Using night light emissions for the prediction of local wealth

Abstract

Nighttime illumination can serve as a proxy for economic variables in particular in developing countries, where data are often not available or of poor quality. Existing research has demonstrated this for coarse levels of analytical resolution, such as countries, administrative units or large grid cells. In this article, we conduct the first fine-grained analysis of night lights and wealth in developing countries. The use of large-scale, geo-referenced data from the Demographic and Health Surveys allows us to cover 39 less developed, mostly non-democratic countries with a total sample of more than 34,000 observations at the level of villages or neighborhoods. We show that light emissions are highly accurate predictors of economic wealth estimates even with simple statistical models, both when predicting new locations in a known country and when generating predictions for previously unobserved countries.

Measuring the size and growth of cities using nighttime light

“…However, this technique cannot fix the existent spatial correlation that blurring causes among contiguous light pixels, and that is increasing with luminosity. This issue is addressed by Abrahams et al (2018), who develop a de-blurring procedure 16 High resolution luminosity satellite data has been used as a proxy for economic activity across countries, at the sub-national level and for very small areas (Henderson et al 2011, Bleakley & Lin 2012, Michalopoulos & Papaioannou 2013, Martinez 2018, Lowe 2014, Storeygard 2012, Weidmann & Schutte 2016, Pinkovskiy 2013. The literature on night lights as a proxy for development is summarized by Donaldson & Storeygard (2016).…”

“…In fact, we compare the distribution of urban sprawl, urban population and population density across world regions and document several robust stylized facts (see section 3). In particular, while the distribution of urban population in cities larger than 100,000 people is relatively similar across regions, regardless of their 4 Nighttime light has been shown to be a reliable proxy for economic activity both nationally and in geographically small areas (Henderson et al 2011, Bleakley & Lin 2012, Michalopoulos & Papaioannou 2013, Lowe 2014, Storeygard 2012, Weidmann & Schutte 2016, Pinkovskiy 2013, Goldblatt et al 2018). This implies that it can be used to identify geographically integrated economic markets, thus linking our practical and conceptual definitions.…”

Rebels, Revenue and Redistribution: The Political Geography of Post-Conflict Power-Sharing in Africa

“…As the availability of electricity reflects economic development more broadly, night lights capture a wide range of economic outcomes that can result from preferential political treatment in post-conflict states (Henderson, Storeygard and Weil 2012;Weidmann and Schutte 2017). The first outcome of resource redistribution captured by night lights is electricity itself.…”

“…To compare this effect size to other variables, and to account for the potentially misleading predictive accuracy of the R 2 in our models, we perform an out-of-sample prediction exercise using 10-fold cross-validation. To assess the relative importance of our power-sharing dummy, we include common correlates of night light emissions in this predictive model (Henderson, Storeygard and Weil 2012;Weidmann and Schutte 2017). 14 In contrast to the explanatory approach employed so far, the goal of this exercise is not to isolate the effect of representation in power-sharing from potential confounders.…”

Introduction

“…It is more important than ever to get a good handle on missing-data problems, since it is hard to obtain a forecast for a unit for which we lack crucial data. An innovation to these issues proposed in this special issue is to use forecasting techniques to improve data with estimated values, as Weidmann & Schutte (2017) do when they use light emission to predict economic wealth.…”