World Development has a great collection of short pieces on RCTs.
….practitioners should be aware of the limitations of prioritizing unbiasedness, with RCTs as the a priori tool-of-choice. This is not to question the contributions of the Nobel prize winners. Rather it is a plea for assuring that the “tool-of-choice” should always be the best method for addressing our most pressing knowledge gaps in fighting poverty.
… RCTs are often easier to do with a non-governmental organization (NGO). Academic “randomistas,” looking for local partners, appreciate the attractions of working with a compliant NGO rather than a politically sensitive and demanding government. Thus, the RCT is confined to what NGO’s can do, which is only a subset of what matters to development. Also, the desire to randomize may only allow an unbiased impact estimate for a non-randomly-selected sub-population—the catchment area of the NGO. And the selection process for that sub-sample may be far from clear. Often we do not even know what “universe” is represented by the RCT sample. Again, with heterogeneous impacts, the biased non-RCT may be closer to the truth for the whole population than the RCT, which is (at best) only unbiased for the NGO’s catchment area.
A key critique of the use of randomized experiments in development economics is that they largely have been used for micro-level interventions that have far less impact on poverty than sustained growth and structural transformation. I make a distinction between two types of policy interventions and the most appropriate research strategy for each. The first are transformative policies like stabilizing monetary policy or moving people from poor to rich countries, which are difficult to do, but where the gains are massive. Here case studies, theoretical introspection, and before-after comparisons will yield “good enough” results. In contrast, there are many policy issues where the choice is far from obvious, and where, even after having experienced the policy, countries or individuals may not know if it has worked. I argue that this second type of policy decision is abundant, and randomized experiments help us to learn from large samples what cannot be simply learnt by doing.
Reasonable people would agree that the question should drive the choice of method, subject to the constraint that we should all strive to stay committed to the important lessons of the credibility revolution.
Beyond the questions about inference, we should also endeavor to address the power imbalances that are part of how we conduct research in low-income states. We want to always increase the likelihood that we will be asking the most important questions in the contexts where we work; and that our findings will be legible to policymakers. Investing in knowing our contexts and the societies we study (and taking people in those societies seriously) is a crucial part of reducing the probability that our research comes off as well-identified instances of navel-gazing.
Finally, what is good for reviewers is seldom useful for policymakers. We could all benefit from a bit more honesty about this fact. Incentives matter.