Introduction
Each time you hail a ride with Uber or book a room through Airbnb, you are participating in the so-called sharing economy. The sharing economy and its sister terms—“collaborative,” “platform,” or “gig” economy—refer to a set of techniques and practices that facilitate trusted transactions between strangers on a digital platform.
Instead of hailing taxis or booking hotel rooms, today’s consumers can download an app or visit a website to connect with individuals willing to provide access to their private cars or homes. The sharing economy, of course, did not emerge spontaneously. Antecedents include everything from Internet classifieds such as Craigslist to the carpools of the 1950s.
What distinguishes today’s services is the widespread availability of smartphones and other connected devices, as well as technologies like rating systems, that facilitate trust among strangers.
Sharing economy rhetoric tends to lump together small enterprises motivated by a common social bond, such as local food and housing cooperatives, with billion-dollar global businesses like Uber and Airbnb that readily integrate the language of sharing and connectivity into their branding.
This conflation is a salient feature of what the sharing economy has come to represent—a disruptive force to established industries led by technology companies. We, however, draw a distinction between the variety of businesses that the rhetoric of the sharing economy evokes, like selling grandma’s pies on the corner, and the billion-dollar companies that operate for profit at a global scale. The latter have become a universal focus of the tensions wrought by platforms, technology, and business, and they are the focus of this Essay—though the larger themes of changing commerce that sharing economy proponents promote through an emphasis on sharing, such as reduced ownership of goods, are common to smaller operations.
The upsides of this multibillion-dollar phenomenon are obvious. The sharing economy helps people leverage more of their personal resources and make better use of what Professor Yochai Benkler calls the “excess capacity” of many goods and services.
When used only by their owners, goods like computers and cars will spend a lot of time idle.
By making it easy and cheap to connect to others, we can “share” this excess capacity with the world. Assuming a degree of trust, we might even invite others to share our private spaces—our extra bedroom (Airbnb),
our car (Uber or Lyft),
or our dinner table (Feastly or EatWith).
Sharing economy firms also create new ways to earn income, especially for those who cannot—or do not—wish to work a traditional shift or otherwise face impediments to entering the mainstream workforce.
Additionally, sharing economy analogs can place competitive pressure on legacy services, presumably lowering consumer costs and increasing quality. Taxi companies, for instance, have responded to the convenience of Uber and Lyft by offering consumers the ability to hail cabs through apps instead of calling into a dispatch, such as Arro in New York City
or iTaxi in Miami.
Concerns are also evident. Many argue that sharing economy firms do not compete on a level playing field. Uber and Airbnb, for example, offer the functional equivalent of taxi and hotel services but, by characterizing themselves as mere providers of a software app,
avoid many of the safety, hygiene, and other regulatory requirements that apply to taxis and hotels. A number of class action lawsuits on behalf of Uber and Lyft drivers allege that ride-hailing services skirt labor protections by characterizing drivers as independent contractors entitled to fewer protections.
Another lawsuit argues, conversely, that Uber drivers are independent contractors whom the platform requires to engage in a form of algorithmic price-fixing by setting the prices for each ride and preventing competition.
Together these concerns amount to a claim of regulatory arbitrage;
sharing economy firms flourish by reproducing existing services without the same societal restrictions.
Disability-rights advocates argue that the sharing economy’s relative freedom from legal obligation entails fewer accommodations for disabilities such as wheelchair accessibility.
Others allege discrimination based on race or country of origin: A recent study commissioned by the National Bureau of Economic Research finds “significant evidence of racial discrimination” in that people of color face longer waiting times when hailing an Uber or Lyft along controlled routes in Seattle and Boston.
Another paper (coauthored by Rosenblat) suggests that the passenger-sourced rating system may facilitate employment discrimination against Uber drivers because it masks consumer bias, which can ultimately lead to lower pay, loss of employment, and other adverse employment outcomes for affected drivers.
Professor Nancy Leong and Aaron Belzer go so far as to question the sufficiency of public accommodation laws under the Civil Rights Acts to address various instances of aggregated bias on Airbnb and other sharing economy platforms.
These and related concerns are important and real. But they threaten to overshadow a fundamental critique of the sharing economy that has seen little attention to date. Put simply, platforms like Airbnb, Lyft, and Uber possess deeply asymmetric information about and power over consumers and other participants in the sharing economy. And they are beginning to leverage that power in problematic ways. The sharing economy seems poised to do a great deal of taking—extracting more and more value from participants while continuing to enjoy the veneer of a disruptive, socially minded enterprise.
Today’s companies relentlessly study consumer behavior and use what they discover to maximize their bottom line.
This is true in the mainstream economy. Items cost $9.99 because firms exploit a cognitive bias that causes consumers to perceive the price as closer to $9.00 than to $10.00.
Grocery stores place sugary cereal at eye level for a toddler hoping to increase the nag factor.
As recent work by one of us argues, digital transactions provide especially significant opportunities for firms to discover and exploit the limits of each consumer’s ability to pursue her rational self-interest.
When a company can design an environment from scratch, track consumer behavior in that environment, and change the conditions throughout that environment based on what the firm observes, the possibilities to manipulate are legion. Companies can reach consumers at their most vulnerable, nudge them into overconsumption, and charge each consumer the most she may be willing to pay.
Sharing economy firms, by virtue of sitting between the consumers and providers of services under the scaffolding of a software app, can monitor and channel the behavior of all users. This is partly how they manage to deliver new value to consumers. But their position as all-knowing intermediaries also presents unique opportunities for market manipulation. The stakes are greater too: For many participants, the sharing economy represents a primary or important supplementary source of income.
Experimentation by the platform is not just annoying but affects their livelihood. Meanwhile, consumers may understand that they “pay” for free internet services such as Facebook with their data and yet assume that sharing economy firms are different because of the distinct experiences and rhetoric that surround these services.
Although difficult to verify without behind-the-scenes access, there is evidence that sharing economy firms are already taking advantage of their power over participants. One company in particular—the multibillion-dollar “unicorn” Uber—stands out, showcasing what an intermediary in the sharing economy could do should it be inclined to press its advantages aggressively.
The willingness of one highly visible firm to push normative boundaries is important in several respects. First, it showcases the capacity and incentives of platforms of a certain kind to engage in market manipulation should they be, or become, inclined. Second, if left unchecked, such behaviors may socialize certain practices and encourage emulation or tolerance across and beyond the sharing economy.
While Uber’s corporate practices may not be wholly unique, our unique lens into their operations, which originates in Rosenblat’s research, provides us with a new way of seeing the frameworks in which they operate.
The evidence that Uber is abusing its position is mounting. Uber sometimes operates in a legal gray area such that drivers or the company risk citation by local authority for operating without a taxi license.
In March 2017, the New York Times revealed that Uber systematically targets regulatory authorities, like city officials and code inspectors, and law enforcement officers—identified by the phones they use, their location, and other factors through a tool called “Greyball”—and purposely makes it difficult for those officers to find Uber drivers and issue them citations.
The company went so far as to create a “fake version of the app, populated with ghost cars.”
These manipulations may be part of a broader pattern. Consider, for instance, claims that Uber is manipulating the perceptions of consumers on its popular ride-hailing app. Some consumers report opening the application on their phone and seeing plenty of cars driving around their pickup location, visualized with icons. But after the consumer clicks to request an Uber, these “phantom cars” disappear, and the consumer faces a wait.
Or consider the experiments Uber is running on what ride-hailers might be willing to pay. Apparently, in studying its consumers, the Uber data-science team discovered that people whose phone batteries are low are more willing to pay inflated or “surge” pricing—leading to concerns that the company is interested in what amounts to contextual or individualized price-gouging.
The opportunity and incentive to manipulate drivers is even more pronounced. While Uber drivers use the system, they may be offered a plethora of temporary contracts around price and other factors, and they are perennially forced to agree to new terms of service such as new commission structures, when they log in to work.
As contract scholars explore in other contexts, Uber stands to profit from the inability of the driver to keep up with both the dizzying complexity of such documents and their high rate of change.
Even when the terms are fairly clear, the mechanism of the interaction can be inscrutable. For example, drivers understand that Uber will guarantee them an hourly rate if they accept a certain percentage of ride requests, along with meeting other conditions. On rare occasions, drivers will report that these ride requests flash so fast that the driver is unable to click on them in time to meet Uber’s criteria.
Or, more commonly, a driver will wait for five minutes at a pickup location for a missing Uber rider so as to recuperate a cancellation fee, only to be told that Uber’s internal measurement of time disagrees with that of the driver’s app.
Some issues are subtler still: Uber presumably fuels its ambitious mapping and driverless-car programs with data it gets from monitoring drivers.
This may mean that Uber drivers are unwittingly training their own replacements.
While the sharing economy presents new factual challenges, we are not necessarily in uncharted legal territory. The law of consumer protection has long concerned itself with information and power asymmetries among market participants.
Indeed, given the field’s history and focus, it is notable that the burgeoning legal literature around the sharing economy has scarcely engaged with consumer protection law.
A central aim of this Essay is to address this gap and put forward a positive vision of how consumer protection law should engage with the sharing economy.
This is not to say regulators have ignored the sharing economy, but the challenges regulators face when balancing out the interests of multiple stakeholders are many.
In a recent and lengthy report, the Federal Trade Commission (FTC)—a federal agency with responsibility for preserving the conditions of free and fair trade—heaped praise on sharing economy companies for offering new affordances to consumers and disrupting existing markets through novel means of competition.
A few months later, the other shoe dropped: The FTC settled a complaint with Uber alleging that the company misrepresented, in recruitment advertisements, how much drivers (whom the Commission called “entrepreneurial consumers,” consistent with Uber’s own designation of drivers as “entrepreneurs”) could earn.
The Commission has since entered into a consent decree with Uber for its alleged failure to adequately safeguard user data, including against employees who do not require access.
Such interventions, however, while welcome, have evolved little over the previous half century and feel antiquated in an age of digital platforms. Apart from requiring basic information security, the FTC’s approach to Uber in 2017 is strikingly similar to its handling of the 1979 case involving the multilevel marketer Amway.
As with Uber, the FTC praised Amway for its innovative model of consumer-driven sales of home goods, a technique that permitted Amway to “interject[] a vigorous new competitive presence” into a market dominated by a few major distributors such as Procter & Gamble.
And as with Uber, the FTC restrained Amway from overestimating in published materials how much an Amway consumer-salesperson could make selling its goods.
But there are key differences between the Amway of 1979 and the Uber of today. Amway governed its network of distributors through written materials, the terms of which seldom changed. Its business model was different from its competitors’ but straightforward: Consumers bought goods from Amway, redistributed them in local neighborhoods, and recruited new consumers in exchange for a commission. This remains Amway’s model thirty years later.
Uber is, by contrast, a multivalent digital platform with ambitions to revolutionize global logistics.
It meticulously tracks participants in real time, constantly iterating on approach and design.
In light of these new affordances, it defies imagination that the only problematic practice Uber engages in happens to be the same plainly visible sin of Amway: overestimating incomes in recruitment ads.
The thesis of this Essay, coauthored by a legal scholar and a technology ethnographer who studies ride-hailing and labor in the sharing economy, is that the advantages of information and power that platforms like Uber possess over participants merit a deeper response from consumer protection law.
Regulators face two key challenges in crafting this response. First, regulators must gain a deeper understanding of the acts and practices of digital platforms. This can be accomplished, we argue, by exercising existing authority to demand more granular information from firms about their practices and by incentivizing third parties, such as the research team that uncovered the Volkswagen emissions scandal, to demand and analyze such information.
