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An Objective Corruption Risk Index Using Public Procurement Data

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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.

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  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: (in Hungarian)

  5. For a gentle introduction see:

  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:

  8. NUTS = Nomenclature of territorial units for statistics. For more info see:

  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:?

  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.


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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.

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Correspondence to Mihály Fazekas.

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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).

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