OK, a last thought on the development initiatives and markets thread: let’s leave the predictive markets thing aside for the moment, and get to what I think is a more serious question for development initiatives – do we use all the information we might to evaluate the likely impact of our programs? I think a lot of folks misread the intent of my initial post – I was NOT suggesting we bet on mortality rates and other direct measures of project effectiveness. That is something I could see as an academic exercise, but is way too morbid for my tastes, even in that setting.
But everyone who lunged in that direction seemed to miss the point that any major development initiative will, if it succeeds, have radiating impacts through different markets. That is, a successful food security initiative will change harvest sizes of different crops, thereby influencing commodities markets. A successful public health intervention might increase the size of the workforce, or its efficiency. And so on. My simple thought was that any fund investor worth his/her salt should be examining these initiatives and their expected outcomes to decide 1) if the initiative worked, what markets might be affected, how and when and 2) do they think the initiative will actually work.
If there is no movement around these initiatives, it seems to me that these two factors might be important – at the first step in this decision-making, investors might decide that in the event of a successful intervention, the markets affected might not be accessible or profitable, or the timeframe of any movement in the market might be so long as to make immediate response unnecessary. Thus, we would see no market response to the announcement of an intervention. At that point, it doesn’t matter if the intervention will work or not – that assessment never comes into the picture.
However, in at least some cases, I have to think that there are initiatives out there (in a world of rising food prices, I am a bit fixated on food security at the moment) that would affect significant markets, and not only at a national scale (where markets might be illiquid or otherwise inaccessible). Take the case of cocoa and Cote d’Ivoire this past winter: the civil conflict in CIV cut off a significant amount of global supply, and futures markets got skittish over the further constriction of trade, driving cocoa prices upward. This is a niche crop, heavily produced by only a few countries, but the price movement could have meant big dollars for a fund that correctly anticipated this trend. Surely there are (or will be) food security initiatives that could similarly affect the overall supplies of and access to particular (perhaps niche) crops for entire regions, or even shift global availability/perception enough to shift commodities prices in much larger, more transparent markets in the short term. Don’t fixate on national markets for these initiatives – what about really big development movers that could affect global supplies of grain in an era where all the slack has been taken out of various global grain markets? You can’t tell me that everyone at these trading desks is simply ignoring the food security world . . . surely they are at least assessing through step 1) above. So if there is no market response to these initiatives, either the timeframe of movement is too distant to warrant interest, or the traders simply don’t think these initiatives will succeed enough to significantly influence the markets in which they trade. Perhaps the price of oil and its impact on transport is much, much more important than increasing harvest size when it comes to shaping food commodities prices . . . in which case, it would probably be good for those designing food security initiatives to know this at the outset and address it in project design (for example by thinking about transportation issues as integral to the initiative).
Of course, there is option 3): traders have no idea what sorts of initiatives are out there, and are operating in ignorance of these potential large drivers. This is entirely possible, but a bit hard to believe . . .
Category: research
Quick thought: clarifying the development initiatives and markets post
Lots of comments pouring in via twitter regarding my earlier post on development initiatives and markets. First, I found it interesting that readers went in two directions – they either took the post to be about prediction markets alone, or they caught the reference to hedge funds and realized that I was talking about “betting” in a much more general sense: that is, in the sense of hedge fund investment, which is really a set of (ideally) well-researched, carefully-hedged bets on the direction of particular stocks, commodities and sometimes whole segments of the market.
