The Free State Foundations offers its congratulations to Paul Milgrom and Robert Wilson, the winners of the 2020 Nobel Prize in Economic Sciences. Milgrom and Wilson's "best-known contribution" is their work in the field of spectrum auctions.
Today it seems rather obvious: auctions serve as an efficient and workable tool to leverage market forces in order to put scarce spectrum resources to their best and highest use. Simply put, those who bid the most, value it the most.
But a number of factors – differences between private (subjective) and common valuations, imperfect information, license coverage areas, and more – once posed challenges to the effective use of auctions in the spectrum context.
Thanks to their work in auction theory, however, the Commission was able to transition to auctions, to the benefit of consumers of telecommunications, the U.S. Treasury, and industry.
Thus, prior to the first spectrum auction designed twenty-five years ago by these two Stanford University professors and frequent collaborators, the FCC relied upon a lottery system – and, before that, so-called "beauty contests" – to determine how spectrum would be distributed.
As the prize committee explained:
[H]ow do you design an auction that achieves the efficient allocation of radio-frequency bands, while at the same time benefitting taxpayers to the greatest possible extent? ... Milgrom and Wilson – partly with Preston McAfee – invented an entirely new auction format, the Simultaneous Multiple Round Auction (SMRA). This auction offers all objects (radio frequency bands in different geographic areas) simultaneously. By starting with low prices and allowing repeated bids, the auction reduces the problems caused by uncertainty and the winner's curse. When the FCC first used an SMRA in July 1994, it sold 10 licences in 47 bidding rounds for a total of 617 million dollars – objects which the American government had previously allocated practically for free.
Between 1994 and 2014, SMRAs run by the Commission generated over $120 billion in revenues.
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