Targeting humanitarian aid using administrative data: model design and validation

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.
SSRN WP
HiCN WP
Presentation (UNHCR/WB Conf., Jan. 2020; 30 min)
Emory Wheel article

Keywords: Econometric targeting; Poverty targeting; Lebanon; Syrian refugee crisis; Unconditional cash transfers; Prediction; LASSO.