PublicationsThe U4 Blog

U4 Brief

Proxy indicators for the corrupt misuse of corporations

The abuse of companies for corrupt purposes has reached the forefront of international anti-corruption efforts. However, we lack systematic evidence on which corporate characteristics are likely to signal corruption, and in which contexts. This can bias our understanding of corruption, making it overly focused on the public sector. Monitoring company age is a specific example of how we can validate indicators, tailored to context. We find company corruption risk indicators among three company characteristics: 1. Company registration, such as many companies on the same address 2. Financial information, such as extreme profitability, and 3. Ownership and management structures, such as hidden owners.

5 October 2017
Download PDF
Proxy indicators for the corrupt misuse of corporations

Main points

  • Government and donors can use already available company data and combine it with transactional data on procurement contracts, mining rights, or grants to track corruption risks at a high degree of detail and in real-time.
  • Government and donors can demand fuller and higher quality disclosure of data on these risk indicators from companies doing business with governments and donors.
  • Transparency requirements generate minimal additional administrative burden as companies typically compile and report such information annually.

Cite this publication

Fazekas, M.; Tóth, B.; (2017) Proxy indicators for the corrupt misuse of corporations. Bergen: U4 Anti-Corruption Resource Centre, Chr. Michelsen Institute (U4 Brief 2017:6)

Download PDF

About the authors

Mihály Fazekas

Mihály Fazekas is an associate professor at the Central European University, Department of Public Policy, with a focus on using Big Data methods to understand the quality of government globally. He is also the scientific director of an innovative think tank, the Government Transparency Institute (GTI). He has a PhD from the University of Cambridge, where he pioneered Big Data methods to measure and understand high-level corruption in Central and Eastern Europe.

Bence Tóth


All views in this text are the author(s)’, and may differ from the U4 partner agencies’ policies.

This work is licenced under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International licence (CC BY-NC-ND 4.0)