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Lights and GDP relationship: What does the computer tell us?

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  • Diep Hoang Phan

    (Monash University)

Abstract

The relationship between nighttime lights and GDP varies from country to country. However, which factors drive variations in the lights–GDP relationship across countries remains unclear. This paper examines the significance of approximately 600 potential drivers of uncertainty in the relationship between night lights and GDP worldwide. I employ three novel modern statistical techniques to select variables within a high-dimensional context: LASSO, minimax concave penalty, and spike-and-slab regression. Institutional quality emerges as the most important factor in explaining the difference between luminosity data and GDP across countries.

Suggested Citation

  • Diep Hoang Phan, 2023. "Lights and GDP relationship: What does the computer tell us?," Empirical Economics, Springer, vol. 65(3), pages 1215-1252, September.
  • Handle: RePEc:spr:empeco:v:65:y:2023:i:3:d:10.1007_s00181-023-02377-y
    DOI: 10.1007/s00181-023-02377-y
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    References listed on IDEAS

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    1. 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).
    2. Nathan Nunn & Diego Puga, 2012. "Ruggedness: The Blessing of Bad Geography in Africa," The Review of Economics and Statistics, MIT Press, vol. 94(1), pages 20-36, February.
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    4. Hal R. Varian, 2014. "Big Data: New Tricks for Econometrics," Journal of Economic Perspectives, American Economic Association, vol. 28(2), pages 3-28, Spring.
    5. Feige, Edgar L. & Urban, Ivica, 2008. "Measuring underground (unobserved, non-observed, unrecorded) economies in transition countries: Can we trust GDP?," Journal of Comparative Economics, Elsevier, vol. 36(2), pages 287-306, June.
    6. Sam Asher & Tobias Lunt & Ryu Matsuura & Paul Novosad, 2021. "Development Research at High Geographic Resolution: An Analysis of Night-Lights, Firms, and Poverty in India Using the SHRUG Open Data Platform," The World Bank Economic Review, World Bank, vol. 35(4), pages 845-871.
    7. Nyrup, Jacob & Bramwell, Stuart, 2020. "Who Governs? A New Global Dataset on Members of Cabinets," American Political Science Review, Cambridge University Press, vol. 114(4), pages 1366-1374, November.
    8. Maxim Pinkovskiy & Xavier Sala-i-Martin, 2016. "Lights, Camera … Income! Illuminating the National Accounts-Household Surveys Debate," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 131(2), pages 579-631.
    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.
    10. Luis R. Martínez, 2022. "How Much Should We Trust the Dictator’s GDP Growth Estimates?," Journal of Political Economy, University of Chicago Press, vol. 130(10), pages 2731-2769.
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    More about this item

    Keywords

    Nighttime lights; Modern statistical tools; Official GDP; Measurement errors;
    All these keywords.

    JEL classification:

    • E01 - Macroeconomics and Monetary Economics - - General - - - Measurement and Data on National Income and Product Accounts and Wealth; Environmental Accounts
    • O47 - Economic Development, Innovation, Technological Change, and Growth - - Economic Growth and Aggregate Productivity - - - Empirical Studies of Economic Growth; Aggregate Productivity; Cross-Country Output Convergence
    • O11 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Macroeconomic Analyses of Economic Development
    • R14 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Land Use Patterns

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