Machine learning with screens for detecting bid-rigging cartels

Martin Huber and David Imhof

International Journal of Industrial Organization, 2019, vol. 65, issue C, 277-301

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 84% of the total of bidding processes as collusive or non-collusive. We also discuss tradeoffs in reducing false positive vs. false negative predictions and find that false negative predictions increase much faster in reducing false positive predictions. Finally, we discuss policy implications of our method for competition agencies aiming at detecting bid-rigging cartels.

Keywords: Bid rigging detection; Screening methods; Machine learning; Lasso; Ensemble methods (search for similar items in EconPapers)
JEL-codes: C21 C45 C52 K40 L40 L41 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (22)

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Related works:
Working Paper: Machine Learning with Screens for Detecting Bid-Rigging Cartels (2018)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:indorg:v:65:y:2019:i:c:p:277-301

DOI: 10.1016/j.ijindorg.2019.04.002

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