The AidData SDG widget visualizes aid flow estimates across the Sustainable Development Goals based on a mapping
of AidData activity and purpose codes to SDG targets. You can find more information here: SDG Financial Tracking
The visualizations are divided by tab, allowing users to facet and filter the data according to donors,
recipients, years, and SDGs in a few different ways.
1) SDG Funding by Donor
affords an intuitive view of relative funding across SDGs for a given donor, recipient, and/or
year(s). A dynamic tree chart responds to these user selections by displaying SDGs as tiles, where
each SDG tile is scaled to the proportional funding amount.
2) Top/Bottom Donors
visualizes the top or bottom donors that provide funding to a particular SDG for given recipients
and years utilizing a bar graph visualization.
Methodology in Brief:
Tracking and analyzing funding to the Sustainable Development Goals (SDGs) will be central to measuring progress,
crowding in resources to priority areas, and helping decision-makers make more informed choices. Unfortunately,
currently available data do not capture sufficient information on the distribution of financing for the SDGs.
The AidData Sustainable Development Goals Estimates attempt to fill this gap by providing project-level
estimates of contributions to the SDGs and their associated targets, letting us see where development financing
is targeted and allowing comparisons among different SDG goals.
This methodology is based on an analysis of development project descriptions and builds on an existing activity
coding schema developed at AidData. Activity codes are based on the OECD's Creditor Reporting System (CRS)
sector and purpose codes, but go one step deeper, providing a more disaggregated breakdown of development
activities that are relevant to each CRS code. Student coders had previously activity coded 58% of projects
in AidData's core research release.
The methodology involves three main steps:
Creating a mapping between activity codes and SDG targets;
Splitting an aid project across designated activities; and
Splitting activity amounts across SDG targets, as an activity may be linked to multiple targets.
Where activity codes are unavailable, we use purpose codes, which are not as granular as activity codes, and
generate estimates based on a naïve diffuse assumption about what activities were involved in a project with a
given purpose code.
While we recognize that components of an aid project may receive differing levels of resources in practice, the
available project documentation seldom contains this level of granularity on how money is distributed at the
sub-project level. Therefore, we split dollar values equitably across activities as the best available option
in the absence of perfect information. Having split the dollar value of a project across unique activities, we
then distribute those activity-dollar amounts evenly across the SDGs targets associated with the activity. From
these calculations, we can sum target-level estimates up to the goal level.
For more detailed information on the statistical methodology, please refer to our methodology paper
Estimating Baseline Aid to the Sustainable Development Goals. AidData is also currently working on a new
iteration of this methodology, which will code projects to the SDGs directly rather than through a crosswalk.
