Harnessing Satellite Data to Improve Social Assistance Targeting in the Eastern Caribbean

Sophia Chen, Ryu Matsuura, Flavien Moreau and Joana Pereira

No 2024/084, IMF Working Papers from International Monetary Fund

Abstract: 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. We leverage recent advances in machine learning and satellite imagery processing to propose an implementable strategy in the face of these constraints. We show that local well-being can be predicted with high accuracy in the Eastern Caribbean region using satellite data and that such predictions can be used to improve targeting by reducing aggregation bias, better allocating resources across areas, and proxying for information difficult to verify.

Keywords: Social assistance targeting; satellite data; machine learning; Eastern Caribbean; Small Island Developing States.; aggregation bias; hard-to-verify information; satellite imagery processing; Income; Natural disasters; Caribbean (search for similar items in EconPapers)
Pages: 45
Date: 2024-04-05
New Economics Papers: this item is included in nep-big, nep-dev and nep-ure
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