UC Berkeley

Using a unique dataset of daily U.S. and U.K. price listings and the associated number of clicks for precisely defined goods from a major shopping platform, this paper explores how prices are set in online markets, which have a number of special properties such as low search costs, low costs of monitoring competitors' prices, and low costs of nominal price adjustment. High-quality data are not only useful to estimate price rigidity and other properties of price adjustment in online commerce but also allow comparing the behavior of those properties with estimates available from brick-and-mortar stores.