Second, regulators must find ways to characterize and address problematic behavior. Regulators can accomplish this by drawing lines between acceptable and unacceptable (or harmful) conduct, as the law must often do, or else by attempting to better align the incentives of sharing economy firms with those of other participants.
Consumer protection law must be capable of restoring a sensible balance between sharing and taking.
Our Essay proceeds as follows. Part I offers a more nuanced conception of the sharing economy than presently exists in the legal literature. While there is no stable consensus definition of the sharing economy, this Part identifies a set of core claims, practices, antecedents, and technologies that underpin ride-hailing and other contemporary sharing services. Part I also canvasses in greater detail the benefits and costs of the sharing economy that commentators have identified to date.
Missing from the standard recitation of benefits and concerns is a fundamental critique of the sharing economy grounded in asymmetries of information and power. Part II advances such a critique. We draw from the theory of digital market manipulation and other work to argue for recognition of a greater range and complexity of dangers. Many of the concerns we emphasize in Part II are necessarily speculative in nature, in part because sharing economy practices occur behind the digital scenes. We therefore ground the discussion in a case study of Uber, which we select for its unique visibility among sharing economy firms and its apparent willingness to push normative boundaries, and because one of us (Rosenblat) has studied Uber’s drivers extensively in her ethnographic fieldwork. Our concerns, of course, apply beyond this single company and across the sharing economy of today and tomorrow. Others are likely to also engage in versions of the behaviors we catalogue, and many are in a powerful position to do so.
Part III advances the argument that consumer protection law—with its long emphasis on asymmetries of information and power—represents a critical but oddly missing lens through which to understand and address the full complexity of the sharing and taking economy. Part III concludes by suggesting ways consumer protection law can evolve to address the techniques used by sharing economy firms.
I. The Story of the Sharing Economy
There is no consensus definition of the sharing economy.
We define the sharing economy loosely as a set of practices and techniques that leverage digital architectures to facilitate trusted transactions between strangers. But at base the sharing economy and its sister terminology, like “collaborative consumption,” the “peer-to-peer” economy, or the “gig economy,” represent a rhetorical device, a story that proponents tell in service of some business or political purpose such as attracting participants and funding or minimizing government intervention.
On this view, the sharing economy poses as a social movement even as it engages in what Professors Elizabeth Pollman and Jordan Barry term regulatory entrepreneurship (or, more pejoratively, regulatory arbitrage).
This Part begins by telling the story of the sharing economy from the vantage of its proponents and then describes the considerable concrete benefits and real dangers that sharing economy commentators have identified to date. This Part presages Part II, in which we introduce and contrast our own novel critique grounded in asymmetries of information and power.
A. Why “Sharing”?
The gist of the sharing economy narrative is that technology helps people collaborate economically at scale. Consider the classic carpool that was introduced and popularized in the 1950s and that persists today. Many people need to get from the suburbs to downtown. If everyone drives, there is traffic congestion, and no one can read the newspaper. Meanwhile, cars are designed to hold four or five people, and so that extra space and gas is wasted. Carpooling by neighbors, who generally know and trust each other, adds value by sharing the responsibility and resources needed to get to work. Broader carpooling might be even better but would introduce search and transaction costs. Worse yet, it could introduce the prospect of unreliable or undesirable drivers or riders. Sharing economy firms address these perceived problems of scaling by introducing apps and rating systems to find, connect, and assess people. Not only can you get downtown via Uber, but you can invite a stranger to dinner (Feastly), let your spare bedroom for the week (Airbnb), or even rent out your power tools (NeighborGoods). People trade or purchase resources from one another; the platform acts as an impartial intermediary to help them connect.
The sharing economy narrative emerges from a variety of sources, including our familiarity with online social networks and a general sense of economic urgency that flows from the wake of the Great Recession and the rise of precarious employment in the United States.
But its intellectual home is really the notion of “commons-based peer production” that Professor Benkler put forward as early as 2002.
Proponents initially envisioned that social values and notions of individual empowerment would flavor the missions of businesses under the sharing economy umbrella. This vision of the sharing economy gets its roots from advocacy groups interested in the structures and decentralized impact of peer-to-peer technologies, like the file-sharing service Napster or the virtual currency Bitcoin.
Prominent sharing economy advocate Peers.org functioned both as a public relations machine for sharing economy firms and later as an advocacy organization for workers.
Today the promise of the sharing economy continues to be based, rhetorically, on ideas of social reciprocity. Advocates of the sharing economy characterize digital platforms as trusted economic communities that enable commerce, while simultaneously responding to the needs of local communities or even addressing historical inequities. For example, Airbnb claims to build a community wherever it creates a marketplace for hosts to auction off their spare bedrooms to visiting tourists,
and Uber helps to provide alternative and more efficient transit access to underserved communities.
These platforms claim to leverage technology to connect people and create the infrastructure to support transactions with common social goals. The business practices of these platforms represent, to paraphrase author Tom Slee, a marriage of commerce and cause.
The sharing economy walks an interesting line: The model is spun as both novel and having many antecedents, which makes it feel simultaneously innovative and familiar. Business professor Arun Sundararajan argues that the sharing economy represents a series of familiar practices—borrowing and lending underused goods, lending a helping hand for services, or self-employment for side work—and reorganizes them digitally in monetizable ways.
For Professor Sundararajan, the rapid growth of the sharing economy is partly a function of this familiarity, which renders the model more palatable to consumers.
Just as Amazon is an extension of brick-and-mortar or catalogue retail, sharing economy firms draw from the phenomena of couch-surfing and carpooling.
Another building block of the sharing economy narrative is the increasing centrality of service-based consumption, such as using the music services Pandora or Spotify instead of purchasing songs.
The rhetoric of sharing contrasts with ideals of ownership and possession, and the sharing economy ideology takes aim at exclusive individual ownership of goods in particular, contending that idle, underutilized assets, like power drills, spare bedrooms, or spare time, contain value that can be “unlocked” efficiently for monetary (and even nonmonetary) benefits.
As sociologist Juliet Schor observes, socioeconomic factors, such as a general increase in the consumption habits of consumers, helped to evolve the secondary digital markets for the redistribution of used goods and, later, services.
By facilitating connectivity and trust between strangers, sharing economy businesses offer a more mature version of their most familiar antecedents, Craigslist and eBay, which both started in 1995 and initially provided a digital space for the recirculation of goods in the nascent growth and popularity of the Internet and Internet exchanges.
Sharing economy firms are diverse and yet carefully consistent in their terminology. They call themselves “platforms” or “technologies” and do not typically own the physical assets, such as homes, cars, or tools, used in the transactions they mediate, although they may offer resources that enable individuals to own or care for their assets, such as car leases or cleaning services.
They also speak of promoting freedom, flexibility, and independence.
This narrative of worker (and consumer) empowerment through networks similarly reinforces the identity of platforms as neutral arbiters of technological transactions, like a credit card processor, rather than traditional employers with social obligations toward their employees. Many workers in the sharing economy are classified as independent contractors, so their employers often communicate job expectations in the language of suggestions or recommendations.
This framing draws on sharing economy values that redefine workers as free, independent entrepreneurs who can work for multiple, competing employers, such as driving for both Uber and Lyft. The prospect that the sharing economy generates new, more flexible opportunities for income is particularly exciting in the face of societal anxiety about dwindling economic opportunities for growth following the financial crisis of 2007. In a 2015 survey coauthored by Uber economist Jonathan Hall and economist Alan Krueger, eighty-five percent of surveyed drivers indicated that flexibility was a driving motivation for their work on the platform.
The promise of freedom and flexibility is perceived as a benefit of the sharing economy more broadly because it fits into a more utopian vision of workers who work by “uncoerced choice.”
Flexibility in the gig economy, as scholar Vili Lehdonvirta observed in 2017, hinges on how dependent one is on the gig work,
echoing Schor and affirmed again by Rosenblat’s research.
The story, then, is one of evolving technological and consumption habits that, along with techniques of trust facilitation such as rating mechanisms, empower new modalities of consumption and work just in time to cushion the economic fallout of the financial crisis. For a modest fee to offset the value they are adding, sharing economy firms act as neutral community marketplaces in which people can come together and purchase or sell excess capacity in the form of rides, tasks, rooms, and other resources. These new modalities are both familiar, in that they have recognizable antecedents, and powerfully disruptive of the less social or socially minded patterns of commerce.
And this story seems to be working—at least for some. The ongoing popularity of the sharing economy as a business trope can be partly credited to the remarkable financial success of its leading symbols in garnering venture capital funding in Silicon Valley, and in their global scale: Airbnb, which was founded as a start-up in 2008, is valued at $30 billion
and has home or room listings in 65,000 cities (as of July 2017),
and Uber, another start-up from 2009 that is valued at nearly $70 billion,
has services in perhaps
633 cities worldwide.
There is no central source of information on the size of the sharing economy, although several reports make efforts to measure it by different metrics from a macroeconomic perspective. Studies by the Pew Research Center, the Brookings Institution, and the JPMorgan Chase Institute all report significant and growing participation.
Although frequently billed as a national or global phenomenon, the sharing economy is largely centered on urban populations. A Pew study in May 2016 found that seventy-three percent of Americans still had not heard of the phrase “sharing economy” and that only fifteen percent of Americans have used ride-hailing apps like Uber or Lyft, which the study found to be available primarily in and around metropolitan areas.
Meanwhile, sharing economy firms seem to confront fewer—or at least different—regulations than the taxi, hotel, restaurant, and other legacy firms with which they compete. As Professors Pollman and Barry explore, sharing economy firms like Uber and Airbnb have proven adept at exploiting gray areas (or simply flouting laws) while growing to a size and popularity that gives them the political clout to combat efforts to regulate them.
These authors refer to this strategy as “regulatory entrepreneurship”—defined as a business model that acknowledges how “changing the law is a material part of the company’s business plan and vision for success.”
Crucial to this success is a strong rhetorical strategy that positions the sharing economy as familiar enough for consumers to adopt and enjoy it, but novel and “disruptive” enough to merit new regulatory strategies and to generate scorn for policymakers who stand in the way of its innovations.
B. Sharing’s Rewards
The sharing economy is tied up in whimsical notions about a decentralized, social marketplace. But whether or not you credit this underlying narrative, any evenhanded assessment of the sharing economy must acknowledge a host of concrete benefits also emphasized by economists and regulators.
They include: maximizing the utility of personal assets; flexible schedules for workers; (some) income security; increasing the quality and quantity of goods and services available through greater competition; and local access to new infrastructure resources. We canvass them briefly below. Many of the very same techniques and technologies that permit sharing economy firms to deliver this new value are also what allow them to engage in problematic manipulation.
1. Efficiency and Income Flexibility. — The sharing economy promises to unlock various resources with excess capacity, such as a household’s guestroom. The connectivity and trust mechanisms developed by platforms increase efficiency in the sense that underutilized resources can find a higher-value use.
Sharing economy firms also promote efficiency by lowering search costs and by permitting consumers more and better options.
Discussing transportation, Professor Brishen Rogers offers the prospect that city dwellers could dispense with car ownership entirely.
In addition to freeing up resources for individuals or families, fewer cars on the road holds positive implications for the environment and traffic congestion.
Among the most valuable resources is an individual’s time. A central benefit of the sharing (or “gig”) economy is to provide more and more diverse opportunities to make money.
Many cannot work even a part-time job due to the schedules and shifts that typify traditional employment.
Imagine a parent who drops her child off at public school and must pick her up again in the early afternoon. Some days this parent has errands to run, but others she is sitting at home. TaskRabbit, Amazon Mechanical Turk, and Lyft all offer this person a flexible means to supplement the family income or save for her child’s college tuition—and all on her own schedule—driving between drop-off and pickup.
