Search With Learning for Differentiated Products: Evidence from E-Commerce
提出一种在消费者对效用分布不确定时估计搜索成本的方法，利用MP3播放器的在线浏览和购买数据，发现搜索成本显著，忽略学习过程会导致严重偏差。
This article provides a method to estimate search costs in a differentiated product environment in which consumers are uncertain about the utility distribution. Consumers learn about the utility distribution by Bayesian updating their Dirichlet process prior beliefs. The model provides expressions for bounds on the search costs that can rationalize observed search and purchasing behavior. Using individual-specific data on web browsing and purchasing behavior for MP3 players sold online we show how to use these bounds to estimate search costs as well as the parameters of the utility distribution. Our estimates indicate that search costs are sizable. We show that ignoring consumer learning while searching can lead to severely biased search cost and elasticity estimates.