A while back, I had a blog post on a report for ActionAid, written by Alex Evans, on critical uncertainties for development between the present and 2020. One of the big uncertainties Alex identified were environmental shocks, though in that version of the report he limited these shocks to climate-driven environmental shocks. In my post, I suggested to Alex that he widen his scope, for environmental shocks might also include ecosystem collapse, such as in major global fisheries – such environmental shocks are not really related to climate change, but are still of great importance. The collapse of the Gulf of Guinea large marine ecosystem (largely due to commercial overfishing from places other than Africa) has devastated local fish hauls, lowering the availability of protein in the diets of coastal areas and driving enormous pressure on terrestrial fauna as these populations seek to make up for the lost protein. Alex was quite generous with my comments, and agreed with this observation wholeheartedly.
And then today, I stumbled on this – a simple visualization of Atlantic Fisheries in 1900 and 2000, by fish haul. The image is striking (click to expand):
Now, I have no access to the datasets used to construct this visualization, and therefore I can make no comments on its accuracy (the blog post on the Guardian site is not very illuminating). However, this map could be off by quite a bit in terms of how good hauls were in 1900, and how bad they are now, and the picture would still be very, very chilling. As I keep telling my students, all those new, “exotic” fish showing up in restaurants are not delicacies – they are just all that is left in these fisheries.
This is obviously a development problem, as it compromises livelihoods and food supplies. Yet I don’t see anyone addressing it directly, even aid organizations engaged with countries on the coast of the Gulf of Guinea, where this impact is most pronounced. And how long until even the rich really start to feel the pinch?
Go here to see more visualizations – including one of the reach of the Spanish fishing fleet that makes clear where the pressure on the Gulf of Guinea is coming from.
Category: Africa
On explanation in development research
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.
On field experience and playing poor
There is a great post up at Good on “Pretending to be Poor” experiments, where participants try to live on tiny sums of money (i.e. $1.50/day) to better understand the plight of the global poor. Cord Jefferson refers to this sort of thing as “playing poor”, at least in part because participants don’t really live on $1.50 a day . . . after all, they are probably not abandoning their secure homes, and probably not working the sort of dangerous, difficult job that pays such a tiny amount. Consuming $1.50/day is one thing. Living on it is entirely another. (h/t to Michael Kirkpatrick at Independent Global Citizen for pointing out the post).
This, for me, brings up another issue – the “authenticity” of the experiences many of us have had while doing fieldwork (or working in field programs), an issue that has been amplified by what seems to be the recent discovery of fieldwork by the RCT trials for development crowd (I still can’t get over the idea that they think living among the poor is a revolutionary idea). The whole point of participant observation is to better understand what people do and why they do it by experiencing, to some extent, their context – I find it inordinately difficult to understand how people even begin to meaningfully parse social data without this sort of grounding. In a concrete way, having malaria while in a village does help one come to grips with the challenges this might pose to making a living via agriculture in a rather visceral way. So too, living in a village during a drought that decimated a portion of the harvest, by putting me in a position where I had to go a couple of (intermittent) days without food, and with inadequate food for quite a few more, helped me to come to grips with both the capacity and the limitations of the livelihoods strategies in the villages I write about in Delivering Development, and at least a limited understanding of the feelings of frustration and inadequacy that can arise when things go wrong in rural Africa, even as livelihoods strategies work to prevent the worst outcomes.
