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

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Abstract
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 and combines geospatial data with survey 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 other 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 study have important implications for agencies seeking to improve targeting, especially with shrinking humanitarian funding.

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