This was followed by a post by Eric Djimeu  on the 3ie blog asks what else development economics should be learning from clinical trials, in which he writes:
How should we understand clinical equipoise?
My problem with these posts is that they seem to be understanding clinical equipoise in terms of needing uncertainty about whether or not some intervention makes people better off, without taking into account the costs of doing so relative to “how much” better off the intervention makes people. But we don’t live in a world of no budget constraints, and so the standard of clinical equipoise needs to be more along the lines of doubts over whether this use of funds makes people better off relative to any other possible use of funds in the country, or for international organizations, the world. Anyone who thinks there is not considerable uncertainty about this question is likely deluding themselves.
What does this mean in practice?
- We need to do a much better job of documenting intervention costs in our studies – this should include both the direct costs of any treatment given to individuals (e.g. the amount of grants given as transfers, or the cost of malaria nets) as well as the administrative costs involved in implementing these. It is hard to justify a study on the grounds of it being needed to compare the cost-benefit of different interventions if cost is not provided! This also relates to recent discussion by Chris Blattman on his blog of whether we should be benchmarking interventions against simply giving individuals the equivalent amount in cash : as Chris notes “I’ve seen many, many, many projects that spend $1500 training and all the “other stuff” in order to give people $300 or a cow. Is it fair to ask, what if we’d just given them $1800? Or what if we’d given six people cows? Seriously, your one guy does six times better than that?”
- There are hardly any treatments where entire world coverage is the likely outcome, so we are almost always in the case of having to choose who to give something good to, and of someone who could benefit from it not receiving it. This presents two reasons for randomization and experimentation: first, experimenting to learn how to better target individuals, when there is uncertainty as to the distribution of benefits. E.g. if we have 1000 more malaria nets to give, should we give them to pregnant mothers in Sierra Leone or families with young children in Chad? Second, the usual story of random assignment being an ethical way to give everyone who would benefit the same chance from doing so applies once you have narrowed it down to groups who you expect to benefit most.
- Finally, even in the rare cases where it is possible to try to get 100% world or country coverage, there is debate about the ethics of doing so compared to spending the money on other things. This shows up in the case of trying to eradicate polio – where there is debate over whether disease eradication is ethical  (here is the case for ). So Paul Farmer’s point that we know better healthcare is good is surely not sufficient – we need to know how good relative to other things we could be doing with the same money.
- Finally, we need to think beyond individuals and also think about the role of the collective good. This comes about most strongly in the case of interventions that may be privately undesirable but publicly desirable , but also applies when there are positive or negative spillovers – another area we need more research about.