Off limits

Kevin Lewis

October 09, 2019

Shining a Light on Dark Patterns
Jamie Luguri & Lior Strahilevitz
University of Chicago Working Paper, August 2019

Dark patterns are user interfaces whose designers knowingly confuse users, make it difficult for users to express their actual preferences, or manipulate users into taking certain actions. They typically exploit cognitive biases and prompt online consumers to purchase goods and services that they do not want, or to reveal personal information they would prefer not to disclose. Research by computer scientists suggests that dark patterns have proliferated in recent years, but there is no scholarship that examines dark patterns’ effectiveness in bending consumers to their designers’ will. This article provides the first public evidence of the power of dark patterns. It discusses the results of the authors’ large-scale experiment in which a representative sample of American consumers were randomly assigned to a control group, a group that was exposed to mild dark patterns, or a group that was exposed to aggressive dark patterns. All groups were told they had been automatically enrolled in an identity theft protection plan, and the experimental manipulation varied what acts were necessary for consumers to decline the plan. Users in the mild dark pattern condition were more than twice as likely to remain enrolled as those assigned to the control group, and users in the aggressive dark pattern condition were almost four times as likely to remain enrolled in the program. There were two other striking findings. First, whereas aggressive dark patterns generated a powerful backlash among consumers, mild dark patterns did not – suggesting that firms employing them generate substantial profits. Second, less educated subjects were significantly more susceptible to mild dark patterns than their well-educated counterparts. Both findings suggest that there is a particularly powerful case for legal interventions to curtail the use of mild dark patterns. The article concludes by examining legal frameworks for ameliorating the dark patterns problem. Many dark patterns appear to violate federal and state laws restricting the use of unfair and deceptive practices in trade. Moreover, in those instances where consumers enter into contracts after being exposed to dark patterns, their consent could be deemed voidable under contract law principles. The article proposes a quantitative bright-line rule for identifying impermissible dark patterns. Dark patterns are presumably proliferating because firms’ secret and proprietary A-B testing has revealed them to be profit maximizing. We show how similar A-B testing can be used to identify those dark patterns that are so manipulative that they ought to be deemed unlawful.

Housing, urban growth and inequalities: The limits to deregulation and upzoning in reducing economic and spatial inequality
Andrés Rodríguez-Pose & Michael Storper
Urban Studies, forthcoming

Urban economics and branches of mainstream economics – what we call the ‘housing as opportunity’ school of thought – have been arguing that shortages of affordable housing in dense agglomerations represent a fundamental barrier to economic development. Housing shortages are considered to limit migration into thriving cities, curtailing their expansion potential, generating rising social and spatial inequalities and inhibiting national growth. According to this dominant view, relaxing zoning and other planning regulations in the most prosperous cities is crucial to unleash the economic potential of cities and nations and to facilitate within-country migration. In this article, we contend that the bulk of the claims of the housing as opportunity approach are fundamentally flawed and lead to simplistic and misguided policy recommendations. We posit that there is no clear and uncontroversial evidence that housing regulation is a principal source of differences in home availability or prices across cities. Blanket changes in zoning are unlikely to increase domestic migration or to improve affordability for lower-income households in prosperous areas. They would, however, increase gentrification within metropolitan areas and would not appreciably decrease income inequality. In contrast to the housing models, we argue that the basic motors of all these features of the economy are the current geography of employment, wages and skills.

Whose Voice Do We Hear in the Marketplace? Evidence from Consumer Complaining Behavior
Devesh Raval
Marketing Science, forthcoming

Consumer voice has increasingly become a major factor in the marketplace through consumer complaints, but little is known about who chooses to complain and how complainants compare with consumers of the product. Any differences in complaint rates across groups can reflect either different propensities to complain or different consumer experiences, making it difficult to assess the degree of self-selection. I utilize a set of law enforcement actions to separate these two explanations by comparing characteristics of complaining consumers to those of victims, and I find much lower complaint rates in heavily minority areas compared with nonminority areas, relative to their respective victimization rates. I find evidence against information-based accounts for why victims from minority areas are less likely to complain and in favor of explanations related to lower levels of trust or general social capital. I then provide a statistical weighting approach to remedy the problem of self-selection and apply it to develop an implied victimization rate using complaints from the Consumer Sentinel database.

