Measuring Inequality using Geospatial Data

The main challenge in studying economic inequality is limited data availability, which is particularly problematic in developing countries. We construct a measure of economic inequality for 234 countries/territories from 1992 to 2013 using satellite data on night lights and gridded population data. Key methodological innovations include the use of varying levels of data aggregation, and a calibration of the lights-prosperity relationship to match traditional inequality measures based on income data. We obtain a measure that is significantly correlated with cross-country variation in income inequality. We provide three applications of the data in the fields of health economics and international finance. %Our results show that light- and income-based inequality measures lead to similar results in cross-country regressions. Moreover, within countries, nighttime light emissions capture income, as well as, more enduring economic activity, such as housing and infrastructure investments. Our results show that light- and income-based inequality measures lead to similar results in terms of cross-country correlations, but not for the dynamics of inequality within countries. Namely, we find that the light-based inequality measure can capture more enduring features of economic activity that are not directly captured by income.