Detecting bid-rigging coalitions in different countries and auction formats

TL;DR

This paper introduces a machine learning-based screening method to detect bid-rigging coalitions across different countries and auction formats, achieving high classification accuracy and identifying key bid features.

Contribution

It presents a novel coalition-based screening approach using machine learning to identify collusive groups in procurement auctions across multiple countries and auction types.

Findings

Correctly classifies 90% of collusive and competitive coalitions

Coalition-based bid variance and uniformity are key predictors

Effective across Swiss, Japanese, and Italian procurement data

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.