03 January 2012

When rigorous evaluation is NOT necessary?

Sometimes simple descriptive survey data is basically fine to tell us what we need to know. But if you want to get published in a top economics journal, you need a really convincing statistical demonstration of causality (unlike, say, some top medical journals *cough* American Journal of Clinical Nutrition *cough*).

Case #1 - Oster & Thornton's very cool paper on menstruation and school attendance. They found two things
(a) - from a simple survey/school attendance records - girls in Nepal miss an average of 0.4 days per 180 day school year due to menstruation.

(b) - a randomized intervention providing sanitary products has little impact on reducing that 0.4 days.

Now - the interesting part here is part a - based on simple descriptive statistics - which tells us everything we need to know. Part b - the randomized intervention - is basically irrelevant once we already know that girls are not missing school due to menstruation

Case #2 - Friedman, Kremer, Miguel & Thornton - on education and attitudes to democracy and ethnicity. By offering randomized primary school scholarships they get a very clean identification of the causal impact of education on attitudes. Which is great. But we also knew that there is a ton of descriptive statistics on this already.

From this simple cross-tabulation of Afrobarometer data (the website is pretty cool by the way) - I'm guessing that primary school probably doesn't have a huge impact on support for democracy, because almost everyone supports it to begin with. 


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