But the key part of that last sentence was “at least a limited understanding.” Being there is not the same thing as sharing the experience of poverty, development, or disaster. When I had malaria, I knew what clinics to go to, and I knew that I could afford the best care available in Cape Coast (and that care was very good) – I was not a happy guy on the morning I woke up with my first case, but I also knew where to go, and that the doctor there would treat me comprehensively and I would be fine. So too with the drought – the villages I was living in were, at most, about 5 miles (8km) from a service station with a food mart attached. Even as I went without food for a day, and went a bit hungry for many more, I knew in the back of my mind that if things turned dire, I could walk that distance and purchase all of the food I needed. In other words, I was not really experiencing life in these villages because I couldn’t, unless I was willing to throw away my credit card, empty my bank account, and renounce all of my upper-class and government colleagues and friends. Only then would I have been thrown back on only what I could earn in a day in the villages and the (mostly appalling) care available in the rural clinic north of Eguafo. I was always critically aware of this fact, both in the moment and when writing and speaking about it since. Without that critical awareness, and a willingness to downplay our own (or other’s) desire to frame our work as a heroic narrative, there is a real risk in creating our own versions of “playing poor” as we conduct fieldwork.
I'm a talking head . . .
Geoff Dabelko, Sean Peoples, Schuyler Null and the rest of the good folks at the Environmental Change and Security Program at the Woodrow Wilson Center for Scholars were kind enough to interview me about some of the themes in Delivering Development. They’ve posted the video on te ECSP’s blog, The New Security Beat (you really should be checking them out regularly). So, if you want to see/hear me (as opposed to read me), you can go over to their blog, or just click below.
Homage to Misrata
Having lived in Spain for a couple of years, I’ve been deeply moved by the history of the Spanish Civil War, especially the experience of Catalunya during that war. For a moment, the anarchists actually ran Barcelona (such as anarchists can run anything at the scale of a city) and tried to create a vision of something different. Something not capitalist, not communist, but uniquely libratory. That vision was squashed between the Fascists of Franco and the Communists in their own ranks, both groups afraid of the challenge to their own political structures that the anarchists’ alternative vision posed. The world ignored the eradication of the anarchists, the West excusing their inaction in the name of halting the advance of communism.
While the parallels are weak, at best, I feel some sort of odd parallel between anarchist Barcelona and contemporary Misrata. In the fears that the Libyan rebels might be worse than Gaddafi are the echoes of those who feared the anarchists more than Fascism. The journalists like Tim Hetherington and Chris Hondros share something of a kinship with Orwell and his fellow fighters on the side of the anarchists, the ones who carried the story of Catalunya out of Barcelona as it fell, and never let the story die.
I wonder how history is going to judge our half-assed engagement in Libya.
Why do we insist on working at the national level again?
The BBC has posted an interesting map of Nigeria that captures the spatiality of politics, ethnicity, wealth, health, literacy and oil. There are significant problems with this map. The underlying data has fairly large error bars that are not acknowledged, and the presentation of the data is somewhat problematic; for example, the ethnic “areas” in the country are represented only by the majority group, hiding the heterogeneity of these areas, and other data is aggregated at the state level, blurring heterogenous voting patterns, incomes, literacy rates and health situations. I really wish that those who create this sort of thing would do a better job addressing some of these issues, and pointing out the issues they cannot address to help the reader better evaluate the data.
But even with all of these caveats, this map is a striking illustration of the problems with using national-level statistics to guide development policy and programs. Look at the distributions of wealth, health and literacy in the country – error bars or no, this data clearly demonstrates that national measures of wealth cannot guide useful economic policy, national measures of literacy might obscure regional or ethnic patterns of educational neglect, and national vaccination statistics tell us nothing about the regional variations in disease ecology and healthcare delivery that shape health outcomes in this country.
This is not to say that states don’t matter – they matter a lot. However, when we use national-scale data for just about anything, we are making very bad assumptions about the heterogeneity of the situation in that country . . . and we are probably missing key opportunities and challenges we should be addressing in our work.
Future challenges, future solutions
On Global Dashboard Alex Evans discusses a report he wrote for ActionAid on critical uncertainties for development between the present and 2020. Given Alex got to distill a bunch of futures studies, scenarios and outlooks into this report, I have to say this: I want his job.
