Today I wanted to take the opportunity to talk about a new initiative that the Africa Region and the Research Group at the World Bank are launching today. The idea here is that we don't know enough about how to effectively address the underlying causes of gender inequality. Let me start by explaining what I mean by underlying causes. Take the case of female farmers. There is a lot of literature out there which shows that women have lower agricultural yields than men. And some of it shows that this is because women have lo
Is in danger of being messed up. Here is why: There are two fundamental reasons for doing impact evaluation: learning and judgment. Judgment is simple – thumbs up, thumbs down: program continues or not. Learning is more amorphous – we do impact evaluation to see if a project works, but we try and build in as many ways to understand the results as possible, maybe do a couple of treatment arms so we see what works better than what. In learning evaluations, real failure is a lack of statistical power, more so than the program working or
As part of a new series looking how institutions are approaching impact evaluation, DI virtually sat down with Nick York, Head of Evaluation and Gail Marzetti, Deputy Head, Research and Evidence Division. For Part I of this series, see yesterday’s post. Today we focus on DFID’s funding for research and impact evaluation.
As part of a new series looking how institutions are approaching impact evaluation, DI virtually sat down with Nick York, Head of Evaluation and Gail Marzetti, Deputy Head, Research and Evidence Division
I am in the midst of a trip working on impact evaluations in Ghana and Tanzania and these have really brought home the potential and pitfalls of working with program’s monitoring data.
In many evaluations, the promise is significant. In some cases, you can even do the whole impact evaluation with program monitoring data (for example when a specific intervention is tried out with a subset of a program’s clients). However, in most cases a combination of monitoring and survey data is required.
In a New York Times column last Friday David Brooks discussed a book by Jim Manzi, and extolled the idea of randomized field trials as a way for the US to make better policies.
While it’s nice to welcome Citizen Brooks into the fold, there are a couple of points in his article worth exploring a bit.
So this past week I was in Ghana following up on some of the projects I am working on there with one of my colleagues. We were designing an agricultural impact evaluation with some of our counterparts, following up on the analysis of the second round of a land tenure impact evaluation and a financial literacy intervention, and exploring the possibility of some work in the rural financial sector. In no particular order, here are some of the things I learned and some things I am still wondering about:
One of the things I learned from other folks at the Bank I work with is the usefulness of doing a workshop early in the early design of an impact evaluation to bring the project and the impact evaluation team together to hammer out design. With one of my colleagues, I did one of these during my recent trip to Ethiopia and a bunch of things stuck out.
I was in a meeting the other week where we were wrestling with the issue of how to capture better labor supply in agricultural surveys. This is tough – the farms are often far from the house, tasks are often dispersed across time, with some of them being a small amount of hours – either in total or on a given day. Families can have more than one farm, weakening what household members know about how the others spend their time. One of the interesting papers that came up was a study by Elena Bardasi, Kathleen Beegle, Andrew Dllon and Pieter Serneels. Before turning to their results its worth spending a bit more time discussing what could be going on.
Two things would seem to matter (among others). First, who you ask could shape the information you get. We’ve had multiple posts in the past about imperfections in within household information. These posts have talked about income and consumption and while labor would arguably be easier to observe, it may suffer from the same strategic motives for concealment and thus be underreported when the enumerator asks someone other than the actual worker to respond on this.