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.
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Research Fellows
Onur Altindag
Assistant Professor of Economics, Bentley UniversityAssistant Professor...
![Targeting Humanitarian Aid Using Administrative Data: Model Design and Validation](https://erf.org.eg/app/uploads/2021/05/1620139342_882_88232_nl_stephenoconnell-150x150.png)
Authors
Stephen D. O’Connell
Assistant Professor of Economics, Emory University
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Authors
Aytuğ Şaşmaz
Harvard University Department of Government
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Authors
Zeynep Balcıoğlu
Northeastern University Department of Political Science
![Targeting Humanitarian Aid Using Administrative Data: Model Design and Validation](https://erf.org.eg/app/themes/website2020/resources/assets/images/placeholder.jpg)
Authors
Paola Cadoni
UNHCR Lebanon
![Targeting Humanitarian Aid Using Administrative Data: Model Design and Validation](https://erf.org.eg/app/themes/website2020/resources/assets/images/placeholder.jpg)
Authors
Matilda Jerneck
UNHCR Lebanon
![Targeting Humanitarian Aid Using Administrative Data: Model Design and Validation](https://erf.org.eg/app/themes/website2020/resources/assets/images/placeholder.jpg)
Authors
Aimee Kunze Foong
UNHCR Lebanon