restrain individual members of such organizations from lawfully carrying out the legitimate objects thereof….”). In practice, however, it seems the agencies have historically treated labor markets differently. See, e.g., Naidu, Posner, & Weyl, supra note 22.

[82] See, e.g., Roger G. Noll, Buyer Power and Economic Policy, 72 Antitrust L.J. 589, 589 (2005) (“[B]uyer power arises from monopsony (one buyer) or oligopsony (a few buyers), and is the mirror image of monopoly or oligopoly.”); id. at 591 (“Asymmetric treatment of monopoly and monopsony has no basis in economic analysis.”).

[83] Of course, monopoly markets in intermediate products (i.e., products sold not to end users, but to manufacturers who use them as inputs for products that are, in turn, sold to end users) may indeed sit in the same place in the supply chain as the typical monopsony market. Some, but not all, of the complications associated with monopsony analysis are relevant to these monopoly situations, as well.

[86] For purposes of this discussion, “monopoly” refers to any merger (or other conduct) that would increase market power by a seller in a product market, and “monopsony” refers to any merger (or other conduct) that would increase market power by a buyer in an input market (including a labor market).

[87] Keith Brand, Martin Gaynor, Patrick McAlvanah, David Schmidt, & Elizabeth Schneirov, Economics at the FTC: Office Supply Retailers Redux, Health Care Quality Efficiencies Analysis, and Litigation of an Alleged Get Rich Quick Scheme, 45 Rev. Indus. Org. 325 (2014).

[88] Id.

[89] Some efficiency-enhancing mergers will be identifiable, of course. For example, if the merger raises quantities and prices for all inputs, that must be efficiency enhancing. The problem, as always, is with the hard cases.

[90] See C. Scott Hemphill & Nancy L. Rose, Mergers that Harm Sellers, 127 Yale L.J. 2078 (2018).

[91] In theory, one could force a monopsony model to be identical to monopoly. The key difference is about the standard economic form of these models that economists use. The standard monopoly model looks at one output good at a time, while the standard factor-demand model uses two inputs, which introduces a tradeoff between, say, capital and labor. See Sonia Jaffe, Robert Minton, Casey B. Mulligan, and Kevin M. Murphy, Chicago Price Theory (2019) at Ch. 10. One could generate harm from an efficiency for monopoly (as we show for monopsony) by assuming the merging parties each produce two different outputs, apples and bananas. An efficiency gain could favor apple production and hurt banana consumers. While this sort of substitution among outputs is often realistic, it is not the standard economic way of modeling an output market.

[92] Herbert Hovenkamp, Worker Welfare and Antitrust, 90 U. CHI. L. REV. 511, 529 (2023) (“To the extent that such actions lead to higher prices or reduced product output, labor as well as consumers suffer.”).

[93] Marinescu & Hovenkamp, supra note 16 at 1042 (“The key message from economic theory is that as one moves away from the competitive equilibrium towards a situation of monopsony in the labor market, wages and production both generally tend to decrease.”).

[94] See United States v. Bertelsmann SE & Co. KGaA, et al., supra note 24.

[95] Id. at 23 (“The defendants do not dispute that if advances are significantly decreased, some authors will not be able to write, resulting in fewer books being published, less variety in the marketplace of ideas, and an inevitable loss of intellectual and creative output.”)

[96] John Asker & Volker Nocke, Collusion, Mergers, and Related Antitrust Issues, in 5 HANDBOOK oF INDUSTRIAL ORGANISATION 177, 221-22 (Kate Ho, Ali Hortasçu & Alessandro Lisseri eds., 2021).

[97] But see United States v. Bertelsmann SE & Co. KGaA, et al., supra note 24, at 28 (“Thus, even if alternative submarkets exist at other advance levels, or if there are broader markets that might be analyzed, the viability of such additional markets does not render the one identified by the government unusable.”). Of course, in that case, the parties (and the court) did identify downstream harms. See id. at 23.

[98] See generally, Geoffrey A. Manne & E. Marcellus Williamson, Hot Docs vs. Cold Economics: The Use and Misuse of Business Documents in Antitrust Enforcement and Adjudication, 47 Ariz. L. Rev. 609, 619 (2005).

[99] Hemphill & Rose, supra note 90. The authors make a useful distinction between mergers that generate classical monopsony and those that increase buyer leverage. As explained below, however, increased buyer bargaining leverage is just a transfer from sellers to buyers. If it truly has no effect on output, as supposed for Hemphill & Rose, it is not anticompetitive. If antitrust is to weigh in on splitting the surplus and conclude that a merger that leads to more of the surplus going to the buyer is anticompetitive, the courts would be implicitly saying that either the division before the merger was optimal or that more surplus going to sellers is always better. While people may have an intuition that more surplus going to sellers of labor (i.e., workers) is better, do we have the same intuition for all types of sellers? Moreover, would we be willing to apply the same logic to mergers to monopoly? If so, and mergers that increase buyer leverage are bad and mergers that increase seller leverage are bad (again with no effect on output), are we concluding all mergers are bad, full stop?

[100] Marinescu & Hovenkamp, supra note 16, at 1040 (emphasis added).

[101] Such bargaining models have been awarded Nobel prizes. See Peter Diamond, Wage Determination and Efficiency in Search Equilibrium, 49 Rev. Econ. Stud. 217 (1982); Christopher A. Pissarides, Equilibrium Unemployment Theory (2017).

[102] See, e.g., Richard Rogerson, Robert Shimer, & Randall Wright, Search-Theoretic Models of the Labor Market: A Survey, XLIII J. ECON. LIT. 959,961 (2005) (“Bargaining is one of the more popular approaches to wage determination in the literature…”).

[103] See, e.g., John Van Reenan, Labor Market Power, Product Market Power and the Wage Structure: A Note 224 (Program on Innovation and Diffusion, Working Paper No. 085, 2023), https://poid.lse.ac.uk/PUBLICATIONS/abstract.asp?index=10529, (“Here, when firms achieve more product market power there are higher profits and therefore more of a potential surplus to be split between employers and employees. Workers (at least those who keep their jobs), may welcome greater monopoly power as they are able to extract higher wage rents, which would not be the case for a firm earning thin or no margins in an extremely competitive product market. Consequently, this generates the opposite implication at the firm level – more product market power generates higher, not lower, wages.”).

[104] Complaint, In the Matter of Kroger/Albertsons, supra note 2, at ¶ 63 (“Union grocery labor is a relevant market in which to analyze the probable effects of the proposed acquisition.”).

[105] Indeed, increased bargaining power is the purpose of a union. Whether the coordination leads to equivalent, lesser, or greater bargaining power than that of employers in a given case depends on many specifics. But the whole point of both the union and the labor antitrust exemption is to facilitate the exercise of this increased bargaining power on the labor side.

[106] Lynn Petrak, Local Union Supports Kroger-Albertsons Merger, Progressive Grocer (Feb. 21, 2024), https://progressivegrocer.com/local-union-supports-kroger-albertsons-merger.

[107] Press Release, America’s Largest Union of Essential Grocery Workers Announces Opposition to Kroger and Albertsons Merger, United Food and Commercial Workers (May 5, 2023), https://www.ufcw.org/press-releases/americas-largest-union-of-essential-grocery-workers-announces-opposition-to-kroger-and-albertsons-merger.

[108] See Petrak, supra note 106.

[109] Roman Inderst & Christian Wey, Countervailing Power and Dynamic Efficiency, 9 J. Eur. Econ. Ass’n 702, 715 (2011).

[110] For further discussion of the problems of reconciling upstream and downstream market effects when labor markets are taken into account, see Section V, infra.

[111] FTC v. Whole Foods Mkt., Inc., 548 F.3d 1028, 1063 (D.C. Cir. 2008). See also Geoffrey Manne, Premium, Natural, and Organic Bullsh**t, Truth on the Market (Jun. 6, 2007), https://truthonthemarket.com/2007/06/06/premium-natural-and-organic-bullst (“In other words, there is a serious risk of conflating a ‘market’ for business purposes with an actual antitrust-relevant market.”).

[112] Unsurprisingly, there is no SOC code that corresponds to such a market definition, and the FTC did not allege it. See Occupational Employment and Wage Statistics, May 2023 Occupation Profiles, Bureau of Labor Statistics (last visited Apr. 23, 2024), https://www.bls.gov/oes/current/oes_stru.htm#41-0000.

[113] Complaint, In the Matter of Kroger/Albertsons, supra note 2, at ¶ 63.

[114] See Brian Albrecht, Dirk Auer, Eric Fruits, & Geoffrey A. Manne, Food-Retail Competition, Antitrust Law, and the Kroger/Albertsons Merger, Int’l. Ctr. for Law & Econ. White Paper 2023-10-17 (2023), https://laweconcenter.org/resources/food-retail-competition-antitrust-law-and-the-kroger-albertsons-merger.

[115] See generally Section A, infra.

[116] See, e.g., Amos Golan, Julia Lane, & Erika McEntarfer, The Dynamics of Worker Reallocation within and across Industries, 74 Economica. 1 (2007). (“About 27% of workers who had previously exhibited a substantial degree of attachment to their employer reallocate in a given year. About two-thirds of this reallocation is job-to-job reallocation, split roughly evenly between, within and across broadly defined industries.)

[117] See United States v. Bertelsmann SE & Co. KGaA, et al., supra note 24.

[118] See, e.g., Press Release, Justice Department Obtains Permanent Injunction Blocking Penguin Random House’s Proposed Acquisition of Simon & Schuster, US Dep’t of Justice (Oct. 31, 2022), https://www.justice.gov/opa/pr/justice-department-obtains-permanent-injunction-blocking-penguin-random-house-s-proposed (“‘The decision is also a victory for workers more broadly,’ said AAG Kanter. ‘It reaffirms that the antitrust laws protect competition for the acquisition of goods and services from workers.’”). Notably, both the complaint and the court’s decision also noted (rightly or wrongly) downstream effects in the product market. See id. at 23.

[119] Transcript: Public Workshop on Competition in Labor Markets, Antitrust Div. of the U.S. Justice Dep’t (Sep. 23, 2019), available at https://www.justice.gov/atr/page/file/1209071/download.

[120] See, Albrecht, Auer, Fruits, & Manne, supra note 114.

[121] See infra Section III.B (“More fundamentally, regardless of the data source that is used, market definition issues remain. The variety of concentration estimates stemming from different geographic units and shifting occupational groupings demonstrates the lack of clarity around reasonable market boundaries.”).

[122] 2023 Merger Guidelines, supra note 3, at 27.

[123] Id.

[124] For a recent summary, see Carl Sanders & Christopher Taber, Life-Cycle Wage Growth and Heterogeneous Human Capital, 4 Ann. Rev. Econ. 399 (2012).

[125] See, e.g., Edward Lazear, Firm?Specific Human Capital: A Skill?Weights Approach, 117 J. Pol. Econ. 914 (2009) (noting that “no skills need be truly ‘firm specific’ in the sense of there being no other firm at which they have value. On the contrary, the skills appear to be general because in isolation they are used at a number of firms in the market. But the weights differ by firm”). See also Jesper Bagger, François Fontaine, Fabien Postel-Vinay, & Jean-Marc Robin, Tenure, Experience, Human Capital, and Wages: A Tractable Equilibrium Search Model of Wage Dynamics, 104 Am. Econ. Rev. 1551 (2014).

[126] Complaint, In the Matter of Kroger/Albertsons, supra note 2.

[127] Id. at ¶ 63.

[128] Id.

[129] See, e.g., Robert Topel, Specific Capital, Mobility, and Wages: Wages Rise with Job Seniority, 99 J. Pol. Econ. 145 (1991).

[130] See Non-Compete Clause Rule, Final Rule, supra note 1, at 283. See also Comments of Scholars of Law & Economics and ICLE in the Matter of Non-Compete Clause Rulemaking, supra note 20, at 29.

[131] Non-Compete Clause Rule, Final Rule, id., at 283.

[132] See id. at 283-86 (citing Evan Starr, Consider This: Wages, Training, and the Enforceability of Covenants Not to Compete, 72 Indus. & Labor Rel. Rev. 783 (2019) (finding that moving from mean NCA enforceability to no NCA enforceability would decrease the number of workers receiving training by 14.7% in occupations that use NCAs at a relatively high rate); Jessica Jeffers, The Impact of Restricting Labor Mobility on Corporate Investment and Entrepreneurship, Working Paper (Sep. 7, 2022), https://ssrn.com/abstract=3040393 (finding that knowledge-intensive firms invest 32% less in capital equipment following decreases in the enforceability of NCAs); Matthew S. Johnson, Michael Lipsitz, & Alison Pei, Innovation and the Enforceability of Non-Compete Agreements, NBER Working Paper Series (Jul. 2023) (finding that greater non-compete enforceability increases R&D expenditure). At least one more study finding similar results was previously cited in the proposed Non-Compete Clause Rule (see supra note 1, at 3505), but not included in the final rulee. See Matthew S. Johnson & Michael Lipsitz, Why Are Low-Wage Workers Signing Noncompete Agreements?, J. Human Resources 0619-10274R2 (May 12, 2020) (finding that hair salons that use NCAs train their employees at a higher rate and invest in customer attraction through the use of digital coupons at a higher rate, both by 11 percentage points)).

[133] Naidu, Posner, & Weyl, supra note 22.

[134] See especially Section I.B, infra.

[135] Marinescu & Hovenkamp, supra note 16, at 1062-63. See also Hovenkamp, Worker Welfare and Antitrust, supra note 92, at 521.

[136] Hemphill & Rose, supra note 90, at 2092.

[137] As Marinescu & Hovenkamp note (attributing the point to Hemphill & Rose), “[i]n this case, there is merely a transfer away from workers and towards the merging firms. Yet… such a transfer is a harm for antitrust law as it results from a reduction in competition.” Id. at 1062 (citing Hemphill & Rose, id., at 2104-05).

