Tag: livelihoods

On livelihoods and global environmental change

In a comment on my earlier post critiquing the recent ENSO and conflict piece that appeared in nature Nature , Joe pointed out that my argument that the authors of the piece did not understand livelihoods was not necessarily clear to the reader.  I think this is completely fair – I am buried in livelihoods . . . it is a concept at the core of what I have researched for the past 14 years, and therefore what may seem obvious to me is not so obvious to everyone else.
First, to clarify: I think the top-line issue I was shorthanding in my response to Solomon was the causal framework: it is totally unclear to me how they think environmental change is translated into conflict.  It is possible that they had no explicit notion of how this connection is made, but I think that would create an enormous set of problems for the study as it would make it impossible to know what variables to control for in the study (to some extent, I think this is a problem with the study anyway).  However, the study, and Solomon’s response, led me to believe that they did have a very basic framing of this connection, where weather impacts livelihoods which impacts behavior.  In this apparent framing, it seems to me that they treated livelihoods as a straightforward set of activities – and the impact of weather on those activities could be easily and generally understood, and the human outcomes of those impacts could also be easily and generally understood.  If this is true, it is a serious misunderstanding of livelihoods.
There is a lot of stuff I could say about livelihoods – my current intellectual project involves rethinking how we understand livelihoods, because I think current analytical frameworks cannot really engage with actual livelihoods decision-making on the ground.  As a result, a lot of our understandings of what people do, and why they do it, are wide of the mark, and the interventions we design to improve/augment/replace existing means of making a living in particular places are often misguided and prone to “surprise” outcomes.
First, a quick definition of livelihoods as they are treated in the contemporary literature: “the capabilities, assets (stores, resources, claims and access) and activities required for a means of living” (Chambers and Conway, 1992:7).  As Brent McCusker and I have argued:

this definition of livelihoods moves past income toward a more holistic consideration of the manner in which a person obtains a living. In practice, this definition has resulted in a number of approaches to livelihoods that focus closely on access to various types of assets drawn upon by individuals to make a living. These approaches tend to categorize these assets as one of five types of capital: natural, physical, human, financial and social. Land comes under natural capital, “the natural resource base (land, water, trees) that yields products utilized by human populations for their survival,” though an improved field might come under the heading of physical capital, which generally includes “assets brought into existence by economic production processes.”

My problem with the livelihoods approach that dominates the literature, and subtly undergirds the Nature piece I was critiquing, is not the broad definition of livelihoods.  Instead, the problem lies in the subtle assumption of this approach that, in its focus on the requirements for a means of living, concentrates on material circumstances and outcomes as a metric for the success and viability of particular livelihoods.  As I have demonstrated repeatedly (for example here, and in my book Delivering Development), livelihoods are double-edged: they are aimed at both meeting certain material requirements of life and maintaining the privileges of the powerful.  Above certain very, very low thresholds, the social goals of livelihoods actually trump the material goals.  Therefore, if we want to understand livelihoods decisions and outcomes, we must understand the social context at least as well as we do the material conditions in a particular place.  Using generalized assumptions about human motivations to explain responses to livelihoods shifts will smooth over really significant differences in decision-making, and therefore obscure any possible causal connection between things like environmental change and the incidence of conflict – material maximization/deprivation is only part of the story of human motivations, and a relatively small part at that.
How does this all relate to the Nature piece and my criticism? While the authors never specified the means by which this would happen in the piece, only offering general speculation in their response to my criticism, I found Solomon’s response to my blog post really telling:

The study is trying to understand whether choosing to engage in conflict is a “livelihood decision” that individuals in modern societies select more often when El Nino events occur. Our findings tells us that for some reason, people’s willingness to engage in organized violence changes when the global climate changes. One hypothesis is that perhaps “predation” (i.e. the forceful extraction of property from others) is a form of “adaptation” to climate changes.

