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

Lyons, Angela C.; Castano, Alejandro Montoya; Kass-Hanna, Joséphine; Han, Min; Soliman, Abdel-Hamid

doi:10.2139/ssrn.4404113

Cited by 1 publication

(1 citation statement)

References 14 publications

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“…We also expanded the health dimension to encompass indicators for healthcare access, households with special health care needs and food security such as diet diversity. Factors such as food diversity, social inclusion and connectivity to mobile phones and social networks have been documented as being particularly important to households' resiliency and their capacity to cope with and recover from negative shocks, especially households that have been forcibly displaced (Gerlitz et al, 2017; Hahn et al, 2009; Khawaja et al, 2020; Lyons & Kass‐Hanna, 2020a, 2020b; Rajesh et al, 2018).…”

Section: Construction Of the MLImentioning

confidence: 99%

A multidimensional approach to measuring vulnerability to poverty among refugee populations

Lyons

Kass-Hanna

Castano

2023

J of Intl Development

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This study constructs a multidimensional livelihood index (MLI) using data from Syrian refugees in Lebanon to identify households who are currently poor. The MLI is then used to predict households' vulnerability to future poverty using a three‐stage Feasible Generalized Least Squares (FGLS) model. The analysis identifies more precisely which households and geographical locations are vulnerable to experiencing prolonged poverty. This study is among the first to adapt the multidimensional poverty framework to the context of protracted forced displacement. The approach provides insights into how humanitarian and development organizations can more optimally target assistance to alleviate immediate and future poverty.

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