There are many instances where you have a bunch of variables and you need to boil them down to one or just a few. For example, you may be testing the effect of an education program on students’ confidence, self-efficacy, and learning levels. You will likely have at least one measure (likely more) for each of those core outcome variables and want to combine them into one to avoid having to do any multiple hypothesis corrections.
TLDR; The experience of RSBY, a government subsidized health insurance program, shows why India desperately needs civil service reform. RSBY showed early promise but ultimately failed because state RSBY teams were unable to perform basic monitoring tasks that even a small, reasonably competent team could have accomplished. About ten years ago, I spent a summer working as a World Bank consultant on RSBY, a (then) new centrally government subsidized health insurance program for in-patient care.
Researchers at the World Bank recently released an excellent new dataset on learning outcomes by country.The dataset is here and a journal article describing the dataset is here. This is the first dataset on learning outcomes (rather than educational attainment) which includes most low income countries. (Hanushek and Woesmann created a cross country dataset on learning outcomes some time back but it only included 77 countries.) How well does India do according to the HLO dataset?
In my last blog post, I shared some thoughts on Sarah Rose’s excellent recent working paper on how USAID could become more evidence based. TLDR: I argue that USAID doesn’t necessarily need to generate evidence (since there are plenty of other orgs that do that) but it does need to the use the latest and best evidence. Unfortunately, this is not always the case. Roses’ paper has several interesting recommendations for ensuring that USAID makes better use of evidence.
Sarah Rose at the Center for Global Development recently published an excellent note on how to make USAID programming more evidence-based. As a former member of one of the groups mentioned in the article (the Evaluation and Impact Assessment group at the erstwhile Global Development Lab) and a long-time evaluator, this is a topic dear to my cold, data-driven heart. I realize that probably marks me as a member of very small fraternity, but people really should care more about making donors more evidence-based!