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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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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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