Using the Moran’s I to detect bid rigging in Brazilian procurement auctions
Author
Abstract
Suggested Citation
DOI: 10.1007/s00168-020-01018-x
Download full text from publisher
As the access to this document is restricted, you may want to search for a different version of it.
References listed on IDEAS
- Abrantes-Metz, Rosa M. & Froeb, Luke M. & Geweke, John & Taylor, Christopher T., 2006. "A variance screen for collusion," International Journal of Industrial Organization, Elsevier, vol. 24(3), pages 467-486, May.
- Marcel Boyer & Rachidi Kotchoni, 2015.
"How Much Do Cartel Overcharge?,"
Review of Industrial Organization, Springer;The Industrial Organization Society, vol. 47(2), pages 119-153, September.
- Boyer, Marcel & Kotchoni, Rachidi, 2014. "How Much Do Cartels Overcharge?," TSE Working Papers 14-462, Toulouse School of Economics (TSE), revised Jul 2015.
- Marcel Boyer & Rachidi Kotchoni, 2015. "How Much Do Cartels Overcharge," Post-Print hal-01386061, HAL.
- Ishii, Rieko, 2009. "Favor exchange in collusion: Empirical study of repeated procurement auctions in Japan," International Journal of Industrial Organization, Elsevier, vol. 27(2), pages 137-144, March.
- Aryal, Gaurab & Gabrielli, Maria F., 2013.
"Testing for collusion in asymmetric first-price auctions,"
International Journal of Industrial Organization, Elsevier, vol. 31(1), pages 26-35.
- Gaurab Aryal & Maria F. Gabrielli, 2011. "Testing for Collusion in Asymmetric First-Price Auctions," ANU Working Papers in Economics and Econometrics 2011-564, Australian National University, College of Business and Economics, School of Economics.
- Patrick Bajari & Lixin Ye, 2003.
"Deciding Between Competition and Collusion,"
The Review of Economics and Statistics, MIT Press, vol. 85(4), pages 971-989, November.
- Patrick Bajari & Lixin Ye, 2001. "Deciding Between Competition and Collusion," Working Papers 01008, Stanford University, Department of Economics.
- David Imhof & Yavuz Karagök & Samuel Rutz, 2018. "Screening For Bid Rigging—Does It Work?," Journal of Competition Law and Economics, Oxford University Press, vol. 14(2), pages 235-261.
- Imhof, David, 2017. "Simple Statistical Screens to Detect Bid Rigging," FSES Working Papers 484, Faculty of Economics and Social Sciences, University of Freiburg/Fribourg Switzerland.
- Erutku, Can, 2012. "Testing post-cartel pricing during litigation," Economics Letters, Elsevier, vol. 116(3), pages 339-342.
- Marcel Boyer & Rachidi Kotchoni, 2015. "How Much Do Cartel Overcharge? (The "Working Paper" Version)," CIRANO Working Papers 2015s-37, CIRANO.
Citations
Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
Cited by:
- David Imhof & Hannes Wallimann, 2021. "Detecting bid-rigging coalitions in different countries and auction formats," Papers 2105.00337, arXiv.org.
- Brown, David P. & Eckert, Andrew & Silveira, Douglas, 2023. "Screening for collusion in wholesale electricity markets: A literature review," Utilities Policy, Elsevier, vol. 85(C).
Most related items
These are the items that most often cite the same works as this one and are cited by the same works as this one.- Huber, Martin & Imhof, David, 2019.
"Machine learning with screens for detecting bid-rigging cartels,"
International Journal of Industrial Organization, Elsevier, vol. 65(C), pages 277-301.
- Huber, Martin & Imhof, David, 2018. "Machine Learning with Screens for Detecting Bid-Rigging Cartels," FSES Working Papers 494, Faculty of Economics and Social Sciences, University of Freiburg/Fribourg Switzerland.
- Clark, Robert & Coviello, Decio & de Leverano, Adriano, 2020.
"Complementary bidding and the collusive arrangement: Evidence from an antitrust investigation,"
ZEW Discussion Papers
20-052, ZEW - Leibniz Centre for European Economic Research.
- Robert Clark & Decio Coviello & Adriano De Leverano, 2020. "Complementary bidding and the collusive arrangement: Evidence from an antitrust investigation," Working Paper 1446, Economics Department, Queen's University.
- Brown, David P. & Eckert, Andrew & Silveira, Douglas, 2023. "Screening for collusion in wholesale electricity markets: A literature review," Utilities Policy, Elsevier, vol. 85(C).
- Johannes Wachs & J'anos Kert'esz, 2019. "A network approach to cartel detection in public auction markets," Papers 1906.08667, arXiv.org.
- Granlund, David & Rudholm, Niklas, 2023. "Calculating the probability of collusion based on observed price patterns," Umeå Economic Studies 1014, Umeå University, Department of Economics, revised 13 Oct 2023.
