A Machine Learning Approach for Flagging Incomplete Bid-rigging Cartels

Hannes Wallimann (), David Imhof and Martin Huber
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Hannes Wallimann: Faculty of Economics and Social Sciences, Postal: Bd de Pérolles 90, CH-1700 Fribourg, http://www.unifr.ch/ses/

No 513, FSES Working Papers from Faculty of Economics and Social Sciences, University of Freiburg/Fribourg Switzerland

Abstract: We propose a new method for flagging bid rigging, which is particularly useful for detecting incomplete bid-rigging cartels. 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. We demonstrate that our algorithm 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 (search for similar items in EconPapers)
Pages: 72 pages
Date: 2020-04-01
New Economics Papers: this item is included in nep-big, nep-cmp, nep-ecm, nep-law and nep-ore
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Related works:
Journal Article: A Machine Learning Approach for Flagging Incomplete Bid-Rigging Cartels (2023)
Working Paper: A Machine Learning Approach for Flagging Incomplete Bid-rigging Cartels (2020)
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