Entries tagged with “Senegal”.
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Thu 28 Apr 2016
Posted by Ed under Adaptation, Climate Change, development, environment, Food Security, policy
Comments Off on He’s not a weatherman, and the rapper is not the star
As many of you know, I tend to post when provoked to rage by something in the press/literature/world. These days, I am massively overtasked, which means I need special levels of rage to post. So hooray to Tom Friedman, who in his utterly frustrating column yesterday actually managed to get me there.
I’m going to set aside my issues with the Friedman-standard reductionist crap in the column. Ken Opalo killed it anyway, so just read his post. Instead, I want to spend a few words excoriating Friedman for his lazy, stereotypical portrayal of my friend and colleague Ousmane Ndiaye in that column. First, as has been noted a few times, Ousmane is a climatologist with a Ph.D. This is NOT THE SAME THING AS A WEATHERMAN. Just Google the two, for heaven’s sake. What Ousmane is trained in is high-end physical science, and he is good at it. Really good at it.
But what is really remarkable about Ousmane, and totally elided in Friedman’s lazy, lazy writing, is that he is no office-bound monotonic weatherman. First, Ousmane is really, really funny. I’ve never seen him not funny, ever – even in serious meetings. Which makes me wonder how hard Friedman, who writes “”His voice is a monotone,” is working to fit Ousmane into the box of “scientist” as Friedman understands it.
Second, Ousmane does remarkable work engaging farmers across Senegal. I have seen him in farmer meetings, talking about seasonal forecasts. He cares deeply about these farmers, and how well he is able to communicate forecasts to them. I’ve also seen him at Columbia University, in scientific meetings, moving between professors and development donors, talking about new ideas and new challenges that need to be addressed. He moves between these worlds easily, a skill far too lacking in the climate change community.
What I am saying here is simple: Friedman missed the fact that he had the star right in front of him, clicking away at the computer. He needed a counterpoint for his rapper, and a sad caricature of Ousmane became that counterpoint. And because of the need to present Ousmane as the boring scientist, Friedman totally missed how unbelievably apocalyptic the figures he was hearing really are, especially for rain-fed agriculturalists in Senegal. A 2C rise in temperature over the last 60 or so years means that, almost certainly, some varieties of important cereals are no longer germinating, or having trouble germinating. The fact Senegal is currently 5C over normal temperature is unholy – and were this to hold up, would totally crush this year’s harvest (planting starts in about a month, so keep an eye on this) because very little would germinate properly at that level.
Ousmane was describing the apocalypse, and Friedman was fixated on a clicking mouse. Friedman owes Ousmane an apology for this pathetic caricature, and he owes the rest of us an apology for the ways in which his lazy plot and the characters he needed to occupy it resulted in a complete burial of the lede: climate change is already reaching crisis levels in some parts of the world.
P.S., if you want to see some of the work that has started to emerge from working alongside Ousmane, check out this and this.
Tue 31 Dec 2013
First up on my week up update posts is a re-introduction to my reworked livelihoods approach. As some of you might remember, the formal academic publication laying out the theoretical basis for this approach came out in early 2013. This approach presented in the article is the conceptual foundation for much of the work we are doing in my lab. This pub is now up on my home page, via the link above or through a link on the publications page.
The premise behind this approach, and why I developed it in the first place, is simple. Most livelihoods approaches implicitly assume that the primary motivation for livelihoods decisions is the maximization of some sort of material return on that activity. Unfortunately, in almost all cases this is a massive oversimplification of livelihoods decision-making processes, and in many cases is fundamentally incorrect. Think about the number of livelihoods studies where there are many decisions or behaviors that seem illogical when held up to the logic of material maximization (which would be any good livelihoods study, really). We spend a lot of time trying to explain these decisions away (idiosyncrasy, incomplete information, etc.). But this makes no sense – if you are living on $1.25 a day, and you are illogical or otherwise making decisions against interest, you are likely dead. So there must be a logic behind these decisions, one that we must engage if we are to understand why people do what they do, and if we are to design and implement development interventions that are relevant to the needs of the global poor. My livelihoods approach provides a means of engaging with and explaining these behaviors built on explicit, testable framings of decision-making, locally-appropriate divisions of the population into relevant groupings (i.e. gender, age, class), and the consideration of factors from the local to the global scale.
The article is a straight-ahead academic piece – to be frank, the first half of the article is not that accessible to those without backgrounds in social theory and livelihoods studies. However, the second half of the article is a case study that lays out what the approach allows the user to see and explain, which should be of interest to most everyone who works with livelihoods approaches.
For those who would like a short primer on the approach and what it means in relatively plain English, I’ve put up a “top-line messages” document on the preprints page of my website.
Coming soon is an implementation piece that guides the user through the actual use of the approach. I field-tested the approach in Kaffrine, Senegal with one of my graduate students from May-July 2013. I am about to put the approach to work in a project with the Red Cross in the Zambezi Basin in Zambia next month. In short, this is not just a theoretical pipe dream – it is a real approach that works. In fact, the reason we are working with Red Cross is because Pablo Suarez of Boston University and the Red Cross Climate Centre read the academic piece and immediately grasped what it could do, and then reached out to me to bring me into one of their projects. The implementation piece is already fully drafted, but I am circulating it to a few people in the field to get feedback before I submit it for review or post it to the preprints page. I am hoping to have this up by the end of January. Once that is out the door, I will look into building a toolkit for those who might be interested in using the approach.
