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Price-Parity Clauses: The Good, The Bad, and the…Anticompetitive?

Andrew Mercado —  21 October 2022

[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.]

Price-parity clauses have, until recently, been little discussed in the academic vertical-price-restraints literature. Their growing importance, however, cannot be ignored, and common misconceptions around their use and implementation need to be addressed. While similar in nature to both resale price maintenance and most-favored-nations clauses, the special vertical relationship between sellers and the platform inherent in price-parity clauses leads to distinct economic outcomes. Additionally, with a growing number of lawsuits targeting their use in online platform economies, it is critical to fully understand the economic incentives and outcomes stemming from price-parity clauses. 

Vertical price restraints—of which resale price maintenance (RPM) and most favored nation clauses (MFN) are among many—are both common in business and widely discussed in the academic literature. While there remains a healthy debate among academics as to the true competitive effects of these contractual arrangements, the state of U.S. jurisprudence is clear. Since the Supreme Court’s Leegin and State Oil decisions, the use of RPM is not presumed anticompetitive. Their procompetitive and anticompetitive effects must instead be assessed under a “rule of reason” framework in order to determine their legality under antitrust law. The competitive effects of MFN are also generally analyzed under the rule of reason.

Distinct from these two types of clauses, however, are price-parity clauses (PPCs). A PPC is an agreement between a platform and an independent seller under which the seller agrees to offer their goods on the platform for their lowest advertised price. While sometimes termed “platform MFNs,” the economic effects of PPCs on modern online-commerce platforms are distinct.

This commentary seeks to fill a hole in the PPC literature left by its current focus on producers that sell exclusively nonfungible products on various platforms. That literature generally finds that a PPC reduces price competition between platforms. This finding, however, is not universal. Notably absent from the discussion is any concept of multiple sellers of the same good on the same platform. Correctly accounting for this oversight leads to the conclusion that PPCs generally are both efficient and procompetitive.

Introduction

In a pair of lawsuits filed in California and the District of Columbia, Amazon has come under fire for its restrictions around pricing. These suits allege that Amazon’s restrictive PPCs harm consumers, arguing that sellers are penalized when the price for their good on Amazon is higher than on alternative platforms. They go on to claim that these provisions harm sellers, prevent platform competition, and ultimately force consumers to pay higher prices. The true competitive result of these provisions, however, is unclear.

That literature that does exist on the effects these provisions have on the competitive outcomes of platforms in online marketplaces falls fundamentally short. Jonathan Baker and Fiona Scott Morton (among others) fail to differentiate between PPCs and MFN clauses. This distinction is important because, while the impacts on consumers may be similar, the mechanisms by which the interaction occurs is not. An MFN provision stipulates that a supplier—when working with several distributors—must offer its goods to one particular distributor at terms that are better or equal to those offered to all other distributors.

PPCs, on the other hand, are agreements between sellers and platforms to ensure that the platform’s buyers have access to goods at better or equal terms as those offered the same buyers on other platforms. Sellers that are bound by a PPC and that intend to sell on multiple platforms will have to price uniformly across all platforms to satisfy the PPC. PPCs are contracts between sellers and platforms to define conduct between sellers and buyers. They do not determine conduct between sellers and the platform.

A common characteristic of MFN and PPC arrangements is that consumers are often unaware of the existence of either clause. What is not common, however, is the outcomes that stem from their use. An MFN clause only dictates the terms under which a good is sold to a distributor and does not constrain the interaction between distributors and consumers. While the lower prices realized by a distributor may be passed on as lower prices for the consumer, this is not universally true. A PPC clause, on the other hand, constrains the interactions between sellers and consumers, necessitating that the seller’s price on any given platform, by definition, must be as low as the price on all other platforms. This leads to the lowest prices for a given good in a market.

Intra-Platform Competition

The fundamental oversight in the literature is any discussion of intra-platform competition in the market for fungible goods, within which multiple sellers sell the same good on multiple platforms. Up to this point, all the discussion surrounding PPCs has centered on the Booking.com case in the European Union.

In Booking.com, the primary platform, Booking.com, instituted price-parity clauses with sellers of hotel rooms on its platform, mandating that they sell rooms on Booking.com for equal to or less than the price on all other platforms. This pricing restriction extended to the hotel’s first-party website as well.

