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Economic Development in Pixels: The Limitations of Nightlights and New Spatially Disaggregated Measures of Consumption and Poverty

Author

Listed:
  • John D. Huber
  • Laura Mayoral
Abstract
We develop a novel methodology that uses machine learning to produce accurate estimates of consumption per capita and poverty in 10x10km cells in sub-Saharan Africa over time. Using the new data, we revisit two prominent papers that examine the effect of institutions on economic development, both of which use “nightlights” as a proxy for development. The conclusions from these papers are reversed when we substitute the new consumption data for nightlights. We argue that the different conclusions about institutions are due to a previously unrecognized problem that is endemic when nightlights are used as a proxy for spatial economic well-being: nightlights suffer from nonclassical measurement error. This error will typically lead to biased estimates in standard statistical models that use nightlights as a spatially disaggregated measure of economic development. The bias can be either positive or negative, and it can appear when nightlights are used as either a dependent or an independent variable. Our research therefore underscores an important limitation in the use of nightlights, which has become the standard measure of spatial economic well-being for studies focusing on developing parts of the world. It also demonstrates how machine learning models can generate a useful alternative to nightlights, with important implications for the conclusions we draw from the analyses in which such data are employed.

Suggested Citation

  • John D. Huber & Laura Mayoral, 2024. "Economic Development in Pixels: The Limitations of Nightlights and New Spatially Disaggregated Measures of Consumption and Poverty," Working Papers 1433, Barcelona School of Economics.
    • Handle: RePEc:bge:wpaper:1433
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    File URL: https://bse.eu/sites/default/files/working_paper_pdfs/1433.pdf
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    References listed on IDEAS

    as
    1. Oded Galor & Omer Ozak, 2015. "Land Productivity and Economic Development: Caloric Suitability vs. Agricultural Suitability," Working Papers 2015-5, Brown University, Department of Economics.
    2. 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.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    economic develpment; poverty; institutions; nightlights; nonclassical measurement error; machine learning;
    All these keywords.

    JEL classification:

    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
    • P46 - Political Economy and Comparative Economic Systems - - Other Economic Systems - - - Consumer Economics; Health; Education and Training; Welfare, Income, Wealth, and Poverty
    • P48 - Political Economy and Comparative Economic Systems - - Other Economic Systems - - - Legal Institutions; Property Rights; Natural Resources; Energy; Environment; Regional Studies

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