[138] See, e.g., Kartell v. Blue Shield of Mass., Inc., 749 F.2d 922 (1st Cir. 1984). See also Steven C. Salop, Question: What Is the Real and Proper Antitrust Welfare Standard? Answer: The True Consumer Welfare Standard, 22 Loy. Consumer L. Rev. 336, 342 (2010) (“However, Judge Breyer treated Blue Cross essentially as an agent for the customers it insured, rather than as an intermediary firm that purchased inputs and sold outputs as a monopolistic reseller. The court apparently assumed (perhaps wrongfully) that Blue Cross would pass on its lower input costs to its customers in the form of lower insurance premiums.”).

[139] See Jan M. Rybnicek & Joshua D. Wright, Outside In or Inside Out?: Counting Merger Efficiencies Inside and Out of the Relevant Market, in 2 William E. Kovacic: An Antitrust Tribute—Liber Amicorum (Nicolas Charbit & Elisa Ramundo, eds., 2014) at 10 (“Despite the incorporation of efficiencies analysis into modern merger evaluation, and the advances in economics that allow efficiencies to be identified and calculated more accurately than at the time of Philadelphia National Bank, antitrust doctrine in the United States still supports a regime that fails to take into account efficiencies arising outside of the relevant market.”).

[140] U.S. Dep’t. of Justice & Fed. Trade Comm’n, Commentary on the Horizontal Merger Guidelines (2006) at 57. See also Gregory J. Werden, Cross-Market Balancing of Competitive Effects: What Is the Law, and What Should It Be?, 43 J. Corp. L. 119, 121 (2017) (“Since 1997, however, the Horizontal Merger Guidelines have asserted the inextricably linked exception.”); U.S. Dep’t. of Justice & Fed. Trade Comm’n, Horizontal Merger Guidelines (2010) at § 10, n.14 (“In some cases, however, the Agencies in their prosecutorial discretion will consider efficiencies not strictly in the relevant market, but so inextricably linked with it that a partial divestiture or other remedy could not feasibly eliminate the anticompetitive effect in the relevant market without sacrificing the efficiencies in the other market(s). Inextricably linked efficiencies are most likely to make a difference when they are great and the likely anticompetitive effect in the relevant market(s) is small so the merger is likely to benefit customers overall.”).

[141] See, e.g., Brunswick Corp. v. Pueblo Bowl-O-Mat, Inc., 429 U.S. 477, 487 (1977) (“Every merger of two existing entities into one, whether lawful or unlawful, has the potential for producing economic readjustments that adversely affect some persons. But Congress has not condemned mergers on that account; it has condemned them only when they may produce anticompetitive effects.”). See also Robert H. Bork, The Antitrust Paradox: A Policy at War with Itself (2021) at 110 (“Those who continue to buy after a monopoly is formed pay more for the same output, and that shifts income from them to the monopoly and its owners, who are also consumers. This is not dead-weight loss due to restriction of output but merely a shift in income between two classes of consumers. The consumer welfare model, which views consumers collectively, does not take this income effect into account.”).

[142] See, e.g., Herbert Hovenkamp & Fiona Scott Morton, The Life of Antitrust’s Consumer Welfare Model, ProMarket (Apr. 10, 2023), https://www.promarket.org/2023/04/10/the-life-of-antitrusts-consumer-welfare-model (“A useful definition of ‘consumer welfare’ is that antitrust should be driven by concerns for trading partners….”).

[143] Dennis Carlton, Does Antitrust Need to Be Modernized?, 21 J. Econ. Persp. 155, 158 (2007).

[144] Hovenkamp & Scott Morton, supra note 34.

[145] See also Hemphill & Rose, supra note 90, at 2106. Hemphill & Rose distinguish monopsony power from increased buyer leverage, which does not result in a deadweight loss but is simply a redistribution from sellers to buyers. Leverage will be partially passed through to consumers as lower prices. Standard monopsony increases in bargaining power will not generate lower prices, since “[a]n increase in monopsony power increases the firm’s perceived marginal cost and reduces output. Far from lowering output prices, the increased monopsony power raises price in output markets (if the firm faces downward sloping demand for its output) or else leaves it unchanged.”

[146] Statement of the Federal Trade Commission Concerning the Proposed Acquisition of Medco Health Solutions by Express Scripts, Inc., FTC File No. 111-0210 (Apr. 2, 2012) at 7, available at https://www.ftc.gov/sites/default/files/documents/closing_letters/proposed-acquisition-medco-health-solutions-inc.express-scripts-inc./120402expressmedcostatement.pdf.

[147] Roman Inderst & Greg Shaffer, Buyer Power in Merger Control, in ABA Antitrust Section Handbook, Issues in Competition Law and Policy (Wayne Dale Collins, ed. 2008) at 1611, 1612-13 (emphasis added).

[148] Salop, supra note 138, at 342 (“Efficiency benefits count under the true consumer welfare standard, but only if there is evidence that enough of the efficiency benefits pass through to consumers so that consumers (i.e., the buyers) would directly benefit on balance from the conduct.”).

[149] It is worth noting that, although the analogy between Blue Cross and Kroger here seems quite apt and powerful, there can be little doubt that Salop would not condone this mode of analysis in a case against Kroger. Whether (if correct) that is a function of one person’s idiosyncratic preferences or an expression of the complication inherent in assessing consumer welfare in monopsony cases is uncertain.

[150] Werden, Cross-Market Balancing of Competitive Effects, supra note 140, at 129. The referenced language from Chicago Board of Trade and Sylvania is: “The true test for legality is whether the restraint imposed is such as merely regulates and perhaps thereby promotes competition or whether it is such as may suppress or even destroy competition.” Chi. Bd. of Trade v. U.S., 246 U.S. 231, 238 (1918); “Under this rule, the factfinder weighs all of the circumstances of a case in deciding whether a restrictive practice should be prohibited as imposing an unreasonable restraint on competition.” Cont’l T.V. v. GTE Sylvania, 433 U.S. 36, 49 (1977).

[151] Nat’l Collegiate Athletic Ass’n v. Alston, 141 S. Ct. 2141, 2154 (2021).

[152] Id. at 2152.

[153] Id.

[154] To be clear, the legal process for evaluating this tradeoff is not a strict balancing, but a “less-restrictive alternative” test—exactly as the Court laid out and applied in Amex. See id. at 2162 (“The court then proceeded to what corresponds to the third step of the American Express framework, where it required the student-athletes ‘to show that there are substantially less restrictive alternative rules that would achieve the same procompetitive effect as the challenged set of rules.’”).

[155] See, e.g., Gregory J. Werden, Monopsony and the Sherman Act: Consumer Welfare in a New Light, 74 Antitrust L.J. 707, 735 (2007) (“Predatory pricing that excludes competitors and results in monopsony is condemned by the Sherman Act, just as the Act condemns predatory pricing that excludes competitors and obtains a monopoly.… Protecting consumer welfare is the principal goal of the Sherman Act, but it is only a goal: The Sherman Act protects the people by protecting the competitive process. The competitive process could not be undermined any more clearly than it is when competing buyers conspire to eliminate the competition among themselves, and it matters not one whit under the Sherman Act whether the conspiracy threatens the welfare of conspirators’ customers or the welfare of end users. It is enough that the conspiracy threatens the welfare of the trading partners exploited by the conspiracy. Harm to them implies harm to people protected by the Sherman Act.”).

[156] See discussion supra, text at notes 11 and 92.

[157] See, e.g., Sean P. Sullivan, Modular Market Definition, 55 U.C. Davis L. Rev. 1091, 1118 (2021) (“One traditional purpose of market definition has been to act like a microscope trained upon a specific area of concern. The full, interconnected web of commerce—of all possible products and technologies and consumptive uses and trading partners—is simply too big and too overwhelming to provide useful context for antitrust analysis.”).

[158] See Illinois Brick Co. v. Illinois, 431 U.S. 720, 731-32 (1977) (“The principal basis for the decision in Hanover Shoe was the Court’s perception of the uncertainties and difficulties in analyzing price and output put decisions… and of the costs to the judicial system and the efficient enforcement of the antitrust laws of attempting to reconstruct those decisions in the courtroom.”); Hanover Shoe, Inc. v. United Shoe Machinery Corp., 392 U.S. 481, 493 (1968).

[159] Ohio v. Am. Express Co., 138 S. Ct. 2274, 2287 (2018) (“Accordingly, we will analyze the two-sided market for credit-card transactions as a whole to determine whether the plaintiffs have shown that Amex’s anti-steering provisions have anticompetitive effects.”). See also U.S. v. Am. Express Co., 88 F. Supp. 3d 143, 216-17 (E.D.N.Y. 2015) (“Merchants facing increased credit card acceptance costs will pass most, if not all, of their additional costs along to their customers in the form of higher retail prices…. [C]ustomers who do not carry or qualify for an Amex card are nonetheless subject to higher retail prices at the merchant, but do not receive any of the premium rewards or other benefits conferred by American Express on the cardholder side of its platform…. Thus, in the most extreme case, a lower-income shopper who pays for his or her groceries with cash… is subsidizing, for example, the cost of the premium rewards conferred by American Express on its relatively small, affluent cardholder base in the form of higher retail prices.”).

[160] See, e.g., Michael Katz & Jonathan Sallet, Multisided Platforms and Antitrust Enforcement, 127 Yale L.J. 2142 (2018).

[161] Id. (“For all these reasons, ‘[i]n two-sided transaction markets, only one market should be defined.’ Any other analysis would lead to ‘mistaken inferences’ of the kind that could ‘chill the very conduct the antitrust laws are designed to protect.’”) (cleaned up and citations omitted).

[162] Id. at 2286.

[163] Geoffrey A. Manne, In Defence of the Supreme Court’s “Single Market” Definition in Ohio v American Express, 7 J. Antitrust Enf. 104, 110 (2019).

[164] See Jonathan H. Adler, Conservation Through Collusion: Antitrust as an Obstacle to Marine Resource Conservation, 61 Wash. & Lee L. Rev 3, 78 (2004) (“The purported aim of antitrust law is to improve consumer welfare by proscribing actions and arrangements that reduce output and increase prices. Conservation aims to improve human welfare by maximizing the long-term productive use of natural resources, an aim that often requires limiting consumption to sustainable levels. While such conservation measures might increase prices in the short-run, when successful they enhance consumer welfare by increasing long-term production and ensuring the availability of valued resources over time.”)

[165] See Clayton Act, 15 U.S.C. § 18 (2018); U.S. v. Philadelphia Nat’l Bank, 374 U.S. 321 (1963). See also Daniel A. Crane, Balancing Effects Across Markets, 80 Antitrust L.J. 397, 397 (2015) (noting that PNB is usually read to hold that “it is improper to weigh a merger’s procompetitive effects in one market against the merger’s anticompetitive effects in another.”). See also Merger Guidelines, supra note 3, at 27.

[166] Rybnicek & Wright, supra note 139, at 10.

[167] U.S. v. Baker Hughes Inc., 908 F.2d 981, 984 (D.C. Cir. 1990).

[168] See, e.g., Saint Alphonsus Med. Ctr.-Nampa v. St. Luke’s Health Sys., 778 F.3d 775, 790 (9th Cir. 2015) (“[A] defendant can rebut a prima facie case with evidence that the proposed merger will create a more efficient combined entity and thus increase competition.”); FTC v. Tenet Health Care, 186 F.3d 1045, 1054-55 (8th Cir. 1999) (“[Courts should consider] evidence of enhanced efficiency in the context of the competitive effects of the merger… [as] the merged entity may well enhance competition.”).

[169] Although its decision was not limited to the acceptance of “innovation” effects, the court rejected the contention that such “efficiencies” would not accrue to consumers in the relevant market, instead accepting that innovation itself was a cognizable efficiency. See New York v. Deutsche Telekom AG, 439 F. Supp. 3d 179, 215-16 (S.D.N.Y. 2020) (“Scott Morton stated that because these speeds are far beyond the levels that consumers now require, and because the value of speed to consumers diminishes the more that speeds exceed the level that consumers can practically use, there is no reliable way to determine how consumers would value speeds higher than roughly 250 mbps…. This argument is too limiting. The same may have been said about airplane speeds and pilotless flying machines in 1920. It unduly discounts the rate at which technological innovation, new products, and consumer applications develop to take advantage of enhanced capabilities, and the extent to which this merger might specifically help accelerate that process.”).

[170] Basel J. Musharbash & Daniel A. Hanley, Toward a Merger Enforcement Policy That Enforces the Law: The Original Meaning and Purpose of Section 7 of the Clayton Act, Working Paper (2024) at 58-59, https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4745310.

[171] Indeed, as Musharbash & Hanley go on to note, the phrase “in any line of commerce” does not map onto the traditional conception of market definition used in merger analysis and defined by substitutability of products: “[A] ‘line of commerce’ is a category of business occupation which is defined by characteristics that separate or distinguish it from other categories of business occupation. Under this definition, the fact that a group of business occupations offer substitute products from the perspective of consumers certainly could, at least in theory, qualify them as a “line” of commerce, but nothing in the phrase signifies that such substitutability is the only permissible basis for identifying a line of commerce. Indeed, using other characteristics that reasonably distinguish one business occupation from another — such as distinct products or services, peculiar know-how and operations, or divergent supply chains and distribution channels — to identify a line of commerce would be more consistent with the phrase’s textual import. For the word line was ordinarily used to identify, with varying degrees of generality, the type of business a party was engaged in, not the markets it sold to or participated in.” Id. at 61.

[172] See, e.g., Viktoria H. S. E. Robertson, Antitrust Market Definition for Digital Ecosystems, Concurrences No. 2-2021 (2021) at 5, https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3844551 (“However, the picture would not be complete without also considering the macro level of the digital ecosystem, which is needed in order to understand the various competitive constraints (or the absence of such constraints) that are at work. The dif?culty for market de?nition is to account for the various layers of competition that are present in the market realities of digital ecosystems in order to allow for the substantive analysis of a speci?c market behaviour or concentration. The challenge lies in providing an approach that does justice to the complexity of these markets, but without unnecessarily adding to that complexity.”).

