The world's biggest regression discontinuity design?

The public school systems in both Malawi and Uganda (the two countries where I recently spent time doing fieldwork) revolve around a set of massively-important exams that determine whether you get to move on from one level of education to another, and often eligibility for jobs as well. One of the people I was working with in Uganda described primary school there as spending seven years studying for a single test.

It’s hard to overstate the importance of these tests. Uganda’s first such exam is the Primary Leaving Examination, or PLE, which you take after Primary 7 (roughly equivalent to 6th grade as there is no kindergarten). A more-or-less universal practice in the schools I visited in Northern Uganda was to kick out all the poorly-performing P6 pupils before the beginning of P7, leaving just a core of all-stars who spend the year prepping for the test. I’m guessing this is done, in large part, to optimize how good the school looks relative to its competition.

Pressure is high on the pupils as well – it’s common for the names of top performers to be published in newspapers (and hence, by process of elimination, everyone knows who did badly as well). An op-ed I read while in Lira – which I wish I’d cut out and kept, as I can’t find it online – pointed out that this pressure has a cost, and proposed a neat experiment that could be carried out on a grand scale. It’s clear that pupils and their families enjoy the immediate fame of having their names show up in the paper, the author said, but how do they fare down the road? The author proposed that someone should follow up to see how many newspaper-famous PLE success stories end up making it through secondary school.

What’s interesting about this idea is that we could, conceivably, not only do a raw comparison, but actually isolate the causal effect of passing the PLE versus failing it (or of getting a higher grade versus a lower one). The idea is that these exams have hard score cutoffs for passing (or getting a certain grade) and if administered honestly, students can’t control their exact score. Consider a group of exam-takers who are all basically right at the cutoff. Idiosyncratic events on the day of the exam, or random errors, will push them above or below the passing mark. Hence if you look just at that group, you effectively have random assignment to the “pass, name in the paper, life of success and riches” condition or the “fail, no newspaper fame, everybody feels bad for you” condition. You can see how much passing the exam impacts, for example, your wages later in life or the number of kids you have or how many years of school you eventually finish. This approach is called a “regression discontinuity” or “RD” design, and it’s pretty hot in education research these days.

The cool thing about doing this in Malawi or Uganda is that it’s not just a particular school or program – you could study the impact of passing an exam that basically everyone in the country takes. But you’d need the exam scores, plus followup data with a random subset (or all) of the pupils in the country. I can think of ways to do this, but none of them are feasible – unless, to pick one example, some folks at UNEB and the Ugandan Census want to let me at their raw, identifiable data.