Skip to main content

Advertisement

SpringerLink
Log in

An Objective Corruption Risk Index Using Public Procurement Data

  • Published:
European Journal on Criminal Policy and Research Aims and scope Submit manuscript

Abstract

In order to address the lack of reliable indicators of corruption, this article develops a composite indicator of high-level institutionalised corruption through a novel ‘Big Data’ approach. Using publicly available electronic public procurement records in Hungary, we identify “red flags” in the public procurement process and link them to restricted competition and recurrent contract award to the same company. We use this method to create a corruption indicator at contract level that can be aggregated to the level of individual organisations, sectors, regions and countries. Because electronic public procurement data is available in virtually all developed countries from about the mid-2000s, this method can generate a corruption index based on objective data that is consistent over time and across countries. We demonstrate the validity of the corruption risk index by showing that firms with higher corruption risk score had relatively higher profitability, higher ratio of contract value to initial estimated price, greater likelihood of politicians managing or owning them and greater likelihood of registration in tax havens, than firms with lower scores on the index. In the conclusion we discuss the uses of this data for academic research, investigative journalists, civil society groups and small and medium business.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

Notes

  1. Other types of grand corruption can be found in privatisation, altering regulation, large scale smuggling operations, etc. Public procurement is arguably one of the most important types as for example it amounts to roughly one third of public spending in OECD countries.

  2. It would be extremely difficult to use this method in a large cross-national framework, because it requires detailed knowledge of the cost of constructing public works across regions within a country (Golden and Picci 2005: 43). It is also restricted to construction, so could not be used to measure corruption in other sectors. Moreover, it is not appropriate for time-series data, as it “is not a measure of the flow of corrupt transactions.” (Ibid: 43).

  3. While modifying contract conditions does not belong to the set of company selection techniques, it can be part of an arsenal supporting the selection of the ‘right’ company. For example, the pre-selected company wins in a competitive process by promising low price and high quality knowing that later contract modifications will allow it to earn the agreed corruption rent.

  4. See: http://www.kozbeszerzes.hu/nid/KE (in Hungarian)

  5. For a gentle introduction see: http://en.wikipedia.org/wiki/Web_crawler

  6. Further robustness checks were done excluding issuers which awarded few contracts. Regression results confirm the robustness of models on samples with issuers awarding at least 5, 10 and 50 contracts. Regression outputs can be obtained from the authors.

  7. CPV = Common procurement vocabulary. For more info see: http://simap.europa.eu/codes-and-nomenclatures/codes-cpv/codes-cpv_en.htm

  8. NUTS = Nomenclature of territorial units for statistics. For more info see: http://epp.eurostat.ec.europa.eu/portal/page/portal/nuts_nomenclature/introduction

  9. A potential criticism against excluding such markets is that they may be the most corrupt markets biasing results. However, taking the full period of 2009–2012 means that there was a change of national government and many changes in local and regional administrations. Hence, the probability that the same market remained captured by the same 1 or 2 firms is very low implying that the observed low number of unique winners reflects market specificity rather than corruption. In addition, models run on the full sample revealed qualitatively identical cut-points and coefficents.

  10. Log contract values are used instead of actual contract values because the contract value distribution is highly skewed with a few large contracts distorting results.

  11. For details see:? http://ec.europa.eu/internal_market/publicprocurement/docs/explan-notes/classic-dir-framework_en.pdf

  12. Standard deviation of character lengths from the population mean is 3435 for the whole 2009–2012 period. So, eligibility criteria 2639 characters above its market average is about three quarters standard deviation difference.

  13. Abuse of weekends is possible as legally required time periods are defined in calendar days so the effective time companies would have for bid preparation can further be decreased by including weekends and national holidays in the submission period.

  14. Restricted sample results are not reported here. Regression outputs can be obtained from the authors.

  15. Calculating CRI for court decisions which established corruption in public procurement could serve as a more robust upper bound for the CRI scale.

  16. This difference remains unchanged when taking into account the number of managers with different names in the database suggesting that the relationship is not an artefact of matching politicians and businessman based on name only.

References

  • Amaral, M., Saussier, S., & Yvrande-Billon, A. (2009). Auction procedures and competition in public services: the case of urban public transport in France and London. Utilities Policy, 17, 166–175.

    Article  Google Scholar 

  • Andersson, S., & Heywood, P. M. (2009). The politics of perception: use and abuse of transparency International’s approach to measuring corruption. Political Studies, 57, 746–767.

    Article  Google Scholar 

  • Arndt, C., & Oman, C. (2006). Uses and abuses of governance indicators. Paris: OECD.