The sharing economy has a relatively low barrier to entry for job opportunities as well, which may be especially salient for marginalized populations excluded from the workplace by low education, a criminal record, or other factors.
Sharing economy workers can also switch jobs at will: They are generally classified as independent contractors who are free to work for multiple competing employers and are labeled partners or entrepreneurs by sharing economy advocates.
Although there is high turnover within the industry, some offer this as proof that temporary employment in the sharing economy benefits those who are in career transition or who face challenges related to their family, education, or health.
The sharing economy may thus reduce overall income volatility, particularly for those who live paycheck to paycheck in an economic climate in which real wages have declined since 2009 for most households, with the exception of the top fifth percentile.
The sharing economy facilitates more transactions with greater efficiency between users through technology, but it can also have economic impact on related industry actors and consumer populations. Broadly accessible services can prompt industry specialization for legacy businesses (for example, business travelers who want reliable experiences or families with kids who might prefer to stay in a hotel rather than in a stranger’s home), which presents users with a greater variety of options, and the chauffeur industry can cater to specialized, niche, or luxury services for high-end consumers.
2. Greater Competition. — Many commentators, among them regulatory bodies, have praised the innovative means by which sharing economy firms compete in legacy markets.
Although some worry that sharing economy firms compete unfairly with legacy firms by performing a functionally equivalent service under fewer constraints, it is Economics 101 that the introduction of new competitors into existing markets should have a positive effect on price and quality.
Some studies indicate that Airbnb has affected the hotel market in precisely this manner—by driving down the price of a hotel room.
This economic effect is obscured by the scarcity of substantive studies;
however, particularly visible are the changes to legacy firms as they adapt to shifting consumer expectations. Today, taxi services have apps that permit consumers to call for a car and pay for the service at the push of the button, just like they can on Uber and Lyft.
The sharing economy platforms may also popularize the use of transit and accommodations services without unseating existing businesses. For example, one study found that the rise of Uber changed the makeup of the labor market within the transportation services industry but that it also grew the pie overall, rather than cutting off thinner slices of it.
3. Access to New Resources. — The introduction of sharing economy firms can have other positive effects, especially in cities where sharing economy activity tends to concentrate. Supplementary income from part-time work in the sharing economy may enable people to pay their rent, cover daily living expenses, or pursue their passions or goals. For example, in cities like New York or San Francisco where Airbnb’s business is under attack by hotels or unsympathetic regulators,
Airbnb takes the stance that the income people earn through its platform allows them to afford their rent. Indeed, the numbers are impressive: In San Francisco alone, Airbnb asserts that its platform has generated 430 jobs and $56 million in local spending.
The company noted, too, that of total guest spending, hosts’ households received $12.7 million.
Research on the impact of the sharing economy in low-income communities demonstrates that it can increase access to resources and opportunities, facilitate networking opportunities (such as when Uber drivers or passengers make connections that lead to new job opportunities), or help fill gaps in public transportation.
For example, people who may not be able to get to work on time because they lack car ownership or access to robust public transit could stand to benefit from ride-hail technologies that provide them with better mobility.
Improving access to resources and shoring up access to transit in underserved areas can both bolster a belief in civic community
and remedy existing inequities in public infrastructure and commercial services, such as longstanding patterns of discrimination against people of color by taxis.
Similarly, accommodations options available on platforms like Airbnb may reduce the cost to tourists or travelers who wish to visit a city if they can find a place to stay in someone’s spare room more cheaply than in a hotel. Some cities, like Altamonte Springs, Florida, are even experimenting with subsidizing ride-hail services like Uber and Lyft to meet the cities’ transportation needs.
C. Sharing’s Perils
Neither clever rhetoric nor clear benefits have managed to entirely insulate sharing economy firms from criticism. The excitement around these new companies has come to be tempered by a series of concerns. Worries come from a variety of sources, which in turn shape the basis and character of their critique. Competitors to the sharing economy, for instance, maintain that Uber, Airbnb, and other firms are not competing fairly.
Cities focus on the safety and mobility of urban residents. Drivers and other service providers question why they should miss out on the benefits associated with employment. Others raise concerns around privacy and discrimination.
These concerns do not necessarily have a common nexus, except for the general absence of regulatory supervision. This section summarizes the literature and reporting critical of the sharing economy and catalogs the various downsides critics have advanced. The section acts as a prelude to the arguably deeper critique we advance in Part II.
1. Regulatory Arbitrage. — The first set of concerns centers on the claim that sharing economy firms are not competing fairly. In part through a strategy that embraces forgiveness over permission, these firms replicate legacy services such as transportation, lodging, cleaning, and even dining without the encumbrance of regulation. Critics perceive this as a problem for at least two reasons. First, it is very difficult for an existing service bound by regulations to compete with a firm that is not. And indeed, Uber and Airbnb have prompted outcries by taxis and hotels the nation over.
Second, the regulations around legacy services exist for a reason: to protect visitors and city residents. The critique is that, because they skirt legal requirements through regulatory arbitrage, sharing economy firms are underinsured, less safe, less sanitary, and so on.
Proponents of the sharing economy may counter that regulations are outdated and constitute barriers to entry. More particularly, proponents may argue that sharing economy firms have developed new mechanisms to ensure safety, quality, and other values. Chief among these is the ability to rate and comment on services, coupled with an enforcement mechanism when a provider falls below consumer expectations. But the question then becomes whether these systems are adequate to protect participants. Cities such as Austin, Texas, think not and have responded with ordinances reintroducing certain requirements, such as fingerprint-based background checks for Uber and Lyft drivers, and have instituted new regulations the city sees as better tailored to govern the realities of ride-hailing.
Related to issues of regulatory arbitrage is the concern that, by characterizing all participants in the sharing economy as “consumers” of a technology, including providers of services (i.e., workers), sharing economy firms manage to avoid labor laws.
This circumvention of labor law has generated a number of critiques and class action lawsuits. For example, several lawsuits in California allege that drivers for Uber and Lyft are “employees” and not “independent contractors” as these firms claim.
In response, Uber has pivoted to characterizing drivers as “end users” or “consumers” of its software, akin to passengers.
The allegation of employment misclassification is not limited to California or even the United States; London drivers have also sued Uber alleging violations of the UK Employment Rights Act.
2. Discrimination. — One of the more troubling existing critiques of the sharing economy is that sharing economy firms facilitate discrimination. As alluded to above, one benefit of the sharing economy is that vulnerable or marginalized populations might have greater access to services in their neighborhoods and greater opportunities to earn income.
But investigations have also yielded evidence that both service providers and service consumers in the sharing economy face racial and other discrimination. A National Bureau of Economic Research study shows that African Americans wait longer for rides on ride-hailing services.
Another analysis by Rosenblat and various coauthors concluded that the Uber rating system can mask passenger-sourced discrimination, which may, for example, lead to lower ratings for drivers with protected-class characteristics and could result in lower pay or leave them more vulnerable to termination by the platform.
Other evidence suggests that African Americans have trouble finding accommodations on Airbnb due to discrimination by hosts.
The prospect that the sharing economy supports discrimination led Leong and Belzer to argue for an update of public accommodation laws.
They surveyed existing mechanisms for addressing race discrimination in the context of public accommodations, including the groundbreaking Civil Rights Act of 1964 and identified limitations in these mechanisms to regulate the sharing economy.
The authors recommended a variety of updates, including altering the standard for discriminatory intent and mandating greater transparency.
3. Privacy. — Finally, there have been allegations that sharing economy firms pose a threat to information privacy. Like other digital platforms, sharing economy firms have access to a tremendous volume and variety of information about the behaviors of consumers. Sharing economy firms likely collect more information than is needed to accomplish their core goals of reducing search costs and facilitating trust. Uber needs to know where riders are in order to connect them to drivers. But it bothers some that Uber continues to record a passenger’s whereabouts after she has left the car.
Moreover, there is evidence that sharing economy firms, like other start-ups, may have inadequate internal safeguards around privacy. Many were alarmed, for instance, to learn that senior managers at Uber in 2014 enjoyed an unrestricted God’s eye view of the system, which permitted a senior manager to track the arrival of a reporter who was interviewing him.
Concerns over information privacy, nonetheless, are not unique to sharing economy firms. The collection, processing, and use of personal information is part of a broader trend whereby digitally as well as physically based firms make a close study of consumers.
And yet, as we explore in the next Part, there may be reasons to be particularly concerned with the information practices of sharing economy firms. Thus far, however, the people raising privacy concerns about Uber or Airbnb do so on similar terms as those raising concerns with Facebook or Google.
II. Taking in the Sharing Economy
Part I offers a sense of the discourse around the sharing economy to date. Proponents weave the emerging set of practices and techniques into a story about social innovation. They emphasize the many benefits of the sharing economy to consumers, workers, and communities in general. And they position opponents as Luddites who are overinvested in the status quo.
Detractors, meanwhile, see this narrative as a kind of “share-washing” and point instead to the sharing economy’s downsides: the various businesses, groups, or individuals who stand to lose out or get hurt. These downsides, while real and concerning, share only a loose conceptual foundation. Uber’s poor internal privacy safeguards appear to have little to do with the question of whether drivers are employees or independent contractors. Notwithstanding talk of exceptionalism or disruption, little about their configuration ultimately prevents sharing economy firms from improving the way they treat workers, buying more insurance, or policing better against discrimination. Indeed, allegations of discrimination have already led to concrete changes to the way sharing economy firms operate.
The back and forth between innovation and exploitation threatens to overshadow a foundational critique regarding the ways sharing economy firms leverage their status as intermediary platforms. Drawing from each author’s work on information and power asymmetries, this Part argues that sharing economy firms, which observe in detail the activities of all participants under the scaffolding of an app, have both the means and the incentives to engage in complex techniques of self-dealing scarcely reflected in the legal academic literature. We first develop an account of digital market manipulation—referring to the emerging technologies and techniques that empower firms to discover and exploit the limits of consumers’ ability to pursue their self-interest. We then give preliminary evidence of digital market manipulation in the context of our case study, the popular ride-hailing service Uber.
A. Digital Market Manipulation
Knowledge itself is power. Under the right circumstances, having better or earlier information than others confers a tremendous advantage. This helps explain why contemporary commerce involves ever-more-detailed study of consumer habits.
The more a firm knows about consumers, the better it is able to meet their needs and monetize their attention. But even one of the most common business models of Internet firms—free content in exchange for ads tailored to one’s interest—is mutating. Increasingly, firms use what they know about consumers not only to match them to content they might prefer but also to nudge consumers to pay more, to work for less, and to behave in other ways that advantage the firm.
In a pair of influential articles, Professors Jon Hanson and Douglas Kysar develop the theory of market manipulation to describe the exploitation by firms of the cognitive biases of consumers.
Cognitive bias refers to the ways people depart from rational decisionmaking because of reliance on heuristics or other mental limitations.
Traditional economic models generally assume away irrational behavior, whereas behavioral economics recognizes the role of cognitive bias as individuals attempt to pursue their self-interest.
Hanson and Kysar’s particular contribution to the literature is to illustrate the ways companies purposely leverage consumer limitations in order to extract rent.
Everything costs $9.99, they observe, because our brains see a greater distance from $10 than 1 cent.
Indeed, firms are not only in a position to exploit consumer cognitive bias; they may find themselves displaced from the market by firms that are willing to do so if they do not.
One of us (Calo) has contributed to this debate by updating Hanson and Kysar for the digital age.