The list he produces is quite interesting. In distilled form, they are:
1. What is the global balance of power in 2020?
2. Will job creation keep pace with demographic change to 2020?
3. Is there serious global monetary reform by 2020?
4. Who will benefit from the projected ‘avalanche of technology’ by 2020?
5. Will the world face up to the equity questions that come with a world of limits by 2020?
6. Is global trade in decline by 2020?
7. How has the nature of political influence changed by 2020?
8. What will the major global shocks be between now and 2020?
All are fair questions. And, in general, I like his 10 recommendations for addressing these challenges:
1. Be ready (because shocks will be the key drivers of change)
2. Talk about resilience (because the poor are in the firing line)
3. Put your members in charge (because they can bypass you)
4. Talk about fair shares (because limits change everything)
5. Specialise in coalitions (and not just of civil society organisations)
6. Take on the emerging economies (including from within)
7. Brings news from elsewhere (because innovation will come from the edges)
8. Expect failure (and look for the silver lining)
9. Work for poor people, not poor countries (as most of the former are outside the latter)
10. Be a storyteller (because stories create worldviews)
I particularly like #10 here, as it was exactly this idea that motivated me to write Delivering Development. And #7 is more or less the political challenge I lay out in the last 1/4 of the book. #9 is a clear reference to Andy Sumner’s work on the New Bottom Billion, which everyone should be looking at right now. In short, Alex and I are on the same page here.
I have two bits of constructive criticism to offer that I think would strengthen this report – and would be easy edits. First, I think Alex has made a bit of a mistake in limiting his concern for environmental shocks to climate shocks. These sorts of shocks are, of course, critical (hell, welcome to my current job), but there are other shocks out there that are perhaps not best captured as climate shocks on such a short timescale. For example, ecological collapse from overuse/misuse of ecosystem resources (see the Millennium Ecosystem Assessment) may have nothing at all to do with climate change – overfishing is currently crushing most major global fisheries, and the connection between this behavior and climate change is somewhat distant, at best. We’ve been driving several ecosystems off cliffs for some time now, and one wonders when resilience will fail and a state change will set in. It is near-impossible to know what the new state of a stressed ecosystem will be after a state change, so this is really a radical uncertainty we need to be thinking about.
Second, I am concerned that Stevens’ claim about the collapse of globalization bringing about “savage” negative impacts on the developing world. Such a claim strikes me as overgeneralized and therefore missing the complexity of the challenge such a collapse might bring – and it is a bit ironic, given his admonition to “talk about resilience” above. I think that some people (urban dwellers in particular) would likely be very hard hit – indeed, the term savage might actually apply to those who are heavily integrated into global markets simply by the fact they are living in large cities whose economies are driven by global linkages. And certainly those in marginal rural environments who are already subject to crop failure and other challenges will likely suffer greatly from the loss of market opportunities and perhaps humanitarian assistance (look at contemporary inland Somalia for an illustration of what I am talking about here). However, others (the bulk of rural farmers with significant subsistence components to their agricultural activities, or the option to convert activities to subsistence) have the option to pull back from market engagement and still make a stable living. Opportunity will certainly dry up for these people, at least for a while, as this is usually a strategy for managing temporary economic fluctuations. This is certainly a negative impact, for if development does nothing else, it must provide opportunities for people. However, this sort of negative impact doesn’t rise to “savage” – which to me implies famine, infant mortality, etc. I think we make all-to-easy connections between the failure of globalization/development (I’m not sure they are all that different, really, a point I discuss in Delivering Development). Indeed, a sustained loss of global connection might, in the long run, create a space for local innovations and market development that could lead to a more robust future.
So to “be ready” requires, I think, a bit of a broadening of our environmental concerns, and a major effort to engage the complexity of engagement with the global economy among the rural poor in the world. Both are quite doable – and are really minor edits to a very nice report (which I still wish I wrote).
