Targeting of food aid programs: Evidence from Egypt

dc.creatorMahmoud, Mai
dc.creatorKurdi, Sikandra
dc.date2025-12-31
dc.date2026-01-02T22:02:22Z
dc.date2026-01-02T22:02:22Z
dc.date.accessioned2026-06-27T15:37:32Z
dc.descriptionIn-kind food aid programs remain prominent world-wide. Targeting in these programs is complex due to potential distortions in consumption. This paper advances the literature by moving beyond poverty-based targeting to address nutritional objectives. Using data from a randomized controlled trial (RCT), we apply machine learning (ML) techniques to analyze heterogeneity in impacts across nutritional outcomes, aiming to inform targeting based on observable characteristics. We find that such characteristics significantly predict heterogeneity in treatment effects, though relevant predictors differ by outcome and treatment type. Building on recent literature advocating for balancing of deprivation and expected impact, we show that, in our context, the trade-off between targeting the most impacted versus the most deprived households is limited. Instead, the main challenge is prioritizing among competing nutritional objectives. Our findings indicate that ML methods can inform outcome-specific targeting criteria, though these criteria vary across outcomes and are imperfectly correlated.
dc.formatapplication/pdf
dc.identifierhttps://hdl.handle.net/10568/179370
dc.identifier.urihttp://hdl.handle.net/123456789/109814
dc.languageen
dc.publisherInternational Food Policy Research Institute
dc.relationhttps://hdl.handle.net/10568/132231
dc.rightsOpen Access
dc.sourceMahmoud, Mai; and Kurdi, Sikandra. 2025. Targeting of food aid programs: Evidence from Egypt. IFPRI Discussion Paper 2393. Washington, DC: International Food Policy Research Institute. https://hdl.handle.net/10568/179370
dc.subjectnutrition
dc.subjecteconometric models
dc.subjectfood aid
dc.subjectmachine learning
dc.subjecttargeting
dc.subjectfood aid
dc.titleTargeting of food aid programs: Evidence from Egypt
dc.typeWorking Paper

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