Application of Association Analysis to Detect Collusive Behaviour in Public Tenders

Authors

  • Łukasz Ziarko University of Łódź, Faculty of Economics and Sociology Department of Economic and Social Statistics, Łódź, Poland https://orcid.org/0000-0002-9726-5552

DOI:

https://doi.org/10.18778/0208-6018.351.01

Keywords:

association analysis, bid-rigging, cartel detection

Abstract

The purpose of this study is to examine the conditions required for the application of association analysis in the identification of the collusive behaviour of contractors in public tenders. It also focuses on determining the values of the confidence and lift measures that will describe the rules specific to a tender cartel. Worldwide research has aimed to develop effective and easy‑to‑use screening tests to identify cartel cases in public procurement. The recent research focuses on price (its distribution, variance, range) and classifiers allowing for detection of contractors whose mode of operation deviates from that commonly observed. This study follows the direction of current research. The main results of the study include the confirmation of the applicability of the method for the detection of colluding entities and the determination of the value of the confidence and lift measures specific to cartel cases. The policymakers, law enforcement agencies, contracting authorities and competitors of the cartels can use the proposed method to eliminate or at least to limit the scale of the problem. The main shortcoming of the application of the results is the inability to apply them to cartels pursuing an avoidance strategy. Further research will be conducted to develop a conceptual application of association analysis to all cartel strategies.

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References

Abrantes‑Metz R. M., Froeb L. M., Geweke J., Taylor C. T. (2006), A variance screen for collusion, “International Journal of Industrial Organization”, vol. 24, pp. 467–486, https://doi.org/10.1016/j.ijindorg.2005.10.003
Google Scholar

ACFE (2016), Report to the Nations on Occupational Fraud and Abuse 2016, Association of Certified Fraud Examiners, Austin.
Google Scholar

Agrawal R., Imieliński T., Swami A. (1993), Mining Association Rules Between Sets of Items in Large Databases, “ACM SIGMOD Record”, vol. 22, pp. 207–216, https://doi.org/10.1145/170036.170072
Google Scholar

Anysz H., Foremny A. (2019), Analityczne metody detekcji zmów przetargowych w budownictwie, XI Konferencja Stowarzyszenia Kosztorysantów Budowlanych. Koniunktura i jej wpływ na ceny robót budowlanych, Warsaw, 21–22 March 2019.
Google Scholar

Bajari P., Ye L. (2003), Deciding Between Competition and Collusion, “Review of Economics & Statistics”, vol. 85, pp. 971–989.
Google Scholar

Cabral L. M.B. (2000), Introduction to Industrial Organization, 1st ed., Massachusetts Institute of Technology, Cambridge.
Google Scholar

Church J., Ware R. (2000), Industrial Organization: A Strategic Approach, McGraw‑Hill Publishing Co., International Edition.
Google Scholar

Competition Protection Act (2007), Act of 16 February 2007 on Competition and Consumer Protection, Competition Law, 2007, Journal of Laws of 2007, No 50, item 331, with subsequent changes.
Google Scholar

Fazekas M., Tóth B. (2016), Assessing the potential for detecting collusion in Swedish public procurement, Konkurrensverket, Stockholm.
Google Scholar

Foremny A., Anysz H. (2018), The collusion detection in public procurements – selected methods applied for the road construction industry in Poland, MATEC Web of Conferences, vol. 219, https://doi.org/10.1051/matecconf/201821904002
Google Scholar

Gabaix X., Laibson D., Li D., Li H., Resnick S., Vries C. G. de (2016), The impact of competition on prices with numerous firms, “Journal of Economic Theory”, vol. 165, pp. 1–24, https://doi.org/10.1016/j.jet.2016.04.001
Google Scholar

Hand D., Mannila H., Smyth P. (2005), Eksploracja danych, Wydawnictwa Naukowo‑Techniczne, Warszawa.
Google Scholar

Harrington J. E. (2006), How Do Cartels Operate?, “Foundations and Trends in Microeconomics”, vol. 2, pp. 1–105, https://doi.org/10.1561/0700000021
Google Scholar

