Open The Echo Chamber

So, it seems I have been challenged/called out/what-have-you by the folks at Imagine There Is No . . . over what I would do (as opposed to critique) about development.  At least I think that is what is going on, given that I received this tweet from them:

@edwardrcarr what would You do with 1 Billion $ for #development? bit.ly/rQrUOd #The.1.Bill.$.Question

In general, I think this is a fair question.  Critique is nice, but at the end of the day I strive to build something from my critiques.  As I tell my grad students, I can train a monkey to take something apart – there isn’t much talent to that.  On the other hand, rebuilding something from whatever you just dismantled actually requires talent.  I admit to being a bit concerned about calling what I build “better”, mostly because such judgments gloss over the fact that any development intervention produces winners and losers, and therefore even a “better” intervention will probably not be better for someone.  I prefer to think about doing things differently, with an eye toward resolving some of the issues that I critique.

So, I will endeavor to answer – but first I must point out that asking someone what s/he would do for development with $1 billion is a very naive question.  I appreciate its spirit, but there isn’t much point to laying down a challenge that has little alignment with how the world works.  I think this is worth pointing out in light of the post on Imagine There Is No . . ., as they seem to be tweaking Bill Easterly for not having a good answer to their question.  However, for anyone who has ever worked for a development agency, the question “on what would you spend a billion dollars” comes off as a gotcha question because it is sort of nonsensical.  While the question might be phrased to make us think about an ideal world, those of us engaged in the doing of development who take its critique and rethinking seriously immediately start thinking about the sorts of things that would have to happen to make spending $1 billion possible and practical.  Those problems are legion . . . and pretty much any answer you give to the question is open to a lot of critique, either from a practical standpoint (great idea that is totally impractical) or from the critique side (and idea that is just replicating existing problems).  When caught in a no-win situation, the best option is not to answer at all.  Sure, we should imagine a perfect world (after all, according to A World Of Difference, I am “something of a radical thinker”), but we do not work in that world – and people live in the Global South right now, so anything we do necessarily must engage with the imperfections of the now even as we try to transcend them.

Given all of this, I offer the following important caveats to my answer:

1) I am presuming that I will receive this money as individual and not as part of any existing organization, as organizations have structures, mandates and histories that greatly shape what they can do.

2) I am presuming that I have my own organization, and that it already has sufficient staff to program $1 billion dollars – so a lot of contracting officers and lawyers are in place.  Spending money is a lot harder than you’d think.

3) I am presuming that I answer only to myself and the folks in the Global South.  Monitoring and evaluation are some of the biggest constraints on how we do development today.  As I said in my talk at SAIS a little while ago, it is all well and good to argue that development merely catalyzes change in complex systems, which makes its outcomes inherently unpredictable.  It is entirely another to program against that understanding – if the possible outcomes of a given intervention are hard to predict, how do you know which indicators to choose?  How can you build an evaluation system that allows you to capture unintended positive and negative outcomes as the project matures without looking like you are fudging the numbers?  This sounds like constrained thinking, but it is reality for anyone working in a big donor agency, and for all of the folks who implement the work of those agencies.

4) I am presuming there are enough qualified staff out there willing to quit what they are doing and come work for this project . . . and I am going to need a hell of a lot of staff.

5) I am presuming that I am expected to accomplish something in the relatively short term – i.e. 3-5 years, as well as trigger transformative changes in the Global South over the long haul.  If you don’t produce some results relatively soon, people will bail out on you.

All of these, except for 5), are giant caveats that basically divorce the question and its answer from reality.  I just need to point that out.  Because of these caveats, my answer here cannot be interpreted as a critique of my current employer, or indeed any other development organization – an answer that would also serve as a critique of those institutions would have to engage with their realities, blowing out a lot of my caveats above . . . sorry, but that’s reality, and it is really important to acknowledge the limits of any answer to such a loaded question.

So, here goes.  If I had $1 billion, I would spend it 1) figuring out what people really do to manage the challenges they face day-to-day, 2) identifying which of these activities are most effective at addressing those challenges and why, 3) evaluating whether any of these activities can be brought to scale or introduced to new places, and 4) bringing these ideas to scale.