It is possible that Solomon’s reference to conflict as a livelihoods decision was simply echoing the terms of my criticism.  However, both the article and his response seems to reflect an implicit framing of the environment-to-conflict connection as somehow passing through livelihoods in a straightforward manner.  Because the authors never actually unpack how the environment impacts livelihoods, and in turn how those impacts are translated into human impacts, they become guilty of the same issue that plagues nearly everyone using the livelihoods framework these days: they implicitly embrace an over-generalized framing of livelihoods decisions that relies too heavily on a relatively minor driver of decision-making (material conditions), and completely ignores the dominant factors that shape the character of particular activities and therefore result in particular outcomes for the well-being of those living under that strategy.  I am sure that predation does occur.  I am also absolutely certain that this is not a general response – it does not happen very often (plenty of empirical studies show other behaviors).  It is not interesting to know that it occurs – we already know that.  What is interesting and important is why it occurs.  Going for “story time” explanations of complex behavior does not contribute to our understanding of human behavior, or the impact of climate change on human well-being.
I am working on a reframing of livelihoods that elevates the social component to its proper place in livelihoods decision-making (in review at the Journal of Development Studies).  The thinking behind this reframing is intensely theoretical and really, really academic (for a taste of what I mean, see this piece I wrote with Brent).  My goal in the forthcoming piece is to take this really esoteric theory and turn it into an approach that can be understood and employed widely.  With any luck it will be accepted and published relatively soon . . . I will put up a pre-print as soon as I am able.  But even with this reframing, we are going to have to work really hard at understanding when large-scale studies such as the one I have been critiquing are appropriate for furthering our understanding of things we really need to know, when they merely illustrate what we already know, and when they present really problematic findings with a misleading level of certainty.



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Conflict and El Nino: How did this get through peer review?

I knew it was going to be a bad day when I opened my email this morning to a message from a colleague that linked to a new study in Nature: “Civil conflicts are associated with the global climate.” (the actual article is paywalled).  Well, that is assertive . . . especially because despite similar claims in the past, I have yet to see any study make such a definitive, general connection successfully.  Look, the problem here is simple: the connection between conflict and the environment is shaky, at best. For all of the attention that Thomas Homer-Dixon gets for his work, the simple fact is that for interstate conflict, there are more negative cases than positive case . . . that is, where a particular environmental stressor exists, conflict DOES NOT happen far more often than it does.  Intrastate conflict is much, much more complex, though there are some indications that the environment does play a triggering/exacerbating role in conflict at this scale.
Sadly, this article does not live up to its claims.  It is horrifically flawed, to the point that I cannot see how its conclusions actually tell us anything about the relationship between El Nino and conflict, let alone climate and conflict.  Even a cursory reading reveals myriad problems with the framing of the research design, the regression design, and the interpretation of the regression outputs (though, to be honest, the interpretation really didn’t matter, as whatever was coming out of the regressions was beyond salvation anyway) that lead me to question how it even got through peer review.  My quick take:
Let’s start with the experimental design:

… We define annual conflict risk (ACR) in a collection of countries to be the probability that a randomly selected country in the set experiences conflict onset in a given year. Importantly, this ACR measure removes trends due to the growing number of countries.

In an impossible but ideal experiment, we would observe two identical Earths, change the global climate of one and observe whether ACR in the two Earths diverged. In practice, we can approximate this experiment if the one Earth that we do observe randomly shifts back and forth between two different climate states. Such a quasi-experiment is ongoing and is characterized by rapid shifts in the global climate between La Niña and El Niño.

This design makes sense only if you assume that the random back-and-forth shifting did not trigger adaptive livelihoods decisions that, over time, would have served to mitigate the impact of these state shifts (I am being generous here and assuming the authors do not think that changes in rainfall directly cause people to start attacking one another, though they never really make clear the mechanisms linking climate states and human behavior).  The only way to assume non-adaptive livelihoods is to know next to nothing about how people make livelihoods decisions.  Assuming that these livelihoods are somehow optimized for one state or the other such that a state change would create surprising new conditions that introduced new stresses is more or less to assume that the populations affected by these changes were somehow perpetually surprised by the state change (even though it happened fairly frequently).  After 14 years of studying rural livelihoods in sub-Saharan Africa, I find that absolutely impossible to believe.  Flipping back and forth between states does not give you two Earths, it gives you one Earth that presented certain known challenges to people’s livelihoods.

To identify a relation between the global climate and ACR, we compare societies with themselves when they are exposed to different states of the global climate. Heuristically, a society observed during a La Niña is the ‘control’ for that same society observed during an El Niño ‘treatment’.

No, it is not.  This is a false parsing of the world, and as a result they are regressing junk.
This is not the only problem with the research design. Another huge problem with this study is its treatment of the impact of ENSO-related state changes on people.  These state changes in the climate do not have the same impact everywhere, even in strongly teleconnected places.  The ecology and broader environment of the tropics is hardly monolithic (though it is mostly treated this way), and a strong teleconnection can mean either drought or flooding . . . in other words, the el Nino teleconnection creates a variety of climatological phenomena that play out in a wide range of environments that are exploited by an even larger number of livelihoods strategies, creating myriad environmental and human impacts.  These impacts cannot be aggregated into a broad driver of conflict – basically, their entire regression (which, mind you, is framed around a junk “counterfactual”) is populated with massively over-aggregated data such that any causal signal is completely lost in the noise.
Most reasonable approaches to the environment-conflict connection now treat environmental stresses as an exacerbating factor, or even a trigger, for other underlying factors.  Such an approach seems loosely borne out in the Nature article.  The authors note that in the “teleconnected group, low-income countries are the most responsive to ENSO, whereas similarly low income countries in the weakly affected group do not respond significantly to ENSO.”  This certainly sounds like a broad stressor (state change in the climate) is influencing other, more directly pertinent drivers of conflict.  But then we get to their statement of limitations:

