Targeting humanitarian aid using administrative data: Model design and validation

Onur Altindag, Stephen O'Connell, Aytuğ Şaşmaz, Zeynep Balcıoğlu, Paola Cadoni, Matilda Jerneck and Aimee Kunze Foong

Journal of Development Economics, 2021, vol. 148, issue C

Abstract: We develop and assess the performance of an econometric prediction model that relies on administrative data held by international agencies to target over $380 million annually in unconditional cash transfers to Syrian refugees in Lebanon. Standard metrics of prediction accuracy suggest targeting using administrative data is comparable to a short-form Proxy Means Test, which requires a survey of the entire target population. We show that small differences in accuracy across approaches are largely attributable to a few data fields. These results are robust to a blind validation test performed on a random sample collected after the model derivation, as well as the type of estimator used for prediction. We discuss relative costs, which are likely to feature prominently when alternative approaches are considered in practice.

Keywords: Poverty targeting; Proxy means test; Cash transfers; Refugees; Forced displacement; Lebanon (search for similar items in EconPapers)
JEL-codes: I32 I39 O12 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (20)

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Related works:
Working Paper: Targeting humanitarian aid using administrative data: model design and validation (2020)
Working Paper: TARGETING HUMANITARIAN AID USING ADMINISTRATIVE DATA: MODEL DESIGN AND VALIDATION (2019)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:deveco:v:148:y:2021:i:c:s0304387820301395

DOI: 10.1016/j.jdeveco.2020.102564

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