Auctions versus Posted Prices in Online Markets

Auctions versus Posted Prices in Online Markets

Liran Einav

Stanford University and National Bureau of Economic Research

Chiara Farronato

Harvard University and National Bureau of Economic Research

Jonathan Levin

Stanford University and National Bureau of Economic Research

Neel Sundaresan

Microsoft

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-off between competitive price discovery and convenience. Evidence from eBay fits the theory. We then show that the decline in auctions was not driven by compositional shifts in seller experience or items sold, but by changing seller incentives. We estimate the demand facing sellers and document fall-

We are grateful to Pat Bajari, Nathan Hendren, Preston McAfee, Justin Rao, David Reiley, four anonymous referees, and the editor (Phil Reny) for helpful comments. We appreciate support from the National Science Foundation, the Stanford Institute for Economic Policy Research, the Toulouse Network on Information Technology, and the Alfred P. Sloan Foundation. The data for this study (provided as supplementary material online) were obtained through a contract between Einav, Farronato, and Levin and eBay Research. Neel Sundaresan was an employee of eBay at the time this research was done.

Electronically published January 11, 2018 [ Journal of Political Economy, 2018, vol. 126, no. 1] ? 2018 by The University of Chicago. All rights reserved. 0022-3808/2018/12601-0002$10.00

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ing sale probabilities and falling relative demand for auctions. Both favor posted prices; our estimates suggest the latter is more important for the auction decline. Survey evidence provides further support.

I. Introduction

One of the classic questions in economic theory concerns the best way to make a sale. Should a seller post a price, run an auction, or try to haggle with buyers? How does this depend on the good being sold, the prospective demand, and the market environment? If a seller faces buyers with private information about their willingness to pay and there are no further transaction costs, an auction is optimal (Harris and Townsend 1981; Myerson 1981; Riley and Samuelson 1981). An auction aggregates information and helps the seller identify the appropriate buyer and price. However, auctions can have high transaction costs. They take time and require communication with multiple buyers. If buyers appear gradually, are impatient, or are few in number, price posting may be preferable (e.g., Wang 1993; Ziegler and Lazear 2003). Casual empiricism aligns nicely with these trade-offs. Auctions are used for art, wine, and sales of large or rarely traded assets. Posted prices are used for more standardized goods. They became the norm in retail markets after being introduced by department stores in the 1840s to make shopping more convenient for buyers (Surowiecki 2011).

In the early days of the internet, many observers speculated that technology would shift retail markets in the direction of more dynamic pricing mechanisms. The Economist (2000) wrote that the internet had introduced "the possibility of a permanent worldwide bazaar in which no prices are ever fixed for long, all information is instantly available, and buyers and sellers spend their lives haggling to try to get the best deals" (see also Hall 2002). By 2001, eBay had become a dominant platform for consumer auctions and was the third-ranked website in terms of time spent by consumers.1 Its growth was enabled by the development of proxy bidding that allowed buyers to submit a maximum bid and have the computer respond to opponent bids up to this maximum. This lowered transaction costs by allowing bidders to participate in a dynamic auction without paying constant attention.

Since this time, online commerce has grown enormously, but internet auctions have not. Today most online commerce takes place at posted retail prices. Figure 1 shows the evolution on eBay: the share of listings and transaction volume attributable to auction sales has fallen well below

1 Source: .

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FIG. 1.--Auction share on eBay over time. For each month, the figure shows the average daily share of active eBay listings (black) and transaction revenues (gray) based on comparing pure auction listings to all pure auction and posted price listings. Less common formats, such as hybrid auctions or "best offer," are not included. The sharp drop in fall 2008 coincides with a decision in September 2008 to allow "good till canceled" posted price listings (see Sec. VII).

50 percent.2 Figure 2 shows a similar pattern, this time for Google searches involving the terms "online auctions" and "online prices." Nonetheless, the continuing use of auctions along with posted prices makes the internet, and eBay, a natural laboratory for studying how sellers choose between auctions and posted prices, how sale probabilities and prices compare between the two pricing mechanisms, and how the incentive to use auctions might have changed over time. The goal of this paper is to address these questions.