Although we observe that the ACR of low-income countries is most strongly associated with ENSO, we cannot determine if (1) they respond strongly because they are low-income, (2) they are low income because they are sensitive to ENSO, or (3) they are sensitive to ENSO and low income for some third unobservable reason. Hypothesis (1) is supported by evidence that poor countries lack the resources to mitigate the effects of environmental changes. However, hypothesis (2) is plausible because ENSO existed before the invention of agriculture and conflict induces economic underperformance.

Even here, they have really oversimplified things: the way this is framed, either the environment causes the conflict (pretty much established by the literature that this is not the case), the environment causes economic problems that cause the conflict, or it is something else entirely.  Every other possible factor in the world is in that third category, and most current work on this subject concentrate on other drivers of conflict (only some of which are economic) and how they intersect with environmental stresses.
This paper is a mess.  But it got into print and made waves in a lot of popular outlets (for example, here and here).  Why?  Because it is reviving the long-dead corpse of environmental determinism…people really want the environment to in some way determine human behavior (we like simple explanations for complex events), even if that determination takes place via influences nuanced by local environmental variation, etc.  Environmental determinism fell apart in the face of empirical evidence in the 1930s.  But it makes for a good, simple narrative of explanation where we can just blame conflict on climate cycles that are beyond our control, and look past the things like colonialism that created the foundation for modern political economies of conflict.  This absolves the Global North of responsibility for these conflicts, and obscures the many ways in which these conflicts could be addressed productively.



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Why should the aid/relief/development community care about global environmental change (Pt. 3)?

