Note

Stalking in the Grocery Aisles: Using Section 5 of the FTC Act to Curtail Big Data Driven Price Discrimination

 Introduction

Most people have a baseline understanding that stores collect information about them, but they probably do not recognize the extent to which it is used for the benefit of the store and against the consumer. People quickly scroll through consent agreements to get onto stores’ free Wi-Fi and ignore signs warning them about facial recognition.1 Even if a customer takes notice of the warnings, they most likely do not know how the data is used.2 One way big box stores or grocery stores are using this data is to price discriminate.3 Price discrimination is when a store charges different customers different prices without having a cost-based reason to do so.4 Today, stores do this by offering individuals discounts.5 All marketplaces participate in this type of discount pricing, however this Note focuses on how data collected at brick-and-mortar stores increases this particular type of stores’ ability to price discriminate.

These individualized discounts allow stores to charge each customer a different price, and if they are good at it, they will charge each individual their reservation price—the price close to the maximum they are willing to pay.6 This new way of price discrimination has many effects on the broader market. Specifically, it can hurt low-income individuals and entrench monopolies.7 The current approach to the problem is to try to require more consent, presumably so companies will get less data and be less effective at price discrimination.8

This Note argues that the notice and consent regime is no longer effective in controlling big data driven price discrimination and antitrust laws should instead be used to regulate the worst cases of price discrimination. Part I of this Note discusses how companies collect data on their customers, how they use this data to price discriminate, and how current laws use consent to try to regulate the data market. Part II discusses the harms associated with the price discrimination: overall welfare, equity, and competition. Part III dives into why the current consent regime is failing to address this problem. Finally, Part IV discusses how antitrust laws can be used to tackle this issue. Specifically, Part IV looks at the history of Section 5 of the Federal Trade Commission (“FTC”) Act, how it could be applied to price discrimination, and why it would be effective.

How Companies Use Big Data to Price Discriminate

This Section will introduce how stores collect data on shoppers’ habits and how they can translate those patterns into methods to charge customers different prices. Finally, it discusses how society is currently trying to regulate this type of data use.

How Companies Collect the Data

Although big data is often referred to as a single entity, by its definition it must come from multiple sources. In general, “[b]ig data refers to the ability to gather large volumes of data, often from multiple sources, and with it produce new kinds of observations, measurements and predictions.”9 This Section looks at a few methods brick-and-mortar stores use to collect data on their customers, specifically geolocation tracking and facial recognition.

Many brick-and-mortar stores physically track customers through the store in order to learn about their habits.10 Stores can learn who walked through the door, how long that person stood in the shampoo aisle and maybe even their facial expressions while standing there.11 Many businesses offer tracking services to retail stores, and they all work a little differently.12 Most commonly, companies use Wi-Fi and Bluetooth to track individuals through a store; however, more stores are starting to use facial recognition to do the same thing.

Geolocation Tracking Through Wi-Fi and Bluetooth

Retail stores will often use a combination of Wi-Fi and Bluetooth technologies to collect information on their customers. When Wi-Fi is enabled on a smart phone, the signal is constantly looking for a router to join.13 When it finds a router, the smart phone will send its Media Access Control number (“MAC”) to the router.14 If the store has installed a particular software on their Wi-Fi, the store will be able to triangulate the phone’s location using different strengths of the Wi-Fi signal to track a phone within ten feet of accuracy.15 If an individual does not log into the Wi-Fi, the store likely will not know the identity of the MAC owner, but the store will still be able to track the phone.16 However, if an individual logs onto a store’s Wi-Fi, their data is likely no longer anonymous.17 In addition to knowing the customer’s MAC number, the store will be able to see which websites the customer visited while on their Wi-Fi and if the store installs cookies on the phone, the store can track which websites the customer visits long after they have left the store.18 Most individuals consent to this type of data collection by agreeing to terms and conditions to join the store’s Wi-Fi.19

Stores also use Bluetooth to track individuals through their store, especially if an individual has the store’s app installed on their phone.20 Many stores’ apps have Bluetooth features built in that will connect with Bluetooth beacons throughout the retail store.21 When a phone connects with a beacon, the beacon is able to triangulate the phone’s location to track an individual through a store.22 Like Wi-Fi, this technology is generally accurate within ten feet.23 Traditionally, this technology only worked if the individual had the corporation’s app installed on their phone; several stores have found a way around this by using third-party apps.24 Many popular apps, such as weather apps, have the same Bluetooth technology in their applications so the app owner can collect the location data and sell it to a store.25 This expands stores’ abilities to track people around their store, because the store is no longer limited to people who have downloaded their app.

This type of data collection allows stores to see where a person spends their time in a store. When combined with data about what an individual purchased, stores can figure out what a consumer was thinking about buying but did not purchase. This knowledge can be used to participate in price discrimination.26

Tracking Using Facial Recognition

Some stores have been experimenting with facial recognition to track customers, but its popularity is hard to discern because of stores’ unwillingness to admit to using these techniques. Facial Recognition Technology (“FRT”) uses cameras to identify individuals in a crowd.27 There have been news reports of stores toying with the idea of using facial recognition, but some moved away from the idea or became less transparent about it.28

The makers of FRT have been more open about the capabilities of the technology. One company that has talked openly about their FRT product is FaceFirst.29 FaceFirst is a national company that produces FRT and claims to have “cameras at dozens of malls in the United States.”30 One way that FaceFirst is using the technology is to recognize possible shoplifters:

[Its] software is designed to scan faces as far as 50 to 100 feet away. As people walk through a store entrance, the video camera captures multiple images of each shopper and chooses the clearest one. The software analyzes that image and compares it to a database of “dishonest customers” that the retailer has compiled; if there is a match, the software sends an alert to store employees within seconds of that person walking through the door.31

In addition, FaceFirst has developed technology that will recognize “VIP” customers.32 This allows stores to upload pictures of their best customers so they will be notified when they enter the store and the customer can be given “VIP treatment.”33 The store can also track where individuals spend their time in the store, which is an indication of an individual’s interest in a given product.34

Facial recognition has several advantages over Bluetooth or Wi-Fi tracking. First, it can work without customers doing anything. Even if the customer does not have a smart phone or has Wi-Fi and Bluetooth turned off, FRT can still identify them.35 In addition, FRT can collect more detailed information. Some FRT companies claim to “offer retailers the ability to detect the current emotions of the people walking through their aisles. One such company’s software ‘extracts at least 90,000 data points from each frame, . . . which are sorted by emotional categories, such as anger, disgust, joy, surprise or boredom.’”36 The retail store has even more data points that can be used for targeted advertising or price discrimination.

How Companies Use Data to Price Discriminate

Stores use a combination of the data collected in-store and online to help maximize profits. This Note focuses on how stores use data to price discriminate. Price discrimination occurs when a seller charges buyers different prices that are not justified by different marginal costs of selling the product.37 There are many different ways to participate in price discrimination, and the exact mechanisms for this are discussed below.38

There are three types of price discrimination. First-degree price discrimination occurs when each customer is charged an individual price close to or at their maximum price, also known as their reservation price.39 This can also be called perfect price discrimination.40 Perfect price discrimination is very difficult to do. However, given the technologies of today, it is becoming more common and possible.41 Second-degree price discrimination occurs when a seller charges a purchaser different prices depending on the quantity of a product purchased or the version of the product.42 This is also called “non-linear pricing” and is common in food sales where buying a larger quantity of something often results in a lower price per unit.43 Finally, third-degree price discrimination is when a seller charges different demographic groups different prices.44 These are often seen with student or senior discounts.45 Second- and third-degree price discrimination are much easier to achieve and are generally much more common than first-degree price discrimination.

Brick-and-mortar stores are currently participating in three types of price discrimination.46 First, stores are exploring the demand curve, meaning charging different prices based on location or time. Second, they are developing targeted ads to try to direct customers to certain products. These two mechanisms of price discrimination more clearly fit into second- or third-degree price discrimination. Finally, they are participating in personalized pricing which is first-degree price discrimination.

Exploring the Demand Curve

The most common way companies can participate in price discrimination is to change the price based on where or when the item is purchased; this is called exploring the demand curve.47 Stores do this to learn more about their demand curve, which provides information they can exploit to maximize profits. When companies change the price, they can discern average reservation prices.48 Once a store knows when people stop buying the product, they know to charge just under that amount.

Online retailers can easily explore the demand curve by quickly changing the price of a product.49 This type of price changing is more difficult at brick-and-mortar stores where changing prices is harder. Instead, brick-and-mortar stores charge one price at one store and another price at another store.50 This experiment is less accurate because the customers at the different stores are likely to have different reservation prices.51 However, brick-and-mortar stores still get an idea of what different reservation prices are. Once stores have a better idea of what customers’ reservation prices are, they can use that to maximize their profits by charging as close to that price as possible.52

Steering

Steering is another very common method of price discrimination. Steering occurs when a store directs ads at or organizes their store in a way that encourages customers to buy certain products.53 This occurs on both an individualized level and a demographic level.

