16 September 2019: The Organisation for Economic Co-operation and Development (OECD) has launched an interactive website to inform policy leaders and decision-makers on resources to achieve the 2030 Agenda for Sustainable Development. The ‘SDG Financing Lab’ shows that SDG 14 (life below water) and SDG 15 (life on land) receive 3.5% of total donor commitments, the least among the 17 SDGs.

The OECD developed the website as a direct response to the OECD Global Outlook’s “wake-up call” to improve indicators and tools to analyze the volume and alignment of financial flows to the SDGs. The 2017 Development Assistance Committee (DAC) High-level Meeting, titled ‘A New DAC: Innovation for the 2030 Agenda,’ called for developing innovative tools and accelerating responses to development cooperation. In response to this need, the OCED developed the SDG Financing Lab to quantify the contribution of different donors to the SDGs and to help increase transparency and improve the impact of aid.

Users can search the website to compare development financing across the SDGs and identify which Goals are the “darlings” of donors and which are the “orphans”. The highest amounts of funding go to SDG 9 (industry, innovation and infrastructure), which received 12% of the total; SDG 8 (decent work and economic growth), which received 10%; SDG 2 (zero hunger), which received 9%; and SDG 7 (affordable and clean energy) and SDG 10 (reduced inequalities), which both received 8% of total aid flows.

Users can also track how donor contributions have changed over time. For SDG 10, for instance, donors provided US$8,951.20 million in 2012, which increased to US$13,835.09 million in 2014, US$21.276.07 million in 2015 and US$25,754.45 million in 2016. Donor funding for SDG 10 then decreased slightly in 2017 to US$25,605.90 million in 2017. Other SDGs show more stable funding over time.

The ‘SDG Financing Lab’ ranks donors by aid volume for each Goal and also allows users to explore individual providers’ portfolios to see major donors to particular SDGs and countries. For SDG 1 (no poverty), for example, the top ten donors are the International Development Association (IDA), European Union (EU) institutions, the Asian Development Bank (ADB), Japan, France, Germany, International Fund for Agricultural Development (IFAD), the African Development Fund, Norway and Australia. On SDG 5 (gender equality), the top ten donors are Canada, the UK, Sweden, the US, EU Institutions, Norway, Germany, Australia, Spain, and the Netherlands. For SDG 14, the top ten donors are the International Development Association (IDA), Japan, Germany, EU institutions, the US, the United Arab Emirates (UAE), Norway, the Global Environment Facility (GEF), Food and Agriculture Organization of the UN (FAO), and the UK.

Users can view aid flows from the perspective of the donor or the recipient. Recipients can be viewed by country, by region and by income groups, such as least developed countries (LDCs) and more advanced developing countries and territories (MADCTs). The Africa region, for instance, receives the most donor aid for SDG 8 (15%), SDG 9 (14%), SDG 17 (partnerships for the Goals, 13%), SDG 3 (good health and well-being), as well as SDGs 10 and 2. In contrast, Asia receives the most donor aid for SDG 16 (peace, justice and strong institutions; 12%), SDG 4 (quality education, 12%), SDG 17 (11%) and SDG 3 (8%). SDG 7, SDG 2, SDG 8, and SDG 13 (climate action)all received 7% of Asia’s SDG financing. The website also includes links to browse official development statistics and to discover SDG-related content.

The OECD uses its Credit Reporting System (CRS) database, which provides information on aid activities by country, sector and individual project, to map official development assistance (ODA) to the SDGs. This data represents over US$1.5 trillion in aid financing and over 1.3 million aid projects over the last six years. OECD elaborates upon this methodology in a working paper titled, ‘Linking Aid to the SDGs: A machine learning approach.’ [SDG Financing Lab] [OECD Development Statistics Databases] [Publication: Linking Aid to the SDGs: A machine learning approach]