TARGETING HUMANITARIAN AID USING ADMINISTRATIVE DATA: MODEL DESIGN AND VALIDATION

Onur Altindag, Stephen D. O’Connell (), Aytug Sasmaz (), Zeynep Balcioglu (), Paola Cadoni (), Matilda Jerneck () and Aimee Foong ()
Additional contact information
Stephen D. O’Connell: Emory University Department of Economics and IZA Institute of Labor Economics
Aytug Sasmaz: Harvard University Department of Government
Zeynep Balcioglu: Northeastern University Department of Political Science
Paola Cadoni: UNHCR Lebanon
Matilda Jerneck: UNHCR Lebanon
Aimee Foong: UNHCR Lebanon
Authors registered in the RePEc Author Service: Stephen D. O'Connell

No 1343, Working Papers from Economic Research Forum

Abstract: We develop and assess the performance of an econometric targeting model for a large scale humanitarian aid program providing unconditional cash and food assistance to refugees in Lebanon. We use regularized linear regression to derive a prediction model for household expenditure based on demographic and background characteristics; from administrative data that are routinely collected by humanitarian agencies. Standard metrics of prediction accuracy suggest this approach compares favorably to the commonly used “scorecard” Proxy Means Test, which requires a survey of the entire target population. We confirm these results through a blind validation test performed on a random sample collected after the model derivation.

Pages: 33
Date: 2019-09-20, Revised 2019-09-20
New Economics Papers: this item is included in nep-agr and nep-big
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Citations: View citations in EconPapers (4)

Published by The Economic Research Forum (ERF)

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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 (2020)
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Persistent link: https://EconPapers.repec.org/RePEc:erg:wpaper:1343

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