Identifying non-competitive bids in construction contract auctions |
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| Authors: | Martin Skitmore |
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| Institution: | 1. School of Social Work, College for Public Health and Social Justice, Saint Louis University, Saint Louis, MO, United States;2. Yonsei University, Seoul, Republic of Korea;3. School of Social Work, Boston University, Boston, MA, United States;1. Department of Electrical and Computer Engineering, Coordinated Science Laboratory, University of Illinois, Urbana, IL 61801, USA;2. Department of Economics, University of Illinois, Urbana, IL 61801, USA;1. Université de Sherbrooke, 2500 boul. de l’Université, Sherbrooke J1K 2R1, Canada;2. Université de Bourgogne Franche Comté, Le2I (UMR CNRS 6306), 21079 Dijon, France |
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| 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. |
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| Keywords: | Construction Contract Auctions Non-competitive bids Outliers Goodness-of-fit Small samples |
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