Deciding Between Competition and Collusion

Deciding Between Competition and Collusion - Deciding...

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Deciding Between Competition and Collusion Patrick Bajari and Lixin Ye 1 May 18, 2001 Abstract In many studies in empirical industrial organization, the economist needs to decide between several non- nested models of industry equilibrium. In this paper, we develop a new approach to the model selection problem that can be used when the economist must decide between models with bid-rigging and models without bid-rigging. We elicit from industry experts a prior distribution over markups across auctions. This induces a prior distribution over structural cost parameters. We then use Bayes Theorem to compute posterior probabilities for several non-nested models of industry equilibrium. In many settings, we believe that it is useful to formally incorporate the a prior beliefs of industry experts into estimation, especially in small samples where asymptotic approximations may be unreliable. 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. 1 Introduction. Most current research in empirical industrial organization focuses on a single industry. Economists often speak infor- mally to industry experts and read publications written by industry experts in the course of doing research. Carefully eliciting the views of industry experts is viewed by most empirical industrial organization economists as an essential step in conducting thorough research. However, almost no formal use is made of expert opinion in estimation. In this research, we use the tools of statistical decision theory to decide between competitive and collusive models of industry equilibrium. We elicit from industry experts a prior distribution over the structural cost parameters that enter into our models. This is done using a two step procedure. First, we elicit a cumulative distribution over markups for all of the projects in our data set. Second, the cumulative distribution of markups is used to induce a 1 We would like to thank Patrick Muchmore, Nancy Dinh and Susan Yun for their dedicated assistance in collecting the data set. Seminar participants at Stanford, the 1999 Stanford/Berkeley I.O. Fest, the University of Chicago, and Yale University provided helpful comments. We would like to thank the Minnesota, North Dakota and South Dakota Departments of Transportation and numerous City and County governments in the Midwest for their kind assistance. Also, thanks to Construction Market Data and Bituminous Paving Inc. for their insights into the construction market. Thanks to Lanier Bendard, George Deltas, Jon Levin, Paul Milgrom, Matt White and Ed Vytlacil for helpful conversations. Funding from SIEPR and the National Science Foundation is gratefully acknowledged. Parts of this research were completed while the first author was a National Fellow at the Hoover Institution.
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