For now, let’s take up the issue of predictive markets. I love Bill Easterly’s response tweet, asking what development initiative I (or anyone else) would bet my own money on. I think prediction markets are interesting tools. They are hardly perfect, as like other markets they are subject to bubbles and manipulation, but there is some evidence to suggest that they do yield interesting information under the right conditions. It would be interesting to set up parallel prediction markets, and populate one with development professionals at agencies and NGOs, one with development academics, and one that blends the two, and then have them start to buy and sell the likelihood of success (as defined by the initiative, both in terms of outcomes and timeframe) for any number of development initiatives. While I doubt these parallel markets would move in lockstep, I wonder if they would come to radically different assessments of these initiatives. And we could examine how well they worked as predictive devices. I’m pretty sure most academics would have started shorting the Millennium Village Project at its inception (academic paper here) . . . so what things would the development blogosphere/twittersphere short today? What would you go long on (that is, what would you hold in the expectation it would meet expectations and rise in value)? Have at it in the comments . . .
I’ll address the wider meaning of “betting” that I was also aiming at later . . .
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.
Why should the aid/relief/development community care about global environmental change (Pt. 4)?
So, how do we fix the way we think about development to address the challenges of global environmental change? Well, there are myriad answers, but in this post I propose two – we have to find ways of evaluating the impact of our current projects such that those lessons are applicable to other projects that are implemented in different places and at various points in the future . . . and we have to better evaluate just where things will be in the future as we think about the desired outcomes of development interventions.
To achieve the first of these two is relatively easy, at least conceptually: we need to fully link up the RCT4D crowd with the qualitative research/social theory crowd. We need teams of people that can bring the randomista obsession with sampling frames and serious statistical tools – in other words, a deep appreciation for rigor in data collection – and connect it to the qualitative social theoretical emphasis on understanding causality by interrogating underlying social process – in other words, a deep appreciation for rigor in data interpretation. Such teams work to cover the weaknesses of their members, and could bring us new and very exciting insights into development interventions and social process.
Of course, everyone says we need mixed methodologies in development (and a lot of other fields of inquiry), but we rarely see projects that take this on in a serious way. In part, this is because very few people are trained in mixed methods – they are either very good at qualitative methods and interpretation, or very good at sampling and quantitative data analysis. Typically, when a team gets together with these different skills, one set of skills or the other predominates (in policy circles, quant wins every time). To see truly mixed methodologies, this cannot happen – as soon as one trumps the other, the value of the mixing declines precipitously.
For example, you need qualitative researchers to frame the initial RCT – an RCT framed around implicit, unacknowledged assumptions about society is unlikely to “work” – or to capture the various ways in which an intervention works. At the same time, the randomista skill of setting up a sampling frame and obtaining meaningful large-scale data sets requires attention to how one frames the question, and where the RCT is to be run . . . which impose important constraints on the otherwise unfettered framings of social process coming from the qualitative side, framings that might not really be testable in a manner that can be widely understood by the policy community. Then you need to loop back to the qualitative folks to interpret the results of the initial RCT – to move past whether or not something worked to the consideration of the various ways in which it did and did not work, and a careful consideration of WHY it worked. Finally, these interpretations can be framed and tested by the qualitative members of the team, starting an iterative interpretive process that blends qualitative and quantitative analysis and interpretation to rigorously deepen our understanding of how development works (or does not work).
The process I have just described will require teams of grownups with enough self-confidence to accept criticism and to revise their ideas and interpretations in the face of evidence of varying sorts. As soon as one side of this mixed method team starts denigrating the other, or the concerns of one side start trumping those of the other, the value of this mixing drops off – qualitative team members become fig leaves for “story time” analyses, or quantitative researchers become fig leaves for weak sampling strategies or overreaching interpretations of the data. This can be done, but it will require team leaders with special skill sets – with experience in both worlds, and respect for both types of research. There are not many of these around, but they are around.
Where are these people now? Well, interestingly the answer to this question leads me to the second answer for how development might better answer the challenges of global environmental change: development needs to better link itself with the global environmental change community. Despite titles that might suggest otherwise (UNEP’s Fourth Global Environment Outlook was titled Environment for Development), there is relatively little interplay between these communities right now. Sure, development folks say the right things about sustainability and climate change these days, but they are rarely engaging the community that has been addressing these and many other challenges for decades. At the same time, the global environmental change community has a weak connection to development, making their claims about the future human impacts of things like climate change often wildly inaccurate, as they assume current conditions will persist into the future (or they assume equally unrealistic improvements in future human conditions).