OK, ok, you say: I get it, global environmental change matters to development/aid/relief.  But aside from thinking about project-specific intersections between the environment and development/aid/relief, what sort of overarching challenges does global environmental change pose to the development community?  Simply put, I think that the inevitability of various forms of environmental change (a level of climate change cannot be stopped now, certain fisheries are probably beyond recovery, etc.) over the next 50 or so years forces the field of development to start thinking very differently about the design and evaluation of policies, programs, and projects . . . and this, in turn, calls into question the value of things like randomized control trials for development.
In aid/development we tend to be oriented to relatively short funding windows in which we are supposed to accomplish particular tasks (which we measure through output indicators, like the number of judges trained) that, ideally, change the world in some constructive manner (outcome indicators, like a better-functioning judicial system).  Outputs are easier to deliver and measure than outcomes, and they tend to operate on much shorter timescales – which makes them perfect for end-of-project reporting even though they often bear little on the achievement of the desired outcomes that motivated the project in the first place (does training X judges actually result in a better functioning judicial system?  What if the judges were not the problem?).  While there is a serious push in the development community to move past outputs to outcomes (which I generally see as a very positive trend), I do not see a serious conversation about the different timescales on which these two sorts of indicators operate.  Outputs are very short-term.  Outcomes can take generations.  Obviously this presents significant practical challenges to those who do development work, and must justify their expenditures on an annual basis.
This has tremendous implications, I think, for development practice in the here and now – especially in development research.  For example, I think this pressure to move to outcomes but deliver them on the same timescale as outputs has contributed to the popularity of the randomized control trials for development (RCT4D) movement.  RCT4D work gathers data in a very rigorous manner, and subjects it to interesting forms of quantitative analysis to determine the impact of a particular intervention on a particular population.  As my colleague Marc Bellemare says, RCTs establish “whether something works, not how it works.”
The vast majority of RCT4D studies are conducted across a few months to years, directly after the project is implemented.  Thus, the results seem to move past outputs to impacts without forcing everyone to wait a very long time to see how things played out.  This, to me, is both a strength and a weakness of the approach . . . though I never hear anyone talking about it as a weakness.  The RCT4D approach seems to suggest that the evaluation of project outcomes can be effectively done almost immediately, without need for long-term follow-up.  This sense implicitly rests on the forms of interpretation and explanation that undergird the RCT4D approach – basically, what I see as an appallingly thin approach to the interpretation of otherwise interesting and rigorously gathered data. My sense of this interpretation is best captured by Andrew Gelman’s (quoting Fung) use of the term “story time”, which he defines as a “pivot from the quantitative finding to the speculative explanation.” It seems that many practitioners of RCT4D seem to think that story time is unavoidable . . . which to me reflects a deep ignorance of the concerns for rigor and validity that have existed in the qualitative research community for decades.  Feel free to check the methods section of any of my empirically-based articles (i.e. here and here): they address who I interviewed, why I interviewed them, how I developed interview questions, and how I knew that my sample size had grown large enough to feel confident that it was representative of the various phenomena I was trying to understand.  Toward the end of my most recent work in Ghana, I even ran focus groups where I offered my interpretations of what was going on back to various sets of community members, and worked with them to strengthen what I had right and correct what I had wrong.  As a result, I have what I believe is a rigorous, highly nuanced understanding of the social causes of the livelihoods decisions and outcomes that I can measure in various ways, qualitative and quantitative, but I do not have a “story time” moment in there.
The point here is that “story time”, as a form of explanation, rests on uncritical assumptions about the motivations for human behavior that can make particular decisions or behaviors appear intelligible but leave the door open for significant misinterpretations of events on the ground.  Further, the very framing of what “works” in the RCT4D approach is externally defined by the person doing the evaluation/designing the project, and is rarely revised in the face of field realities . . . principally because when a particular intervention does not achieve some externally-defined outcome, it is deemed “not to have worked.”  That really tends to shut down continued exploration of alternative outcomes that “worked” in perhaps unpredictable ways for unexpected beneficiaries.  In short, the RCT4D approach tends to reinforce the idea that development is really about delivering apolitical, technical interventions to people to address particular material needs.
The challenge global environmental change poses to the RCT4D randomista crowd is that of the “through ball” metaphor I raised in my previous post.  Simply put, identifying “what works” without rigorously establishing why it worked is broadly useful if you make two pretty gigantic assumptions: First, you have to assume that the causal factors that led to something “working” are aspects of universal biophysical and social processes that are translatable across contexts.  If this is not true, an RCT only gives you what works for a particular group of people in a particular place . . . which is not really that much more useful than just going and reading good qualitative ethnographies.  If RCTs are nothing more than highly quantified case studies, they suffer from the same problem as ethnography – they are hard to aggregate into anything meaningful at a broader scale.  And yes, there are really rigorous qualitative ethnographies out there . . .
Second, you have to assume that the current context of the trial is going to hold pretty much constant going forward.  Except, of course, global environmental change more or less chucks that idea for the entire planet.  In part, this is because global environmental change portends large, inevitable biophysical changes in the world.  Just because something works for improving rain-fed agricultural outputs today does not mean that the same intervention will work when the enabling environmental conditions, such as rainfall and temperature, change over the next few decades.  More importantly, though, these biophysical changes will play out in particular social contexts to create particular impacts on populations, who will in turn develop efforts to address those impacts. Simply put, when we introduce a new crop today and it is taken up and boosts yields, we know that it “worked” by the usual standards of agricultural development and extension.  But the take-up of new crops is not a function of agricultural ecology – there are many things that will grow in many places, but various social factors ranging from the historical (what crops were introduced via colonialism) to gender (who grows what crops and why) are what lead to particular farm compositions.  For example, while tree crops (oil palm, coconut, various citrus, acacia for charcoal) are common on farms around the villages in which I have worked in Ghana, almost none of these trees are found on women’s farms.  The reasons for this are complex, and link land tenure, gender roles, and household power relations into livelihoods strategies that balance material needs with social imperatives (for extended discussions, see here and here, or read my book).
Unless we know why that crop was taken up, we cannot understand if the conditions of success now will exist in the future . . . we cannot tell if what we are doing will have a durable impact.  Thus, under the most reliable current scenario for climate change in my Ghanaian research context, we might expect the gradual decline in annual precipitation, and the loss of the minor rainy season, to make tree crops (which tend to be quite resilient in the face of fluctuating precipitation) more and more attractive.  However, tree crops challenge the local communal land tenure system by taking land out of clan-level recirculation, and allowing women to plant them would further challenge land tenure by granting them direct control over access to land (which they currently lack).  Altering the land tenure system would, without question, set off a cascade of unpredictable social changes that would be seen in everything from gender roles to the composition of farms.  There is no way to be sure that any development intervention that is appropriate to the current context will be even functional in that future context.  Yet any intervention we put into place today should be helping to catalyze long-term changes . . .
Simply put: Global environmental change makes clear the limitations of our current thinking on aid/development (of which RCT4D is merely symptomatic).   Just like RCTs, our general framing of development does not move us any closer to understanding the long-term impact of our interventions.  Further, the results of RCTs are not generalizable past the local context (which most good randomistas already know), limiting their ability to help us transform how we do development.  In a world of global environmental change, our current approaches to development just replicate our existing challenges: they don’t really tell us if what we are doing will be of any lasting benefit, or even teach us general lessons about how to deliver short-term benefits in a rigorous manner.
 
