Optimal Long-Term Contracting with Learning

• 成果类型: Article
• 署名作者: He, Zhiguo; Wei, Bin; Yu, Jianfeng; Gao, Feng
• 署名单位: University of Chicago; National Bureau of Economic Research; Federal Reserve System - USA; Federal Reserve Bank - Atlanta; Tsinghua University; University of Minnesota System; University of Minnesota Twin Cities; Tsinghua University
• 刊物名称: REVIEW OF FINANCIAL STUDIES
• ISSN/ISSBN: 0893-9454
• DOI: 10.1093/rfs/hhx007
• 发表日期: 2017
• 页码: 2006
• 关键词: continuous-time; moral hazard; incentive contracts; adverse selection; dynamic contracts; security design; COMPENSATION; performance; AGENCY; MODEL

摘要

We introduce uncertainty into Holmstrom and Milgrom (1987) to study optimal long-term contracting with learning. In a dynamic relationship, the agent's shirking not only reduces current performance, but also increases the agent's information rent due to the persistent belief manipulation effect. We characterize the optimal contract using the dynamic programming technique in which information rent is the unique state variable. In the optimal contract, the optimal effort is front-loaded and stochastically decreases over time. Furthermore, the optimal contract exhibits an option-like feature in that incentives increase after good performance. Implications about managerial incentives and asset management compensations are discussed.