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Monitoring global development aid with machine learning

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  • Malte Toetzke

    (ETH Zurich)

  • Nicolas Banholzer

    (ETH Zurich)

  • Stefan Feuerriegel

    (LMU Munich)

Abstract

Monitoring global development aid provides important evidence for policymakers financing the Sustainable Development Goals (SDGs). To overcome the limitations of existing monitoring, we develop a machine learning framework that enables a comprehensive and granular categorization of development aid activities based on their textual descriptions. Specifically, we cluster the descriptions of ~3.2 million aid activities conducted between 2000 and 2019 totalling US$2.8 trillion. As a result, we generated 173 activity clusters representing the topics of underlying aid activities. Among them, 70 activity clusters cover topics that have not yet been analysed empirically (for example, greenhouse gas emissions reduction and maternal health care). On the basis of our activity clusters, global development aid can be monitored for new topics and at new levels of granularity, allowing the identification of unexplored spatio-temporal disparities. Our framework can be adopted by development finance and policy institutions to promote evidence-based decisions targeting the SDGs.

Suggested Citation

  • Malte Toetzke & Nicolas Banholzer & Stefan Feuerriegel, 2022. "Monitoring global development aid with machine learning," Nature Sustainability, Nature, vol. 5(6), pages 533-541, June.
    • Handle: RePEc:nat:natsus:v:5:y:2022:i:6:d:10.1038_s41893-022-00874-z
    DOI: 10.1038/s41893-022-00874-z
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    Cited by:

    1. Lisa Chauvet & Marin Ferry, 2023. "L’efficacité de l’aide : quelles évolutions de la littérature depuis deux décennies ? WP329," Working Papers hal-04141543, HAL.
    2. Claire E. Robertson & Nicolas Pröllochs & Kaoru Schwarzenegger & Philip Pärnamets & Jay J. Bavel & Stefan Feuerriegel, 2023. "Negativity drives online news consumption," Nature Human Behaviour, Nature, vol. 7(5), pages 812-822, May.
    3. Johannes Jakubik & Stefan Feuerriegel, 2022. "Data‐driven allocation of development aid toward sustainable development goals: Evidence from HIV/AIDS," Production and Operations Management, Production and Operations Management Society, vol. 31(6), pages 2739-2756, June.

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