🤖 AI Summary
This study addresses the challenge of jointly optimizing the allocation of limited treatment resources and subsequent recommendations to maximize adoption when individuals’ initial qualities are unknown. The authors propose a two-stage model in which a sender first allocates treatment and then recommends selected individuals to receivers. Drawing on mechanism design and contract theory, they derive a non-monotonic optimal joint mechanism: individuals of intermediate quality receive treatment with lower probability, yet if treated, they are always recommended. This structure overcomes the suboptimality inherent in sequential or decoupled designs and strictly outperforms strategies that optimize allocation and recommendation independently. The framework offers practical relevance in contexts such as educational interventions, industrial policy, and startup incubation programs.
📝 Abstract
We study a model in which a sender allocates limited treatment to agents with heterogeneous quality and later recommends selected agents to a receiver, seeking to maximize the number of agents accepted by the receiver. All agents value treatment, which improves agents' quality, but treatment must be allocated before the sender observes agents' initial quality; recommendation occurs only after quality is learned. A natural benchmark is to design the two instruments separately: allocate treatment randomly first, and then recommend agents from the top down afterward. Our main result shows that the sender can do strictly better by coordinating treatment allocation with recommendations. In the optimal joint mechanism, treatment is non-monotone in quality: an intermediate group has a lower treatment probability than both higher- and lower-quality agents, but is compensated with a guaranteed recommendation when treatment is realized. We provide an implementation through contracts that induce self-selection and discuss applications to education, industrial policy, and startup incubation. The takeaway is simple: coordinate treatment allocation and recommendation.