We study the interplay between informational frictions and second-degree price discrimination. Our theory recognizes that consumers differ in their tastes for quality as well as in the information they possess about available offers, which leads to dispersion over price–quality menus in equilibrium. While firms are ex ante identical, we show that their menus are ordered so that more generous menus leave more surplus to consumers of all valuations. We explore the cross-section of equilibrium menus and variations in market conditions to generate empirical predictions on prices, qualities, and markups across firms, and within a firm’s product line. For instance, more competition may raise prices for low-quality goods; yet, consumers are better off, as the qualities they receive also increase. The predictions of our model illuminate empirical findings in many markets, such as those for cell phone plans, yellow-pages advertising, cable TV, and air travel.

We study bargaining with divisibility and interdependent values. A buyer and a seller trade a divisible good. The seller is privately informed about its quality, which can be high or low. Gains from trade are positive and decreasing in quantity. The buyer makes offers over time. Divisibility introduces a new channel of competition between the buyer’s present and future selves. The buyer’s temptation to split the purchases of the high-quality good is detrimental to him. As bargaining frictions vanish and the good becomes arbitrarily divisible, the high-quality good is traded smoothly over time and the buyer’s payoff shrinks to zero.

We study a dynamic stopping game between a principal and an agent. The principal gradually learns about the agent's private type from a noisy performance measure that can be manipulated by the agent via a costly and hidden action. We fully characterize the unique Markov equilibrium of this game. We find that terminations/market crashes are often preceded by a spike in manipulation intensity and (expected) performance. Moreover, due to endogenous signal manipulation, too much transparency can inhibit learning and harm the principal. As the players get arbitrarily patient, the principal elicits no useful information from the observed signal.