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[The following is a guest post from Andrew Mercado, a research assistant at the Mercatus Center at George Mason University and an adjunct professor and research assistant at George Mason’s Antonin Scalia Law School.]

The Competition and Transparency in Digital Advertising Act (CTDAA), introduced May 19 by Sens. Mike Lee (R-Utah), Ted Cruz (R-Texas), Amy Klobuchar (D-Minn.), and Richard Blumenthal (D-Conn.), is the latest manifestation of the congressional desire to “do something” legislatively about big digital platforms. Although different in substance from the other antitrust bills introduced this Congress, it shares one key characteristic: it is fatally flawed and should not be enacted.  

Restrictions

In brief, the CTDAA imposes revenue-based restrictions on the ownership structure of firms engaged in digital advertising. The CTDAA bars a firm with more than $20 billion in annual advertising revenue (adjusted annually for inflation) from:

  1. owning a digital-advertising exchange if it owns either a sell-side ad brokerage or a buy-side ad brokerage; and
  2. owning a sell-side brokerage if it owns a buy-side brokerage, or from owning a buy-side or sell-side brokerage if it is also a buyer or seller of advertising space.

The proposal’s ownership restrictions present the clearest harm to the future of the digital-advertising market. From an efficiency perspective, vertical integration of both sides of the market can lead to enormous gains. Since, for example, Google owns and operates an ad exchange, a sell-side broker, and a buy-side broker, there are very few frictions that exist between each side of the market. All of the systems are integrated and the supply of advertising space, demand for that space, and the marketplace conducting price-discovery auctions are automatically updated in real time.

While this instantaneous updating is not unique to Google’s system, and other buy- and sell-side firms can integrate into the system, the benefit to advertisers and publishers can be found in the cost savings that come from the integration. Since Google is able to create synergies on all sides of the market, the fees on any given transaction are lower. Further, incorporating Google’s vast trove of data allows for highly relevant and targeted ads. All of this means that advertisers spend less for the same quality of ad; publishers get more for each ad they place; and consumers see higher-quality, more relevant ads.

Without the ability to own and invest in the efficiency and transaction-cost reduction of an integrated platform, there will likely be less innovation and lower quality on all sides of the market. Further, advertisers and publishers will have to shoulder the burden of using non-integrated marketplaces and would likely pay higher fees for less-efficient brokers. Since Google is a one-stop shop for all of a company’s needs—whether that be on the advertising side or the publishing side—companies can move seamlessly from one side of the market to the other, all while paying lower costs per transaction, because of the integrated nature of the platform.

In the absence of such integration, a company would have to seek out one buy-side brokerage to place ads and another, separate sell-side brokerage to receive ads. These two brokers would then have to go to an ad exchange to facilitate the deal, bringing three different brokers into the mix. Each of these middlemen would take a proportionate cut of the deal. When comparing the situation between an integrated and non-integrated market, the fees associated with serving ads in a non-integrated market are almost certainly higher.

Additionally, under this proposal, the innovative potential of each individual firm is capped. If a firm grows big enough and gains sufficient revenue through integrating different sides of the market, they will be forced to break up their efficiency-inducing operations. Marginal improvements on each side of the market may be possible, but without integrating different sides of the market, the scale required to justify those improvements would be insurmountable.

Assumptions

The CTDAA assumes that:

  1. there is a serious competitive problem in digital advertising; and
  2. the structural separation and regulation of advertising brokerages run by huge digital-advertising platforms (as specified in the CTDAA) would enhance competition and benefit digital advertising customers and consumers.

The first assumption has not been proven and is subject to debate, while the second assumption is likely to be false.

Fundamental to the bill’s assumption that the digital-advertising market lacks competition is a misunderstanding of competitive forces and the idea that revenue and profit are inversely related to competition. While it is true that high profits can be a sign of consolidation and anticompetitive outcomes, the dynamic nature of the internet economy makes this theory unlikely.

As Christopher Kaiser and I have discussed, competition in the internet economy is incredibly dynamic. Vigorous competition can be achieved with just a handful of firms,  despite claims from some quarters that four competitors is necessarily too few. Even in highly concentrated markets, there is the omnipresent threat that new entrants will emerge to usurp an incumbent’s reign. Additionally, while some studies may show unusually large profits in those markets, when adjusted for the consumer welfare created by large tech platforms, profits should actually be significantly higher than they are.