A theory of digital market manipulation layers in the roles of personal information and digital design. The contemporary consumer is a mediated consumer, meaning that her interactions occur through a platform that a company can closely monitor and that it took great pains to design and architect.
By tracking consumer habits in close detail, not only are firms in a position to exploit the general cognitive biases consumers share across a population, but they are also able to identify the specific and often highly idiosyncratic limitations of each consumer. Moreover, the ability to architect virtually every aspect of a digital interaction, such as a website or app, creates far greater opportunities for manipulation than the static selection of price, an offer of rebate, or the other analog approaches Hanson and Kysar explore.
Contrast the Hanson and Kysar example of $9.99 pricing to the ability of firms to charge an individual more because they know she is intoxicated or depressed and therefore less able to exercise self-control.
A theory of digital market manipulation accounts for several new capacities of contemporary firms to identify and exploit cognitive biases. For instance, the ability to study consumer behaviors on a massive scale permits firms to discover many more instances of bias. Rather than draw from a set of known cognitive limitations, such as a propensity to stick with defaults, companies can now use pattern recognition to spot the many idiosyncratic ways consumers depart from rational decisionmaking within their digital ecosystem.
This furnishes firms with far more options for advantage taking. Moreover, whereas traditional marketers have been content to use what they know about consumers to match them with goods and services they might prefer, firms are increasingly using what they know to better persuade consumers—a practice known as persuasion profiling.
Data advantages are especially critical to sharing economy firms. A recent profile of Uber’s founder, Travis Kalanick, affirmed the “existential” importance of data to that company.
In addition to its central objective of running a vehicle dispatch system, the company’s ambitions include everything from revolutionizing logistics and providing a granular new mapping service to training cars to drive themselves—all goals heavily reliant on the collection and analysis of enormous volumes of data.
According to Forbes, the company ultimately “seeks to become the planet’s operating system for transportation.”
Sharing economy firms such as Airbnb, Lyft, and Uber sit between transactions among multiple parties, which places them in a position to study both the provider of the service and its consumer, individually and collectively. Moreover, the firms design each participant’s entire digital experience from scratch. They build and update the apps or website portals that service providers and service users access. They structure the business model and acceptable forms of transaction. And they write the terms of service and privacy policies that every participant clicks through in order to use each service. This combination of visibility and sociotechnical design confers upon sharing economy firms exquisite control of the interactions they facilitate.
The sharing economy accordingly represents fertile ground for digital market manipulation. However, there are dynamics at play in the sharing economy that differ qualitatively even from social networks or online commerce generally. First, peoples’ livelihoods are at stake. Many participants in the sharing economy derive irreplaceable income from the transactions these platforms facilitate.
Indeed, as we discuss in Part I, this is one of the sharing economy’s most significant benefits. But it also means that the systematic exploitation of bias in this context is especially fraught.
Second, while consumers are starting to understand the notion of paying for “free” services with data, they have no such mental model for sharing. There is an increasingly common saying in privacy circles: If you are not the customer, you’re the product.
Consumers use Facebook or Google without paying money and so, arguably, they tacitly accept the value proposition that these companies will monetize their information and attention. This mental model, or representation of reality, may not translate to the sharing economy, which can appear on first blush to have a simpler business model: It connects consumers to providers for a fee. You are the customer, quite literally, so you do not necessarily think of yourself as a product, too.
Third, sharing economy firms can influence not only the perceptions and behaviors of consumers of goods and services but also those of the suppliers. Control of this “two-sided” marketplace creates additional channels for digital market manipulation, including interparticipant interaction.
For example, sharing economy firms interested in changing host behavior for reasons of profit margins could use their knowledge of and access to guests through the application to police the host behaviors indirectly by encouraging guests to down-rate activities that harm Airbnb profits.
Finally, sharing economy firms can and do leverage their access to consumers and other participants in order to influence important stakeholders such as potential regulators. As Professors Pollman and Barry explain, this strategy is at the very heart of regulatory entrepreneurship: Tell a good story, become indispensable to consumers, and then ask forgiveness instead of permission.
We would add that sharing economy firms, as digital platforms, are especially well positioned to identify, encourage, and coordinate participants willing to contact regulators on the firm’s behalf. Imagine, for example, the sudden emergence of a button on an app the consumer can press to call the specific legislator taking aim at the sharing economy firm.
In short, the sharing economy presents at least as many opportunities for digital market manipulation as any previous or contemporary market modality. Of course, it is one thing to illustrate that a firm could engage in manipulative techniques in theory, and quite another to show they do so in practice. Professors Hanson and Kysar accompanied their theoretical work with an article cataloguing the actual practices of firms that exploit consumer cognitive bias.
This was possible in part because analog market manipulation is detectable once a person is looking for it: The price or placement of an item or the terms of refund are evident. Digital market manipulation, being a product of behind-the-scenes processing of personal information, is less visible.
Nevertheless, drawing from a variety of sources including an ethnographic study of sharing economy participants, the next section illustrates several actual and potential instances of digital market manipulation by the popular ride-hailing platform Uber.
B. Some Evidence of Digital Market Manipulation in the Sharing Economy
It is one thing to claim that sharing economy firms have an opportunity or even an incentive to abuse their intermediary position. It is quite another to demonstrate that they are actively engaged in abusive practices. We do not have access to the decisions or processes that go on behind the digital scenes of a server or an app. Thus, evidencing the extent to which sharing economy firms are leveraging their asymmetric access to information and unilateral power over the design of interfaces and other architecture is difficult. Nevertheless, some of the acts and practices of sharing economy firms are observable enough to raise serious questions. By studying the observations of sharing economy participants, we can begin to piece together enough evidence of digital market manipulation to support a much deeper investigation.
For this purpose, we have selected Uber as a case study. The ride-hailing service has emerged as something of a poster child for the sharing economy. The largest of the sharing economy firms by valuation,
it has amassed so much cultural capital as to give rise to an epithet—the “Uberization” of X or “Uber for X” (for example, “Uber for Healthcare”) are used as branding material for companies looking to emulate Uber’s achievements.
One of us (Rosenblat) has conducted extensive fieldwork with Uber drivers, which has uncovered many of the issues represented in this section. We suspect, however, that a sustained analysis of virtually any large sharing economy firm—including Uber’s competitor Lyft or housing analogue Airbnb—would yield a similar set of concerns and questions.
This section first addresses possible manipulations of ride-hailers. Next it examines the even greater capacity of Uber to manipulate ride-providers. Finally, the section addresses systematic issues that could affect all participants.
1. Taking from the Traditional Consumer. — Consumers utilize Uber’s services by downloading a software application that Uber designs from scratch. The application opens to a map of the user’s present location. Represented on the app are icons of vehicles alongside the wait time for the nearest available driver. A user may open her app and see many vehicles around her, suggesting that an Uber driver is close by should she decide to hail one. As research by author Rosenblat and Professor Luke Stark reveals, the representation of nearby Uber cars can be illusory.
Clicking the button to request an Uber prompts a connection to the nearest driver, who may be much farther away. The consumer may then face a wait time as an actual Uber driver wends her way toward the pickup location. Those icons that appeared where cars were not present are familiar to some consumers as “phantom cars.”
Uber has offered a variety of responses to allegations of phantom cars, including that the cars achieve a “visual effect” akin to a screensaver.
The idea is to suggest visually that Uber is searching for nearby partners, not that cars are literally present at the location of the icons. Upon multiple inquiries to Uber’s Customer Support Representatives, a particular driver eventually received the following explanation for this discrepancy via email:
The app is simply showing there are partners on the road at the time. This is not a representation of the exact numbers of drivers or their location. This is more of a visual effect letting people know that partners are searching for fares. I know this seems a [sic] misleading to you but it is meant as more of a visual effect more than an accurate location of drivers in the area. It would be better of you to think of this as a screen saver on a computer. Once a rider request[s] a trip there will be actual information about the partners [sic] location showing up in the app.
One problem with this response is that the visual vocabulary of Uber’s app is inconsistent. Upon hailing an Uber, a consumer sees an icon of her driver’s actual location in real time. Nothing in the interface distinguishes between the manufactured display of phantom cars and the actual representation of a hailed vehicle. In Professor Woodrow Hartzog’s parlance, Uber engages in “abusive design” by suggesting visually that cars are nearby when they are not, presumably to entice the consumer to commit to hailing Uber instead of Lyft or a taxi.
Further, the company is using the consumer’s location on a map to lend verisimilitude to the illusion.
In response to controversy that the presence of phantom cars generated, Uber initially denied their existence but has since offered additional explanation:
Latency is one reason [an accurate depiction of cars] is not always possible. Another reason is that the app only shows the nearest eight cars to avoid cluttering the screen. Also, to protect the safety of drivers, in some volatile situations, the app doesn’t show the specific location of individual cars until the ride is requested.
Regardless, Uber’s ability to blur the boundaries between an accurate representation of real-time supply (drivers) and a representation of general driver supply illustrates the potential for deception to emerge in user interactions with the platform.
A second set of examples involves the willingness of a user to pay surge prices. Uber determines a price according to a proprietary surge-pricing algorithm. When transportation demand is very high—such as during rush hour or a sporting event—Uber’s algorithm changes the pricing in response.
Drivers like surge pricing because they make more money, although a common piece of advice among drivers is “don’t chase the surge” because the system can be unreliable.
Presumably consumers do not like surge pricing to the extent they have to pay more.
Indeed, charging higher prices during periods of extreme demand has echoes of price gouging—the practice of charging much more for goods and services in the face of an emergency or shortage.
Uber’s own data scientists have revealed that the firm makes a close study of exactly when consumers might be willing to pay more. For example, Uber researchers found that individuals are more willing to pay surge pricing when the batteries on their phones are low.
This makes sense, of course, because the alternative is for the consumer to be stranded without access to a means of communication. Uber claims it does not currently leverage this information—we take the company at its word.
But the very fact that Uber monitors battery life raises questions about the information to which Uber has access as well as the criteria the firm might find suitable for use in pricing.
Another example involves introducing artificial precision into surge pricing. Numerous studies and common sense tell us that consumers tend to condemn price gouging, or raising prices during times of very low supply and high demand, as unfair.
Many jurisdictions prohibit the practice as a matter of law.
At the heart of price gouging is the idea that a seller decides to exploit anomalous circumstances to charge people much more. Uber does set the base rates at which drivers are paid, and at which passengers pay, but in addition, it uses pricing algorithms to assess surge premiums, and it sometimes assesses a higher price for passengers to pay through “up-front pricing.” In this sense, Uber does not set prices the way that merchandise is listed at a physical store, but instead, it determines initial prices using algorithms that effectively experiment with what a consumer is willing to pay. Algorithmic pricing is also evident in other businesses, such as online retail outlets, which use customers’ information, such as their location comparable to other available retail options or their demographic, to target them with different prices for the same items.
The social science around algorithms has found that people tend to trust algorithms and “Big Data” as reflecting mathematical realities.
Travis Kalanick has reinforced this perception by stating, for instance, that “[we are] not setting the price. . . . We have algorithms to determine what that market is.”
But Uber’s data scientists found that users distrust algorithmic surge pricing that merely doubles their costs (x2.0) because they perceive this as a form of artificial price gouging. This suggests that Uber should introduce the appearance of more precision (e.g., x2.2).
Designing prices that people will perceive as algorithmic or mathematical instead of arbitrary is the digital equivalent of charging $9.99.
Uber also appears to be charging different prices to similarly situated consumers—a practice known as dynamic price discrimination, which some consumers and commentators find alarming. Research by computer scientists Le Chen, Alan Mislove, and Christo Wilson measured the prices Uber’s Application Programming Interface returned for surge in various areas to various passengers and examined those prices against the prices passengers actually received.
