I recently came across a paper by Kelsey Jack which is a white paper for the J-PAL and CEGA Agricultural Technology Adoption Initiative (ATAI). This paper systematically explores the barriers to technology adoption that come from market inefficiencies, what we know about these, and what research is going on (under ATAI) to fill these gaps.
At a recent seminar someone joked that the effect size in any education intervention is always 0.1 standard deviations, regardless of what the intervention actually is. So a new study published last week in Science which has a 2.5 standard deviation effect certainly deserves attention. And then there is the small matter of one of the authors (Carl Wieman) being a Nobel Laureate in Physics and a Science advisor to President Obama.
Regardless of whether we do empirical or theoretical work, we all have to utilize information given to us by others. In the field of development economics, we rely heavily on surveys of individuals, households, facilities, or firms to find out about all sorts of things. However, this reliance has been diminishing over time: we now also collect biological data, try to incorporate more direct observation of human behavior, or conduct audits of firms.
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.
OK, let’s put two blog posts in a pot and stir. In a previous post on measuring consumption, Jed gave us some food for thought, while over on Aid Thoughts, Matt is talking about how a respondent is seeing the enumerator on the sly to conceal land that he doesn’t want his wife to know about. Put it together, and what do you have?
Diseases like malaria, diarrhea and intestinal worms plague hundreds of millions of people in the developing world. A major puzzle for development researchers and practitioners is why the poor do not purchase available prevention technologies that could reduce the burden of these diseases. While much of the recent literature has focused on price elasticities of demand and behavioral explanations, another potential explanation is that liquidity constraints prevent the poor from undertaking profitable health investments.
Following up on Michael’s post yesterday, I wanted to add a couple of thoughts.
If one wants to grow an oak tree, it helps to have both an acorn and a working knowledge of the conditions under which an acorn is most likely to become an oak tree. One also needs to know how long the germination process is likely to take – in the case of the red oak, upwards of two years from flowering to acorn to sapling. Absent such knowledge, one might reasonably (but incorrectly) infer that, upon seeing no outward signs of life six months after planting the acorn, one’s efforts had been in vain.
Following on David’s rant on external validity yesterday, which turned out to be quite popular, I decided to keep the thread going. Despite the fact that the debate is painted in ‘either/or’ terms, my feeling is that there are things that careful researchers/evaluators can do to improve the external validity of their studies.