Development workers constantly gnash their teeth over whether their work has any effect. But they’re faced with a fundamental problem: In undeveloped countries, accurate economic data can be impossible to come by. To solve that problem, a team of economists has developed a methodology to track GDP using the intensity of night-time lighting–data that can be readily gleaned from satellite images. The idea is that as countries become wealthier, they build up their roads and infrastructures, which translates directly into how brightly lit they are at night.
As the New Scientist reports:
By comparing 11 years of satellite data on night-time light intensity with GDP estimates, David Weil and colleagues at Brown University in Providence, Rhode Island, developed a method for estimating changesin GDP from light measurements alone. When applied to some far-flungplaces, the formula casts doubts on official figures. Their paper hasbeen submitted to The American Economic Review.
Inthe Democratic Republic of the Congo, for example, World Bank figuressuggest that GDP shrank by 2.6% between 1992 and 2003. Weil’sfindings point to a 2.4% increase over the same period. “Thatinclines me to think that the Congo’s problems are with its statistical information, not its economy,” says Weil.
The New Scientist also has a brilliant gallery of images, showing exactly how this lights-at-night method works–the U.S., for example, lights up like a Roman candle at night because it’s also the richest country on earth.
But there are a couple niggles which prevent night-time images from being a straight-up infographic detailing GDP. For one, light intensity and economic output aren’t cleanly linked; lights can get brighter as population grows. And also, it still remains to be seen whether the growth in light patterns from rich nations can really be extrapolated onto poor nations.
Nonetheless, it seems possible that light-intensity might become a new tool in the toolbelt of development economists–especially since satellite images are some of the finest-grained, most frequently updated data sets in the world.
Read more at New Scientist.