Detecting Red-Flag Bidding Patterns in Low-Bid Procurement for Highway Projects with Pattern Mining
Publication: Journal of Management in Engineering
Volume 40, Issue 1
Abstract
Competitive bidding is a popular technique State Highway Agencies use for selecting contractors for public construction work. It intends to ensure that construction projects are awarded at the lowest price. However, public owners may award contracts at high prices due to collusive bid arrangements among bidders. The current practices and state-of-the-art methods of bid collusion detection are ineffective because they greatly rely on engineers’ estimates, which can be inaccurate, and the similarity in the cost structure of the bids, which experienced collusive bidders can manipulate. This study contributes to the body of knowledge with a novel data-driven collusion red-flag detection framework (CRFD) that utilizes pattern mining techniques and statistical tests for detecting contractors with a red-flag pattern, which refers to a significant difference in the winning rates of a contractor with and without the co-occurrence of other particular contractors. A mechanism is also proposed to incorporate potential, influential factors into the CRFD to increase the detection power or examine possible collusion between different scenarios. The proposed method is expected to assist project owners in detecting early signs of bid collusion and eventually help them significantly enhance their award decision-making.
Get full access to this article
View all available purchase options and get full access to this article.
Data Availability Statement
Some or all data, models, or codes used during the study were provided by a third party. Direct requests for these materials may be made to the provider as indicated in the Acknowledgments.
Acknowledgments
The authors acknowledge that the South Dakota Department of Transportation provided the bid tabulation data for this study.
References
AASHTO. 2013. Practical guide to cost estimating. Washington, DC: AASHTO.
Ameyaw, E. E., E. Pärn, A. P. C. Chan, D.-G. Owusu-Manu, D. J. Edwards, and A. Darko. 2017. “Corrupt practices in the construction industry: Survey of Ghanaian experience.” J. Manage. Eng. 33 (6): 1–11. https://doi.org/10.1061/(ASCE)ME.1943-5479.0000555.
Anysz, H., A. Foremny, and J. Kulejewski. 2019. “Comparison of ANN classifier to the neuro-fuzzy system for collusion detection in the tender procedures of road construction sector.” IOP Conf. Ser.: Mater. Sci. Eng. 471 (11): 112064. https://doi.org/10.1088/1757-899X/471/11/112064.
Baader, G., and H. Krcmar. 2018. “Reducing false positives in fraud detection: Combining the red flag approach with process mining.” Int. J. Account. Inf. Syst. 31 (Dec): 1–16. https://doi.org/10.1016/j.accinf.2018.03.004.
Bajari, P., and L. Ye. 2003. “Deciding between competition and collusion.” Rev. Econ. Stat. 85 (4): 971–989. https://doi.org/10.1162/003465303772815871.
Ballesteros-Pérez, P., M. C. González-Cruz, A. Cañavate-Grimal, and E. Pellicer. 2013. “Detecting abnormal and collusive bids in capped tendering.” Autom. Constr. 31 (May): 215–229. https://doi.org/10.1016/j.autcon.2012.11.036.
Ballesteros-Pérez, P., M. Skitmore, R. Das, and M. L. del Campo-Hitschfeld. 2015. “Quick abnormal-bid-detection method for construction contract auctions.” J. Constr. Eng. Manage. 141 (7): 04015010. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000978.
Bel, G., and X. Fageda. 2011. “Big guys eat big cakes: Firm size and contracting in urban and rural areas.” Int. Public Manage. J. 14 (1): 4–26. https://doi.org/10.1080/10967494.2011.547746.
Bergman, M. A., J. Lundberg, S. Lundberg, and J. Y. Stake. 2020. “Interactions across firms and bid rigging.” Rev. Ind. Organ. 56 (1): 107–130. https://doi.org/10.1007/s11151-018-09676-0.
Chee, C. H., J. Jaafar, I. A. Aziz, M. H. Hasan, and W. Yeoh. 2019. “Algorithms for frequent itemset mining: A literature review.” In Artificial intelligent revision. Berlin: Springer.
Cheung, S. O., and L. Shen. 2017. “Concentration analysis to measure competition in megaprojects.” J. Manage. Eng. 33 (1): 1–11. https://doi.org/10.1061/(ASCE)ME.1943-5479.0000464.
Chotibhongs, R., and D. Arditi. 2012. “Detection of collusive behavior.” J. Constr. Eng. Manage. 138 (11): 1251–1258. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000542.
Clark, R., D. Coviello, J. F. Gauthier, and A. Shneyerovy. 2018. “Bid rig ging and entry deterrence in public procurement: Evidence from an investigation into collusion and corruption in Quebec.” J. Law Econ. Organ. 34 (3): 301–363. https://doi.org/10.1093/jleo/ewy011.
