Linking Aid to the Sustainable Development Goals – a machine learning approach

Arnaud Pincet, Shu Okabe and Martin Pawelczyk

No 52, OECD Development Co-operation Working Papers from OECD Publishing

Abstract: 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).

Keywords: Artificial Intelligence; Credit Reporting System; Innovation; Machine Learning; Official Development Finance; Sectors; Sustainable Development Goals; Text Mining (search for similar items in EconPapers)
JEL-codes: C38 C45 C55 F21 F35 O11 (search for similar items in EconPapers)
Date: 2019-02-01
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Citations: View citations in EconPapers (4)

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Persistent link: https://EconPapers.repec.org/RePEc:oec:dcdaaa:52-en

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