Three months ago, African countries reached an inflection point: for the first time since the start of the Sustainable Development Goals (SDGs), more people in sub-Saharan Africa were escaping extreme poverty than entering it. In Côte d’Ivoire, one of four countries we examine for a report debuting a new methodology to track financing to the SDGs, the rate of extreme poverty is projected to collapse from 17.2 to 4.9 percent by 2030.
Yet even if these broad trends continue, Africa will still fall short of achieving SDG 1 (ending extreme poverty), with 377 million projected to still be living on less than $1.90 a day by 2030. As the UN’s High-Level Political Forum on Sustainable Development begins this week to report on global progress towards the SDGs, it’s increasingly clear that the world is not on track to achieving many of the other Global Goals by the deadline either.
One significant barrier to progress, highlighted by Homi Kharas and Lorenz Noo in a recent Brookings blog, is the lack of quality, disaggregated data on development outcomes. Four years into the SDGs era, we’re still unable to clearly assess progress on important Sustainable Development Goals like zero hunger (Goal 2), as some of the most commonly used indicators on hunger tell different, conflicting stories.
A similar problem exists for data on one of the key inputs of global development: financing. Achieving the Global Goals would require the international community to mobilize significant additional financing over the next decade—$2.5 trillion USD, by one estimate. Tracking and analyzing this funding is central to measuring progress and making more informed choices on where to allocate and prioritize resources. Yet, despite increasing interest in integrating the SDGs into national planning processes and the creation of broad estimates of financing needed to achieve them, little data is currently being generated to track funding for the SDGs in a granular way—for example, by country or donor portfolio, sector, or investment type.
Our new policy report, Financing the SDGs: Evidence in Four Countries, helps fill this gap. It debuts AidData’s updated methodology for tracking financing related to the SDGs and applies it to track $44 billion in donor financing to the SDGs in four countries—much of which can even be tracked to specific SDG targets. In the education sector, for example, 83% of the $2.5 billion in funding related to Goal 4 (Quality Education) in these four countries can be tracked to a specific Goal 4 target. To date, this is the only methodology of which we are aware that can track financing from a diverse set of funding mechanisms to the Sustainable Development Goals at the target level.
Explore the policy report, Financing the SDGs: Evidence in Four Countries, and our updated methodology for tracking financing related to the Global Goals.
For each of the four case study countries—Colombia, Cambodia, Rwanda, and Côte d’Ivoire—we provide a baseline of SDG-related funding not only immediately after the launch of the Sustainable Development Goals, but also for the years immediately before. We do this because, while the Global Goals may be new packaging, the majority of the underlying ideas they represent predate the Sustainable Development Agenda.
By examining the development project descriptions of donors, we can link projects implemented before the SDGs began to the development issue now represented by the Global Goals and their targets. Here, we use data from the OECD’s Creditor Reporting System (CRS) database on all official development finance between 2010 and 2016 to identify individual projects that can be linked to specific SDGs or targets, and then quantify total financing by SDG.
This process builds on our 2017 methodology, which we used to track financing to the SDGs in Colombia and in our flagship Realizing Agenda 2030 report on financing towards and global progress on the SDGs. Rather than cross-walking OECD CRS sector codes to the relevant SDGs, our updated methodology has human coders analyze project descriptions to directly link projects to the relevant SDGs and targets. This standardized approach and improved granularity enhances our ability to monitor and evaluate funding gaps and trends in financing for the SDGs.
The four countries we use as case studies represent diverse development contexts and trajectories, from low- to upper middle-income levels and across three regions. By examining the composition of funding to various SDGs and their targets, as well as trends in financing over time, we are able to explore in-depth how a country’s individual context and history impacts its SDG-related donor funding. We also look at SDG financing from the perspectives of donors, to see how their own interests are reflected in their development portfolios across different countries.
Better data on the landscape of financing for the SDGs would help improve accountability and awareness for both donors and recipient country governments. We are now considering approaches for scaling up our methodology, including automation through machine learning techniques. With additional support going forward, we could expand the type of financing covered by our methods to include domestic resources, private sector investments, and emerging donors to gather evidence for an even broader range of countries and contexts. By providing a more robust view of who is funding which goals and where, we hope to make it easier to leverage the SDGs to more effectively direct future financial flows where they will have the most impact.
