Optimal Dynamic Contracting: The First-Order Approach and Beyond

54 Pages Posted: 1 Dec 2015

See all articles by Marco Battaglini

Marco Battaglini

Cornell University

Rohit Lamba

Pennsylvania State University - College of the Liberal Arts

Date Written: November 2015

Abstract

We study a dynamic principal-agent model in which the agent's types are serially correlated. In these models, the standard approach consists of first solving a relaxed version in which only local incentive compatibility constraints are considered, and then in proving that the local constraints are sufficient for implementability. We explore the conditions under which this approach is valid and can be used to characterize the profit maximizing contract. We show that the approach works when the optimal allocation in the relaxed problem is monotonic in the types, a condition that is satisfied in most solved examples. Contrary to the static model, however, monotonicity is generally violated in many interesting economic environments. Moreover, when the time horizon is long enough and serial correlation is sufficiently high, global incentive compatibility constraints are generically binding. By fully characterizing a simple two period example, we uncover a number of interesting features of the optimal contract that cannot be observed in spatial environments in which the standard approach works. Finally, we show that even in complex environments, approximately optimal allocations can be easily characterized by focusing on a particular class of contracts in which the allocation is forced to be monotonic.

Keywords: contract theory, dynamic contracts

JEL Classification: D86

Suggested Citation

Battaglini, Marco and Lamba, Rohit, Optimal Dynamic Contracting: The First-Order Approach and Beyond (November 2015). CEPR Discussion Paper No. DP10956, Available at SSRN: https://ssrn.com/abstract=2697589

Marco Battaglini (Contact Author)

Cornell University ( email )

Ithaca, NY 14853
United States

Rohit Lamba

Pennsylvania State University - College of the Liberal Arts ( email )

University Park, PA 16802-3306
United States

HOME PAGE: http://www.rohitlamba.com

Do you want regular updates from SSRN on Twitter?

Paper statistics

Downloads
0
Abstract Views
552
PlumX Metrics