Not knowing the competition: evidence and implications for auction design
This article incorporates content that was previously circulated under the title “Selective Entry in Auctions: Estimation and Evidence.” Kong would like to thank the New Mexico State Land Office for generous assistance in collecting and interpreting the data. Special thanks are due to Stephen Wust, Joe Mraz, and Dan Fuqua. Chris Barnhill, Kevin Hammit, Tracey Noriega, Philip White, and Lindsey Woods also provided valuable industry insights. The author would like to thank Xiaohong Chen, Jeremy Fox, Emmanuel Guerre, Kei Kawai, Vijay Krishna, Laurent Lamy, John Lazarev, Alessandro Lizzeri, Xun Tang, anonymous referees, and participants of the NYU CRATE lunch (2014), NYU Stern Friday seminar (2015), SITE Workshop (2015), World Congress of the Econometric Society (2015 Montreal), NYU alumni conference (2017), and Midwest Econometrics Group (2017 TAMU) for helpful comments and suggestions. The author is deeply indebted to Isabelle Perrigne and Quang Vuong for their invaluable guidance and support. All remaining errors are the author's. Financial support from the NYU Center for Research in Applied and Theoretical Econometrics is gratefully acknowledged.
Abstract
In a government auction program where first-price auctions generate significantly higher revenue than English auctions, I document evidence that bidders are uncertain about the number of auction entrants. Motivated by additional data evidence, I estimate a structural model of auctions in which rivals' participation is stochastic, allowing for bidders' risk aversion and asymmetry. Counterfactual simulations reveal that bidders' uncertainty about the number of entrants, combined with risk aversion, substantially softens the revenue impact of low competition in first-price auctions. This explains the observed revenue patterns and uncovers an empirically important reason for sellers to favor first-price auctions over English auctions.
