Lessons in Data

Last week I experienced another great example of just how much careful data analysis matters in research. Anne Bernier and I began working on a new blog post for AidData on development assistance to Morocco, looking specifically at aid before and after sweeping domestic legal reforms in 2004 which granted greater rights and status to women. After I pulled information on aid commitments to Morocco over the last decade, I began examining the projects for noticeable changes between the years before the reforms and the years after–and specifically if anything about the “gender-related aid” changed after the reforms, indicating a response by donors.

First, I tracked total amounts, and that told a story of increasing gender-related aid, both in number of projects and in dollar value of the commitments. However, when I looked at gender-related aid dollars each year as a percentage of total aid to Morocco, I found quite a different result.  The percentage fluctuated a bit but was generally around 1% both before and after 2004–meaning gender-related aid to Morocco didn’t increase relative to other types of aid given during those years. And, of course, to reconcile these two findings, I just needed to look at a third area: total aid to Morocco, which was increasing over these years–so simply the same percentage of a larger pie. From there Anne and I looked elsewhere, which you can read about in the actual blog (http://blog.aiddata.org/2010/08/aid-women-and-progress-in-morocco.html), and found some pretty interesting results!

Well, there it is, my short and sweet lesson in learning from data!

Comments

  1. Sneha Raghavan says:

    Firstly, i read your blog entry and absolutely loved it. It must be so satisfying to see it up there and knowing that you created something out of nothing- or rather, that you figured out the limitations to the correlations between the moroccan reform and aid packages. Fascinating! Congratulations on all your hard work 🙂

    Secondly, i completely agree about the need to think analytically about numbers and not just take them for what they are. Quantative reasoning is perhaps meaningless without qualitative thought. What’s that quote about statistics being like a bikini? It shows a lot but still conceals the most important things. Something to that degree.