I’m really excited by this approach, and the things that are emerging from it in different places (Mali, Zambia, and Senegal, at the moment). I would love feedback on the concept or its use – I’m not a defensive or possessive person when it comes to ideas, as I think debate and critique tend to make things stronger. The reason I am developing a new livelihoods approach is because the ones we have simply don’t explain the things we need to know, and the other tools of development research that dominate the field at the moment (i.e. RCTs) cannot address the complex, integrative questions that drive outcomes at the community level. So consider all of this a first draft, one that you can help bring to final polished form!
Wed 29 May 2013
Posted by Ed under Academia, Africa, Climate Change, Delivering Development, development, Development Institutions, Livelihoods, policy, research
Comments Off on Why big panel/baseline surveys often set us back, and why it doesn’t have to be that way:
I’ve just spent nearly three weeks in Senegal, working on the design, monitoring, and evaluation of a CCAFS/ANACIM climate services project in the Kaffrine Region. It was a fantastic time – I spent a good bit of time out in three villages in Kaffrine implementing my livelihoods as governmentality approach (for now called the LAG approach) to gather data that can inform our understanding of what information will impact which behaviors for different members of these communities.
This work also included a week-long team effort to build an approach to monitoring and evaluation for this project that might also yield broader recommendations for M&E of climate services projects in other contexts. The conversations ranged from fascinating to frustrating, but in the process I learned an enormous amount and, I think, gained some clarity on my own thinking about project design, monitoring, and evaluation. For the purposes of this blog, I want to elaborate on one of my long-standing issues in development – the use of panel surveys, or even broad baseline surveys, to design policies and programs.
At best, people seem to assume that the big survey instrument helps us to identify the interesting things that should be explained through detailed work. At worst, people use these instruments to identify issues to be addressed, without any context through which to interpret the patterns in the data. Neither case is actually all that good. Generally, I often find the data from these surveys to be disaggregated/aggregated in inappropriate manners, aimed at the wrong issues, and rife with assumptions about the meaning of the patterns in the data that have little to do with what is going on in the real world (see, for example, my article on gendered crops, which was inspired by a total misreading of Ghanaian panel survey data in the literature). This should be of little surprise: the vast bulk of these tools are designed in the abstract – without any prior reference to what is happening on the ground.
What I am arguing here is simple: panel surveys, and indeed any sort of baseline survey, are not an objective, inductive data-gathering process. They are informed by assumptions we all carry with us about causes and effects, and the motivations for human behavior. As I have said time and again (and demonstrated in my book Delivering Development), in the world of development these assumptions are more often than not incorrect. As a result, we are designing broad survey instruments that ask the wrong questions of the wrong people. The data from these instruments is then interpreted through often-inappropriate lenses. The outcome is serious misunderstandings and misrepresentations of life on globalization’s shoreline. These misunderstandings, however, carry the hallmarks of (social) scientific rigor even as they produce spectacular misrepresentations of the decisions, events, and processes we must understand if we are to understand, let alone address, the challenges facing the global poor. And we wonder why so many projects and policies produce “surprise” results contrary to expectations and design? These are only surprising because the assumptions that informed them were spectacularly wrong.
This problem is easily addressed, and we are in the process of demonstrating how to do it in Kaffrine. There are baseline surveys of Kaffrine, as well as ongoing surveys of agricultural production by the Senegalese agricultural staff in the region. But none of these is actually tied to any sort of behavioral model for livelihoods or agricultural decision-making. As a result, we can’t rigorously interpret any patterns we might find in the data. So what we are doing in Kaffrine (following the approach I used in my previous work in Ghana) is spending a few weeks establishing a basic understanding of the decision-making of the target population for this particular intervention. We will then refine this understanding by the end of August through a full application of the LAG approach, which we will use to build a coherent, complex understanding of livelihoods decision-making that will define potential pathways of project impact. This, in turn, will shape the design of this program in future communities as it scales out, make sense of the patterns in the existing baseline data and the various agricultural services surveys taking places in the region, and enable us to build simple monitoring tools to check on/measure these pathways of impact as the project moves forward. In short, by putting in two months of serious fieldwork up front, we will design a rigorous project based on evidence for behavioral and livelihoods outcomes. While this will not rule out surprise outcomes (African farmers are some pretty innovative people who always seem to find a new way to use information or tools), I believe that five years from now any surprises will be minor ones within the framework of the project, as opposed to shocks that result in project failure.
Incidentally, the agricultural staff in Kaffrine agrees with my reading of the value of their surveys, and is very excited to see what we can add to the interpretation of their data. They are interested enough to provide in-town housing for my graduate student, Tshibangu Kalala, who will be running the LAG approach in Kaffrine until mid-July. Ideally, he’ll break it at its weak points, and by late July or early August we’ll have something implementable, and by the end of September we should have a working understanding of farmer decision-making that will help us make sense of existing data while informing the design of project scale up.