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Optimal contracts under competition when uncertainty from adverse selection and moral hazard are present

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  • Packham, N.

Abstract

In a continuous-time setting where a risk-averse agent controls the drift of an output process driven by a Brownian motion, optimal contracts are linear in the terminal output; this result is well-known in a setting with moral hazard and – under stronger assumptions – adverse selection. We show that this result continues to hold when in addition reservation utilities are type-dependent. This type of problem occurs in the study of optimal compensation problems involving competing principals.

Suggested Citation

  • Packham, N., 2018. "Optimal contracts under competition when uncertainty from adverse selection and moral hazard are present," Statistics & Probability Letters, Elsevier, vol. 137(C), pages 99-104.
    • Handle: RePEc:eee:stapro:v:137:y:2018:i:c:p:99-104
    DOI: 10.1016/j.spl.2018.01.014
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    References listed on IDEAS

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    1. Roland Bénabou & Jean Tirole, 2016. "Bonus Culture: Competitive Pay, Screening, and Multitasking," Journal of Political Economy, University of Chicago Press, vol. 124(2), pages 305-370.
    2. Jullien, Bruno, 2000. "Participation Constraints in Adverse Selection Models," Journal of Economic Theory, Elsevier, vol. 93(1), pages 1-47, July.
    3. Holmstrom, Bengt & Milgrom, Paul, 1987. "Aggregation and Linearity in the Provision of Intertemporal Incentives," Econometrica, Econometric Society, vol. 55(2), pages 303-328, March.
    4. Jaeyoung Sung, 2005. "Optimal Contracts Under Adverse Selection and Moral Hazard: A Continuous-Time Approach," The Review of Financial Studies, Society for Financial Studies, vol. 18(3), pages 1021-1073.
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    Cited by:

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    2. Christina E. Bannier & Eberhard Feess & Natalie Packham & Markus Walzl, 2021. "Differentiation and Risk Aversion in Imperfectly Competitive Labor Markets," Journal of Institutional and Theoretical Economics (JITE), Mohr Siebeck, Tübingen, vol. 177(1), pages 1-27.
    3. Nasekin, Sergey & Chen, Cathy Yi-Hsuan, 2018. "Deep learning-based cryptocurrency sentiment construction," IRTG 1792 Discussion Papers 2018-066, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".

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