We combine a simple model with rich data from eBay. The data include all listings from 2003, when auctions were dominant, to 2009, when posted prices had overtaken auctions. We use these data to shed light on which items tend to be sold by auction, which sellers favor auctions, and how a seller's behavior changes as he or she becomes more experienced. Follow-

2 Figure 1 uses data from the US eBay platform, . As shown in online app. D, the eBay platforms in other countries across the world, with the exception of India, have experienced similar trends. Note that fig. 1 also shows a very sharp drop in the share of auction listings in September 2008, due to a change in eBay policy that allowed 30-day posted price listings to roll over from one month to the next ("good till canceled"). The figure omits less prevalent sales mechanisms such as "hybrid" auctions that allow a preemptive posted price purchase or "best offer" listings that allow a buyer to negotiate down from the posted price.

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FIG. 2.--Google search volume for online auctions and online prices. The figure presents results from "Google Trends" for search terms "online auctions" and "online prices." The y -axis is a Google-generated index for the weekly volume of Google searches for each of the two search terms, which should make the weekly volume figures comparable over time and across the two search terms.

ing Elfenbein, Fisman, and McManus (2012, 2015) and Einav et al. (2015), we also take advantage of a ubiquitous feature of eBay's platform, namely, that sellers frequently list the same item multiple times, either simultaneously or over time, while varying their sale format or other pricing parameters. We construct a large data set of matched listings during the 2003?9 period, focusing on sellers who listed the same item by auction and by posted price. There are many such cases, which enable an "applesto-apples" comparison of auctions and posted prices.

The key trade-off introduced above is that auctions enable price discovery and buyer competition but are less convenient for buyers. In Section II, we propose a model of this trade-off to help us interpret patterns in the data. The model is parsimonious. It has just a few parameters, which capture how reduced uncertainty about an item's value, greater retail competition, and greater demand for convenience all favor posted prices. After describing the setting and data, we document in Section IV that the use of auctions on eBay corresponds broadly with the theory. Auctions are used by smaller and less experienced sellers, for used goods, and for goods that are more idiosyncratic. Auction listings are also more likely to sell than matched posted price listings, but at lower prices, a prediction that comes naturally from the model.

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We then turn to the question of why auctions became less popular over time. One immediate hypothesis is that there were changes in the composition of internet sellers or of items being sold online. We show in Section V.A that on eBay this does not appear to explain much of the move to posted prices. Instead, the shift to price posting has occurred within natural groupings of sellers and products. A second possibility is a change in consumer preferences for convenience. Fifteen years ago, the novelty of internet auctions might have been a form of online entertainment, while today more buyers may be less patient, seek immediate gratification, or use other online diversions for entertainment. Finally, the price discovery benefits of auctions may have declined because of increased retail competition, on eBay and more generally with the rise of Amazon and improvements in internet search.

Our model captures the latter effects as increases in distinct parameters: the "hassle cost" of auctions l and the "best alternative" for buyers u. It suggests that either mechanism might explain why sellers would be motivated to shift to posted prices. We provide direct evidence on the changing incentives for sellers in Section V.B by documenting the growth of a large "auction discount." In 2003, auction prices were, on average, within 5 percent of matched posted price sales. By 2009, the discount had grown to over 16 percent. The drop was not due to sellers lowering their auction reserve prices. In fact, reserve prices generally increased relative to matched posted prices. Instead, the growing auction discount suggests a fall in the demand for auction listings relative to an analogous posted price listing.

We develop this idea further in Section VI. We use variation in posted prices and auction start prices within sets of matched listings to estimate the combinations of sale probability and sale price that can be achieved with different sale formats. The estimates allow us to derive "listing-level" demand curves. Comparing the estimated listing-level demand curves from 2003 and 2009 leads to a striking observation. Listing-level demand fell sharply for both formats, but much more for auctions. We connect the demand estimates to the model and by calibrating the model parameters infer how changes in competition and buyer preference for posted prices shifted seller incentives. We find that increased competition did push sellers toward posted prices but played a less important role than the secular fall in auction demand. This is particularly true in categories such as collectibles, jewelry, and clothing that feature relatively differentiated products. In commodity categories such as electronics and computers, the increase in competition plays a bigger role in explaining the shift toward price posting.

