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Measuring poverty in India with machine learning and remote sensing

Author

Listed:
  • Adel Daoud
  • Felipe Jordan
  • Makkunda Sharma
  • Fredrik Johansson
  • Devdatt Dubhashi
  • Sourabh Paul
  • Subhashis Banerjee

Abstract

In this paper, we use deep learning to estimate living conditions in India. We use both census and surveys to train the models. Our procedure achieves comparable results to those found in the literature, but for a wide range of outcomes.

Suggested Citation

  • Adel Daoud & Felipe Jordan & Makkunda Sharma & Fredrik Johansson & Devdatt Dubhashi & Sourabh Paul & Subhashis Banerjee, 2021. "Measuring poverty in India with machine learning and remote sensing," Papers 2202.00109, arXiv.org, revised Oct 2022.
    • Handle: RePEc:arx:papers:2202.00109
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    References listed on IDEAS

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    1. Daoud, Adel & Kim, Rockli & Subramanian, S.V., 2019. "Predicting women's height from their socioeconomic status: A machine learning approach," Social Science & Medicine, Elsevier, vol. 238(C), pages 1-1.
    2. Sara Randall & Ernestina Coast, 2015. "Poverty in African Households: the Limits of Survey and Census Representations," Journal of Development Studies, Taylor & Francis Journals, vol. 51(2), pages 162-177, February.
    3. 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.
    4. Halleröd, Björn & Rothstein, Bo & Daoud, Adel & Nandy, Shailen, 2013. "Bad Governance and Poor Children: A Comparative Analysis of Government Efficiency and Severe Child Deprivation in 68 Low- and Middle-income Countries," World Development, Elsevier, vol. 48(C), pages 19-31.
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