Next up: The Final Chapter – Fixing It



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Much of the scientific case for climate change, in a single graphic

This graphic, from Skeptical Science, is just awesome. I spend a good bit of my time thinking about climate change and its impacts on the global poor – mostly how we might address both global poverty and climate change, maximizing synergies and minimizing trade-offs between these efforts.  I’ve been a lead author of two major global environmental assessments (the Millennium Ecosystem Assessment and GEO-4) and I am now a review author of the IPCC’s AR5.  Despite all of this, I find that people still question my understanding of climate change – they want me to be deluded by false data, or somehow motivated by another political agenda that I can only accomplish through an environmental hoax.  In short, they want me to be either stupid or a liar.  Not that anyone will say that to my face, of course, but that is really what it boils down to.
So, I greatly appreciate when someone comes up with a means of communicating what we know about the changing climate that is both simple and clear.  In one post, Skeptical Science has managed this.  Everyone should take a look and have a quick read.  First, the graphic:

Second, the explanation of the graphic:
1) If greenhouse warming is taking place, the stratosphere should cool while the troposphere warms (heat is being trapped in the troposphere). Check.
2) If greenhouse warming is taking place, nights should warm faster than days, as the nighttime radiation of heat into space will be limited by the greenhouse effect. Check.
3) For similar reasons, if greenhouse warming is taking place, winters should warm faster than summers. Check.
4) If greenhouse warming is taking place, and #1 is true, the troposphere/stratosphere boundary should rise as the warmer troposphere expands relative to the stratosphere. Check.
5) If greenhouse warming is taking place, out of the total carbon we find in the atmosphere, a rising percentage will be fossil carbon.  There is really only one way for a lot of fossil carbon into the atmosphere, and that is burning fossil fuels (remember, oil, natural gas and coal come from the decomposition of long-dead animals). Check.
6) If greenhouse warming is taking place, the oceans should be warming up overall, not shifting heat around.  Check.
In short, every theoretical predictor of the greenhouse effect is being realized in empirical measurement – again, not models, but the actual instrument record.  So, unless folks are willing to argue that all thermometers, weather satellites, weather balloons, and tools for measuring atmospheric chemistry are wrong or somehow perverted to a hoax, there is no empirical basis to argue that greenhouse warming is not taking place – nor is there much of an argument to be made, given the rising presence of fossil carbon in the atmosphere, that humans have nothing to do with it . . .
Time to start dealing with reality, instead of denying it.  What is happening in the global climate is affecting how we do development – or at least it should be.  Changes in the global climate have manifest in various environmental shifts that in turn are impacting livelihoods, migration decisions, and the food security of the global poor.  I’ll address this in a subsequent post . . .

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Stories, development and adaptation

Mike Hulme has an article in the July issue of Nature Climate Change titled “Meet the humanities,”[paywalled] in which he argues that “An introduction needs to be made between the rich cultural knowledge of social studies and the natural sciences.”  Overall, I like this article – Hulme understands the social science side of things, not least through his own research and his work as editor of Global Environmental Change, one of the most influential journals on the human dimensions of global change*.  Critically, he lays out how, even under current efforts to include a wider range of disciplines in major climate assessments, the conversation has been dominated for so long by the biophysical sciences and economics that it is difficult for other voices to break in:

policy discussions have become “improving climate predictions” and “creating new economic policy instruments”; not “learning from the myths of indigenous cultures” or “re-thinking the value of consumption.”

