Policy makers repeatedly face this generalizability puzzle—whether the results of a specific program generalize to other contexts—and there has been a long-standing debate among policy makers about the appropriate response. But the discussion is often framed by confusing and unhelpful questions, such as: Should policy makers rely on less rigorous evidence from a local context or more rigorous evidence from elsewhere? And must a new experiment always be done locally before a program is scaled up?
These questions present false choices. Rigorous impact evaluations are designed not to replace the need for local data but to enhance their value. This complementarity between detailed knowledge of local institutions and global knowledge of common behavioral relationships is fundamental to the philosophy and practice of our work at the Abdul Latif Jameel Poverty Action Lab (J-PAL). […]
To give a sense of our philosophy, it may help to first examine four common, but misguided, approaches about evidence-based policy making that our work seeks to resolve.
Can a study inform policy only in the location in which it was undertaken? Kaushik Basu has argued that an impact evaluation done in Kenya can never tell us anything useful about what to do in Rwanda because we do not know with certainty that the results will generalize to Rwanda. To be sure, we will never be able to predict human behavior with certainty, but the aim of social science is to describe general patterns that are helpful guides, such as the prediction that, in general, demand falls when prices rise. Describing general behaviors that are found across settings and time is particularly important for informing policy. The best impact evaluations are designed to test these general propositions about human behavior.
Should we use only whatever evidence we have from our specific location? In an effort to ensure that a program or policy makes sense locally, researchers such as Lant Pritchett and Justin Sandefur argue that policy makers should mainly rely on whatever evidence is available locally, even if it is not of very good quality. But while good local data are important, to suggest that decision makers should ignore all evidence from other countries, districts, or towns because of the risk that it might not generalize would be to waste a valuable resource. The challenge is to pair local information with global evidence and use each piece of evidence to help understand, interpret, and complement the other.
Should a new local randomized evaluation always precede scale up? One response to the concern for local relevance is to use the global evidence base as a source for policy ideas but always to test a policy with a randomized evaluation locally before scaling it up. Given J-PAL’s focus on this method, our partners often assume that we will always recommend that another randomized evaluation be done—we do not. With limited resources and evaluation expertise, we cannot rigorously test every policy in every country in the world. We need to prioritize. For example, there have been more than 30 analyses of 10 randomized evaluations in nine low- and middle- income countries on the effects of conditional cash transfers. While there is still much that could be learned about the optimal design of these programs, it is unlikely to be the best use of limited funds to do a randomized impact evaluation for every new conditional cash transfer program when there are many other aspects of antipoverty policy that have not yet been rigorously tested.
Must an identical program or policy be replicated a specific number of times before it is scaled up? One of the most common questions we get asked is how many times a study needs to be replicated in different contexts before a decision maker can rely on evidence from other contexts. We think this is the wrong way to think about evidence. There are examples of the same program being tested at multiple sites: For example, a coordinated set of seven randomized trials of an intensive graduation program to support the ultra-poor in seven countries found positive impacts in the majority of cases. This type of evidence should be weighted highly in our decision making. But if we only draw on results from studies that have been replicated many times, we throw away a lot of potentially relevant information. […]
Focusing on mechanisms, and then judging whether a mechanism is likely to apply in a new setting, has a number of practical advantages for policy making. […] We suggest the use of a four-step generalizability framework that seeks to answer a crucial question at each step:
Step 1: What is the disaggregated theory behind the program?
Step 2: Do the local conditions hold for that theory to apply?
Step 3: How strong is the evidence for the required general behavioral change?
Step 4: What is the evidence that the implementation process can be carried out well?
Bates, M. A., & Glennerster, R. (2017). “The generalizability puzzle”. Stanford Social Innovation Review, Summer 2017. Leland Stanford Jr. University.
Added to diary 22 March 2018