Evidence of dynamic entry in digital markets can be found in a recently announced product offering from a small (but more than $6 billion in revenue) competitor in digital advertising. Following the outcry associated with Google’s alleged abuse with Project Bernanke, the Trade Desk developed OpenPath. This allowed the Trade Desk, a buy-side broker, to handle some of the functions of a sell-side broker and eliminate harms from Google’s alleged bid-rigging to better serve its clients.

In developing the platform, the Trade Desk said it would discontinue serving any Google-based customers, effectively severing ties with the largest advertising exchange on the market. While this runs afoul of the letter of the law spelled out in CTDAA, it is well within the spirit its sponsor’s stated goal: businesses engaging in robust free-market competition. If Google’s market power was as omnipresent and suffocating as the sponsors allege, then eliminating traffic from Google would have been a death sentence for the Trade Desk.

While various theories of vertical and horizontal competitive harm have been put forward, there has not been an empirical showing that consumers and advertising customers have failed to benefit from the admittedly efficient aspects of digital-brokerage auctions administered by Google, Facebook, and a few other platforms. The rapid and dramatic growth of digital advertising and associated commerce strongly suggests that this has been an innovative and welfare-enhancing development. Moreover, the introduction of a new integrated brokerage platform by a “small” player in the advertising market indicates there is ample opportunity to increase this welfare further.  

Interfering in brokerage operations under the unproven assumption that “monopoly rents” are being charged and that customers are being “exploited” is rhetoric unmoored from hard evidence. Furthermore, if specific platform practices are shown inefficiently to exclude potential entrants, existing antitrust law can be deployed on a case-specific basis. This approach is currently being pursued by a coalition of state attorneys general against Google (the merits of which are not relevant to this commentary).   

Even assuming for the sake of argument that there are serious competition problems in the digital-advertising market, there is no reason to believe that the arbitrary provisions and definitions found in the CTDAA would enhance welfare. Indeed, it is likely that the act would have unforeseen consequences:

  • It would lead to divestitures supervised by the U.S. Justice Department (DOJ) that could destroy efficiencies derived from efficient targeting by brokerages integrated into platforms;
  • It would disincentivize improvements in advertising brokerages and likely would reduce future welfare on both the buy and sell sides of digital advertising;
  • It would require costly recordkeeping and disclosures by covered platforms that could have unforeseen consequences for privacy and potentially reduce the efficiency of bidding practices;
  • It would establish a fund for damage payments that would encourage wasteful litigation (see next two points);
  • It would spawn a great deal of wasteful private rent-seeking litigation that would discourage future platform and brokerage innovations; and
  • It would likely generate wasteful lawsuits by rent-seeking state attorneys general (and perhaps the DOJ as well).

The legislation would ultimately harm consumers who currently benefit from a highly efficient form of targeted advertising (for more on the welfare benefits of targeted advertising, see here). Since Google continually invests in creating a better search engine (to deliver ads directly to consumers) and collects more data to better target ads (to deliver ads to specific consumers), the value to advertisers of displaying ads on Google constantly increases.

Proposing a new regulatory structure that would directly affect the operations of highly efficient auction markets is the height of folly. It ignores the findings of Nobel laureate James M. Buchanan (among others) that, to justify regulation, there should first be a provable serious market failure and that, even if such a failure can be shown, the net welfare costs of government intervention should be smaller than the net welfare costs of non-intervention.

Given the likely substantial costs of government intervention and the lack of proven welfare costs from the present system (which clearly has been associated with a growth in output), the second prong of the Buchanan test clearly has not been met.

Conclusion

While there are allegations of abuses in the digital-advertising market, it is not at all clear that these abuses have had a long-term negative economic impact. As shown in a study by Erik Brynjolfsson and his student Avinash Collis—recently summarized in the Harvard Business Review (Alden Abbott offers commentary here)—the consumer surplus generated by digital platforms has far outstripped the advertising and services revenues received by the platforms. The CTDAA proposal would seek to unwind much of these gains.

If the goal is to create a multitude of small, largely inefficient advertising companies that charge high fees and provide low-quality service, this bill will deliver. The market for advertising will have a far greater number of players but it will be far less competitive, since no companies will be willing to exceed the $20 billion revenue threshold that would leave them subject to the proposal’s onerous ownership standards.

If, however, the goal is to increase consumer welfare, increase rigorous competition, and cement better outcomes for advertisers and publishers, then it is likely to fail. Ownership requirements laid out in the proposal will lead to a stagnant advertising market, higher fees for all involved, and lower-quality, less-relevant ads. Government regulatory interference in highly successful and efficient platform markets are a terrible idea.

