Bidding theory predicts low er selling winning bids (higher buying winning bids) as numbers of bidders incre ase. Alternative versions predict that winning bids fall (rise) with the second order statistic, the maximum order statistic, or 1/N. The authors test these pre dictions using data on underwriters' spreads on tax exempt bonds, offshore oil t racts, and National Forest timber. They estimate winning bids using general vari ables for the product and dummies for 1, 2, . . .11 bidders. In all cases mor e bidders meansignificantly lower (selling) winning bids. The expected maximum order statistic fits better than either the second order statistic or1/N, in te n out of twelve cases.

From bidding data, we estimate the underlying value distribution for Forest Service timber. We find that bidder values decrease $2/mbf (thousand board feet) with each mile from the tract and that small firms (fewer than 500 employees) have values that are $72/mbf lower than large firms. The empirical value distribution is used to simulate various hypothetical scenarios designed to inform public policy. The most anticompetitive mergers raise price by less than 3%, and a 4% decline in marginal costs through greater merger efficiencies is enough to offset a 1% anticompetitive price increase. Eliminating the SBA set-aside program would raise timber revenues by 15%. A policy of granting bidding preferences to small and more-distant bidders would raise revenue by approximately one-tenth of one percent.