Perspective
I sat through an outstanding FEWS-NET briefing today at work – some of the material falls under the heading of sensitive but unclassified (SBU), which basically means I can’t give details on it here. However, the publicly-available information from the briefing (link here – click on the near-term and medium-term tabs) makes it clear that there are really bad things taking place in parts of the Horn of Africa right now that are likely to result in large areas being extremely food insecure, which FEWS-NET defines as:
Households face substantial or prolonged shortfalls in their ability to meet basic food requirements. Reduced food intake is widespread, resulting in significantly increased rates of acute malnutrition and increasing mortality. Significant erosion of assets is occurring, and households are gradually moving towards destitution.
To summarize, people are dying due to food insecurity in the Horn of Africa right now, and it is going to get a whole lot worse for the next 6 or so months.
The briefing was very well run and presented, and the question session afterward was generally quite informative. FEWS-NET is a remarkable tool – I think it is probably the best food insecurity assessment tool in the world right now – and I am engaged with thinking about how to make their assessments and projections even more accurate. So I had a sort of technical disconnect from the meaning of the data during the briefing – to me, the numbers were data points that could be parsed differently to better understand what was actually taking place.
I returned to my desk, head buzzing with ways to reframe some of the analysis, but before I could get to writing anything down, an email came in telling me that the wife of one of my closest friends had passed away from ovarian cancer. She was 41, and leaves behind my friend and their very young son. For some reason, in that moment all of my data points became people, tens of thousands of mothers, fathers and children whose loss was beyond tragic.
That was it for me. I logged out, walked out of the office, and went to get my oldest daughter out of preschool an hour early. Somebody needs to parse the data, to reframe and retheorize what we see happening in places like the Horn of Africa so we can respond better and reduce the occurrence and impact of future events. But not me, not today.
Tomorrow, maybe.
What else we don't know about adaptation
RealClimate had an interesting post the other day about adaptation – specifically, how we bring together models that operate at the global-to-regional scales with an understanding of current and future impacts of climate change, which we feel at the local scale. This post was written from a climate science perspective – and so focuses on modeling capabilities and needs as related to the biophysical world. In doing so, I think that one key uncertainty in our use of downscaled models for adaptation planning is huge – the likely pathways of human response to changes in the climate over the next several decades. In places like sub-Saharan Africa, how people respond to climate change will have impacts on land use decisions, and therefore land cover . . . and land cover is a key component of local climate. In other words, as we downscale climate models, we need to start adding new types of data to them – social data on adaptation decision-making, so that we might project plausible future pathways and build them into these downscaled models.
For example, many modeling exercises currently suggest that a combination of temperature increases and changes in the amount and pattern of rainfall in parts of southern Africa will make it very difficult to raise maize there over the next few decades. This is a major problem, as maize is a staple of the region. So, what will people do? Will they continue to grow maize that is less hardy and takes up less CO2 and water as it grows, will they switch to a crop that takes up more CO2 than maize ever did, or will they begin to abandon the land and migrate to cities, creating pockets of fallow land and/or opening a frontier for mechanized agriculture (both outcomes likely to have significant impacts on greenhouse gas emissions and water cycling, among other things)? Simply put, we don’t really know. But we need to know, and we need to know with reasonably high resolution. That is, it is not enough to simply say “they will stop planting maize and plant X.” We need to know when this transition will take place. We need to know if it will happen suddenly or gradually. We need to know if that transition will itself be sustainable going forward, or if other changes will be needed in the near future. All of this information needs to be part of iterative model runs that capture land cover changes and biogeochemical cycling changes associated with these decisions to better understand future local pathways of climate change impacts and the associated likely adaptation pathways that these populations will occupy.
The good news* is that I am on this – along with my colleague Brent McCusker at West Virginia University (see pubs here and here). Between the two of us, we’ve developed a pretty solid understanding of adaptation and livelihoods decision-making, and have spent a good bit of time theorizing the link between land use change and livelihoods change to enable the examination of the issues I have raised above. We have a bit of money from NSF to run a pilot this summer (Brent will manage this while I am a government employee), and I plan to spend next year working on how to integrate this research program into the global climate change programming of my current employer.