Dijkgraaf, E., and R. Gradus. 2007. “Collusion in the Dutch waste collection market.” Local Gov. Stud. 33 (4): 573–588. https://doi.org/10.1080/03003930701417601.
Dulaimi, M. F., and H. G. Shan. 2002. “The factors influencing bid mark-up decisions of large- and medium-size contractors in Singapore.” Construct. Manage. Econ. 20 (7): 601–610. https://doi.org/10.1080/01446190210159890.
Ebekozien, A. 2020. “Corrupt acts in the Nigerian construction industry: Is the ruling party fighting corruption?” J. Contemp. African Stud. 38 (3): 348–365. https://doi.org/10.1080/02589001.2020.1758304.
Erfani, A., K. Zhang, and Q. Cui. 2021. “TAB bid irregularity: Data-driven model and its application.” J. Manage. Eng. 37 (5): 04021055. https://doi.org/10.1061/(ASCE)ME.1943-5479.0000958.
European Commission. 2013. EU competition law, cartel legislation and other reference texts on 1 January 2013. Luxembourg: Publications Office of the European Union.
FHWA (Federal Highway Administration). 2017. Suggestions for the detection and prevention of construction contract bid rigging. Washington, DC: FHWA.
Fournier-Viger, P., J. Chun-Wei Lin, T. Truong-Chi, and R. Nkambou. 2019. “A survey of high utility itemset mining.” In High-utility pattern mining: Theory, algorithms and applications, 1–45. New York: Wiley.
Fournier-Viger, P., W. Gan, Y. Wu, M. Nouioua, W. Song, T. Truong, and H. Duong. 2022. Pattern mining: Current challenges and opportunities. Edited by U. K. Rage, V. Goyal, and P. K. Reddy, 34–49. Berlin: Springer.
García Rodríguez, M. J., V. Rodríguez-Montequín, P. Ballesteros-Pérez, P. E. D. Love, and R. Signor. 2022. “Collusion detection in public procurement auctions with machine learning algorithms.” Autom. Constr. 133 (Jan): 104047. https://doi.org/10.1016/j.autcon.2021.104047.
Gupta, S. 2001. “The effect of bid rigging on prices: A study of the highway construction industry.” Rev. Ind. Organ. 19 (Dec): 451–465. https://doi.org/10.1023/A:1012568509136.
Haghbina, S., M. Hoseinalipour, and A. Mohebifar. 2014. “Investigation of Iranian general contractors’ collaboration in social network of construction.” J. Appl. Environ. Biol. Sci. 4 (3): 17–24. https://doi.org/10.1023/A:1012568509136.
Harper, I., S. McCluskey, P. Anderson, and M. O’Bryan. 2015. Competition policy review. Canberra, Australia: Commonwealth of Australia.
Harrington, J. E., and D. Imhof. 2022. “Cartel screening and machine learning.” In Computational antitrust. Palo Alto, CA: Stanford Univ.
Horne, J., J. Venter, and H. Lochner. 2018. “The red flag system as the gatekeeper in tender fraud prevention and detection of misrepresentation.” Int. J. African Renaiss. Stud. 13 (1): 129–143. https://doi.org/10.1080/18186874.2018.1478655.
Huber, M., and D. Imhof. 2019. “Machine learning with screens for detecting bid-rigging cartels.” Int. J. Ind. Organ. 65 (Jun): 277–301. https://doi.org/10.1016/j.ijindorg.2019.04.002.
Imhof, D. 2019. “Detecting bid-rigging cartels with descriptive statistics.” J. Compet. Law Econ. 15 (4): 427–467. https://doi.org/10.1093/joclec/nhz019.
Imhof, D., Y. Karagök, and S. Rutz. 2018. “Screening for bid rigging-does it work?” J. Compet. Law Econ. 14 (2): 235–261. https://doi.org/10.1093/joclec/nhy006.
Imhof, D., and H. Wallimann. 2021. “Detecting bid-rigging coalitions in different countries and auction formats.” Int. Rev. Law Econ. 68 (Dec): 106016. https://doi.org/10.1016/j.irle.2021.106016.
ITD (Idaho Transportation Department). 2020. Construction cost estimating guide. Boise, ID: ITD.
Jaspers, J. D. 2000. “Business cartels and organised crime: Exclusive and inclusive systems of collusion.” Trends Organ. Crime 22 (4): 414–432. https://doi.org/10.1007/s12117-018-9350-y.
