  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.