In this case, it was alleged that consumers were worse off because the PPC unambiguously increased prices for hotel rooms. This is because, even if the hotel was willing to offer a lower price on its own website, it was unable to do so due to the PPC. This potential lower price would come about due to the low (possibly zero cost) commission a hotel must pay to sell on its own website. On the hotel’s own website, the room could be discounted by as much as the size of the commission that Booking.com took as a percentage of each sale. Further, if a competing platform chose to charge a lower commission than Booking.com, the discount could be the difference in commission rates.

While one other case, E-book MFN, is tangentially relevant, Booking.com is the only case where independent third-party sellers list a good or service for sale on a platform that imposes a PPC. While there is some evidence of harm in the market for the online booking of hotel rooms, however, hotel-room bookings are not analogous to platform-based sales of fungible goods. Sellers of hotel rooms are unable to compete to sell the same room; they can sell similarly situated, easily substitutable rooms, but the rooms are still non-fungible.

In online commerce, however, sellers regularly sell fungible goods. From lip balm and batteries to jeans and air filters, a seller of goods on an e-commerce site is among many similarly situated sellers selling nearly (or perfectly) identical products. These sellers not only have to compete with goods that are close substitutes to the good they are selling, but also with other sellers that offer an identical product.

Therefore, the conclusions found by critics of Booking.com’s PPC do not hold when removing the non-fungibility assumption. While there is some evidence that PPCs may reduce competition among platforms on the margin, there is no evidence that competition among sellers on a given platform is reduced. In fact, the PPC may increase competition by forcing all sellers on a platform to play by the same pricing rules.

We will delve into the competitive environment under a strict PPC—whereby sellers are banned from the platform when found to be in violation of the clause—and introduce the novel (and more realistic) implicit PPC, whereby sellers have incentive to comply with the PPC, but are not punished for deviation. First, however, we must understand the incentives of a seller not bound by a PPC.

Competition by sellers not bound by price-parity clauses

An individual seller in this market chooses to sell identical products at different prices across different platforms, given that the platforms may choose various levels of commission per sale. To sell the highest number of units possible, there is an incentive for sellers to steer customers to platforms that charge the lowest commission, and thereby offer the seller the most revenue possible.

Since the platforms understand the incentive to steer consumers toward low-commission platforms to increase the seller’s revenue, they may not allocate resources toward additional perks, such as free shipping. Platforms may instead compete vigorously to reduce costs in order offer the lowest commissions possible. In the long run, this race to the bottom might leave the market with one dominant and ultra-efficient naturally monopolistic platform that offers the lowest possible commission.

While this sounds excellent for consumers, since they get the lowest possible prices on all goods, this simple scenario does not incorporate non-price factors into the equation. Free shipping, handling, and physical processing; payment processing; and the time spent waiting for the good to arrive are all additional considerations that consumers factor into the equation. For a higher commission, often on the seller side, platforms may offer a number of these perks that increase consumer welfare by a greater amount than the price increase often associated with higher commissions.

In this scenario, because of the under-allocation of resources to platform efficiency, a unified logistics market may not emerge, where buyers are able to search and purchase a good; sellers are able to sell the good; and the platform is able to facilitate the shipping, processing, and handling. By fragmenting these markets—due to the inefficient allocation of capital—consumer welfare is not maximized. And while the raw price of a good is minimized, the total price of the transaction is not.

Competition by sellers bound by strict price-parity clauses

In this scenario, each platform will have some version of a PPC. When the strict PPC is enforced, a seller is restricted from selling on that platform when they are found to have broken parity. Sellers choose the platforms on which they want to sell based on which platform may generate the greatest return; they then set a single price for all platforms. The seller might then make higher returns on platforms with lower commissions and lower returns on platforms with higher commissions. Fundamentally, to sell on a platform, the seller must at least cover its marginal cost.

Due to the potential of being banned for breaking parity, sellers may have an incentive to price so low that, on some platforms, they do not turn a profit (due to high commissions) while compensating for those losses with profits earned on other platforms with lower commissions. Alternatively, sellers may choose to forgo sales on a given platform altogether if the marginal cost associated with selling on the platform under parity is too great.

For a seller to continue to sell on a platform, or to decide to sell on an additional platform, the marginal revenue associated with selling on that platform must outweigh the marginal cost. In effect, even if the commission is so high that the seller merely breaks even, it is still in the seller’s best interest to continue on the platform; only if the seller is losing money by selling on the platform is it economically rational to exit.

