An Introduction to Aid Data

Curriculum

6 Sections
35 Lessons
Lifetime

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Introduction to aid data
Welcome to the world of aid data! In this first module, we’ll be starting from first principles: what do we actually mean when we say ‘aid’, and why is looking at aid data important?
5
A guide to IATI data
One major source of aid data is made available through the International Aid Transparency Initiative (IATI). There are a few different ways that you can access raw IATI data, so here’s a quick run through of what you can get, and where it is, including IATI data itself as well as data about IATI data (metadata).
6
Cleaning IATI data with OpenRefine
So once you've got the raw IATI data, what next? Often it might need 'cleaning' - here, we'll use a sample IATI dataset to learn how to use a powerful cleaning tool, OpenRefine.
5
An introduction to OECD-DAC data
Another major source of data available about international development data, is the OECD. Here, we run through a few ways that they make this data available, and where to find what you're looking for.
8
Inspiration module: how is aid data used in the media?
We've seen a few ways of where to find aid data, how to clean it and use it - but how is this applied in the real world? Here are a couple of examples of 'reverse engineering' aid data that we might come across in the media - maybe it will give you some ideas!
6
Jargon busting the world of aid
This module is intended as a reference point while you're going through the other modules. In each case, the relevance of the term to working specifically with data is highlighted to try and make it clear what you should be looking out for when you come across the term in your work.
5

Weaknesses of the OECD-DAC data

Excludes much of the ‘aid bundle’: for example, remittances, charitable donations from the public, foreign investment, funds that don’t meet the ‘official’ definition of ODA – which means that the financial flows here are just a tiny portion of resources which are intended to reduce poverty in poor countries.

Slow to publish: full datasets are not published until December of the following year (although limited preliminary data is published in April)

Difficult to match up with money received: the data here is on money that flows out of donor countries, not necessarily the money that flows into** **low-income countries. There are multiple reasons why these two might not match up as lots of ODA is actually spent in the donor country (but still qualifies as ODA, if the economic welfare of a developing country is the ‘main’ aim). For example, some of it goes towards debt relief, or student costs in the donor country.

Difficult to match inputs with outcomes: as there are no economic/social indicators included here, it’s difficult to see whether projects had the desired outcomes – eg. increased spending on malaria prevention vs. a reduction in malaria prevalence

Not so useful for:

helping recipient countries manage their aid flows – for this, a dedicated Aid Management Platform is much more useful
tracking aid beyond recipient government level – what happened after it went to a specific government?

Strengths of the OECD-DAC data

An Introduction to Aid Data

Curriculum

Weaknesses of the OECD-DAC data

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