EconPapers    
Economics at your fingertips  
 

Optimal Rewards in Contests

Aner Sela, Todd Kaplan and Chen Cohen

No 4704, CEPR Discussion Papers from Centre for Economic Policy Research

Abstract: We study all-pay contests under incomplete information where the reward is a function of the contestant's type and effort. We analyse the optimal reward for the designer when the reward is either multiplicatively separable or additively separable in effort and type. In the multiplicatively separable environment the optimal reward is always positive while in the additively separable environment it may also be negative. In both environments, depending on the designer's utility, the optimal reward may either increase or decrease in the contestants' effort. Finally, in both environments, the designer's payoff depends only upon the expected value of the effort-dependent rewards and not the number of rewards.

Keywords: Contests; All-pay auctions; Optimal design (search for similar items in EconPapers)
JEL-codes: D44 D72 O31 (search for similar items in EconPapers)
Date: 2004-10
References: Add references at CitEc
Citations: View citations in EconPapers (8)

Downloads: (external link)
https://cepr.org/publications/DP4704 (application/pdf)

Related works:
Journal Article: Optimal rewards in contests (2008) Downloads
Working Paper: The Optimal Rewards in Contests (2005) Downloads
Working Paper: The Optimal Rewards in Contests (2004) Downloads
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:cpr:ceprdp:4704

Ordering information: This working paper can be ordered from
https://cepr.org/publications/DP4704

Access Statistics for this paper

More papers in CEPR Discussion Papers from Centre for Economic Policy Research 33 Great Sutton Street, London EC1V 0DX, UK.
Bibliographic data for series maintained by CEPR ().

 
Page updated 2026-05-29
Handle: RePEc:cpr:ceprdp:4704