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Cited by 73Corruption and Economic DevelopmentPublic Procurement and PolicyComplex Network Analysis Techniques

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Corruption risk in contracting markets: a network science perspective

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Wachs, Johannes, Fazekas, Mihály, Kertész, János·2021·International Journal of Data Science and Analytics
DOI 10.1007/s41060-019-00204-1
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Abstract

We use methods from network science to analyze corruption risk in a large administrative dataset of over 4 million public procurement contracts from European Union member states covering the years 2008–2016. By mapping procurement markets as bipartite networks of issuers and winners of contracts, we can visualize and describe the distribution of corruption risk. We study the structure of these networks in each member state, identify their cores, and find that highly centralized markets tend to have higher corruption risk. In all EU countries we analyze, corruption risk is significantly clustered. However, these risks are sometimes more prevalent in the core and sometimes in the periphery of the market, depending on the country. This suggests that the same level of corruption risk may have entirely different distributions. Our framework is both diagnostic and prescriptive: It roots out where corruption is likely to be prevalent in different markets and suggests that different anti-corruption policies are needed in different countries.

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Fig. 2 The 2008-2016 single-bidding rate of each country correlated with various perception-based measures of corruption, all measured in 2013. When correlations are negative, the perception-based measure
Fig. 2 The 2008-2016 single-bidding rate of each country correlated with various perception-based measures of corruption, all measured in 2013. When correlations are negative, the perception-based measureCC-BY · From the PDF
Fig. 3 The distribution of the number of contracts awarded and won, for issuers (in gray) and winners (in white), respectively, of procurement contracts in each country, aggregated from 2008 to 2016. The distribu-
Fig. 3 The distribution of the number of contracts awarded and won, for issuers (in gray) and winners (in white), respectively, of procurement contracts in each country, aggregated from 2008 to 2016. The distribu-CC-BY · From the PDF
Fig. 4 The weighted core of the Hungarian market from 2008 to 2016. Gray nodes denote issuers of contracts, while white nodes denote winners. Nodes are included in the core if they engage in many contracts with other highly active nodes, defined iteratively. Edges are colored red
Fig. 4 The weighted core of the Hungarian market from 2008 to 2016. Gray nodes denote issuers of contracts, while white nodes denote winners. Nodes are included in the core if they engage in many contracts with other highly active nodes, defined iteratively. Edges are colored redCC-BY · From the PDF
Fig. 5 Comparing the centralization of the procurement markets, measured as the share of all contracts which are between core issuers and winners, and corruption risk quantified by single bidding and the European Quality of Government Index (EQI), respectively. All measures
Fig. 5 Comparing the centralization of the procurement markets, measured as the share of all contracts which are between core issuers and winners, and corruption risk quantified by single bidding and the European Quality of Government Index (EQI), respectively. All measuresCC-BY · From the PDF
Fig. 6 Comparing the relative prevalence of single bidding in the core of EU procurement markets with their overall single-bidding rates and the relative core sizes, respectively. The dotted line represents the null
Fig. 6 Comparing the relative prevalence of single bidding in the core of EU procurement markets with their overall single-bidding rates and the relative core sizes, respectively. The dotted line represents the nullCC-BY · From the PDF
model: The expected prevalence of single bidding in the core when single bidding is randomized within sectors
model: The expected prevalence of single bidding in the core when single bidding is randomized within sectorsCC-BY · From the PDF
Fig. 7 The 2014 Hungarian procurement market. We plot the largest connected component of the network, filtering out nodes involved in less than three contracts for the sake of visualization. Nodes are buyers and suppliers of contracts, connected by an edge if they contract with one another. Edges are colored red if the single-bidding rate on the edge exceeds the average rate of single bidding that year. Single bidding is significantly overrepresented among the edges in the top left cluster
Fig. 7 The 2014 Hungarian procurement market. We plot the largest connected component of the network, filtering out nodes involved in less than three contracts for the sake of visualization. Nodes are buyers and suppliers of contracts, connected by an edge if they contract with one another. Edges are colored red if the single-bidding rate on the edge exceeds the average rate of single bidding that year. Single bidding is significantly overrepresented among the edges in the top left clusterCC-BY · From the PDF
Fig. 8 Clustering of single bidding in procurement market networks by country, averaged over 2008-2016. Higher values indicate higher variation of single-bidding rates across communities in the market relative to a shuffled null model, indicated by the baseline value of 1
Fig. 8 Clustering of single bidding in procurement market networks by country, averaged over 2008-2016. Higher values indicate higher variation of single-bidding rates across communities in the market relative to a shuffled null model, indicated by the baseline value of 1CC-BY · From the PDF
Fig. 9 The relative prevalence of corruption risk in the core of the market plotted against its tendency to cluster, plotted for countries with above average single-bidding rates in the EU, 2008-2016
Fig. 9 The relative prevalence of corruption risk in the core of the market plotted against its tendency to cluster, plotted for countries with above average single-bidding rates in the EU, 2008-2016CC-BY · From the PDF

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Metadata

Authors
Wachs, J., Fazekas, M., & Kertész, J.
Affiliations
  • RWTH Aachen University (DE)
  • Central European University - Budapest Campus (HU)
Corresponding author
Wachs, Johannes
ORCID
Venue
International Journal of Data Science and Analytics
Publisher
Springer Science and Business Media LLC
Published
2020-01-18
Volume
12
Issue
1
Pages
45-60
ISSN
  • 2364-415X (ISSN-L)
  • 2364-4168
DOI
10.1007/s41060-019-00204-1
OpenAlex
W2973493443
Cited by
73
References
1
Citations by year
2026: 12 · 2025: 10 · 2024: 8 · 2023: 14 · 2022: 14 · 2021: 12 · 2020: 3
FWCI
14.38
Type
journal article
Language
en
Open access
Licence
Locations
  • International Journal of Data Science and Analytics (publishedVersion, cc-by)
  • RePEc: Research Papers in Economics (submittedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • Open Research (Auckland University of Technology) (submittedVersion, cc-by)
  • RWTH Publications (RWTH Aachen) (submittedVersion, other-oa)
  • WU Research (submittedVersion)
  • arXiv (Cornell University)
  • RWTH Publications (RWTH Aachen)
Funders
Hungarian Scientific Research Fund (K129124)
Topics
  • Social Sciences › Sociology and Political Science › Corruption and Economic Development
  • Social Sciences › Business, Management and Accounting › Strategy and Management › Public Procurement and Policy
  • Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
Keywords
corruption risk, public procurement, bipartite graphs, network centrality, network clustering, market structure, anti-corruption policy, EU member states

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