Domestic revenues are crucial to eradicating extreme poverty, providing 10 times more finance than official development assistance (ODA) in Africa. But government revenue data is complicated, hard to access, and – to most people – meaningless. We set out to change this.
Domestic Revenues could contribute an additional 3-5% of GDP to fund social spending in Sub-Saharan Africa alone.
Despite the rhetoric that revenues will increase to plug the SDG finance gap the data shows the opposite: overall revenue relative to GDP has been in decline in Africa since 2012. This trend was driven by overall declines in revenue from resource rich countries, but most others saw increases in revenue, with surprisingly varied drivers.
Why aren’t these numbers in the headlines?
Well, first the data is old – most datasets are from at least a few years ago. Second, the data is patchy, with limited country coverage, Third, estimates seem to vary greatly between sources, so nobody is sure which to use.
Thankfully there is one dataset that is significantly more accurate and detailed than the rest: The United Nations University -WIDER/International Centre for Tax and Development (ICTD) dataset. But this meticulously compiled data (covering 40 indicators for 196 countries) comes at a cost, and even picking which bit of information to look at can be confusing.
We saw a need for a tool that could quickly decipher the UNU-WIDER dataset’s thousands of data points, allowing citizens and policy makers to quickly view, analyse and share the information that was important to them.
That’s where Tableau comes in…
Tableau reshapes and displays data in graphs, charts and maps, so that it is interactive, and users can pick indicators and country groupings of interest. It enabled us to create a simple view that shows top line tax, resource and total revenue figures, displaying their value and trends over time. It can also show the user a more detailed breakdown of the revenue and its different sources. Everything is interactive, different options can be picked and compared by the user, from the country or country grouping, to year or indicator.
Even for the revenue data savvy, displaying it isn’t straightforward. The data is limited to what countries provide, meaning that often there are large gaps, or sub-components don’t add up to aggregate amounts – making tax composition comparisons problematic. It meant that we needed analysis to focus on trends, limiting our options for creating visualisations that are accurate and useful.
There is a lot of work to be done. The dataset compiled by UNU-WIDER is displayed as a percentage of GDP, so while it allows a comparison of different countries and relative contributions, it gives no idea of the absolute financing amount of each component over time. We need to convert this into dollars and deflate them so that changes will be comparable over time. After this absolute figures can be compared with expenditures, or even financing needs. For instance, if oil revenues in Nigeria declined by US$10 million, this may be the equivalent of the entire budget allocation of Antiretroviral care in the country. In this case, the average citizen is able to use the tool to make far more compelling cases to advocate for increases in revenue and expenditure where it is needed most.
We hope this is useful – but welcome feedback, advice and opportunities for collaboration. Contact [email protected]