Harrington J. E. (2008), Detecting Cartels, [in:] P. Buccirossi (ed.), Handbook of Antitrust Economics. Massachusetts Institute of Technology, Cambridge, pp. 213–258.
Google Scholar

Huber M., Imhof D. (2018), Machine Learning with Screens for Detecting Bid‑Rigging Cartels, Working Papers SES University of Fribourg, vol. 494.
Google Scholar

Hyytinen A., Steen F., Toivanen O. (2018), Cartels uncovered, “American Economic Journal: Microeconomics”, vol. 10, issue 4, pp. 190–222, https://doi.org/10.1257/mic.20160326
Google Scholar

Imhof D. (2017), Simple Statistical Screens to Detect Bid Rigging, Working Papers SES, University of Fribourg, vol. 484.
Google Scholar

Imhof D., Karagök Y., Rutz S. (2016), Screening for bid rigging‑does it work?, Working Papers SES, University of Fribourg, vol. 468.
Google Scholar

McGowan L. (2010), The Antitrust Revolution in Europe. Exploring the European Commission’s Cartel Policy, Edward Elgar Publishing Limited, Cheltenham – Northampton.
Google Scholar

Mena‑Labarthe C. (2012), Mexican Experience in Screens for Bid‑Rigging, “CPI Antitrust Chronicle”, no. 1, pp. 1–8.
Google Scholar

Morozov I., Podkolzina E. (2013), Collusion Detection in Procurement Auctions, Basic Research Program.Workin Papers. Series: Economics (WP BRP 25/EC/2013), National Research University Higher School of Economics, Moscow, pp. 118–129.
Google Scholar

OCCP (2014), Polityka konkurencji na lata 2014–2018, UOKiK, Warszawa.
Google Scholar

OECD (2016), Fighting bid rigging in public procurement, Report on implementing the OECD Recommendation, Daf/Comp(2009)1/Final.
Google Scholar

Osowski S. (2013), Metody i narzędzia eksploracji danych, Wydawnictwo BTC, Legionowo.
Google Scholar

Porter R. H., Zona D. J. (1997), Ohio School Milk Markets. An Analysis of Bidding (No. 6037), NBER Working Paper Series, no. 6037, National Bureau of Economic Research, Cambridge, https://doi.org/10.1360/zd-2013-43-6-1064
Google Scholar

PPO (2019), The annual report of the President of the Public Procurement Office for 2019.
Google Scholar

Shaik S., Allen A. J., Edwards S., Harris J. (2012), Market Structure. Conduct Performance Hypothesis Revisited Using Stochastic Frontier Efficiency Analysis, “Journal of the Transportation Research Forum”, vol. 48, issue 3, pp. 3–18, http:/doi.org/10.22004/ag.econ.207141
Google Scholar

Tan P.‑N., Steinbach M., Karpatne A., Kumar V. (2019), Introduction to Data Mining, 2nd ed., Pearson, New York.
Google Scholar

The President of the OCCP (2007), Decision No RLU–30/2007 of 17.07.2007.
Google Scholar

The President of the OCCP (2013), Decision No RKT–46/2013 of 16.12.2013.
Google Scholar

The President of the OCCP (2017), Decision No RŁO–8/2017 of 28.12.2017.
Google Scholar

Tirole J. (1988), The Theory of Industrial Organization, Massachusetts Institute of Technology, Cambridge.
Google Scholar

Ziarko Ł. (2016), Eksploracja danych w identyfikacji praktyk antykonkurencyjnych, [in:] A. Fornalczyk, T. Skoczny (eds.), Economic of Competition Protection. Vertical Restraints, University of Warsaw, Warsaw, pp. 273–291.
Google Scholar

Published

2020-12-15

How to Cite

Ziarko, Łukasz. (2020). Application of Association Analysis to Detect Collusive Behaviour in Public Tenders. Acta Universitatis Lodziensis. Folia Litteraria Romanica, 6(351), 7-22. https://doi.org/10.18778/0208-6018.351.01

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Articles