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Shedding light on development: Leveraging the new nightlights data to measure economic progress

Author

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  • Prachi Jhamb
  • Susana Ferreira
  • Patrick Stephens
  • Mekala Sundaram
  • Jonathan Wilson

Abstract

Nightlights (NTL) have been widely used as a proxy for economic activity, despite known limitations in accuracy and comparability, particularly with outdated Defense Meteorological Satellite Program (DMSP) data. The emergence of newer and more precise Visible Infrared Imaging Radiometer Suite (VIIRS) data offers potential, yet challenges persist due to temporal and spatial disparities between the two datasets. Addressing this, we employ a novel harmonized NTL dataset (VIIRS + DMSP), which provides the longest and most consistent database available to date. We evaluate the association between newly available harmonized NTL data and various indicators of economic activity at the subnational level across 34 countries in sub-Saharan Africa from 2004 to 2019. Specifically, we analyze the accuracy of the new NTL data in predicting socio-economic outcomes obtained from two sources: 1) nationally representative surveys, i.e., the household Wealth Index published by Demographic and Health Surveys, and 2) indicators derived from administrative records such as the gridded Human Development Index and Gross Domestic Product per capita. Our findings suggest that even after controlling for population density, the harmonized NTL remain a strong predictor of the wealth index. However, while urban areas show a notable association between harmonized NTL and the wealth index, this relationship is less pronounced in rural areas. Furthermore, we observe that NTL can also significantly explain variations in both GDP per capita and HDI at subnational levels.

Suggested Citation

  • Prachi Jhamb & Susana Ferreira & Patrick Stephens & Mekala Sundaram & Jonathan Wilson, 2025. "Shedding light on development: Leveraging the new nightlights data to measure economic progress," PLOS ONE, Public Library of Science, vol. 20(2), pages 1-19, February.
    • Handle: RePEc:plo:pone00:0318482
    DOI: 10.1371/journal.pone.0318482
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    References listed on IDEAS

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    1. John Gibson & Susan Olivia & Geua Boe‐Gibson, 2020. "Night Lights In Economics: Sources And Uses," Journal of Economic Surveys, Wiley Blackwell, vol. 34(5), pages 955-980, December.
    2. Luke Sherman & Jonathan Proctor & Hannah Druckenmiller & Heriberto Tapia & Solomon M. Hsiang, 2023. "Global High-Resolution Estimates of the United Nations Human Development Index Using Satellite Imagery and Machine-learning," NBER Working Papers 31044, National Bureau of Economic Research, Inc.
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    4. Gibson, John & Olivia, Susan & Boe-Gibson, Geua & Li, Chao, 2021. "Which night lights data should we use in economics, and where?," Journal of Development Economics, Elsevier, vol. 149(C).
    5. Christopher Yeh & Anthony Perez & Anne Driscoll & George Azzari & Zhongyi Tang & David Lobell & Stefano Ermon & Marshall Burke, 2020. "Using publicly available satellite imagery and deep learning to understand economic well-being in Africa," Nature Communications, Nature, vol. 11(1), pages 1-11, December.
    6. J. Vernon Henderson & Adam Storeygard & David N. Weil, 2012. "Measuring Economic Growth from Outer Space," American Economic Review, American Economic Association, vol. 102(2), pages 994-1028, April.
    7. Anna Bruederle & Roland Hodler, 2018. "Nighttime lights as a proxy for human development at the local level," PLOS ONE, Public Library of Science, vol. 13(9), pages 1-22, September.
    8. Nejat Anbarci & Monica Escaleras & Charles A. Register, 2012. "From Cholera Outbreaks to Pandemics: The Role of Poverty and Inequality," The American Economist, Sage Publications, vol. 57(1), pages 21-31, May.
    9. Keola, Souknilanh & Andersson, Magnus & Hall, Ola, 2015. "Monitoring Economic Development from Space: Using Nighttime Light and Land Cover Data to Measure Economic Growth," World Development, Elsevier, vol. 66(C), pages 322-334.
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    2. Li, MingDa & Liverpool-Tasie, Lenis Saweda O. & Reardon, Thomas, 2026. "Unpacking interstate tomato trade in Nigeria: A Gravity-Based Structural PPML Approach," 100th Annual Conference, March 23-25, 2026, Wadham College, University of Oxford, Oxford, UK 397920, Agricultural Economics Society (AES).

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