Which Communities Complain to Policymakers? Evidence from Consumer Sentinel
Devesh Raval
Economic Inquiry, forthcoming

Consumer complaints provide a signal of the problems that different American communities face. I use a large database of millions of complaints to examine how per capita complaint rates vary across communities, as well as heterogeneity in who complains to different agencies and about different consumer protection issues. I find higher complaint rates in more heavily Black, more educated, higher income, older, and more urban communities and lower complaint rates in more heavily Hispanic and higher household size communities. The demographics of complaints are quite different for the Consumer Financial Protection Bureau, with much higher rates of complaints from Black and college educated areas compared to the Federal Trade Commission or Better Business Bureaus. I also find much higher rates of finance related complaints from Black communities across all reporting agencies.

On the Redesign of Accident Liability for the World of Autonomous Vehicles
Steven Shavell
NBER Working Paper, September 2019

This article studies a model of liability for automobile accidents in the coming world in which automobiles will be autonomous. In that world, travelers will not be drivers, rendering liability premised on driver fault irrelevant as a means of reducing accident dangers. Moreover, no other conventional principle of individual or of manufacturer liability would serve well to do so. Indeed, in the model considered, strict manufacturer liability, recommended by many commentators, would actually tend to leave accident risks unchanged from their levels in the absence of liability. However, a new form of strict liability –– the hallmark of which is that damages would be paid to the state –– would be superior to conventional rules of liability in alleviating accident risks and would be easy to administer.

Search Advertising and Information Discovery: Are Consumers Averse to Sponsored Messages?
Navdeep Sahni & Charles Zhang
Stanford Working Paper, August 2019

We analyze a large-scale field experiment conducted on a US search engine in which 3.3 million users were randomized into seeing more, or less advertising. Our data rejects that users are, overall, averse to search advertising targeted to them. At the margin, users prefer the search engine with higher level of advertising. The usage of the search engine (in terms of number of searches, and number of search sessions) is higher among users who see higher levels of advertising, relative to the control group. This difference in usage persists even after the experimental treatment ends. The increase in usage is higher for users on the margin who, in the past, typed a competing search engine's name in the search query and navigated away from our focal search engine. On the supply side, higher level of advertising increases traffic to newer websites. Consumer response to search advertising is also more positive when more businesses located in the consumer's state create new websites. Quantitatively, the experimental treatment of a higher level of advertising increases ad clicks which leads to between 4.3% to 14.6% increase in search engine revenue. Overall, patterns in our data are consistent with an equilibrium in which advertising conveys relevant “local” information, which is unknown to the search engine, and therefore missed by the organic listings algorithm. Hence, search advertising makes consumers better off on average. On the margin, the search engine does not face a trade-off between advertising revenue and search engine usage.

Collusive Investments in Technological Compatibility: Lessons from U.S. Railroads in the Late 19th Century
Daniel Gross
NBER Working Paper, September 2019

Collusion is widely condemned for its negative effects on consumer welfare and market efficiency. In this paper, I show that collusion may also in some cases facilitate the creation of unexpected new sources of value. I bring this possibility into focus through the lens of a historical episode from the 19th century, when colluding railroads in the U.S. South converted 13,000 miles of railroad track to standard gauge over the course of two days in 1886, integrating the South into the national transportation network. Route-level freight traffic data reveal that the gauge change caused a large shift in market share from steamships to railroads, but did not affect total shipments or prices on these routes. Guided by these results, I develop a model of compatibility choice in a collusive market and argue that collusion may have enabled the gauge change to take place as it did, while also tempering the effects on prices and total shipments.