They found a discrepancy, with users in the same surge zone at the same time receiving different prices, which Uber explained as a bug in the system.
A possible technical explanation for this discrepancy has to do with server infrastructure.
Achieving consistency of prices across a distributed network of services is challenging.
Uber may also be manipulating consumer access to various tiers of service. Uber offers a variety of services under its umbrella, with variations in price and quality of service. Anecdotally speaking, for some consumers, the cheaper service uberPool appears as a default, requiring the consumer to overcome default bias in search of another option.
For other consumers, perhaps those that Uber somehow understands to be better resourced or who potentially have a habit of preferring one tier of service to another, the more expensive uberX appears as a default. The displayed services may also be labeled by Uber in the Uber rider app as “popular,” indicating that they are highlighted based on aggregate consumer behavior and not based on individual habits; they may also be labeled under the umbrellas of “economy” or “premium” services.
In other instances, two different passengers standing in the same spot at the same time might receive different price quotes to the same destination. This, speculatively, indicates that Uber possibly targets users with prices based on their perceived willingness to pay more. There could, of course, be technical errors that produce discrepancies or extremely sensitive pricing algorithms that are responsive to momentary supply and demand and that might result in higher price quotes for sequential passengers in similarly situated positions.
Uber may even hide uberX entirely by showing no available drivers.
Ultimately we do not know,
and any technical caveat may suffice to explain only some of the inconsistences in Uber pricing.
2. Taking from the Entrepreneurial Consumer. — Sharing economy firms try to avoid characterizing themselves as traditional employers. Rather, they claim to provide a technology-based service to different categories of users. Contractually, Uber tends to refer to drivers as independent contractors, which helps limit its obligations under labor and tort law.
Uber and other sharing economy firms characterize all participants as consumers: In the United Kingdom, for example, Uber actually uses the label “customer” in its terms of service when drivers download the app,
and in a U.S. class action regarding employment classification, Uber took the position that drivers are consumers of its software, like passengers, for which they pay a “licensing fee” to Uber.
The construction of all participants as consumers is reinforced by the FTC’s verbiage in its complaint against Uber, in which it refers to drivers as “entrepreneurial consumers.”
At any rate, the opportunity to manipulate drivers is, if anything, considerably more expansive than the opportunity to manipulate ride-hailers.
All users of the platform rely on Uber to fulfill the expectations it scaffolds onto users about how its system works. When issues of deception or other types of problems emerge, such as phantom cars, it can be challenging to dissect which part of the problem is a business practice, a technical issue, or a sociotechnical misunderstanding. But discrepancies in the system will generally tend to be more impactful on drivers in that they can result in lost pay or even suspension from the service.
Drivers transact with the company and with riders in accordance with contractual terms written by Uber. This is hardly anomalous; one-sided or boilerplate contracts are commonplace in contemporary markets.
But Uber’s business model and the transactions it supports are particularly complex. They involve many different configurations and contingencies, such as short-term promotions and new contractual conditions. Drivers must perennially agree to new terms of service in order to log in to work—akin to signing a new employee manual every few days.
As Professor Oren Bar-Gill reports in his book Seduction by Contract, contracts are getting more complex over time as an empirical matter.
Bar-Gill argues that the increasingly dense contracts may represent a purposeful attempt to exploit human limitations in processing complexity.
And as Professor David Horton explores, constant unilateral changes to contracts result in “shadow terms” consumers are not aware of.
Worse still, Uber could roll out an aggressive term for a specific period of time and then erase it—a sort of fleeting unconscionability.
Together, these forces leave drivers at a clear disadvantage. Drivers may not even have a record of the specific terms governing a particular period of time, let alone a clearly legible record of their transactions.
Another concrete way Uber presses its advantage over drivers is by hiding information about the marketplace. Uber imposes restrictions on the information available to drivers before they accept a trip, which prohibits them from making informed choices about the rides they agree to carry out: For example, Uber has a policy of blind ride acceptance, such that the driver does not know the destination of the passenger (and hence, the remunerative value of the trip) before she accepts it.
This practice is touted as a means to ensure system efficiency and prohibit destination-based discrimination.
If a driver accepts the trip and afterward declines it, her cancellation rating is affected, which can put her job in jeopardy.
Drivers who do not meet the threshold requirements that Uber sets for their behaviors, such as a high ride-acceptance rate, a low ride-cancellation rate, and a high passenger rating, risk being “deactivated” (temporary suspension or permanent firing) by Uber from the platform.
In other instances, Uber does not hide information entirely but relies on general impressions or predictions instead of making concrete numbers available. For example, while Uber originally showed drivers precise surge premiums in a given area in association with heat maps that display varying levels of surge through color schemes—yellow means demand is rising, orange means surge may appear soon, and red means it is surging—it changed the design of its app in October 2015 to show heat maps with those color schemes but without precise prices.
In effect, the app encourages drivers to believe in surge and travel to receive surge rides, but it fails to provide a precise indicator or a guarantee of what that price is. Heat maps thus function as a behavioral engagement tool but can effectively operate as a bait-and-switch mechanism similar to the use of phantom cars to entice ride-hailers. These constraints on drivers’ freedom to make fully informed and independent choices reflect the broad information and power asymmetries that characterize the relationship between Uber and its drivers and illustrate how the Uber platform narrows the choices that drivers are free to make.
Uber’s algorithmic dispatcher also effectively forces drivers to accept trips they might otherwise reject through a combination of design and policies, such as ride-acceptance quotas
(even as it nudges passengers to accept services that they might not prefer). This soft coercion manifests most acutely around the uberPool service. Many drivers dislike uberPool, a carpool service, because they must pick up multiple passengers at different points, endure the grief of passengers who dislike being delayed en route to their destination to accommodate other passengers, and suffer lower passenger evaluations as a result, for about the same pay as an uberX ride, in which they take one passenger or passenger group from A to B.
Drivers have typically not been permitted by the company to opt out of uberPool, and their acceptance rates for routine and for premium or incentive offers, including guarantees, can be affected by their ride-acceptance ratings on uberPool, which is a point of concern for some drivers.
A January 2017 driver survey by Harry Campbell, who runs a popular rideshare blog for Uber, Lyft, and other ride-hail drivers, found that “56.5% of drivers disagreed with the statement that they are satisfied with their uberPool experience.”
In order to decline “automatic” trip requests from uberPool, drivers can try to write in to Uber Customer Support Representatives. Driver reports in online forums indicate that some have had success, but others are not able to opt out.
Uber’s practices further complicate the situation by providing different terms to drivers depending on when they started to drive for the service. For example, after uberBlack drivers protested against being forced to accept low-paid uberX trips, Uber rescinded its policy requiring drivers to accept those trips in 2014,
only to reintroduce similar terms for new drivers in New York City a few years later.
It is worth noting that while Uber’s general terms and policies, including those related to opt-outs and cancellation fees, usually apply to all Uber drivers, each market may create exceptions. Moreover, perennial modifications may be implemented over time, as is evident in Uber’s rescinded and then reintroduced policy for uberBlack drivers to pick up uberX trips in New York.
Hourly guaranteed pay has been another source of concern. While Uber (and Lyft) sets base rates per mile and per minute at which drivers earn, it also offers a range of incentive pay—like surge pricing. The incentive pay offers drivers a premium to drive when and where demand is high and gives hourly guarantees
that provide drivers an hourly rate, such as $22 or $40 per hour for a given shift, if they meet specific criteria during the guarantee period(s).
Uber does not disclose the criteria by which select drivers are invited to participate in guarantees, which vary frequently, but drivers who do opt in report mixed experiences in claiming guaranteed pay.
Hourly ride guarantees typically come with some version of the following conditions that drivers must meet in order to be eligible for the guarantee: accept ninety percent of ride requests; complete two trips per hour; work between prescribed hours, such as 12:00 am–3:00 am; be online for fifty minutes of each hour; maintain a high average passenger-sourced rating, such as 4.7 out of five stars; and accept trips from within a circumscribed radius.
Drivers in Rosenblat’s ongoing research report that they occasionally get “phantom” or fleeting ride requests.
In a typical anecdote, a driver sees a ride request notification flash across her screen for a split second, rather than for the standard fifteen seconds—too quickly for her to assess its merits or even blindly accept it. Or, drivers report that they accept every ride request they see yet, upon receiving their pay stubs, discover they were not paid the guaranteed rate. When they inquire with Uber Support, they are told that they did not meet the ride-acceptance rate. For example, while a driver may perceive that she accepted 100% of ride requests, Uber Support may say she accepted only 78% of ride requests according to Uber’s internal data.
Drivers have no way of monitoring app activities to provide an objective account and perceive they would be in violation of their terms of service were they to try to reverse-engineer the app in an effort to hold the system accountable for their pay.
Some of these instances of missed ride requests may involve drivers who are distracted or otherwise miss a legitimate ride request. But reports of phantom flash requests signal either an underhanded business practice that is designed to minimize how many drivers actually receive the guaranteed pay or possibly technical issues that interfere with the mechanisms in place to ensure drivers are paid for their work. Network glitches might result in delays between the time a request from Uber is sent to the driver and the appearance of that request on the driver’s screen. Or, the driver may accept the ride, but there may be a delay in how long it takes for that driver’s intent to be conveyed to Uber. If the driver has a slow phone, or another data-intensive app is open and running, the Uber app may not take priority.
Drivers’ phones may have varying signal strengths as they connect to the Uber and mobile phone networks, and there can be other sources of lag across the network. And drivers may have phones of varying quality and different data plans. Importantly, drivers are not well positioned to appreciate the many factors that could interfere with their expectations for the service they have licensed for Uber and upon which they depend for their income.
Hourly guarantees and ride request rates are not the only issues for which technical infrastructure may not support the social expectations of pricing for drivers on Uber’s system. For example, drivers across online forums and in interviews from Rosenblat’s ongoing fieldwork report that they are not always paid for cancellation fees. Uber’s policy in most cities
is that drivers receive a cancellation fee, such as $5 or $10, depending on their city and their tier of service.
In a common scenario, the driver indicates she has arrived to pick up a passenger within the app, but the passenger takes longer than five minutes to get in the car. Drivers generally must be within the estimated time of arrival generated by the app, or within five minutes of it, and at the time they have arrived, they should be located within the passenger’s geofence.
After five minutes elapse, drivers can report to Uber that the passenger did not show up and claim the cancellation fee.
Drivers have mixed experiences in actually being paid that cancellation fee. When they make further inquiries to Uber Support, they receive responses indicating that they did not, in fact, wait for five minutes, even if they assert that they waited six or seven.
One dissatisfied driver requested time stamps for a specific trip to verify that he should merit the cancellation fee, to which Uber’s representative replied: “We’re unable to provide screenshots of our software, but I can tell you that it was 4 minutes and 59 seconds. I understand that this is frustrating we can’t add the fee unless it’s the full 5 minutes, as this is something that happens automatically in our system.”
In a system in which payment or nonpayment of a fee hinges on seconds, the absence of a mutually accountable time notation is problematic. This problem is so prevalent that one enterprising driver developed a rideshare timer available to be purchased and downloaded for the cost of one cancellation fee ($5), with the express goal of helping drivers claim their fees.
Driver frustration with cancellation fees abounds in online driver forums. While some drivers undoubtedly do not wait the full five minutes, reports of unpaid cancellation fees are recurrent, and drivers have no way within the app to validate how much time has elapsed. The issue of unpaid cancellation fees indicates either that Uber is engaged in a nefarious business practice of shorting driver pay through the design of its timerless app or that something is awry with the technical infrastructure that accounts for driver pay.