Basically, I would spend $1 billion dollars on the argument “the new big idea is no more big ideas.”

Why would I do this, and do it this way?  Well, I believe that in a general way those of us working in development have very poor information about what is actually happening in the Global South, in the places where the challenges to human well-being are most acute.  We have a lot of assumptions about what is happening and why, but these are very often wrong.  I wrote a whole book making this point – rather convincingly, if some of the reviews are to be believed.  Because we don’t know what is happening, and our assumptions are wide of the mark, a lot of the interventions we design and implement are irrelevant (at best) or inappropriate (at worst) to the intended beneficiaries.  Basically, the claim (a la Sachs and the Millennium Villages Project) that there are proven development interventions is crap.  If we had known, proven interventions WE WOULD BE USING THEM.  To assume otherwise is to basically slander the bulk of people working on development as either insufficiently motivated (if we weren’t so damn lazy, and we really cared about poor people, we could fix all of the problems in the world with these proven interventions) or to argue that there simply needs to be more money spent on these interventions to fix everything (except in many cases there is little evidence that funding is the principal cause of project failure).  Of course, this is exactly what Sachs argues when asking for more support for the MVP, or when he is attacking anyone who dares critique the project.

The only way to really know what is happening is to get out there and talk to people.  When you do, what you find is that the folks we classify as the “global poor” are hardly helpless.  They are remarkably capable people who make livings under very difficult circumstances with very little resource and limited fallback options.  They know their environments, their economy, and their society far better than anyone from the outside ever will.  They are, in short, remarkable resources that should be treated as treasured repositories of human knowledge, not as a bunch of children who can’t work things out for themselves.  $1 billion would get us a lot of people in a lot of places doing a lot of learning . . . and this sort of thing can be programmed to run over 6 months to a year to run fieldwork, do some data analysis, and start producing tailored understandings of what works and why in different places . . . which then makes it relatively easy to start identifying opportunities for scale-up.  Actually, the scale-up could be done really easily, and could be very responsive to local needs, if we would just set up a means of letting communities speak to one another in a free and open manner – a network that let people in the Global South ask each other questions, and offer their answers and solutions, to one another.  Members of this project from the Global North, from the Universities and from development organizations, could work with communities to convey the lessons the project has gleaned from various activities in various places to help transfer ideas and technology in a manner that facilitates their productive introduction in new contexts.  So I suppose I would have to carve part of the $1 billion off for that network, but it would come in under the scale-up component of my project.  Eventually, I suspect this sort of network would also become a means of learning about what is happening in the Global South as well . . .

With any luck at all, by year 3 we would see the cross-fertilization of all kinds of locally-appropriate ideas and technology happening around the world and the establishment of a nascent network that could build on this momentum to yield even more information about what people are already doing, and what challenges they really face.  We would have started a process that has immediate impacts, but can work in tandem with the generational timescales of social change that are necessary to bring about major changes in any place.  We would have started a process that likely could not be stopped.  How it would play out is anyone’s guess . . . but it would sure look different than whatever we are doing now.

Yep, no sooner do I post on failure and how we account for it and learn from it, then I come upon a big fail of my own.  That I can learn from. Irony, anyone?

As many of you know, I have been working in Ghana since 1997.  I’ve spent some 20 months there, though it has been a while since I was last on the ground (I need to change that) – basically, the last meaningful research trip I took was in the summer of 2006.  That work, along with the fieldwork that came before it, was so rich that I am still working through what it all means – and it has led me down the path of a book about why development doesn’t work as we expect, and now a (much more academic) complete rethinking of the livelihoods framework that many in development use to assess how people make a living.

One of my big findings (at least according to some of my more senior colleagues) is that inequality and (depending on how you look at it) injustice are not accidental products of “bad information” or “false consciousness” in livelihoods strategies, but integral parts of how people make a living (article to this effect here, with related work here and here, as well as a long discussion in Delivering Development).  One constraint specific to the livelihoods in the villages in which I have been working is the need to balance the material needs of the household with the social requirement that men make more money than their wives.  I have rich empirical data demonstrating this to be true, and illustrating how it plays out in agricultural practice (which makes up about 65% of most household incomes).

