阎芃寰

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.

摘要译文

我们研究了一个模型：发送者将有限的干预配额分配给质量可能各不相同的个体，并随后向接收者推荐被选中的个体，目标是最大化被接收者接受的个体数量。所有个体都希望获得干预，因为干预可以提高个体的质量；但是，干预必须在发送者观察到个体的初始质量之前完成分配，而推荐只有在质量被获知之后才会发生。一个自然的基准做法是分别设计这两种手段：先随机分配干预，然后再从高质量到低质量依次推荐个体。我们的主要结果表明，发送者可以通过协调干预分配与推荐而取得严格更好的结果。在最优联合机制中，干预分配关于质量是非单调的：一个中间质量群体获得干预的概率低于高质量和低质量个体，但作为补偿，当他们获得干预时，会百分之百被推荐。我们为这一机制给出了一种通过合约诱导自选择的实施方式，并讨论了其在教育、产业政策和创业孵化中的应用。本文的核心启示很简单：应当协调干预分配与推荐。