[173] See Credit Suisse Securities (USA) v. Billing, 551 U.S. 264, *19-*20, *1-*2 (2007) (holding that where “(1) an area of conduct [is] squarely within the heartland of… regulations; (2) [there is] clear and adequate… authority to regulate; (3) [there is] active and ongoing agency regulation; and (4) [there is] a serious conflict between the antitrust and regulatory regimes…, [such] laws are ‘clearly incompatible’ with the application of the antitrust laws…[,]” thus “implicitly precluding the application of the antitrust laws to the conduct alleged”). See also Philadelphia Nat. Bank, 374 U.S. at 398-74 (Harlan, J. dissenting) (“Sweeping aside the ‘design fashioned in the Bank Merger Act’ as ‘predicated upon uncertainty as to the scope of § 7 of the Clayton Act,’ the Court today holds § 7 to be applicable to bank mergers and concludes that it has been violated in this case. I respectfully submit that this holding, which sanctions a remedy regarded by Congress as inimical to the best interests of the banking industry and the public, and which will in large measure serve to frustrate the objectives of the Bank Merger Act, finds no justification in either the terms of the 1950 amendment of the Clayton Act or the history of the statute.”).


[174] Complaint, In the Matter of Kroger/Albertsons, supra note 2.

[175] See supra, notes 137-140 and accompanying text.

[176] See, e.g., Naidu, Posner & Weyl, supra note 22.

[177] See, e.g., Yeh, et al., supra note 52; Kirov & Traina, supra note 42.

[178] Id.

[179] See, e.g., David Arnold, Mergers and Acquisitions, Local Labor Market Concentration, and Worker Outcomes, unpublished manuscript (April 2, 2021), available at https://darnold199.github.io/madraft.pdf.

[180] See, e.g., Bagger, et al., supra note 125.

[181] See Complaint, In the Matter of Kroger/Albertsons, supra note 2, at ¶ 63.

[182] See Section V, infra.

[183] See, e.g., Hovenkamp, Worker Welfare and Antitrust, supra note 92, at 543 (“Consumer welfare—when it is properly defined—and worker welfare travel in tandem. When a practice harms consumers by raising prices and reducing output, it harms labor as well. There is no a priori reason for thinking that worker harm is less severe than consumer harm. A properly designed antitrust policy must focus on both sets of interests.”).

[184] See infra, Section V.

[185] See 2023 Merger Guidelines, supra note 3.

[186] Rinz, supra note 61.

[187] Lipsius, supra note 71.

[188] Autor, Patterson, & Reenen, supra note 61.

[189] Benmelech, Bergman, & Kim, supra note 49.

[190] Berger, Herkenhoff, & Mongey, supra note 49.

[191] Rinz, supra note 61.

[192] Lipsius, supra note 71.

[193] Rinz, supra note 61, at S259.

[194] Autor, Patterson & Reenen, supra note 61.

[195] Id. at 13.

[196] Id. at 24, Figure A4.

[197] Id. at 6.

[198] Id. at 2

[199] Benmelech, Bergman, and Kim, supra note 49.

[200] Id.

[201] Id. at 202.

[202] Berger, Herkenhoff, & Mongey, supra note 49.

[203] Id.

[204] Handwerker & Dey, supra note 64.

[205] Berger, Herkenhoff, & Mongey, supra note 49.

[206] Azar, Marinescu, and Steinbaum, supra note 38.

[207] Handwerker & Dey, supra note 64, at 135.

[208] Id.

[209] Azar, Marinescu, Steinbaum, & Taska, supra note 74.

[210] Id. at *2 (According to this perspective, ease of finding when searching may be a better measure of the relevant outside option for workers. More job openings means more feasible outside options which is basically all models means less market power by employers: “we measure concentration using job openings rather than employment because we view vacancies as a better gauge of how likely searching workers (whether employed or unemployed) are to receive a job offer.”).

[211] Id. at Table 1.

[212] Id. at *5 (“Using online job board data from CareerBuilder.com, Marinescu and Wolthoff (2019) show that, within a 6-digit SOC, the elasticity of applications with respect to wages is negative. Therefore, the 6-digit SOC is too broad of a market according to the [small significant non-transitory reduction in wage test].”); Ioana Marinescu & Ronald Wolthoff, Opening the Black Box of the Matching Function: The Power of Words, 38 J. LAB. ECON. 535 (2020).

[213] Id. at *4 (“According to the USDA documentation, “commuting zones were developed without regard to a minimum population threshold and are intended to be a spatial measure of the local labor market.” Marinescu and Rathelot (2018) also show that 81% of applications on CareerBuilder.com are within the commuting zone, with the probability of submitting an application strongly declining in the distance between the applicant’s and the job’s zip code.”); Ioana Marinescu & Roland Rathelot, Mismatch Unemployment and the Geography of Job Search, 10 Am. Econ J. Macroeconomics 42 (2018).

[214] U.S. Dept. of Just. & Fed. Trade Comm’n, Horizontal Merger Guidelines (2010).

[215] Azar, Marinescu, Steinbaum, & Taska, supra note 74, at *13.

[216] Azar, Berry, & Marinescu, supra note 50 (The authors argue the SOC-6 by commuting zone is a plausible definition of a market, based on the market supply elasticity they back out from their estimated job vacancy elasticities).

[217] Gregor Schubert, Anna Stansbury, & Bledi Taska, Employer Concentration and Outside, Working Paper (2024), https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3599454.

[218] Id. at Table 2, Panel A.

[219] Id.

[220] Merger Guidelines, supra note 3, at 6.

[221] Schubert, Stansbury, & Taska, supra note 217, at Table 2, Panel A.

[222] Azar, Marinescu, Steinbaum, & Taska, supra note 74, at 13.

[223] Schubert, Stansbury, & Taska, supra note 217.

[224] Azar, Marinescu, and Steinbaum, supra note 38.

[225] Id. at Table 1. The authors argue this market is likely too large. (“Using the vacancies data set from the same source as the one used in this paper, Marinescu and Wolthoff (2020) show that, within a six-digit SOC, the elasticity of applications to a given job posting with respect to posted wages is negative. Therefore, the six-digit SOC is likely too broad to be a labor market, since we would expect applications to increase in response to posted wages in a frictional labor market”) Marinescu & Wolthoff, supra note 212.

[226] Azar, Marinescu, Steinbaum, & Taska, supra note 74.

[227] Azar, Marinescu, and Steinbaum, supra note , at Table 2.

[228] See infra Appendix Section I.E


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Antitrust & Consumer Protection

A Competition Law & Economics Analysis of Sherlocking

ICLE White Paper Abstract Sherlocking refers to an online platform’s use of nonpublic third-party business data to improve its own business decisions—for instance, by mimicking the successful products . . .


Sherlocking refers to an online platform’s use of nonpublic third-party business data to improve its own business decisions—for instance, by mimicking the successful products and services of edge providers. Such a strategy emerges as a form of self-preferencing and, as with other theories about preferential access to data, it has been targeted by some policymakers and competition authorities due to the perceived competitive risks originating from the dual role played by hybrid platforms (acting as both referees governing their platforms, and players competing with the business they host). This paper investigates the competitive implications of sherlocking, maintaining that an outright ban is unjustified. First, the paper shows that, by aiming to ensure platform neutrality, such a prohibition would cover scenarios (i.e., the use of nonpublic third-party business data to calibrate business decisions in general, rather than to adopt a pure copycat strategy) that should be analyzed separately. Indeed, in these scenarios, sherlocking may affect different forms of competition (inter-platform v. intra-platform competition). Second, the paper argues that, in either case, the practice’s anticompetitive effects are questionable and that the ban is fundamentally driven by a bias against hybrid and vertically integrated players.

I. Introduction

The dual role some large digital platforms play (as both intermediary and trader) has gained prominence among the economic arguments used to justify the recent wave of regulation hitting digital markets around the world. Many policymakers have expressed concern about potential conflicts of interest among companies that have adopted this hybrid model and that also control important gateways for business users. In other words, the argument goes, some online firms act not only as regulators who set their platforms’ rules and as referees who enforce those rules, but also as market players who compete with their business users. This raises the fear that large platforms could reserve preferential treatment for their own services and products, to the detriment of downstream rivals and consumers. That, in turn, has led to calls for platform-neutrality rules.

Toward this aim, essentially all of the legislative initiatives undertaken around the world in recent years to enhance competition in digital markets have included anti-discrimination provisions that target various forms of self-preferencing. Self-preferencing, it has been said, serves as the symbol of the current competition-policy zeitgeist in digital markets.[1] Indeed, this conduct is considered functional to leveraging strategies that would grant gatekeepers the chance to entrench their power in core markets and extend it into associated markets.[2]

Against this background, so-called “sherlocking” has emerged as one form of self-preferencing. The term was coined roughly 20 years ago, after Apple updated its own app Sherlock (a search tool on its desktop-operating system) to mimic a third-party application called Watson, which was created by Karelia Software to complement the Apple tool’s earlier version.[3] According to critics of self-preferencing generally and sherlocking in particular, biased intermediation and related conflicts of interest allow gatekeepers to exploit their preferential access to business users’ data to compete against them by replicating successful products and services. The implied assumption is that this strategy is relevant to competition policy, even where no potential intellectual-property rights (IPRs) are infringed and no slavish imitation sanctionable under unfair-competition laws is detected. Indeed, under such theories, sherlocking would already be prevented by the enforcement of these rules.

To tackle perceived misuse of gatekeepers’ market position, the European Union’s Digital Markets Act (DMA) introduced a ban on sherlocking.[4] Similar concerns have also motivated requests for intervention in the United States,[5] Australia,[6] and Japan.[7] In seeking to address at least two different theories of gatekeepers’ alleged conflicts of interest, these proposed bans on exploiting access to business users’ data are not necessarily limited to the risk of product imitation, but may include any business decision whatsoever that a platform may make while relying on that data.

In parallel with the regulatory initiatives, the conduct at-issue has also been investigated in some antitrust proceedings, which appear to seek the very same twofold goal. In particular, in November 2020, the European Commission sent a statement of objections to Amazon that argued the company had infringed antitrust rules through the systematic use of nonpublic business data from independent retailers who sell on the Amazon online marketplace in order to benefit Amazon’s own retail business, which directly competes with those retailers.[8] A similar investigation was opened by the UK Competition and Markets Authority (CMA) in July 2022.[9]

Further, as part of the investigation opened into Apple’s App Store rule requiring developers to use Apple’s in-app purchase mechanism to distribute paid apps and/or paid digital content, the European Commission also showed interest in evaluating whether Apple’s conduct might disintermediate competing developers from relevant customer data, while Apple obtained valuable data about those activities and its competitors’ offers.[10] The European Commission and UK CMA likewise launched an investigation into Facebook Marketplace, with accusations that Meta used data gathered from advertisers in order to compete with them in markets where the company is active, such as classified ads.[11]

There are two primary reasons these antitrust proceedings are relevant. First, many of the prohibitions envisaged in regulatory interventions (e.g., DMA) clearly took inspiration from the antitrust investigations, thus making it important to explore the insights that competition authorities may provide to support an outright ban. Second, given that regulatory intervention will be implemented alongside competition rules (especially in Europe) rather than displace them,[12] sherlocking can be assessed at both the EU and national level against dominant players that are not eligible for “gatekeeper” designation under the DMA. For those non-gatekeeper firms, the practice may still be investigated by antitrust authorities and assessed before courts, aside from the DMA’s per se prohibition. And, of course, investigations and assessments of sherlocking could also be made even in those jurisdictions where there isn’t an outright ban.

The former sis well-illustrated by the German legislature’s decision to empower its national competition authority with a new tool to tackle abusive practices that are similar and functionally equivalent to the DMA.[13] Indeed, as of January 2021, the Bundeskartellamt may identify positions of particular market relevance (undertakings of “paramount significance for competition across markets”) and assess their possible anticompetitive effects on competition in those areas of digital ecosystems in which individual companies may have a gatekeeper function. Both the initiative’s aims and its list of practices are similar to the DMA. They are distinguished primarily by the fact that the German list is exhaustive, and the practices at-issue are not prohibited per se, but are subject to a reversal of the burden of proof, allowing firms to provide objective justifications. For the sake of this analysis, within the German list, one provision prohibits designated undertakings from “demanding terms and conditions that permit … processing data relevant for competition received from other undertakings for purposes other than those necessary for the provision of its own services to these undertakings without giving these undertakings sufficient choice as to whether, how and for what purpose such data are processed.”[14]

Unfortunately, none of the above-mentioned EU antitrust proceedings have concluded with a final decision that addresses the merits of sherlocking. This precludes evaluating whether the practice would have survived before the courts. Regarding the Apple investigation, the European Commission dropped the case over App Store rules and issued a new statement of objections that no longer mentions sherlocking.[15] Further, the European Commission and the UK CMA accepted the commitments offered by Amazon to close those investigations.[16] The CMA likewise accepted the commitments offered by Meta.[17]

Those outcomes can be explained by the DMA’s recent entry into force. Indeed, because of the need to comply with the new regulation, players designated as gatekeepers likely have lost interest in challenging antitrust investigations that target the very same conduct prohibited by the DMA.[18] After all, given that the DMA does not allow any efficiency defense against the listed prohibitions, even a successful appeal against an antitrust decision would be a pyrrhic victory. From the opposite perspective, the same applies to the European Commission, which may decide to save time, costs, and risks by dropping an ongoing case against a company designated as a gatekeeper under the DMA, knowing that the conduct under investigation will be prohibited in any case.

Nonetheless, despite the lack of any final decision on sherlocking, these antitrust assessments remain relevant. As already mentioned, the DMA does not displace competition law and, in any case, dominant platforms not designated as gatekeepers under the DMA still may face antitrust investigations over sherlocking. This applies even more for jurisdictions, such as the United States, that are evaluating DMA-like legislative initiatives (e.g., the American Innovation and Choice Online Act, or “AICOA”).

