Measuring Inequality Using Geospatial Data

Jul 19, 2026

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The main challenge in studying inequality is limited data availability, especially in developing countries. This study develops light-based geospatial income inequality (LGII) for 234 countries and territories from 1992 to 2013 using satellite night-lights and gridded population data. It introduces methods using varying data aggregation levels and calibrates the lights–prosperity relationship to align with income-based inequality measures. LGII shows strong correlation with cross-country income inequality and, within countries, relates to energy efficiency and population data quality. Applications connect the measure to health economics and international finance.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages21

Reading Time53 min

AccessFree to download

Introduction

Global inequality and data constraints

Proposed geospatial measure (LGII)

Comparison with traditional income-based inequality measures

Methodological contribution

Nighttime light proxy and calibration

Data aggregation and observation expansion

Applications

Health economics

International finance

Pub lic Disclosure Authorized Pub lic Disclosure Authorized The World Bank Economic Review, 37(4), 2023, 549–569 [https://doi.org10.1093/wber/lhad026](https://doi.org10.1093/wber/lhad026) Article Measuring Inequality Using Geospatial Data Jaqueson K. Galimberti, Stefan Pichler , and Regina Pleninger Abstract The main challenge in studying inequality is limited data availability, which is particularly problematic in developing countries. This study constructs a measure of light-based geospatial income inequality (LGII) 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. The new LGII measure is significantly correlated with cross-country variation in income inequality. Within countries, the light-based inequality measure is also correlated with measures of energy efficiency and the quality of population data. Two applications of the data are provided in the fields of health economics and international finance. The results show that light-and income-based inequality measures lead to similar results, but the geospatial data offer a significant expansion of the number of observations. JEL classification: D63, E01, I14, O11, O47, O57 Keywords: nighttime lights, inequality, gridded population 1. Introduction The past decades have witnessed a significant increase in economic inequality with important social and economic consequences (see, e.g., Piketty and Saez 2014; Lakner and Milanovic 2016) . As a result, the Jaqueson K. Galimberti is an economist in the Economic Research and Development Impact Department at the Asian Development Bank, Manila, Philippines; a research fellow at the KOF Swiss Economic Institute, ETH Zurich; and a research associate at the Centre for Applied Macroeconomic Analysis, Australian National University; his email address is [jgalimberti@adb.org](jgalimberti@adb.org). Stefan Pichler is an associate professor at the University of Groningen; a research fellow at Aletta Jacobs School of Public Health; a research fellow at the KOF Swiss Economic Institute, ETH Zurich; and a research fellow at IZA Bonn; his email address is [s.pichler@rug.nl. Regina Pleninger](s.pichler@rug.nl. Regina Pleninger) is an economist at the World Bank, Washington DC, USA; and a research fellow at the KOF Swiss Economic Institute, ETH Zurich; her email address is [rpleninger@worldbank.org. The](rpleninger@worldbank.org. The) authors thank the editor and anonymous referees as well as Richard Bluhm, Bruno Caprettini, Florian Eckert, Vera Eichenauer, Harry Garretsen, Martin Karlsson, Melanie Krause, Philip Vermeulen, and Nicolas Ziebarth for helpful comments and suggestions. Moreover, the authors thank participants at the World Inequality Conference in Paris in 2021, DENS 2020 in St. Gallen, YSEM 2021 in Zurich, and the research seminars at the Competent in Competition and Health center (CINCH) in Essen, at Auckland University of Technology, at the University of Auckland, at the University of Hamburg, and at the University of Groningen. The subnational borders data used in this paper were accessed during a visit to the Center for International Earth Science Information Network (CIESIN), at Columbia University. The authors thank Kytt MacManus and Greg Yetman for their hospitality. The authors also thank the NASA Socioeconomic Data and Applications Center (SEDAC) for the support with the distribution of the data set generated by this project, which can be accessed at [https://doi.org/10.7927/kd8b-2376](https://doi.org/10.7927/kd8b-2376) . This project received funds from the MTEC Foundation Grant, for which the authors gratefully acknowledge the support. The views expressed in this paper are those of the authors, and do not necessarily represent the views

FAQs

What is the main data challenge addressed when studying inequality?

Studying inequality is constrained by limited and inconsistent data availability worldwide, which is especially problematic in developing countries.

How does LGII measure income inequality in this study?

LGII uses satellite nighttime light emissions as a proxy for economic prosperity, combined with geolocated gridded population data to construct light-per-capita based Gini coefficients.

What evidence shows LGII aligns with traditional income inequality measures?

LGII is significantly correlated with cross-country variation in income inequality, and within countries it correlates with energy efficiency and the quality of population data.

Jul 19, 2026

Uploaded by Ivy