These past weeks I’ve visited several southern African nations to assist on-going evaluations of health sector pay-for-performance reforms. It’s been a whirlwind of government meetings, field trips, and periods of data crunching. We’ve made good progress and also discovered roadblocks – in other words business as usual in this line of work. One qualitative data point has stayed with me throughout these weeks, the paraphrased words of one clinic worker: “I like this new program because it makes me feel that the people in charge of the system care about us.”
Last week, I talked about the difficulty of categorizing cash transfer programs neatly into bins of unconditional (UCT) and conditional (CCT) ones. Afterwards, one of the comments gently chastised me for being overly optimistic of thinking about these programs as being in a continuum of intensity of the conditions rather than in a multi-dimensional design space.
A couple of days ago, my wife and I were having one of the moments -- I was convinced we had had a detailed conversation about something and she was convinced that no such conversation had taken place. Now, if you were to show up and do a survey of us, we wouldn't agree.
Many policymakers are interested in the role of conditions in cash transfer programs. Do they improve outcomes of interest more than money alone? Are there trade-offs? Is there a role for conditions for political rather than technocratic reasons? It’s easy to extend the list of questions for a good while. However, before one can get to these questions, there is a much more basic question that needs to be answered (for any policymaker contemplating running one of these programs at any level): “What do you mean by a conditional (or unconditional) cash transfer program?”
I’ve read several research proposals in the past few months, as well engaged in discussions, that touch on the same question: how to use the spatial variation in a program’s intensity to evaluate its causal impact. Since these proposals and conversations all mentioned the same fairly recent paper by Markus Frolich and Michael Lechner, I eagerly sat down to read it.
How many points do you need to qualify to migrate to Australia? What is the cost of applying? How much money do you need to set up a bank account in the Cayman Islands? What is the procedure for getting money out of these accounts when you want to spend it?
- From the indecision blog – as a young researcher, how do you find out what your “thing” is, that is, your research agenda - interesting hypothesis that for many researchers research preferences “reveal themselves”.
- From the 3ie blog – does economics need a more systematic approach to replication to be considered a hard science? – interesting link contained within to an AER editor’s report on the replication policy there.
- New results published in the New England Journal of Medicine from the Oregon Health Experiment look at impacts of access to Medicaid on simple health measures like cholesterol and blood pressure (see our discussion of the original set of results here), and for summaries of the new results either the Washington Post Wonkblog or NPR). One of the big measurement issues is of course that even with a sample of approx 6,000 treated and 6,000 control, it is not clear there are enough cases over 2 years of the sort of health events that easier access to medical care can fix.
- After Markus’s post this week showing how a package of grants and training helped women grow small businesses in Bangladesh, Chris Blattman has a post on new results from an evaluation he did in Uganda, which also finds positive impacts of training and grants on getting women to start businesses. We’ll wait for a working paper to render our thoughts on this – there are worrying issues (phased in randomization where the control group was guaranteed treatment at a known later date, potentially causing them to delay current business activities) and intriguing-sounding findings (general equilibrium effects on village economies) that pique my interest.
Can we break poverty traps? An interesting new paper by Oriana Bandiera, Robin Burgess, Narayan Das, Selim Gulesci, Imran Rasul, and Munshi Sulaiman adds to this emergent literature with a definitive “yes we can.” Bandiera, et. al. evaluate a program run by the NGO BRAC which provides a significant infusion of capital, coupled with training, for Bangladeshi women.
Shifting from opt-in to opt-out defaults is one of the clearest success stories for policy to emerge from behavioral economics, as evidenced by the large increases in organ donor rates and contributions to retirement savings plans obtained when opt-out defaults are used instead of opt-in.
However, there are several limits of opt-out policies: