Measuring Inequality Using Geospatial Data

Jaqueson K. Galimberti, Stefan Pichler, Regina Pleninger

Research output: Contribution to journalArticleAcademicpeer-review

1 Citation (Scopus)
20 Downloads (Pure)

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.

Original languageEnglish
Pages (from-to)549-569
Number of pages21
JournalWorld Bank Economic Review
Volume37
Issue number4
DOIs
Publication statusPublished - Nov-2023

Keywords

  • gridded population
  • inequality
  • nighttime lights

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