Truth-Telling and the Regulator. Experimental Evidence from Commercial Fishermen
Moritz Drupp, Menusch Khadjavi & Martin Quaas
European Economic Review, forthcoming

Understanding what determines the truth-telling of economic agents towards their regulator is of major economic importance from banking to the management of common-pool resources such as European fisheries. By enacting a discard-ban on unwanted fish-catches without increasing monitoring activities, the European Union (EU) depends on fishermen's truth-telling. Using a coin-tossing task in an artefactual mail field experiment with 120 German commercial fishermen, we test whether truth-telling in a baseline setting differs from behavior in two treatments that exploit fishermen's widespread ill-regard of their regulator, the EU. We find, first, that fishermen misreport coin tosses more strongly to their advantage in a treatment where they are faced with the EU flag, and, second, that misreporting is consistent with behavior in other hidden tasks. We also find some supportive evidence for our first result in a conceptual replication with 1,200 UK citizens who voted ‘leave’ in the Brexit referendum. Our findings imply that lying is more extensive towards an ill-regarded regulator and that policy needs to account for this endogenously eroding honesty base.

Too Much Data: Prices and Inefficiencies in Data Markets
Daron Acemoglu et al.
NBER Working Paper, September 2019

When a user shares her data with an online platform, she typically reveals relevant information about other users. We model a data market in the presence of this type of externality in a setup where one or multiple platforms estimate a user’s type with data they acquire from all users and (some) users value their privacy. We demonstrate that the data externalities depress the price of data because once a user’s information is leaked by others, she has less reason to protect her data and privacy. These depressed prices lead to excessive data sharing. We characterize conditions under which shutting down data markets improves (utilitarian) welfare. Competition between platforms does not redress the problem of excessively low price for data and too much data sharing, and may further reduce welfare. We propose a scheme based on mediated data-sharing that improves efficiency.

Nonrivalry and the Economics of Data
Charles Jones & Christopher Tonetti
NBER Working Paper, September 2019

Data is nonrival: a person's location history, medical records, and driving data can be used by any number of firms simultaneously. Nonrivalry leads to increasing returns and implies an important role for market structure and property rights. Who should own data? What restrictions should apply to the use of data? We show that in equilibrium, firms may not adequately respect the privacy of consumers. But nonrivalry leads to other consequences that are less obvious. Because of nonrivalry, there may be large social gains to data being used broadly across firms, even in the presence of privacy considerations. Fearing creative destruction, firms may choose to hoard data they own, leading to the inefficient use of nonrival data. Instead, giving the data property rights to consumers can generate allocations that are close to optimal. Consumers balance their concerns for privacy against the economic gains that come from selling data to all interested parties.

Improving Regulatory Effectiveness Through Better Targeting: Evidence from OSHA
Matthew Johnson, David Levine & Michael Toffel
Harvard Working Paper, August 2019

We study how a regulator can best allocate its limited inspection resources. We direct our analysis to a US Occupational Safety and Health Administration (OSHA) inspection program that targeted dangerous establishments and allocated some inspections via random assignment. We find that inspections reduced serious injuries by an average of 9% over the following five years. We use new machine learning methods to estimate the effects of counterfactual targeting rules OSHA could have deployed. OSHA could have averted over twice as many injuries if its inspections had targeted the establishments where we predict inspections would avert the most injuries. The agency could have averted nearly as many additional injuries by targeting the establishments predicted to have the most injuries. Both of these targeting regimes would have generated over $1 billion in social value over the decade we examine. Our results demonstrate the promise, and limitations, of using machine learning to improve resource allocation.

Market Concentration, Market Shares, and Retail Food Prices: Evidence from the U.S. Women, Infants, and Children Program
Meilin Ma et al.
Applied Economic Perspectives and Policy, September 2019, Pages 542–562

We explore pricing in local food-retailing markets where supermarkets operate versus those occupied solely by smaller food retailers. Using data from the Women, Infants, and Children program in the Greater Los Angeles area, we show that supermarkets do not raise prices in local markets or as a function of market concentration or firm market shares. Smaller food retailers charge substantially higher prices on average than supermarkets. Their prices increase with market concentration and shares of sales, especially when small retailers face no direct competition from supermarkets. Given the dominance of small retailers in some low-income areas, our findings have important implications regarding local market power, food costs, and supermarket entry.

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