Prosperity and economic development matter a great deal in people’s everyday lives. But measuring prosperity across the globe in a scientific way can be tricky. Reliable statistics are particularly hard to come by in developing countries.
[interstitial_link url=”https://www.washingtonpost.com/blogs/monkey-cage/wp/2015/07/20/data-poverty-makes-it-harder-to-fix-real-poverty-thats-why-the-un-should-push-countries-to-gather-and-share-data/”]Data poverty makes it harder to fix real poverty. That’s why the UN should push countries to gather and share data[/interstitial_link]
This is where satellites come in handy — as a way to estimate wealth using night-light-emissions images. Previous research has shown that night lights correlate very well with the economic performance of large geographic entities such as countries. But does this approach also work when predicting wealth at a much finer resolution — looking at villages or households?
We think it does. Our new paper in the Journal of Peace Research is part of a special issue on “Forecasting in Peace Research.” For our analysis, we use detailed economic data on wealth at the household level from the “Demographic and Health Survey” (DHS) project funded by USAID. We match the geographic information of the surveyed household clusters with the corresponding night-light emissions from the DMSP-OLS project provided by the National Oceanographic and Atmospheric Administration.
Figure 1 gives an example: The underlying map of bright spots and dark regions shows the night-light emissions from the Pakistani city of Hyderabad. The numeric values are the wealth estimations from the DHS data: Higher values correspond to more wealth (on a 1-5 scale). The result is striking: Wealthy survey clusters are located in bright areas, whereas the poor survey cluster (1.82) emits almost no light at all.
We wanted to see if this pattern holds more generally, so we repeated this exercise for more than 34,000 survey clusters from 39 countries. Figure 2 below shows results for three sample countries: Albania, Cameroon and Liberia. Across these cases, the same pattern emerges: Poor households emit little to no light, and the wealthiest households (levels 4-5) are predominantly located in the brightest areas of the countries. At the same time, however, we see that the absolute amount of light emitted by the rich varies tremendously across countries: In Albania, it is almost three times the value of Liberia.
In our paper, we show how the problem of cross-national comparability (and other issues) can be resolved. Using simple statistical methodology, our results indicate that household-level wealth can be predicted from night-light emissions with high accuracy and resolution.
So why is this an important finding?
First, it shows that night-lights data can be exploited at much finer resolutions than before. Using these data to predict economic performance at the level of countries is good, but using them to predict household-level wealth is even better. This is particularly useful for environments where we don’t have alternative data to use in scientific analyses.
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Second, these results pave the way for more research at the subnational level. By combining them with spatial data on violence, state reach, health care and many other issues, it is possible to examine the economic preconditions and consequences of politics at the local level. For example, what areas benefit or suffer most from a new agricultural policy? Or does inequality matter for collective action at the village level? Now, we can find out.
Nils B. Weidmann is a professor of political science and head of the “Communication, Networks and Contention” research group at the University of Konstanz, Germany. Sebastian Schutte is a Marie Curie fellow at the Zukunftskolleg and the Department of Politics and Public Administration at the University of Konstanz, Germany. The Journal of Peace Research special issue on “Forecasting in Peace Research” is edited by Håvard Hegre, Nils Metternich, Håvard Mokleiv Nygård and Julian Wucherpfennig, and will be published in 2017.