The implication is that Uber promises to pay if drivers wait for five minutes, but it withholds the information drivers need to ensure that the time they wait is accounted for, which is in turn necessary to receive payment. By contrast, for example, Uber’s competitor Lyft keeps a timer in the app so that drivers can see when five minutes have elapsed. Even if Lyft experiences the same technical time discrepancies as Uber, the placement of an in-app timer aligns better with a general lay or driver understanding of time as an objective measure and mitigates the issue of unfairness in the user experience of the app.
Uber changed its policies in the United States in an announcement on June 20, 2017, on a number of features,
including cancellation fees, which are now available after two minutes have passed (although this policy could change again). Uber added a timer feature to the app in some cities, with promises to introduce it in additional cities soon, to remedy issues and confusion with unpaid cancellation fees.
3. The Wisdom of the Captured. — The nature of the sharing economy, and the sharing economy firm, continues to evolve. A company such as Uber possesses far grander ambitions than simply connecting drivers and riders. Uber recently launched its own mapping service, for instance, and has invested significantly in driverless cars.
Ultimately, the company hopes to revolutionize transportation logistics.
In May 2016, Uber informed some drivers that it had added a “phone movement notification” to alert drivers when their phone may be unsecured by a mount, though not all drivers use a phone mount (e.g., some keep it in their laps or to the side).
We speculate that one explanation for this feature, since it does not necessarily impact a driver’s ability to do her job, is that it can be used to better collect mapping data for Uber from the driver’s phone. In any event, it is clear that Uber’s motivations involve both profiting in the short run from virtual dispatch services and advancing its many other goals. Conflicts may arise between Uber’s systemic goals and the everyday interactions of other participants—a phenomenon Rosenblat and her coauthor Tim Hwang refer to as the “wisdom of the captured.”
Imagine if Uber drivers were obligated to use a particular mapping service to choose a route. While drivers currently have the choice to use Uber’s in-app navigation tool, Google Maps, or Waze, they are held accountable to using the route generated by these services: Uber mediates complaints from passengers that a driver took an inefficient route by examining whether the driver deviated from the route suggested by her GPS.
Now imagine the navigation software behind the mapping service is trying to improve performance by sending participants along an inefficient route to test road conditions and generate data on the roads less traveled—a technique known in technical literature as the “multi-arm bandit algorithm.”
This may improve the product overall at the expense of the drivers, who waste travel time and risk conflicts with passengers.
More generally, Uber drivers may one day face competitive pressure from Uber’s own fleet of self-driving vehicles. Contemporary self-driving technology relies in large measure on machine learning, which in turn is trained on data from actual human driving.
We cannot know for sure, but assuming Uber is training its own systems on the limitless driver data to which it has access, Uber participants may be unwittingly training their replacements.
As Rosenblat and Hwang note:
Uber’s commitment to self-driving cars, enabled in part by the data gathered by their drivers, is arguably the clearest articulation yet that Uber will make choices that benefit the system over individual drivers. Self-driving cars would directly compete with and impact the human drivers of Uber’s system, effectively automating them out of a job.
III. The (New) Role of Consumer Protection Law
To summarize the discussion so far: The “sharing economy” is an umbrella term referring to not only a particular set of techniques and practices but also a rhetorical strategy aimed at attracting support and fending off restriction. Sharing economy firms, which have predecessors in Craigslist and elsewhere, leverage pervasive connectivity in order to facilitate trusted transactions between strangers on digital platforms. This creates economic and other value but also raises concerns, including those about racial bias, safety, and fairness to competitors and workers. We introduce a further complexity in that sharing economy firms can leverage their unparalleled access to information and control over user experience to the disadvantage of those very users. Connecting our concerns is the common theme of how access to information coupled with control of design permits sharing economy firms, such as the subject of our case study, to manipulate their users.
Information plays a role, of course, in many conversations around the promise and perils of the sharing economy. A recent report showed bias in Airbnb transactions by observing who was able to secure a booking or how users rated their experiences and then parsing this information demographically.
Another report showed discriminatory bias against Uber and Lyft passengers with African American first names, who experienced longer wait times.
What remains undertheorized, however, is the specific role of information asymmetry between platforms and users and the new capabilities that accompany it. The problem is not simply that Uber has access to detailed information about its ecosystem; the problem is that only Uber does. This access, coupled with its vantage as a digital intermediary, permits Uber to shape outcomes to its advantage.
Addressing the harms—actual and potential—that we raised in Part II requires grappling directly with the information asymmetry between sharing economy firms and other entities. Serious abuses may require the intervention of the criminal justice system. But speaking generally, consumer protection law, with its long emphasis on information and power asymmetry in the marketplace, is well suited to the task of addressing these asymmetries.
To address the sharing economy, however, consumer protection law will need to evolve beyond its present confines in two ways. First, regulators will need to develop a better understanding of the technology and business practices of sharing economy firms in order to uncover the entire range of offending practices. Second, regulators or courts will need to find a means by which to address those practices (assuming sunlight alone is not a sufficient antidote). This could involve drawing lines between permissible and impermissible practices or developing a set of criteria to determine that a given digital act or practice rises to the level of unfairness or deception. It could also manifest in an attempt to alter the incentives of firms to better align them with those of their consumers. Or it could necessitate a hybrid approach like making platforms “fiduciaries” of the users whose data they possess.
The remainder of this Part proceeds as follows. The first section gives a brief background on consumer protection law, paying particular attention to the role of information asymmetry. The second section addresses the failure of consumer protection law to keep up with the realities of digital commerce. A final section describes how consumer protection law might be updated to address the particular challenges of the sharing economy.
A. Consumer Protection: Origins and Purposes
Consumer protection law covers a wide and varied area. As law and economics scholar Kenneth Dam puts it, “The only thing that seems to hold the subject together is our desire to protect someone whom we call the consumer.”
A painstaking analysis could thereby encompass any statute, regulation, or court decision aimed at protecting consumers in any capacity or context. This could include everything from product liability suits, to product recalls by federal agencies, to fair lending rules, to municipal requirements that restaurants post the results of health inspections. Such a comprehensive canvassing of consumer protection law is beyond the scope of this Essay.
For purposes of our discussion, we focus on the contemporary core of consumer protection law: policing the marketplace against anticompetitive, unfair, or deceptive practices.
Contemporary accounts trace the origins of consumer protection law to the Progressive Era response to the laissez faire business practices of the nineteenth century.
With the passage of the Sherman Antitrust Act in 1890 and the Federal Trade Commission and Clayton Acts in 1914 (following the Supreme Court’s decision limiting antitrust enforcement in Standard Oil v. United States
), Congress sought to rein in the excesses of the various “trusts” or monopolies that had grown up in the preceding decades of unfettered industry.
The concern was that large monopolies were engaging in predatory pricing and other unfair practices as well as exercising political power to harm new entrants, all to the detriment of American consumers.
Although the purpose of Congress’s intervention was ultimately to protect consumers from the predation of powerful firms, the initial means it employed was to direct agencies and courts to protect American businesses from one another. That is, government intervened on behalf of businesses that had been shut out of the marketplace or otherwise hampered in their operations by the predatory practices of a monopolistic incumbent.
In the short run, such practices may appear to benefit the consumer because his or her purchases are being subsidized by the would-be monopolist. The anticipated, long-term result of policing unfair methods, however, is more robust competition, which in turn drives up the quality and variety of goods and services and drives down the prices consumers pay for them over time.
Antitrust laws and other prohibitions on unfair competition help preserve consumers’ range of choice in the market by preventing established businesses from excluding new entrants through the use of unfair techniques of competition—for example, by dropping prices so low that no one else can stay in business and then raising prices again in the absence of competitors.
These ongoing efforts to stamp out unfair competition are directed at ensuring consumers have sufficient choices around goods and services in the marketplace.
This set of problems is sometimes called “external” consumer protection because it involves activities and circumstances that exist outside of the mind of the individual consumer.
Problems arise when one firm abuses its power over others and thereby limits the range of goods and services available to the consumer.
To help consumers navigate the free market, Congress conferred upon the FTC the additional authority to police against “unfair or deceptive acts or practices in or affecting commerce.”
Under this authority, issued in 1938, the FTC immediately began to prohibit firms from introducing fraudulent or misleading information into the marketplace.
For example, the FTC might pursue a large home-and-beauty company for faking a demonstration of the efficacy of its razor by pretending it can shave sandpaper.
Enforcement against deception, bolstered by a wide variety of federal and state requirements that companies affirmatively disclose certain categories of information, continues to be a major area of activity at the FTC.
Within a few decades, however, the FTC had also built up its enforcement against “unfair” practices—such as aggressive door-to-door sales or unconscionable contract terms—not involving outright deception but nevertheless perceived to take advantage of consumers.
This set of problems is sometimes called “internal” consumer protection because it involves the internal mental state and affordances of the consumer herself.
Problems arise when firms abuse their asymmetry of information or power over consumers, resulting in gain to the former and harm to the latter.
In the 1960s, faced with rampant consumerism that strained the agency’s capacity to address unfair or deceptive practices across the entire nation, the FTC also encouraged the states to promulgate their own consumer protection laws.
Most states did.
These “little-FTC acts” often contained a private cause of action, permitting not only regulators but also aggrieved individuals to pursue claims of deception and unfairness.
These acts supplemented existing common law fraud remedies, which were available to consumers but limited by the requirement that the plaintiff show an intent, not just a tendency, to mislead.
This era also saw the ascendance of many laws and regulations aimed at improving the baseline safety or efficacy of a range of products, requirements that firms inform or warn consumers about their products and services, and the maturation of product liability in tort law.
Together, these developments, which have waxed and waned over time with public perceptions of the limits of government power, have come to represent what we think of as consumer protection law today.
One way to think about the interrelation between the role of consumer protection law in policing against unfair competition among firms (e.g., through antitrust laws) and its role in policing against unfair and deceptive practices aimed at consumers (e.g., through truth-in-lending laws) is to consider what is needed to produce a theoretical “sovereign consumer.”
A sovereign consumer has both real choices in the marketplace and a meaningful ability to exercise them. Unfair competition laws involve careful calibration of the system as a whole to enable sufficient market participants. The more (and more diverse) the market participants, the greater the range of consumer choices in goods and services, including with respect to quality and price.
Unfair and deceptive practices laws instead aim to help consumers who possess market choices to navigate those choices in practice.
Prohibitions on deceptive practices, such as misleading claims about a product’s efficacy or price, help ensure that the consumer has access to good information as she makes decisions on what products and services to consume. Prohibitions on unfair practices, such as coercing the consumer into making a purchase, help ensure the consumer is free to act on the information she receives. Thus, an example of a deceptive practice would be to claim that information posted to a social network is private when in fact it becomes public,
and an example of an unfair practice would be a service person dismantling a consumer’s stove and refusing to reassemble it until the consumer agrees to purchase new parts.
Despite some consensus around the idea that consumer protection law exists to preserve consumer sovereignty in these ways, fundamental disagreement remains about the exact societal interests at stake. The influential Chicago School is grounded in economics and sees preserving allocative and productive efficiency as being at the heart of what lawmakers and courts intend by policing both antitrust and unfairness and deception.
Others convincingly challenge the efficiency rationale, conceptualizing consumer protection as a kind of prohibition on unhealthy and immoral levels of consumer exploitation. Professor Robert Lande, for example, looks to the legislative records of each of the pieces of federal legislation we mention above—the Sherman, Clayton, and FTC Acts—and concludes that “Congress was concerned principally with preventing ‘unfair’ transfers of wealth from consumers to firms with market power.”
