We initiate the study of naïveté-based discrimination, the practice of conditioning offers on external information about consumers’ naïveté. Knowing that a consumer is naive increases a monopolistic or competitive firm's willingness to generate inefficiency to exploit the consumer's mistakes, so naïveté-based discrimination is not Pareto-improving, can be Pareto-damaging, and often lowers total welfare when classical preference-based discrimination does not. Moreover, the effect on total welfare depends on a hitherto unemphasized market feature: the extent to which the exploitation of naive consumers distorts trade with different types of consumers. If the distortion is homogeneous across naive and sophisticated consumers, then under an arguably weak and empirically testable condition, naïveté-based discrimination lowers total welfare. In contrast, if the distortion arises only for trades with sophisticated consumers, then perfect naïveté-based discrimination maximizes social welfare, although imperfect discrimination often lowers welfare. If the distortion arises only for trades with naive consumers, then naïveté-based discrimination has no effect on welfare. We identify applications for each of these cases. In our primary example, a credit market with present-biased borrowers, firms lend more than is socially optimal to increase the amount of interest naive borrowers unexpectedly pay, creating a homogeneous distortion. The condition for naïveté-based discrimination to lower welfare is then weaker than prudence.

We identify a competition-policy-based argument for regulating the secondary features of complex or complexly priced products when consumers have limited attention. Limited attention implies that consumers can only “study” a small number of complex products in full, while—by failing to check secondary features—they can superficially “browse” more. Interventions limiting ex post consumer harm through safety regulations, caps on certain fees, or other methods induce consumers to do more or more meaningful browsing, enhancing competition. We show that for a pro-competitive effect to obtain, the regulation must apply to the secondary features, and not to the total price or value of the product. As an auxiliary positive prediction, we establish that because low-value consumers are often more likely to study—and therefore less likely to browse—than high-value consumers, the average price consumers pay can be increasing in the share of low-value consumers. We discuss applications of our insights to health-insurance choice, the European Union’s principle on unfair contract terms, food safety in developing countries, and the shopping behaviour of (and prices paid by) low-income and high-income consumers.

We analyse conditions facilitating profitable deception in a simple model of a competitive retail market. Firms selling homogenous products set anticipated prices that consumers understand and additional prices that naive consumers ignore unless revealed to them by a firm, where we assume that there is a binding floor on the anticipated prices. Our main results establish that “bad” products (those with lower social surplus than an alternative) tend to be more reliably profitable than “good” products. Specifically, (1) in a market with a single socially valuable product and sufficiently many firms, a deceptive equilibrium—in which firms hide additional prices—does not exist and firms make zero profits. But perversely, (2) if the product is socially wasteful, then a profitable deceptive equilibrium always exists. Furthermore, (3) in a market with multiple products, since a superior product both diverts sophisticated consumers and renders an inferior product socially wasteful in comparison, it guarantees that firms can profitably sell the inferior product by deceiving consumers. We apply our framework to the mutual fund and credit card markets, arguing that it explains a number of empirical findings regarding these industries.

A (partially naïve) quasi-hyperbolic discounter repeatedly chooses whether to complete a task. Her net benefits of task completion are drawn independently between periods from a time-invariant distribution. We show that the probability of completing the task conditional on not having done so earlier increases towards the deadline. Conversely, we establish nonidentifiability by proving that for any time-preference parameters and any dataset with such (weakly increasing) task-completion probabilities, there exists a stationary payoff distribution that rationalizes the agent’s behavior if she is either sophisticated or fully naïve. Additionally, we provide sharp partial identification for the case of observable continuation values.

We develop a model of multi-dimensional misspecified learning in which an overconfident agent learns about groups in society from observations of his and others’ successes. We show that the average person sees his group relative to other groups too positively, and this in-group bias exhibits systematic comparative-statics patterns. First, a person is most likely to have negative opinions about other groups he competes with. Second, while information about another group’s achievements does not lower a person’s prejudice, information about economic or social forces affecting the group can, and personal contact with group members has a beneficial effect that is larger than in classical settings. Third, the agent’s beliefs are subject to “bias substitution”, whereby forces that decrease his bias regarding one group tend to increase his biases regarding unrelated other groups.

We study the effects of defaults on charitable giving in a large-scale field experiment on an online fundraising platform. We exogenously vary default options along two choice dimensions: the charitable donation decision and the “co-donation” decision regarding how much to contribute to supporting the platform. We document a strong effect of defaults on individual behavior but nevertheless find that aggregate donation levels are unaffected by defaults. In contrast, co-donations increase in the default amount. We complement our experimental results with a structural model that investigates whether personalizing defaults based on individuals' donation histories can increase donation revenues.