In other words, I know damn well that men get very itchy about anything that allows women to become more productive, as this calls one of the two goals of existing livelihoods strategies into question.  Granted, I figured this out for the first time around 2007, and have only very recently (i.e. articles in review) been able to get at this systematically, but still, I knew this.

And I completely overlooked it when trying to implement the one village improvement project with which I have been involved.  Yep, I totally failed to apply my own lessons to myself.

What happened?  Well, to put it simply, I had some money available after the 2006 fieldwork for a village improvement project, which I wanted the residents of Dominase and Ponkrum to identify and, to the extent possible, design for themselves.  We had several community meetings that meandered (as they do) and generally seemed to reflect the dominant voices of men.  However, at the end of one of these meetings, one of my extraordinarily talented Ghanaian colleagues from the University of Cape Coast had the experience and the awareness to quietly wander off to a group of women and chat with them.  I noticed this but did not say anything.  A few minutes later, he strolled by, and as he did he said to me “we need to build a nursery.”  Kofi had managed to elicit the womens’ childcare needs, which were much more practical and actionable than any other plans we had heard.  At the next community meeting we raised this, and nobody objected – we just got into wrangling over details.  I left at the end of the field season, confident we could get this nursery built and staffed.

Five years later, nothing has happened.  They formed the earth blocks, but nobody cleared the agreed-upon area for the nursery.  It was never a question of money, and my colleagues at the University of Cape Coast checked in regularly.  Each time, they left with promises that something would get going, and nothing ever did.  I don’t fault the UCC team – the community needed to mobilize some labor so they would have buy-in for the project, and would take responsibility for the long-term maintenance of the structure. This is on the community – they just never built it.

And it wasn’t until yesterday, when talking about this with a colleague, that I suddenly realized why – childcare would lessen one demand on women that limits their agricultural productivity and incomes.  Thus, with a nursery in place women’s incomes would surely rise . . . and men have no interest in that, as this is not the sort of intervention that would drive a parallel increase in their own incomes.  I have very robust data that demonstrates that men move to control any increase in their wives incomes that might threaten the social order of the household, even if that decreases overall household income and access to food.

So why, oh why, did I ever think that men would allow this nursery to be built?  Of course they wouldn’t.

I can excuse myself between 2006-2008 for missing this, as I was still working through what was going on in these livelihoods.  But for the last three years I knew about this fundamental component of livelihoods, and how robust this aspect of livelihoods decision-making really is, even under conditions of change such as road construction.  I have been looking at how others misinterpret livelihoods and design/implement bad interventions for years, all the while doing that very thing myself.

Healer, heal thyself.

Ah, that familiar refrain – a mix of love and derision provoked by the vagaries of life in my favorite West African country: the power cuts out randomly in the midst of a big soccer match, “Oh, Ghana!” The new road washes out because of inadequate culverts? “Oh, Ghana!” And now, the country’s economy grows 34% in the second quarter of 2011 – expanding the GDP by 3.4 percent in that quarter alone (h/t to Andy Sumner for pointing this out to me)?

Wait, isn’t that good news?

Well, on its face, yes – this surge in growth suggests there is a lot more money at play in Ghana, and that will hopefully result in new and better jobs, greater revenues for the state, and eventually better services for the population.  But there are two big caveats that really, really worry me here.

1. The growth was driven mostly by growth in the mining and quarrying sector – of which oil has about a 2/3 share. So the economy has grown, but it is still commodity-dependent.  Admittedly, they now have oil on top of cocoa and gold, but these don’t exactly track independently of one another.  Building your whole economy on three commodities is not a path to a stable, sustainable future.
2. Ghana does not seem to have a plan to spend all of this new revenue in a manner that will trigger the virtuous process I was describing above.  Without a plan, the possibility of misuse and redirection of funds into private accounts rises dramatically (h/t to Mark Weston).

