A data buyer faces a decision problem under uncertainty. He can augment his initial private information with supplemental data from a data seller. His willingness to pay for supplemental data is determined by the quality of his initial private information. The data seller optimally o¤ers a menu of statistical experiments. We establish the properties that any revenue-maximizing menu of experiments must satisfy. Every experiment is a non-dispersed stochastic matrix, and every menu contains a fully informative experiment. In the cases of binary states and actions, or binary types, we provide an explicit construction of the optimal menu of experiments.Keywords: information design, price of information, statistical experiments, mechanism design, price discrimination, hypothesis testing.JEL Codes: D42, D82, D83.We thank the co-editor, Je¤ Ely, and three anonymous referees for their productive suggestions. We are grateful for conversations with Ben Brooks,

The mechanisms by which information is traded can shape the creation and the distribution of surplus in many important markets. Information about individual borrowers guides banks' lending decisions, information about consumers' characteristics facilitates targeted online advertising, and information about a patient's genome enhances health care delivery. In all these settings, information buyers (i.e., lenders, advertisers, and health care providers) have private knowledge relevant to their decision problem at the time of contracting (e.g., independent knowledge of a borrower, prior interactions with specific consumers, access to a patient's family history). Thus, potential data buyers seek to acquire supplemental information to improve the quality of their decision making.In this paper, we develop a framework to analyze the sale of supplemental information. We consider a data buyer who faces a decision problem under uncertainty. A monopolist data seller owns a database containing information about a "state" variable that is relevant to the buyer's decision. Initially, the data buyer has only partial information about the state. This information is private to the data buyer

A principal owns a firm, hires an agent of uncertain productivity, and designs a dynamic policy for evaluating his performance. The agent observes ongoing evaluations and decides when to quit. When not quitting, the agent is paid a wage that is linear in his expected productivity; the principal claims the residual performance. After quitting, the players secure fixed outside options. I show that equilibrium is Pareto efficient. For a broad class of performance technologies, the equilibrium wage deterministically grows with tenure. My analysis suggests that endogenous performance evaluation plays an important role in shaping careers in organizations. (JEL D21, D82, D83, J24, J31, J41, M51)

We study dynamic games in which senders with state-independent payoffs communicate to a single receiver. Senders' private information evolves according to an aperiodic and irreducible Markov chain. We prove an analog of a folk theorem-that any feasible and individually rational payoff can be approximated in a perfect Bayesian equilibrium if players are sufficiently patient. In particular, there are equilibria in which the receiver makes perfectly informed decisions in almost every period, even if no informative communication can be sustained in the stage game. We conclude that repeated interaction can overcome strategic limits of communication.

Short-lived buyers arrive to a platform over time and randomly match with sellers. The sellers stay at the platform and sequentially decide whether to accept incoming requests. The platform designs what buyer information the sellers observe before deciding to form a match. We show full information disclosure leads to a market failure because of excessive rejections by the sellers. If sellers are homogeneous, then coarse information policies are able to restore efficiency. If sellers are heterogeneous, then simple censorship policies are often constrained efficient as shown by a novel method of calculus of variations.