Within the boundaries of the platform, sellers bound by a PPC have a strong incentive to vigorously compete. Additionally, they have an incentive to compete vigorously across platforms to generate the highest possible revenue and offset any losses from high-commission platforms.

Platforms have an incentive to vigorously compete to attract buyers and sellers by offering various incentives and additional services to increase the quality of a sale. Examples of such “add-ons” include fulfilment and processing undertaken by the platform, expedited shipping and insured shipping, and authentication services and warranties.

Platforms also have an incentive to find the correct level of commission based on the add-on services that they provide. A platform that wants to offer the lowest possible prices might provide no or few add-ons and charge a low commission. Alternatively, the platform that wants to provide the highest possible quality may charge a high commission in exchange for many add-ons.

As the value that platforms can offer buyers and sellers increases, and as sellers lower their prices to maintain or increase sales, the quality bestowed upon consumers is likely to rise. Competition within the platform, however, may decline. Highly efficient sellers (those with the lowest marginal cost) may use strict PPCs—under which sellers are removed from the platform for breaking parity—to price less-efficient sellers out of the market. Additionally, efficient platforms may be able to price less-efficient platforms out of the market by offering better add-ons, starving the platforms of buyers and sellers in the long run.

Even with the existence of marginally higher prices and lower competition in the marketplace compared to a world without price parity, the marginal benefit for the consumer is likely higher. This is because the add-on services used by platforms to entice buyers and sellers to transact on a given platform, over time, cost less to provide than the benefit they bestow. Regardless of whether every single consumer realizes the full value of such added benefits, the likely result is a level of consumer welfare that is greater under price parity than in its absence.

Implicit price parity: The case of Amazon

Amazon’s price-parity-policy conditions access to some seller perks on the adherence to parity, guiding sellers toward a unified pricing scheme.  The term best suited for this type of policy is an “implicit price parity clause” (IPPC). Under this system, the incentive structure rewards sellers for pricing competitively on Amazon, without punishing alternative pricing measures. For example, if a seller sets prices higher on Amazon because it charges higher commissions than other platforms, that seller will not eligible for Amazon’s Buy Box. But they are still able to sell, market, and promote their own product on the platform. They still show up in the “other sellers” dropdown section of the product page, and consumers can choose that seller with little more than a scroll and an additional click.

While the remainder of this analysis focuses on the specific policies found on Amazon’s platform, IPPCs are found on other platforms, as well. Walmart’s marketplace contains a similar parity policy along with a similarly functioning “buy” box. eBay, too, offers a “best price guarantee,” through which the site offers match the price plus 10% of a qualified competitor within 48 hours. While this policy is not identical in nature, it is in result: prices that are identical for identical goods across multiple platforms.

Amazon’s policy may sound as if it is picking winners and losers on its platform, a system that might appear ripe for corruption and unjustified self-preferencing. But there are several reasons to believe this is not the case. Amazon has built a reputation of low prices, quick delivery, and a high level of customer service. This reputation provides the company an incentive to ensure a consistently high level of quality over time. As Amazon increases the number of products and services offered on its platform, it also needs to devise ways to ensure that its promise of low prices and outstanding service is maintained.

This is where the Buy Box comes in to play. All sellers on the platform can sell without utilizing the Buy Box. These transactions occur either on the seller’s own storefront, or by utilizing the “other sellers” portion of the purchase page for a given good. Amazon’s PPC does not affect the way that these sales occur. Additionally, the seller is free in this type of transaction to sell at whatever price it desires. This includes severely under- or overpricing the competition, as well as breaking price parity. Amazon’s policies do not directly determine prices.

The benefit of the Buy Box—and the reason that an IPPC can be so effective for buyers, sellers, and the platform—is that it both increases competition and decreases search costs. For sellers, there is a strong incentive to compete vigorously on price, since that should give them the best opportunity to sell through the Buy Box. Because the Buy Box is algorithmically driven—factoring in price parity, as well as a few other quality-centered metrics (reviews, shipping cost and speed, etc.)—the featured Buy Box seller can change multiple times per day.

Relative prices between sellers are not the only important factor in winning the Buy Box; absolute prices also play a role. For some products—where there are a limited number of sellers and none are observing parity or they are pricing far above sellers on other platforms—the Buy Box is not displayed at all. This forces consumers to make a deliberate choice to buy from a specific seller as opposed to from a preselected seller. In effect, the Buy Box’s omission removes Amazon’s endorsement of the seller’s practices, while still allowing the seller to offer goods on the platform.