The more data a store gets, the better it is able to develop its broader marketing techniques, including non-personalized ad campaigns and store layout. This use of data is not exactly price discrimination but is another way for companies to maximize their profits using big data. If stores are organized in such a way that encourages people to buy more products, they will make more money. For example, if a store is tracking customers and notices a customer is having to back-track a lot in order to get everything they need, the store may decide to re-organize its merchandise. If stores notice people often buy two products together, the store may move them closer together or advertise one to a person who buys the other. In addition, if the store notices a lot of people take more time in a certain aisle, but don’t buy anything from the aisle—information they can gather from geolocation tracking or facial recognition tracking—the store may consider lowering the price of the items in that aisle or sending out more advertisements.54

Stores also participate in steering at an individualized level. Stores are able to use individualized data about a consumer in order to encourage them to spend more money at their store. In order to get people to buy more of their products, a store may offer them discounts for items they are thinking about buying. A company may target their advertisements and discounts to products that are similar to items customers have purchased before.55 These targeted advertisements not only encourage individuals to buy the products, but are more effective than general discounts because the individual will not feel bombarded.56 In addition to sending discounts for complementary items, a store may send you a coupon that is directed at a specific item after noticing that you have spent a lot of time looking at that item, either online or in-store.57 If a store is able to track a customer’s social media information,58 they can often figure out how well-off the customer is and target products to them that they think they will be able to afford.59

Personalized Pricing

Personalized pricing most closely simulates first-degree price discrimination.60 Some stores have started to use personalized pricing and this Note predicts it will only become more popular. Direct price discrimination, or charging each individual person a different price, is still pretty rare.61 Instead of using direct methods of price discrimination, many stores have turned to individualized discounts.62 If a company uses personalized discounts, they are able to list the same price for everyone but have every person paying something close to their reservation price with their specific discounts. There is ample evidence of this happening, especially in brick-and-mortar stores where direct price discrimination is harder, and they have access to more information from facial recognition or geolocation tracking.63

Further, stores can price discriminate by targeting particular products to customers who are not currently buying them and offering them at a “sale” price. Stores do this to “inflate the price to consumers willing and able to pay more, while offering the same product to other consumers for less money.”64 Stores are able to use the data collected from things like cookies and geolocation tracking in order to figure out what kind of products a person may be interested in but not currently buying and then offer the customers a discount on these particular products. This is especially important because “[s]o many brands have trained their customers to shop [for] sales.”65 If stores are able “to find out which customers really need those promotions and which don’t,” they will be able to maximize their profits.66

Another way stores can price discriminate is through loyalty programs. Loyalty programs typically require a customer to provide baseline information, such as name, gender, age, and location, before joining.67 The buyer will then see “benefit[s] such as seat upgrades or free flights from an airline frequent flier program, or price discounts on specific items from a grocery store.”68 Having an individualized account also makes it easier to send out the individual discounts.69

Customers are generally more willing to accept personalized discounts than personalized pricing, which is especially true if the data is more reliable and tailored to their spending habits.70 Discounts that are on mobile devices instead of physical coupons are even more likely to be positively received by customers.71 Positive reactions allow more companies to participate in personalized pricing.72

How the Consent Regime is Currently Being Used to “Regulate”
Big Data Driven Price Discrimination

Many individuals recognize that this price discrimination could be a problem. People may not like the idea that they get charged a higher price for light bulbs because they just bought a lamp. Despite the recognition that this is a problem, the collection of data is actually very lightly regulated. Most of the solutions up to this point are within the notice and consent regime.73 There are two main reasons why society has chosen this approach. First, notice and consent allows businesses to ask for and use data of individuals; second, informed consent allows individuals to do their own cost-benefit analysis to figure out if sharing their data is worth it.74 Regulations of all methods of collection—Bluetooth, Wi-Fi, and facial recognition—are lacking in enforcement and scope. Solutions to the lack of regulation will be explored in the next Section.

Regulation of Bluetooth and Wi-Fi

There is no federal regulation specifically targeted at geolocation tracking, including Bluetooth and Wi-Fi, in retail stores.75 Although some states have laws that govern the collection of personally identifiable information, geolocation data from Bluetooth or Wi-Fi does not generally qualify because MAC numbers are not usually considered identifiable information.76

The FTC has released guidelines for companies who are collecting this type of data. The guidelines require companies to: “(1) know[] what information they have and who has access to it; (2) limit[] the collection and retention of information to what is necessary; (3) us[e] secure methods to protect the information; and (4) dispos[e] of information when its retention is no longer necessary.”77 These guidelines are not binding.78 Several laws regulating geolocation tracking have been proposed at the federal level, but none have gotten past committee.79

Regulation of Facial Recognition

Like geolocation tracking, there is no national law regulating the private use of FRT.80 The FTC has also released a guideline for best practices for use of FRT. The recommendations include: “obtain[ing] consumer consent before using their images or biometric data” and “not us[ing] facial recognition technology to help identify anonymous images.”81 These guidelines are recommendations and are not enforceable.82

While there is no federal legislation, several states have attempted to regulate FRT. Specifically, Illinois, Texas, Washington, and California have laws that regulate the use of FRT. Illinois has the strictest law and the only law that has been widely enforced. The Illinois law, Biometric Information Privacy Act (“BIPA”), requires that a company that is collecting biometric data, which includes FRT, inform the individual that the data is being collected, including the duration of the storage and purpose for the data, obtain a written release, and publish a retention schedule and guidelines for destruction.83 The Texas law requires entities that are using a biometric identifier for a commercial purpose to inform the individual when collecting a biometric identifier and receive consent.84 The Washington law requires a company using biometric data for commercial purposes to provide notice and either obtain consent or install “a mechanism to prevent the subsequent use” for commercial purposes.85 Neither the Texas nor Washington laws have been enforced.86 The California law requires notice when collecting personal information which includes biometric data such as face scans.87 Many other states have laws that govern the storage of biometric data, such as face prints for FRT.88 While states have begun to recognize the need to enforce some restraints on the use of this data, issues with this method prevail, as seen in the next Section.

The Harms of Price Discrimination

The impact of price discrimination remains a widely debated topic. This Note will touch briefly on the welfare and surplus effects of price discrimination and go more in-depth on the equity and competition impacts. This Note concludes that discount-based price discrimination has a negative impact on society as a whole.

Price Discrimination has an Ambiguous Impact on
Societal Surplus

It is unclear what effect price discrimination has on the economy when measured in traditional economic terms, such as societal welfare. Societal welfare, or total welfare, is the combination of consumer surplus and producer surplus.89 “Consumer surplus is the difference between the highest price a consumer is willing to pay and the actual price they do pay for the good, or the market price.”90 On the other hand, “producer surplus is the difference between the actual price of a good or service—the market price—and the lowest price a producer would be willing to accept for a good.”91 The impact of price discrimination on total welfare, consumer welfare, and producer welfare depends upon market structure and the type of price discrimination.92

[A] welfare economic stance towards price discrimination is ambiguous. Under the right circumstances, price discrimination can increase total welfare and can even be averagely beneficial for consumers, provided it leads to a substantial increase in total output. On the other hand, price discrimination can lead to a transfer of welfare from consumers to sellers or even to a reduction of total welfare. In any case, consumers with a high willingness to pay will most probably be worse off under price discrimination.93

For perfect first-degree price discrimination, consumer welfare will decrease, producer welfare will increase, and total welfare is ambiguous. In a perfect price discrimination situation, the supplier is charging each consumer the maximum price they are willing to pay, or their point on the demand curve. This results in the producer taking all of consumer surplus because there is no longer any space between the price and the demand curve.94 In a perfect world there would be no dead weight loss, meaning all transactions that are beneficial to both the consumer and seller are still occurring; the only change is a shift of surplus from consumers to producers.95 In fact the total welfare could increase, but this is not always the case.96 Because the impacts on total welfare are so unclear, it is worth looking into the other possible impacts of price discrimination.

Price Discrimination Negatively Impacts Equity

Economists disagree about the equity impact of price discrimination. On one hand, individuals who are able to pay more are able to subsidize individuals who cannot pay as much. This allows more people to consume the product. On the other hand, price discrimination allows for stereotyping and for stores to give more rewards to customers they believe will bring them more money in the long run.97

Price discrimination opens the door to discrimination based on things such as gender or race.98 The data that is collected by stores often includes things like race, gender, income level, sexuality, and more.99 This type of discrimination is particularly troublesome given the trend towards discount-based price discrimination. For example, a store may be less likely to target a discount for laundry detergent to a male, making it so males pay more for laundry detergent than females. As stores start to do more personalized discounts, the sticker price of a product may increase. The higher price allows for stores to charge customers a wider variety of prices. This means that individuals who are not valued by the store or who are not seen as likely purchasers of an item are likely to pay more for the items.100

Since it is generally true that 80 percent of revenue is received from just 20 percent of customers, it is common for stores to focus on those top customers.101 This means that stores will spend less time and money trying to encourage people who do not spend a lot of money to come to the store. This results in fewer discounts and therefore higher prices for individuals who do not spend a lot of money at a given store.102 People who do not spend as much money at stores “will not enjoy anything like the attention and value the loyal customers enjoy. Moreover, some retailers downgrade the benefits of their loyalty program for customers judged to be of less value to the store based on the amounts they spend.”103 While a low-income individual may be a loyal shopper of a particular store, and therefore create a lot of revenue for them, they still may not receive as many discounts since they are not purchasing the higher-end products where stores are able to make more of their money.

The recent trend in the retail industry is to spend more effort retaining customers and less effort gaining new customers.104 This is because “it can cost five times more to attract a new customer, than it does to retain an existing one.”105 People who are new entrants to a market will be negatively impacted. People who have not purchased at a store before will have to pay the higher sticker price because they will not be receiving the same discounts as more loyal customers. This affects young individuals and people who are gaining disposable income for the first time the most.

Price Discrimination Decreases Competition

One impact of price discrimination that is discussed less often is the impact of price discrimination on competition. There are two mechanisms in which price discrimination hurts competition. The first is decreasing the need for multiple companies to target different demographics. The second is creating a monopoly within the data market.

Price discrimination allows a monopoly to supply more of the demand curve, increasing the size of their monopoly.106 In a healthy market there will be substitute products at different prices that allow more people to fulfill their need.107 In economics, a perfectly competitive market exists when there are multiple firms competing that cause the price and quantity to exist where the supply and demand curves intersect, every transaction that is beneficial to both the seller and the consumer is occurring.108 In a perfectly competitive market, the optimal price leaves approximately half of the demand curve without a product.109 Other firms can then enter that market and provide products that are less expensive substitutes to the product.110 But in a perfect price discrimination model, one firm is able to supply the entire demand curve.111 This means that there is less need for more companies to sell similar or competing products, making it easier to have a monopoly on a larger market.