Long and short: climate modelers, you need us social scientists, now more than ever. We’re here to work with you . . .
*Calling this good news presumes that you see me as competent, or at least that you see Brent as competent enough to make up for my incompetence.
Satellite Sentinels: We can do better than this (but it won't be as sexy)
The Satellite Sentinel Project released a report the other day that detailed what appears to be violence in the villages of Maker Abior and Todach in the Abeyei region of Sudan. The imagery in the report is fairly standard DigitalGlobe 60cm stuff – and nothing fancy has been done to it to enhance analysis – it’s not clear if the imagery is even georectified, though given its largely illustrative use it probably doesn’t matter. In the images are clearly burned buildings, and what certainly appear to be fortified areas where the Sudan Armed Forces are moving in equipment, fortifying defenses and improving storage facilities. They claim to have imagery related to a parallel buildup of forces on the South Sudan side of the border.
But what do these images really tell us that good, on-the-ground intelligence does not? Nothing. In fact, I would argue that these images might be leading to unwarranted conclusions . . . or the Satellite Sentinel Project needs to do a much better job of explaining how the imagery enhances their conclusions. For example:
- How are the structures on the South Sudan side of the border representative of military buildup? Do they share a construction or layout with other known military encampments? Or is this conclusion completely supplied by on-the-ground intelligence? If the answer is the latter, what exactly to these images add to the analysis?
- How are the burned structures in Maker Abior and Todach linked to the military buildup in the subsequent pictures? There is no imagery of an attack in progress – and there will likely never be this sort of smoking gun evidence from this project. Data is gathered irregularly, and often at fairly wide intervals – so what you will end up with are a lot of before and after photos that can only be explained by on the ground intelligence. In this case, it seems the on-the-ground intelligence has provided (at best) a weak link between this buildup and whatever happened in Maker Abior and Todach . . . but in presenting the imagery in this sort of a sequence, it appears that the evidence for the connection is much stronger than the data allows.
These are major issues that the project should be thinking through carefully. Inadvertent misrepresentation of events on the ground will greatly damage not only this project’s legitimacy, but indeed any efforts to use remotely sensed data to identify/verify events on the ground in this region.
Please note: I am NOT suggesting that there is no violence in the region, or that what is happening isn’t hugely problematic. However, I want our interpretations and responses to be based upon clear evidence, not loose circumstantial data strung together into potentially flimsy arguments about what has happened, and what might happen next.
So, what can we do about the problems in this region with this sort of data? Well, for one thing the project might think about how to use its considerable remotely-sensed imagery resources to fill some significant gaps in data and interpretation about the political economy of natural resources in this region. Abeyei has a long history of conflict between different groups using natural resources for their livelihoods – especially conflicts that occur when pastoral/semipastoral groups move their cattle through agricultural areas, damaging fields (this is a thin distinction – really, most everyone in this region makes a living through a mixture of pastoralism and agriculture. The question is which group’s crops are impacted by the other’s cattle.). This may be one of the most significant challenges facing this region – how to address this ongoing challenge, especially once there is a border dividing the transhumance routes these different groups have used to move their cattle to new watering and feeding areas. Given the potential impact of a border on these routes, and therefore access to needed natural resources, we’ve already seen the Dinka to the south and the Messiriya to the north laying out territorial and resources claims far in excess of any previously recognized situation. It is nearly impossible to adjudicate these claims because, as my colleague David Decker at the University of South Carolina – Sumter has argued, there is very little literature on the political ecology of this region. The bulk of our understanding of natural resources, livelihoods and political economy that we do have are derived from colonial accounts more than a half century old. With good intelligence, some serious on-the-ground research and the mobilization of people like David, and the integration of satellite imagery of the region that we can use to analyze (no more pretty pictures, just serious analysis) things like land cover, soil moisture, biomass, etc. we might at least create a stopgap for this knowledge gap that can then enable a settlement in this area that meets the widest range of livelihoods needs possible, lowering the potential for future conflict.