Abstract
We study a design problem for an effort-maximizing principal in a two-player contest with two dimensions of asymmetry. Players have different skill levels and an information gap exists, as only one player knows the skill difference. The principal has two policy instruments to redress the lack of competitive balance due to asymmetry; she can commit to an information-revealing mechanism, and she can discriminate one of the players by biasing his effort. We characterize the optimal level of discrimination to maximize aggregate effort, showing how this is in turn inextricably linked to the choice of information revelation. Applications are found in newcomer-incumbent situations in an internal labor market, sales-force management, and research contests.
Highlights
Competition in social, political and economic spheres is often analyzed as a contest in which resources are sunk in order to win a prize
We demonstrate that there is an interesting interplay between these two policy instruments, and that the optimal level and direction of discrimination is inextricably linked to the choice of information revelation
When it is thought that the informed newcomer is very likely to be skill-inferior the designer does not benefit from revealing this to the uninformed opponent, and she chooses to discriminate in favor of the informed player; we show further how the magnitude of the discrimination depends on the skill level
Summary
Competition in social, political and economic spheres is often analyzed as a contest in which resources are sunk in order to win a prize. We set up a simple model that effectively captures the incumbentnewcomer scenario, and in which the principal has two policy instruments at her disposal She can commit to a signaling mechanism which may reveal - at least partially - the hidden information; she can use a policy which treats one of the players preferentially by biasing positively his effort level in the contest. Zhang and Zhou (2016) consider first a structure in which the hidden prize value is binary, which yields an expected effort function that is globally convex or concave depending on the valuation by the informed player and the two possible valuations of the uninformed; a signal is optimal that gives either full disclosure of the hidden state, or no disclosure. We use the notation p to denote a generic distribution wherever needed, while q always refers to the prior, which is a parameter of the model
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