Hulme is not arguing that we are wrong to be trying to improve climate predictions or develop new economic policy instruments – instead, he is subtly asking if these are the right tools for the job of addressing climate change and its impacts.  My entire research agenda is one of unearthing a greater understanding of why people do what they do to make a living, how they decide what to do when their circumstances change, and what the outcomes of those decisions are for their long-term well being.  Like Hulme, I am persistently surprised at the relative dearth of work on this subject – especially because the longer I work on issues of adaptation and livelihoods, the more impressed I am with the capacity of communities to adjust to new circumstances, and the less impressed I am with anyone’s ability to predictably (and productively) intervene in these adjustments.
This point gets me to my motivation for this post.  Hulme could not cover everything in his short commentary, but I felt it important to identify where a qualitative social science perspective can make an immediate impact on how we think about adaptation (which really is about how we think about development, I think).   I remain amazed that so many working in development fail to grasp that there is no such things as a completely apolitical, purely technical intervention. For example, in development we all too often assume that a well is just a well – that it is a technical intervention that delivers water to people.  However, a well is highly political – it reshapes some people’s lives, alters labor regimes, could empower women (or be used as an excuse to extract more of their labor on farms, etc.) – all of this is contextual, and has everything to do with social relations and social power.  So, we can introduce the technology of a well . . . but the idea and meaning of a well cannot be introduced in the same manner – these are produced locally, through local lenses. It is this basic failure of understanding that lies at the heart of so many failed development projects that passed technical review and various compliance reviews: they were envisioned as neutral and technical, and were probably very well designed in those arenas.  However, these project designers gave little concern to the contextual, local social processes that would shape the use and outcomes of the intervention, and the result was lots of “surprise” outcomes.
When we start to approach these issues from a qualitative social scientific standpoint, or even a humanities standpoint (Hulme conflates these in his piece, I have no idea why.  They are not the same), the inherent politics of development become inescapable.  This was the point behind my article “The place of stories in development: creating spaces for participation through narrative analysis.”  In that article, I introduce the story I used to open Delivering Development to illustrate how our lived experience of development often plays out in ways best understood as narratives, “efforts to present information as a sequence of connected events with some sort of structural coherence, transforming ‘the real into an object of desire through a formal coherence and a moral order that the real.”  These narratives emerge in the stories we are told and that we overhear in the course of our fieldwork, but rarely make it into our articles or reports (though they do show up on a few fantastic aid blogs, like Shotgun Shack and Tales from the Hood).  They become local color to personal stories, not sources of information that reveal the politics of our development efforts (though read the two aforementioned blogs for serious counterpoints).
In my article, I demonstrated how using the concept of narrative, drawn from the humanities, has allowed me to identify moments in which I am placed into a plot, a story of development and experience not of my making:

In this narrative [“the white man is so clever,” a phrase I heard a lot during fieldwork], I was cast as the expert, one who had knowledge and resources that could improve their lives if only I would share it with them. [The community] cast themselves in the role of recipients of this knowledge, but not participants in its formation.  This narrative has been noted time and again in development studies (and post-colonial studies), and in the era of participation we are all trained to subvert it when we see it emerge in the work of development agencies, governments, and NGOs. However, we are less trained to look for its construction by those living in the Global South. In short, we are not trained to look for the ways in which others emplot us.

The idea of narrative is useful not only for identifying when weird neocolonial moments crop up, but also for destabilizing those narratives – what I call co-authoring.  For example, when I returned to the site of my dissertation fieldwork a few years later, I found that my new position as a (very junior) professor created a new set of problems:

This new identity greatly hindered my first efforts at fieldwork after taking this job, as several farmers openly expected me to tell them what to plant and how to plant it. I was able to decentre this narrative when, after one farmer suggested that I should be telling him what to plant instead of asking him about his practices, I asked him ‘Do I look like a farmer?’ He paused, admitted that I did not, and then started laughing. This intervention did not completely deconstruct his narrative of white/developed and black/developing, or my emplotment in that narrative. I was still an expert, just not about farming. This created a space for him to speak freely to me about agriculture in the community, while still maintaining a belief in me as the expert.

Certainly, this is not a perfect outcome.  But this is a lot better than the relationship that would have developed without an awareness of this emerging narrative, and my efforts to co-author that narrative.  Long and short, the humanities have a lot to offer both studies of climate change impacts and development – if we can bring ourselves to start taking things like stories seriously as sources of data.  As Hulme notes, this is not going to be an easy thing to do – there is a lot of inertia in both development and climate change studies.  But changes are coming, and I for one plan to leverage them to improve our understandings of what is happening in the world as a result of our development efforts, climate change, global markets, and any number of other factors that impact life along globalization’s shoreline – and to help co-author different, and hopefully better, outcomes than what has come before.
 
*full disclosure: I’ve published in Global Environmental Change, and Hulme was one of the editors in charge of my article.



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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.

Posted on by Ed 3

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.

Posted on by Ed 0

How to blow up behavioral econ/RCT via qualitative research?

