{
  "title": "Harnessing Satellite Data to Improve Social Assistance Targeting in the Eastern Caribbean",
  "publication": "IMF Working Papers, April 5, 2024",
  "sourceUrl": "https://www.imf.org/en/publications/wp/issues/2024/04/05/harnessing-satellite-data-to-improve-social-assistance-targeting-in-the-eastern-caribbean-547416",
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  "summary": "Prioritizing populations most in need of social assistance is an important policy decision. In the Eastern Caribbean, social assistance targeting is constrained by limited data and the need for rapid support in times of large economic and natural disaster shocks.",
  "sections": [
    {
      "heading": "Executive summary",
      "content": "- Prioritizing populations most in need of social assistance is an important policy decision.\n- In the Eastern Caribbean, social assistance targeting is constrained by limited data and the need for rapid support in times of large economic and natural disaster shocks.\n- The authors leverage recent advances in machine learning and satellite imagery processing to propose an implementable strategy under these constraints.\n- Main empirical claim: local well-being can be predicted with high accuracy in the Eastern Caribbean region using satellite data.\n- Policy implication: satellite-based predictions can improve targeting by reducing aggregation bias, better allocating resources across areas, and proxying for information difficult to verify."
    },
    {
      "heading": "Findings and implications for targeting",
      "content": "- Predictive performance\n  - Local well-being is predicted with high accuracy using satellite data (as reported by the study).\n- Targeting improvements enabled by satellite predictions\n  - Reducing aggregation bias.\n  - Better allocating resources across areas.\n  - Providing proxies for hard-to-verify information relevant to eligibility or need.\n- Context-specific relevance\n  - Strategy responds to the need for rapid support during large economic and natural disaster shocks.\n  - Tailored to Small Island Developing States in the Eastern Caribbean context."
    },
    {
      "heading": "Methodology and technical approach",
      "content": "- Data and techniques\n  - Use of satellite imagery processing combined with machine learning advances.\n- Implementation emphasis\n  - The paper proposes an implementable strategy suitable where traditional household or administrative data are limited or slow to update.\n\n---\n\n Content in this bundle\n\n- Working Paper\n  - Working Paper (Markdown version){rel=\"alternate\" type=\"text/markdown\"}\n  - Working Paper (PDF){rel=\"external\" type=\"application/pdf\"}\n\n---\n\nSource: https://www.imf.org/en/publications/wp/issues/2024/04/05/harnessing-satellite-data-to-improve-social-assistance-targeting-in-the-eastern-caribbean-547416"
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    "Authors: Sophia Chen, Ryu Matsuura, Flavien Moreau, Joana Pereira",
    "Published: April 5, 2024",
    "Series: IMF Working Papers",
    "DOI: https://doi.org/10.5089/9798400274312.001",
    "Prioritizing populations most in need of social assistance is an important policy decision.",
    "In the Eastern Caribbean, social assistance targeting is constrained by limited data and the need for rapid support in times of large economic and natural disaster shocks.",
    "The authors leverage recent advances in machine learning and satellite imagery processing to propose an implementable strategy under these constraints.",
    "Main empirical claim: local well-being can be predicted with high accuracy in the Eastern Caribbean region using satellite data.",
    "Policy implication: satellite-based predictions can improve targeting by reducing aggregation bias, better allocating resources across areas, and proxying for information difficult to verify.",
    "Predictive performance",
    "Targeting improvements enabled by satellite predictions",
    "Context-specific relevance",
    "Data and techniques",
    "Implementation emphasis",
    "**Working Paper**"
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