EconPapers    
Economics at your fingertips  
 

Monitoring global development aid with machine learning

Malte Toetzke (), Nicolas Banholzer and Stefan Feuerriegel
Additional contact information
Malte Toetzke: ETH Zurich
Nicolas Banholzer: ETH Zurich
Stefan Feuerriegel: LMU Munich

Nature Sustainability, 2022, vol. 5, issue 6, 533-541

Abstract: 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.

Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: Track citations by RSS feed

Downloads: (external link)
https://www.nature.com/articles/s41893-022-00874-z Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:natsus:v:5:y:2022:i:6:d:10.1038_s41893-022-00874-z

Ordering information: This journal article can be ordered from
https://www.nature.com/natsustain/

DOI: 10.1038/s41893-022-00874-z

Access Statistics for this article

Nature Sustainability is currently edited by Monica Contestabile

More articles in Nature Sustainability from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().

 
Page updated 2022-07-23
Handle: RePEc:nat:natsus:v:5:y:2022:i:6:d:10.1038_s41893-022-00874-z