Score contribution per author:
α: calibrated so average coauthorship-adjusted count equals average raw count
We develop an approach to identify and test for bid rigging in procurement auctions. First, we introduce a general auction model with asymmetric bidders. Second, we study the problem of identification in our model. We state a set of conditions that are both necessary and sufficient for a distribution of bids to be generated by a model with competitive bidding. Third, we discuss how to elicit a prior distribution over a firm's structural cost parameters from industry experts. Given this prior distribution, we use Bayes's theorem to compare competitive and collusive models of industry equilibrium. Finally, we apply our methodology to a data set of bidding by construction firms in the Midwest. The techniques we propose are not computationally demanding, use flexible functional forms, and can be programmed using most standard statistical packages. © 2003 President and Fellows of Harvard College and the Massachusetts Institute of Technology.