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Research report

Machine learning approach for flagging incomplete bid-rigging cartels

BP2-STS

    01.03.2020

72

Bid rigging detection
screening methods
descriptive statistics
machine learning
random forest
lasso
ensemble methods
English 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.
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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 ; 513
License
License undefined
Identifiers
  • RERO DOC 328358
  • RERO R009035542
Persistent URL
https://folia.unifr.ch/unifr/documents/308631
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