Price discrimination could also hurt a lot of smaller competitors. As price discrimination becomes more common it may become a necessity to stay afloat. If smaller companies are unable to obtain the data to price discriminate, they may be unable to compete. This is particularly a problem for opt-in policies. Consumers are often averse to opting into multiple or many sites.112 Instead they may be more likely to opt-into a few “large networks with a broad scope, rather than to less established firms.”113 This means that individuals may be likely to consent to bigger companies having their data because they know the rewards will be bigger, but not consent to smaller companies using their data. If smaller companies cannot use data that larger companies can use, they will not be able to compete at the same level.

How the Current Approach is Failing

Most of the current regulation of big data has focused on the collection rather than on the use of the data.114 Specifically, regulation has focused on consent.115 Scholars have long criticized the modern consent regime.116 Although consent may seem like a good idea in theory, the model is not working as designed. Individuals do not understand what they are agreeing to and do not feel as though they have the ability to accept or decline a consent agreement. In addition to these traditional reasons, consent does not work in this particular context because there are externalities in the market.

Consumers are Unable to Understand the Impact of Their
Consent

The first reason the consent regime is failing is because individuals do not understand what they are agreeing to.117 Many studies have shown that people do not read privacy agreements nor engage in the decision-making process necessary for real consent.118 Specifically with regards to things like geolocation tracking “Americans have had little understanding of the specific ways in which tracking takes place or of the general lack of government regulations overseeing such surveillance.”119 One implication of this is that people think they do not have to read consent agreements because they believe government regulation will protect them from the worst of the harms.120

Even if individuals were to take the time to read the terms and conditions, they would not be able to make decisions that are optimal for themselves. There are cognitive issues that make it nearly impossible for an individual to make a rational decision regarding sharing their data.121 One specific problem is that people do not understand the cumulative nature of sharing their data.122 For instance, if an individual shares their birthday and name with one company they may not know that that company may be able to use that information to gain access to a lot of other information about the individual.

Consumers Lack Negotiating Power

The second problem with the way the consent regime is functioning today is that it primarily uses adhesion contracts.123 An adhesion contract is a contract that is drafted by one side and accepted by the other side without input.124 Most people today do not feel like they have the option of saying no to these adhesion contracts, even if they want to.125 Given that “[m]ost people philosophically do not agree with the idea of trade-offs” with regards to their privacy, it is likely many people would say no if they felt they had the option.126 Despite this, most people fail to even “opt out of the collection, use, or disclosure of their data when presented with the choice to do so.”127 In fact, less than five percent of individuals chose to opt out of data collection when given the opportunity.128 This is most likely because they feel they do not have an option; they know their data will be out there whether they give consent or not.

In addition, the number of times individuals are expected to consent to sharing their data prevents notice and consent from working properly. If a person were to fully understand all of the terms and conditions that they were accepting every day, these constant decisions would slow down society. One study found that fully understanding what is being agreed to could cost $781 billion per year in the United States alone.129 There are too many negative consequences with the consent regime for it to be the main way society protects an individual’s privacy.

Consumers’ Decisions Affect More Than Themselves

These first two failures of the consent regime are well-recognized and common. A less well-known issue with consent is that it fails to take into consideration externalities. An externality occurs when a cost or benefit associated with a choice is not felt by the person making the choice.130 This results in either an over- or under-supply of a product compared to what would be socially optimal.131 This Note argues that the consent regime should be abandoned when an individual’s decision to share their data affects other people. Given that individuals are unable to weigh the benefits of sharing their data with the societal consequences of sharing that data, such as price discrimination, it may not be best to leave the choice up to them.

Price discrimination is an externality in the data sharing market. Individuals may want to share their data because it will lead to things like more discounts and therefore lower prices. What they are not thinking about is the impact that this shared data could have on a store’s ability to price discriminate. As discussed, this price discrimination could not only obliterate consumer surplus in the market as a whole but could also lead to equity and antitrust issues.132 This externality will result in more data in the market, allowing more price discrimination to occur.

Using Antitrust Laws to Limit Price Discrimination

Given the recognition that price discrimination is a problem and the failure of the consent approach, the law should look elsewhere to regulate big data driven price discrimination. This Note recommends using an indirect method to regulate the quantity of data shared. By preventing companies from participating in the most extreme forms of price discrimination, the government will be able to lower the value of the data and therefore decrease how much is being shared. This could be done through existing antitrust laws, specifically Section 5 of the FTC Act. This Part introduces the history of Section 5, followed by how Section 5 could be used to reduce price discrimination and why it would be successful.

Background on Section 5 of the FTC Act

Section 5 of the FTC Act gives the FTC the power to regulate “unfair or deceptive acts.”133 There has been much debate over what was meant by “unfair or deceptive.”134 Section 5 is often used to fill in the gaps that exist in current antitrust law or to prevent things that are almost violations of the Clayton or Sherman Act but are not full violations.135 Expansion of Section 5 to include things such as price discrimination has garnered criticism, highlighting how the unpredictable nature of the law could have detrimental effects on the economy.136 However, if done carefully, the use of Section 5 of the FTC Act to limit price discrimination would not only be a proper use of the FTC’s authority but would have positive impacts on the economy.

The FTC’s interpretation of Section 5 has ebbed and flowed as new commissioners come to the FTC.137 In a 1980 statement, the FTC said in order for something to be unfair or deceptive the injury caused by the act “must satisfy three tests. It must be substantial; it must not be outweighed by any countervailing benefits to consumers or competition that the practice produces; and it must be an injury that consumers themselves could not reasonably have avoided.”138 This standard is still referenced today for how to identify a practice as unfair.139

In addition to the definition of unfair, the FTC released guidance in 2015 on how Section 5 should be implemented.

[T]he Commission will be guided by the public policy underlying the antitrust laws, namely, the promotion of consumer welfare; the act or practice will be evaluated under a framework similar to the rule of reason, that is, an act or practice challenged by the Commission must cause, or be likely to cause, harm to competition or the competitive process, taking into account any associated cognizable efficiencies and business justifications; and the Commission is less likely to challenge an act or practice as an unfair method of competition on a standalone basis if enforcement of the Sherman or Clayton Act is sufficient to address the competitive harm arising from the act or practice.140

Although this statement was rescinded in summer 2021,141 it remains the most recent statement on how the FTC will enforce Section 5. There are two reasons this is still a relevant interpretation of Section 5. First, it is a widely accepted interpretation of Section 5 enforcement. Although much speculation came with the 2015 statement, the statement itself was not a dramatic change from previous FTC enforcement.142 Second, anything that meets the 2015 statement is likely to be included in any new guidance for Section 5 enforcement. In the press release accompanying the rescindment, the Commission noted that the 2015 statement “constrained the agency’s use of its authority to stop anticompetitive business tactics under Section 5 of the FTC Act,” and called the statement “shortsighted.”143 This indicates that the Commission is moving towards a more expansive interpretation of Section 5, even though they have yet to release any guidance on the issue.

Applying Section 5 to Price Discrimination

Section 5 is the catchall provision of antitrust enforcement. This Section will first discuss the history and interpretation of Section 5, the common applications, and how price discrimination fits under Section 5. The Section concludes with how the FTC can use Section 5 to curtail price discrimination.

Price Discrimination Meets the Definition of Unfair

Price discrimination utilized by stores meets interpretations of “unfair” described by the Supreme Court and the FTC. The most important Supreme Court interpretation of Section 5 came in 1972 with FTC v. Sperry & Hutchinson Co.144 In Sperry & Hutchinson, the Supreme Court endorsed a broad interpretation of Section 5, concluding that a definition of fairness could include “public values beyond simply those enshrined in the letter or encompassed in the spirit of the antitrust laws.”145 In endorsing an early FTC memo on Section 5, the Supreme Court recognized that something was “unfair” when “it is within at least the penumbra of some common-law, statutory, or other established concept of unfairness[,] . . . is immoral, . . . [and] causes substantial injury to consumers . . . .”146 This was most recently echoed by the Supreme Court in FTC v. Indiana Federation of Dentists.147 In that case the Supreme Court said “[t]he standard of ‘unfairness’ under the FTC Act is, by necessity, an elusive one, encompassing not only practices that violate the Sherman Act and the other antitrust laws, but also practices that the Commission determines are against public policy for other reasons.”148 These cases indicate the Supreme Court is open to having broader interpretations of Section 5.

In addition to Supreme Court law, all three of the 1980 requirements defining unfair practices—substantiality, net negative impact on economy, and no consumer led solution—are met for big data driven price discrimination. The 1980 definition of “unfair” has not been replaced or rescinded, and many courts still rely on the definition today.149

The first part of the test, substantiality, is met by the three distinct harms discussed above: surplus/welfare, equity, and competition.150 The impacts of price discrimination should not be limited to a discussion of overall welfare. The effects on equity and competition should be considered. Society has long held that competition is good for consumers.151 It encourages innovation and helps secure a more reasonable price. Price discrimination can undermine these benefits by creating a larger monopoly.152 In addition, price discrimination also leaves many people behind, increasing inequality.153 Therefore, a broader look at price discrimination passes the substantiality test.

The second part of the test, not outweighed by benefits, has a similar analysis. Although an individual may benefit from price competition in the short run,154 they will not benefit in the long run.155 Consumers will particularly be hurt as price discrimination allows monopolies to get larger, most likely resulting in decreased innovation and options.156 In addition, not every individual will benefit from the price discrimination. People who spend more money at a store and are therefore seen as more valuable customers will benefit more than others.157 There is also no guarantee of a substantial benefit to society.158 Therefore, price differentiation is not outweighed by benefits and passes the second step.

The third part of the test, consumers cannot solve, is also satisfied here. As discussed, the current approach to price discrimination, the consent regime, is not working.159 Although individuals may be able to opt out of data collection in their own circumstance, they do not have the control over what other people share. The more other people share, the more a corporation is able to estimate what each individual’s reservation price is and participate in first-degree price discrimination.

