The recommendation principle states that without losing generality, a sender can focus on experiments where messages directly correspond to the receiver’s action space. We study the general validity of a recommendation principle and fully characterize the conditions on the posterior beliefs' space under which it holds in Bayesian persuasion settings. Using our characterization, we construct a simple verifiable test that asserts the validity of the recommendation principle. We apply our test to examples in the literature where the principle has been assumed or proved.

This paper introduces a dynamic model of public housing allocation in which units that arrive stochastically must be matched upon arrival with applicants on a waiting list. We establish a lower bound on the number of possible violations of ex-post Pareto efficiency or envy-freeness that grows linearly with the applicant pool. We construct a novel strategy-proof direct mechanism that yields interim Pareto efficient and envy-free outcomes by optimizing applicants’ preferences over buildings and expected waiting times through building-specific queues. Using data on preferences for public housing, we estimate that adopting the proposed mechanism would improve welfare by $2,300–$4,900 per applicant.

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I study the relationship between a matching platform's design and users' welfare. Increasing users' number of prospects has a positive choice effect (users are more likely to find a desirable partner) and a negative competition effect (users are less likely to match). The interaction of choice and competition effects has three significant consequences. First, welfare is not strictly increasing but is single-peaked in users' number of prospects, leading to ambiguous platform network effects. Second, market sides even out in size through agents' optimal enrollment decisions. Finally, a designer generally fails to maximize users' welfare and platform enrollment simultaneously. 

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