    Google Scholar 

  • Auriol, E., Flochel, T., & Straub, S. (2011). Public Procurement and Rent-Seeking: The Case of Paraguay (No. 11–224). TSE Working Papers: 11–224, Toulouse: Toulouse School of Economics (TSE).

  • Borgatti, S. P., Everett, M. G., & Johnson, J. C. (2013). Analyzing social networks. London: Sage.

  • Charron, N., Dijkstra, L., & Lapuente, V. (2010). Mapping Quality of Government in the European Union: A Study of National and Sub-National Variation (No. 2010:22). Gothenburg.

  • Charron, N., Dahlström, C., Fazekas, M., Lapuente, V. (2015). Carriers, connections, and corruption risks in Europe. QoG Working Paper Series: 2015:6, University of Gothenburg: Quality of Government Institute, Gothenburg.

  • Coviello, D., & Gagliarducci, S. (2010). Building Political Collusion: Evidence from Procurement Auctions (No. 4939). IZA DP No. 4939, Bonn: Institute for the Study of Labor (IZA).

  • European Commission (2011). Establishing an EU Anti-corruption reporting mechanism for periodic assessment (“EU Anti-corruption Report”). Brussels.

  • Fazekas, M., & Tóth, I. J. (2012a). Hibák, javítások és előzetes eredmények - magyarországi közbeszerzések 2010–2011. Budapest: Corruption Research Centre.

    Google Scholar 

  • Fazekas, M., & Tóth, I. J. (2012b). Public procurement, corruption and State Capacity in Hungary – objective measures and new insights. Budapest: Corruption Research Centre.

    Google Scholar 

  • Fazekas, M., Tóth, I. J., & King, L. P. (2013). Corruption manual for beginners: Inventory of elementary “corruption techniques” in public procurement using the case of Hungary. Working Paper: GTI-WP/2013:01. Government Transparency Institute, Budapest.

  • Fazekas, M., Chvalkovská, J., Skuhrovec, J., Tóth, I. J., & King, L. P. (2014). Are EU funds a corruption risk? The impact of EU funds on grand corruption in Central and Eastern Europe. In A. Mungiu-Pippidi (Ed.), The anticorruption frontline. The ANTICORRP Project, vol. 2. (pp. 68–89). Berlin: Budrich.

  • Golden, M. A., & Picci, L. (2005). Proposal for a new measure of corruption, illustrated with Italian data. Economics & Politics, 17(1), 37–75. doi:10.1111/j.1468-0343.2005.00146.x.

    Article  Google Scholar 

  • Goldman, E., Rocholl, J., & So, J. (2013). Politically connected boards of directors and the allocation of procurement contracts. Review of Finance, 17(5), 1617–1648. doi:10.1093/rof/rfs039.

    Article  Google Scholar 

  • Good, P. I. (2006). Resampling methods a practical guide to data analysis (3rd ed.). Berlin: Birkhauser.

    Google Scholar 

  • Gounev, P., & Bezlov, T. (2010). Examining the links between organised crime and corruption. Sofia: Center for the Study of Democracy.

    Google Scholar 

  • Grodeland, A. B. (2005). Bulgaria, Czech Republic, Romania and Slovenia: The use of contacts and informal networks in public procurement. In OECD (Ed.), Fighting corruption and promoting integrity in public procurement (pp. 59–76). Paris: OECD.

    Google Scholar 

  • Heggstad, K. K., & Froystad, M. (2011). The basics of integrity in procurement. Bergen, Norway: U4 Anti-Corruption Resource Centre Chr. Michelsen Institute (CMI).

  • Hyytinen, A., Lundberg, S., & Toivanen, O. (2008). Politics and Procurement: Evidence from Cleaning Contracts (No. 233). HECER Discussion paper No. 233.

  • Ishii, R. (2009). Favor exchange in collusion: empirical study of repeated procurement auctions in Japan. International Journal of Industrial Organization, 27(2), 137–144.

    Article  Google Scholar 

  • Kaufmann, D., Mastruzzi, M., & Kraay, A. (2010). The worldwide governance indicators. Methodology and analytical issues (No. 5430). Washington, DC.

  • Kenny, C., & Musatova, M. (2010). “Red Flags of Corruption” in World Bank Projects: An Analysis of Infrastructure Contracts (No. Policy Research Working Paper 5243) (p. Policy Research Working Paper 5243, World Bank, Washington DC). Washington, DC.

  • Kurtz, M. J., & Schrank, A. (2007a). Growth and governance: a defense. Journal of Politics, 69(2), 563–569.

    Article  Google Scholar 

  • Kurtz, M. J., & Schrank, A. (2007b). Growth and governance: models, measures, and mechanisms. The Journal of Politics, 69(2), 538–554.

