Estimating urban GDP growth using nighttime lights and machine learning techniques in data poor environments: The case of South Sudan

Patrick McSharry and Joseph Mawejje

Technological Forecasting and Social Change, 2024, vol. 203, issue C

Abstract: Estimating economic performance in data poor, fragile, and conflict environments with weak capacities for the generation of timely and quality official statistics is challenging. Drawing on recent advances in the use of remote sensing data to estimate economic activities, this paper identifies the observable variables that are correlated with nighttime lights in urban South Sudan. With a focus on the three major cities of Juba, Malakal and Wau, a machine learning aided regression model constructed using a backward stepwise selection approach demonstrates that population, carbon dioxide, private sector credit, as well as climate and conflict events, can explain much of the variability in the nighttime lights time series for Juba – the capital city. Nighttime lights in the next period can be accurately forecasted using these variables. After forecasting future nighttime lights, it is then possible to transform these into estimates of urban real GDP growth.

Keywords: Nighttime light; Machine learning; Remote Sensing; GDP forecasting; URBAN; South Sudan (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:tefoso:v:203:y:2024:i:c:s0040162524001951

DOI: 10.1016/j.techfore.2024.123399

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