The analysis of big datasets to find evidence of corruption – for example, the method developed by Mihály Fazekas to identify “red flags” of corruption risks in procurement contract data—requires statistical skills and software, both of which are in short supply in many parts of the developing world, such as sub-Saharan Africa.
Yet some ambitious recent initiatives are trying to address this problem. Lately I’ve had the privilege to be involved in one such initiative, led by Oxford mathematician Balázs Szendrői, that helps empower a group of young African mathematicians to analyse “big data” on public corruption.
The first step in this project was to develop software; this may seem trivial, but many cash-strapped African universities simply don’t have the resources to purchase the latest statistical software packages. The African Maths Initiative (AMI), a Kenyan NGO that works to create a stronger mathematical community and culture of mathematics across Africa, has helped to solve this problem by developing a new open-source program, R-Instat (which builds on the popular but difficult-to-learn statistics package R), funded through crowd-sourcing. Still in development, it is on track for launch in July this year. AMI has also helped develop a menu on R-Instat that can be used specifically for analysing procurement data and identifying corruption risk indicators.