Search for differentiated products: identification and estimation

Abstract

When consumers search for differentiated products, a given search decision can be explained either by low search cost or by low tastes for the set of products already found. We propose an identification strategy that allows to estimate the search cost distribution in the presence of unobserved tastes. The required data takes the form of conditional search decisions: observations of search actions combined with previously observed product displays. We develop an application using clickstream data from a hotel search platform. Estimates of price elasticity of demand in the search model differ from those in the static model, reflecting the bias due to endogeneity of search‐generated choice sets.