中文摘要
复杂商品和服务的购买者通常依赖专家中介的建议，而这些中介自身对可选方案的质量也并非完全知情。这些中介在多大程度上能够了解相关质量并据此采取行动？我们利用450万例美国联邦医疗保险受益人的关节置换手术数据，在初级保健医生（PCP）向专科医生转诊的情境下研究这一问题。我们首先记录了地理市场内专科医生在质量和成本方面存在的显著异质性，并提供了基于研究设计（design-based）的证据，表明初级保健医生会具体依据其自身患者的治疗结果来调整转诊行为。随后，我们采用一个关于初级保健医生转诊选择的结构性学习模型，以量化信息摩擦造成的损失，并模拟信息改善后可能出现的重新配置。除学习效应外，该模型还考虑了习惯持续性（habit persistence）与产能约束对可能的重新配置所构成的限制。我们发现，在不存在信息摩擦的情况下，约四分之一的患者会被转诊至不同的专科医生，患者治疗结果将获得幅度较小但具有实际意义的改善。
Abstract
Buyers of complex goods and services often rely on the advice of expert intermediaries who are themselves imperfectly informed about the quality of the available options. How well do these intermediaries learn about that quality and act on it? We study this question in the setting of referrals from primary care physicians (PCPs) to specialists, using data on 4.5 million joint replacement surgeries for Medicare beneficiaries. We first document substantial heterogeneity in specialist quality and costs within geographic markets, and we present design-based evidence showing that PCPs adjust their referrals specifically based on the outcomes of their own patients. We then employ a structural learning model of PCP referral choices to quantify the losses from informational frictions and to simulate possible reallocations with improved information. Beyond learning, the model also accounts for limitations on possible reallocations due to habit persistence and capacity constraints. We find that about one-quarter of patients would be referred to a different specialist in the absence of informational frictions, with small but meaningful improvements in patient outcomes.