Those planning and implementing spectrum auctions generally have two primary objectives: increasing economic efficiency (or ensuring that spectrum is made available to those who can gain the most value from use of the spectrum) and revenue maximization (or ensuring that governments receive appropriate revenues from sales of the spectrum). There can, of course, sometimes be a trade-off between these two objectives. For example, a portion of the spectrum available for auction might be designated for new entrants to increase competition at the expense of some loss of revenue from incumbents who might be willing to pay more for additional spectrum than new entrants. Spectrum auction design has a significant impact on the extent to which the primary objectives of economic efficiency and revenue maximization are met. In his path-breaking examination of the policies of the Federal Communications Commission, Coase (1959) defined the terms of the debate on spectrum allocation. Coase argued that administrative licensing of spectrum was inefficient and that establishing a market for spectrum in which spectrum owners would be allowed to purchase, sell, lease, subdivide and aggregate spectrum would lead to more efficient spectrum allocation. A number of studies have built on Coase’s insights to argue that administrative spectrum allocation and assignment techniques were inefficient, and that spectrum auctions produce superior results. Studies building on Coase’s insights include Cramton’s (1995, 1997, 2001) comprehensive analyses of FCC spectrum auctions, Chakravorti et al.’s (1995) study on Personal Communications Services spectrum auctions, de Vany’s (1998) analysis of the implementation of market-based spectrum policy, Hazlett’s (1990, 1998, 2001, 2008) wide-ranging work including studies of property rights and spectrum allocation, Binmore and Klemperer’s (2002) analysis of the British 3G telecom auction, Klemper’s (2002a) analysis of the European 3G telecom auctions, and McMillan’s (1994, 1995) surveys of spectrum auctions. While these studies have produced rich insights into the determinants of spectrum prices, there appears to be relatively little quantitative analysis of the impact of alternative auction mechanisms on spectrum prices. The purpose of this paper is to help fill this gap by building and estimating a model of spectrum prices which focuses on the impact of alternative auction mechanisms. An outline of the paper is as follows. Section 2 provides a brief overview of auction theory, Section 3 summarizes the data and approach, Section 4 provides the results of the regression analysis, and Section 5 states the conclusions
