The documentation for this data set can be found in the following open access peer-reviewed publication:
Galimberti, J. K., S. Pichler, and R. Pleninger. 2023. Measuring inequality using geospatial data. The World Bank Economic Review, 37(4):549-569, https://doi.org/10.1093/wber/lhad026.
Notes on the data and their interpretation:
The Global Database of Light-based Geospatial Income Inequality (LGII) Measures, Version 1 data set contains Gini-coefficients of inequality for 234 countries and territories from 1992 to 2013. The measurement unit is the Gini-Coefficient (Range: 0-1), with higher values representing higher inequality. These measures are constructed using worldwide geospatial satellite data on nighttime lights emission as a proxy for economic prosperity, matched with varying sources of data on geo-located population counts. The nighttime lights data were supplied by the National Oceanic and Atmospheric Administration (NOAA), National Centers for Environmental Information (NCEI), Earth Observation Group (EOG), and Operational Linescan System (OLS) instruments. The population data used consisted of CIESIN's Gridded Population of the World (GPW) collection, and the Oak Ridge National Laboratory (ORNL) LandScan (LSC) data set. The nighttime lights and population data were combined to produce an array of geospatially-informed Gini-coefficients, which were then weighted to optimize their correlation with a benchmark - specifically, the Standardized World Income Inequality Database (SWIID), to generate a parsimonious composite inequality metric.
These data are generated using Operational Linescan System (OLS) instruments and specifically employ an unfiltered version (referred to as 'average visible') that captures even low-intensity emissions from less densely populated regions. A constant elasticity model, uniformly implemented at the pixel level, is employed to convert luminosity into economic output indicators. The elasticity parameter is empirically determined through an agnostic calibration approach, guided by extant measures and quality assessments of income inequality. Two primary sources furnish the population data integral to the calculations. The first is the GPWv4.11 data set, assembled using population census figures gathered from a multitude of organizations, including national statistical agencies, and is conjoined with spatially explicit administrative boundary data sets. The second is the LSC data set, which employs a multi-variable disaggregation technique to apportion census counts within administrative regions, informed by auxiliary data including land cover, transportation networks, topography, and high-resolution satellite imagery. The aggregation of these luminosity and population data sets produces an array of geospatially-informed Gini-coefficients. To generate a parsimonious composite inequality metric, the Gini values are subsequently weighted to optimize their correlation with a benchmark - specifically, the Standardized World Income Inequality Database (SWIID). The weighting factor, denoted as "lambda", is adjustable, enabling customization to achieve superior cross-country (coded as L100 in the data set) or within-country (coded as L000) congruence with income inequality metrics. Intermediate measures are also provided, with a recommended lambda calibration of 0.5 (coded as L050 in the data set).
The magnitudes of the Light-based Geospatial Income Inequality (LGII) Gini-coefficients are generally higher than Gini-coefficients based on income surveys – on average, about 0.3 points higher than the benchmark SWIID. This difference can be attributed to spatial aggregation and the use of night lights as a proxy for income.
The LGII Gini-coefficients are recommended for interpretation comparatively across countries. One of the merits of this data set is that it has light-based Gini-coefficients that are more highly correlated with income-based Gini-coefficients than the previous literature, especially in cross-country comparisons. It should be noted that even inequality measures based on income surveys are subject to uncertainties, mostly due to income mismeasurement. The calibration of the LGII Gini-coefficients takes into account a measure of uncertainty around the income inequality benchmark.
The time series presented in these data should be treated cautiously. The LGII measure does not capture time variation in the benchmark income inequality as well as it captures variations across countries. Inequality is a slowly moving phenomenon, so the time variation observed over the 1992-2013 period is relatively small compared to the variation across countries. Trends in inequality are normally studied across several decades of data, sometimes centuries, which is feasible only for a handful of countries. Additionally, the data sources used in this data set are not ideal for comparisons over time; both night lights and gridded population (especially LandScan) have issues here.
