Measuring Inequality using Geo-Spatial Data

Stefan Pichler¹ (with Jaqueson K. Galimberti¹ and Regina Pleininger¹)

1 ETH Zurich

The main limitation in the study of income inequality is data availability, especially in developing countries. Our aim is to construct a measure of income inequality for all countries world-wide using geo-spatial satellite data on nighttime lights emission as well as gridded population data. We match population with the night lights data and calculate a Gini-coefficients for all countries of the world from 1992-2013. We use this data in two applications: Measuring the relationship between out-of-pocket health expenditures with inequality, find a significant and positive relationship. Similarly, we find that epidemic disasters are associated with higher inequality.