Fri 20 May 2011
I was at a talk today where folks from Michigan State were presenting research and policy recommendations to guide the Feed the Future initiative. I greatly appreciate this sort of presentation – it is good to get real research in the building, and to see USAID staff that have so little time turn out in large numbers to engage. Once again, folks, its not that people in the agencies aren’t interested or don’t care, its a question of time and access.
In the course of one of the presentations, however, I saw a moment of “explanation” for observed behavior that nicely captures a larger issue that has been eating at me as the randomized control trials for development (RCT4D) movement gains speed . . . there isn’t a lot of explanation there. There is really interesting data, rigorously collected, but explanation is another thing entirely.
In the course of the presentation, the presenter put up a slide that showed a wide dispersion of prices around the average price received by farmers for their maize crops around a single market area (near where I happen to do work in Malawi). Nothing too shocking there, as this happens in Malawi, and indeed in many places. However, from a policy and programming perspective, it’s important to know that the average price is NOT the same thing as what a given household is taking home. But then the presenter explained this dispersion by noting (in passing) that some farmers were more price-savvy than others.
1) there is no evidence at all to support this claim, either in his data or in the data I have from an independent research project nearby
2) this offhand explanation has serious policy ramifications.
This explanation is a gross oversimplification of what is actually going on here – in Mulanje (near the Luchenza market area analyzed in the presentation), price information is very well communicated in villages. Thus, while some farmers might indeed be more savvy than others, the prices they are able to get are communicated throughout the village, thus distributing that information. So the dispersion of prices is the product of other factors. Certainly desperation selling is probably part of the issue (another offhand explanation offered later in the presentation). However, what we really need, if we want a rigorous understanding of the causes of this dispersion and how to address it, is a serious effort to grasp the social component of agriculture in this area – how gender roles, for example, shape household power dynamics, farm roles, and the prices people will sell at (this is a social consideration that exceeds explanation via markets), or how social networks connect particular farmers to particular purchasers in a manner that facilitates or inhibits price maximization at market. These considerations are both causal of the phenomena that the presenter described, and the points of leverage on which policy might act to actually change outcomes. If farmers aren’t “price savvy”, this suggests the need for a very different sort of intervention than what would be needed to address gendered patterns of agricultural strategy tied to long-standing gender roles and expectations.
This is a microcosm of what I am seeing in the RCT4D world right now – really rigorous data collection, followed by really thin interpretations of the data. It is not enough to just point out interesting patterns, and then start throwing explanations out there – we must turn from rigorous quantitative identification of significant patterns of behavior to the qualitative exploration of the causes of those patterns and their endurance over time. I’ve been wrestling with these issues in Ghana for more than a decade now, an effort that has most recently led me to a complete reconceptualization of livelihoods (shifting from understanding livelihoods as a means of addressing material conditions to a means of governing behaviors through particular ways of addressing material conditions – the article is in review at Development and Change). However, the empirical tests of this approach (with admittedly tiny-n size samples in Ghana, and very preliminary looks at the Malawi data) suggest that I have a better explanatory resolution for explained behaviors than possible through existing livelihoods approaches (which would end up dismissing a lot of choices as illogical or the products of incomplete information) – and therefore I have a better foundation for policy recommendations than available without this careful consideration of the social.
See, for example, this article I wrote on how we approach gender in development (also a good overview of the current state of gender and development, if I do say so myself). I empirically demonstrate that a serious consideration of how gender is constructed in particular places has large material outcomes on whose experiences we can understand, and therefore the sorts of interventions we might program to address particular challenges. We need more rigorous wrestling with “the social” if we are going to learn anything meaningful from our data. Period.
In summary, explanation is hard. Harder, in many ways, than rigorous data collection. Until we start spending at least as much effort on the explanation side as we do on the collection side, we will not really change much of anything in development.