[The following is a guest post from Andrew Mercado, a research assistant at the Mercatus Center at George Mason University and an adjunct professor and research assistant at George Mason’s Antonin Scalia Law School.]

Barry Schwartz’s seminal work “The Paradox of Choice” has received substantial attention since its publication nearly 20 years ago. In it, Schwartz argued that, faced with an ever-increasing plethora of products to choose from, consumers often feel overwhelmed and seek to limit the number of choices they must make.

In today’s online digital economy, a possible response to this problem is for digital platforms to use consumer data to present consumers with a “manageable” array of choices and thereby simplify their product selection. Appropriate “curation” of product-choice options may substantially benefit consumer welfare, provided that government regulators stay out of the way.   

New Research

In a new paper in the American Economic Review, Mark Armstrong and Jidong Zhou—of Oxford and Yale universities, respectively—develop a theoretical framework to understand how companies compete using consumer data. Their findings conclude that there is, in fact, an impact on consumer, producer, and total welfare when different privacy regimes are enacted to change the amount of information a company can use to personalize recommendations.

The authors note that, at least in theory, there is an optimal situation that maximizes total welfare (scenario one). This is when a platform can aggregate information on consumers to such a degree that buyers and sellers are perfectly matched, leading to consumers buying their first-best option. While this can result in marginally higher prices, understandably leading to higher welfare for producers, search and mismatch costs are minimized by the platform, leading to a high level of welfare for consumers.

The highest level of aggregate consumer welfare comes when product differentiation is minimized (scenario two), leading to a high number of substitutes and low prices. This, however, comes with some level of mismatch. Since consumers are not matched with any recommendations, search costs are high and introduce some error. Some consumers may have had a higher level of welfare with an alternative product, but do not feel the negative effects of such mismatch because of the low prices. Therefore, consumer welfare is maximized, but producer welfare is significantly lower.

Finally, the authors suggest a “nearly total welfare” optimal solution in suggesting a “top two-best” scheme (scenario three), whereby consumers are shown their top two best options without explicit ranking. This nearly maximizes total welfare, since consumers are shown the best options for them and, even if the best match isn’t chosen, the second-best match is close in terms of welfare.

Implications

In cases of platform data aggregation and personalization, scenarios one, two, and three can be represented as different privacy regimes.

Scenario one (a personalized-product regime) is akin to unlimited data gathering, whereby platforms can use as much information as is available to perfectly suggest products based on revealed data. From a competition perspective, interfirm competition will tend to decrease under this regime, since product differentiation will be accentuated, and substitutability will be masked. Since one single product will be shown as the “correct” product, the consumer will not want to shift to a different, welfare-inferior product and firms have incentive to produce ever more specialized products for a relatively higher price. Total welfare under this regime is maximized, with producers using their information to garner a relatively large share of economic surplus. Producers are effectively matched with consumers, and all gains from trade are realized.

Scenario two (a data-privacy regime) is one of near-perfect data privacy, whereby the platform is only able to recommend products based on general information, such as sales trends, new products, or product specifications. Under this regime, competition is maximized, since consumers consider a large pool of goods to be close substitutes. Differences in offered products are downplayed, which has the tendency to reduce prices and increase quality, but at the tradeoff of some consumer-product mismatch. For consumers who want a general product and a low price, this is likely the best option, since prices are low, and competition is high. However, for consumers who want the best product match for their personal use case, they will likely undertake search costs, increasing their opportunity cost of product acquisition and tending toward a total cost closer to the cost under a personalized-product regime.

Scenario three (a curated-list regime) represents defined guardrails surrounding the display of information gathered, along the same lines as the personalized-product regime. Platforms remain able to gather as much information as they desire in order to make a personalized recommendation, but they display an array of products that represent the first two (or three to four, with tighter anti-preference rules) best-choice options. These options are displayed without ranking the products, allowing the consumer to choose from a curated list, rather than a single product. The scenario-three regime has two effects on the market:

  1. It will tend to decrease prices through increased competition. Since firms can know only which consumers to target, not which will choose the product, they have to effectively compete with closely related products.
  2. It will likely spur innovation and increase competition from nascent competitors.

From an innovation perspective, firms will have to find better methods to differentiate themselves from the competition, increasing the probability of a consumer acquiring their product. Also, considering nascent competitors, a new product has an increased chance of being picked when ranked sufficiently high to be included on the consumer’s curated list. In contrast, the probability of acquisition under scenario one’s personalized-product regime is low, since the new product must be a better match than other, existing products. Similarly, under scenario two’s data-privacy regime, there is so much product substitutability in the market that the probability of choosing any one new product is low.

