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Category: Development

Published February 2, 2012

US Food Aid Does Have an Impact in Developing Countries, Just Not the One You Think It Has (Updated)

A new working paper by Nathan Nunn and Nancy Qian:

This paper examines the effect of US food aid on conflict in recipient countries. To establish a causal relationship, we exploit time variation in food aid caused by fluctuations in US wheat production together with cross-sectional variation in a country’s tendency to receive any food aid from the United States. Our estimates show that an increase in US food aid increases the incidence, onset and duration of civil conflicts in recipient countries. Our results suggest that the effects are larger for smaller scale civil conflicts. No effect is found on interstate warfare.

This is bound to make waves among food policy scholars and in Washington, DC, where the Farm Bill, part of which sets guidelines for the provision of food aid, is due to be renewed this year.

I have not yet had a chance to read the paper (I’m teaching two classes this semester, so most of my reading time goes to those; I’ve been on the same “pleasure”-reading book since before Christmas), so please take the following with a grain of salt since it’s off the top of my head, but I wonder whether it might have made for cleaner identification to use weather shocks (specifically, extreme weather events and natural disasters) as a source of exogenous variation instead of fluctuations in US wheat production.

In other words, it could perhaps be the case that US wheat production affects conflict through means other than US food aid, so using unpredictable shocks to the supply of US food aid might make for more solid identification. But as I said, I have not yet had a chance to read the paper, and Nunn and Qian are both careful empiricists, so they probably address my concern somewhere in the paper.

UPDATE: Jon Prettyman, a Masters of Public Policy student advisee of mine, just emailed with this: “I saw the Nunn and Qian paper on several blogs today and I’m reading through it now, primarily because it sounds an awful lot like my thesis, and came across the answer to the question from your post.  They did use weather in an earlier draft of the paper, but found that wheat production yields similar estimates and is easier to interpret.”

Published February 1, 2012

Is It Time for a T Party in Impact Evaluation?

When we write a dynamic model in economics, we typically use the subscript t to denote a given time period, and we usually say that t = 1, 2, …, T, where T denotes the last time period considered by our model. Likewise, we usually use T to denote the number of time periods considered in a longitudinal data set.

With that in mind, the World Bank’s David McKenzie argues for more T in the experiments conducted by development economists in a forthcoming article in the Journal of Development Economics:

The vast majority of randomized experiments in economics rely on a single baseline and single follow-up survey. If multiple follow-ups are conducted, the reason is typically to examine the trajectory of impact effects, so that in effect only one follow-up round is being used to estimate each treatment effect of interest. While such a design is suitable for study of highly autocorrelated and relatively precisely measured outcomes in the health and education domains, this article makes the case that it is unlikely to be optimal for measuring noisy and relatively less autocorrelated outcomes such as business profits, household incomes and expenditures, and episodic health outcomes. Taking multiple measurements of such outcomes at relatively short intervals allows one to average out noise, increasing power. When the outcomes have low autocorrelation and budget is limited, it can make sense to do no baseline at all. Moreover, I show how for such outcomes, more power can be achieved with multiple follow-ups than allocating the same total sample size over a single follow-up and baseline. I also highlight the large gains in power from ANCOVA analysis rather than difference-in-differences analysis when autocorrelations are low and a baseline is taken. This article discusses the issues involved in multiple measurements, and makes recommendations for the design of experiments and related non-experimental impact evaluations.

This brings to mind what one of my friends who works in the microfinance industry had told me the last time we argued about the effects of microfinance on poverty: “It can take a long time to get out of poverty even in the best of scenarios, so evaluating the impact of microfinance after just one or two years tends to shortchange microfinance.”

Moreover, this makes me less worried about not having had the luxury of conducting a baseline survey for the randomized controlled trial I am conducting with Michael Carter and Catherine Guirkinger on the impacts of crop insurance on the welfare of cotton producers in southern Mali. Thankfully, we will be doing at least two rounds of follow-up survey in order to study the dynamic effects of our intervention, and we are working on finding funding for a third round.

UPDATE: In the time between the moment I wrote this post on Sunday morning and the moment it was published, David offered his own blog post on his paper on the World Bank’s Development Impact blog.

Published January 24, 2012

Contributing to Public Goods: My 20 Rules for Refereeing

The development economics blogosphere has been abuzz with talk of refereeing lately. Here are some words of advice from Quarterly Journal of Economics editor Larry Katz in an interview with Berk Özler on the Development Impacts blog, here is David McKenzie on the same blog, and here is Chris Blattman.

I cannot possibly claim to be among the best referees, but by my count, I have refereed 56 57 papers and two book manuscripts since 2005, and I do take pride in my refereeing, which might explain why I was asked to become associate editor at the American Journal of Agricultural Economics for 2012-2015.

As such, I figured I should chime in with my own advice about how to referee papers. I cannot say I always follow every single one of the 20 rules that follow but those are, by and large, the rules I try to live by as a referee. Some of those rules are derived from a similar list by Chris Barrett, who gave the students in his graduate empirical development micro class a list of such rules.

Because the following list is highly idiosyncratic, I would be very happy to hear about your own rules in the comments. And because this is a specialized post, I’m placing the list under the fold.

Continue readingContributing to Public Goods: My 20 Rules for Refereeing