Against this background, drawing on recent EU cases, this paper questions the alleged anticompetitive implications of sherlocking, as well as claims that the practice fails to comply with existing antitrust rules.

First, the paper illustrates that prohibitions on the use of nonpublic third-party business data would cover two different theories that should be analyzed separately. Whereas a broader case involves all the business decisions adopted by a dominant platform because of such preferential access (e.g., the launch of new products or services, the development or cessation of existing products or services, the calibration of pricing and management systems), a more specific case deals solely with the adoption of a copycat strategy. By conflating these theories in support of a blanket ban that condemns any use of nonpublic third-party business data, EU antitrust authorities are fundamentally motivated by the same policy goal pursued by the DMA—i.e., to impose a neutrality regime on large online platforms. The competitive implications differ significantly, however, as adopting copycat strategies may only affect intra-brand competition, while using said data to improve other business decisions could also affect inter-platform competition.

Second, the paper shows that, in both of these scenarios, the welfare effects of sherlocking are unclear. Notably, exploiting certain data to better understand the market could help a platform to develop new products and services, to improve existing products and services, or more generally to be more competitive with respect to both business users and other platforms. As such outcomes would benefit consumers in terms of price and quality, any competitive advantage achieved by the hybrid platform could be considered unlawful only if it is not achieved on the merits. In a similar vein, if sherlocking is used by a hybrid platform to deliver replicas of its business users’ products and services, that would likely provide short-term procompetitive effects benefitting consumers with more choice and lower prices. In this case, the only competitive harm that would justify an antitrust intervention resides in (uncertain) negative long-term effects on innovation.

As a result, in any case, an outright ban of sherlocking, such as is enshrined in the DMA, is economically unsound since it would clearly harm consumers.

The paper is structured as follows. Section II describes the recent antitrust investigations of sherlocking, illustrating the various scenarios that might include the use of third-party business data. Section III investigates whether sherlocking may be considered outside the scope of competition on the merits for bringing competitive advantages to platforms solely because of their hybrid business model. Section IV analyzes sherlocking as a copycat strategy by investigating the ambiguous welfare effects of copying in digital markets and providing an antitrust assessment of the practice at issue. Section V concludes.

II. Antitrust Proceedings on Sherlocking: Platform Neutrality and Copycat Competition

Policymakers’ interest in sherlocking is part of a larger debate over potentially unfair strategies that large online platforms may deploy because of their dual role as an unavoidable trading partner for business users and a rival in complementary markets.

In this scenario, as summarized in Table 1, the DMA outlaws sherlocking, establishing that to “prevent gatekeepers from unfairly benefitting from their dual role,”[19] they are restrained from using, in competition with business users, “any data that is not publicly available that is generated or provided by those business users in the context of their use of the relevant core platform services or of the services provided together with, or in support of, the relevant core platform services, including data generated or provided by the customers of those business users.”[20] Recital 46 further clarifies that the “obligation should apply to the gatekeeper as a whole, including but not limited to its business unit that competes with the business users of a core platform service.”

A similar provision was included in the American Innovation and Choice Online Act (AICOA), which was considered, but not ultimately adopted, in the 117th U.S. Congress. AICOA, however, would limit the scope of the ban to the offer of products or services that would compete with those offered by business users.[21] Concerns about copycat strategies were also reported in the U.S. House of Representatives’ investigation of the state of competition in digital markets as supporting the request for structural-separation remedies and line-of-business restrictions to eliminate conflicts of interest where a dominant intermediary enters markets that place it in competition with dependent businesses.[22] Interestingly, however, in the recent complaint filed by the U.S. Federal Trade Commission (FTC) and 17 state attorneys general against Amazon that accuses the company of having deployed an interconnected strategy to block off every major avenue of competition (including price, product selection, quality, and innovation), there is no mention of sherlocking among the numerous unfair practices under investigation.[23]

Evaluating regulatory-reform proposals for digital markets, the Australian Competition and Consumer Commission (ACCC) also highlighted the risk of sherlocking, arguing that it could have an adverse effect on competition, notably on rivals’ ability to compete, when digital platforms exercise their strong market position to utilize nonpublic data to free ride on the innovation efforts of their rivals.[24] Therefore, the ACCC suggested adopting service-specific codes to address self-preferencing by, for instance, imposing data-separation requirements to restrain dominant app-store providers from using commercially sensitive data collected from the app-review process to develop their own apps.[25]

Finally, on a comparative note, it is also useful to mention the proposals advanced by the Japanese Fair Trade Commission (JFTC) in its recent market-study report on mobile ecosystems.[26] In order to ensure equal footing among competitors, the JFTC specified that its suggestion to prevent Google and Apple from using nonpublic data generated by other developers’ apps aims at pursuing two purposes. Such a ban would, indeed, concern not only use of the data for the purpose of developing competing apps, products, and services, but also its use for developing their own apps, products, and services.

TABLE 1: Legislative Initiatives and Proposals to Ban Sherlocking

As previously anticipated, sherlocking recently emerged as an antitrust offense in three investigations launched by the European Commission and the UK CMA.

In the first case, Amazon’s alleged reliance on marketplace sellers’ nonpublic business data has been claimed to distort fair competition on its platform and prevent effective competition. In its preliminary findings, the Commission argued that Amazon takes advantage of its hybrid business model, leveraging its access to nonpublic third-party sellers’ data (e.g., the number of ordered and shipped units of products; sellers’ revenues on the marketplace; the number of visits to sellers’ offers; data relating to shipping, to sellers’ past performance, and to other consumer claims on products, including the activated guarantees) to adjust its retail offers and strategic business decisions to the detriment of third-party sellers, which are direct competitors on the marketplace.[27] In particular, the Commission was concerned that Amazon uses such data for its decision to start and end sales of a product, for its pricing system, for its inventory-planning and management system, and to identify third-party sellers that Amazon’s vendor-recruitment teams should approach to invite them to become direct suppliers to Amazon Retail. To address the data-use concern, Amazon committed not to use nonpublic data relating to, or derived from, independent sellers’ activities on its marketplace for its retail business and not to use such data for the purposes of selling branded goods, as well as its private-label products.[28]

A parallel investigation ended with similar commitments in the UK.[29] According to the UK CMA, Amazon’s access to and use of nonpublic seller data could result in a competitive advantage for Amazon Retail arising from its operation of the marketplace, rather than from competition on the merits, and may lead to relevant adverse effects on competition. Notably, it was alleged this could result in a reduction in the scale and competitiveness of third-party sellers on the Amazon Marketplace; a reduction in the number and range of product offers from third-party sellers on the Amazon Marketplace; and/or less choice for consumers, due to them being offered lower quality goods and/or paying higher prices than would otherwise be the case.

It is also worth mentioning that, by determining that Amazon is an undertaking of paramount significance for competition across markets, the Bundeskartellamt emphasized the competitive advantage deriving from Amazon’s access to nonpublic data, such as Glance Views, sales figures, sale quantities, cost components of products, and reorder status.[30] Among other things, with particular regard to Amazon’s hybrid role, the Bundeskartellamt noted that the preferential access to competitively sensitive data “opens up the possibility for Amazon to optimize its own-brand assortment.”[31]

A second investigation involved Apple and its App Store rule.[32] According to the European Commission, the mandatory use of Apple’s own proprietary in-app purchase system (IAP) would, among other things, grant Apple full control over the relationship its competitors have with customers, thus disintermediating those competitors from customer data and allowing Apple to obtain valuable data about the activities and offers of its competitors.

Finally, Meta faced antitrust proceedings in both the EU and the UK.[33] The focus was on Facebook Marketplace—i.e., an online classified-ads service that allows users to advertise goods for sale. According to the European Commission and the CMA, Meta unilaterally imposes unfair trading conditions on competing online-classified ads services that advertise on Facebook or Instagram. These terms and conditions, which authorize Meta to use ads-related data derived from competitors for the benefit of Facebook Marketplace, are considered unjustified, as they impose an unnecessary burden on competitors and only benefit Facebook Marketplace. The suspicion is that Meta has used advertising data from Facebook Marketplace competitors for the strategic planning, product development, and launch of Facebook Marketplace, as well as for Marketplace’s operation and improvement.

Overall, these investigations share many features. The concerns about third-party business-data use, as well as about other forms of self-preferencing, revolve around the competitive advantages that accrue to a dominant platform because of its dual role. Such advantages are considered unfair, as they are not the result of the merits of a player, but derived purely and simply from its role as an important gateway to reach end users. Moreover, this access to valuable business data is not reciprocal. The feared risk is the marginalization of business users competing with gatekeepers on the gatekeepers’ platforms and, hence, the alleged harm to competition is the foreclosure of rivals in complementary markets (horizontal foreclosure).

The focus of these investigations was well-illustrated by the European Commission’s decision on Amazon’s practice.[34] The Commission’s concern was about the “data delta” that Amazon may exploit, namely the additional data related to third-party sellers’ listings and transactions that are not available to, and cannot be replicated by, the third-party sellers themselves, but are available to and used by Amazon Retail for its own retail operations.[35] Contrary to Amazon Retail—which, according to Commission’s allegations, would have full access to and would use such individual, real-time data of all its third-party sellers to calibrate its own retail decisions—sellers would have access only to their own individual listings and sales data. As a result, the Commission came to the (preliminary) conclusion that real-time access to and use of such volume, variety, and granularity of non-publicly available data from its retail competitors generates a significant competitive advantage for Amazon Retail in each of the different decisional processes that drive its retail operations.[36]

On a closer look, however, while antitrust authorities seem to target the use of nonpublic third-party business data as a single theory of harm, their allegations cover two different scenarios along the lines of what has already been examined with reference to the international legislative initiatives and proposals. Indeed, the Facebook Marketplace case does not involve an allegation of copying, as Meta is accused of gathering data from its business users to launch and improve its ads service, instead of reselling goods and services.

FIGURE 1: Sherlocking in Digital Markets

As illustrated above in Figure 1, while the claim in the latter scenario is that the preferential data use would help dominant players calibrate business decisions in general, the former scenario instead involves the use of such data for a pure copycat strategy of an entire product or service, or some of its specific features.

In both scenarios the aim of the investigations is to ensure platform neutrality. Accordingly, as shown by the accepted commitments, the envisaged solution for antitrust authorities is to impose  data-separation requirements to restrain dominant platforms from using third-party commercially sensitive data. Putting aside that these investigations concluded with commitments from the firms, however, their chances of success before a court differ significantly depending on whether they challenge a product-imitation strategy, or any business decision adopted because of the “data delta.”

A. Sherlocking and Unconventional Theories of Harm for Digital Markets

Before analyzing how existing competition-law rules could be applied to the various scenarios involving the use of third-party business data, it is worth providing a brief overview of the framework in which the assessment of sherlocking is conducted. As competition in the digital economy is increasingly a competition among ecosystems,[37] a lively debate has emerged on the capacity of traditional antitrust analysis to adequately capture the peculiar features of digital markets. Indeed, the combination of strong economies of scale and scope; indirect network effects; data advantages and synergies across markets; and portfolio effects all facilitate ecosystem development all contribute to making digital markets highly concentrated, prone to tipping, and not easily contestable.[38] As a consequence, it’s been suggested that addressing these distinctive features of digital markets requires an overhaul of the antitrust regime.

Such discussions require the antitrust toolkit and theories of harm to illustrate whether and how a particular practice, agreement, or merger is anticompetitive. Notably, at issue is whether traditional antitrust theories of harm are fit for purpose or whether novel theories of harm should be developed in response to the emerging digital ecosystems. The latter requires looking at the competitive impact of expanding, protecting, or strengthening an ecosystem’s position, and particularly whether such expansion serves to exploit a network of capabilities and to control access to key inputs and components.[39]

A significant portion of recent discussions around developing novel theories of harm to better address the characteristics of digital-business models and markets has been devoted to the topic of merger control—in part a result of the impressive number of acquisitions observed in recent years.[40] In particular, the focus has been on analyzing conglomerate mergers that involve acquiring a complementary or unrelated asset, which have traditionally been assumed to raise less-significant competition concerns.

In this regard, an ecosystem-based theory seems to have guided the Bundeskartellamt in its assessment of Meta’s acquisition of Kustomer[41] and by the CMA in Microsoft/Activision.[42] A more recent example is the European Commission’s decision to prohibit the proposed Booking/eTraveli merger, where the Commission explicitly noted that the transaction would have allowed Booking to expand its travel-services ecosystem.[43] The Commission’s concerns were related primarily to the so-called “envelopment” strategy, in which a prominent platform within a specific market broadens its range of services into other markets where there is a significant overlap of customer groups already served by the platform.[44]

Against this background, putative self-preferencing harms represent one of the European Commission’s primary (albeit contentious)[45] attempts to develop new theories of harm built on conglomerate platforms’ ability to bundle services or use data from one market segment to inform product development in another.[46] Originally formulated in the Google Shopping decision,[47] the theory of harm of (leveraging through) self-preferencing has subsequently inspired the DMA, which targets different forms of preferential treatment, including sherlocking.

