Illuminating the Nile: estimating subnational GDP in Egypt using nighttime lights and machine learning

Abstract

Egypt has been reporting several subnational socioeconomic indicators for more than three decades. However, utilizing these valuable datasets for monitoring long temporal trends in local development and inequalities has been hindered by the lack of a key indicator, the Gross Domestic Product (GDP), which was only reported subnationally starting 2013. This paper aims to address this data gap, by employing satellite-generated nighttime lights (NTL) and machine learning, to estimate subnational GDP in Egypt from 1992 to 2012. The paper relies on the harmonized global nighttime lights dataset that extends from 1992 to 2021, to carry out a twofold process. First, it validates NTL as a useful proxy for subnational economic activity in Egypt using econometric methods; then it estimates missing GDP using machine learning algorithms. Results show that the concentration of nearly the entire Egyptian population densely around the Nile River is challenging to nighttime lights accuracy; however, upon accounting for population density and agricultural activity, NTL could serve as a valuable proxy for subnational GDP in Egypt, and consequently a coherent GDP dataset is constructed since 1992.