Global inequality remotely sensed

Abstract

Economic inequality is notoriously difficult to quantify as reliable data on household incomes are missing for most of the world. Here, we show that a proxy for inequality based on remotely sensed nighttime light data may help fill this gap. Individual households cannot be remotely sensed. However, as households tend to segregate into richer and poorer neighborhoods, the correlation between light emission and economic thriving shown in earlier studies suggests that spatial variance of remotely sensed light per person might carry a signal of economic inequality. To test this hypothesis, we quantified Gini coefficients of the spatial variation in average nighttime light emitted per person. We found a significant relationship between the resulting light-based inequality indicator and existing estimates of net income inequality. This correlation between light-based Gini coefficients and traditional estimates exists not only across countries, but also on a smaller spatial scale comparing the 50 states within the United States. The remotely sensed character makes it possible to produce high-resolution global maps of estimated inequality. The inequality proxy is entirely independent from traditional estimates as it is based on observed light emission rather than self-reported household incomes. Both are imperfect estimates of true inequality. However, their independent nature implies that the light-based proxy could be used to constrain uncertainty in traditional estimates. More importantly, the light-based Gini maps may provide an estimate of inequality where previously no data were available at all.

Nighttime Lights and County-Level Economic Activity in the United States: 2001 to 2019

“…There are several ways to numerically contrast Figures 1 and 2. A salient approach is to use spatial inequality statistics, as ever more studies use DMSP data to estimate inequality [39,40,48]. The overstated lit area in Figure 1 from DMSP blurring [24] makes it harder to distinguish areas of concentrated activity from other areas.…”

Spatial Income Inequality, Convergence, and Regional Development in a Lower Middle‐Income Country: Satellite Evidence from the Philippines

“…Thus, higher intensity of light implies higher level of economic activity and income, which is increasing in both income per capita and the number of people (Henderson, Storeygard, and Weil 2012; Keola, Andersson, and Hall 2015; Gibson et al 2021). Mirza et al (2021), using a global sample of 57 countries, examine the relationship between light‐based inequality indicators and official estimates of inequality and confirm a robust positive relationship. Mveyange (2018), focusing on a sample of 48 African economies, finds a similar result that NTLs are a decent proxy for spatial income inequality.…”

“…The principal culprit behind this is the notorious difficulty of constructing measures of spatial inequality mainly because of the poor availability of subnational income accounts. Practical constraints include limited time coverage of regional economic data, incomparability at population subscales, dependence on misreported income surveys, and low‐quality data collection (Mirza et al 2021). In the Philippines, although subnational gross domestic product (GDP) data are available for its 17 administrative regions, income accounts at the province level, which are necessary to calculate inequality measures at the administrative region level, are unavailable.…”

“…In the case of Organisation for Economic Co‐operation and Development (OECD) countries, data collection is quite easy, since several regional datasets are available in the OECD Regional Statistics, EUROSTAT, and Cambridge Econometrics (CAMECON) among others (Lessmann 2014). As a result, the vast majority of studies in the literature on spatial inequality are limited to a particular country sample with a general bias toward upper middle‐ and high‐income economies (Mirza et al 2021; Lessmann and Seidel 2017).…”

Twice Is Nice: The Benefits of Two Ground Measures for Evaluating the Accuracy of Satellite-Based Sustainability Estimates

“…Efforts to use satellite data are particularly attractive because of the ubiquitous and rapidly expanding availability of imagery, much of it in the public domain. Recent work has demonstrated promising results for using satellites to estimate outcomes such as agricultural crop yields, village-level measures of wealth based on asset ownership, average household consumption, income inequality, and the prevalence of informal settlements [4][5][6].…”