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A machine learning approach to assessing multidimensional poverty and targeting assistance among forcibly displaced populations

Author

Listed:
  • Lyons, Angela C.
  • Montoya Castano, Alejandro
  • Kass-Hanna, Josephine
  • Zhang, Yifang
  • Soliman, Aiman

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 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 effective and efficient targeting strategies. Our proposed approach helps: (1) identify the households most in need of assistance based on a flexible, multidimensional poverty metric and (2) operationalize this method without resorting to impractical and expensive data collection procedures. Our findings highlight the importance of a comprehensive and versatile 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 impact the effectiveness of targeting strategies. The insights from this study have important implications for agencies seeking to improve targeting and increase the efficiency of shrinking humanitarian funding.

Suggested Citation

  • Lyons, Angela C. & Montoya Castano, Alejandro & Kass-Hanna, Josephine & Zhang, Yifang & Soliman, Aiman, 2025. "A machine learning approach to assessing multidimensional poverty and targeting assistance among forcibly displaced populations," World Development, Elsevier, vol. 192(C).
  • Handle: RePEc:eee:wdevel:v:192:y:2025:i:c:s0305750x25000981
    DOI: 10.1016/j.worlddev.2025.107013
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    More about this item

    Keywords

    Multidimensional poverty; Forced displacement; Refugees; Poverty targeting; Humanitarian assistance; Machine learning;
    All these keywords.

    JEL classification:

    • I3 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty
    • I32 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty - - - Measurement and Analysis of Poverty
    • I38 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty - - - Government Programs; Provision and Effects of Welfare Programs
    • O1 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development
    • O19 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - International Linkages to Development; Role of International Organizations
    • O53 - Economic Development, Innovation, Technological Change, and Growth - - Economywide Country Studies - - - Asia including Middle East
    • R23 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Household Analysis - - - Regional Migration; Regional Labor Markets; Population
    • H1 - Public Economics - - Structure and Scope of Government

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