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Nighttime lights revisited : the use of nighttime lights data as a proxy for economic variables

Author

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  • Addison,Douglas M.
  • Stewart,Benjamin P.
Abstract
The growing availability of free or inexpensive satellite imagery has inspired many researchers to investigate the use of earth observation data for monitoring economic activity around the world. One of the most popular earth observation data sets is the so-called nighttime lights from the Defense Meteorological Satellite Program. Researchers have found positive correlations between nighttime lights and several economic variables. These correlations are based on data measured in levels, with a cross-section of observations within a single time period across countries or other geographic units. The findings suggest that nighttime lights could be used as a proxy for some economic variables, especially in areas or times where data are weak or unavailable. Yet, logic suggests that nighttime lights cannot serve as a good proxy for monitoring the within-in country growth rates all of these variables. Examples examined this paper include constant price gross domestic product, non-agricultural gross domestic product, manufacturing value added, and capital stocks, as well as electricity consumption, total population, and urban population. The study finds that the Defense Meteorological Satellite Program data are quite noisy and therefore the resulting growth elasticities of Defense Meteorological Satellite Program nighttime lights with respect to most of these socioeconomic variables are low, unstable over time, and generate little explanatory power. The one exception for which Defense Meteorological Satellite Program nighttime lights could serve as a proxy is electricity consumption, measured in 10-year intervals. It is hoped that improved data from the recently launched Suomi National Polar-Orbiting Partnership satellite will help expand or improve these outcomes. Testing this should be an important next step.

Suggested Citation

  • Addison,Douglas M. & Stewart,Benjamin P., 2015. "Nighttime lights revisited : the use of nighttime lights data as a proxy for economic variables," Policy Research Working Paper Series 7496, The World Bank.
    • Handle: RePEc:wbk:wbrwps:7496
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    References listed on IDEAS

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    Cited by:

    1. Suchita Srinivasan, 2023. "Social Policies and Adaptation to Extreme Weather: Evidence from South Africa," CER-ETH Economics working paper series 23/381, CER-ETH - Center of Economic Research (CER-ETH) at ETH Zurich.
    2. Heger, Martin Philipp & Neumayer, Eric, 2019. "The impact of the Indian Ocean tsunami on Aceh’s long-term economic growth," Journal of Development Economics, Elsevier, vol. 141(C).
    3. Soma Sarkar, 2021. "Rapid assessment of cyclone damage using NPP-VIIRS DNB and ancillary data," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 106(1), pages 579-593, March.
    4. Jasiński, Tomasz, 2019. "Modeling electricity consumption using nighttime light images and artificial neural networks," Energy, Elsevier, vol. 179(C), pages 831-842.
    5. Paul Minard, 2020. "Institutions and China's comparative development," Papers 2001.02804, arXiv.org.
    6. José García-Montalvo & Marta Reynal-Querol & Juan Carlos Muñoz Mora, 2021. "Measuring Inequality from Above," Working Papers 1252, Barcelona School of Economics.
    7. 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).
    8. Adriana Kocornik-Mina & Thomas K. J. McDermott & Guy Michaels & Ferdinand Rauch, 2020. "Flooded Cities," American Economic Journal: Applied Economics, American Economic Association, vol. 12(2), pages 35-66, April.
    9. 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.
    10. John Gibson & Susan Olivia & Geua Boe-Gibson, 2019. "A Test of DMSP and VIIRS Night Lights Data for Estimating GDP and Spatial Inequality for Rural and Urban Areas," Working Papers in Economics 19/11, University of Waikato.
    11. Konno, Akio & Kato, Hironori & Takeuchi, Wataru & Kiguchi, Riku, 2021. "Global evidence on productivity effects of road infrastructure incorporating spatial spillover effects," Transport Policy, Elsevier, vol. 103(C), pages 167-182.
    12. Uzoma Iloanugo & Indranil Dutta & M. Emranul Haque, 2020. "Do Amnesty Policies Reduce Conflict? Evidence from the Niger-Delta Amnesty Program," Economics Discussion Paper Series 2011, Economics, The University of Manchester.
    13. Ademmer, Martin & Beckmann, Joscha & Bode, Eckhardt & Boysen-Hogrefe, Jens & Funke, Manuel & Hauber, Philipp & Heidland, Tobias & Hinz, Julian & Jannsen, Nils & Kooths, Stefan & Söder, Mareike & Stame, 2021. "Big Data in der makroökonomischen Analyse," Kieler Beiträge zur Wirtschaftspolitik 32, Kiel Institute for the World Economy (IfW Kiel).
    14. Stein, Merlin, 2021. "Re-evaluating RCTs with nightlights - An example from biometric smartcards in India," University of Tübingen Working Papers in Business and Economics 152, University of Tuebingen, Faculty of Economics and Social Sciences, School of Business and Economics.
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    Keywords

    Economic Theory&Research; Demographics; Population Policies; Population&Development; Industrial Economics; Population Sciences; Economic Growth;
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