Finally, estimates for small geographies, such as small island states, need to be treated with caution. This is because the number of observations available for computation of the Gini-coefficients is small, especially in terms of census areas.
Recommended data set citation:
Galimberti, J. K.1,2,3, S. Pichler2,4,5,6, and R. Pleninger2,7. 2023. Global Database of Light-based Geospatial Income Inequality (LGII) Measures, Version 1. Palisades, New York: NASA Socioeconomic Data and Applications Center (SEDAC). https://doi.org/10.7927/kd8b-2376. Accessed DAY MONTH YEAR.
1 Asian Development Bank, Economic Research and Development Impact Department, Philippines
2 KOF Swiss Economic Institute, ETH Zurich, Switzerland
3 Centre for Applied Macroeconomic Analysis, Australian National University, Australia
4 University of Groningen, Netherlands
5 Aletta Jacobs School of Public Health, Netherlands
6 IZA Bonn, Germany
7 World Bank, USA
The authors wish to thank Professor Eric Edmonds, Editor at World Bank Economic Review, and anonymous referees; Richard Bluhm, Bruno Caprettini, Florian Eckert, Vera Eichenauer, Harry Garretsen, Martin Karlsson, Melanie Krause, Philip Vermeulen, and Nicolas Ziebarth for helpful comments and suggestions; 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; Kytt MacManus and Greg Yetman of the Center for International Earth Science Information Network (CIESIN) for their hospitality; and the NASA Socioeconomic Data and Applications Center (SEDAC) for their support with the distribution of the data set generated by this project. The subnational borders data used in this work were accessed during a visit to CIESIN. This project received funds from the Management, Technology and Economics (MTEC) Foundation Grant, for which the authors gratefully acknowledge their support. The views expressed in this paper are those of the authors, and do not necessarily represent the views of their corresponding institutional affiliations.
Hati kwa ajili ya seti hii ya data inaweza kupatikana katika ijayo wazi upatikanaji peer-kukaguliwa kuchapishwa: Galimberti, JK, S. Pichler, na R. Pleninger. 2023 - Mwaka wa Uhamiaji Kupima ukosefu wa usawa kwa kutumia data ya kijiografia. The World Bank Economic Review, 37 (4), 59-569, 10109 (w) "Kama ilivyoelezwa katika taarifa ya Global Database of Light-Based Geospatial Income Inequality (LGII) ya mwaka 2013, idadi ya watu wanaopatikana katika nchi hizo ni ""kubwa"" na ""kubwa"" kwa mujibu wa takwimu za Gini." Kiwango cha kipimo ni Gini-Coefficient (Range: 0-1), na maadili ya juu zaidi inawakilisha usawa wa juu. Vipimo hivi vimejengwa kwa kutumia data ya satelaiti ya kijiografia ulimwenguni kote juu ya uzalishaji wa taa za usiku kama wakala wa ustawi wa kiuchumi, ikilinganishwa na vyanzo tofauti vya data juu ya hesabu za idadi ya watu wa kijiografia. Takwimu za taa za usiku zilitolewa na Usimamizi wa Kitaifa wa Bahari na Anga (NOAA), Vituo vya Kitaifa vya Habari za Mazingira (NCEI), Kikundi cha Uchunguzi wa Dunia (EOG), na vifaa vya Operesheni ya Line Scanning System (OLS). Takwimu za idadi ya watu zilizotumiwa zilijumuisha mkusanyiko wa idadi ya watu wa ulimwengu wa CIESIN (GPW) na seti ya data ya Oak Ridge National Laboratory (ORNL) LandScan (LSC). "Lighting usiku na idadi ya watu data walikuwa pamoja kuzalisha safu ya geospatially taarifa Gini-coefficients, ambayo walikuwa kisha uzito ili kuboresha uhusiano wao na benchmark, hasa Standardized World Income Inequality Database (SWIID), kuzalisha ""parsimonious"" mchanganyiko usawa kipimo." Takwimu hizi hutengenezwa kwa kutumia vifaa vya Operational Linescan System (OLS) na hasa hutumia toleo lisilo na kichujio (linarejelea kama 'kiwango cha wastani kinachoonekana') ambalo huchukua hata uzalishaji wa nguvu ya chini kutoka mikoa isiyo na watu wengi. Mfano wa elasticity ya mara kwa mara, iliyotengenezwa kwa usawa katika ngazi ya pixel, hutumiwa kubadilisha mwangaza kuwa viashiria vya pato la kiuchumi. Elasticity parameter ni empirically kuamua kwa njia ya agnostic calibration mbinu, kuongozwa na vipimo zilizopo na tathmini ya ubora wa usawa wa mapato. Vyanzo viwili vya msingi hutoa data ya idadi ya watu muhimu kwa mahesabu. "Mfano wa kwanza ni ""GPW v4.11"" seti ya data, iliyokusanywa kwa kutumia takwimu za sensa za idadi ya watu zilizokusanywa kutoka kwa mashirika mengi, pamoja na mashirika ya takwimu ya kitaifa, na imeunganishwa na seti za data za mipaka ya kiutawala ya wazi." LSC ni seti ya data ya LSC, ambayo hutumia mbinu ya kutenganisha tofauti ya kutenganisha idadi ya sensa ndani ya mikoa ya kiutawala, inayoarifiwa na data ya msaidizi ikiwa ni pamoja na kifuniko cha ardhi, mitandao ya usafiri, topografia, na picha za satelaiti za azimio la juu. Kuunganishwa kwa seti hizi za data za mwangaza na idadi ya watu hutokeza safu ya coefficients za Gini zilizoelezwa kwa kijiografia. Kwa mfano, kwa mfano, kwa mfano, kwa mfano, kwa mfano, kwa mfano, kwa mfano, kwa mfano, kwa mfano, kwa mfano, kwa mfano, kwa mfano, kwa mfano, kwa mfano, kwa mfano, kwa mfano, kwa mfano, kwa mfano. "Kama ""lambda,"" ni adjustable, kuwezesha customization kufikia bora msalaba-nchi (coded kama L100 katika seti ya data) au ndani ya nchi (coded kama L000) congruence na kipimo cha mapato usawa." Kwa mfano, kwa mfano, kiwango cha lambda cha 0.5 (L50) kinapaswa kuonyeshwa. Kiwango cha Gini-coefficients ya Light-based Geospatial Income Inequality (LGII) ni juu zaidi kuliko Gini-coefficients kulingana na tafiti za mapato - kwa wastani, karibu pointi 0.3 juu kuliko SWIID. Tofauti hii inaweza kuhusishwa na mkusanyiko wa nafasi na matumizi ya taa za usiku kama proxy kwa mapato. LGII Gini-coefficients ni ilipendekeza kwa ajili ya tafsiri kulinganisha kati ya nchi. Moja ya sifa za seti hii ya data ni kwamba ina mwanga-msingi Gini-coefficients kwamba ni zaidi sana kuhusiana na mapato-msingi Gini-coefficients kuliko fasihi ya awali, hasa katika kulinganisha msalaba-nchi. Ni lazima ieleweke kwamba hata vipimo vya ukosefu wa usawa kulingana na tafiti za mapato ni chini ya kutokuwa na uhakika, hasa kutokana na kupima vibaya mapato. Kwa mfano, kiwango cha Gini