Using Section 5 to Curtail Price Discrimination is in Line with
Current Interpretations of Section 5

As discussed, in 2015 the FTC set out three standards for when to apply Section 5 to “unfair” conduct. Once deemed “unfair,” price discrimination must meet all three requirements set out in the 2015 standard. This means price discrimination must be (1) problematic for the consumer welfare standard, when judged against (2) the rule of reason, for it to be (3) outside the scope of a Clayton Act or Sherman Act violation. Broken down into its three parts, price discrimination clearly meets all these elements.

First is the consumer protection standard, which means the government will only pursue cases where the action harms consumers, not cases where the action is only harmful to other producers.160 The FTC tends to target actions that result in lower output or higher prices.161 At first glance, price discrimination may seem good for consumers because they are getting more discounts and lower prices; however, individualized discounts do not include many other people. Most stores are practicing individualized pricing by offering people specific discounts.162 These discounts will go to people who shop at a store often and whom the store wants to keep returning.163 In order to accommodate for lower prices for those customers, stores could increase the sticker price, the price that people would pay without any discounts. This higher price is particularly worrisome in the consumer protection context, which preferences keeping prices low for consumers. In addition, price discrimination helps entrench monopolies.164 Monopolies result in less innovation, fewer options, and higher prices, all of which is bad for consumers and goes against the consumer welfare standard.165

The second moving principle for enforcement of Section 5 is to use the rule of reason. The rule of reason is an approach to antitrust cases that has its roots in Standard Oil v. United States.166 This case involved a possible violation of the Sherman Act, and the Court embraced the rule of reason, concluding that every fact was relevant to see if there was an “undue restraint” on trade.167 Today the rule of reason is used in most antitrust cases when the conduct is not per-se illegal.

It is likely the rule of reason factors indicate that big data driven price discrimination should be illegal. Scholars have long debated whether price discrimination should be a violation of antitrust laws.168 Although most analysts conclude price discrimination does not violate antitrust laws, many are operating under the assumption that first-degree price discrimination is not possible.169 However, given the change in companies’ abilities to participate in first-degree price discrimination, it is time to re-evaluate this assumption. Economists should conduct more research about how stores give out discounts, what is happening to the sticker price, who is getting the discounts, who is not getting the discounts, and much more. Given that perfect price discrimination was seen as theoretical for so long, most of the research on the issue is either hypothetical or looks at a very specific case of price discrimination. As price discrimination continues to grow more popular it is important that economists and the FTC continue to trace its impact.170 Because of these factors, it is a legitimate likelihood that price discrimination based on big data information would be illegal under the rule of reason analysis.

In addition, for Section 5 analysis, as opposed to other antitrust laws, the rule of reason should look at impacts beyond overall societal welfare. As Senator Cummins noted during the passage of Section 5:

We are not simply trying to protect one man against another; we are trying to protect the people of the United States, and, of course, there must be in the imposture or in the vicious practice or method something that has a tendency to affect the people of the country or be injurious to their welfare.171

This indicates that Section 5 rule of reason should be read more broadly than rule of reason for other Antitrust Laws, to include things like equity loss or loss to competition on a larger level. Given a broader interpretation of rule of reason for Section 5 and the rise of first-degree price discrimination, it is very possible, if not likely, that the type of price discrimination described in this Note would pass a rule of reason test.

The third part of the 2015 guidelines, that it is outside the scope of the Sherman or Clayton Acts, is also met. Big data driven price discrimination is the perfect case for Section 5 enforcement because it is close to a violation of Section 2 of the Sherman Act but would likely not be successful in court. Section 2 of the Sherman Act makes monopolization illegal.172 In order to prove monopolization, the prosecution or plaintiff must prove a) the corporation has monopoly power and b) the monopoly is not the result of “superior product, business acumen, or historic accident.”173 A business can be innovative in trying to get new business but “[w]hen a competitor achieves or maintains monopoly power through conduct that serves no purpose other than to exclude competition, such conduct is clearly improper.”174

Price discrimination likely does not meet both elements. To the first element, stores likely have no clear monopoly. In order to violate Section 2, the company must have either “acquire[d] or maintain[ed] [its] monopoly power through improper means”175 with “a specific intent to destroy competition or build monopoly.”176 Stores would argue that they are not acting to create a monopoly but to increase quantity sold. Although it might seem like price discrimination is a type of monopolization that could be litigated under Section 2 it is unlikely to be successful, therefore leaving ample room for Section 5 enforcement. To the second element, stores could argue that their ability to get data to price discriminate is not based on their size but on better business practices, such as privacy policies. The use of the data might be just superior business practices. Therefore, price discrimination belongs under a Section 5 claim.

Robinson–Patman Is an Ineffective Tool Against Consumer
Level Price Discrimination

Section 5 is a better solution than the Robinson–Patman Act against this type of price discrimination. The Robinson–Patman Act was passed in 1936 and “bans certain discriminatory prices, services, and allowances in dealings between merchants.”177 The Act is most commonly “invoked when a manufacturer charges different prices to two competing dealers of the manufacturer[.]”178 It is not invoked on the consumer level, but instead at the dealer level. The “statute makes it unlawful for a supplier to ‘discriminate’ in price between two of its dealers where the requisite effects on ‘competition’ are shown.”179 The Robinson–Patman Act requires proof of injury to competition.180 There are two types of injuries to competition that qualify under the Robinson–Patman Act. First is primary line injury, which “occurs when one manufacturer reduces its prices in a specific geographic market and causes injury to its competitors in the same market.”181 This injury does not apply to price discrimination in stores within the same geographic market. Secondary line injury “occurs when the seller’s low prices to the favored customer allow this customer to undersell and thereby ruin its own direct competitors.”182 Here, the price discrimination is to direct consumers, not resellers, so it cannot be a secondary line injury. Although it may be possible to re-interpret the Robinson–Patman Act to include price discrimination of the type described in this Note, courts are more willing to use Section 5 than to completely change course on the Robinson–Patman case law.183

How the FTC Would Curtail Price Discrimination

There are two ways the FTC could use its Section 5 power to regulate big data driven price discrimination. The first is to open up an enforcement action against specific stores. The second is to use their rulemaking authority to prohibit certain types of price discrimination.

The FTC has the ability to bring action against a corporation whom it has “reason to believe . . . has been or is using any unfair method of competition or unfair or deceptive act.”184 The FTC will be able to develop this “reason to believe” through their investigatory powers defined in Section 6 of the FTC Act.185 Once there is a proper foundation, the FTC can initiate a proceeding by filing a complaint saying an order should be given to have the entity “cease and desist” its conduct.186 The complaint will specify a time in which the entity can come to the FTC to state why an order should not be issued against them.187 If the FTC then determines “that the method of competition or the act or practice in question is prohibited” by Section 5, the Commission must report its findings in writing and serve an order on the entity “to cease and desist from using such method of competition or such act or practice.”188 This order can then be appealed to a Circuit Court.189 After an order has become final, anyone who violates “shall forfeit and pay to the United States a civil penalty of not more than $10,000 for each violation, which shall accrue to the United States and may be recovered in a civil action brought by the Attorney General of the United States.”190 A violation can also result in a court issuing a permanent injunction.191 The FTC should use their enforcement powers if an investigation into discount price discrimination finds several particularly egregious cases but no clear pattern.

The FTC could also use their rulemaking authority to limit price discrimination.192 Once the FTC has identified a common practice193 that they believe is in violation of Section 5, they may propose a rule, must publish the proposed rule, and allow for public commenting and hearings.194 Once the rule has taken effect, any violation “shall constitute an unfair or deceptive act or practice in violation of [S]ection [5].”195 The rulemaking authority should be used if the commission finds more widespread issues with personalized pricing.

Using Section 5 to Curtail Price Discrimination Would
Be Successful

There are prominent criticisms of this more active use of Section 5 and more paternalistic approach to data regulation. Some believe the FTC Act should be limited to more clear violations of the Sherman or Clayton Acts and not be expanded. It can be argued that expansion of Section 5 will curtail innovation by making it unclear what activities are and are not illegal. Others believe that individuals should maintain complete control over their data and have the ability to trade it for lower prices. Finally, the government might not be able to regulate the complicated pricing mechanisms used by companies. However, these criticisms should not prevent the FTC from taking action.

Using Section 5 to Curtail Price Discrimination is Within
the Bounds of the FTC Act

Legislative history of Section 5 of the FTC Act indicates it is not limited to Sherman or Clayton Act Violations. The FTC Act was passed in 1914 and created the FTC. Section 5 of the Act gave the FTC the power to regulate “unfair methods of competition.”196 The law was passed because Congress “was worried both that the Sherman Act had left antitrust law largely in the hands of the judiciary and that then-existing antitrust law was too limited in scope.”197 The creation of the FTC took some of the antitrust power away from the judiciary branch and gave it to an organization that would have more substantive knowledge about competition and markets.198 Instead of being confined by specific limitations, the open-endedness of “unfair methods of competition” would allow enforcement to change over time as markets evolved.199 The agency could stop anti-competitive behavior before it became a full blown violation of the Sherman or Clayton Acts, preventing further harm to society.200 In addition, it allowed the FTC to stop behavior that is “beyond the confines of the Sherman and Clayton Acts.”201 The legislative history also shows that Congress intended the Act to have some limits, such as focusing on anti-competitive behavior instead of all unfair business practices.202

In addition to the legislative history, courts’ interpretations of Section 5 allow for broader enforcement. There are several Supreme Court cases that endorse a broader interpretation of “unfair” in the context of Section 5. However, several lower court decisions seem to limit the FTC’s power.203 The three main lower court cases, Boise Cascade,204du Pont,205 and Official Airline Guides,206 appear at first glance to limit the FTC’s power, but they should not be overread. These cases may limit the FTC’s power in certain areas but do not destroy it.207Boise Cascade limits the FTC’s actions to cases “firmly grounded in modern economic theories of harm to competition and the competitive process.”208Official Airline Guides was a unique case of FTC overreach that was not grounded in economic theory.209 In du Pont, the Second Circuit was worried about the “arbitrary nature of the Commission’s case,” while still recognizing that while “the Commission may under § 5 enforce the antitrust laws, including the Sherman and Clayton Acts, it is not confined to their letter.”210 This recognition is important as it allows expansion of Section 5. Although these cases can help provide guidance for how to bring a Section 5 case, they do not eliminate the FTC’s power to bring such a case.

