Eva Vivalt

There are many more things that can be done using AidGrade’s data set of impact evaluation results. (And many things I am planning to do using completely different data sets!)

A selection of other questions that can be answered using the same data (collaborators welcome):

– What is the shape of different interventions’ effects over time? I tend to have repeat observations, e.g. 3, 6 or 12 months after the start of each intervention or after the program concluded.

– Regarding the finding that government-implemented programs sadly do not perform as well as NGO-implemented programs, even after controlling for sample size: what could explain this? Do the effects vary by the type of government or NGO?

– When do you observe more specification searching in the social sciences? I largely peeled this part off my job market paper to submit to another journal.

– What are the general equilibrium effects of the interventions?

– How do policymakers respond to the exogenous information shock provided by the results of a new study?

You might have seen that I am looking for an RA. It might be possible to collaborate on one or more of these projects if there were interest.

The biggest barrier to maintaining a constantly-updated, comprehensive data set on different studies’ results (necessary for meta-analysis) is getting all those data.

Do you know anyone who could help build an app that encourages researchers to “See how your results compare” — so that if they enter in their data, they get some nice graphics about where their results fall in the distribution of all studies done to that point, perhaps disaggregated by region or other study characteristics?

Let’s leverage researchers’ curiosity about their own data. Navel-gazing for the public good.

Development economics has relied increasingly on randomized controlled trials (RCTs), championed by the likes of the folks at J-PAL, IPA, CEGA, and many others. On the other hand, the strategy has its discontents, who fear that a lot of money is going into evaluations that may not have much practical value, as impact evaluations may not have much external validity.

I was worried that my paper “How Much Can We Generalize from Impact Evaluations?”, which draws upon a unique data set of roughly 600 impact evaluations across 20 different types of development programs, would stoke the flames and end up criticized by both sides. It didn’t, because we’re all economists and care a lot about data. At the end of the day, to what extent results generalize, and when, is an empirical question. I think this “war” is poorly named, because we can all agree that it is critically important to look carefully at the data.

I am very heartened by initiatives like the Berkeley Initiative for Transparency in the Social Sciences (BITSS) which emphasize getting to the right answer, not getting an answer. I suspect that meta-analysis will continue to grow in use in economics, and that the answer to the question “how much do results generalize?” will continue to be tested.

For my part, I intend for AidGrade’s data to be constantly updated and publicly available, and to continue to allow people to conduct their own meta-analyses instantly online, by selecting papers they wish to include (more filters to be added). I will be applying for grants to develop online training modules to help crowdsource the data (moving to a Wiki style), which will enable this to keep going and expanding in perpetuity, becoming more and more useful as more studies are completed.

We are in a new era. If I can borrow Chris Blattman’s conceptualization of “impact evaluations 1.0” (just run it) vs. “impact evaluations 2.0” (run it with an emphasis on mechanisms), I’d suggest a slightly modified “impact evaluations 3.0”: run it, with an emphasis on mechanisms, but then synthesize your results with those from other studies to build something bigger than the sum of your parts.