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Guiding Social Protection Targeting Through Satellite Data in São Tomé and Príncipe

Author

Listed:
  • Peter Fisker
  • Jordi Gallego-Ayala
  • David Malmgren Hansen
  • Thomas Pave Sohnesen
  • Edmundo Murrugarra

Abstract

No abstract is available for this item.

Suggested Citation

  • Peter Fisker & Jordi Gallego-Ayala & David Malmgren Hansen & Thomas Pave Sohnesen & Edmundo Murrugarra, 2022. "Guiding Social Protection Targeting Through Satellite Data in São Tomé and Príncipe," World Bank Publications - Reports 38222, The World Bank Group.
    • Handle: RePEc:wbk:wboper:38222
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    References listed on IDEAS

    as
    1. Thomas Pave Sohnesen & Niels Stender, 2017. "Is Random Forest a Superior Methodology for Predicting Poverty? An Empirical Assessment," Poverty & Public Policy, John Wiley & Sons, vol. 9(1), pages 118-133, March.
    2. Emily Aiken & Suzanne Bellue & Dean Karlan & Chris Udry & Joshua E. Blumenstock, 2022. "Machine learning and phone data can improve targeting of humanitarian aid," Nature, Nature, vol. 603(7903), pages 864-870, March.
    3. Linden McBride & Austin Nichols, 2018. "Retooling Poverty Targeting Using Out-of-Sample Validation and Machine Learning," The World Bank Economic Review, World Bank, vol. 32(3), pages 531-550.
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