Online Demand Under Limited Consumer Search
利用亚马逊的产品搜索数据，联合估计消费者信息搜索和耐用品在线需求，发现消费者通常只搜索10-15个选项，且产品推荐通过降低搜索成本使大多数家庭受益。
Using aggregate product search data from Amazon.com, we jointly estimate consumer information search and online demand for consumer durable goods. To estimate the demand and search primitives, we introduce an optimal sequential search process into a model of choice and treat the observed market-level product search data as aggregations of individual-level optimal search sequences. The model builds on the dynamic programming framework by Weitzman [Weitzman, M. L. 1979. Optimal search for the best alternative. Econometrica 47(3) 641–654] and combines it with a choice model. It can accommodate highly complex demand patterns at the market level. At the individual level, the model has a number of attractive properties in estimation, including closed-form expressions for the probability distribution of alternative sets of searched goods and breaking the curse of dimensionality. Using numerical experiments, we verify the model's ability to identify the heterogeneous consumer tastes and search costs from product search data. Empirically, the model is applied to the online market for camcorders and is used to answer manufacturer questions about market structure and competition and to address policy-maker issues about the effect of selectively lowered search costs on consumer surplus outcomes. We demonstrate that the demand estimates from our search model predict the actual product sales ranks. We find that consumer search for camcorders at Amazon.com is typically limited to 10–15 choice options and that this affects estimates of own and cross elasticities. In a policy simulation, we also find that the vast majority of the households benefit from Amazon.com's product recommendations via lower search costs.