Working Papers
We address the question of whether regulators should disclose consumer traits in competitive risk-sharing markets, such as insurance or credit markets, when consumers suffer from correlation neglect. We find that disclosure improves welfare if the trait has a strong causal effect on consumer's risk. However, when correlation plays a dominant role, disclosure of the trait leads to mispricing and lowers market participation. Our results highlight that causal effects support transparency, while correlational effects may justify privacy protection.
We study monotone persuasion in the linear case, where posterior distributions over states are summarized by their mean. We solve the two leading cases where optimal unrestricted signals can be nonmonotone. First, if the objective is s-shaped and the state is discrete, then optimal monotone signals are upper censorship, whereas optimal unrestricted signals may require randomization. Second, if the objective is m-shaped and the state is continuous, then optimal monotone signals are interval disclosure, whereas optimal unrestricted signals may require nonmonotone pooling. We illustrate our results with an application to media censorship.
A decision maker acquires and processes information about an uncertain state of nature by an inquiry: a contingent sequence of questions to be asked before a decision is reached. Inquiry is a costly activity, with the cost proportional to its length. We characterize optimal inquiries and uncover two behavioral implications associated with costly inquiry: attention span reduction (i.e., favoring shorter inquiries by focusing on a subset of decisions and assigning them different priorities) and confirmation bias (i.e., seeking evidence through inquiry to confirm a prior guess of which decisions are optimal). This framework can be used to understand prominent cognitive biases, such as framing and search satisficing in healthcare and tunnel vision in criminal investigation.
We investigate the impact of information disclosure, via a statistical instrument, on consumer welfare in competitive insurance markets with limited screening. We demonstrate that, under natural constraints on information disclosure, no statistical instrument is "safe" to implement. There always exists a nonnegligible set of prior beliefs about the risk types of consumers, compatible with an observed market situation, under which additional information disclosure strictly worsens welfare.
While much is known about statistical and behavioral discrimination in hiring decisions, little attention has been given to how the organization of the interviewing process affects fairness. Using a sequential search model, we define fairness through two principles—equal treatment of equals and invariance under reordering—and fully characterize the hiring procedures that satisfy them. We show that such procedures have a simple structure and can be preregistered with a regulator to ensure fair treatment of applicants. Our analysis reveals that several common hiring practices are unfair according to these principles of fairness.
A sender designs how to disclose information about the state of the world to persuade a receiver to accept a proposal. The sender is ignorant about both the receiver's type and his risk attitude. The sender applies the principle of maximum entropy to resolve her ignorance. We show that the maximum-entropy utility is risk neutral if nothing is known about the agent's utility, and it is CARA if the average utility is known. Furthermore, the optimal signal is either fully revealing or completely uninformative if nothing is known about the distribution of the agent's type, or if its mean is known; the optimal signal is a censorship if the mean and variance are known. To derive our results, we propose a novel representation of preference over lotteries.
We study bargaining under incomplete information, with applications to trade and to provision of public good. In our setting, agents not only agree on how they share their output, but also on how much output they produce. We are interested in bargaining rules that do not depend on priors. We find a unique rule that satisfies a set of axioms. Under this rule, the higher the surplus, the more output is produced. Moreover, the produced output is shared as in the Nash bargaining solution. We present a dynamic protocol that implements this rule for any priors. Heterogeneous discount factors and degrees of risk aversion can be included.
We consider a model where two e-commerce platforms, such as internet auctions, compete for sellers who are heterogeneous in their time preferences. Contrary to the literature which argues that if two platforms coexist in equilibrium, then the “law of one price” must hold, we demonstrate that two platforms may set different prices and have positive equilibrium profits by exploiting heterogeneity of sellers' time preferences. In such an equilibrium less patient sellers choose the more popular, but more expensive, platform, while more patient sellers prefer the less popular and cheaper one.