Development needs to hang out with the scenario builders of the global environmental change community to better understand the world we are trying to influence twenty years hence – the spot to which we are delivering the pass, to take up a metaphor from an earlier post on this topic. We need to get with the biophysical scientists who can tell us about the challenges and opportunities the expect to see two or more decades hence. And we need to find the various teams that are already integrating biophysical scientists and social scientists to address these challenges – the leaders already have to speak quant and qual, science and humanities, to succeed at their current jobs. The members of these teams have already started to learn to respect their colleagues’ skills, and to better explain what they know to colleagues who may not come at the world with the same framings, data or interpretations. They are not perfect, by any stretch (I voice some of my concerns in Delivering Development), but they are great models to go on.
Meanwhile, several of my colleagues and I are working on training a new generation of interdisciplinary scholars with this skill set. All of my current Ph.D. students have taken courses in qualitative methods, and have conducted qualitative fieldwork . . . but they also have taken courses on statistics and biogeographic modeling. They will not be statisticians or modelers, but now they know what those tools can and cannot do – and therefore how they can engage with them. The first of this crew are finishing their degrees soon . . . the future is now. And that gives me reason to be realistically optimistic about things . . .
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.
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
Why should the aid/relief/development community care about global environmental change (Pt. 2)?
Yesterday, I took the relief community to task for not spending more time seriously thinking about global environmental change. To be clear, this is not because that community pays no attention, or is unaware of the trend toward increasing climate variability and extreme weather events in many parts of the world that seems to be driving ever-greater needs for intervention. That part of the deal is pretty well covered by the humanitarian world, though some folks are a bit late to the party (and it would be good to see a bit more open, informal discussion of this – most of what I have seen is in very formal reports and presentations). I am more concerned that the humanitarian community gives little or no thought to the environmental implications of its interventions – in the immediate rush to save lives, we are implementing projects and conducting activities that have a long-term impact on the environment at scales ranging from the community to the globe. We are not, however, measuring these impacts in really meaningful ways, and therefore run the risk of creating future problems through our current interventions. This is not a desirable outcome for anyone.
But what of the development community, those of us thinking not in terms of immediate, acute needs as much as we are concerned with durable transformations in quality of life that will only be achieved on a generational timescale? You’d think that this community (of which I count myself a part) would be able to grasp the impact of climate change on people’s long-term well-being, as both global environmental changes (such as climate change and ecosystem collapse) and development gains unfold over multidecadal timescales. Yet the integration of global environmental change into development programs and research remains preliminary and tentative – and there is great resistance to such integration from many people in this community.
Sometimes people genuinely don’t get it – they either don’t think that things like climate change are real problems, or fail to grasp how it impacts their programs. These are the folks who would lose at the “six degrees of Kevin Bacon” game – I’ve said it before, and I will say it again: global environmental change is development’s Kevin Bacon: I can link environmental change to any development challenge in three steps or less. Sometimes the impacts are really indirect, which can make this hard to see. For example, take education: in some places, climate change will alter growing seasons such that farm productivity will be reduced, forcing families to use more labor to get adequate food and income, which might lead parents to pull their kids from school to get that labor. Yep, at least some education programs are impacted by climate change, an aspect of global environmental change.
Other times, though, I think that the resistance comes from a very legitimate place: many working in this field are totally overtaxed as it is. They know that various aspects of global environmental change are problems in the contexts in which they work, but lack the human and financial resources to accomplish most of their existing tasks. Suddenly they hear that they will have to take something like climate change into account as they do their work, which means new responsibilities that will entail new training, but often come without new personnel or money. It is therefore understandable when these folks, faced with these circumstances, greet the demand for the integration of global environmental change considerations into their programs with massive resistance.