Below is a list of how the regimes stack up:

  • Personalized-Product: Total welfare is maximized, but prices are relatively higher and competition is relatively lower than under a data-privacy regime.
  • Data-Privacy: Consumer welfare and competition are maximized, and prices are theoretically minimized, but at the cost of product mismatch. Consumers will face search costs that are not reflected in the prices paid.
  • Curated-List: Consumer welfare is higher and prices are lower than under a personalized-product regime and competition is lower than under a data-privacy regime, but total welfare is nearly optimal when considering innovation and nascent-competitor effects.

Policy in Context

Applying these theoretical findings to fashion administrable policy prescriptions is understandably difficult. A far easier task is to evaluate the welfare effects of actual and proposed government privacy regulations in the economy. In that light, I briefly assess a recently enacted European data-platform privacy regime and U.S. legislative proposals that would restrict data usage under the guise of bans on “self-preferencing.” I then briefly note the beneficial implications of self-preferencing associated with the two theoretical data-usage scenarios (scenarios one and three) described above (scenario two, data privacy, effectively renders self-preferencing ineffective). 

GDPR

The European Union’s General Data Protection Regulation (GDPR)—among the most ambitious and all-encompassing data-privacy regimes to date—has significant negative ramifications for economic welfare. This regulation is most like the second scenario, whereby data collection and utilization are seriously restricted.

The GDPR diminishes competition through its restrictions on data collection and sharing, which reduce the competitive pressure platforms face. For platforms to gain a complete profile of a consumer for personalization, they cannot only rely on data collected on their platform. To ensure a level of personalization that effectively reduces search costs for consumers, these platforms must be able to acquire data from a range of sources and aggregate that data to create a complete profile. Restrictions on aggregation are what lead to diminished competition online.

The GDPR grants consumers the right to choose both how their data is collected and how it is distributed. Not only do platforms themselves have obligations to ensure consumers’ wishes are met regarding their privacy, but firms that sell data to the platform are obligated to ensure the platform does not infringe consumers’ privacy through aggregation.

This creates a high regulatory burden for both the platform and the data seller and reduces the incentive to transfer data between firms. Since the data seller can be held liable for actions taken by the platform, this significantly increases the price at which the data seller will transfer the data. By increasing the risk of regulatory malfeasance, the cost of data must now incorporate some risk premium, reducing the demand for outside data.

This has the effect of decreasing the quality of personalization and tilting the scales toward larger platforms, who have more robust data-collection practices and are able to leverage economies of scale to absorb high regulatory-enforcement costs. The quality of personalization is decreased, since the platform has incentive to create a consumption profile based on activity it directly observes without considering behavior occurring outside of the platform. Additionally, those platforms that are already entrenched and have large user bases are better able to manage the regulatory burden of the GDPR. One survey of U.S. companies with more than 500 workers found that 68% planned to spend between $1 and $10 million in upfront costs to prepare for GDPR compliance, a number that will likely pale in comparison to the long-term compliance costs. For nascent competitors, this outlay of capital represents a significant barrier to entry.

Additionally, as previously discussed, consumers derive some benefit from platforms that can accurately recommend products. If this is the case, then large platforms with vast amounts of accumulated, first-party data will be the consumers’ destination of choice. This will tend to reduce the ability for smaller firms to compete, simply because they do not have access to the same scale of data as the large platforms when data cannot be easily transferred between parties.

SelfPreferencing

Claims of anticompetitive behavior by platforms are abundant (e.g., see here and here), and they often focus on the concept of self-preferencing. Self-preferencing refers to when a company uses its economies of scale, scope, or a combination of the two to offer products at a lower price through an in-house brand. In decrying self-preferencing, many commentators and politicians point to an alleged “unfair advantage” in tech platforms’ ability to leverage data and personalization to drive traffic toward their own products.

It is far from clear, however, that this practice reduces consumer welfare. Indeed, numerous commentaries (e.g., see here and here) circulated since the introduction of anti-preferencing bills in the U.S. Congress (House; Senate) have rejected the notion that self-preferencing is anti-competitive or anti-consumer.

There are good reasons to believe that self-preferencing promotes both competition and consumer welfare. Assume that a company that manufactures or contracts for its own, in-house products can offer them at a marginally lower price for the same relative quality. This decrease in price raises consumer welfare. The in-house brand’s entrance into the market represents a potent competitive threat to firms already producing products, who in turn now have incentive to lower their own prices or raise the quality of their own goods (or both) to maintain their consumer base. This creates even more consumer welfare, since all consumers, not just the ones purchasing the in-house goods, are better off from the entrance of an in-house brand.