Joshua, E. S. N., K. A. Vardhan, N. T. Rao, and D. Bhattacharyya. 2023. “An enhanced K-means clustering algorithm to improve the accuracy of clustering using centroid identification based on compactness factor.” Smart Technol. in Data Sci. and Commun., Lect. Notes Networks and Systems 558 (Jan): 251–256. https://doi.org/10.1007/978-981-19-6880-8_26.
Kawai, K., J. Nakabayashi, J. Ortner, and S. Chassang. 2022. “Using bid rotation and incumbency to detect collusion: A regression discontinuity approach.” SSRN Electron. J. 90 (1): 376–403. https://doi.org/10.1093/restud/rdac013.
Kim, K. J., K. Kim, E. W. Kim, and T. H. T. Nguyen. 2021. “Estimating damages of bid-rigging in design-build contracts based on simulation model.” KSCE J. Civ. Eng. 25 (5): 1568–1577. https://doi.org/10.1007/s12205-021-1571-y.
Le, C., T. Le, H. D. Jeong, and E.-B. Lee. 2019. “Geographic information system–based framework for estimating and visualizing unit prices of highway work items.” J. Constr. Eng. Manage. 145 (8): 04019044. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001672.
Le, C., T. Nguyen, and T. Le. 2022. “Detecting red-flag bidding patterns in low-bid procurement for highway projects with pattern mining.” In Proc., 9th Int. Conf. on Construction Engineering and Project Management, 11–17. Seoul: Korea Institute of Construction Engineering and Management.
Le, C., K. J. Shrestha, H. D. Jeong, and I. Damnjanovic. 2021. “A sequential pattern mining driven framework for developing construction logic knowledge bases.” Autom. Constr. 121 (Jan): 103439. https://doi.org/10.1016/j.autcon.2020.103439.
Le, Y., M. Shan, A. P. C. Chan, and Y. Hu. 2014. “Overview of corruption research in construction.” J. Manage. Eng. 30 (4): 1–7. https://doi.org/10.1061/(ASCE)ME.1943-5479.0000300.
Lee, J.-S. 2022. “Simulating competitive bidding in construction collusive bidding cases.” J. Manage. Eng. 38 (5): 04022050. https://doi.org/10.1061/(ASCE)ME.1943-5479.0001081.
Li, M., M. Baek, and B. Ashuri. 2021. “Forecasting ratio of low bid to owner’s estimate for highway construction.” J. Constr. Eng. Manage. 147 (1): 04020157. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001970.
Locatelli, G., G. Mariani, T. Sainati, and M. Greco. 2017. “Corruption in public projects and megaprojects: There is an elephant in the room.” Int. J. Project Manage. 35 (3): 252–268. https://doi.org/10.1016/j.ijproman.2016.09.010.
Long, W., X. Wang, Y. Liang, and K. Ye. 2023. “Characteristics of collusive practices in bidding: Mixed methods study in China.” J. Constr. Eng. Manage. 149 (6): 1–12. https://doi.org/10.1061/JCEMD4.COENG-12938.
Luna, J. M., P. Fournier-Viger, and S. Ventura. 2019. “Frequent itemset mining: A 25 years review.” WIREs Data Min. Knowl. Discov. 9 (6): e1329. https://doi.org/10.1002/widm.1329.
Lyra, M. S., B. Damásio, F. L. Pinheiro, and F. Bacao. 2022. “Fraud, corruption, and collusion in public procurement activities, a systematic literature review on data-driven methods.” Appl. Network Sci. 7 (1): 83. https://doi.org/10.1007/s41109-022-00523-6.
Modrušan, N., K. Rabuzin, and L. Mršić. 2022. “Review of public procurement fraud detection techniques powered by emerging technologies.” Int. J. Adv. Comput. Sci. and Appli. 12 (2): 1–9. https://doi.org/10.14569/IJACSA.2021.0120272.
Morselli, C., and M. Ouellet. 2018. “Network similarity and collusion.” Soc. Networks 55 (Oct): 21–30. https://doi.org/10.1016/j.socnet.2018.04.002.
ODOT (Oregon Department of Transportation). 2020. Estimating manual. Eugene, OR: ODOT.
OECD (Organisation for Economic Co-Operation and Development). 2009. Guidelines for fighting bid rigging: Helping governments to obtain best value for money. Berlin: OECD.
Oke, A., C. Aigbavboa, and Z. Mangena. 2017. “Prevention of collusion for innovative construction.” Procedia Eng. 196 (Jan): 491–497. https://doi.org/10.1016/j.proeng.2017.07.229.
Oo, B.-L., D. Drew, and H.-P. Lo. 2008. “A comparison of contractors’ decision to bid behaviour according to different market environments.” Int. J. Project Manage. 26 (4): 439–447. https://doi.org/10.1016/j.ijproman.2007.06.001.
