A Machine Learning Approach for Flagging Incomplete Bid-Rigging Cartels

Hannes Wallimann (), David Imhof () and Martin Huber
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Hannes Wallimann: Lucerne University of Applied Sciences and Arts
David Imhof: University of Fribourg

Computational Economics, 2023, vol. 62, issue 4, No 12, 1669-1720

Abstract: 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.

Keywords: Bid rigging detection; Screening methods; Descriptive statistics; Machine learning; Random forest; Lasso; Ensemble methods (search for similar items in EconPapers)
JEL-codes: C21 C45 C52 D22 D40 K40 L40 L41 (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (2)

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Working Paper: A Machine Learning Approach for Flagging Incomplete Bid-rigging Cartels (2020)
Working Paper: A Machine Learning Approach for Flagging Incomplete Bid-rigging Cartels (2020)
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DOI: 10.1007/s10614-022-10315-w

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