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Transnational machine learning with screens for flagging bid-rigging cartels

BP2-STS

    01.10.2020

33

Bid rigging
screening methods
machine learning
random forest
ensemble methods
English 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.
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Faculty
Faculté des sciences économiques et sociales et du management
Language
  • English
Classification
Economics
Other electronic version

Faculté SES

Series statement
  • Working Papers SES ; 519
License
License undefined
Identifiers
  • RERO DOC 329575
  • RERO R009091060
Persistent URL
https://folia.unifr.ch/unifr/documents/308994
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