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 ()
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Aytuğ Şaşmaz: Harvard University Department of Government
Zeynep Balcıoğlu: Northeastern University Department of Political Science
Paola Cadoni: UNHCR Lebanon
Matilda Jerneck: UNHCR Lebanon
Aimee Kunze Foong: UNHCR Lebanon

No 327, HiCN Working Papers from Households in Conflict Network

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)
Pages: 52 pages
Date: 2020-06
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https://hicn.org/wp-content/uploads/2020/06/HiCN-WP-327.pdf First version, 2020 (application/pdf)

Related works:
Journal Article: Targeting humanitarian aid using administrative data: Model design and validation (2021)
Working Paper: TARGETING HUMANITARIAN AID USING ADMINISTRATIVE DATA: MODEL DESIGN AND VALIDATION (2019)
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Persistent link: https://EconPapers.repec.org/RePEc:hic:wpaper:327

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