Multi-Attribute Auctions with Unobserved Heterogeneity in Supplier Qualities and Buyer Tastes

Multi-attribute auctions play a very important role in our economy: (i) they are widely used in industry procurement; (ii) many markets for services are organized in a form reminiscent of private multi-attribute auctions; (iii) recently many Internetbased markets adopted multi-attribute structure. Multi-attribute auctions differ from standard low price auctions in that (a) a buyer chooses a winner on the basis of several indicators instead of taking only a price quote into account; (b) the exact weighting scheme that is used to determine the winner is not announced. Often the exact set of characteristics included in the score is left unspecified. These features create natural obstacles to the study of multi-attribute auctions since they imply that auctioneers’ tastes as well as some of the relevant bidder characteristics may not be observed in the data. This paper shows that the multi-attribute auction model with unobserved bidder characteristic and unobserved auctioneer tastes is identified from the data. Our identification argument allows for endogenous auction participation. An important identification step deals with recovering bidders’ quality rankings. This step essentially proposes an algorithm to recover bidder classification into unobserved groups. We use identification argument to develop an estimation methodology. We apply our methodology to the data for on-line multi-attribute procurement auctions for services and estimate the distribution of buyers’ tastes, sellers’ unobserved characteristics and the distribution of sellers’ private costs. We use these estimates to evaluate the effect of announcing weighting scheme used in the auction on bidders’ pricing and participation decisions.