Abstract
While much is known about optimal design of auctions within the context of a single item for sale, little is known about optimal design of large platform markets like eBay and auto auction houses that house large numbers of concurrent auctions. We attempt a macro-level empirical market design exercise by combining a unique dataset on tablet sales collected from eBay over the course of a year with methodologies developed by Bodoh-Creed (2011) and Backus and Lewis (2013). The former proposes a tractable approach to studying dynamic auction markets when the number of participants on both sides is sufficiently large. The model also delivers predictions on optimal fee schedule design -- specifically, in terms of listing fees and percentage charges for a sale -- for an auction house wishing to maximize profits by attracting the appropriate mix of buyers and sellers into the market. We begin by empirically investigating the key assumptions of the model which deliver (computational and empirical) tractability, and find that they are reasonable. We then estimate consumer demand, market supply, and the distributions of market entrants (this part still in progress). These figures are plugged into the Bodoh-Creed (2011) framework in order to compute optimal fee schedules and draw comparisons to actual fee schedules, as well as to make policy prescriptions.
Published Version
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