A growing body of empirical literature finds that consumers are relatively limited in how much they search over product characteristics. We assemble a dataset of search and purchase behavior from eBay to quantify the returns, and thus implied costs, to consumer search on the internet. The...

We build a model of online behavioral manipulation driven by AI advances. A platform dynamically offers one of n products to a user who slowly learns product quality. User learning depends on a product's "glossiness,' which captures attributes that make products appear more attractive than they...

We asked 97 cigarette smokers to make a series of 12 binary choices between experimental cigarette packages with varying warnings and background colors. Each smoker had to decide which of the two packages contained cigarettes less risky for his health. We tested whether the smokers, confronted...

Regulators around the world are discussing, or taking action to limit, self-preferencing by large platforms. This paper explores Amazon's search rankings of its own products as the European Union's Digital Markets Act (DMA) was coming into effect. Using data on over 8 million Amazon search...

Data on sales of memory modules are used to explore several aspects of e-retail demand. There is a strong relationship between e-retail sales to a given state and sales tax rates that apply to purchases from online retailers. This suggests that there is substantial substitution between online...

We develop and estimate a model of consumer search with spatial learning. Consumers make inferences from previously searched objects to unsearched objects that are nearby in attribute space, generating path dependence in search sequences. The estimated model rationalizes patterns in data on...