Marshall Burke and Apoorva Lal, AtlasAI
Our last post looked at how national averages can be deceptive when trying to get an accurate picture of economic well-being. Using new satellite- and deep-learning-based estimates of asset wealth across the continent, we showed that about half of the variation in economic well-being across the continent in a given year is within countries rather than between them. This tells us that just making comparisons at the country level — which is what is currently done when things like the Sustainable Development Goals get tracked — could give a very incomplete picture of who is wealthy and who is poor.
What about when we look at how things are changing over time? Maybe there are a lot of differences in average wealth levels within countries, but over time everyone is being lifted up in some countries but not in others. If so, this might tell us that we can get away with country-level tracking when looking at growth rates.
Unfortunately, the data don’t really support this notion either. Using the same satellite imagery/ deep learning pipeline we’ve developed here at AtlasAI and described in our last post, we can look at changes in asset wealth over the last few decades at a very local level. As before, we are measuring asset wealth as an index, where higher values indicate households that own more assets. In our training data, which comes from household surveys done across the continent, households in the top quintile of this index almost all have electricity and own a radio, tv, fridge, and phone. In the bottom quintile, almost all do not have electricity and do not own any of these items.
The animation below shows what happens when we track asset wealth over time in Nigeria, from the country level down to the district level. At the country level, asset wealth was overall growing across the country, rising about 0.25 standard deviations between 2005 and 2017. (Consistent with the story from Nigerian GDP data, this growth was strong prior to 2015 but then basically slowed or stopped after that.) But again this country level average hides enormous variation at both the state and the district level.
We estimate that some districts were growing quite rapidly, for instance by more than 0.5 standard deviations over the 15 year period. Other districts actually showed declines in asset wealth over the period, by as much as 0.2 standard deviations. The map below shows this clearly. Conflict-stricken areas of northeast Nigeria, we estimate, showed meaningful declines in asset wealth over the period, while parts of the center and south of the country showed robust growth.
Interestingly, for Nigeria we find a modestly positive relationship between how wealthy a district was at the start of our dataset, and subsequent growth (left plot below). This is the opposite of what would be predicted by standard models of convergence — i.e. that poor places should catch up over time as resources and technology get transferred to these areas. For other countries in Africa — e.g. Ghana, right plot — things look more like what standard economic thought would predict: poor places have grown faster over the last few decades.
Looking across Africa, we again see huge variation in where asset wealth is growing and where it’s not. Known success stories (Ghana, Zambia, parts of East Africa) stick out, as do known problem areas that have struggled with conflict (southern Cote d’Ivoire, DRC, Somalia).
Overall, we calculate that 69% of variation in growth rates is within countries rather than between them. Put another way, country level factors (e.g. country level institutions) can explain less than a third of the overall variation in asset growth across Africa, as measured in our data. This number is even higher — 81% — if we focus on just sub-Saharan Africa and omit North African countries. Our data and results further emphasize the importance of subnational data for understanding which areas are on track to meet sustainable development goals, and which areas are not. Look for a public data release on our website in the coming months!