In particular, it is asserting that platform may use self-preferencing to adopt a leveraging strategy with a twofold anticompetitive effect—that is, excluding or impeding rivals from competing with the platform (defensive leveraging) and extending the platform’s market power into associated markets (offensive leveraging). These goals can be pursued because of the unique role that some large digital platforms play. That is, they not only enjoy strategic market status by controlling ecosystems of integrated complementary products and services, which are crucial gateways for business users to reach end users, but they also perform a dual role as both a critical intermediary and a player active in complementors’ markets. Therefore, conflicts of interests may provide incentives for large vertically integrated platforms to favor their own products and services over those of their competitors.[48]

The Google Shopping theory of harm, while not yet validated by the Court of Justice of the European Union (CJEU),[49] has also found its way into merger analysis, as demonstrated by the European Commission’s recent assessment of iRobot/Amazon.[50] In its statement of objections, the Commission argued that the proposed acquisition of iRobot may give Amazon the ability and incentive to foreclose iRobot’s rivals by engaging in several foreclosing strategies to prevent them from selling robot vacuum cleaners (RVCs) on Amazon’s online marketplace and/or at degrading such rivals’ access to that marketplace. In particular, the Commission found that Amazon could deploy such self-preferencing strategies as delisting rival RVCs; reducing rival RVCs’ visibility in both organic and paid results displayed in Amazon’s marketplace; limiting access to certain widgets or commercially attractive labels; and/or raising the costs of iRobot’s rivals to advertise and sell their RVCs on Amazon’s marketplace.[51]

Sherlocking belongs to this framework of analysis and can be considered a form of self-preferencing, specifically because of the lack of reciprocity in accessing sensitive data.[52] Indeed, while gatekeeper platforms have access to relevant nonpublic third-party business data as a result of their role as unavoidable trading partners, they leverage this information exclusively, without sharing it with third-party sellers, thus further exacerbating an already uneven playing field.[53]

III. Sherlocking for Competitive Advantage: Hybrid Business Model, Neutrality Regimes, and Competition on the Merits

Insofar as prohibitions of sherlocking center on the competitive advantages that platforms enjoy because of their dual role—thereby allowing some players to better calibrate their business decisions due to their preferential access to business users’ data—it should be noted that competition law does not impose a general duty to ensure a level playing field.[54] Further, a competitive advantage does not, in itself, amount to anticompetitive foreclosure under antitrust rules. Rather, foreclosure must not only be proved (in terms of actual or potential effects) but also assessed against potential benefits for consumers in terms of price, quality, and choice of new goods and services.[55]

Indeed, not every exclusionary effect is necessarily detrimental to competition.[56] Competition on the merits may, by definition, lead to the departure from the market or the marginalization of competitors that are less efficient and therefore less attractive to consumers from the point of view of, among other things, price, choice, quality or innovation.[57] Automatically classifying any conduct with exclusionary effects were as anticompetitive could well become a means to protect less-capable, less-efficient undertakings and would in no way protect more meritorious undertakings—thereby potentially hindering a market’s competitiveness.[58]

As recently clarified by the CJEU regarding the meaning of “competition on the merits,” any practice that, in its implementation, holds no economic interest for a dominant undertaking except that of eliminating competitors must be regarded as outside the scope of competition on the merits.[59] Referring to the cases of margin squeezes and essential facilities, the CJEU added that the same applies to practices that a hypothetical equally efficient competitor is unable to adopt because that practice relies on using resources or means inherent to the holding of such a dominant position.[60]

Therefore, while antitrust cases on sherlocking set out to ensure a level playing field and platform neutrality, and therefore center on the competitive advantages that a platform enjoys because of its dual role, mere implementing a hybrid business model does not automatically put such practices outside the scope of competition on the merits. The only exception, according to the interpretation provided in Bronner, is the presence of an essential facility—i.e., an input whose access should be considered indispensable, as there are no technical, legal, or economic obstacles capable of making it impossible, or even unreasonably difficult, to duplicate it.[61]

As a result, unless it is proved that the hybrid platform is an essential facility, sherlocking and other forms of self-preferencing cannot be considered prima facie outside the scope of competition on the merits, or otherwise unlawful. Rather, any assessment of sherlocking demands the demonstration of anticompetitive effects, which in turn requires finding an impact on efficient firms’ ability and incentive to compete. In the scenario at-issue, for instance, the access to certain data may allow a platform to deliver new products or services; to improve existing products or services; or more generally to compete more efficiently not only with respect to the platform’s business users, but also against other platforms. Such an increase in both intra-platform and inter-platform competition would benefit consumers in terms of lower prices, better quality, and a wider choice of new or improved goods and services—i.e., competition on the merits.[62]

In Facebook Marketplace, the European Commission and UK CMA challenged the terms and conditions governing the provision of display-advertising and business-tool services to which Meta required its business customers to sign up.[63] In their view, Meta abused its dominant position by imposing unfair trading conditions on its advertising customers, which authorized Meta to use ads-related data derived from the latter in a way that could afford Meta a competitive advantage on Facebook Marketplace that would not have arisen from competition on the merits. Notably, antitrust authorities argued that Meta’s terms and conditions were unjustified, disproportionate, and unnecessary to provide online display-advertising services on Meta’s platforms.

Therefore, rather than directly questioning the platform’s dual role or hybrid business model, the European Commission and UK CMA decided to rely on traditional case law which considers unfair those clauses that are unjustifiably unrelated to the purpose of the contract, unnecessarily limit the parties’ freedom, are disproportionate, or are unilaterally imposed or seriously opaque.[64] This demonstrates that, outside the harm theory of the unfairness of terms and conditions, a hybrid platform’s use of nonpublic third-party business data to improve its own business decisions is generally consistent with antitrust provisions. Hence, an outright ban would be unjustified.

IV. Sherlocking to Mimic Business Users’ Products or Services

The second, and more intriguing, sherlocking scenario is illustrated by the Amazon Marketplace investigations and regards the original meaning of sherlocking—i.e., where a data advantage is used by a hybrid platform to mimic its business users’ products or services.

Where sherlocking charges assert that the practice allows some platforms to use business users’ data to compete against them by replicating their products or services, it should not be overlooked that the welfare effects of such a copying strategy are ambiguous. While the practice could benefit consumers in the short term by lowering prices and increasing choice, it may discourage innovation over the longer term if third parties anticipate being copied whenever they deliver successful products or services. Therefore, the success of an antitrust investigation essentially relies on demonstrating a harm to innovation that would induce business users to leave the market or stop developing their products and services. In other words, antitrust authorities should be able to demonstrate that, by allowing dominant platforms to free ride on their business guests’ innovation efforts, sherlocking would negatively affect rivals’ ability to compete.

A. The Welfare Effects of Copying

The tradeoff between the short- and long-term welfare effects of copying has traditionally been analyzed in the context of the benefits and costs generated by intellectual-property protection.[65] In particular, the economic literature investigating the optimal life of patents[66] and copyrights[67] focuses on the efficient balance between dynamic benefits associated with innovation and the static costs of monopoly power granted by IPRs.

More recently, product imitation has instead been investigated in the different scenario of digital markets, where dominant platforms adopting a hybrid business model may use third-party sellers’ market data to design and promote their own products over their rivals’ offerings. Indeed, some studies report that large online platforms may attempt to protect their market position by creating “kill zones” around themselves—i.e., by acquiring, copying, or eliminating their rivals.[68] In such a novel setting, the welfare effects of copying are assessed regardless of the presence and the potential enforcement of IPRs, but within a strategy aimed at excluding rivals by exploiting the dual role of both umpire and player to get preferential access to sensitive data and free ride on their innovative efforts.[69]

Even in this context, however, a challenging tradeoff should be considered. Indeed, while in the short term, consumers may benefit from the platform’s imitation strategy in terms of lower prices and higher quality, they may be harmed in the longer term if third parties are discouraged from delivering new products and services. As a result, while there is empirical evidence on hybrid platforms successfully entering into third parties’ adjacent market segments, [70] the extant academic literature finds the welfare implications of such moves to be ambiguous.

A first strand of literature attempts to estimate the welfare impact of the hybrid business model. Notably, Andre Hagiu, Tat-How Teh, and Julian Wright elaborated a model to address the potential implications of an outright ban on platforms’ dual mode, finding that such a structural remedy may harm consumer surplus and welfare even where the platform would otherwise engage in product imitation and self-preferencing.[71] According to the authors, banning the dual mode does not restore the third-party seller’s innovation incentives or the effective price competition between products, which are the putative harms caused by imitation and self-preferencing. Therefore, the authors’ evaluation was that interventions specifically targeting product imitation and self-preferencing were preferable.

Germa?n Gutie?rrez suggested that banning the dual model would generate hardly any benefits for consumers, showing that, in the Amazon case, interventions that eliminate either the Prime program or product variety are likely to decrease welfare.[72]

Further, analyzing Amazon’s business model, Federico Etro found that the platform and consumers’ incentives are correctly aligned, and that Amazon’s business model of hosting sellers and charging commissions prevents the company from gaining through systematic self?preferencing for its private-label and first-party products.[73] In the same vein, on looking at its business model and monetization strategy, Patrick Andreoli-Versbach and Joshua Gans argued that Amazon does not have an obvious incentive to self-preference.[74] Indeed, Amazon’s profitability data show that, on average, the company’s operating margin is higher on third-party sales than on first-party retail sales.

Looking at how modeling details may yield different results with regard to the benefits and harms of the hybrid business model, Simon Anderson and O?zlem Bedre-Defoile maintain that the platform’s choice to sell its own products benefits consumers by lowering prices when a monopoly platform hosts competitive fringe sellers, regardless of the platform’s position as a gatekeeper, whether sellers have an alternate channel to reach consumers, or whether alternate channels are perfect or imperfect substitutes for the platform channel.[75] On the other hand, the authors argued that platform product entry might harm consumers when a big seller with market power sells on its own channel and also on the platform. Indeed, in that case, the platform setting a seller fee before the big seller prices its differentiated products introduces double markups on the big seller’s platform-channel price and leaves some revenue to the big seller.

Studying whether Amazon engages in self-preferencing on its marketplace by favoring its own brands in search results, Chiara Farronato, Andrey Fradkin, and Alexander MacKay demonstrate empirically that Amazon brands remain about 30% cheaper and have 68% more reviews than other similar products.[76] The authors acknowledge, however, that their findings do not imply that consumers are hurt by Amazon brands’ position in search results.

Another strand of literature specifically tackles the welfare effects of sherlocking. In particular, Erik Madsen and Nikhil Vellodi developed a theoretical framework to demonstrate that a ban on insider imitation can either stifle or stimulate innovation, depending on the nature of innovation.[77] Specifically, the ban could stimulate innovation for experimental product categories, while reducing innovation in incremental product markets, since the former feature products with a large chance of superstar demand and the latter generate mostly products with middling demand.

Federico Etro maintains that the tradeoffs at-issue are too complex to be solved with simple interventions, such as bans on dual mode, self-preferencing, or copycatting.[78] Indeed, it is difficult to conclude that Amazon entry is biased to expropriate third-party sellers or that bans on dual mode, self-preferencing, or copycatting would benefit consumers, because they either degrade services and product variety or induce higher prices or commissions.

Similar results are provided by Jay Pil Choi, Kyungmin Kim, and Arijit Mukherjee, who developed a tractable model of a platform-run marketplace where the platform charges a referral fee to the sellers for access to the marketplace, and may also subsequently launch its own private-label product by copying a seller.[79] The authors found that a policy to either ban hybrid mode or only prohibit information use for the launch of private-label products may produce negative welfare implications.

Further, Radostina Shopova argues that, when introducing a private label, the marketplace operator does not have incentive to distort competition and foreclose the outside seller, but does have an incentive to lower fees charged to the outside seller and to vertically differentiate its own product in order to protect the seller’s channel.[80] Even when the intermediary is able to perfectly mimic the quality of the outside seller and monopolize its product space, the intermediary prefers to differentiate its offer and chooses a lower quality for the private-label product. Accordingly, as the purpose of private labels is to offer a lower-quality version of products aimed at consumers with a lower willingness to pay, a marketplace operator does not have an incentive to distort competition in favor of its own product and foreclose the seller of the original higher-quality product.

In addition, according to Jean-Pierre Dubé, curbing development of private-label programs would harm consumers and Amazon’s practices amount to textbook retailing, as they follow an off-the-shelf approach to managing private-label products that is standard for many retail chains in the West.[81] As a result, singling out Amazon’s practices would set a double standard.

Interestingly, such findings about predictors and effects of Amazon’s entry in competition with third-party merchants on its own marketplace are confirmed by the only empirical study developed so far. In particular, analyzing the Home & Kitchen department of Germany’s version of Amazon Marketplace between 2016 and 2021, Gregory S. Crawford, Matteo Courthoud, Regina Seibel, and Simon Zuzek’s results suggest that Amazon’s entry strategy was more consistent with making Marketplace more attractive to consumers than expropriating third-party merchants.[82] Notably, the study showed that, comparing Amazon’s entry decisions with those of the largest third-party merchants, Amazon tends to enter low-growth and low-quality products, which is consistent with a strategy that seeks to make Marketplace more attractive by expanding variety, lessening third-party market power, and/or enhancing product availability. The authors therefore found that Amazon’s entry on Amazon Marketplace demonstrated no systematic adverse effects and caused a mild market expansion.

Massimo Motta and Sandro Shelegia explored interactions between copying and acquisitions, finding that the former (or the threat of copying) can modify the outcome of an acquisition negotiation.[83] According to their model, there could be both static and dynamic incentives for an incumbent to introduce a copycat version of a complementary product. The static rationale consists of lowering the price of the complementary product in order to capture more rents from it, while the dynamic incentive consists of harming a potential rival’s prospects of developing a substitute. The latter may, in turn, affect the direction the entrant takes toward innovation. Anticipating the incumbent’s copying strategy, the entrant may shift resources from improvements to compete with the incumbent’s primary product to developing complementary products.

Jingcun Cao, Avery Haviv, and Nan Li analyzed the opposite scenario—i.e., copycats that seek to mimic the design and user experience of incumbents’ successful products.[84] The authors find empirically that, on average, copycat apps do not have a significant effect on the demand for incumbent apps and that, as with traditional counterfeit products, they may generate a positive demand spillover toward authentic apps.

Massimo Motta also investigated the potential foreclosure effects of platforms adopting a copycat strategy committed to non-discriminatory terms of access for third parties (e.g., Apple App Store, Google Play, and Amazon Marketplace).[85] Notably, according to Motta, when a third-party seller is particularly successful and the platform is unable to raise fees and commissions paid by that seller, the platform may prefer to copy its product or service to extract more profits from users, rather than rely solely on third-party sales. The author acknowledged, however, that even though this practice may create an incentive for self-preferencing, it does not necessarily have anticompetitive effects. Indeed, the welfare effects of the copying strategy are a priori ambiguous.[86] While, on the one hand, the platform’s copying of a third-party product benefits consumers by increasing variety and competition among products, on the other hand, copying might be wasteful for society, in that it entails a fixed cost and may discourage innovation if rivals anticipate that they will be systematically copied whenever they have a successful product.[87] Therefore, introducing a copycat version of a product offered by a firm in an adjacent market might be procompetitive.