    Article  Google Scholar 

  • Lambsdorff, J. G. (2006). Measuring corruption – the validity and precision of subjective indicators (CPI). In C. Sampford, A. Shacklock, C. Connors, & F. Galtun (Eds.), Measuring Corruption (pp. 81–100). Aldershot: Ashgate.

    Google Scholar 

  • Morozov, I., & Podkolzina, E. (2013). Collusion detection in procurement auctions (No. WP BRP 25/EC/2013). Moscow.

  • Mungiu-Pippidi, A. (2006). Corruption: diagnosis and treatment. Journal of Democracy, 17(3), 86–99.

    Article  Google Scholar 

  • Mungiu-Pippidi, A. (Ed.). (2011). Contextual choices in fighting corruption: Lessons learned. Oslo: Norwegian Agency for Development Cooperation.

    Google Scholar 

  • North, D. C., Wallis, J. J., & Weingast, B. R. (2009). Violence and social orders. A conceptual framework for interpreting recorded human history. Cambridge: Cambridge University Press.

    Book  Google Scholar 

  • OECD. (2009). Towards a sound integrity framework: instruments, processes, structures and conditions for implementation. Paris: OECD.

    Google Scholar 

  • Olken, B. A. (2007). Monitoring corruption: evidence from a field experiment in Indonesia. Journal of Political Economy, 115(2), 200–249. doi:10.1086/517935.

    Article  Google Scholar 

  • Olken, B. A., & Pande, R. (2012). Corruption in developing countries. Annual Review of Economics, 4(1):479-509.

  • Padhi, S. S., & Mohapatra, P. K. (2011). Detection of collusion in government procurement auctions. Journal of Purchasing and Supply Management, 17(4), 207–221.

    Article  Google Scholar 

  • Pricewaterhouse Coopers. (2013). Identifying and reducing corruption in public procurement in the EU. Brussels: PricewaterhouseCoopers and Ecorys.

    Google Scholar 

  • Radelet, S. (2002). Qualifying for the millennium challenge account. Washington DC: Center for Global Development.

  • Radelet, S. (2003). Bush and foreign aid. Foreign Affairs, 82(5), 104–117.

    Article  Google Scholar 

  • Rose, R., & Peiffer, C. (2012). Paying bribes to get public services: A global guide to concepts and survey measures (No. SPP 494). Glasgow: Centre for the Study of Public Policy.

    Google Scholar 

  • Rothstein, B., & Teorell, J. (2008). What is quality of government? A theory of impartial government institutions. Governance, 21(2), 165–190.

    Article  Google Scholar 

  • Soreide, T. (2002). Corruption in public procurement. Causes, consequences and cures. Bergen, Norway.

  • Tax Justice Network. (2013). Financial secrecy index 2013. Chesham: Methodology.

    Google Scholar 

  • Transparency International. (2012a). Corruption perceptions index 2012. Berlin: Transparency International.

    Google Scholar 

  • Transparency International. (2012b). NIS assessment toolkit. Berlin: Transparency International.

    Google Scholar 

Download references

Acknowledgments

The authors would like to express their gratitude for two EU funded projects at the Budapest Corvinus University (TAMOP 4.2.2.B and ANTICORRP (Grant agreement no: 290529)) even though they relied extensively on their voluntary contributions for realising this project. They would also like to express special thanks to colleagues at the Corruption Research Center Budapest working on the Hungarian public procurement database (MakAB), especially Kinga Csizmás, Ágnes Czibik, Zoltán Kelemen and Tamás Uhrin. Furthermore, we would like to thank the colleagues at the University of Cambridge, Hungarian Economic Association, and U4 Anti-Corruption Resource Center for their insightful comments on earlier drafts of this paper.

Author information

Authors and Affiliations

  1. Department of Sociology, University of Cambridge, Free School Lane, Cambridge, CB23RQ, UK

    Mihály Fazekas & Lawrence Peter King

  2. Government Transparency Institute, Budapest, Hungary

    Mihály Fazekas

  3. Institute of Economics, Hungarian Academy of Sciences, Budapest, Hungary

    István János Tóth

Authors
  1. Mihály Fazekas
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. István János Tóth
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Lawrence Peter King
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mihály Fazekas.

Electronic Supplementary Material

Below is the link to the electronic supplementary material.

ESM 1

(DOC 501 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Fazekas, M., Tóth, I.J. & King, L.P. An Objective Corruption Risk Index Using Public Procurement Data. Eur J Crim Policy Res 22, 369–397 (2016). https://doi.org/10.1007/s10610-016-9308-z

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10610-016-9308-z

Keywords

Search

Navigation