Just a quick thought, given the interest in my last two posts. I have (obviously) been driven a bit crazy by the RCT/behavioral economics folks who suddenly seem to be coming around to either qualitative methods to explain their results . . . which are generally results that qualitative researchers have known about for a long time.  In other words, it seems to me that there is a real danger here that a sort of waving at qualitative methods (i.e. the “bad journalism” approach, where you just do a bunch of interviews without considering who to interview, how to interview them, why interviews vs. focus groups vs. whatever, etc.) might become yet another way to prolong the hegemony of economics over development thinking.
I’m worried about this because while I think economic approaches and theory have purchase on explanation to varying degrees depending on the subject at hand and the scale of analysis, in the end any effort to explain the emergence of and means of addressing a development issue or challenge at a scale that might have meaningful impact that relies on the economic alone will not result in a particularly complete explanation for observed events, nor will it help us understand likely outcome pathways in future similar situations.  Put another way, only sometimes can economics get us to a “good enough” solution that enables really productive development work.
What if I told you that I had really good, concrete empirical data from a really tiny dataset (two villages in Ghana, but the entire population of those two villages, so no sampling issues) that clearly demonstrated that any effort to explain livelihoods decision-making in these villages cannot be productively explained by economic approaches – whether crude (i.e. assumptions about maximizing behavior) or complex (game theoretic approaches)?  Instead, the data makes it remarkably clear that the economic, while a component of decision-making, is just one component of a project of household governance – it is a clearly external (etic, for you anthro types out there) heuristic that improperly parses the social processes that lead to livelihoods decisions. In short, I can show where the explanatory power of “the economic” stops, and where meaningful explanation requires a re-embedding of the economic in larger social processes that cannot be reduced to the economic (and, at the same time, which demonstrates that the economic cannot be reduced to any of these other processes).
Basically, I’m starting to walk you through my retheorization of livelihoods as what Foucault called governmentality . . . but I could also work this up as a means of discrediting the RCT and behavioral economics turn toward the qualitative by arguing that these efforts are tails wagging the dog . . .
Thoughts?

Posted on by Ed 2

Qualitative research was (already) here . . .

You know, qualitative social scientists of various stripes have long complained of their marginalization in development.  Examples abound of anthropologists, geographers, and sociologists complaining about the influence of the quantitatively-driven economists (and to a lesser extent, some political scientists) over development theory and policy.  While I am not much for whining, these complaints are often on the mark – quantitative data (of the sort employed by economists, and currently all the rage in political science) tends to carry the day over qualitative data, and the nuanced lessons of ethnographic research are dismissed as unimplementable, ideosyncratic/place-specific, without general value, etc.  This is not to say that I have an issue with quantitative data – I believe we should employ the right tool for the job at hand.  Sadly, most people only have either qualitative or quantitative skills, making the selection of appropriate tools pretty difficult . . .
But what is interesting, of late, is what appears to be a turn toward the lessons of the qualitative social sciences in development . . . only without actually referencing or reading those qualitative literatures.  Indeed, the former quantitative masters of the development universe are now starting to figure out and explore . . . the very things that the qualitative community has known for decades. What is really frustrating and galling is that these “new” studies are being lauded as groundbreaking and getting great play in the development world, despite the fact they are reinventing the qualitative wheel, and without much of the nuance of the current qualitative literature and its several decades of nuance.
What brings me to today’s post is the new piece on hunger in Foreign Policy by Abhijit Banerjee and Esther Duflo.  On one hand, this is great news – good to see development rising to the fore in an outlet like Foreign Policy.  I also largely agree with their conclusions – that the poverty trap/governance debate in development is oversimplified, that food security outcomes are not explicable through a single theory, etc.  On the other hand, from the perspective of a qualitative researcher looking at development, there is nothing new in this article.  Indeed, the implicit premise of the article is galling: When they argue that to address poverty, “In practical terms, that meant we’d have to start understanding how the poor really live their lives,” the implication is that nobody has been doing this.  But what of the tens of thousands of anthropologists, geographers and sociologists (as well as representatives of other cool, hybridized fields like new cultural historians and ethnoarchaeologists).  Hell, what of the Peace Corps?
Whether intentional or not, this article wipes the qualitative research slate clean, allowing the authors to present their work in a methodological and intellectual vacuum.  This is the first of my problems with this article – not so much with its findings, but with its appearance of method.  While I am sure that there is more to their research than presented in the article, the way their piece is structured, the case studies look like evidence/data for a new framing of food security.  They are not – they are illustrations of the larger conceptual points that Banerjee and Duflo are making.  I am sure that Banerjee and Duflo know this, but the reader does not – instead, most readers will think this represents some sort of qualitative research, or a mixed method approach that takes “hard numbers” and mixes it in with the loose suppositions that Banerjee and Duflo offer by way of explanation for the “surprising” outcomes they present.  But loose supposition is not qualitative research – at best, it is journalism. Bad journalism. My work, and the work of many, many colleagues, is based on rigorous methods of observation and analysis that produce validatable data on social phenomena.  The work that led to Delivering Development and many of my refereed publications took nearly two years of on-the-ground observation and interviewing, including follow-ups, focus groups and even the use of archaeology and remotely-sensed data on land use to cross-check and validate both my data and my analyses.
The result of all that work was a deep humility in the face of the challenges that those living in places like Coastal Ghana or Southern Malawi manage on a day-to-day basis . . . and deep humility when addressing the idea of explanation.  This is an experience I share with countless colleagues who have spent a lot of time on the ground in communities, ministries and aid organizations, a coming to grips with the fact that massively generalizable solutions simply don’t exist in the way we want them to, and that singular interventions will never address the challenges facing those living in the Global South.
So, I find it frustrating when Banerjee and Duflo present this observation as in any way unique:

What we’ve found is that the story of hunger, and of poverty more broadly, is far more complex than any one statistic or grand theory; it is a world where those without enough to eat may save up to buy a TV instead, where more money doesn’t necessarily translate into more food, and where making rice cheaper can sometimes even lead people to buy less rice.