B. Antitrust Assessment: Competition, Innovation, and Double Standards

The economic literature has demonstrated that the rationale and welfare effects of sherlocking by hybrid platforms are definitively ambiguous. Against concerns about rivals’ foreclosure, some studies provide a different narrative, illustrating that such a strategy is more consistent with making the platform more attractive to consumers (by differentiating the quality and pricing of the offer) than expropriating business users.[88] Furthermore, copies, imitations, and replicas undoubtedly benefit consumers with more choice and lower prices.

Therefore, the only way to consider sherlocking anticompetitive is by demonstrating long-term deterrent effects on innovation (i.e., reducing rivals’ incentives to invest in new products and services) outweigh consumers’ short-term advantages.[89] Moreover, deterrent effects must not be merely hypothetical, as a finding of abuse cannot be based on a mere possibility of harm.[90] In any case, such complex tradeoffs are at odds with a blanket ban.[91]

Moreover, assessments of the potential impact of sherlocking on innovation cannot disregard the role of IPRs—which are, by definition, the main primary to promote innovation. From this perspective, intellectual-property protection is best characterized as another form of tradeoff. Indeed, the economic rationale of IPRs (in particular, of patents and copyrights) involves, among other things, a tradeoff between access and incentives—i.e., between short-term competitive restrictions and long-term innovative benefits.[92]

According to the traditional incentive-based theory of intellectual property, free riding would represent a dangerous threat that justifies the exclusive rights granted by intellectual-property protection. As a consequence, so long as copycat expropriation does not infringe IPRs, it should be presumed legitimate and procompetitive. Indeed, such free riding is more of an intellectual-property issue than a competitive concern.

In addition, to strike a fair balance between restricting competition and providing incentives to innovation, the exclusive rights granted by IPRs are not unlimited in terms of duration, nor in terms of lawful (although not authorized) uses of the protected subject matter. Under the doctrine of fair use, for instance, reverse engineering represents a legitimate way to obtain information about a firm’s product, even if the intended result is to produce a directly competing product that may steer customers away from the initial product and the patented invention.

Outside of reverse engineering, copying is legitimately exercised once IPRs expire, when copycat competitors can reproduce previously protected elements. As a result of the competitive pressure exerted by new rivals, holders of expired IPRs may react by seeking solutions designed to block or at least limit the circulation of rival products. They could, for example, request other IPRs to cover aspects or functionalities different from those previously protected. They could also bring (sometimes specious) legal action for infringement of the new IPR or for unfair competition by slavish imitation. For these reasons, there have been occasions where copycat competitors have received protection from antitrust authorities against sham litigation brought by IPR holders concerned about losing margins due to pricing pressure from copycats.[93]

Finally, within the longstanding debate on the intersection of intellectual-property protection and competition, EU antitrust authorities have traditionally been unsympathetic toward restrictions imposed by IPRs. The success of the essential-facility doctrine (EFD) is the most telling example of this attitude, as its application in the EU has been extended to IPRs. As a matter of fact, the EFD represents the main antitrust tool for overseeing intellectual property in the EU.[94]

After Microsoft, EU courts have substantially dismantled one of the “exceptional circumstances” previously elaborated in Magill and specifically introduced for cases involving IPRs, with the aim of safeguarding a balance between restrictions to access and incentives to innovate. Whereas the CJEU established in Magill that refusal to grant an IP license should be considered anticompetitive if it prevents the emergence of a new product for which there is potential consumer demand, in Microsoft, the General Court considered such a requirement met even when access to an IPR is necessary for rivals to merely develop improved products with added value.

Given this background, recent competition-policy concerns about sherlocking are surprising. To briefly recap, the practice at-issue increases competition in the short term, but may affect incentives to innovate in the long-term. With regard to the latter, however, the practice neither involves products protected by IPRs nor constitutes a slavish imitation that may be caught under unfair-competition laws.

The case of Amazon, which has received considerable media coverage, is illustrative of the relevance of IP protection. Amazon has been accused of cloning batteries, power strips, wool runner shoes, everyday sling bags, camera tripods, and furniture.[95] One may wonder what kind of innovation should be safeguarded in these cases against potential copies. Admittedly, such examples appear consistent with the findings of the already-illustrated empirical study conducted by Crawford et al. indicating that Amazon tends to enter low-quality products in order to expand variety on the Marketplace and to make it more attractive to consumers.

Nonetheless, if an IPR is involved, right holders are provided with proper means to protect their products against infringement. Indeed, one of the alleged targeted companies (Williams-Sonoma) did file a complaint for design and trademark infringement, claiming that Amazon had copied a chair (Orb Dining Chair) sold by its West Elm brand. According to Williams-Sonoma, the Upholstered Orb Office Chair—which Amazon began selling under its Rivet brand in 2018—was so similar that the ordinary observer would be confused by the imitation.[96] If, instead, the copycat strategy does not infringe any IPR, the potential impact on innovation might not be considered particularly worrisome—at least at first glance.

Further, neither the degree to which third-party business data is unavailable nor the degree to which they are relevant in facilitating copying are clear cut. For instance, in the case of Amazon, public product reviews supply a great deal of information[97] and, regardless of the fact that a third party is selling a product on the Marketplace, anyone can obtain an item for the purposes of reverse engineering.[98]

In addition, antitrust authorities are used to intervening against opportunistic behavior by IPR holders. European competition authorities, in particular, have never before seemed particularly responsive to the motives of inventors and creators versus the need to encourage maximum market openness.

It should also be noted that cloning is a common strategy in traditional markets (e.g., food products)[99] and has been the subject of longstanding controversies between high-end fashion brands and fast-fashion brands (e.g., Zara, H&M).[100] Furthermore, brick-and-mortar retailers also introduce private labels and use other brands’ sales records in deciding what to produce.[101]

So, what makes sherlocking so different and dangerous when deployed in digital markets as to push competition authorities to contradict themselves?[102]

The double standard against sherlocking reflects the same concern and pursues the same goal of the various other attempts to forbid any form of self-preferencing in digital markets. Namely, antitrust investigations of sherlocking are fundamentally driven by the bias against hybrid and vertically integrated players. The investigations rely on the assumption that conflicts of interest have anticompetitive implications and that, therefore, platform neutrality should be promoted to ensure the neutrality of the competitive process.[103] Accordingly, hostility toward sherlocking may involve both of the illustrated scenarios—i.e., the use of nonpublic third-party business data either in adopting any business decision, or just copycat strategies, in particular.

As a result, however, competition authorities end up challenging a specific business model, rather than the specific practice at-issue, which brings undisputed competitive benefits in terms of lower prices and wider consumer choice, and which should therefore be balanced against potential exclusionary risks. As the CJEU has pointed out, the concept of competition on the merits:

…covers, in principle, a competitive situation in which consumers benefit from lower prices, better quality and a wider choice of new or improved goods and services. Thus, … conduct which has the effect of broadening consumer choice by putting new goods on the market or by increasing the quantity or quality of the goods already on offer must, inter alia, be considered to come within the scope of competition on the merits.[104]

Further, in light of the “as-efficient competitor” principle, competition on the merits may lead to “the departure from the market, or the marginalization of, competitors that are less efficient and so less attractive to consumers from the point of view of, among other things, price, choice, quality or innovation.”[105]

It has been correctly noted that the “as-efficient competitor” principle is a reminder of what competition law is about and how it differs from regulation.[106] Competition law aims to protect a process, rather than engineering market structures to fulfill a particular vision of how an industry is to operate.[107] In other words, competition law does not target firms on the basis of size or status and does not infer harm from (market or bargaining) power or business model. Therefore, neither the dual role played by some large online platforms nor their preferential access to sensitive business data or their vertical integration, by themselves, create a competition problem. Competitive advantages deriving from size, status, power, or business model cannot be considered per se outside the scope of competition on the merits.

Some policymakers have sought to resolve these tensions in how competition law regards sherlocking by introducing or envisaging an outright ban. These initiatives and proposals have clearly been inspired by antitrust investigations, but they did so for the wrong reasons. Instead of taking stock of the challenging tradeoffs between short-term benefits and long-term risks that an antitrust assessment of sherlocking requires, they blamed competition law for not providing effective tools to achieve the policy goal of platform neutrality.[108] Therefore, the regulatory solution is merely functional to bypass the traditional burden of proof of antitrust analysis and achieve what competition-law enforcement cannot provide.

V. Conclusion

The bias against self-preferencing strikes again. Concerns about hybrid platforms’ potential conflicts of interest have led policymakers to seek prohibitions to curb different forms of self-preferencing, making the latter the symbol of the competition-policy zeitgeist in digital markets. Sherlocking shares this fate. Indeed, the DMA outlaws any use of business users’ nonpublic data and similar proposals have been advanced in the United States, Australia, and Japan. Further, like other forms of self-preferencing, such regulatory initiatives against sherlocking have been inspired by previous antitrust proceedings.

Drawing on these antitrust investigations, the present research shows the extent to which an outright ban on sherlocking is unjustified. Notably, the practice at-issue includes two different scenarios: the broad case in which a gatekeeper exploits its preferential access to business users’ data to better calibrate all of its business decisions and the narrow case in which such data is used to adopt a copycat strategy. In either scenario, the welfare effects and competitive implications of sherlocking are unclear.

Indeed, the use of certain data by a hybrid platform to improve business decisions generally should be classified as competition on the merits, and may yield an increase in both intra-platform (with respect to business users) and inter-platform (with respect to other platforms) competition. This would benefit consumers in terms of lower prices, better quality, and a wider choice of new or improved goods and services. In a similar vein, if sherlocking is used to deliver replicas of business users’ products or services, the anti-competitiveness of such a strategy may only result from a cumbersome tradeoff between short-term benefits (i.e., lower prices and wider choice) and negative long-term effects on innovation.

An implicit confirmation of the difficulties encountered in demonstrating the anti-competitiveness of sherlocking comes from the recent complaint issued by the FTC against Amazon.[109] Current FTC Chairwoman Lina Khan devoted a significant portion of her previous academic career to questioning Amazon’s practices (including the decision to introduce its own private labels inspired by third-party products)[110] and to supporting the adoption of structural-separation remedies to tackle platforms’ conflicts of interest that induce them to exploit their “systemic informational advantage (gleaned from competitors)” to thwart rivals and strengthen their own position by introducing replica products.[111] Despite these premises and although the FTC’s complaint targets numerous practices belonging to what has been described as an interconnected strategy to block off every major avenue of competition, however, sherlocking is surprisingly off the radar.

Regulatory initiatives to ban sherlocking in order to ensure platform neutrality with respect to business users and a level playing field among rivals would sacrifice undisputed procompetitive benefits on the altar of policy goals that competition rules are not meant to pursue. Sherlocking therefore appears to be a perfect case study of the side effects of unwarranted interventions in digital markets.

[1] Giuseppe Colangelo, Antitrust Unchained: The EU’s Case Against Self-Preferencing, 72 GRUR International 538 (2023).

[2] Jacques Cre?mer, Yves-Alexandre de Montjoye, & Heike Schweitzer, Competition Policy for the Digital Era (2019), 7, https://op.europa.eu/en/publication-detail/-/publication/21dc175c-7b76-11e9-9f05-01aa75ed71a1/language-en (all links last accessed 3 Jan. 2024); UK Digital Competition Expert Panel, Unlocking Digital Competition, (2019) 58, available at https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/785547/unlocking_digital_competition_furman_review_web.pdf.

[3] You’ve Been Sherlocked, The Economist (2012), https://www.economist.com/babbage/2012/07/13/youve-been-sherlocked.

[4] Regulation (EU) 2022/1925 on contestable and fair markets in the digital sector and amending Directives (EU) 2019/1937 and (EU) 2020/1828 (Digital Markets Act) (2022), OJ L 265/1, Article 6(2).

[5] U.S. S. 2992, American Innovation and Choice Online Act (AICOA) (2022), Section 3(a)(6), available at https://www.klobuchar.senate.gov/public/_cache/files/b/9/b90b9806-cecf-4796-89fb-561e5322531c/B1F51354E81BEFF3EB96956A7A5E1D6A.sil22713.pdf. See also U.S. House of Representatives, Subcommittee on Antitrust, Commercial, and Administrative Law, Investigation of Competition in Digital Markets, Majority Staff Reports and Recommendations (2020), 164, 362-364, 378, available at https://democrats-judiciary.house.gov/uploadedfiles/competition_in_digital_markets.pdf.

[6] Australian Competition and Consumer Commission, Digital Platform Services Inquiry Report on Regulatory Reform (2022), 125, https://www.accc.gov.au/about-us/publications/serial-publications/digital-platform-services-inquiry-2020-2025/digital-platform-services-inquiry-september-2022-interim-report-regulatory-reform.

[7] Japan Fair Trade Commission, Market Study Report on Mobile OS and Mobile App Distribution (2023), https://www.jftc.go.jp/en/pressreleases/yearly-2023/February/230209.html.

[8] European Commission, 10 Nov. 2020, Case AT.40462, Amazon Marketplace; see Press Release, Commission Sends Statement of Objections to Amazon for the Use of Non-Public Independent Seller Data and Opens Second Investigation into Its E-Commerce Business Practices, European Commission (2020), https://ec.europa.eu/commission/presscorner/detail/en/ip_20_2077.

[9] Press Release, CMA Investigates Amazon Over Suspected Anti-Competitive Practices, UK Competition and Markets Authority (2022), https://www.gov.uk/government/news/cma-investigates-amazon-over-suspected-anti-competitive-practices.

