Detecting bid-rigging coalitions in different countries and auction formats

David Imhof and Hannes Wallimann

International Review of Law and Economics, 2021, vol. 68, issue C

Abstract: We propose an original application of screening methods using machine learning to detect collusive groups of firms in procurement auctions. As a methodical innovation, we calculate coalition-based screens by forming coalitions of bidders in tenders to flag bid-rigging cartels. Using Swiss, Japanese and Italian procurement data, we investigate the effectiveness of our method in different countries and auction settings, in our cases first-price sealed-bid and mean-price sealed-bid auctions. We correctly classify 90% of the collusive and competitive coalitions when applying four machine learning algorithms: lasso, support vector machine, random forest, and super learner ensemble method. Finally, we find that coalition-based screens for the variance and the uniformity of bids are in all the cases the most important predictors according to the random forest.

Keywords: Cartel detection; Screening; Machine learning; Procurement data (search for similar items in EconPapers)
JEL-codes: C45 C52 D22 D40 K40 L40 L41 (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (9)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:irlaec:v:68:y:2021:i:c:s0144818821000405

DOI: 10.1016/j.irle.2021.106016

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