Essays in information economics

Abstract

This dissertation contains two independent papers. The first paper studies the optimal refund mechanism when an uninformed buyer can privately acquire information about his valuation over time. In principle, a refund mechanism can specify the odds that the seller requires the product returned while issuing a (partial) refund, which we call stochastic return. It guarantees the seller a strictly positive minimum revenue and facilitates intermediate buyer learning. In the benchmark model, stochastic return is always sub-optimal. The optimal refund mechanism takes simple forms: the seller either deters learning via a well-designed non-refundable price or encourages full learning and escalates price discrimination via free return. This result is robust to both positive learning and negative learning framework. In the second paper, an imperfectly informed sender chooses a publicly observable experiment and sends a cheap talk message to a receiver after privately observing the information outcome. With binary states and finite actions, we characterize an algorithm to determine the highest equilibrium payoff the sender can possibly achieve under any arbitrary (state-dependent) preferences. The sender’s marginal incentives are crucial to construct an equilibrium and the receiver’s randomization can smooth the sender’s incentive to misreport and thereby increase the equilibrium payoff. We characterize sufficient conditions for information design to be valuable under different payoff structures. Lastly, we study continuum action space and build a connection between our results and Lipnowski and Ravid (2020).