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arXiv · 2105.00337

Detecting bid-rigging coalitions in different countries and auction formats

David Imhof·Hannes Wallimann
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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.

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David Imhof, Hannes Wallimann. 2021-05-01. Detecting bid-rigging coalitions in different countries and auction formats. https://arxiv.org/abs/2105.00337

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