中文摘要
我研究消费者评论对企业参与市场的激励所产生的影响。企业生产的商品质量异质且未知，产品质量通过消费者评论逐渐显现，企业同时面临进入与退出决策。平台将过往评论加以汇总，构建针对单个企业的评级，用以引导消费者搜寻。当平台将所有评论都纳入评级时——即完全透明——消费者会在评级最高的企业处排队。这一“需求悬崖”使得企业的延续价值作为评级的函数呈现S形，既导致进入率偏低，又产生非合意的选择效应——高质量企业提前退出。企业在起步阶段偏好更多反馈，在站稳脚跟后偏好更少反馈，而均衡恰好产生了与之相反的形态。随后，我研究评级机制的设计。平台必须在为消费者提供准确信息的需要与鼓励高质量企业进入并保持经营的需要之间加以权衡。关键洞见在于：最优评级机制包含上删节（upper censorship），即将评论从高评级企业的评级中加以抑制，以此作为提供激励的手段。这使得“攀爬评级之坡”的任务不再那么令人生畏，从而刺激企业参与。一项利用Yelp!所提供数据进行的探索性校准估计表明，采用最优政策可带来约7%的消费者福利提升。
Abstract
I study the impact of consumer reviews on the incentives for firms to participate in the market. Firms produce goods of heterogeneous, unknown quality that is gradually revealed via consumer reviews, and face both entry and exit decisions. A platform combines past reviews to construct firm-specific ratings that help guide consumer search. When the platform integrates all reviews into ratings - full transparency - consumers form queues at the highest-rated firms. This demand cliff induces an S-shaped continuation value for firms as a function of ratings, generating both low entry rates as well as unwanted selection effects - high-quality firms exit early. Whereas firms prefer more feedback when starting out and less feedback when established, equilibrium induces precisely the reverse profile. I then study the design of ratings systems. The platform must balance the need to provide consumers with accurate information against the need to encourage high-quality firms to enter and remain active. The key insight is that optimal rating systems involve upper censorship, i.e. the suppression of reviews from highly-rated firms' ratings, as a means of incentive provision. This makes the task of "climbing the ratings hill" less daunting, stimulating participation. An exploratory calibration using data provided by Yelp! estimates a consumer welfare gain of roughly 7% from adopting the optimal policy.