aggressively than Weak bidders, so that bs (v) < bw (v), for all v ∈ (0; v ). In addition, they
provide sufficient conditions on the bidders’ distributions of values to observe different
rankings of first- and second-price auctions in terms of the seller’s revenues (see their
Propositions 4.3, 4.4 and 4.5). Actually, since it is always a dominant strategy for a bidder to
bid her/his own valuation in second-price auctions, these revenue rankings are the results of
bidders’ equilibrium behavior in first-price auctions. On the one hand, if the valuations of the
Strong bidder are all greater than the maximum valuation of the Weak bidder, then the Strong
bidder will always outbid the Weak bidder; Maskin and Riley (2000a) refer to this as the
“Getty effect”. When this happens, second-price auctions yield lower revenues than first-price
auctions. On the other hand, if there is a positive probability for the Weak bidder to submit no
bid at all (i.e. the range of values of the weak bidder is partially in the negative domain or
below the seller’s reserve price), then there is an incentive for the Strong bidder to low-ball,
i.e., to submit very low bids (close or equal to the seller’s reserve price) for a range of low
values. It is this incentive to low-ball that makes first-price auctions yield lower expected
revenues than second-price auctions. Li and Riley (1999) extend this analysis to asymmetric
auctions with more than two bidders, and who may display constant absolute risk averse
preferences and/or have affiliated values. One striking result of their analysis is that the effect
of low-balling on the seller’s expected revenue holds even if bidders are extremely risk averse
and have uniformly distributed values.
In what follows, we assume bidders to display homogenous constant relative risk averse
preferences. Such preferences are well adapted to auction experiments because they