Detecting bid-rigging coalitions in different countries and auction formats

David Imhof and Hannes Wallimann

Papers from arXiv.org

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 the random forest.

Date: 2021-05
New Economics Papers: this item is included in nep-big, nep-cmp and nep-des
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Citations: View citations in EconPapers (9)

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