Optimal Long-Term Contracting with Learning

58 Pages Posted: 10 Nov 2016 Last revised: 30 Aug 2017

See all articles by Zhiguo He

Zhiguo He

Stanford University - Knight Management Center

Bin Wei

Federal Reserve Bank of Atlanta

Jianfeng Yu

Tsinghua University - PBC School of Finance

Feng Gao

Tsinghua University

Multiple version iconThere are 2 versions of this paper

Date Written: 2016-11-01

Abstract

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 decreases stochastically 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.

Keywords: executive compensation, moral hazard, Bayesian learning, hidden information, belief manipulation, private savings, continuous time, stock options

JEL Classification: D8, D86, M12

Suggested Citation

He, Zhiguo and Wei, Bin and Yu, Jianfeng and Gao, Feng, Optimal Long-Term Contracting with Learning (2016-11-01). FRB Atlanta Working Paper No. 2016-10, Available at SSRN: https://ssrn.com/abstract=2867335

Zhiguo He (Contact Author)

Stanford University - Knight Management Center ( email )

655 Knight Way
Stanford, CA 94305-7298
United States

Bin Wei

Federal Reserve Bank of Atlanta ( email )

1000 Peachtree Street N.E.
Atlanta, GA 30309-4470
United States

HOME PAGE: http://https://www.frbatlanta.org/research/economists/wei-bin.aspx

Jianfeng Yu

Tsinghua University - PBC School of Finance ( email )

No. 43, Chengfu Road
Haidian District
Beijing 100083
China

Feng Gao

Tsinghua University ( email )

Beijing, 100084
China

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