Peer-to-peer markets such as eBay, Uber, and Airbnb allow small suppliers to compete with traditional providers of goods or services. We view the primary function of these markets as making it easy for buyers to find sellers and engage in convenient, trustworthy transactions. We discuss elements of market design that make this possible, including search and matching algorithms, pricing, and reputation systems. We then develop a simple model of how these markets enable entry by small or flexible suppliers, and how they impact existing firms. Finally, we consider the regulation of peer-to-peer markets and the economic arguments for different approaches to licensing and certification, data, and employment regulation.

In the short-run, peer producers decide whether to host on a particular day. Because of the flexible nature of their supply, we hypothesize that these producers will be highly responsive to market conditions, hosting travelers when prices are high, and using accommodation for private use when prices are low. In contrast, because hotels have a fixed number of rooms dedicated to travelers' accommodation, they will typically choose to transact even when demand is relatively low, while they won't be able to expand capacity during peaks in demand. These di↵erences imply that peer supply elasticity should be higher than hotels' supply elasticity on average. We validate this prediction by estimating a peer supply elasticity that is twice as high as hotels' elasticity. The heterogeneous entry of peer hosts across cities and over time has surplus implications. We estimate our short-run equilibrium model to quantify the e↵ect of Airbnb on total welfare and its distribution across travelers, peer hosts, and hotels. Travelers benefit from Airbnb for two reasons. First, flexible sellers o↵er a di↵erentiated product relative to hotels. Second, they also compete with hotels by expanding the number of rooms available. This second e↵ect is particularly important in periods of high demand when hotels are capacity constrained and can thus charge higher prices. Consequently, we find that the increase in consumer surplus from Airbnb is concentrated in city-days of peak demand, which the accommodation industry defines as compression nights. In those cities and periods, flexible sellers allow more travelers to stay in a city without greatly a↵ecting the number of travelers staying at hotels. Our data mainly come from two sources: proprietary data from Airbnb, and data from STR, which tracks supply and demand data for the hotel industry. We obtain data on average prices and rooms sold at a city, day, and accommodation type level between 2011 and 2014 for the 50 largest US cities. 1 We first document heterogeneity in the number of Airbnb listings across cities and over time. Cities like New York and Los Angeles have grown more quickly, reaching supply shares exceeding 15% and 5% respectively in 2014, while cities like Oklahoma City and Memphis have grown more slowly, with less than 1% supply shares at the of 2014. Within each city over time, the number of available rooms is higher during peak travel times such as Christmas and the summer. The geographic and time heterogeneity suggests that hosts flexibly choose when to list their rooms for rent on Airbnb, and are more likely to do so in cities and times when the returns to hosting are highest. In Section 2, we incorporate this intuition into a model of the market for accommodations. In this model, rooms for accommodations can be provided by dedicated or flexible sellers, and products are di↵erentiated. We include two time-horizons. The long-run horizon is characterized by the entry decision of flexible sellers given the new Airbnb platform. We model the decision of flexible sellers to jo...

Auctions were very popular in the early days of internet commerce, but today online sellers mostly use posted prices. We model the choice between auctions and posted prices as a trade-o¤ between competitive price discovery and convenience. Evidence from eBay …ts the theory: auctions are favored by less experienced sellers and for idiosyncratic products, and auction listings sell at a discount but with higher probability relative to comparable posted price listings. We then show that the decline in auctions was not driven by changes in the type of sellers and items. Instead, seller incentives changed. We estimate the demand facing individual sellers at di¤erent points in time, and document falling sale probabilities and a fall in the relative demand for auctions. Both favor posted prices; our estimates suggest the latter is more important for explaining the shift away from auctions. We provide supporting evidence from a survey of eBay sellers, and discuss why sellers might use a mix of auctions and posted prices in order to price discriminate.

At least one co-author has disclosed a financial relationship of potential relevance for this research. Further information is available online at http://www.nber.org/papers/w19021.ack NBER working papers are circulated for discussion and comment purposes. They have not been peerreviewed or been subject to the review by the NBER Board of Directors that accompanies official NBER publications.