Oh, Ghana!

Even the oddly good news – agricultural (economic) growth seems to be matching the growth of mining and quarrying – isn’t really that good.  At first glance, this news seems to suggest that ag production is increasing, or that more of that production is getting to market before spoiling, trends that would benefit much of the Ghanaian population.  Maybe not, though – Ghana’s light-crop cocoa crop doubled over the same period last year, suggesting this increase is largely pegged to cocoa.  Worse, a big chunk of this improvement is tied to good weather, which is difficult to gamble on year-to-year.

Oh, Ghana!

Marc Bellemare at Duke has been using Delivering Development in his development seminar this semester.  On Friday, he was kind enough to blog a bit about one of the things he found interesting in the book: the finding that women were more productive than men on a per-hectare basis.  As Marc notes, this runs contrary to most assumptions in the agricultural/development economics literature, especially some rather famous work by Chris Udry:

Whereas one would expect men and women to be equally productive on their respective plots within the household, Udry finds that in Burkina Faso, men are more productive than women at the margin when controlling for a host of confounding factors.

This is an important finding, as it speaks to our understanding of inefficiency in household production . . . which, as you might imagine given Udry’s findings, is often assumed to be a problem of men farming too little and women farming a bit too much land.  So Marc was a bit taken aback to read that in coastal Ghana the situation is actually reversed – women are more productive than men per unit area of land, and therefore to achieve optimal distributions of agricultural resources (read:land) in these households we would actually have to shift land out of men’s production into women’s production.

I knew that this finding ran contrary to Udry and some other folks, but I did not think it was that big a deal: Udry worked in the Sahel, which is quite a different environment and agroecology than coastal Ghana.  Further, he worked with folks of a totally different ethnicity engaged with different markets.  In short, I chalked his findings up to the convergence of any number of factors that had played out somewhat differently in my research context.  I certainly don’t see my findings as generalizable much beyond Akan-speaking peoples living in rural parts of Ghana . . .

All of that said, Marc points out that with regard to my findings:

Of course, this would need to be subjected to the proper empirical specification and to a battery of statistical tests . . .

Well, that is an interesting question.  So, a bit of transparency on my data (it is pretty transparent in my refereed pubs, but the book didn’t wade into all of that):

Weaknesses:

• The data was gathered during the main rainy season, typically as the harvest was just starting to come in.  This required folks to make some degree of projection about the productivity of their fields at least a month into the future, and often several months into the future
• The income figures for each crop, and therefore for total agricultural productivity, were self-reported. I was not able to cross-check these reported figures by counting the actual amount of crop coming off each farm.
• I also gathered information on expenses, and when I totaled up expenses and subtracted them from reported income, every household in the village was running in the red.  I know that is not true, having lived there for some 18 months of my life.
• There is no doubt in my mind that production figures were underestimated, and expenses overestimated, in my data – this fits into patterns of income reporting among the Akan that are seen elsewhere in the literature.
• Therefore, you cannot trust the reported figures as accurate absolute measures of farm productivity.

Strengths:

• The data was replicated across three field seasons.  The first two field seasons, I conducted all data collection with my research assistant.  However, in the final year of data collection, I lead a team of four interviewers from the University of Cape Coast, who worked with local guides to identify farms and farmers to interview – in the last year, we interviewed every willing farmer in the village (nearly 100% of the population).
• It turns out that my snowball sample of households in the first two years of data collection actually covered the entire universe of households operating under non-exceptional household circumstances (i.e. they are not samples, they are reports on the activities of the population).
• In other words, you don’t have to ask about my sampling – there was no sampling.  I just described the activities of the entire relevant population in all three years.
• This removes a lot of concerns people have about the size of my samples – some household strategies only had 7 or 8 households working with them in a given year, which makes statistical work a little tricky  Well, turns out there is no real need for stats, as this is everyone!
• The only exception to this: female-headed households.  I grossly underinterviewed them in years 1 and 2 (inadvertently), and the women I did interview do not appear to be representative of all female-headed households.  I therefore can only make very limited claims about trends in these households.
• Even with completely new interviewers who had no preconceived notions about the data, the income findings came in roughly the same as when I gathered the data. That’s replicability, folks! Well, at least as far as qualitative social science gets in a dynamic situation.
• Though the data was gathered at only one point in the season, at that point farmers were already seeing how the first wave of the harvest was doing and could make reasonable projections about the rest of the harvest.

