Identifying non-competitive bids in construction contract auctions

Abstract

Construction contract auctions are characterised by (1) anticipated high outliers due to the presence of non-competitive bids, (2) very small samples and (3) uncertainty of the appropriate underlying density function model of the bids. This paper describes the simultaneous identification of high outliers and density function by systematically identifying and removing candidate (high) outliers and examining the composite goodness-of-fit of the resulting reduced samples with the normal and lognormal density functions. Six different identification strategies are tested empirically by application, both independently and in pooled form, to several sets of auction data gathered from around the world. The results indicate the normal density to be the most appropriate model and a multiple of the auction standard deviation to be the best identification strategy.