The final section of the paper provides some broader context and additional evidence. We discuss the role that changes in eBay policies, such as its search algorithm and listing fees, might have played in the transition to posted prices. We also look in more detail at buyer behavior. We

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show that compositional changes in the set of buyers do not appear to have driven the reduced demand for auction listings. But we also show that at any given time, there is significant heterogeneity in the pool of buyers. Frequent buyers tend to focus more on auctions, and they get better bargains when they are bidding. This suggests that customer segmentation could be one reason why sellers have continued to use auctions for items that are not particularly idiosyncratic or unusual. From this perspective, the rationale for auctions is less about price discovery and closer to the use of couponing or other strategies that target price-sensitive buyers. Finally, we provide some supporting evidence for our basic story by reporting on a survey we conducted of long-time eBay sellers.

Our paper relates to theoretical work on auctions and posted prices and to research on online auctions. The theory papers include Wang (1993), Lu and McAfee (1996), Kultti (1999), and Ziegler and Lazear (2003). Ziegler and Lazear focus on the use of posted prices in retail markets, and their perspective is close in spirit to ours. They propose a model in which sellers can vary in impatience to sell, and buyers arrive gradually. They show that posted prices are desirable if the seller is impatient, if buyers arrive slowly, or if buyers have attractive outside options. Our model similarly captures the idea that better outside options for buyers, for instance, due to increased retail competition, push sellers toward price posting. There is also a small literature on "buy-it-now" auctions (e.g., Budish and Takeyama 2001), which we do not study here but analyzed in Einav et al. (2015). This mechanism allows buyers to preempt an auction by purchasing at a posted price and provides a way to segment impatient high-value buyers from price-sensitive ones, an idea we discuss in Section VII.A.

There is also a large literature studying online auctions (Bajari and Horta?su [2004] is an early survey) and several papers that specifically compare online auctions and posted prices. Zeithammer and Liu (2006) and Hammond (2010, 2013) both note the coexistence of auctions and posted prices on eBay and explain it on the basis of seller heterogeneity. Zeithammer and Liu (2006) argue that posting an effective price requires gathering information, which will be worth the investment only for sellers with larger inventory. Using data from sales of Canon digital cameras on eBay in September and October 2005, they provide evidence that sellers with more inventory favor posted prices. Hammond (2010) shows a similar pattern in sales of compact discs on eBay. Hammond (2013) develops the analysis further by estimating a model of sale format choice using data on eBay CD sales. A key idea is that sellers with high opportunity costs of selling prefer posted prices (something that is also true in our model), and Hammond finds that variation in seller opportunity costs can explain the coexistence of auctions and posted prices in his CD sales data.

Bauner (2015) studies the use of auctions and price posting using eBay sales of Major League Baseball tickets. He finds that not only seller costs

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but heterogeneity across buyers, broadly divided into "fixed price lovers" and "neutrals," helps to explain the coexistence of auction and fixed price sales. Bauner's work, as well as Hammond's (2013), models sellers' choice of sales mechanism within a market equilibrium. Our analysis relies on analyzing sellers' residual demand, without a complete characterization of where this residual demand comes from. Our discussion of buyer heterogeneity and price discrimination in Section VII.A helps clarify the connection between these two empirical approaches. Sweeting (2012) also provides evidence on auction and posted price sales in a separate study of Major League Baseball ticket resale. Sweeting's paper focuses on dynamic price adjustment, but he observes that auction prices are typically below posted price transaction levels. Finally, Ariely and Simonson (2003) and Malmendier and Lee (2011) analyze auction prices relative to posted prices in an earlier time period and document some cases in which auction prices have been anomalously high.

II. A Model: Price Discovery versus Convenience

We start by modeling a seller's choice of sales mechanism. The model is designed to emphasize the trade-off between price discovery and convenience. The model is extremely parsimonious, but it allows us to show how buyer preferences, or changes in competition or information, can affect the optimal choice of sales mechanism. We also discuss some extensions of the model later in the paper.

