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-disclosing 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 inextricably determines the choice of information disclosure. 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 inextricably determines the choice of information disclosure
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 incumbent-newcomer 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.. The asymmetric information relates to the value of the prize, and the effort-maximizing designer must reveal the state optimally by committing to a signaling mechanism.14 Kamenica and Gentzkow (2011) show generally that full disclosure is an optimal policy if the payoff of the sender (principal) as a function of the belief of the receiver (uninformed contestant) is globally convex, whilst no disclosure is best when it is globally concave; if the payoff function of the sender has concave and convex portions, partial disclosure is optimal. 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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