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Predicting poverty with vegetation index

Author

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  • Binh Tang
  • Yanyan Liu
  • David S. Matteson

Abstract

Accurate and timely predictions of the poverty status of communities in developing countries are critical to policymakers. Previous work has applied convolutional neural networks (CNNs) to high‐resolution satellite imagery to perform community‐level poverty prediction. Although promising, such imagery has limitations in predicting poverty among poor communities. We provide the first evidence that a publicly available, moderate‐resolution vegetation index (the normalized difference vegetation index [NDVI]), can be used with CNNs to produce accurate poverty predictions contemporaneously among poor communities heavily dependent on agriculture. We also show that the NDVI can effectively detect consumption variation over time. To our knowledge, this is the first attempt to use remote sensing data to predict future‐period consumption expenditure at the community level.

Suggested Citation

  • Binh Tang & Yanyan Liu & David S. Matteson, 2022. "Predicting poverty with vegetation index," Applied Economic Perspectives and Policy, John Wiley & Sons, vol. 44(2), pages 930-945, June.
  • Handle: RePEc:wly:apecpp:v:44:y:2022:i:2:p:930-945
    DOI: 10.1002/aepp.13221
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    References listed on IDEAS

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    1. Linden McBride & Christopher B. Barrett & Christopher Browne & Leiqiu Hu & Yanyan Liu & David S. Matteson & Ying Sun & Jiaming Wen, 2022. "Predicting poverty and malnutrition for targeting, mapping, monitoring, and early warning," Applied Economic Perspectives and Policy, John Wiley & Sons, vol. 44(2), pages 879-892, June.
    2. Kathleen Beegle & Luc Christiaensen & Andrew Dabalen & Isis Gaddis, 2016. "Poverty in a Rising Africa," World Bank Publications - Books, The World Bank Group, number 22575.
    3. Lee, Yong Suk, 2018. "International isolation and regional inequality: Evidence from sanctions on North Korea," Journal of Urban Economics, Elsevier, vol. 103(C), pages 34-51.
    4. 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.
    5. Chris Browne & David S Matteson & Linden McBride & Leiqiu Hu & Yanyan Liu & Ying Sun & Jiaming Wen & Christopher B Barrett, 2021. "Multivariate random forest prediction of poverty and malnutrition prevalence," PLOS ONE, Public Library of Science, vol. 16(9), pages 1-23, September.
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    Cited by:

    1. García-Suaza, Andres & Varela, Daniela, 2024. "Nightlight, landcover and buildings: understanding intracity socioeconomic differences," Documentos de Trabajo 21025, Universidad del Rosario.

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