Making the case for disaggregated data: A look at Ethiopia
Cat Langdon takes a look at disaggregated data on Ethiopia, and examines how it can be used and improved to help us track the needs of the poorest people.
The 2030 Agenda for Sustainable Development lays out an ambitious vision of a future where no one lives in extreme poverty. At the heart of the Sustainable Development Goals is a commitment to ensure that ‘no one is left behind’ and that no goal is considered met unless met for all. But if we are to truly realise this ambition we need to move beyond existing statistics that track national averages and move towards more and better data disaggregated down to the level of individuals. In other words, we need to count people, not averages.
The P20 Initiative does this by focusing on the people who live in the poorest 20% of the population globally: the P20. The initiative tracks progress of people out of poverty using the best available data. It makes the case for greater investment in the production and use of disaggregated data so that policies and resources can be accurately targeted towards those who are currently being left behind.
The need for disaggregated data is clear. National data masks disparities at the subnational, community and household levels The new global map visualisation on our Development Data Hub illustrates the problem. If we take the example of Ethiopia, a voluntary national review country at this year’s UN High-Level Political Forum, we can see that 43% of the population are amongst the global P20, around 40.5 million individual people.
However, when we zoom in to the subnational level we can see the picture becomes much more varied. The Somali and Afar regions in the north and north-east of the country have the highest percentage of people in the global P20 at 80% and 71%, respectively. This is in stark contrast to predominately urban regions such as Dire Dawa and Harari where the percentage of those in the global P20 falls to 12% and 19%, respectively.
The picture becomes even more granular when we look at the survey level, where each dot on the map represents a cluster of surveyed households. In the Harari region we can see large disparities between survey clusters, with some reporting up to 72% of residents among the global P20, and some reporting 0%. What also becomes clear at the survey level is that the distribution of household survey clusters is uneven across the country. Survey clusters are much denser in urban areas, reflecting both higher population density and ease of access for survey enumerators. By contrast, there are no survey clusters in some of the more remote and sparsely populated areas of the country, and in the absence of data it is impossible to know if they contain households or individuals who are being left behind.
This global visualisation clearly illustrates the disparities that are masked if we only look at national averages as indicators of progress. Disaggregating the available data on poverty to individual regions within a country gives a more granular picture, but if we are serious about ensuring that no one – wherever they live, irrespective of their gender, age, disability, ethnicity, religion or sexual orientation – is left behind, we need data disaggregated down to the level of individual people.
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