I think the first problem contributes to the second – it is difficult to prioritize people and funding for a challenge that is poorly understood in the development community, and whose impacts on the project or initiative at hand might be difficult to see. But we must do this – various forms of global environmental change are altering the future world at which we are aiming with our development programs and projects. While an intervention appropriate to a community’s current needs may result in improvements to human well-being in the short term, the changes brought on by that intervention may be maladaptive in ten or twenty years and end up costing the community much more than it gained initially.
Global environmental change requires us to think about development like a fade route in football (American), or the through ball in soccer (the other football). In both cases, the key is to put the ball where the target (the receiver of the pass) is going to be, not where they are now. Those who can do this have great success. Those that cannot have short careers. Development needs to start working on its timing routes, and thinking about where our target communities are going to be ten and twenty years from now as we design our programs and projects.
So, how do we start putting our projects through on goal? One place to start would be by addressing two big barriers: the persistence of treating global environmental change as a development sector like any other, and the failure of economics to properly cost the impacts of these changes.
First, global environmental change is not a sector. It is not something you can cover in a section of your project plan or report, as it impacts virtually all development sectors. Climate change alters the range and number of vectors for diseases like malaria. Overfishing to meet the demands of consumers in the Global North can crush the food security of poor coastal populations in the Global South. Deforestation can intensify climate change, lead to soil degradation that compromises food security, and even distort economic policy (you can log tropical hardwoods really quickly and temporarily boost GNP in a sort of “timber bubble”, but eventually you run out of trees and those 200-500 year regrowth times means that the bubble will pop and a GNP downturn is the inevitable outcome of such a policy). If global environmental change is development’s Kevin Bacon, it is pretty much omnipresent in our programs and projects – we need to be accounting for it now. That, in turn, requires us to start thinking much longer term – we cannot design projects with three to five year horizons – that is really the relief-to-recovery horizon (see part 1 for my discussion of global environmental change in that context). Global environmental change means thinking about our goals on a much longer timescale, and at a much more general (and perhaps ambitious) scale. The uncertainty bars on the outcomes of our work get really, really huge on these timescales . . . which to me is another argument for treating development as a catalyst aimed at triggering changes in society by facilitating the efforts of those with innovative, locally-appropriate ideas, as opposed to imposing and managing change in an effort to achieve a narrow set of measurable goals at all costs. My book lays out the institutional challenges to such a transformation, such as rethinking participation in development, which we will have to address if this is ever to work.
Second, development economics needs to catch up to everyone else on the environment. There are environmental economists, but not that many – and there are virtually no development economists that are trained in environmental economics. As a result, most economic efforts to address environmental change in the context of development are based on very limited understandings of the environmental issues at hand – and this, in turn, creates a situation where much work in development economics either ignores or, in its problematic framings of the issue, misrepresents the importance of this challenge to the development project writ large. Until development economists are rewarded for really working on the environment, in all its messiness and uncertainty (and that may be a long way off, given how marginal environmental economists are to the discipline), I seriously doubt we are going to see enough good economic work linking development and the environment to serve as a foundation for a new kind of thinking about development that results in durable, meaningful outcomes for the global poor. In the meantime, it seems to me that there is a huge space for geographers, anthropologists, sociologists, political scientists, new cultural historians, and others to step up and engage this issue in rich, meaningful ways that both drive how we do work now and slowly force new conversations on both economics and the practice of development.
I do admit, though, that my expanding circle of economics colleagues (many of which I connected to via this blog and twitter) have given me entrée into a community of talented people that give me hope – they are interested and remarkably capable, and I hope they continue to engage me and my projects as they go forward . . . I think there is a mutual benefit there.
Let me be clear: the continuing disconnect between development studies and environmental studies is closing, and there are many, many opportunities to continue building connections between these worlds. This blog is but one tiny effort in a sea of efforts, which gives me hope – with lots of people at work on this issue, someone is bound to succeed.
