Linking aid to the Sustainable Development Goals : a machine learning approach / Arnaud Pincet, Shu Okabe, Martin Pawelczyk

Okabe, Shu [VerfasserIn] ; Pawelczyk, Martin [VerfasserIn]

Englisch

Paris : OECD, 2019

1 Online-Ressource (circa 58 Seiten) : Illustrationen

DOI: 10.1787/4bdaeb8c-en

*Development

Entwicklungshilfe / UN-Entwicklungsziele / UN-Entwicklungsziele / Künstliche Intelligenz / Lernen

Amtsdruckschrift / Government document

Journal of Economic Literature: C38

Journal of Economic Literature: C45

Journal of Economic Literature: C55

Journal of Economic Literature: F21

Journal of Economic Literature: F35

Journal of Economic Literature: O11

Official Development Assistance amounted USD 146.6 billions in 2017 but do we know how much of this aid contributed to the Sustainable Development Goals (SDGs)? And to what SDG in particular? This paper present a new methodology using machine learning designed to link project-based flows to the Sustainable Development Goals. It provide first estimates of DAC and non-DAC donors aid contribution for the goal and show that similar analysis can be done at the recipient level and for other type of textual database such as private sector reports; opening wide array for policy analysis. The methodology presented in this working paper uses semantic analysis of the text description of each project present in the Creditor Reporting System (CRS).

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