A seller has a single item to sell and a cost c of making the sale. We assume there are two or more buyers, each with the same value v for the item, and a common fixed reservation utility u. We assume that v is drawn from a log-concave distribution F, where the distribution (but not the realization) is known to the seller.3 The seller can choose either posting a price or running a second-price auction. If the item is sold via auction, there is a hassle cost that reduces the buyer valuation from v to a lower amount v 2 l, where l is assumed to be identical across buyers and known to the seller.

The seller's problem is to choose a sales mechanism and either a posted price or a reserve price. Suppose the seller opts for a posted price p. The item will sell if v 2 u p, or with probability QF ?p? 5 1 2 F ? p 1 u?. So the expected profit is pF ?p? 5 ?p 2 c?Q F ?p?. If instead the seller runs an auction and sets a reserve price r, the item will sell if v 2 u 2 l r , or with probability Q A?r ? 5 1 2 F ?p 1 u 1 l?. If the item does sell, the

3 In online app. A, we discuss a more general version of the model in which there are n 1 bidders, and the value of bidder i is given by vi 5 v 1 wi, where v is common across bidders and w1, . . . , wn are independent across bidders. Under appropriate distributional assumptions, all of the comparative statics discussed in this section continue to hold in this more general model.

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auction price will be v 2 u 2 l, so the expected price conditional on sale is pA?r ? 5 E?v 2 u 2 ljv 2 u 2 l r . Theseller'sexpectedprofitistherefore pA?r ? 5 ? pA?r ? 2 cQ A?r ?.

Figure 3 illustrates the difference between the two sale mechanisms. The black line shows the posted price demand curve; that is, for any price p, the probability of sale is Q F(p). The gray line shows the implied "auction demand curve." That is, each possible reserve price r is converted to its implied sale probability Q A(r) and the expected price conditional on sale pA(r). The demand curves are drawn assuming that consumer values are distributed uniformly on [0, 1], with u 5 0 and l 5 0:2. The dashed gray line shows the probability of sale associated with different reserve prices, that is, the auction sale probability Q A(r) for each reserve price r.

An immediate observation is that the posted price demand curve is steeper than the auction demand curve.4 This is intuitive: the auction uniformly reduces the willingness to pay of all buyers including those with the highest value, but it also creates competition that increases the final price above the reserve price. The second effect is largest for low reserve prices (or high sale probability). Both effects are visible in the picture if we consider the auction sale curve. The vertical distance between the posted price demand curve and the auction sale curve is the hassle cost l, while the distance between the auction sale curve and auction demand curve represents the effect of competition--the expected amount by which the auction price will rise above the reserve price.

Now consider the seller's choice of sale format. We will not take the view that every seller chooses the optimal sales strategy for every listing. Indeed our empirical approach will be premised on the idea that sellers engage in considerable experimentation. Nonetheless, it is useful to consider the profit-maximizing incentives. A profit-maximizing seller will want to choose a point on the upper envelope of the demand curves: using a fixed price if she aims to sell at a high price and an auction if she aims to sell with high probability. For a fixed price listing, the optimal price p* maximizes pF ?p? 5 ?p 2 c?1 2 F ?p 1 u? and satisfies the first-order condition: p* 5 c 1 ?1 2 F ?p* 1 u?=f ?p* 1 u?. For an auction, the optimal reserve price is r * 5 c. Overall, the seller does best to use a posted price if and only if pF ?p*? pA?r *?.

The model gives rise to several comparative statics predictions. (1) All else equal, a higher level of c pushes the seller toward a posted price. For the example shown in figure 3, an auction is optimal for c 0:317 while a posted price is preferable for c 0:317. (2) All else equal, a higher level of u (i.e., lower buyer value relative to the next-best alternative) also pushes

4 More generally, assuming F is log concave, then as long as l E?v 2 v, where v is the lowest possible value of v, the fixed price demand curve will start above the auction demand curve and cross it exactly once to end below it.

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