For anyone working in food security – that is, anyone who has been reading the literature coming out of anthropology, geography, sociology, and even some areas of ag econ, this is not a revelation – this is standard knowledge.  A few years ago I spent a lot of time and ink on an article in Food Policy that tried to loosely frame a schematic of local decision-making that leads to food security outcomes – an effort to systematize an approach to the highly complex sets of processes and decisions that produce hunger in particular places because there is really no way to get a single, generalized statistic or finding that will explain hunger outcomes everywhere.
In other words: We know.  So what do you have to tell us?
The answer, unfortunately, is not very much . . . because in the end they don’t really dive into the social processes that lead to the sorts of decisions that they see as interesting or counterintuitive.  This is where the heat is in development research – there are a few of us working down at this level, trying to come up with new framings of social process that move us past a reliance solely on the blunt tool of economistic rationality (which can help explain some behaviors and decisions) toward a more nuanced framing of how those rationalities are constructed by, and mobilize, much larger social processes like gender identification.  The theories in which we are dealing are very complex, but they do work (at least I think my work with governmentality is working – but the reviewers at Development and Change might not agree).
And maybe, just maybe, there is an opening to get this sort of work out into the mainstream, to get it applied – we’re going to try to do this at work, pulling together resources and interests across two Bureaus and three offices to see if a reframing of livelihoods around Foucault’s idea of governmentality can, in fact, get us better resolution on livelihoods and food security outcomes than current livelihoods models (which mostly assume that decisionmaking is driven by an effort to maximize material returns on investment and effort). Perhaps I rest too much faith on the idea of evidence, but if we can implement this idea and demonstrate that it works better, perhaps we will have a lever with which to push oversimplified economistic assumptions out of the way, while still doing justice to the complexity of social process and explanation in development.

Posted on by Ed 6

Why is this still surprising?

Alertnet has a post on climate change and the poor that opened with one of my least favorite narrative techniques – surprise about local capacity and knowledge for adaptation.

I was struck by the local community’s scientific knowledge about climate change. I’d often heard that such communities know a tremendous amount about changing weather patterns – and can easily tell a good year from a bad one in terms of droughts or floods – but that they don’t know much about ‘greenhouse gas emissions’ or ‘climate change’.

Not so in Manikganj District. The community performed a drama for us and it was clear that they knew exactly what these relatively western scientific terms mean both in theory and in practice.

I was also struck by how the community – supported by the local nongovernmental organisation, GSK – has developed a range of strategies and activities to cope with longer floods, higher floodwater levels and the erosion that each year washes away more and more of their crop and homestead land into the nearby Padma River. There was no drama here. And I was deeply affected by the industrious positive way the people of Manikganj District meet these challenges and carry on with daily life.

I confess to a bit of fatigue at the continued voicing of surprise at finding out that those in the Global South who are dealing with the impacts of climate change actually have ideas, and often successful strategies, for managing those impacts.  Implicitly, every time we do this we back our readers up to a place of comfort (“those poor/dark people don’t know much”) and then get to act surprised when it turns out they do actually have knowledge and capabilities.  I’d like to think that the first half of my book flips this script, arguing consistently that the people I have been working with are staggeringly capable, and therefore it is the breakdown of livelihoods and adaptation that is interesting, not its existence.
To be fair, though, I blame myself and my colleagues working on adaptation and livelihoods for the persistence of this narrative technique.  Once we get past that all-to-common intro, the post gets into concrete discussions of how people are adapting – good, useful grounded description.  But what I long for, and what I am working on (articles in review and in prep, folks) is moving beyond the descriptive case toward a more systematic understanding of adaptation and livelihoods decision-making that enables some level of generalization and systematization. Indeed, by failing to approach livelihoods and adaptation decision-making in this manner, we enable the very frustrating lead-in technique I described above – every case becomes unique, and every effort to manage the impacts of climate change is therefore an isolated surprise.  If, in fact, it is not at all surprising that people have at least some knowledge and capacity for addressing climate change (and this is really not surprising at all, dammit), we need to get past simple description to capturing processes that might be leveraged into better early warning, better programming, and better understandings of what people are experiencing on the ground.

Posted on by Ed 3