At the relevant times, Congress was concerned with the capability of powerful firms to set prices and engage in practices that would annex any economic windfall from commerce to industry and minimize the windfall flowing to consumers. Lawmakers saw this wholesale appropriation of social surplus from commerce as immoral (that is, “unfair”).
Whatever consumer protection law’s normative beginnings, regulators tend to be reluctant to bring actions solely on the basis of public interest. Indeed, in the late ’70s, Congress responded to perceived overreaching by the FTC by delaying its funding.
The Commission eventually issued a statement better clarifying the contours of its own power.
Today, federal regulators such as the FTC look for materially misleading statements or evidence of a significant harm that the consumer cannot reasonably avoid and that has no countervailing benefit to competition.
State actions at common law are more adventurous but still not fairly characterized as policing morality.
Several aspects of consumer protection law are clear however one conceives its origins and purposes. First, the law assumes the marketplace will function improperly and to the detriment of consumers absent government intervention of some sort. Second, determining what acts or practices are permissible requires the regulator or court to look at both the real-world options consumers have in the marketplace and the prospect that consumers will be able to exercise meaningful choices regarding goods and services in practice. Detecting and addressing harmful asymmetries of information and power among firms, and between firms and consumers, is thus at the heart of consumer protection law.
Throughout the remainder of this Part we will emphasize the interaction between the sharing economy and the Federal Trade Commission. The FTC, with its general mission of market and consumer protection, is the most likely and well-positioned agency to address sharing economy ills. And the FTC has already engaged with the sharing economy in highly visible ways. Many of the insights of this section also apply with equal force to other agencies at the federal or local level. As Professor Danielle Citron shows, state attorneys general are taking a more and more active role policing privacy through state statutory authority that closely mirrors that of the FTC.
B. Consumer Protection in 2017: From Amway to Uber
Consumer protection law is surprisingly absent from sharing economy discourse, which tends to focus on other important considerations such as labor laws, racial bias, access to services, and safety.
These issues intersect with consumer protection, of course. The regulations that states and municipalities would extend to ride-hailing or room-hosting, which already attach to taxis and hotels, exist in part to protect consumers from harm.
The difference is one of emphasis: The discourse around consumer protection law specifically looks to information and power asymmetry to determine whether, in context, a particular business practice interferes with individuals or the market in harmful ways.
Today the role of information in consumer protection is even more critical than in the past. Firms in every industry are awash in data, relying on the collection and increasingly sophisticated processing of intelligence about consumers.
Information has itself become the subject of consumer protection under the rubric of privacy. The FTC has emerged in recent years as a species of consumer privacy watchdog, investigating companies, including Uber, for invasive privacy practices or poor data security as unfair or deceptive acts and practices under Section V of the FTC Act.
In a sense, the FTC’s assertiveness in the area of privacy is strange for an agency devoted to the promotion of free trade. Economic orthodoxy suggests that markets generally benefit from the free flow of information between consumers and firms, leading many economists to criticize privacy as an artificial restraint on trade.
One way to explain the agency’s interest in privacy, however, is to understand it as a way to help preserve basic symmetries of information between firms and consumers.
As discussed in Part II, firms with access to detailed data about consumers have the ability and the incentive to leverage this information to the consumer’s detriment. Overindulgence in such activity, at a minimum, undermines the market by promoting resentment and distrust.
Given its emphasis on information symmetry, consumer protection law seems well positioned to help unpack and address the sorts of data-driven problems that may arise when a platform possesses and leverages asymmetries of information and power. But so far consumer protection law has yet to catch up to a commercial world fueled by data.
To be clear: Consumer protection authorities have hardly ignored the sharing economy. In June 2015, the FTC convened a daylong workshop entitled “The ‘Sharing’ Economy: Issues Facing Platforms, Participants, and Regulators” and solicited comments from the public.
The FTC summarized the workshop and public comments in a November 2016 staff report.
The lengthy report describes at a high level of generality what makes for a successful sharing economy platform and engages in sustained discussion of the competition issues that sharing economy platforms may generate. In general, the report is hopeful that sharing economy firms will increase competition overall through a “gale of creative destruction.”
The report flags other potential issues that threaten to compromise the sharing economy ecosystem—including low information about the quality of goods and services—and identifies reputation systems and other mechanisms by which sharing economy firms address these issues.
The report raises a range of concerns around safety, sanitation, and privacy but stops short of suggesting intervention.
Indeed, the report specifically assesses the pros and cons of regulatory intervention in the sharing economy, with many experts and FTC staff concluding that regulation would be premature.
Overall, the FTC’s 2015 workshop and 2016 report are best characterized as cautiously supportive of the sharing economy. A few months later, however, the FTC made it clear that its commitment to enforcing Section V’s prohibition on unfair or deceptive practices applies in full force to sharing economy firms. The agency announced a $20 million settlement with Uber over allegations that the company misled drivers—whom the Commission called “enterprising consumers”—about how much they could earn.
The complaint alleges that Uber disseminated advertisements that overestimated the likely hours and yearly income of drivers.
Moreover, according to the FTC, Uber induced participation in a Vehicles Solution Program that helps drivers lease or purchase a car, again by overestimating the likely returns on investment and making public claims for which the company had insufficient support.
The FTC relied on deception again when, in August of 2017, the Commission alleged that Uber failed to use best practices in safeguarding user data, notwithstanding Uber’s public representations to the contrary on its website and terms of service.
The FTC and Uber entered into a consent decree whereby Uber agreed to improve its internal and external safeguards.
The consent decree makes no attempt to change Uber’s business practices except as they relate to protecting access to consumer information.
What the FTC’s complaints show is that sharing economy firms, like everyone else, are subject to federal prohibitions on deceiving consumers, broadly defined. No less than a hardware store or vitamin supplement company, Uber cannot make a material claim in its marketing materials for which it lacks evidence. But is Uber—with its carefully managed, complex data ecosystem—really like a chain of hardware stores? We submit it is not.
There has been a sea change in the affordances and techniques of modern business and consumer protection law has yet to catch up. One way to see this is to compare the 2017 Uber complaint with the Commission’s 1979 investigation of Amway. Back in the spotlight with the installation of Amway heiress Betsy DeVos as Secretary of Education, Amway is a multilevel marketing company originally founded in 1949 that facilitates peer-to-peer sales of home and beauty products.
The model involves entrepreneurial consumers (to borrow the FTC’s term for Uber drivers) who purchase goods from Amway and resell them in their own neighborhoods.
In addition to small profits from the sale of goods, Amway sellers receive bonuses or commissions for signing up new sellers.
Following the receipt of complaints, the FTC initiated an investigation of Amway. Initially, FTC staff took a highly skeptical position, and their detailed analysis of defense counsel’s charges lent credence to the characterization of Amway as a “pyramid distribution scheme” with the “potential for massive deception.”
But ultimately the Commission arrived at a place of cautious optimism. The Commission praised Amway’s “highly unusual distribution system” for its capacity to bypass “near insurmountable” barriers to competition with established firms such as Procter & Gamble and “interject[] a vigorous new competitive presence into this highly concentrated market.”
Having backed away from claims that Amway was a pyramid scheme, and having praised the company for its competitive disruption and empowerment of entrepreneurial consumers, the FTC nevertheless identified certain unlawful practices. Specifically, the Commission ordered Amway to cease “misrepresenting in any manner the past, present, or future profits, earnings, or sales from such participation,” even by implication.
The Commission also admonished Amway for attempting to fix the prices at which distributors offered Amway products for sale through the printed materials it disseminated.
The FTC takes a strikingly similar strategy in 2017 toward a very different company. Just as the Commission praised Amway for competitive innovation, the agency’s staff report praises sharing economy firms for their “disruptive innovation.”
And just as the FTC went on to rebuke and limit Amway for its published claims over earnings potential, the Commission also rebuked and limited Uber for its own claims around earnings—the only difference being that Amway published in the local paper and Uber published on popular websites.
The combined effect of these interventions is, first, to establish the value-add of a market disrupter by emphasizing its positive competitive effects and the opportunities it creates for consumers. A second effect is to clarify that ordinary rules apply to the disrupter by placing modest limitations on the market disrupter such as a prohibition on deception in written advertisements. Meanwhile, the consumer observes that a federal watchdog both reviewed the sharing economy firm’s record and acted upon it.
Amway and Uber are not without their parallels. We tend to agree with the Commission in both instances that a novel strategy to disseminate goods and services can have positive effects on the overall market in terms of price and quality. Both companies faced skepticism fueled by incumbent competitors, and both faced similar challenges such as high rates of turnover.
But whereas Amway was and remains a multilevel marketer of household goods, Uber’s ambitions extend well beyond ride-sharing into mapping, logistics, and untold other domains. Amway polices its ecosystem with contracts and a “Code of Ethics and Rules of Conduct” available for everyone to examine,
while Uber and other sharing economy firms leverage dynamic digital platforms consisting of thousands of users who serve as ceaseless flows of information about participants. Much happens beneath the surface. Interestingly, whereas the FTC was able to show specific measures by which Amway controlled prices within its distribution ecosystem through specific contractual terms, no such discussion appears in the FTC’s complaint against Uber, which constantly changes its terms and displays them only on the drivers’ apps.
Uber can and likely does leverage its access to information and control of the interface to its advantage.
The company manipulates what ride-hailers and providers see and limits or channels all participants’ behaviors toward Uber’s ends. Assuming the phenomena participants report cannot be explained by cancellations or technical issues, at least some of these acts or practices implicate consumer protection law. For example, if it turns out Uber is limiting the number of drivers who receive hourly pay guarantees by flashing phantom requests on purpose, this would be at best a misleading act or practice.
Even if problems with the system, such as latency, explain the flash requests, these phantom requests might still rise to the level of an unfair practice—that is, a material harm to (entrepreneurial) consumers that they cannot reasonably avoid. A thorough vetting by a twenty-first-century agency—particularly a pioneer in bringing technological savvy to government—would involve investigating these practices as or more deeply than written ads.
C. Updating Consumer Protection Law
What challenges face a contemporary consumer protection authority interested in addressing the full range of activities of the sharing economy? We assume for purposes of our argument that regulators are unlikely to intervene absent articulable harm to consumers. This assumption is not necessarily obvious. While the FTC’s unfairness standard requires a showing of harm, the agency is empowered to address deception even absent such a showing.
State attorneys general have similar leeway in bringing enforcement actions under state law.
And, as discussed above, there is evidence in the legislative record of a moral dimension to consumer protection law that concerns itself with unjust enrichment of firms at the expense of consumers.
For purposes of argument, we adopt the more conservative view that justifying intervention into business practices requires pointing to harm to consumers and thereby sets a higher bar than strictly necessary.
Assuming harm is the proper lodestar for consumer protection law, we envision essentially two tasks for the regulator. The first is detecting harms that are not manifest from observable public statements. This represents a nontrivial task roughly akin to the problem of discovering lines of software code that instruct a vehicle to cheat on an emissions test. The second is addressing those harms in a way that does not foreclose legitimate experimentation by platforms. Companies often have perfectly acceptable reasons for observing consumers, for treating consumers differently, and even for nudging consumer behavior toward profitable ends. Having gained a complete picture of digital techniques and practices, regulators like the FTC still have to determine what rises to the level of unfair or deceptive. We address each challenge in turn.
1. Detecting Harm. — Uber is positioned to do so much more than overestimate earnings or returns to potential drivers in advertisements. The company observes and structures millions of transactions under the scaffolding of its app and uses what it observes to channel participant behavior toward a variety of ends.
