Hannes Wallimann, David Imhof, Martin Huber
arXiv 12 Apr 2020 · Econometrics · publishedComputational Economics (2022) · 2 citations (OpenAlex)
arXiv:2004.05629 · PDF · DOI · OpenAlex · Extracted main text
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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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Imhof, D., Y. Karagoek, and S. Rutz, Screening for bid rigging, does… (2018) self | 0.976 | 29 | 5 | 93% |
| 2 | Huber, M., and D. Imhof, Machine learning with screens for detecting… (2019) self | 0.969 | 22 | 4 | 91% |
| 3 | Imhof, D., Detecting bid-rigging cartels with descriptive statistics… (2020) self | 0.956 | 8 | 4 | 88% |
| 4 | Abrantes-Metz, R. M., L. M. Froeb, J. F. Geweke, and C. T. Taylor, A… (2006) | 0.737 | 3 | 2 | 100% |
| 5 | OECD, Roundtable on ex officio cartel investigations and the use of… (2014) | 0.737 | 3 | 2 | 100% |
| 6 | Abrantes-Metz, R. M., M. Kraten, A. D. Metz, and G. Seow, Libor mani… (2012) | 0.644 | 2 | 2 | 100% |
| 7 | Breiman, L., Random forests, Machine Learning, 45(1), 5–32 (2001) | 0.644 | 2 | 2 | 100% |
| 8 | Esposito, F., and M. Ferrero, Variance screens for detecting collusi… (2006) | 0.644 | 2 | 2 | 100% |
| 9 | Jimenez, J. L., and J. Perdiguero, Does rigidity of pprice hide coll… (2012) | 0.644 | 2 | 2 | 100% |
| 10 | Bergman, M. A., J. Lundberg, S. Lundberg, and J. Y. Stake, Interacti… (2019) | 0.585 | 3 | 1 | 100% |
Showing the top 10 of 37 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Collusion Detection with Graph Neural Networks | 0.811 | 4 | 2 |