This paper examines how relational contracting affects the pattern of trade across the economy. We suppose a firm (principal) repeatedly chooses among of a set of potential trading partners (agents) under the threat of holdup. The possibility of ex-post opportunism allows agents to collect rents, which act like a fixed cost that the principal must pay when initiating a new relationship. The principal responds by dividing agents into "insiders", with whom she has previously traded, and "outsiders", with whom she has never traded. If the principal is sufficiently patient, the profit-maximising contract then uses insiders efficiently, while being biased against outsiders. This optimal strategy can be implemented by a "maintenance contract" that is robust to asymmetric information.
A seller wishes to sell multiple goods by a deadline, for example, the end of a season. Potential buyers enter over time and can strategically time their purchases. Each period, the profit-maximizing mechanism awards units to the buyers with the highest valuations exceeding a sequence of cutoffs. We show that these cutoffs are deterministic, depending only on the inventory and time remaining; in the continuous-time limit, the optimal mechanism can be implemented by posting anonymous prices. When incoming demand decreases over time, the optimal cutoffs satisfy a one-period-look-ahead property and prices are defined by an intuitive differential equation.We are grateful to the editor, Jesse Shapiro, and the referees for many excellent comments. We thank
This paper proposes a tractable model of Bayesian learning on large random networks where agents choose whether to adopt an innovation. We study the impact of the network structure on learning dynamics and product diffusion. In directed networks, all direct and indirect links contribute to agents' learning. In comparison, learning and welfare are lower in undirected networks and networks with cliques. In a rich class of networks, behavior is described by a small number of differential equations, making the model useful for empirical work.
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