Carrying out evaluations to affect policy is the big motivation of many development economists. Usually, grant proposals and such will ask researchers to document “How will your results affect policy?”. In this post, we address a corollary of that problem statement: “when and how should your results affect policy?”. All the work that goes into the evaluation design at the start drums up a lot of enthusiasm among policymakers, and may open windows of opportunity for policy influence long before the final results from the evaluation are available.
- On the CGD blog, Jessica Goldberg corrects the weird NYT post by Casey Mulligan critiquing experiments
- Free online course on using randomized experiments to evaluate social programs to be offered by J-PAL: this is a 4 week course starting April 1st.
Worldwide, one in five children of upper-primary-school age remain out of school. Girls in developing countries are disproportionately affected, with a quarter of them not completing primary school.
I’m working on an impact evaluation in Colombia right now, and we are in the process of looking at baseline data from firms. The data are a bit noisy at the moment, and part of what makes it hard for me to look at is that many of the costs are in the millions (e.g. an energy bill of 1,442,990). The exchange rate currently is 1USD = 2055 Colombian pesos.
- Reminder: Today (Friday 28th) is the deadline for submissions to the World Bank’s ABCDE conference. While the title of the conference is “The Role of Theory in Development Economics”, they are looking for a broad range of “papers on innovative ways that analytical and deductive methods, as well as issues of methodology such as the use of randomizations, can be used in development.”.
Two weeks ago, I blogged about some productive impacts of cash transfer programs. For these effects, as well as the myriad other blog posts and papers on this topic out there, a key point is that the benefits of these transfers extend well beyond the actual individual recipient of the transfer.
I came across a new working paper written by researchers at Google and Microsoft with the title “on the near impossibility of measuring the returns to advertising”. They begin by noting the astounding statistic that annual US advertising revenue is $173 billion, or about $500 per American per year. That’s right, more than the GDP per capita of countries like Burundi, Madagascar and Eritrea is spent just on advertising!
This week we're introducing our new series that we decided to call 'Ask Guido.' Guido Imbens has kindly agreed to answer technical questions every so often and we are thrilled. For this first installment, Guido starts by answering a question about standard errors and the appropriate level of clustering in matching.
One question that often comes up in empirical work concerns the appropriate way to calculate standard errors, and in particular the correct level of clustering. Here is a specific version of the question that someone posed, slightly paraphrased:
We are delighted that Dave Evans has agreed to be a semi-regular contributor to this blog, agreeing to post about once a month. David is a Senior Economist in the Chief Economist's Office for the Africa region of the World Bank, and coordinates impact evaluation work across sectors in the Africa region.
- For Valentine’s Day: of course there is a literature on this – from PhD comics comes abstracts of real papers such as “Me Do Wu My Val: The Creation of Valentine’s Day in Accra, Ghana”; and “Influence of Valentine’s Day and Halloween on Birth Timing”.
- Cyrus Samii on too much concern about “representativeness” and “generalizability”.