It seems implausible that Uber would engage in textbook deception in public advertisements, which everyone can see, but never manipulate circumstances beneath the surface. And, as Part II shows, we have already encountered indications that Uber may be engaging in questionable behaviors.
Some of these practices are obvious. For example, a consumer can complain to regulators that while it first appeared there were drivers nearby, once she initiated the request those drivers disappeared and she had to wait. Others require more work to uncover. The FTC had ready access to Amway’s paper contracts in the 1970s. In theory a regulator could also discover just how often Uber changes its contracts with drivers and whether any versions of those contracts are too complex to follow or contain objectionable terms. But this would require a great deal of diligence and the cooperation of drivers or the company. Then there are practices about which we can only speculate. We have no way of knowing whether the fleeting ride requests or lost cancellation fees that drivers report, and which result in lower income, are the product of user error, poor design, or intent. Still other practices may be entirely invisible, such as Uber’s practice of evading police in jurisdictions where drivers or the company might be issued a citation.
Regulators have at least two significant means by which to explore what sharing economy firms are doing behind the digital scenes. The first is direct investigation. The FTC can and does invite industry to workshops, like its sharing economy workshop, to talk about what they do.
Industry participants control their own message in these contexts. But the agency is also empowered by statute to do its own digging. Not only may the FTC subpoena witnesses and compel the production of documentary evidence in the course of an investigation,
but it can also require the filing of annual or special reports or answers to specific questions.
Law professor Rory Van Loo briefly discusses this underutilized regulatory affordance in the context of retail.
Building in part on digital market manipulation, Professor Van Loo argues that the retail industry has become increasingly adept at gathering and leveraging consumer information in problematic ways.
He recommends regulatory oversight on par with financial regulation when agencies become much more familiar with business practices.
Van Loo cites specifically to the FTC’s underutilized investigatory powers in the course of his discussion.
Getting data from sharing economy firms won’t be easy. Reading the headlines around the well-publicized feud between ride-hailing services and Austin, Texas, over municipal regulatory requirements, it would appear that the city was exclusively concerned with how well Uber and Lyft drivers were vetted for felonies and how many wheelchair-accessible cars needed to be on the road at any given time.
These are important issues, which the ride-hailing services were ready to concede to some extent: The model legislation the companies spent millions promoting to Austin voters, ultimately unsuccessfully, had provisions for better vetting and for ensuring accessibility.
Uber and Lyft’s proposal rejected the fingerprint-based vetting system the city preferred—which the press covered in detail.
But there was an equally big gap in the provisions around accessing Uber’s and Lyft’s data. Austin wanted the services to report regularly on a range of specific questions or see their licenses to operate revoked.
The companies’ proposed bill was more limited, providing for independent audit of some driver records and the filing of quarterly reports.
The second means by which to explore sharing economy acts and practices is to incentivize third-party researchers to investigate firms. In 2015, the car giant Volkswagen famously conceded that its vehicles were built to perform differently in road conditions than on mandatory emissions tests.
The Volkswagen code instructing the car to perform more efficiently during emissions testing was discovered when an international nonprofit commissioned research into how cars might preform more poorly than expected in real-world conditions.
In testing several Volkswagen diesel models, a team at West Virginia University found an apparently intentional discrepancy.
Similarly, testing by then–Stanford Ph.D. candidate Jonathan Mayer discovered Google’s alleged circumvention of the Safari browser’s cookie-blocking feature, leading to a multimillion-dollar fine against the company.
The practice of academics discovering impropriety is not unique to the digital world—it was finance professor David Yermack who first uncovered the scandal around improper backdating of stock options in 1997.
But regulators can, and sometimes already do, call upon or fund independent researchers specifically to analyze digital practices and attempt to uncover unfair or deceptive practices.
To the extent regulators pursue the second strategy, there are several ancillary challenges. The first involves removing perceived and actual barriers to research. Researchers who investigate sharing economy firms may need to reverse engineer platforms, scrape data, impersonate consumers, and perform other activities aimed at exploring firm practices. In so doing, they risk legal pushback—valid or not.
For example, a firm might argue that a researcher violated the terms of service and therefore exceeded authorized authority for purposes of the Computer Fraud and Abuse Act.
Or the firm may advance the questionable argument that reverse engineering its algorithm constitutes a trade secret problem or runs afoul of the anticircumvention provision of the Digital Millennium Copyright Act.
Regulators should support and publicize clear-cut exceptions to such rules and others to empower researchers to uncover potential sources of harm.
The second challenge involves validating external findings. The FTC likely cannot proceed on the assertions of researchers alone and may need to find ways to corroborate the researchers’ findings—including by running its own tests. Knowing what questions to pose to the researchers and assessing the information that the agency gathers require a measure of technical expertise. Fortunately, the FTC and other authorities—such as the Federal Communications Commission and state attorneys general— have been in the process of building up their technical capacities for some time.
This affordance, coupled with the underutilized investigatory powers of the FTC, positions some regulators to gain access to the information that they need to police the digital marketplace and protect consumers.
Finally, the potential harms we explore in Part II do not pertain only to traditional consumer harms that entail deception or coercion of a person purchasing a good or service. As far back as the Amway action, the FTC recognized that salespeople were consumers in some contexts, and, indeed, the Uber complaint refers to drivers specifically as “entrepreneurial consumers.”
In addition to increasing the scope of consumer protection law, regulators should be vigilant against new means of disadvantaging those who participate in the sharing economy. For example, participants in the sharing economy may be unwittingly training their robotic replacements.
Further, A/B testing or feature integration in a context in which people are using the platform for their livelihood has different effects and ramifications than in other contexts. Knowingly sending a driver on a longer route in an effort to fill in a blank spot in the relevant mapping software has costs in terms of both the driver’s time and her reputation.
Indeed, to the extent drivers are failing to gain traction in the context of employment class action lawsuits, they may increasingly turn to consumer protection law to vindicate some of the same interests.
2. Addressing Harms. — Presumably a deeper understanding of sharing economy practices would yield additional examples of problematic behavior, beyond false advertising as to potential earnings. Some of these could be as straightforwardly problematic as Volkswagen cheating on emissions tests. But delving into sharing economy techniques and practices would also yield plenty of innocuous behavior and many close calls. It would not be feasible or wise of regulators to intervene every time that a design decision inconveniences a ride-hailer or provider, let alone when it shows one price or product to one participant and a different price or product to another participant. The threat to innovation could be significant, which is why some authorities and theorists gravitate toward a harm standard for intervention in the first place.
Thus, not only would contemporary regulators need to become more adept at discovering potential harms, but they would also need to develop effective and defensible means of addressing those harms. Again, there exist at least two kinds of approaches to address this problem: (1) changing incentives to lessen the likelihood of exploitation and (2) finding a way to distinguish the acceptable channeling of user behavior from the illegitimate one. The first approach, incentives, acknowledges that the range of potential abusive behavior is enormous and that it would be very difficult to draw lines between harmful, neutral, and beneficial practices. This approach recommends making structural changes to business models in an attempt to better align the incentives of firms and consumers. Thus, for example, it would recommend requiring Facebook to offer a paid option in exchange for commitments not to mine the user’s personal information for other purposes.
Or it would recommend establishing internal mechanisms to guard against abuse, such as a review board for consumer research.
The idea is to find structural ways to help mitigate and minimize the circumstances under which the firm will be tempted to leverage its information and design advantages against consumers.
A second approach, line-drawing, bites the bullet and seeks to differentiate between legally tolerable and intolerable activities. In defense of this approach, it should be said that the law is replete with line-drawing. Courts must already assess when influence is “undue,” what sorts of expectations are “reasonable,” and so on.
In consumer protection law, agencies and courts already have to determine what sorts of representations rise to the level of a material deception. The law can analyze, for example, whether conveying the impression that a ride is nearby by displaying phantom cars in the user’s vicinity constitutes deception or a form of visual puffery.
Given the invisibility of decisionmaking processes to the consumer, many acts or practices may not involve deception per se. Rather, they involve using information about a consumer against her or introducing other material or structural disadvantages. An agency would have to determine whether individual practices rise to the level of unfairness, defined as substantial and unavoidable consumer harm. A standard proposed in various contexts is to look to vulnerability.
Thus, we might ask whether a hypothetical practice of charging people more for rides if their battery is low constitutes a form of individualized price gouging.
But not all unfair conduct can be said to target vulnerability. For example, the prospect that a firm display ride requests too fast for a driver to accept in order to manage membership in an hourly guarantee incentive program, and the obfuscation of time in order to reduce the likelihood a driver will collect a cancellation fee, would both seem to rise to the level of unfairness even against otherwise autonomous consumers.
A closely related approach might be to leverage privacy-centric concepts such as secondary use or access.
Under established, if not always enforced, privacy principles, firms are supposed to check with consumers before using their data in ways that go beyond the purpose for which they were provided.
There are several problems with this approach. First, a consumer can be surprised (or delighted) without the practice necessarily rising to the level of actionable harm. And second, firms already make broad and vague disclosures in privacy policies and terms of service covering a very wide range of potential uses.
Some also argue that restrictions on how already collected information is used represent a restraint on free speech.
Under established principles of fair information practice, consumers are supposed to be able to access information concerning them.
Sharing economy firms could share detailed analytics with participants to permit them to better understand the decisions being made about them and to police against abusive practices.
Finally, we want to mention a hybrid approach between incentives and line-drawing: fiduciaries. Several scholars have recently emphasized the role of firms as custodians of data.
The idea is that consumers entrust information to intermediaries, and this generates a fiduciary relationship, including a set of specific obligations.
This approach has several advantages. First, it imports a relatively mature area of law—an area that, like consumer protection law in general, is premised upon information and power asymmetries. The area of law constructs specific obligations such as loyalty, the contours of which are relatively well defined.
Second, the approach shares with incentive-based methods an avoidance of interfering with granular design decisions and gets around the standard First Amendment objections.
Note that these solutions may emanate from different legal sources. The FTC is limited in its authority today to policing unfair and deceptive practices under Section V and to enforcing other specific laws as delegated by Congress. Additional authority, in the form of federal or state statutes, may be necessary to alter sharing economy business models or to impose fiduciary obligations on platforms. Moreover, none of these approaches dispenses with the requirement that contemporary regulators monitor for consumer harms. The incentives approach requires validation: Has the intervention sufficiently aligned the interests of the firm with those of the consumer to lead to tolerable levels of advantage taking? The line-drawing approach has to sort harmful from tolerable conduct. And the fiduciary approach requires a means by which to ensure fiduciary obligations are being met.
Conclusion
At one level, we should embrace the sharing economy as a novel form of technology-enabled commerce. Sharing does, in fact, possess many of the virtues its proponents suggest. But we must also be vigilant, lest the rhetoric of sharing and the allure of disruption limit the critique of the sharing economy to the handful of problems scholars and others have already identified. There exists within the sharing economy a deeper concern, grounded in the asymmetries of information. In taking. Sharing economy firms have the ability to monitor and channel the behavior of all participants and may be using this capacity to everyone’s detriment but their own.
Consumer protection law has been oddly silent in debates about the sharing economy. Very few sharing economy papers address themselves to consumer protection. The FTC’s complaints against Uber could have been filed against any contemporary company, or against an innovator from thirty years ago. Consumer protection law, with its longtime emphasis on asymmetries of information and power, may still be our best means by which to domesticate the deepest problems of the sharing economy. But consumer protection law should evolve to address the new affordances of intermediaries like Uber and other digital platforms. There are a variety of potential configurations, but the contemporary regulator must first understand and then find a way to address the prospect of abuse. This is no easy set of tasks, but it is a crucial one.