Detecting bid-rigging coalitions in different countries and auction formats
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.
Cite
@article{arxiv.2105.00337,
title = {Detecting bid-rigging coalitions in different countries and auction formats},
author = {David Imhof and Hannes Wallimann},
journal= {arXiv preprint arXiv:2105.00337},
year = {2021}
}