Expanded Enforcement Will Not Curtail Innovation

Many businesses worry that the interpretation endorsed in Sperry & Hutchinson will lead to an unpredictable enforcement of Section 5 and stifle business and innovation.211 There are several reasons this is untrue. First, in order to bring a Section 5 action, there has to be clear evidence that it is harmful to society. This requires expanded research and investigation. It is not unpredictable enforcement if academics and government economists have been researching and thinking about this type of behavior. Once a case is brought once, case precedent will prevent the specific type of behavior without stifling other types of innovation. Second, Section 5 has lower punishments than other antitrust laws.212 “Thus, Section 5 is a way to address anticompetitive practices without the imposition of treble damages and automatic follow-on class action litigation.”213 These lower damages will result in less of a chilling effect on innovative business.

This Approach Can Allow Individuals to Maintain Control
of Much of Their Data

Many people say the government should not be heavily involved in the regulation of data collection because individuals have a right to their own data, and they should be able to decide who accesses their data and how it is used.214 This argument fails to consider that most people assume more regulations are protecting them than actually exist.215 People have already adjusted their expectations to allow for more government regulation. The data can be regulated in a way that still incorporates notice and consent.216 The key is to find a middle ground. It is easiest to think of data collection on a spectrum. On one end of the spectrum is data that creates a big public benefit and should always be collected. An example of this would be data collected for credit scores.217 This end of the spectrum should be regulated by the public, requiring people to disclose certain things. On the other end of the spectrum is data that should never be made public. This should also be regulated by the public, preventing disclosure of certain information that could hurt society as a whole. In the middle is a wide range of data collection that could still work on a notice and consent framework.218 This Note does not argue for getting rid of all notice and consent, but rather moving the collection of Wi-Fi and Bluetooth signals and facial recognition tracking data into the end of the spectrum that is more heavily regulated against release. This prevents the negative externality of price discrimination.

Government Regulation Would Allow Beneficial Price
Discrimination

Another argument against this approach is that price discrimination can actually increase overall market efficiency and should therefore be encouraged, not discouraged.219 Some argue price discrimination benefits society as a whole. The first argument for price discrimination is that it has the ability to increase surplus. Some argue perfect price discrimination will increase market surplus, even if consumer surplus decreases. This arguably results in the market doing better. The obvious answer to this critique is that it does not take into account the equity issues or the consumer protection standard.

The second argument for price discrimination is it can actually help consumers because consumers who would not be able to afford the product beforehand are able to afford the product after price discrimination. Although this could be the case in some price discrimination tactics, it most likely would not be for the discount price discrimination method. Stores will likely only give discounts to people who already spend a lot of money at their store, hurting low-income families.220

Finally, there is an argument that price discrimination actually increases competition. This would occur when more companies are able to enter new markets, especially for complimentary products in a different price range. Although this could happen, current companies could also just as easily increase their monopoly power to a broader market. This type of price discrimination creates a new barrier to entry as companies need large amounts of data to compete at the same level as big box stores. This would not be a problem under current Section 5 law. Any case that was unable to prove a broadening of the monopoly market would be unsuccessful. The laws are currently set up to allow for some price discrimination, and this Note advocates for using the full force of Section 5 of the FTC Act to combat price discrimination.

Conclusion

First-degree price discrimination is no longer a theoretical concept—it is happening. Big data driven price discrimination is a phenomenon that cannot be ignored. Although more research needs to be done on how companies are using these new sources of information, it is clear that some price discrimination is occurring and that it has the possibility to be very harmful to society. Requiring more consent will not solve this problem. Individuals making consent choices are unable to take into consideration how their decision impacts society as a whole. Therefore, it is time to turn to a new method of regulation. Section 5 has very recently come into increasing light as the FTC considers expanding its use. It is time to use Section 5’s capabilities to rail in the worst cases of price discrimination.

  1. [1]. See infra Section IV.A (explaining how consumers do not read consent agreements).

  2. [2]. See infra Section IV.A (discussing how consumers are unable to understand consent agreements).

  3. [3]. See infra Section II.B (explaining how stores participate in price discrimination).

  4. [4]. Frederik Zuiderveen Borgesius & Joost Poort, Online Price Discrimination and EU Data Privacy Law, 40 J. Consumer Pol’y 347, 351 (2017) (citing George J. Stigler, The Theory of Price 210 (4th ed. 1987)).

  5. [5]. See infra Section II.B.3 (discussing how stores are participating in personalized pricing).

  6. [6]. Borgesius & Poort, supra note 4, at 351.

  7. [7]. See infra Part III (discussing the harms of price discrimination).

  8. [8]. See infra Section II.C (examining current consent regulations).

  9. [9]. Exec. Off. of the President of the U.S., Big Data and Differential Pricing 2 (2015), https://obamawhitehouse.archives.gov/sites/default/files/whitehouse_files/docs/Big_Data_Report_Nonembargo_v2.pdf [https://perma.cc/9SKH-Q6XZ].

  10. [10]. Ava Farshidi,The New Retail Experience and Its Unaddressed Privacy Concerns: How Rfid and Mobile Location Analytics Are Collecting Customer Information, 7 Case W. Rsrv. J.L. Tech. & Internet 15, 17 (2016).

  11. [11]. Sergio Mannino, How Facial Recognition Will Change Retail, Forbes (May 8, 2020, 7:25 AM), https://www.forbes.com/sites/forbesbusinesscouncil/2020/05/08/how-facial-recognition-will-change-retail [https://perma.cc/XP8A-XERB].

  12. [12]. See generally Joseph Turow, The Aisles Have Eyes: How Retailers Track Your Shopping, Strip Your Privacy, and Define Your Power (2017) (discussing how corporations track individuals through stores).

  13. [13]. See Jessica Gallinaro,Comment, Meet Your New Big Brother: Weighing the Privacy Implications of Physical Retail Stores Using Tracking Technology, 22 Geo. Mason L. Rev. 473, 476–77 (2015).

  14. [14]. Id. at 476.

  15. [15]. The software is often installed by third-party companies such as Euclid Analytics. Turow, supra note 12, at 116 (“Euclid developed a system that could be installed in its clients’ stores to follow pings, enabling the company to note the presence as well as the location of every smartphone in the store.”).

  16. [16]. Id. (explaining how Euclid will keep MAC numbers anonymous if an individual does not log into Wi-Fi).

  17. [17]. Sara Morrison, Why You See Online Ads for Stuff You Buy in the Real World, Vox (Jan. 29, 2020, 1:24 PM), https://www.vox.com/recode/2019/12/19/21011527/retail-tracking-apps-wifi-bluetooth-facebook-ads [https://perma.cc/68N4-83TP] (explaining how some companies “can link a user’s MAC address — and all the information it gets from that address — to personal information provided by the device’s user, like an email address.”).

  18. [18]. Id.

  19. [19]. Id.

  20. [20]. Turow, supra note 12, at 120–21 (“Companies can buy inexpensive [Bluetooth Low Energy (“BLE”)] boxes, which act as beacons, transmitting a signal with a device ID. If a phone app within that range is compatible with that ID, the signal alerts the app to send a message via cellular or Wi-Fi that the phone has made a connection with the BLE beacon in a particular location. With an array of its BLE beacons tuned to its app in a retail location, the app owner can therefore figure out the movement of the phone’s holder as she or he moves through the store.”).

  21. [21]. Morrison, supra note 17.

  22. [22]. Id.

  23. [23]. Turow, supra note 12, at 117.

  24. [24]. Michael Kwet, In Stores, Secret Surveillance Tracks Your Every Move, N.Y. Times: The Privacy Project, https://www.nytimes.com/interactive/2019/06/14/opinion/bluetooth-wireless-tracking-privacy.html [https://perma.cc/8XWA-5T3W].

  25. [25]. Id. (explaining that many non-retail apps, such as weather apps, will insert Bluetooth toolkits into their apps because stores can then pay to receive data on customers).

  26. [26]. See infra Section II.B. (explaining how companies participate in price discrimination).

  27. [27]. Y. Amy Chen,Note, Your Face Is a Commodity, Fiercely Contract Accordingly: Regulating the Capitalization of Facial Recognition Technology Through Contract Law, 34 Notre Dame J.L. Ethics & Pub. Pol’y 501, 505 (2020) (noting that FRT has four components: “(1) a camera to capture an image; (2) an algorithm to create a faceprint; (3) a database of stored images; and (4) an algorithm to compare the captured image to the database of images or a single image in the database”).

  28. [28]. “Target [at one point] posted signs at the entrance of test stores to inform shoppers about its facial recognition software during [a] test” but now refuses to say whether or not they are using facial recognition. Leticia Miranda, Thousands of Stores Will Soon Use Facial Recognition, and They Won’t Need Your Consent, BuzzFeed News (Aug. 17, 2018, 10:28 AM), https://www.buzzfeed
    news.com/article/leticiamiranda/retail-companies-are-testing-out-facial-recognition-at [https://
    perma.cc/C8BZ-Z9Q3]. Walmart was also known to have tested the software in 2015 but did not find it profitable. Id.

  29. [29]. See e.g., Turow, supra note 12, at 230–31 (discussing Face First capabilities); Jeff John Roberts, The Business of Your Face, Fortune (Mar. 27, 2019, 5:00 AM), https://fortune.com/long
    form/facial-recognition [https://perma.cc/W8SC-4RF8] (same).

  30. [30]. Turow, supra note 12, at 227.

  31. [31]. Miranda, supra note 28.

  32. [32]. Turow, supra note 12, at 230.

  33. [33]. Id. (explaining how stores can be alerted when someone enters the store who has been identified as a “VIP” customer).

