«Abstract From bidding data, we estimate the underlying value distribution for Forest Service timber. We nd that bidder values decrease $2=mbf ...»
Because non-cooperative game theory has replaced the structure-performance paradigm as the dominant framework shaping competition policy, it seems appropriate to abandon the old structural metrics in favor of computational ones as a framework for evaluating competition policy Froeb and Werden 15 ; Crooke, Froeb, Tschantz, and Werden 10 . Economists at the Federal Trade Commission and U.S. Department of Justice have already begun to do this in merger cases involving di erentiated-products mergers Werden 31 . The computational approach may be extended to auction markets to address antitrust concerns, like those of Baker 3 who is worried that hospital mergers will reduce competition for Health Maintenance Organization contracts. Computational tools like the ones developed in this paper could replace structural merger analysis in second-price auction markets. Other tools could be used in rst-price auction markets Bajari 2 ; Dalkir, Logan, Masson 11 ; Tschantz, Crooke and Froeb 27 . Perhaps the biggest bene t of such a change would be an expanded role for e ciencies which have no explicit role under a structural standard Werden and Froeb 33 . Even if good data are unavailable, rules-of-thumb implied by the merger model in 5.2 and a compensating marginal-cost reduction could be used to evaluate the e ciency claims of merging parties.
The computational tools presented in the paper could also be used in price- xing cases to compute damages or as a tool to guide enforcement resource-allocation decisions. For example, one of the surprising results of the paper is the small size of the computed price e ects of mergers that include the two most favorably-situated bidders. To get signi cant price e ects, like the 23 percent e ect estimated by Froeb, Koyak, and Werden 13, bidding coalitions would have to include nearly all auction participants.
This is similar to the nding in Bresnahan and Reiss 8 that the bene ts of additional competition are exhausted after only a few rms.
Finally, the computational approach is well suited to analyzing the e ects of bidding preferences. We nd it surprising that bidding preferences could have such large e ects in spectrum auctions Ayres and Cramton 1 , but such small e ects in timber auctions. More work to reconcile or understand these contradictory results would help guide policy.
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