[10] European Commission, 16 Jun. 2020, Case AT.40716, Apple – App Store Practices.

[11] Press Release, Commission Sends Statement of Objections to Meta over Abusive Practices Benefiting Facebook Marketplace, European Commission (2022), https://ec.europa.eu/commission/presscorner/detail/en/ip_22_7728; Press Release, CMA Investigates Facebook’s Use of Ad Data, UK Competition and Markets Authority (2021), https://www.gov.uk/government/news/cma-investigates-facebook-s-use-of-ad-data.

[12] DMA, supra note 4, Recital 10 and Article 1(6).

[13] GWB Digitalization Act, 18 Jan. 2021, Section 19a. On risks of overlaps between the DMA and the competition law enforcement, see Giuseppe Colangelo, The European Digital Markets Act and Antitrust Enforcement: A Liaison Dangereuse, 47 European Law Review 597.

[14] GWB, supra note 13, Section 19a (2)(4)(b).

[15] Press Release, Commission Sends Statement of Objections to Apple Clarifying Concerns over App Store Rules for Music Streaming Providers, European Commission (2023), https://ec.europa.eu/commission/presscorner/detail/en/ip_23_1217.

[16] European Commission, 20 Dec. 2022, Case AT.40462; Press Release, Commission Accepts Commitments by Amazon Barring It from Using Marketplace Seller Data, and Ensuring Equal Access to Buy Box and Prime, European Commission (2022), https://ec.europa.eu/commission/presscorner/detail/en/ip_22_7777; UK Competition and Markets Authority, 3 Nov. 2023, Case No. 51184, https://www.gov.uk/cma-cases/investigation-into-amazons-marketplace.

[17] UK Competition and Markets Authority, 3 Nov. 2023, Case AT.51013, https://www.gov.uk/cma-cases/investigation-into-facebooks-use-of-data.

[18] See, e.g., Gil Tono & Lewis Crofts (2022), Amazon Data Commitments Match DMA Obligations, EU’s Vestager Say, mLex (2022), https://mlexmarketinsight.com/news/insight/amazon-data-commitments-match-dma-obligation-eu-s-vestager-says (reporting that Commissioner Vestager stated that Amazon’s data commitments definitively appear to match what would be asked within the DMA).

[19] DMA, supra note 4, Recital 46.

[20] Id., Article 6(2) (also stating that, for the purposes of the prohibition, non-publicly available data shall include any aggregated and non-aggregated data generated by business users that can be inferred from, or collected through, the commercial activities of business users or their customers, including click, search, view, and voice data, on the relevant core platform services or on services provided together with, or in support of, the relevant core platform services of the gatekeeper).

[21] AICOA, supra note 5.

[22] U.S. House of Representatives, supra note 5; see also Lina M. Khan, The Separation of Platforms and Commerce, 119 Columbia Law Review 973 (2019).

[23] U.S. Federal Trade Commission, et al. v. Amazon.com, Inc., Case No. 2:23-cv-01495 (W.D. Wash., 2023).

[24] Australian Competition and Consumer Commission, supra note 6, 125.

[25] Id., 124.

[26] Japan Fair Trade Commission, supra note 7, 144.

[27] European Commission, supra note 8. But see also Amazon, Supporting Sellers with Tools, Insights, and Data (2021), https://www.aboutamazon.eu/news/policy/supporting-sellers-with-tools-insights-and-data (claiming that the company is just using aggregate (rather than individual) data: “Just like our third-party sellers and other retailers across the world, Amazon also uses data to run our business. We use aggregated data about customers’ experience across the store to continuously improve it for everyone, such as by ensuring that the store has popular items in stock, customers are finding the products they want to purchase, or connecting customers to great new products through automated merchandising.”)

[28] European Commission, supra note 16.

[29] UK Competition and Markets Authority, supra notes 9 and 16.

[30] Bundeskartellamt, 5 Jul. 2022, Case B2-55/21, paras. 493, 504, and 518.

[31] Id., para. 536.

[32] European Commission, supra note 10.

[33] European Commission, supra note 11; UK Competition and Markets Authority, supra note 11.

[34] European Commission, supra note 16. In a similar vein, see also UK Competition and Markets Authority, supra note 16, paras. 4.2-4.7.

[35] European Commission, supra note 16, para. 111.

[36] Id., para. 123.

[37] Cre?mer, de Montjoye, & Schweitzer, supra note 2, 33-34.

[38] See, e.g., Marc Bourreau, Some Economics of Digital Ecosystems, OECD Hearing on Competition Economics of Digital Ecosystems (2020), https://www.oecd.org/daf/competition/competition-economics-of-digital-ecosystems.htm; Amelia Fletcher, Digital Competition Policy: Are Ecosystems Different?, OECD Hearing on Competition Economics of Digital Ecosystems (2020).

[39] See, e.g., Cristina Caffarra, Matthew Elliott, & Andrea Galeotti, ‘Ecosystem’ Theories of Harm in Digital Mergers: New Insights from Network Economics, VoxEU (2023), https://cepr.org/voxeu/columns/ecosystem-theories-harm-digital-mergers-new-insights-network-economics-part-1 (arguing that, in merger control, the implementation of an ecosystem theory of harm would require assessing how a conglomerate acquisition can change the network of capabilities (e.g., proprietary software, brand, customer-base, data) in order to evaluate how easily competitors can obtain alternative assets to those being acquired); for a different view, see Geoffrey A. Manne & Dirk Auer, Antitrust Dystopia and Antitrust Nostalgia: Alarmist Theories of Harm in Digital Markets and Their Origins, 28 George Mason Law Review 1281(2021).

[40] See, e.g., Viktoria H.S.E. Robertson, Digital merger control: adapting theories of harm, (forthcoming) European Competition Journal; Caffarra, Elliott, & Galeotti, supra note 39; OECD, Theories of Harm for Digital Mergers (2023), available at www.oecd.org/daf/competition/theories-of-harm-for-digital-mergers-2023.pdf; Bundeskartellamt, Merger Control in the Digital Age – Challenges and Development Perspectives (2022), available at https://www.bundeskartellamt.de/SharedDocs/Publikation/EN/Diskussions_Hintergrundpapiere/2022/Working_Group_on_Competition_Law_2022.pdf?__blob=publicationFile&v=2; Elena Argentesi, Paolo Buccirossi, Emilio Calvano, Tomaso Duso, Alessia Marrazzo, & Salvatore Nava, Merger Policy in Digital Markets: An Ex Post Assessment, 17 Journal of Competition Law & Economics 95 (2021); Marc Bourreau & Alexandre de Streel, Digital Conglomerates and EU Competition Policy (2019), https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3350512.

[41] Bundeskartellamt, 11 Feb. 2022, Case B6-21/22, https://www.bundeskartellamt.de/SharedDocs/Entscheidung/EN/Fallberichte/Fusionskontrolle/2022/B6-21-22.html;jsessionid=C0837BD430A8C9C8E04D133B0441EB95.1_cid362?nn=4136442.

[42] UK Competition and Markets Authority, Microsoft / Activision Blizzard Merger Inquiry (2023), https://www.gov.uk/cma-cases/microsoft-slash-activision-blizzard-merger-inquiry.

[43] See European Commission, Commission Prohibits Proposed Acquisition of eTraveli by Booking (2023), https://ec.europa.eu/commission/presscorner/detail/en/ip_23_4573 (finding that a flight product is a crucial growth avenue in Booking’s ecosystem, which revolves around its hotel online-travel-agency (OTA) business, as it would generate significant additional traffic to the platform, thus allowing Booking to benefit from existing customer inertia and making it more difficult for competitors to contest Booking’s position in the hotel OTA market).

[44] Thomas Eisenmann, Geoffrey Parker, & Marshall Van Alstyne, Platform Envelopment, 32 Strategic Management Journal 1270 (2011).

[45] See, e.g., Colangelo, supra note 1, and Pablo Iba?n?ez Colomo, Self-Preferencing: Yet Another Epithet in Need of Limiting Principles, 43 World Competition 417 (2020) (investigating whether and to what extent self-preferencing could be considered a new standalone offense in EU competition law); see also European Commission, Digital Markets Act – Impact Assessment Support Study (2020), 294, https://op.europa.eu/en/publication-detail/-/publication/0a9a636a-3e83-11eb-b27b-01aa75ed71a1/language-en (raising doubts about the novelty of this new theory of harm, which seems similar to the well-established leveraging theories of harm of tying and bundling, and margin squeeze).

[46] European Commission, supra note 45, 16.

[47] European Commission, 27 Jun. 2017, Case AT.39740, Google Search (Shopping).

[48] See General Court, 10 Nov. 2021, Case T-612/17, Google LLC and Alphabet Inc. v. European Commission, ECLI:EU:T:2021:763, para. 155 (stating that the general principle of equal treatment obligates vertically integrated platforms to refrain from favoring their own services as opposed to rival ones; nonetheless, the ruling framed self-preferencing as discriminatory abuse).

[49] In the meantime, however, see Opinion of the Advocate General Kokott, 11 Jan. 2024, Case C-48/22 P, Google v. European Commission, ECLI:EU:C:2024:14, paras. 90 and 95 (arguing that the self-preferencing of which Google is accused constitutes an independent form of abuse, albeit one that exhibits some proximity to cases involving margin squeezing).

[50] European Commission, Commission Sends Amazon Statement of Objections over Proposed Acquisition of iRobot (2023), https://ec.europa.eu/commission/presscorner/detail/en/IP_23_5990.

[51] The same concerns and approach have been shared by the CMA, although it reached a different conclusion, finding that the new merged entity would not have incentive to self-preference its own branded RVCs: see UK Competition and Markets Authority, Amazon / iRobot Merger Inquiry – Clearance Decision (2023), paras. 160, 188, and 231, https://www.gov.uk/cma-cases/amazon-slash-irobot-merger-inquiry.

[52] See European Commission, supra note 45, 304.

[53] Id., 313-314 (envisaging, among potential remedies, the imposition of a duty to make all data used by the platform for strategic decisions available to third parties); see also Désirée Klinger, Jonathan Bokemeyer, Benjamin Della Rocca, & Rafael Bezerra Nunes, Amazon’s Theory of Harm, Yale University Thurman Arnold Project (2020), 19, available at https://som.yale.edu/sites/default/files/2022-01/DTH-Amazon.pdf.

[54] Colangelo, supra note 1; see also Oscar Borgogno & Giuseppe Colangelo, Platform and Device Neutrality Regime: The New Competition Rulebook for App Stores?, 67 Antitrust Bulletin 451 (2022).

[55] See Court of Justice of the European Union (CJEU), 12 May 2022, Case C-377/20, Servizio Elettrico Nazionale SpA v. Autorità Garante della Concorrenza e del Mercato, ECLI:EU:C:2022:379; 19 Apr. 2018, Case C-525/16, MEO v. Autoridade da Concorrência, ECLI:EU:C:2018:270; 6 Sep. 2017, Case C-413/14 P, Intel v. Commission, ECLI:EU:C:2017:632; 6 Oct. 2015, Case C-23/14, Post Danmark A/S v. Konkurrencerådet (Post Danmark II), ECLI:EU:C:2015:651; 27 Mar. 2012, Case C-209/10, Post Danmark A/S v Konkurrencera?det (Post Danmark I), ECLI: EU:C:2012:172; for a recent overview of the EU case law, see also Pablo Iba?n?ez Colomo, The (Second) Modernisation of Article 102 TFEU: Reconciling Effective Enforcement, Legal Certainty and Meaningful Judicial Review, SSRN (2023), https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4598161.

[56] CJEU, Intel, supra note 55, paras. 133-134.

[57] CJEU, Servizio Elettrico Nazionale, supra note 55, para. 73.

[58] Opinion of Advocate General Rantos, 9 Dec. 2021, Case C?377/20, Servizio Elettrico Nazionale SpA v. Autorità Garante della Concorrenza e del Mercato, ECLI:EU:C:2021:998, para. 45.

[59] CJEU, Servizio Elettrico Nazionale, supra note 55, para. 77.

[60] Id., paras. 77, 80, and 83.

[61] CJEU, 26 Nov.1998, Case C-7/97, Oscar Bronner GmbH & Co. KG v. Mediaprint Zeitungs- und Zeitschriftenverlag GmbH & Co. KG, Mediaprint Zeitungsvertriebsgesellschaft mbH & Co. KG and Mediaprint Anzeigengesellschaft mbH & Co. KG, ECLI:EU:C:1998:569.

[62] CJEU, Servizio Elettrico Nazionale, supra note 55, para. 85.

[63] European Commission, supra note 11; UK Competition and Markets Authority, supra note 17, paras. 2.6, 4.3, and 4.7.

[64] See, e.g., European Commission, Case COMP D3/34493, DSD, para. 112 (2001) OJ L166/1; affirmed in GC, 24 May 2007, Case T-151/01, DerGru?nePunkt – Duales System DeutschlandGmbH v. European Commission, ECLI:EU:T:2007:154 and CJEU, 16 Jul. 2009, Case C-385/07 P, ECLI:EU:C:2009:456; European Commission, Case IV/31.043, Tetra Pak II, paras. 105–08, (1992) OJ L72/1; European Commission, Case IV/29.971, GEMA III, (1982) OJ L94/12; CJUE, 27 Mar. 1974, Case 127/73, Belgische Radio en Televisie e socie?te? belge des auteurs, compositeurs et e?diteurs v. SV SABAM and NV Fonior, ECLI:EU:C:1974:25, para. 15; European Commission, Case IV/26.760, GEMA II, (1972) OJ L166/22; European Commission, Case IV/26.760, GEMA I, (1971) OJ L134/15.

[65] See, e.g., Richard A. Posner, Intellectual Property: The Law and Economics Approach, 19 The Journal of Economic Perspectives 57 (2005).