For consumers, this vigorous price competition leads to significantly lower prices with a high level of service. When a consumer uses the Buy Box (as opposed to buying directly from a given seller), Amazon is offering an assurance that the price, shipping, cost, speed, and service associated with that seller and that good is the best of all possible options. Amazon is so confident with its  algorithm that the assurance is backed up with a price guarantee; Amazon will match the price of relevant competitors and, until 2021, would foot the bill for any price drops that happened within seven days of purchase.

For Amazon, this commitment to low prices, high volume, and quality service leads to a sustained strong reputation. Since Amazon has an incentive to attract as many buyers and sellers as possible, to maximize its revenue through commissions on sales and advertising, the platform needs to carefully curate an environment that is conducive to repeated interactions. Buyers and sellers come together on the platform knowing that they are going to face the lowest prices, highest revenues, and highest level of service, because Amazon’s implicit price-parity clause (among other policies) aligns incentives in just the right way to optimize competition.

Conclusion

In some ways, an implicit price-parity clause is the Goldilocks of vertical price restraints.

Without a price-parity clause, there is little incentive to invest in the platform. Yes, there are low prices, but a race to the bottom may tend to lead to a single monopolistic platform. Additionally, consumer welfare is not maximized, since there are no services provided at an efficient level to bring additional value to buyers and sellers, leading to higher quality-adjusted prices. 

Under a strict price-parity clause, there is a strong incentive to invest in the platform, but the nature of removing selling rights due to a violation can lead to reduced price competition. While the quality of service under this system may be higher, the quality-adjusted price may remain high, since there are lower levels of competition putting downward pressure on prices.

An implicit price-parity clause takes the best aspects of both no PPC and strict PPC policies but removes the worst. Sellers are free to set prices as they wish but have incentive to comply with the policy due to the additional benefits they may receive from the Buy Box. The platform has sufficient protection from free riding due to the revocation of certain services, leading to high levels of investment in efficient services that increase quality and decrease quality-adjusted prices. Finally, consumers benefit from the vigorous price competition for the Buy Box, leading to both lower prices and higher quality-adjusted prices when accounting for the efficient shipping and fulfilment undertaken by the platform.

Current attempts to find an antitrust violation associated with PPCs—both implicit and otherwise—are likely misplaced. Any evidence gathered on the market will probably show an increase in consumer welfare. The reduced search costs on the platforms alone could outweigh any alleged increase in price, not to mention the time costs associated with rapid processing and shipping.

Further, while there are many claims that PPC policies—and high commissions on sales—harm sellers, the alternative is even worse. The only credible counterfactual, given the widespread permeation of PPC policies, is that all sellers on the Internet only sell through their own website. Not only would this increase the cost for small businesses by a significant margin, but it would also likely drive many out of business. For sellers, the benefit of a platform is access to a multitude (in some cases, hundreds of millions) of potential consumers. To reach that number of consumers on its own, every single independent seller would have to employ a team of marketers that rivals a Fortune 500 company. Unfortunately, the value proposition is not on its side, and until it is, platforms are the only viable option.

Before labeling a specific contractual obligation as harmful and anticompetitive, we need to understand how it works in the real world. To this point, there has been insufficient discussion about the intra-platform competition that occurs because of price-parity clauses, and the potential consumer-welfare benefits associated with implicit price-parity clauses. Ideally, courts, regulators, and policymakers will take the time going forward to think deeply about the costs and benefits associated with the clauses and choose the least harmful approach to enforcement.

Ultimately, consumers are the ones who stand to lose the most as a result of overenforcement. As always, enforcers should keep in mind that it is the welfare of consumers, not competitors or platforms, that is the overarching concern of antitrust.

In Amazon, Antitrust & Competition, Platforms, Price Discrimination, Resale Price Maintenance , , , ,
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The Competition and Transparency in Digital Advertising Act Is Fatally Flawed

Andrew Mercado —  3 June 2022

[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.

In Amazon, Antitrust & Competition, Facebook, Google, Justice Department, Monopolization, Platforms, Price Discrimination, Truth on the Market, Tying , , , , , , ,
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The Paradox of Choice Meets the Information Age

Andrew Mercado —  19 April 2022

[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.

Self–Preferencing

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.

In Antitrust & Competition, Data Privacy & Security, Platforms, Truth on the Market , , , , ,
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