Markus’s previous post on the measurement of sensitive information has started the ball rolling on a major topic that we all confront in field work – accurate measurement. This is an especially acute issue for studies that investigate socially undesirable or stigmatized behaviors such as risky sexual practices or illegal activities.
Jed Friedman's blog
Low birth weight, usually defined as less than 2500 grams at birth, is an important determinant of infant mortality. It is also significantly associated with adverse outcomes well into adulthood such as reduced school attainment and lower earnings. Maternal nutrition is a key determinant of low birth weight and it’s no surprise that nutrition interventions targeted at pregnant mothers can have significant impacts.
David has started a discussion that I find intrinsically interesting and one that well-designed impact evaluations can help clarify: why don’t more people adopt low-cost efficacious health technologies? We may be able to think of examples in our own lives – i.e. “why don’t I take vitamins more regularly?” or “why, if diabetic, don’t I self-test my blood sugar more frequently?” These same questions also resonate for large-scale health programs in many settings.
Markus’ s post yesterday is the first on what will be one recurring blog theme here- measurement. I’ll continue the trend today with a focus on one of the most fundamental welfare constructs in economics: consumption. Specifically, how might the development researcher accurately measure household consumption through survey?
As a PhD student in the late 90s, randomized field trials were not yet common place in empirical development economics. Certain quasi-experimental methods such as regression discontinuity were also fairly exotic. It was the era of the “natural experiment”, when fellow PhD students scoured county newspapers at the university library for research leads. These students were looking for news of policy changes that might plausibly introduce some exogenous variation in the local market environment.
My last post discussed an example of a system intervention (improvements to the pharmaceutical supply chain) and the not uncommon inferential challenge of low power from relatively few units of observation.
A quick look at the burgeoning literature on policy evaluations will reveal a preponderance of evaluations of demand side schemes such as conditional cash transfers. There is an obvious reason for this beyond the promise that such interventions hold: the technology of treatment allows for large sample randomized evaluations, either at the household or community/village level. As long as financing is sufficient to sample an adequate number of study units, study power will not be a concern.