cha LGII ni kiwango cha kiwango cha kiwango cha kiwango cha kiwango cha kiwango cha kiwango cha kiwango cha kiwango cha kiwango cha kiwango cha kiwango cha kiwango cha kiwango cha kiwango cha kiwango cha kiwango cha kiwango cha kiwango cha kiwango cha kiwango cha kiwango cha kiwango cha kiwango cha kiwango cha kiwango cha kiwango. Mfululizo wa wakati ulioonyeshwa katika data hizi unapaswa kutibiwa kwa tahadhari. Ligi ya LGBTI haijashughulikia mabadiliko ya muda katika usawa wa mapato ya msingi kama vile inavyochukua tofauti kati ya nchi. Ukosefu wa usawa ni jambo linaloendelea polepole, kwa hivyo tofauti ya muda iliyoonekana katika kipindi cha 1992-2013 ni ndogo ikilinganishwa na tofauti kati ya nchi. Mwelekeo wa ukosefu wa usawa kwa kawaida huchunguzwa katika data ya miongo kadhaa, wakati mwingine karne, ambayo inawezekana tu kwa nchi chache. Kwa kuongezea, vyanzo vya data vilivyotumiwa katika seti hii ya data sio bora kwa kulinganisha kwa muda; taa za usiku na idadi ya gridi (haswa LandScan) zina masuala hapa. Hatimaye, makadirio kwa ajili ya maeneo madogo ya kijiografia, kama vile majimbo madogo ya kisiwa, wanahitaji kutibiwa kwa tahadhari. Hii ni kwa sababu idadi ya uchunguzi inapatikana kwa ajili ya hesabu ya Gini-coefficients ni ndogo, hasa katika suala la maeneo ya sensa. Kwa mfano, kwa mfano, kwa mfano, kwa mfano, kwa mfano, kwa mfano, kwa mfano, kwa mfano, kwa mfano, kwa mfano, kwa mfano, kwa mfano, kwa mfano, kwa mfano, kwa mfano, kwa mfano, kwa mfano. 2023 - Mwaka wa Uhamiaji Global Database of Light-Based Geospatial Income Inequality (LGII) - Mfano wa Uwiano wa Mapato ya Ulimwenguni Palisades, New York: Kituo cha Takwimu na Maombi ya Kiuchumi ya NASA (SEDAC). "Kama ilivyoelezwa katika ukurasa wa 10 wa ""Mfumo wa Usalama wa Kimataifa"" (Usalama wa Kimataifa wa Kimataifa wa Kimataifa wa Kimataifa wa Kimataifa) na ""Kd8b2376"" (Usalama wa Kimataifa wa Kimataifa wa Kimataifa wa Kimataifa)." Upatikanaji wa siku, mwezi na mwaka "Mwanzoni mwa makala hii, tulizungumzia ""Mtazamo wa Utafiti wa Kiuchumi wa Utafiti wa Kiuchumi wa Utafiti wa Kiuchumi wa Utafiti wa Utafiti wa Utafiti wa Utafiti wa Utafiti wa Utafiti wa Utafiti wa Utafiti wa Utafiti wa Utafiti wa Utafiti wa Utafiti wa Utafiti wa Utafiti wa Utafiti wa Utafiti wa Utafiti wa Utafiti wa Utafiti wa Utafiti wa Utafiti wa Utafiti wa Utafiti wa Utafiti wa Utafiti wa Utafiti wa Utafiti wa Utafiti wa Utafiti wa Utafiti wa Utafiti wa Utafiti wa Utafiti wa Utafiti wa Utafiti wa Utafiti wa Utafiti wa Utafiti wa Utafiti wa Utafiti wa Utafiti wa Utafiti wa Utafiti wa Utafiti wa Utafiti wa Utafiti wa Utafiti wa Utafiti wa Utafiti wa Utafiti wa Utafiti wa Utafiti wa Utafiti wa Utafiti wa U Takwimu za mipaka ya chini ya kitaifa zilizotumiwa katika kazi hii zilipatikana wakati wa ziara ya CIESIN. Mradi huu ulipata fedha kutoka kwa Usimamizi, Teknolojia na Uchumi (MTEC) Foundation Grant, ambayo waandishi wanashukuru kwa msaada wao. Maoni yaliyoonyeshwa katika karatasi hii ni yale ya waandishi, na si lazima kuwakilisha maoni ya ushirika wao husika taasisi.