Transnational machine learning with screens for flagging bid-rigging cartels

Martin Huber and David Imhof

No 519, FSES Working Papers from Faculty of Economics and Social Sciences, University of Freiburg/Fribourg Switzerland

Abstract: We investigate the transnational transferability of statistical screening methods originally developed using Swiss data for detecting bid-rigging cartels in Japan. We find that combining screens for the distribution of bids in tenders with machine learning to classify collusive vs. competitive tenders entails a correct classification rate of 88% to 93% when training and testing the method based on Japanese data from the so-called Okinawa bid-rigging cartel. As in Switzerland, bid rigging in Okinawa reduced the variance and increased the asymmetry in the distribution of bids. When pooling the data from both countries for training and testing the classification models, we still obtain correct classification rates of 82% to 88%. However, when training the models in data from one country to test their performance in the data from the other country, rates go down substantially, due to some screens for competitive Japanese tenders being similar to those for collusive Swiss tenders. Our results thus suggest that a country’s institutional context matters for the distribution of bids, such that a country-specific training of classification models is to be preferred over applying trained models across borders, even though some screens turn out to be more stable across countries than others.

Keywords: Bid rigging; screening methods; machine learning; random forest; ensemble methods (search for similar items in EconPapers)
JEL-codes: C21 C45 C52 D22 D40 K40 (search for similar items in EconPapers)
Pages: 33 pages
Date: 2020-10-26
New Economics Papers: this item is included in nep-big, nep-cmp and nep-law
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Citations: View citations in EconPapers (2)

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Related works:
Journal Article: Transnational machine learning with screens for flagging bid‐rigging cartels (2022)
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