As part of public procurement, many governments adopt small business programs to provide contract opportunities for businesses often with preferences for firms operated by members of groups designated as disadvantaged. The redistribution arising from such programs, however, can introduce significant added costs to government procurement budgets. In this paper, the extent to which small business set-asides increase government procurement costs is examined. The estimates employ data on Japanese public construction projects, where approximately half of the procurement budget is set aside for small and medium enterprises (SMEs). Applying a positive relationship between profitability and firm size obtained by the non-parametric estimation of asymmetric first-price auctions with affiliated private values, a counterfactual simulation is undertaken to demonstrate that approximately 40 percent of SMEs would exit the procurement market if set-asides were to be removed. Surprisingly, the resulting lack of competition would increase government procurement costs more than it would offset the production cost inefficiency.

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Somaini. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research. NBER working papers are circulated for discussion and comment purposes. They have not been peer-reviewed or been subject to the review by the NBER Board of Directors that accompanies official NBER publications.

We document a novel bidding pattern observed in procurement auctions from Japan: winning bids tend to be isolated, and there is a missing mass of close losing bids. This pattern is suspicious in the following sense: its extreme forms are inconsistent with competitive behavior under arbitrary information structures. Building on this observation, we develop systematic tests of competitive behavior in procurement auctions that allow for general information structures as well as nonstationary unobserved heterogeneity. We provide an empirical exploration of our tests, and show they can help identify other suspicious patterns in the data.

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