Measuring Inequality using Geospatial Data - Jaqueson Galimberti; Stefan Pichler; Regina Pleninger (Auckland University of Technology (AUT), Faculty of Business, Economics and Law, 2020)
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 and territories from 1992 to 2013 using satellite data on nighttime light emissions as well as gridded population data. Key methodological innovations include the use of varying levels of data aggregation, and a parsimonious calibration of the lights-prosperity relationship to match traditional inequality measures based on income data. Indeed, we obtain a measure that is significantly correlated with cross-country variation in income inequality. Subsequently, 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 terms of cross-country correlations, but not for the dynamics of inequality measure can capture more enduring features of economic activity that are not directly captured by income.
Keywords: D63; E01; I14; O11; O47; O57; 330; Nighttime lights; inequality; gridded population