[66] See, e.g., Richard Gilbert & Carl Shapiro, Optimal Patent Length and Breadth, 21 The RAND Journal of Economics 106 (1990); Pankaj Tandon, Optimal Patents with Compulsory Licensing, 90 Journal of Political Economy 470 (1982); Frederic M. Scherer, Nordhaus’ Theory of Optimal Patent Life: A Geometric Reinterpretation, 62 American Economic Review 422 (1972); William D. Nordhaus, Invention, Growth, and Welfare: A Theoretical Treatment of Technological Change, Cambridge, MIT Press (1969).

[67] See, e.g., Hal R. Varian, Copying and Copyright, 19 The Journal of Economic Perspectives 121 (2005); William R. Johnson, The Economics of Copying, 93 Journal of Political Economy 158 (1985); Stephen Breyer, The Uneasy Case for Copyright: A Study of Copyright in Books, Photocopies, and Computer Programs, 84 Harvard Law Review 281 (1970).

[68] Sai Krishna Kamepalli, Raghuram Rajan, & Luigi Zingales, Kill Zone, NBER Working Paper No. 27146 (2022), http://www.nber.org/papers/w27146; Massimo Motta & Sandro Shelegia, The “Kill Zone”: Copying, Acquisition and Start-Ups’ Direction of Innovation, Barcelona GSE Working Paper Series Working Paper No. 1253 (2021), https://bse.eu/research/working-papers/kill-zone-copying-acquisition-and-start-ups-direction-innovation; U.S. House of Representatives, Subcommittee on Antitrust, Commercial, and Administrative Law, supra note 8, 164; Stigler Committee for the Study of Digital Platforms, Market Structure and Antitrust Subcommittee (2019) 54, https://research.chicagobooth.edu/stigler/events/single-events/antitrust-competition-conference/digital-platforms-committee; contra, see Geoffrey A. Manne, Samuel Bowman, & Dirk Auer, Technology Mergers and the Market for Corporate Control, 86 Missouri Law Review 1047 (2022).

[69] See also Howard A. Shelanski, Information, Innovation, and Competition Policy for the Internet, 161 University of Pennsylvania Law Review 1663 (2013), 1999 (describing as “forced free riding” the situation occurring when a platform appropriates innovation by other firms that depend on the platform for access to consumers).

[70] See Feng Zhu & Qihong Liu, Competing with Complementors: An Empirical Look at Amazon.com, 39 Strategic Management Journal 2618 (2018).

[71] Andrei Hagiu, Tat-How Teh, and Julian Wright, Should Platforms Be Allowed to Sell on Their Own Marketplaces?, 53 RAND Journal of Economics 297 (2022), (the model assumes that there is a platform that can function as a seller and/or a marketplace, a fringe of small third-party sellers that all sell an identical product, and an innovative seller that has a better product in the same category as the fringe sellers and can invest more in making its product even better; further, the model allows the different channels (on-platform or direct) and the different sellers to offer different values to consumers; therefore, third-party sellers (including the innovative seller) can choose whether to participate on the platform’s marketplace, and whenever they do, can price discriminate between consumers that come to it through the marketplace and consumers that come to it through the direct channel).

[72] See Germa?n Gutie?rrez, The Welfare Consequences of Regulating Amazon (2022), available at http://germangutierrezg.com/Gutierrez2021_AMZ_welfare.pdf (building an equilibrium model where consumers choose products on the Amazon platform, while third-party sellers and Amazon endogenously set prices of products and platform fees).

[73] See Federico Etro, Product Selection in Online Marketplaces, 30 Journal of Economics & Management Strategy 614 (2021), (relying on a model where a marketplace such as Amazon provides a variety of products and can decide, for each product, whether to monetize sales by third-party sellers through a commission or become a seller on its platform, either by commercializing a private label version or by purchasing from a vendor and resell as a first party retailer; as acknowledged by the author, a limitation of the model is that it assumes that the marketplace can set the profit?maximizing commission on each product; if this is not the case, third-party sales would be imperfectly monetized, which would increase the relative profitability of entry).

[74] Patrick Andreoli-Versbach & Joshua Gans, Interplay Between Amazon Store and Logistics, SSRN (2023) https://ssrn.com/abstract=4568024.

[75] Simon Anderson & O?zlem Bedre-Defolie, Online Trade Platforms: Hosting, Selling, or Both?, 84 International Journal of Industrial Organization 102861 (2022).

[76] Chiara Farronato, Andrey Fradkin, & Alexander MacKay, Self-Preferencing at Amazon: Evidence From Search Rankings, NBER Working Paper No. 30894 (2023), http://www.nber.org/papers/w30894.

[77] See Erik Madsen & Nikhil Vellodi, Insider Imitation, SSRN (2023) https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3832712 (introducing a two-stage model where the platform publicly commits to an imitation policy and the entrepreneur observes this policy and chooses whether to innovate: if she chooses not to, the game ends and both players earn profits normalized to zero; otherwise, the entrepreneur pays a fixed innovation cost to develop the product, which she then sells on a marketplace owned by the platform).

[78] Federico Etro, The Economics of Amazon, SSRN (2022), https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4307213.

[79] Jay Pil Choi, Kyungmin Kim, & Arijit Mukherjee, “Sherlocking” and Information Design by Hybrid Platforms, SSRN (2023), https://ssrn.com/abstract=4332558 (the model assumes that the platform chooses its referral fee at the beginning of the game and that the cost of entry is the same for both the seller and the platform).

[80] Radostina Shopova, Private Labels in Marketplaces, 89 International Journal of Industrial Organization 102949 (2023), (the model assumes that the market structure is given exogenously and that the quality of the seller’s product is also exogenous; therefore, the paper does not investigate how entry by a platform affects the innovation incentives of third-party sellers).

[81] Jean-Pierre Dube?, Amazon Private Brands: Self-Preferencing vs Traditional Retailing, SSRN (2022) https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4205988.

[82] Gregory S. Crawford, Matteo Courthoud, Regina Seibel, & Simon Zuzek, Amazon Entry on Amazon Marketplace, CEPR Discussion Paper No. 17531 (2022), https://cepr.org/publications/dp17531.

[83] Motta & Shelegia, supra note 68.

[84] Jingcun Cao, Avery Haviv, & Nan Li, The Spillover Effects of Copycat Apps and App Platform Governance, SSRN (2023), https://ssrn.com/abstract=4250292.

[85] Massimo Motta, Self-Preferencing and Foreclosure in Digital Markets: Theories of Harm for Abuse Cases, 90 International Journal of Industrial Organization 102974 (2023).

[86] Id.

[87] Id.

[88] See, e.g., Crawford, Courthoud, Seibel, & Zuzek, supra note 82; Etro, supra note 78; Shopova, supra note 80.

[89] Motta, supra note 85.

[90] Servizio Elettrico Nazionale, supra note 55, paras. 53-54; Post Danmark II, supra note 55, para. 65.

[91] Etro, supra note 78; see also Herbert Hovenkamp, The Looming Crisis in Antitrust Economics, 101 Boston University Law Review 489 (2021), 543, (arguing that: “Amazon’s practice of selling both its own products and those of rivals in close juxtaposition almost certainly benefits consumers by permitting close price comparisons. When Amazon introduces a product such as AmazonBasics AAA batteries in competition with Duracell, prices will go down. There is no evidence to suggest that the practice is so prone to abuse or so likely to harm consumers in other ways that it should be categorically condemned. Rather, it is an act of partial vertical integration similar to other practices that the antitrust laws have confronted and allowed in the past.”)

[92] On the more complex economic rationale of intellectual property, see, e.g., William M. Landes & Richard A. Posner, The Economic Structure of Intellectual Property Law, Cambridge, Harvard University Press (2003).

[93] See, e.g., Italian Competition Authority, 18 Jul. 2023 No. 30737, Case A538 – Sistemi di sigillatura multidiametro per cavi e tubi, (2023) Bulletin No. 31.

[94] See CJEU, 6 Apr. 1995, Joined Cases C-241/91 P and 242/91 P, RTE and ITP v. Commission, ECLI:EU:C:1995:98; 29 Apr. 2004, Case C-418/01, IMS Health GmbH & Co. OHG v. NDC Health GmbH & Co. GH, ECLI:EU:C:2004:257; General Court, 17 Sep. 2007, Case T-201/04, Microsoft v. Commission, ECLI:EU:T:2007:289; CJEU, 16 Jul. 2015, Case C-170/13, Huawei Technologies Co. Ltd v. ZTE Corp., ECLI:EU:C:2015:477.

[95] See, e.g., Dana Mattioli, How Amazon Wins: By Steamrolling Rivals and Partners, Wall Street Journal (2022), https://www.wsj.com/articles/amazon-competition-shopify-wayfair-allbirds-antitrust-11608235127; Aditya Kalra & Steve Stecklow, Amazon Copied Products and Rigged Search Results to Promote Its Own Brands, Documents Show, Reuters (2021), https://www.reuters.com/investigates/special-report/amazon-india-rigging.

[96] Williams-Sonoma, Inc. v. Amazon.Com, Inc., Case No. 18-cv-07548 (N.D. Cal., 2018). The suit was eventually dismissed, as the parties entered into a settlement agreement: Williams-Sonoma, Inc. v. Amazon.Com, Inc., Case No. 18-cv-07548-AGT (N.D. Cal., 2020).

[97] Amazon Best Sellers, https://www.amazon.com/Best-Sellers/zgbs.

[98] Hovenkamp, supra note 91, 2015-2016.

[99] Nicolas Petit, Big Tech and the Digital Economy, Oxford, Oxford University Press (2020), 224-225.

[100] For a recent analysis, see Zijun (June) Shi, Xiao Liu, Dokyun Lee, & Kannan Srinivasan, How Do Fast-Fashion Copycats Affect the Popularity of Premium Brands? Evidence from Social Media, 60 Journal of Marketing Research 1027 (2023).

[101] Lina M. Khan, Amazon’s Antitrust Paradox, 126 Yale Law Journal 710 (2017), 782.

[102] See Massimo Motta &Martin Peitz, Intervention Triggers and Underlying Theories of Harm, in Market Investigations. A New Competition Tool for Europe? (M. Motta, M. Peitz, & H. Schweitzer, eds.), Cambridge, Cambridge University Press (2022), 16, 59 (arguing that, while it is unclear to what extent products or ideas are worth protecting and/or can be protected from sherlocking and whether such cloning is really harmful to consumers, this is clearly an area where an antitrust investigation for abuse of dominant position would not help).

[103] Khan, supra note 101, 780 and 783 (arguing that Amazon’s conflicts of interest tarnish the neutrality of the competitive process and that the competitive implications are clear, as Amazon is exploiting the fact that some of its customers are also its rivals).

[104] Servizio Elettrico Nazionale, supra note 55, para. 85.

[105] Post Danmark I, supra note 55, para. 22.

[106] Iba?n?ez Colomo, supra note 55, 21-22.

[107] Id.

[108] See, e.g., DMA, supra note 4, Recital 5 (complaining that the scope of antitrust provisions is “limited to certain instances of market power, for example dominance on specific markets and of anti-competitive behaviour, and enforcement occurs ex post and requires an extensive investigation of often very complex facts on a case by case basis.”).

[109] U.S. Federal Trade Commission, et al. v. Amazon.com, Inc., supra note 23.

[110] Khan, supra note 101.

[111] Khan, supra note 22, 1003, referring to Amazon, Google, and Meta.

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Antitrust & Consumer Protection

Once More Unto the Breach, Dear Friends: More Regulatory Overreach by the FTC

TOTM Go big or go home, they say. It’s not really an either-or choice: one can go big, and then go home. Not infrequently, an attempt . . .

Go big or go home, they say. It’s not really an either-or choice: one can go big, and then go home. Not infrequently, an attempt to go big is what gets one sent home.

The Federal Trade Commission (FTC) swung for the fences in April 23’s open meeting. On purely partisan lines, the commission voted 3-2 to adopt a competition regulation that bans the use of noncompete agreements (NCAs) across much of the U.S. economy. With a few small wrinkles, it’s just what the FTC had proposed to do—also by a purely partisan vote—in its January 2023 notice of proposed rulemaking (NPRM).

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Antitrust & Consumer Protection

Steeling to Block a Merger

TOTM In an April 17 address to United Steelworkers in Pittsburgh, President Joe Biden vowed that his administration would “thwart the acquisition of U.S. Steel by a Japanese . . .

In an April 17 address to United Steelworkers in Pittsburgh, President Joe Biden vowed that his administration would “thwart the acquisition of U.S. Steel by a Japanese company,” Nippon Steel, telling the assembled union members that U.S. Steel “has been an iconic American company for more than a century and it should remain totally American.”

Aside from the impropriety of apparently prejudging a proposed combination currently under investigation by the U.S. Justice Department (DOJ), would blocking this merger make any sense on national security or economic grounds? The answer is no.

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Antitrust & Consumer Protection

Antitrust at the Agencies Roundup: Spring Has Sprung

TOTM Last week was the occasion of the “spring meeting”; that is, the big annual antitrust convention in Washington, D.C. hosted by the American Bar Association . . .

Last week was the occasion of the “spring meeting”; that is, the big annual antitrust convention in Washington, D.C. hosted by the American Bar Association (ABA) Antitrust Section. To engage in a bit of self-plagiarism (efficient for me, at least), I had this to say about it last year…

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Antitrust & Consumer Protection

Kroger/Albertsons: Is Labor Bargaining Power an Antitrust Harm?

TOTM The Federal Trade Commission’s (FTC) recent complaint challenging the proposed merger of the supermarkets Kroger Co. and Albertsons Companies Inc. has important implications for antitrust enforcement in . . .

The Federal Trade Commission’s (FTC) recent complaint challenging the proposed merger of the supermarkets Kroger Co. and Albertsons Companies Inc. has important implications for antitrust enforcement in labor markets. Central to the FTC’s case is how it chooses to define the relevant markets, and particularly the commission’s focus on unionized grocery workers. The complaint alleges that the combined firm would dominate these markets, substantially lessening competition for unionized labor.

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Antitrust & Consumer Protection