Conference Paper

A Machine Learning Approach to Targeting Humanitarian Assistance among Forcibly Displaced Populations

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No.

ERF29AC_106

Publisher

ERF

Date

May, 2023

Abstract
Author(s)

Increasing trends in forced displacement and poverty are expected to intensify in coming years. Data science approaches can be useful for governments and humanitarian organizations in designing more robust and effective targeting mechanisms. This study applies machine learning techniques to data collected from Syrian refugees in Lebanon over the last four years to help develop more robust and operationalizable targeting strategies. Our findings highlight the importance of a comprehensive and flexible framework that captures various poverty dimensions along with the commonly used expenditure metric, while also allowing for regular updates to keep up with (rapidly) changing contexts over time. The analysis also points to geographical heterogeneities that are likely to affect the effectiveness of targeting strategies. The insights from this project have important implications for agencies seeking to reduce the inclusion and exclusion errors, especially with shrinking humanitarian funding.