It therefore follows that the entrance of an in-house brand and self-preferencing in the data-utilizing regimes discussed above has the potential to enhance consumer welfare.

In general, the use of data analysis on the platform can allow for targeted product entrance into certain markets. If the platform believes it can make a product of similar quality for a lower price, then it will enter that market and consumers will be able to choose a comparable product for a lower price. (If the company does not believe it is able to produce such a product, it will not enter the market with an in-house brand, and consumer welfare will stay the same.) Consumer welfare will further rise as firms producing products that compete against the in-house brand will innovate to compete more effectively.

To be sure, under a personalized-product regime (scenario one), platforms may appear to have an incentive to self-preference to the detriment of consumers. If consumers trust the platform to show the greatest welfare-producing product before the emergence of an in-house brand, the platform may use this consumer trust to its advantage and suggest its own, potentially consumer-welfare-inferior product instead of a competitor’s welfare-superior product. In such a case, consumer welfare may decrease in the face of an in-house brand’s entrance.

The extent of any such welfare loss, however, may be ameliorated (or eliminated entirely) by the platform’s concern that an unexpectedly low level of house-brand product quality will diminish its reputation. Such a reputational loss could come about due to consumer disappointment, plus the efforts of platform rivals to highlight the in-house product’s inferiority. As such, the platform might decide to enhance the quality of its “inferior” in-house offering, or refrain from offering an in-house brand at all.

A curated-list regime (scenario three) is unequivocally consumer-welfare beneficial. Under such a regime, consumers will be shown several more options (a “manageable” number intended to minimize consumer-search costs) than under a personalized-product regime. Consumers can actively compare the offerings from different firms to determine the correct product for their individual use. In this case, there is no incentive to self-preference to the detriment of the consumer, as the consumer is able to make value judgements between the in-house brand and the alternatives.

If the in-house brand is significantly lower in price, but also lower in quality, consumers may not see the two as interchangeable and steer away from the in-house brand. The same follows when the in-house brand is higher in both price and quality. The only instance where the in-house brand has a strong chance of success is when the price is lower than and the quality is greater than competing products. This will tend to increase consumer welfare. Additionally, the entrance of consumer-welfare-superior products into a competitive market will encourage competing firms to innovate and lower prices or raise quality, again increasing consumer welfare for all consumers.

Conclusion

What effects do digital platform-data policies have on consumer welfare? As a matter of theory, if providing an increasing number of product choices does not tend to increase consumer welfare, then do reductions in prices or increases in quality? What about precise targeting of personal-product choices? How about curation—the idea that a consumer raises his or her level of certainty by outsourcing decision-making to a platform that chooses a small set of products for the consumer’s consideration at any given moment? Apart from these theoretical questions, is the current U.S. legal treatment of platform data usage doing a generally good job of promoting consumer welfare? Finally, considering this overview, are new government interventions in platform data policy likely to benefit or harm consumers?

Recently published economic research develops theoretical scenarios that demonstrate how digital platform curation of consumer data may facilitate welfare-enhancing consumer-purchase decisions. At least implicitly, this research should give pause to proponents of major new restrictions of platform data usage.

Furthermore, a review of actual and proposed regulatory restrictions underscores the serious welfare harm of government meddling in digital platform-data usage.   

After the first four years of GDPR, it is clear that there have been significant negative unintended consequences stemming from omnibus privacy regulation. Competition has decreased, regulatory barriers to entry have increased, and consumers are marginally worse off. Since companies are less able and willing to leverage data in their operations and service offerings—due in large part to the risk of hefty fines—they are less able to curate and personalize services to consumers.

Additionally, anti-preferencing bills in the United States threaten to suppress the proper functioning of platform markets and reduce consumer welfare by making the utilization of data in product-market decisions illegal. More research is needed to determine the aggregate welfare effects of such preferencing on platforms, but all early indications point to the fact that consumers are better off when an in-house brand enters the market and increases competition.

Furthermore, current U.S. government policy, which generally allows platforms to use consumer data freely, is good for consumer welfare. Indeed, the consumer-welfare benefits generated by digital platforms, which depend critically on large volumes of data, are enormous. This is documented in a well-reasoned Harvard Business Review article (by an MIT professor and his student) that utilizes online choice experiments based on digital-survey techniques.

The message is clear. Governments should avoid new regulatory meddling in digital platform consumer-data usage practices. Such meddling would harm consumers and undermine the economy.