In part three, I will take up why global environmental change means that we have to rethink the RCT4D work currently undertaken in development – specifically, why we need much, much better efforts at explanation if this body of work is to give us meaningful, long-term results.
Early warning for climate tipping points
One of the things I am (not so) fond of saying is that when it comes to climate, I am not really worried about what I do know – it’s the things that I don’t know, and cannot predict, that worry me the most. The climate displays many characteristics of a nonlinear complex system, which means that we cannot assume that any changes in this system will come in a steady manner – even a fast but steady manner. Instead, the geologic record suggests that this system changes in a linear manner (i.e. slowly warms up, with related shifts in sea level, precipitation, wind patterns and ocean circulation) up to a certain point before changing state – that is, shifting all of these patterns rather dramatically into a new state that conveys the extra energy in the atmosphere through the Earth system in a different manner. These state changes are frightening to me because they are highly unpredictable (we are not sure where the thresholds for these changes are) and, at their worst, they could introduce biophysical changes like increased temperature and rates of evaporation and decreased rainfall with such speed (i.e. in a decade or two, as opposed to over centuries) that the rate of change outpaces the capacity of biomes to adapt, and the constituent species in those biomes to evolve. This is not some random concern about biodiversity – people seem to forget that agricultural systems are ecosystems; radically simplified ecosystems, to be sure, but still ecosystems. They are actually terribly unstable ecosystems because they are so simple (they have little resilience to change, as there are so few components that shifting any one of them can introduce huge changes to the whole system), and so the sort of nonlinear changes I am describing have particular salience for our food supply. I am not a doomsday scenario kind of guy – I like to think of myself as a hopelessly realistic optimist – but I admit that this sort of thing worries me a lot.
So, to put this another way: we are running like hell down a long hallway toward an open door into a darkened room. We can’t see what’s in the room, and it is coming up fast. Most normal people would probably slow down and enter the room cautiously so as to avoid a nasty collision with something in the dark. When it comes to climate change, though, our current behavior is akin to running right into that room at full speed and hoping with all our might that there is nothing in the way.
This is a really, really stupid way of addressing the challenge of climate change.
The good news on this front is that we are starting to see the emergence of a literature on the early warning for these tipping points. I had a post on this recently, and now the July issue of Nature Climate Change has a review article by Timothy Lenton on early warning of tipping points. It is a really excellent piece – it lays out what we are currently doing, shows the limitations of what we can do, points to significant challenges both in the science and in the policy realm, and tries to chart a path forward. I think Lenton comes in a bit science-heavy in this piece, though. While he raises the issues of false alarms and missed alarms, he spends nearly all his time looking at methods for reducing the occurrence of these events. This is all well and good, but false and missed alarms are inevitable when trying to predict the behavior of complex systems. Yes, we need more and better science, but we also need to be thinking about how we address the loss of policymaker confidence in the wake of false alarms or missed alarms.
To get to this point, I think we need to be looking to arenas where people have a lot of experience communicating levels of risk and the importance of addressing that risk – the insurance industry. Most readers of this blog will have some form of insurance – be it health insurance, life insurance, car insurance, etc. I have all three. Every month, I pay a premium for a product I sincerely hope I never have to use. I’d rather hang on to that money (with a family the size of mine, it gets steep), but the cost of a catastrophic event in any of these areas would be so high that I gladly continue to pay. We need to encourage the insurance industry (they are already working on this issue, as they stand to lose a hell of a lot of money unless they can get their actuarial tables adjusted) to start communicating their sense of the likely future costs of climate change, and the costs associated with potential state changes – and do so in the same way that they sell us insurance policies. Why do we have scientists working on the marketing of our ideas? We are not trained for this, and most of my colleagues lack the salesman’s charisma that the climate change issue so desperately needs.
