tender boxes with red flags

banker boxes with red flags coming out of the top

Building on Phase 1 findings, this project digs deeper into the impact of changes in sociopolitical contexts by focusing on recipient-country regulatory frameworks and their interactions with donor regulations.

Project Summary

This project is a continuation of one in which an innovative methodology was developed for analysing big data from major aid agencies to calculate indicators of corruption risk in aid-funded procurement. This methodology was employed to explore how the type and structure of corruption risks were affected by the institutional control mechanisms employed by donors and the sociopolitical context in recipient countries. Results showed that both factors affect corruption risks and that they interact.

This project extends the large-scale database developed in the initial project to national procurement data from 10 countries. This new database covers developing countries, such as Chile, India, Indonesia, and Mexico, while also including developed economies as comparators, such as Spain and the United Kingdom. The dataset contains more than 6 million contracts, during the period from 2010 to 2018. For some countries where data is of poorer quality, the focus will be more on qualitative and impacts aspects, promoting the improvement of data infrastructure and empowering local users to collect and analyse data through a software tool. In this way, the potential for methodological innovation is maximised by continuing to work with advanced datasets while also promoting the benefits of such analysis as far as possible to other countries that would benefit from using these methods in the medium term.

Policy and Programming Implications

Public procurement accounts for around 50 percent of public spending in developing countries (World Bank 2015), and is the spending channel for a significant share of international development aid. Yet it is an area that is highly prone to corruption. Better understanding of how procurement procedures are manipulated and which interventions are most effective in curbing corruption in procurement is critical to combating corruption, as well as to saving public money, ensuring better provision of public goods, and building confidence in markets.

Research Questions

  • What is the impact of changes in recipient country interventions on corruption risks in aid, as well as national procurement spending?
  • Are there displacement effects of donor anti-corruption policies, i.e., does improving controls in one area prompt corrupt actors to shift corruption to other areas with weaker controls?


This project takes a two-tiered approach to extending the existing database:

  1. Full quantitative analysis for 10 countries with sufficient-quality data; and
  2. Qualitative and impacts approach where data is poorer quality.

A user-friendly interface for analysing the freely available data resulting from this project will be rolled out to a wide range of users, including in-country civil society activists, law enforcement officials, and anticorruption agencies.

ACE Impact


  • Corruption risks in public procurement are greater in countries where political regimes have shorter time horizons and weaker state capacity.
  • Partisan favouritism, whereby a political party in office abuses its power to allocate contracts to its favoured allies, flourishes in conditions where politicians are able to capture and dominate the institutions responsible for (a) implementing and (b) monitoring procurement processes.
  • Donor efforts to curb corruption by increasing oversight and transparency are effective in reducing some corruption risks in procurement [R5], and are particularly valuable in countries with low state capacity. This suggests that donors can control aid in weak state-capacity countries by substituting their own controls for the lack of local controls.
  • Reforms that focus only on specific parts of the process risk displacing corruption to other, less-regulated, parts of the procurement process, reducing their overall impact. It is therefore necessary to design reform in a comprehensive way, and to tailor indicators to political contexts to capture local nuances in corruption techniques [R6].


International Donors
The research team engaged heavily with donors throughout the research, e.g., presenting the methods and findings at internal workshops with the Foreign, Commonwealth, and Development Office (FCDO) (formerly the Department of International Development (DFID)), the World Bank, and the Inter-American Development Bank (IDB), to inform their practices and rules about disbursing aid and monitoring procurement and to provide evidence to support their advocacy work with national governments, so they could collect and publish better quality procurement data and introduce better monitoring systems.

The work fundamentally changed the approach of the World Bank, particularly its Solutions and Innovations in Procurement (SIP) team, which works to identify risks in Bank-financed contracts and to assist governments in improving their own risk management. The team also worked with some World Bank country offices to build awareness of the potential of big data analytics, eg, in August 2017, Sussex research team co-organised an event with the Bank’s country office in Dar es Salaam, the Tanzanian Public Procurement Regulatory Authority (PPRA), and the Tanzanian Prevention and Combatting of Corruption Board.

National Governments
The research team also worked directly with public procurement regulators in two countries, Jamaica and Uganda, to develop online tools to assist their work. These interactive portals allow the regulators (Integrity Commission of Jamaica (ICJ) and Ugandan Public Procurement and Disposal of Assets Authority (PPDA)) to analyse their own procurement data, helping them to spot systemic corruption risks as well as high-risk individual transactions, hence informing policy change and investigation of cases.

Civil Society
The team also worked with the African Maths Initiative (AMI), a Kenya-based non-governmental organisation that works on improving maths education in Africa, to incorporate the red flags methodology into their open-source, user-friendly software package, R-Instat (a front-end to R). The team also worked with AMI to organise workshops for maths students, civil society activists, and researchers in Tanzania (March 2017), Ghana (May 2018), and Uganda (October 2018).

Research Team Members

  • Liz Dávid-Barrett, Professor, University of Sussex; Director of the Centre for the Study of Corruption
  • Mihály Fazekas, Assistant Professor, Central European University; Scientific Director, Government Transparency Institute


Phase 1

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