Nighttime Lights and More

Nighttime lights satellite imagery of China. NOAA/NASA imagery.
Nighttime lights satellite imagery of China. NOAA/NASA.

Joshua Keating at Slate continues his good work popularizing interesting social science research. Last year, he said some nice things about my paper on cities, redistribution, and authoritarian regime survival, writing for Foreign Policy. Today, he writes about the value of satellite imagery of nighttime lights in determining the level of poverty in a country, based on research from Maxim Pinkovskiy and Xavier Sala-i-Martin. The debate is rather technical, but that does not detract from its importance. As the authors write:

How many people are poor, and how fast are they leaving poverty? The World Bank (Chen and Ravallion 2001, 2010) says that a quarter of the people in the developing world lived on an income of less than $1.25 a day (the threshold of absolute poverty defined by the lowest poverty lines in developing countries), down from about 40% in 1992. However, another body of the development literature (to which the authors of this column belong) argues that poverty is much lower (under 5% in 2010), and has declined much faster (Bhalla 2002; Sala-i-Martin 2002, 2004, 2006; Pinkovskiy and Sala-i-Martin 2009 and Sala-i-Martin and Pinkovskiy 2010).

Both literatures use inequality data from household income and consumption surveys to compute income distributions and poverty rates in developing countries, and to conjecture at the shape of the distribution of income for countries and years with no available data. However, from this point, the literature bifurcates. The World Bank anchors estimated country distributions of income to mean income from the surveys they use to compute inequality, whereas Bhalla, Sala-i-Martin and Pinkovskiy rescale the mean of each country distribution to be equal to the GDP per capita for that country in the national accounts. The choice of the mean of the distribution matters empirically because it turns out that, for many developing countries, the survey means are much smaller than the national accounts’ GDP per capita and grow much more slowly; for example, India grows by over 100% between 1994 and 2010 in the national accounts, but only 38% in the surveys. Such differences dwarf any difference in reasonable assumptions about the evolution of inequality in the developing world.

They argue that the nighttime lights data, which is generated via a completely independent process from either the GDP or household income surveys can help in adjudicating this debate. The satellite imagery points to their previous interpretation of low poverty and selecting a mean based on national accounts rather than survey data.

My concern with this analysis is the possibility that the GDP data are manipulated for political purposes. At the subnational level in China, GDP growth figures jump in ways that are not reflected in real economic shifts and that I have described as “juking the stats.” That paper presents some preliminary evidence of systematic differences in national accounts data across regime types as well–namely that dictatorships seem to report higher levels of GDP per capita than democracies that use the same amount of electricity. I’m currently drafting another paper, to be presented at this year’s MPSA conference, that will more seriously investigate this possibility.

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