  34. [34]. Mannino, supra note 11 (“[I]f [FRT] sees you repeatedly go back to a specific item or linger around it for more than a couple of minutes, then this indicates that you have a high level of interest in this item and may require assistance or encouragement to complete the purchase.”).

  35. [35]. Turow, supra note 12, at 228 (“Because facial recognition is passive, it has an advantage over mobile phone trackers.”).

  36. [36]. Id. (citing Elizabeth Dwoskin & Evelyn M. Rusli, The Technology That Unmasks Your Hidden Emotions, Wall St. J. (Jan. 28, 2015, 2:13 PM), https://www.wsj.com/articles/startups-see-your-face-unmask-your-emotions-1422472398 [https://perma.cc/QSA7-E3VX]).

  37. [37]. Borgesius & Poort, supra note 4, at 351.

  38. [38]. See infra Sections III.A–.B (explaining the harms of price discrimination).

  39. [39]. Borgesius & Poort, supra note 4, at 351.

  40. [40]. Id. at 352.

  41. [41]. See infra Section II.B.3 (discussing how stores are participating in price discrimination).

  42. [42]. Mark MacCarthy, New Directions in Privacy: Disclosure, Unfairness and Externalities, 6 I/S: J.L. & Pol’y for Info. Soc’y 425, 463 (2011).

  43. [43]. Borgesius & Poort, supra note 4, at 351–52.

  44. [44]. MacCarthy, supra note 42, at 463–64.

  45. [45]. Id.

  46. [46]. Exec. Off. of the President of the U.S., supra note 9, at 10–13.

  47. [47]. Id. at 10.

  48. [48]. See Borgesius & Poort, supra note 4, at 351.

  49. [49]. Id. at 348–49. One example of an internet company exploring the demand curve is Amazon. In 2000, an Amazon customer noticed that after he cleared his cookies, products on Amazon were cheaper. He accused Amazon of pricing based on what he had shopped for before, a type of price discrimination known as personalized pricing. Amazon said it was not personalized pricing; the website was just “try[ing] out different prices at different times.” Id. at 349. In other words, Amazon was trying to figure out what individuals’ reservation prices were. Id.

  50. [50]. Id. (sharing an example from Staples).

  51. [51]. Different retail stores are going to have different customers because the customer base is representative of the neighborhood the store is in. Different neighborhoods are likely to be made up of people with different income levels, different races, and different opinions.

  52. [52]. See Turow, supra note 12, at 144. The best example of this is in the airline industry. Airlines have been able to gather enough information about customers to estimate their reservation price depending on when they buy a ticket and what add-ons they are willing to pay for, such as seat assignments, luggage, and more. Therefore, the airline companies can charge different prices based on how far out the flight is booked and charge people very close to their reservation price, maximizing their profits. Id. Charging exactly the reservation price allows individuals to maximize their profits because they are able to charge some people a higher price and some people a lower price. For the higher price people, they have a higher profit margin. For the lower price individuals, they are gaining access to a sale they would not have otherwise had.

  53. [53]. Exec. Off. of the President of the U.S., supra note 9, at 11.

  54. [54]. See Mannino, supra note 11 (discussing how stores use facial recognition).

  55. [55]. See id.

  56. [56]. See Exec. Off. of the President of the U.S., supra note 9, at 12 (explaining that personalized discounts increase the likelihood of an individual using the coupon to purchase the product).

  57. [57]. See Mannino, supra note 11 (discussing stores’ use of facial recognition).

  58. [58]. Companies can gain access to customers’ social media data by either prompting the customer to login through social media or buying the data. See id.

  59. [59]. See Vincent Nguyen,Shopping for Privacy: How Technology in Brick-and-Mortar Retail Stores Poses Privacy Risks for Shoppers, 29 Fordham Intell. Prop. Media & Ent. L.J. 535, 558 (2019) (discussing how stores use social media data to price discriminate).

  60. [60]. See Exec. Off. of the President of the U.S., supra note 9, at 14.

  61. [61]. Borgesius & Poort, supra note 4, at 349.

  62. [62]. See Exec. Off. of the President of the U.S., supra note 9, at 12 (explaining how stores use individualized discounts).

  63. [63]. See e.g., Turow, supra note 12, at 1­–2 (“[T]hey can send the shoppers personalized coupons or other messages associated with the goods in a beacon’s proximity.”).

  64. [64]. Nguyen, supra note 59, at 557.

  65. [65]. Turow, supra note 12, at 219 (second alteration in original) (citation omitted).

  66. [66]. Id.

  67. [67]. See Exec. Off. of the President of the U.S., supra note 9, at 12 (sharing types of information collected by stores).

  68. [68]. Id.

  69. [69]. Harlan Landes, Individualized Coupons Aid Price Discrimination, Forbes (Aug. 21, 2012, 5:48 PM), https://www.forbes.com/sites/moneybuilder/2012/08/21/individualized-coupons-aid-price-discrimination/?sh=335cf44445e7 [https://perma.cc/HC4Z-JJQK] (“Two shoppers with loyalty cards, identical except for their past purchasing habits, might receive two different prices for the same product at the same time.”).

  70. [70]. Exec. Off. of the President of the U.S., supra note 9, at 13 (discussing that “if the buyer did not opt-in to the data collection process (as with loyalty programs), or if price differences are not framed as discounts (as with coupons)” the buyer may be more skeptical).

  71. [71]. Turow, supra note 12, at 218 (“Goldberg noted that tailored discounts appearing on mobile devices are less likely to cause shopper pushback than if they were displayed more publicly, such as on e-ink shelves or video screens.”).

  72. [72]. It is true not every customer is always on the hunt for coupons. This does not prevent discount price discrimination from working. If an individual creates an account with a store the store can offer them discounts without them doing anything.

  73. [73]. See Robert H. Sloan & Richard Warner, Beyond Notice and Choice: Privacy, Norms, and Consent, 14 J. High Tech. L. 370, 373–74 (2014); see also Daniel J. Solove, Introduction: Privacy Self-Management and the Consent Dilemma, 126 Harv. L. Rev. 1880, 1880 (2013) (“Privacy self-management takes refuge in consent. It attempts to be neutral about substance ... and instead focuses on whether people consent to various privacy practices. Consent legitimizes nearly any form of collection, use, or disclosure of personal data.”).

  74. [74]. See Sloan & Warner, supra note 73, at 374 (explaining why society has chosen the notice and consent regime to regulate data collection).

  75. [75]. United States v. Jones, 565 U.S. 400, 430 (2012) (Alito, J., concurring) (“To date, however, Congress and most States have not enacted statutes regulating the use of GPS tracking technology for law enforcement purposes.”).

  76. [76]. Morrison, supra note 17.

  77. [77]. Nguyen,supra note 59, at 552.

  78. [78]. Id. at 546.

  79. [79]. Gallinaro,supra note 13, at 478–80 (“Examples of these bills include the proposed Location Privacy Protection Act of 2012and the Geolocation Privacy and Surveillance Act (“GPS Act”).”) (footnote omitted).

  80. [80]. A bill has been introduced that addresses the private use of FRT. Press Release, Senator Jeff Merkley, Merkley, Sanders Introduce Legislation to Put Strict Limits on Corporate Use of Facial Recognition (Aug. 4, 2020), https://www.merkley.senate.gov/news/press-releases/merkley-sanders-introduce-legislation-to-put-strict-limits-on-corporate-use-of-facial-recognition-2020 [https://perma.cc/
    J6DJ-JPZC].

  81. [81]. Nguyen, supra note 59, at 546.

  82. [82]. Id.

  83. [83]. 740 Ill. Comp. Stat. 14/15 (West 2021).

  84. [84]. Tex. Bus. & Com. Code Ann. § 503.001 (West 2021).

  85. [85]. Wash. Rev. Code Ann. § 19.375.020(1) (West 2021).

  86. [86]. Case citations in Westlaw are limited to when the laws are cited in BIPA cases. It is unclear why this is the case.

  87. [87]. Cal. Civ. Code § 1798.110 (West 2021).

  88. [88]. See e.g., Colo. Rev. Stat. Ann. § 6-1-713 (West 2021) (regulating the disposal of personally identifiable information); Thorin Klosowski, The State of Consumer Data Privacy Laws in the US (and Why It Matters), N.Y. Times (Sept. 6, 2021), https://www.nytimes.com/wirecutter/blog/
    state-of-privacy-laws-in-us [https://perma.cc/J42S-FE4W].

  89. [89]. Christina Majaski, Consumer Surplus vs. Economic Surplus: What’s the Difference?, Investopedia (June 28, 2021), https://www.investopedia.com/ask/answers/041715/what-difference-between-consumer-surplus-and-economic-surplus.asp [https://perma.cc/9XNT-W8FP].

  90. [90]. Id. For a visual representation of producer and consumer surplus, see Consumer and Producer Surplus, Econ. Online, https://www.economicsonline.co.uk/Competitive_markets/
    Consumer_and_producer_surplus.html [https://perma.cc/KQZ5-TSX7].

  91. [91]. Majaski, supra note 89.

  92. [92]. MacCarthy, supra note 42, at 465 (discussing how the impacts of price discrimination depend on “the shape of the submarkets that it permits separating, the character of the criteria used to divide those groups, its transparency, and public attitudes toward specific forms of the practice” (quoting William W. Fisher III, When Should We Permit Differential Pricing of Information?, 55 UCLA L. Rev. 1, 37 (2007))).

  93. [93]. Borgesius & Poort, supra note 4, at 355.

  94. [94]. Price Discrimination, Econ. Online, https://www.economicsonline.co.uk/Business_
    economics/Price_discrimination.html [https://perma.cc/5CAB-9CCS]. For a visual representation of how consumer surplus disappears, see id.

  95. [95]. See Daniel J. Gifford & Robert T. Kudrle, The Law and Economics of Price Discrimination in Modern Economies: Time for Reconciliation?, 43 U.C. Davis L. Rev. 1235, 1241 (2010).

  96. [96]. See Borgesius & Poort, supra note 4, at 355.

