A Machine Learning Approach for Flagging Incomplete Bid-Rigging Cartels

Abstract

We propose a detection method for flagging bid-rigging cartels, particularly useful when cartels are incomplete. Our approach combines screens, i.e., statistics derived from the distribution of bids in a tender, with machine learning to predict the probability of collusion. As a methodological innovation, we calculate such screens for all possible subgroups of three or four bids within a tender and use summary statistics like the mean, median, maximum, and minimum of each screen as predictors in the machine learning algorithm. This approach tackles the issue that competitive bids in incomplete cartels distort the statistical signals produced by bid rigging and it outperforms previously suggested methods in applications to incomplete cartels based on empirical data from Switzerland.