- David Imhof & Yavuz Karagök & SAMUEL RUTZ, 2017. "Screening for Bid-rigging. Does it Work?," Working Papers 2017-09, CRESE.
- Imhof, David & Wallimann, Hannes, 2021. "Detecting bid-rigging coalitions in different countries and auction formats," International Review of Law and Economics, Elsevier, vol. 68(C).
- Huber, Martin & Imhof, David, 2023. "Flagging cartel participants with deep learning based on convolutional neural networks," International Journal of Industrial Organization, Elsevier, vol. 89(C).
- Hannes Wallimann & David Imhof & Martin Huber, 2023.
"A Machine Learning Approach for Flagging Incomplete Bid-Rigging Cartels,"
Computational Economics, Springer;Society for Computational Economics, vol. 62(4), pages 1669-1720, December.
- Wallimann, Hannes & Imhof, David & Huber, Martin, 2020. "A Machine Learning Approach for Flagging Incomplete Bid-rigging Cartels," FSES Working Papers 513, Faculty of Economics and Social Sciences, University of Freiburg/Fribourg Switzerland.
- Hannes Wallimann & David Imhof & Martin Huber, 2020. "A Machine Learning Approach for Flagging Incomplete Bid-rigging Cartels," Papers 2004.05629, arXiv.org.
- Garcia Pires, Armando J. & Skjeret, Frode, 2023. "Screening for partial collusion in retail electricity markets," Energy Economics, Elsevier, vol. 117(C).
- Imhof, David & Karagök, Yavuz & Rutz, Samuel, 2016. "Screening for bid-rigging - does it work?," FSES Working Papers 468, Faculty of Economics and Social Sciences, University of Freiburg/Fribourg Switzerland.
- Silveira, Douglas & de Moraes, Lucas B. & Fiuza, Eduardo P.S. & Cajueiro, Daniel O., 2023. "Who are you? Cartel detection using unlabeled data," International Journal of Industrial Organization, Elsevier, vol. 88(C).
- Bedri Kamil Onur Tas, 2023. "Bunching below thresholds to manipulate public procurement," Empirical Economics, Springer, vol. 64(1), pages 303-319, January.
- Mats A. Bergman & Johan Lundberg & Sofia Lundberg & Johan Y. Stake, 2020. "Interactions Across Firms and Bid Rigging," Review of Industrial Organization, Springer;The Industrial Organization Society, vol. 56(1), pages 107-130, February.
- Silveira, Douglas & Vasconcelos, Silvinha & Resende, Marcelo & Cajueiro, Daniel O., 2022.
"Won’t Get Fooled Again: A supervised machine learning approach for screening gasoline cartels,"
Energy Economics, Elsevier, vol. 105(C).
- Douglas Silveira & Silvinha Vasconcelos & Marcelo Resende & Daniel O. Cajueiro, 2021. "Won't Get Fooled Again: A Supervised Machine Learning Approach for Screening Gasoline Cartels," CESifo Working Paper Series 8835, CESifo.
- Imhof, David, 2017. "Simple Statistical Screens to Detect Bid Rigging," FSES Working Papers 484, Faculty of Economics and Social Sciences, University of Freiburg/Fribourg Switzerland.
- Brown, David P. & Eckert, Andrew & Silveira, Douglas, 2023. "Screening for Collusion in Wholesale Electricity Markets: A Review of the Literature," Working Papers 2023-7, University of Alberta, Department of Economics.
- Hannes Wallimann & Silvio Sticher, 2023. "On suspicious tracks: machine-learning based approaches to detect cartels in railway-infrastructure procurement," Papers 2304.11888, arXiv.org.
- Martin Huber & David Imhof & Rieko Ishii, 2022.
"Transnational machine learning with screens for flagging bid‐rigging cartels,"
Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(3), pages 1074-1114, July.
- Huber, Martin & Imhof, David, 2020. "Transnational machine learning with screens for flagging bid-rigging cartels," FSES Working Papers 519, Faculty of Economics and Social Sciences, University of Freiburg/Fribourg Switzerland.
- Bedri Kamil Onur Tas, 2024. "A machine learning approach to detect collusion in public procurement with limited information," Journal of Computational Social Science, Springer, vol. 7(2), pages 1913-1935, October.
More about this item
JEL classification:
- D44 - Microeconomics - - Market Structure, Pricing, and Design - - - Auctions
- H57 - Public Economics - - National Government Expenditures and Related Policies - - - Procurement
- L44 - Industrial Organization - - Antitrust Issues and Policies - - - Antitrust Policy and Public Enterprise, Nonprofit Institutions, and Professional Organizations
Statistics
Access and download statisticsCorrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:anresc:v:66:y:2021:i:2:d:10.1007_s00168-020-01018-x. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.