  97. [97]. Turow, supra note 12, at 11 (“[Lower spenders] will not enjoy anything like the attention and value the loyal customers enjoy. Moreover, some retailers downgrade the benefits of their loyalty program for customers judged to be of less value to the store based on the amounts they spend.”).

  98. [98]. Exec. Off. of the President of the U.S., supra note 9, at 16 (“In the marketing context, disparate treatment occurs when a seller uses race, religion, or some other consumer characteristic as a proxy for demand.”).

  99. [99]. See supra Section II.A (discussing how stores collect data on their customers).

  100. [100]. See Turow, supra note 12, at 10–11.

  101. [101]. Id. at 80.

  102. [102]. Landes, supra note 69 (“New, highly individualized [discounts] will continue and increase price discrimination, wherein those drawing the short end of the pricing stick are inevitably those without leverage — families with low household incomes.”).

  103. [103]. Turow, supra note 12, at 11.

  104. [104]. Id. at 80 (“Many in the industry argued that efficiency meant retailers needed to place more emphasis on retaining good customers than on finding new ones—and that the way to do that was to learn as much as possible about those good customers in order to know what persuasive levers would keep them returning.”).

  105. [105]. Jia Wertz, Don’t Spend 5 Times More Attracting New Customers, Nurture the Existing Ones, Forbes (Sept. 12, 2018, 5:03 PM), https://www.forbes.com/sites/jiawertz/2018/09/12/dont-spend-5-times-more-attracting-new-customers-nurture-the-existing-ones/?sh=606c0af45a8e [https://
    perma.cc/DXF9-ZQXN].

  106. [106]. Borgesius & Poort, supra note 4, at 354 (“By using price discrimination, the monopolist can serve the entire market, with the result that it can increase its economies of scale and network effects. Thus, price discrimination can help to monopolize a market and to make market entry unattractive for competitors.”). But see Exec. Off. of the President of the U.S., supra note 9, at 6 (“Some studies even suggest that differential pricing can intensify competition relative to uniform pricing, by allowing high-margin sellers to compete more aggressively for price-sensitive customers who might otherwise buy from a lower-priced rival.”).

  107. [107]. See Adam Hayes, Substitute, Investopedia (Nov. 27, 2020), https://www.investopedia.
    com/terms/s/substitute.asp [https://perma.cc/H8RX-NWWA].

  108. [108]. See Gifford & Kudrle, supra note 95, at 1256 (“[I]n addition to intuitive reasons, it was inconsistent with the conditions that would characterize a perfectly operating competitive market, where all purchasers would pay the same price for any given product.”).

  109. [109]. See Borgesius & Poort, supra note 4, at 354 (discussing how price discrimination works).

  110. [110]. See generally id. (explaining how stores participate in price discrimination).

  111. [111]. Id.

  112. [112]. Alessandro Acquisti, Curtis Taylor & Liad Wagman, The Economics of Privacy, 54 J. Econ. Literature 442, 456 (2016), https://pubs.aeaweb.org/doi/pdfplus/10.1257/jel.54.2.442 [https://
    perma.cc/VTJ2-TU5S].

  113. [113]. Id.

  114. [114]. See supra Section II.C.

  115. [115]. See id.

  116. [116]. See, e.g., Sloan & Warner, supra note 73, at 373–77 (discussing why notice and consent is currently used and why it is not working).

  117. [117]. See Solove, supra note 73, at 1884–86 (“Despite the embrace of notice and choice, people do not seem to be engaging in much privacy self-management.”).

  118. [118]. See id. at 1884 (footnotes omitted) (“Most people do not read privacy notices on a regular basis.As for other types of notices, such as end-user license agreements and contract boilerplate terms, studies show only a miniscule percentage of people read them.”).

  119. [119]. Turow, supra note 12, at 152.

  120. [120]. This is only one of the reasons people don’t read consent agreements. See infra Section IV.A.

  121. [121]. Solove, supra note 73, at 1880–81, 1888 (“The upshot of this problem is that privacy decisions are particularly susceptible to problems such as bounded rationality, the availability heuristic, and framing effects because privacy is so complex, contextual, and difficult to conceptualize.”).

  122. [122]. Id. at 1889–91 (“Another problem is that even if people made rational decisions about sharing individual pieces of data in isolation, they greatly struggle to factor in how their data might be aggregated in the future.”).

  123. [123]. See, e.g., Adhesion Contract (Contract of Adhesion), Cornell L. Sch.: Legal Info. Inst., https://
    www.law.cornell.edu/wex/adhesion_contract_(contract_of_adhesion) [https://perma.cc/DF3L-FEEW] (explaining how adhesion contracts are the primary form of consent).

  124. [124]. Id. (“The second party typically does not have the power to negotiate or modify the terms of the contract. Adhesion contracts are commonly used for matters involving insurance, leases, deeds, mortgages, automobile purchases, and other forms of consumer credit.”).

  125. [125]. Turow, supra note 12, at 254 (“Contrary to the claim that a majority of Americans consent to discounts because the commercial benefits are worth the costs, we found that Americans do so because they are resigned to the inevitability of surveillance and the power of marketers to harvest their data.”) (emphasis omitted).

  126. [126]. Id. at 253 (emphasis omitted).

  127. [127]. Solove, supra note 73, at 1884–85 (“Most people do not even bother to change the default privacy settings on websites.”).

  128. [128]. MacCarthy, supra note 42, at 435.

  129. [129]. Id. at 428 (according to a 2009 Carnegie Mellon study).

  130. [130]. In an externality the marginal private or marginal production cost (“MPC”) is less than the marginal social cost (“MSC”). The private cost is the cost of the person making the decision about whether or not to participate in the transaction. The social cost is the cost of the transaction to society as a whole. In this case the number of transactions that would occur is at Q1, and the socially optimal number of transactions is at Q. See Positive Externalities, Econ. Online, https://
    www.economicsonline.co.uk/Market_failures/Positive_externalities.html [https://perma.cc/
    PQ4B-SA3P]. See id. for a graphical representative of an externality.

  131. [131]. In the context of data sharing, the MPC would be the cost of the individual making the decision of whether or not to share their data with the corporation. The MSC would be the cost to society of that individual sharing their data with others. The reason MSC is greater than MPC is because the decision of the individual to share their data leads to more price discrimination which is net bad for society, so the cost is higher.

  132. [132]. See supra Sections III.B–.C (discussing the equity and competition harms of price discrimination).

  133. [133]. 15 U.S.C. § 45(a) (2018).

  134. [134]. In theory, Section 5 is not limited by other antitrust laws but has often been limited by the Clayton and Sherman Acts. William Holmes & Melissa Mangiaracina, Antitrust Law Handbook § 7:2 (2020).

  135. [135]. Id.

  136. [136]. Thomas Dahdouh,Section 5, the FTC and Its Critics: Just Who Are the Radicals Here?, 20 Competition: J. Antitrust & Unfair Competition L. Section St. Bar Cal. 1, 1 (2011).

  137. [137]. See Marcy C. Priedeman,Section 5 of the FTC Act: Dark Cloud or Silver Lining?, 19 Competition: J. Antitrust & Unfair Competition L. Section St. Bar Cal. 69, 70 (2010).

  138. [138]. Michael Pertschuk, Paul Rand Dixon, David A. Clanton, Robert Pitofsky & Patricia P. Bailey, FTC Policy Statement on Unfairness, Fed. Trade Comm’n (Dec. 17, 1980), https://www.ftc.
    gov/public-statements/1980/12/ftc-policy-statement-unfairness [https://perma.cc/H4NF-M55G].

  139. [139]. See, e.g., LabMD, Inc. v. FTC, 894 F.3d 1221, 1228–29 (11th Cir. 2018) (citing the 1980 standards).

  140. [140]. Donald S. Clark, Statement of Enforcement Principles Regarding “Unfair Methods of Competition” Under Section 5 of the FTC Act, Fed. Trade Comm’n (Aug. 13, 2015), https://www.ftc.
    gov/system/files/documents/public_statements/735201/150813section5enforcement.pdf [https://perma.cc/Q8NF-Y4QJ].

  141. [141]. Press Release, Fed. Trade Comm’n, FTC Rescinds 2015 Policy that Limited Its Enforcement Ability Under the FTC Act (July 1, 2021), https://www.ftc.gov/news-events/press-releases/2021/07/ftc-rescinds-2015-policy-limited-its-enforcement-ability-under [https://perma.cc/
    7JVW-SUXD] [hereinafter FTC Rescinds].

  142. [142]. Antitrust Law Alert - Federal Trade Commission Provides Statement of Enforcement Principles Regarding Unfair Methods of Competition, Barnes & Thornburg LLP (Aug. 18, 2015), https://bt
    law.com/insights/alerts/2015/antitrust-law-alert-federal-trade-commission-provides-statementof-enforcement-principles [https://perma.cc/EA8R-P867].

  143. [143]. FTC Rescinds, supra note 141.

  144. [144]. See generally FTC v. Sperry & Hutchinson Co., 405 U.S. 233 (1972) (appeal of an FTC cease and desist order).

  145. [145]. Id. at 244.

  146. [146]. Id. at 244 n.5 (quoting 29 C.F.R. § 8355 (1964)).

  147. [147]. See generally FTC v. Indiana Fed’n of Dentists, 476 U.S. 447 (2009) (appealing an FTC cease and desist order).

  148. [148]. Id. at 454 (citations omitted).

  149. [149]. See supra notes 138–39 and accompanying text.

  150. [150]. In addition to the price discrimination impacts, individuals are often harmed by the invasion into their privacy. Impact of privacy violations are beyond the scope of this Note but, as discussed, individuals are often unaware that they are sharing as much data as they think they are.

  151. [151]. See, e.g., Fed. Trade Comm’n, Competition Counts: How Consumers Win When Businesses Compete, https://www.ftc.gov/sites/default/files/attachments/competition-counts/zgen01.pdf [https://perma.cc/RG62-WMRQ] (explaining the consumer protection standard).

