Using random nth price auctions to value non-market goods and services

Public policy decision making often requires balancing the benefits of a policy with the costs. While regulators in the United States and abroad rely heavily on benefit-cost analysis, critics contend that hypothetical bias precludes one of the most popular benefit estimation techniques-contingent surveys-from providing reliable economic values for nonmarket goods and services. This paper explores a new methodology to obtain the total value of nonmarket goods and services-random nth price auctions. The empirical work revolves around examining behavior of 360 participants in a competitive marketplace, where subjects naturally buy, sell, and trade commodities. The field experiment provides some preliminary evidence that hypothetical random nth price auctions can, in certain situations, reveal demand truthfully.