Machine Learning with Screens for Detecting Bid-Rigging Cartels

Martin Huber and David Imhof

No 494, FSES Working Papers from Faculty of Economics and Social Sciences, University of Freiburg/Fribourg Switzerland

Abstract: We combine machine learning techniques with statistical screens computed from the distribution of bids in tenders within the Swiss construction sector to predict collusion through bid-rigging cartels. We assess the out of sample performance of this approach and find it to correctly classify more than 80% of the total of bidding processes as collusive or non-collusive. As the correct classification rate, however, differs across truly non-collusive and collusive processes, we also investigate tradeoffs in reducing false positive vs. false negative predictions. Finally, we discuss policy implications of our method for competition agencies aiming at detecting bid-rigging cartels.

Keywords: Bid rigging detection; screening methods; variance screen; cover bidding screen; structural and behavioural screens; machine learning; lasso; ensemble methods (search for similar items in EconPapers)
JEL-codes: C21 C45 C52 D22 D40 K40 (search for similar items in EconPapers)
Pages: 28 pages
Date: 2018-03-29
New Economics Papers: this item is included in nep-big, nep-cmp, nep-com and nep-law
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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Journal Article: Machine learning with screens for detecting bid-rigging cartels (2019)
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