A Machine Learning Approach for Flagging Incomplete Bid-Rigging Cartels

Wallimann, Hannes; Imhof, David; Huber, Martin

doi:10.1007/s10614-022-10315-w

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Journal article

A Machine Learning Approach for Flagging Incomplete Bid-Rigging Cartels

BP2-STS

Wallimann, Hannes ORCID Lucerne University, Switzerland
Imhof, David University of Fribourg, Switzerland
Huber, Martin University of Fribourg, Switzerland

11.09.2022

Published in:

Computational Economics. - 2022, p. 1-52

English 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.

Faculty

Faculté des sciences économiques et sociales et du management

Department

Département d'économie politique

Language