It’s time for a serious conversation about how science and the for-profit risk management world can start working together to better translate likely future climate impacts into likely future costs that everyone can understand. Science simply does not carry the weight we need in policy circles – the good data and rigorous analysis that are central to scientific legitimacy are, in the policy realm, simply seen as means to achieving a particular viewpoint, not an ever-improving approximation of how the world works. Until the climate science (and social science) community grasps this, I fear we will continue to talk past far too many people – and if we allow this to happen, we become part of the problem.
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.
Savings is a social choice, too . . .
Marc Bellemare’s blog pointed me to an interesting paper by Pascaline Dupas and Jonathan Robinson titled “Why Don’t the Poor Save More? Evidence from Health Savings Experiments.” It is an interesting paper, taking a page from the RCT4D literature to test some different tools for savings in four Kenyan villages. I’m not going to wade into the details of the paper or its findings here (they find some tools to be more effective than others at promoting savings for health expenditures), because they are not what really caught me about this paper. Instead, what struck me was the absence of a serious consideration of “the social” in the framing of the questions asked and the results. Dupas and Robinson expected three features to impact health savings: adequate storage facilities/technology, the ability to earmark funds, and the level of social commitment of the participant. The social context of savings (or, more accurately, barriers to savings) are treated in what I must say is a terribly dismissive way [emphases are mine]:
a secure storage technology can enable individuals to avoid carrying loose cash on their person and thus allow people to keep some physical distance between themselves and their money. This may make it easier to resist temptations, to borrow the terminology in Banerjee and Mullainathan (2010), or unplanned expenditures, as many of our respondents call them. While these unplanned expenditures include luxury items such as treats, another important category among such unplanned expenditures are transfers to others.
A storage technology can increase the mental costs associated with unplanned expenditures, thereby reducing such expenditures. Indeed, if people use the storage technology to save towards a specic goal, such as a health goal in our study, people may consider the money saved as unavailable for purposes other than the specic goal – this is what Thaler (1990) coined mental accounting. By enabling such mental accounting, a designated storage place may give people the strength to resist frivolous expenditures as well as pressure to share with others, including their spouse.
I have seen many cases of unplanned expenditures to others in my fieldwork. Indeed, my village-based field crews in Ghana used to ask for payment on as infrequent a basis as possible to avoid exactly these sorts of expenditures. They would plan for large needed purchases, work until they had earned enough for that purchase, then take payment and immediately make the purchase, making their income illiquid before family members could call upon them and ask for loans or handouts.
However, the phrasing of Dupas and Robinson strikes the anthropologist/ geographer in me as dismissive. These expenses are seen as “frivolous”, things that should be “resisted”. The authors never consider the social context of these expenditures – why people agree to make them in the first place. There seems to be an implicit assumption here that people don’t know how to manage their money without the introduction of new tools, and that is not at all what I have seen (albeit in contexts other than Kenya). Instead, I saw these expenditures as part of a much larger web of social relations that implicates everything from social status to gender roles – in this context, the choice to give out money instead of saving it made much more sense.
In short, it seems to me that Dupas and Robinson are treating these savings technologies as apolitical, purely technical interventions. However, introducing new forms of savings also intervenes in social relations at scales ranging from the household to the extended family to the community. Thus, the uptake of these forms of savings will be greatly effected by contextual factors that seem to have been ignored here. Further, the durability of the behavioral changes documented in this study might be much better predicted and understood – from my perspective, the declining use of these technologies over the 33 month scope of the project was completely predictable (the decline, that is, not the size of the decline). Just because a new technology enables savings that might result in a greater standard of living for the individual or household does not mean that the technology will be seen as desirable – instead, that standard of living must also work within existing social roles and relations if these new behaviors are to endure. Therefore, we cannot really explain the declining use of these technologies over time . . . yet development is, to me, about catalyzing enduring change. While this study shows that the introduction of these technologies has at least a short term transformative effect on savings behavior, I’m not convinced this study does much to advance our understanding of how to catalyze changes that will endure.