With funds devoted to HIV/AIDS declining, there has not been a better time, at least in the past decade or so, to optimize the use of the limited resources between treatment and prevention.
Ten years ago when I was a graduate student piloting questionnaires in rural Indonesia, I sat with a translator and an elderly farmer in his front yard. Mid-way through the interview I asked this farmer the first of several standard questions related to general well-being and life satisfaction: “Thinking about your own life and personal circumstances, how satisfied are you with your life as a whole?” The farmer stared at us with a look of bewilderment on his face. So we asked a second time in a slow sympathetic tone.
So this past week I was in Ghana following up on some of the projects I am working on there with one of my colleagues. We were designing an agricultural impact evaluation with some of our counterparts, following up on the analysis of the second round of a land tenure impact evaluation and a financial literacy intervention, and exploring the possibility of some work in the rural financial sector. In no particular order, here are some of the things I learned and some things I am still wondering about:
My paper “Beyond Baseline and Follow-up: the case for more T in experiments” was recently accepted at the JDE. As with most papers that go through review, the accepted version is a definite improvement on the working paper version.
As the United States prepares for its first presidential election after the Great Recession, inequality has emerged as a central political issue. This is not unremarkable: Americans have historically seemed much less troubled by income differences than, say, Europeans. You may remember a 2004 article by Alberto Alesina, Rafael di Tella and Robert MacCulloch in the Journal of Public Economics, which reported that happiness in the US was much less sensitive to inequality than in Europe.
For many years, researchers have recognized the need to correct standard error estimates for observational dependence within clusters. An earlier post contrasted the typical approach to this matter, the cluster robust standard error (CRSE), and various methods to cluster bootstrap the standard error.
Back in the tail end of last year, I did a post on using workshops with project teams to build impact evaluation design. My friend anonymous requested copies of the presentations. Since I am in the midst of doing another one of these workshops here in Ghana, I thought it would be worth posting them now.
I’ve been meaning to read for the last month this new paper by Orazio Attanasio and co-authors, which is the latest in the still small number of studies to carry out a randomized experiment to measure the impact of microfinance. David Roodman was quick to give his thoughts on it in this post, but I thought I’d also summarize it briefly for you and offer my thoughts.
· A New paper has innovative way of getting data on H1B migrants – they obtained administrative data through a Freedom of Information Act request – and use this for most comprehensive look yet at how high-skilled migrants coming through H1B compare to natives.