  152. [152]. See supra Section III.C.

  153. [153]. See supra Section III.B.

  154. [154]. For example, an individual may be able to buy a product they would not be able to buy at market price.

  155. [155]. See supra Part III (discussing the impact of price discrimination).

  156. [156]. Supra Section III.C.

  157. [157]. Supra Section III.B.

  158. [158]. See supra Section IV.A (explaining why consumers are unable to participate even with proper consent).

  159. [159]. See supra Part IV.

  160. [160]. See, e.g., The Antitrust Laws, Fed. Trade Comm’n, https://www.ftc.gov/tips-advice/
    competition-guidance/guide-antitrust-laws/antitrust-laws [https://perma.cc/LB8Y-LT77] [hereinafter The Antitrust Laws] (“Yet for over 100 years, the antitrust laws have had the same basic objective: to protect the process of competition for the benefit of consumers, making sure there are strong incentives for businesses to operate efficiently, keep prices down, and keep quality up.”).

  161. [161]. Dahdouh, supra note 136, at 14–15.

  162. [162]. See supra Section II.B.3.

  163. [163]. See supra Section III.B.

  164. [164]. See supra Section III.C.

  165. [165]. See, e.g., Mark Thoma, What’s So Bad About Monopoly Power?, CBS News (Sept. 18, 2014, 5:30 AM), https://www.cbsnews.com/news/whats-so-bad-about-monopoly-power [https://perma.cc/
    BSJ5-MBGB
    ] (explaining how monopolies lead to lower output).

  166. [166]. See Louis Altman & Malla Pollack, Callmann on Unfair Competition, Trademarks, & Monopolies § 4:37 (4th ed. 2021).

  167. [167]. See Standard Oil Co. of N.J. v. United States, 221 U.S. 1, 59–62 (1911) (implementing the “undue restraint” standard).

  168. [168]. See, e.g., Robert H. Bork, The Rule of Reason and the Per Se Concept: Price Fixing and Market Division, 74 Yale L.J. 775, 815–28 (1965); see also Gifford & Kudrle, supra note 95, at 1255 (discussing the debate over whether price discrimination violates antitrust laws).

  169. [169]. Gifford & Kudrle, supra note 95, at 1255.

  170. [170]. It is possible for the FTC to investigate this type of behavior by using their Section 9 authority to issue subpoenas. 15 U.S.C. § 49 (2018).

  171. [171]. Dahdouh, supra note 136, at 8 (quoting 51 Cong. Rec. 11105 (1914)).

  172. [172]. 15 U.S.C. § 2 (2018) (starting in part “[e]very person who shall monopolize, or attempt to monopolize, or combine or conspire with any other person or persons, to monopolize any part of the trade or commerce among the several States, or with foreign nations, shall be deemed guilty of a felony”). Monopolization is not directly defined in the statute. Dep’t of Just., Single-Firm Conduct and Section 2 of the Sherman Act: An Overview, Competition and Monopoly: Single-Firm Conduct Under Section 2 of the Sherman Act 7 (2008), https://www.justice.gov/sites/
    default/files/atr/legacy/2008/09/12/236681_chapter1.pdf [https://perma.cc/2NP9-WFXK] [hereinafter Competition and Monopoly] (“Congress gave the Act ‘a generality and adaptability comparable to that found to be desirable in constitutional provisions’ ... .”) (quoting Appalachian Coals, Inc. v. United States, 288 U.S. 344, 360 (1933))).

  173. [173]. Competition and Monopoly, supra note 172, at 5 (quoting United States v. Grinnell Corp., 384 U.S. 563, 570­–71 (1966)).

  174. [174]. Id. at 13.

  175. [175]. Id. at 5.

  176. [176]. Id. at 6 (quoting Times-Picayune Publ’g Co. v. United States, 345 U.S. 594, 626 (1953)).

  177. [177]. The Antitrust Laws, supra note 160.

  178. [178]. Herbert Hovenkamp, The Robinson-Patman Act and Competition: Unfinished Business, 68 Antitrust L.J. 125, 126 (2000).

  179. [179]. Id. at 125 (footnote omitted).

  180. [180]. Price Discrimination: Robinson-Patman Violations, Fed. Trade Comm’n, https://www.ftc.gov/
    tips-advice/competition-guidance/guide-antitrust-laws/price-discrimination-robinson-patman [https://perma.cc/Q5D3-XA7P].

  181. [181]. Id.

  182. [182]. William Markham, Unlawful Price Discrimination: An Obscure, Occasionally Useful Antitrust Doctrine, L. Offs. of William Markham, P.C. (2013), https://www.markhamlawfirm.com/law-articles/
    unlawful-price-discrimination-an-obscure-antitrust-offense-by-william-markham-2013 [https://perma.
    cc/UB3R-K95P].

  183. [183]. Further discussion of the Robinson–Patman Act is outside the scope of this Note.

  184. [184]. 15 U.S.C. § 45(b) (2018).

  185. [185]. Id. § 46(a) (“The Commission shall also have power ... [t]o gather and compile information concerning, and to investigate from time to time the organization, business, conduct, practices, and management of any person, partnership, or corporation engaged in or whose business affects commerce ... .”).

  186. [186]. Id. § 45(b).

  187. [187]. See id.

  188. [188]. Id.

  189. [189]. Id. § 45(c).

  190. [190]. Id. § 45(l).

  191. [191]. Id. § 53(b).

  192. [192]. Id. § 57a(a).

  193. [193]. The practice must be “prevalent.” Id. § 57a(b)(3).

  194. [194]. Id. § 57a(b)–(c).

  195. [195]. Id. § 57a(d)(3).

  196. [196]. Id. § 45(a).

  197. [197]. Dahdouh, supra note 136, at 1.

  198. [198]. Id. at 4–5.

  199. [199]. Id. at 1.

  200. [200]. Id. at 7 (“Senator Reed of Missouri noted that Section 5 deals with ‘the same class of conspiracies exactly as the Sherman Antitrust Act deals with, except that we propose to strike those acts in their incipiency instead of after they have been actually worked out into a complete system of monopoly or restraint of trade.’”) (quoting 51 Cong. Rec. 13118 (1914))).

  201. [201]. Id. at 1, 7–8 (“Senator William Kenyon noted that the new agency ‘can take hold of matters that are not in themselves sufficient to amount to amonopoly or to amount to restraint of trade.’ Senator John Nelson noted that the FTC Act ‘can be used in a lot of cases where there is no trust or monopoly.’ Senator Cummins directly addressed this question in floor debate when he was asked ‘why, if unfair competition is in restraint of trade, [are we] attempting to add statute to statute and give a further remedy for the violation of the [Sherman Act]?’”) (alteration in original) (footnotes omitted) (quoting 51 Cong. Rec. 13156, 13153, 11236 (1914)).

  202. [202]Id. at 4. Senator Cummins also noted:

    that Section 5 “unfairness must be tinctured with unfairness to the public; not merely with unfairness to the rival or competitor . . . . We are not simply trying to protect one man against another; we are trying to protect the people of the United States, and, of course, there must be in the imposture or in the vicious practice or method something that has a tendency to affect the people of the country or be injurious to their welfare.”

    Id. at 8 (quoting 51 Cong. Rec. 11105 (1914)).

  203. [203]. Id. at 10–11.

  204. [204]. See generally Boise Cascade Corp. v. FTC, 637 F.2d 573 (9th Cir. 1980) (holding that just showing parallel action is not enough to violate Section 5).

  205. [205]. See generally E.I. du Pont de Nemours & Co. v. FTC, 729 F.2d 128 (2d Cir. 1984) (holding that an oligopolistic market alone does not violate Section 5).

  206. [206]. See generally Off. Airline Guides, Inc. v. FTC, 630 F.2d 920 (2d Cir. 1980) (holding that in order to bring an essential facilities suit the owner of the essential facility must be in the same market as the person they are preventing from using the essential facility).

  207. [207]. See Dahdouh, supra note 136, at 1.

  208. [208]. Id. at 11.

  209. [209]. Id.

  210. [210]. Id. at 13–14 (quoting du Pont, 729 F.2d at 136).

  211. [211]. Holmes & Mangiaracina, supra note 134, § 7:2.

  212. [212]. Dahdouh, supra note 136, at 4. Congress structured the law with limited remedies, rejecting a treble damages provision. Id.

  213. [213]. Some states do have laws similar to Section 5, but history shows the likelihood of follow-on litigation is low. Id. at 3–4.

  214. [214]. See, e.g., Michele E. Gilman, Five Privacy Principles (from the GDPR) the United States Should Adopt to Advance Economic Justice, 52 Ariz. St. L.J. 368, 400 (2020) (“[R]egulation that in any way inhibits their ability to do so is frequently decried as paternalistic and anti-innovation.” (quoting Lindsey Barrett, Confiding in Con Men: U.S. Privacy Law, the GDPR, and Information Fiduciaries, 42 Seattle U. L. Rev. 1057, 1065–66 (2019)).

  215. [215]. Turow, supra note 12, at 252 (“Most people don’t know the rules of the new digital marketplace, and they think the government protects them more than it does.” (emphasis omitted)).

  216. [216]. See, e.g., MacCarthy, supra note 42, at 475.

  217. [217]. Credit scores are necessary to prevent market failures where asymmetric information leads to inaccurate pricing for things such as loans.

  218. [218]. See MacCarthy, supra note 42, at 478–80.

  219. [219]. Exec. Off. of the President of the U.S., supra note 9, at 17 (“Economic reasoning suggests that differential pricing, whether online or offline, can benefit both buyers and sellers, as described above. Thus, we should be cautious about proposals to regulate online pricing
    – particularly if we believe that online markets are particularly competitive.”).

  220. [220]. See supra Section II.B (discussing how price discrimination harms equity).

*

J.D. Candidate, The University of Iowa College of Law, 2022; B.A., Economics and Political Science Swarthmore College, 2019.



I would like to thank the editors and staff of the Iowa Law Review for their hard work. All remaining errors are my own.