IDEAS home Printed from https://ideas.repec.org/p/wai/econwp/20-03.html
   My bibliography  Save this paper

Three Facts About Night Lights Data

Author

Listed:

Abstract

The DMSP night lights data used in economics are old and not very accurate. Newer VIIRS night lights data have 60 percent higher predictive power for state-level GDP in the United States. Predictive accuracy is far higher in the cross section than for time series changes, either annually or quarterly. Night lights predict more weakly for agriculture than for manufacturing and other industries. These three facts suggest a need for caution in using night lights data, which may be unsuitable for many economics research purposes in many places.

Suggested Citation

  • John Gibson & Geua Boe-Gibson, 2020. "Three Facts About Night Lights Data," Working Papers in Economics 20/03, University of Waikato.
    • Handle: RePEc:wai:econwp:20/03
    as

    Download full text from publisher

    File URL: https://repec.its.waikato.ac.nz/wai/econwp/2003.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Bluhm, Richard & Krause, Melanie, 2022. "Top lights: Bright cities and their contribution to economic development," Journal of Development Economics, Elsevier, vol. 157(C).
    2. repec:lic:licosd:41920 is not listed on IDEAS
    3. 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).
    4. 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.
    5. 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.
    6. Ran Goldblatt & Kilian Heilmann & Yonatan Vaizman, 0. "Can Medium-Resolution Satellite Imagery Measure Economic Activity at Small Geographies? Evidence from Landsat in Vietnam," The World Bank Economic Review, World Bank, vol. 34(3), pages 635-653.
    7. Goldblatt,Ran Philip & Heilmann,Kilian Tobias & Vaizman,Yonatan, 2019. "Can Medium-Resolution Satellite Imagery Measure Economic Activity at Small Geographies ? Evidence from Landsat in Vietnam," Policy Research Working Paper Series 9088, The World Bank.
    8. William Nordhaus & Xi Chen, 2015. "A sharper image? Estimates of the precision of nighttime lights as a proxy for economic statistics," Journal of Economic Geography, Oxford University Press, vol. 15(1), pages 217-246.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Eugenia Go & Kentaro Nakajima & Yasuyuki Sawada & Kiyoshi Taniguchi, 2023. "Satellite-Based Vehicle Flow Data to Assess Local Economic Activities," CIRJE F-Series CIRJE-F-1209, CIRJE, Faculty of Economics, University of Tokyo.
    2. Chong Peng & Weizeng Sun & Xi Zhang, 2022. "Crime under the Light? Examining the Effects of Nighttime Lighting on Crime in China," Land, MDPI, vol. 11(12), pages 1-20, December.
    3. Jesson A. Pagaduan, 2022. "Do higher‐quality nighttime lights and net primary productivity predict subnational GDP in developing countries? Evidence from the Philippines," Asian Economic Journal, East Asian Economic Association, vol. 36(3), pages 288-317, September.
    4. John Gibson, 2021. "Better Night Lights Data, For Longer," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 83(3), pages 770-791, June.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. John Gibson, 2021. "Better Night Lights Data, For Longer," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 83(3), pages 770-791, June.
    2. 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).
    3. 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.
    4. Omoniyi Alimi & Geua Boe-Gibson & John Gibson, 2022. "Noisy Night Lights Data: Effects on Research Findings for Developing Countries," Working Papers in Economics 22/12, University of Waikato.
    5. van der Weide, Roy & Blankespoor, Brian & Elbers, Chris & Lanjouw, Peter, 2024. "How accurate is a poverty map based on remote sensing data? An application to Malawi," Journal of Development Economics, Elsevier, vol. 171(C).
    6. Shapiro, Daniel & Oh, Chang Hoon & Zhang, Peng, 2023. "Nighttime lights data and their implications for IB research," Journal of International Management, Elsevier, vol. 29(5).
    7. Jesson A. Pagaduan, 2022. "Do higher‐quality nighttime lights and net primary productivity predict subnational GDP in developing countries? Evidence from the Philippines," Asian Economic Journal, East Asian Economic Association, vol. 36(3), pages 288-317, September.
    8. John Gibson & Bonggeun Kim & Geua Boe-Gibson, 2022. "How effective are sanctions on North Korea? Popular DMSP night-lights data may bias evaluations due to blurring and poor low-light detection," Working Papers in Economics 22/06, University of Waikato, revised 14 Nov 2022.
    9. Xiaoxuan Zhang & John Gibson & Xiangzheng Deng, 2023. "Remotely too equal: Popular DMSP night‐time lights data understate spatial inequality," Regional Science Policy & Practice, Wiley Blackwell, vol. 15(9), pages 2106-2125, December.
    10. Valentin Lindlacher & Gustav Pirich, 2024. "The Impact of China’s “Stadium Diplomacy” on Local Economic Development in Sub-Saharan Africa," CESifo Working Paper Series 10893, CESifo.
    11. McSharry, Patrick & Mawejje, Joseph, 2024. "Estimating urban GDP growth using nighttime lights and machine learning techniques in data poor environments: The case of South Sudan," Technological Forecasting and Social Change, Elsevier, vol. 203(C).
    12. Bonggeun Kim & John Gibson & Geua Boe‐Gibson, 2024. "Measurement errors in popular night lights data may bias estimated impacts of economic sanctions: Evidence from closing the Kaesong Industrial Zone," Economic Inquiry, Western Economic Association International, vol. 62(1), pages 375-389, January.
    13. Felbermayr, Gabriel & Gröschl, Jasmin & Sanders, Mark & Schippers, Vincent & Steinwachs, Thomas, 2022. "The economic impact of weather anomalies," World Development, Elsevier, vol. 151(C).
    14. Jaqueson K Galimberti & Stefan Pichler & Regina Pleninger, 2023. "Measuring Inequality Using Geospatial Data," The World Bank Economic Review, World Bank, vol. 37(4), pages 549-569.
    15. Lindlacher, Valentin & Pirich, Gustav, 2025. "The Impact of China’s “Stadium Diplomacy” on Local Economic Development in Sub-Saharan Africa," World Development, Elsevier, vol. 185(C).
    16. 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.
    17. Dickinson, Jeffrey, 2020. "Planes, Trains, and Automobiles: What Drives Human-Made Light?," MPRA Paper 103504, University Library of Munich, Germany.
    18. Beyer, Robert & Yao, Jiaxiong & Hu, Yingyao, 2022. "Measuring Quarterly Economic Growth from Outer Space," VfS Annual Conference 2022 (Basel): Big Data in Economics 264007, Verein für Socialpolitik / German Economic Association.
    19. Katarzyna A. Bilicka & André Seidel, 2022. "Measuring Firm Activity from Outer Space," NBER Working Papers 29945, National Bureau of Economic Research, Inc.
    20. Felbermayr, Gabriel & Gröschl, Jasmin & Sanders, Mark & Schippers, Vincent & Steinwachs, Thomas, 2018. "Shedding Light on the Spatial Diffusion of Disasters," VfS Annual Conference 2018 (Freiburg, Breisgau): Digital Economy 181556, Verein für Socialpolitik / German Economic Association.

    More about this item

    Keywords

    DMSP; GDP; night lights; VIIRS; United States;
    All these keywords.

    JEL classification:

    • E23 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Production
    • R12 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Size and Spatial Distributions of Regional Economic Activity; Interregional Trade (economic geography)

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:wai:econwp:20/03. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Geua Boe-Gibson (email available below). General contact details of provider: https://edirc.repec.org/data/dewaknz.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.