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?
One of the things I learned in my first field work experience was that keeping interviews private was critical if you wanted unbiased information. Why? I guess at the time it should have been kind of obvious to me – there are certain questions that a person will answer differently depending on whom else is in the room. We were doing a socio-economic survey of rural households in Ghana, and we thought that income, in particular, would be sensitive, since spouses tended to share information on this selectively and perhaps in a strategic way.
Hot on the heels of More than good Intentions comes an outstanding new book by two of the most prominent leaders of the recent push for more rigorous evaluation – Abhijit Banerjee and Esther Duflo’s Poor Economics: A Radical Rethinking of the way to Fight Global Poverty
I attended this conference at Madison, WI last week, which was quite pleasant except the weather – it snowed!
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.
As a fair number of impact evaluations I work on are programs designed by governments or NGOs, I often initially have to have a tricky discussion when it comes time to do the power calculations to design the impact evaluation. The subject of this conversation is the anticipated effect size. This is a key parameter – if it’s too optimistic you run the risk of an impact evaluation with no effect even when the program had worked to some (lesser) degree, if it’s too pessimistic, then you are wasting money and people’s time in your survey.
Millions of dollars are spent each year trying to improve the productivity of firms in Africa (and those in other developing countries), yet we have very little rigorous evidence as to what works. In a new working paper I look at whether it is even possible to learn whether such policies even work, and what can be done to make progress.
Small number of firms + Large heterogeneity = Not much power
In response to an earlier blog post on marketing experiments, we noted that young creative researchers are working with NGOs to try out new innovative ways to alleviate poverty and spur development. A reader wrote with the following question:
I have been thinking about marriage recently. No, not about my own marital status, but marriage among school-age girls and its effects on future outcomes… While many arguments are made to curb teen marriages (and pregnancies), it is not clear whether these events themselves are the cause of poor future outcomes or they are simply correlated with other background characteristics that are prognostic of future outcomes. A brief survey of the literature indeed suggests that the evidence is mixed; especially when it comes to the effects of teen childbearing on future outcomes.