Padhi, S. S., and P. K. J. Mohapatra. 2011. “Detection of collusion in government procurement auctions.” J. Purchase Supply Manage. 17 (4): 207–221. https://doi.org/10.1016/j.pursup.2011.03.001.
PennDOT. 2018. Cost estimating manual. Harrisburg, PA: PennDOT.
Price, M. K. 2008. “Using the spatial distribution of bidders to detect collusion in the marketplace: Evidence from timber auctions.” J. Reg. Sci. 48 (2): 399–417. https://doi.org/10.1111/j.1467-9787.2008.00557.x.
Seeja, K. R., and M. Zareapoor. 2014. FraudMiner: A novel credit card fraud detection model based on frequent itemset mining. London: Hindawi Publishing Corporation.
Sherstyuk, K. 2002. “Collusion in private value ascending price auctions.” J. Econ. Behav. Organ. 48 (2): 177–195. https://doi.org/10.1016/S0167-2681(01)00215-3.
Signor, R., P. Ballesteros-Perez, and P. E. D. Love. 2021. “Collusion detection in infrastructure procurement: A modified order statistic method for uncapped auctions.” IEEE Trans. Eng. Manage. 70 (2): 464–477. https://doi.org/10.1109/TEM.2021.3049129.
Signor, R., P. E. D. Love, A. T. N. Belarmino, and O. Alfred Olatunji. 2020. “Detection of collusive tenders in infrastructure projects: Learning from operation car wash.” J. Constr. Eng. Manage. 146 (1): 1–10. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001737.
Tóth, B., M. Fazekas, Á. Czibik, and I. J. Tóth. 2015. Toolkit for detecting collusive bidding in public procurement with examples from Hungary. Budapest, Hungary: Corruption Research Center Budapest.
US Department of Justice. 2021. Price fixing, bid rigging, and market allocation schemes. Washington, DC: US Department of Justice.
US Justice Department. 2019. Announcement of the antitrust division’s procurement collusion strike force. Washington, DC: US Justice Department.
Wallimann, H., D. Imhof, and M. Huber. 2022. “A machine learning approach for flagging incomplete bid-rigging cartels.” In Computation economics. Berlin: Springer.
Wang, X., D. Arditi, and K. Ye. 2022. “Coupling effects of economic, industrial, and geographical factors on collusive bidding decisions.” J. Constr. Eng. Manage. 148 (7): 04022042. https://doi.org/10.1061/(ASCE)CO.1943-7862.0002291.
Wang, X., E. K. Owusu, and K. Ye. 2023. “Impacts of external environmental factors on the collusive team scale in bidding: The case of China.” J. Manage. Eng. 39 (4): 1–15. https://doi.org/10.1061/jmenea.meeng-5270.
Wang, X., K. Ye, M. Chen, and Z. Yao. 2021. “A conceptual framework for the inclusion of exogenous factors into collusive bidding price decisions.” J. Manage. Eng. 37 (6): 1–12. https://doi.org/10.1061/(ASCE)ME.1943-5479.0000981.
Yap, J. B. H., I. N. Chow, and K. Shavarebi. 2019. “Criticality of construction industry problems in developing countries: Analyzing Malaysian projects.” J. Manage. Eng. 35 (5): 04019020. https://doi.org/10.1061/(asce)me.1943-5479.0000709.
Yu, Y., I. Martek, M. R. Hosseini, and C. Chen. 2019. “Demographic variables of corruption in the Chinese construction industry: Association rule analysis of conviction records.” Sci. Eng. Ethics 25 (4): 1147–1165. https://doi.org/10.1007/s11948-018-0024-6.
Zhang, Z. 2014. “Too much covariates in a multivariable model may cause the problem of overfitting.” J. Thorac. Dis. 6 (9): 196–197. https://doi.org/10.3978/j.issn.2072-1439.2014.08.33.
Information & Authors
Information
Published In
Journal of Management in Engineering
Volume 40 • Issue 1 • January 2024
Copyright
© 2023 American Society of Civil Engineers.
History
Received: Feb 13, 2023
Accepted: Aug 21, 2023
Published online: Oct 26, 2023
Published in print: Jan 1, 2024
Discussion open until: Mar 26, 2024
ASCE Technical Topics:
- Awards and prizes
- Bids
- Business management
- Construction engineering
- Construction industry
- Construction management
- Contracts and subcontracts
- Detection methods
- Engineering fundamentals
- Engineering profession
- Infrastructure construction
- Methodology (by type)
- Owners
- Personnel (type)
- Personnel management
- Practice and Profession
- Pricing
- Professional development
- Project management
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.