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Measuring Inequality using Geospatial Data

Jaqueson Galimberti (), Stefan Pichler () and Regina Pleninger ()
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Regina Pleninger: KOF Swiss Economic Institute, ETH Zurich

No 2020-07, Working Papers from Auckland University of Technology, Department of Economics

Abstract: 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: Nighttime lights; inequality; gridded population (search for similar items in EconPapers)
JEL-codes: D63 E01 I14 O11 O47 O57 (search for similar items in EconPapers)
Date: 2020-07
New Economics Papers: this item is included in nep-big, nep-geo and nep-mac
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Working Paper: Measuring Inequality using Geospatial Data (2021) Downloads
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