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.

We study the growth of online peer-to-peer markets. Using data from TaskRabbit, an expanding marketplace for domestic tasks at the time of our study, we show that growth varies considerably across cities. To disentangle the potential drivers of growth, we look separately at demand and supply imbalances, network effects, and geographic heterogeneity. First, we find that supply is highly elastic: in periods when demand doubles, sellers perform almost twice as many tasks, prices hardly increase, and the probability of requested tasks being matched falls only slightly. The first result implies that in markets where supply can accommodate demand fluctuations, growth relies on attracting buyers at a faster rate than sellers. Second and perhaps most surprisingly, we find no evidence of network effects in matching: doubling the number of buyers and sellers only doubles the number of matches. Third, we show that the cities where market fundamentals promote efficient matching of buyers and sellers are also those that are the fastest growing. This heterogeneity in matching efficiency is related to two measures of market thickness: geographic density (buyers and sellers living close together) and level of task standardization (buyers requesting homogeneous tasks). Our results have two main implications for peer-to-peer markets in which network effects are limited by the local and time-sensitive nature of the services exchanged. First, marketplace growth largely depends on strategic geographic expansion. Second, a competitive rather than winner-take-all equilibrium may arise in the long run. This paper was accepted by Bruno Cassiman, business strategy.

ICT for comments. We acknowledge support from grants through the Hellman Foundation, the Laura and John Arnold Foundation, the Russell Sage Foundation, and the MIT Initiative on the Digital Economy. The company from which we obtained proprietary data reviewed the paper to make sure that confidential information was reported accurately. None of the authors have any material financial relationship with entities related to this research. The views expressed are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research.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/w26601.ack NBER working papers are circulated for discussion and comment purposes. They have not been peer-reviewed or been subject to the review by the NBER Board of Directors that accompanies official NBER publications.

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.

We study the welfare effects of enabling peer supply through Airbnb in the accommodation industry. We present a model of competition between flexible and dedicated sellers (peer hosts and hotels) who provide differentiated products. We estimate this model using data from major US cities and quantify the welfare effects of Airbnb on travelers, hosts, and hotels. The welfare gains are concentrated in specific locations (New York) and times (New Year’s Eve) when hotel capacity is constrained. This occurs because peer hosts are responsive to market conditions, expand supply as hotels fill up, and keep hotel prices down as a result. (JEL L11, L83, L86, L88, Z31)