Research

The fees that platforms charge to consumers and merchants may be inefficient due to market power, network externalities, and business-stealing externalities. Using a structural model of platform competition estimated on data covering all major US food delivery platforms, I quantify distortions in platform fees. Consumer fees are nearly optimal due to offsetting market power and offline business stealing distortions. Restaurant commissions, by contrast, are nearly twice their socially optimal levels, primarily because platforms do not fully account for consumer benefits from increased restaurant variety on platforms. I also consider whether platform competition corrects inefficiency in platform fees.

We estimate how the spatial assortment of offline stores near consumers affects their online spending. Our data on US business locations and internet usage permit a comparison of this relationship across retailers and product categories, revealing the relative sizes of competitive effects, showrooming effects, and cross-channel complementarities (CCCs). We find that spending at a multichannel retailer’s online store falls (0.5–1.9%) when a rival adds a nearby storefront but rises (4.5–17.9%) when the retailer opens its own storefront, consistent with large CCCs. Offline stores often boost Amazon’s sales, especially for the books category in which showrooming is especially relevant.

This article characterizes the identifiability of BLP-style demand models with network externalities. Market-level data are generally insufficient for identification because product characteristics cannot be varied independently of market shares to identify substitution patterns separately from network externalities. Micro data permit identification: within-market variation in consumer characteristics identifies substitution patterns holding market shares fixed, and across-market variation in the distribution of these characteristics identifies network externalities under certain conditions. I apply the identification logic in estimating demand for dating websites. In counterfactual analysis, I find that website mergers may benefit consumers when they make websites interoperable.

This article develops techniques for the empirical analysis of repeated sequential search over unordered alternatives using data on consumer search processes. I use these techniques to assess why consumers conduct little search in e-commerce and often pay significantly above the minimum available price for a product. Search costs could explain these facts, as could pre-search seller differentiation: consumers with low search costs may not visit stores they dislike based on information known before search. I find that seller differentiation is primarily responsible for limited consideration and market power.