IDEAS home Printed from https://ideas.repec.org/p/oec/dcdaaa/52-en.html
   My bibliography  Save this paper

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

Author

Listed:
  • Arnaud Pincet
  • Shu Okabe
  • Martin Pawelczyk

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).

Suggested Citation

  • Arnaud Pincet & Shu Okabe & Martin Pawelczyk, 2019. "Linking Aid to the Sustainable Development Goals – a machine learning approach," OECD Development Co-operation Working Papers 52, OECD Publishing.
  • Handle: RePEc:oec:dcdaaa:52-en
    DOI: 10.1787/4bdaeb8c-en
    as

    Download full text from publisher

    File URL: https://doi.org/10.1787/4bdaeb8c-en
    Download Restriction: no

    File URL: https://libkey.io/10.1787/4bdaeb8c-en?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Olivera Kostoska & Ljupco Kocarev, 2019. "A Novel ICT Framework for Sustainable Development Goals," Sustainability, MDPI, vol. 11(7), pages 1-31, April.
    2. Asadikia, Atie & Rajabifard, Abbas & Kalantari, Mohsen, 2021. "Systematic prioritisation of SDGs: Machine learning approach," World Development, Elsevier, vol. 140(C).

    More about this item

    Keywords

    Artificial Intelligence; Credit Reporting System; Innovation; Machine Learning; Official Development Finance; Sectors; Sustainable Development Goals; Text Mining;
    All these keywords.

    JEL classification:

    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • F21 - International Economics - - International Factor Movements and International Business - - - International Investment; Long-Term Capital Movements
    • F35 - International Economics - - International Finance - - - Foreign Aid
    • O11 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Macroeconomic Analyses of Economic Development

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:oec:dcdaaa:52-en. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: the person in charge (email available below). General contact details of provider: https://edirc.repec.org/data/oecddfr.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.