The optimal disclosure policy in contests with stochastic entry: A Bayesian persuasion perspective

- We study effort-maximizing disclosure policy of the number of entrants in contests.
- We adopt a setting of imperfectly discriminatory contests with stochastic entry.
- We follow a Bayesian persuasion approach.
- For concave characteristic functions, full disclosure is optimal.
- For convex characteristic functions, full concealment is optimal.

Following a Bayesian persuasion approach, we establish that full disclosure (resp. concealment) is the contest organizer's effort-maximizing policy for disclosing the number of actual contestants if the characteristic function of the imperfectly discriminatory contest technology is strictly concave (resp. convex).