Contexts and Policy Implementation: 4 factors to think about

written by Matt Andrews

I recently blogged about what matters about the context. Here’s a video of a class I taught on the topic at the University of Cape Town over the summer (their winter). It is a short clip where I try to flesh out the 4 factors that I look at when thinking about new policy: 1. Disruption; 2. Strength of incumbents; 3. Legitimacy of alternatives; and 4. Agent alignment (who is behind change and who is not).

How can we learn when we don’t understand the problem?

written by Salimah Samji

Most development practitioners think that they are working on problems. However, what they often mean by the word ‘problem’ is the ‘lack of a solution’. This leads to designing typical, business as usual interventions, without addressing the actual problem. Essentially, they sell solutions to specific problems they have identified and prioritized instead of solving real and distinct problems.

If the problem identification is flawed, then it does not matter whether you do a gold standard RCT or not, you will neither solve the problem nor learn about what works. Here’s a great example. A recent paper entitled, The permanent input hypothesis: the case of textbooks and (no) student learning in Sierra Leone found that a public program providing textbooks to primary schools had no impact on student performance because the majority of books were stored rather than distributed.

Could they not have learned that the textbooks were being locked up, cheaper and faster, through some routine monitoring or audit process (which could have led to understanding why they were locked up and then perhaps trying to find other ways to improve access to the textbooks – assuming that was their goal)? Was an RCT really necessary? More importantly, what was the problem they were trying to solve? What was their causal model or theory of change? If you provide textbooks to children then learning outcomes will improve?

Interestingly, the context section of the paper mentions that “the civil war severely impacted the country’s education system leading to large-scale devastation of school infrastructure, severe shortages of teachers and teaching materials, overcrowding in many classrooms in safer areas, displacement of teachers, frequent disruptions of schooling, psychological trauma among children, poor learning outcomes, weakened institutional capacity to manage the system, and a serious lack of information and data to plan service provision.” In addition, they also found variance between regions and in one remote council, “less than 50 percent of all schools were considered to be in good condition, with almost 20 percent falling under the category “no roof, walls are heavily damaged, needs complete rehabilitation.”

Honestly, in a complex context like this, it isn’t clear or obvious that providing textbooks would make much difference even if they were handed out to the children, especially since they are written in English. Apparently, the teachers teach in Krio in the early years and then switch to English in Grade 4 and 5. Based on the context above, that sounds more like fiction than fact.

In environments like these, real problems are complex and scary, and it is easier to ignore them than to address them. A possible way forward could be to break the problem down into smaller more manageable pieces using tools like problem trees, the ishikawa diagram and the ‘5 whys.’ Then design an intervention, try, learn, iterate and adapt.

For more watch BSC video deconstructing sticky problems and problem driven sequencing.

Rigorous Evidence Isn’t

written by Lant Pritchett

Currently, there are many statements floating around in development about the use of “rigorous evidence” in formulating policies and programs. Nearly all of these claims are fatuous. The problem is, rigorous evidence isn’t.

That is, suppose one generates some evidence about the impact of some programmatic or policy intervention in one particular context that is agreed by all to be “rigorous” because it meets methodological criteria for internal validity of its causal claims. But the instant this evidence is used in formulating policy it isn’t rigorous evidence any more.  Evidence would be “rigorous” about predicting the future impact of the adoption of a policy only if the conditions under which the policy was to be implemented were exactly the same in every relevant dimension as that under which the “rigorous” evidence was generated.  But that can never be so because neither economics—nor any other social science—have theoretically sound and empirically validated invariance laws that specify what “exactly the same” conditions would be.

So most uses of rigorous evidence aren’t.  Take, for instance, the justly famous 2007 JPE paper by Ben Olken on the impact of certain types of monitoring on certain types of corruption. According to Google Scholar as of today, this paper has been cited 637 times.  The question is, for how many of the uses of this “rigorous evidence” is it really “rigorous evidence”?  We (well, my assistant) sampled 50 of the citing papers with 57 unique mentions of Olken (2007).  Only 8 of those papers were about Indonesia (Of course even those 8 are only even arguably “rigorous” applications as they might be about different programs or different mechanisms or different contexts.)  47 of the 57 (82%) of the mentions are neither about Indonesia nor even an East Asia or Pacific country—they might be a review of the literature about corruption in general, about another country, or methodological.  We also tracked whether the words “context” or “external validity” appeared within +/- two paragraphs of the mention. In 34 of the 57 (60%) mentions, the evidence was not about Indonesia and did not mention that the results, while “rigorous” for the time, place and programmatic/policy context, have no claim to be rigorous about any other time, place, or programmatic/policy context.

Another justly famous paper, Angrist and Lavy (1999) in the QJE uses regression discontinuity to identify the impact of class size on student achievement in Israel.  This paper has been cited 1244 times.  I looked through the first 150 citations to this paper (which Google Scholar sorts by the number of times the citing paper has itself been cited) and (other than other papers by the authors) not one mentioned Israel  (not that surprisingly, as Israel is a tiny country) in the title or abstract while China, India, Bangladesh, Cambodia, Bolivia, UK, Wales, USA (various states and cities), Kenya and South Africa all figured.  Angrist and Lavy do not, and do not claim to, provide “rigorous” evidence about any of those contexts.

If one is formulating policies or programs for attacking corruption in highway procurement in Peru or reducing class size in secondary school in Thailand, it is impossible to base those policies on “rigorous evidence” as evidence that is rigorous for Indonesia or Israel isn’t rigorous for these other countries.

Now, some might make the argument that formulation of policies or programs in context X should rely exclusively/primarily/preferentially on evidence that is “rigorous” in context Z because at least we know that in context Z in which it was generated the evidence is internally valid.  This is both fatuous and false as a general proposition.

Fatuous in that no one understands the phrase “policy based on rigorous evidence” to mean “policy based on evidence that isn’t rigorous with respect to the actual policy context to which it is being applied (because there are no rigorous claims to external validity) but rather based on evidence that is rigorous in some other context.”  No one understands it that way because that isn’t rigorous evidence.

It is also false as a general proposition.  It is easy to construct plausible empirical examples in which the evidence suggests that the bias from internal validity is much (much) smaller than the bias from external validity as the contextual variation in “true” impact is much larger than the methodological bias from lack of “clean” causal identification of simple methods.  In these instances, better policy is made using “bad” (e.g. not internally valid) evidence from the same context than “rigorous” evidence from another context (e.g. Pritchett and Sandefur 2013).

Sadly perhaps, there is no shortcut around using judgment and wisdom in assessing all of the available evidence in formulating policies and programs.  Slogans like “rigorous evidence” are an abuse, not a use, of social science.