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EITI stakeholders increasingly face a "so what" challenge: after 20 years of disclosure, where is evidence of impact?
Akompani Ltd is a London consultancy specialising in development cooperation and extractives governance, led by Managing Director Eva Kirch. In 2025 Akompani was commissioned by the German Federal Ministry for Economic Cooperation and Development (BMZ), through GIZ, to answer a question the sector has debated for two decades: does the Extractive Industries Transparency Initiative (EITI) actually improve investment climate and anti-corruption outcomes?
The study, written by Akompani Founder Eva Kirch and Luke Balleny (now: Manager, Energy Minerals and Circularity at World Resources Institute Polsky Center for the Energy Transition), combined desk research and international stakeholder interviews as well as interviews with stakeholders in the DRC and Zambia with a global quantitative analysis. To visualise the quantitative research, Akompani engaged Cambianz’s expertise in data visualisation.
Akompani needed a quantitative backbone for the study, and the underlying data resisted simple answers. Foreign Direct Investment (FDI) inflows, the Corruption Perceptions Index, the World Bank’s Worldwide Governance Indicators, Fraser Mining Survey and Moody's credit ratings all sit on different scales, with patchy coverage across countries and years.
The honest finding was always going to be nuanced. EITI membership correlates with significant gains in some countries, and weak or no effects in others. That kind of result is difficult to surface in a 200-row regression table: the audiences, including ministries, donors, the EITI International Secretariat and international financial institutions, needed to be able to pull complex insights from the data at a glance.
Expert in analyzing and summarising key information from complex data sets, Cambianz built visuals from supplied model data as a country-year panel spanning almost a quarter-century from 2000 to 2023.
The analysis ran linear regressions of EITI membership against FDI and anti-corruption indicators for each country, clustered by country, with results expressed as coefficient, statistical significance and model fit.
The visual layer was developed next for a selection of country case studies, utilising Power BI data modelling and visualization software. Every indicator was normalised to a common 0 to 1 scale so FDI flows, governance scores and credit ratings could sit on a single timeline. Each country received one interactive view: membership period marked, indicators overlaid, and the regression result stated in plain language beneath the chart. A reader selecting the DRC sees FDI climb during membership with the fit (0.87) and significance (p below 0.01) attached: both evidence and its caveats travel together.
