Online Auctions and Buyout
Lepre, Ornella (2008) Online Auctions and Buyout. [Tesi di dottorato] (Inedito)
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The impressive growth of online auctions in recent years has motivated a number of studies aimed at defining and analyzing their distinctive characteristics; in particular, the literature has concentrated on those traits that distinguish online from traditional auctions. One of the most relevant, for its popularity, is the buyout option, a mechanism a seller can use to allow buyers to end an auction early by buying the good at a given "buyout price". While the existing literature has mostly focused on the relationship between buyout and risk aversion, this thesis proposes a new interpretation of the buyout as a channel through which an informed seller can convey his private information on the item on sale. Building on the main hypothesis of asymmetric information between sellers and bidders, the signaling model developed in Chapter 2 shows that, when sellers have a low discount rate, they may find it optimal to signal their information with a buyout price; in this case, higher buyout prices signal items of higher quality or items for which the expected demand is higher. Furthermore, the presence of multiple equilibria allows us to analyze the welfare effects of different levels of buyout prices, another issue that is largley unexplored by the current research. The model developed in Chapter 2 has several empirical implications. According to the interpretation proposed, the buyout option should be used by those sellers who have better information than bidders on the goods on sale. Since we can expect the experience accumulated as a seller to be a resonable proxy of how informed the seller is on the value and the demand of the items on sale, the model predicts a positive correlation between a seller's experience and his choice of offering a buyout option. The model also predicts that bidders with higher valuations are more likely to use the buyout price to buy the object than bidders with low valuations. Finally, if different buyout prices signal different levels of quality or demand, we should observe that, for similar items, auctions that start with a higher buyout price should close at a higher price. All these implications can be tested empirically with data from online auctions. Chapter 3 presents an econometric analysis performed on data gathered from eBay; the results, obtained using two datasets with auctions for different types of objects, appear consistent with the predictions of the model. Specifically, we find a significant correlation between the buyout prices chosen by the seller and the ending prices of the auctions, which indicates that sellers are able to predict with a certain accuracy the ending price of an auction, and that they often set buyout prices accordingly. This correlation turns out to be stronger when sellers are more experienced, further supporting the hypothesis that experienced sellers are generally more informed than bidders. Moreover, the results show that experienced sellers are more likely to offer a buyout option with their auctions, and that bidders are more likely to accept the buyout price when it is offered by an experienced seller.
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