I’m probably forgetting other problems and answers . . . Marc will remind me, I’m sure!  In any case, though, Marc asks a really interesting question at the end of his post:

Assuming the finding holds, it would be interesting to compare the two countries given that Burkina Faso and Ghana share a border. Is the change in gender differences due to different institutions? Different crops?

The short answer, for now, has to be a really unsatisfying “I don’t know.”  Delivering Development lays out in relatively simple terms a really complex argument I have building for some time about livelihoods, that they are motivated by and optimized with reference to a lot more than material outcomes.  The book builds a fairly simple explanation for how men balanced the need to remain in charge of their households with the need to feed and shelter those households . . . but I have elaborated on this in a piece in review at the Development and Change.  I will send them an email and figure out where this is in review – they have been struggling mightily with reviewers (last I heard, they had gone through 13!?!) and put up a preprint as soon as I am able.  This is relevant here because I would need a lot more information about the Burkina setting to work through my new livelihoods framework before I could answer Marc’s question.

Stay tuned!

Over at the Guardian, Damian Carrington has a blog post arguing that “Food is the ultimate security need.”  He bases this argument on a map produced by risk analysts Maplecroft, which sounds quite rigorous:

The Maplecroft index [represented on the map], reviewed last year by the World Food Programme, uses 12 types of data to derive a measure of food risk that is based on the UN FAO’s concept. That covers the availability, access and stability of food supplies, as well as the nutritional and health status of populations.

I’m going to leave aside the question of whether we can or should be linking food security to conflict – Marc Bellemare is covering this issue in his research and has a nice short post up that you should be reading.  He also has a link to a longer technical paper where he interrogates this relationship…I am still wading through it, as it involves a somewhat frightening amount of math, but if you are statistically inclined, check it out.

Instead, I would like to quickly raise some questions about this index and the map that results. First, the construction of the index itself is opaque (I assume because it is seen as a proprietary product), so I have no idea what is actually in there.  Given the character of the map, though, it looks like it was constructed from national-level data.  If it was, it is not particularly useful – food insecurity is not only about the amount of food, but access to that food and entitlement to get access to the food, and these are things that tend to be determined locally.  You cannot aggregate entitlement at the national level and get a meaningful understanding of food insecurity – and certainly not actionable information.

Further, you can’t aggregate food markets or prices at the national level and get anything meaningful with regard to food security – let’s compare Maplecroft’s map with FEWS-NETs maps for the immediate future (August-September 2011):

First Maplecroft:

Now FEWS-NET:

While FEWS-NET does not have global coverage, compare their maps to those of Maplecroft and you see two things: One, FEWS is clearly working at a much finer geographic scale, because they have on-the-ground information about actual markets and access, as well as a deep understanding of climate and livelihoods through which to contextualize their grounded data.  This is what it takes to represent variable vulnerability within a country.  The variability you see on their map illustrates my point about the problems of national-level statistics – clearly food insecurity is a regional-to-local problem in every country, even Somalia.  Two, FEWS is not projecting major risk in the same places as Maplecroft, whose map has painted most of equatorial and dryland Africa as problematic at best.  Now, FEWS-NET’s medium-term projections (October-December 2011):

Again, no real resemblance to the Maplecroft map.

Now, you can argue that the Maplecroft map is aimed at a different goal than the FEWS-NET maps, as Maplecroft is trying to create a risk-assessment picture of food security in the region.  However, Maplecroft’s timescale is unclear (does it cover the next 6 months? 1 year? 5 years?), and its data is so over-aggregated as to be non-actionable.  You can’t build policy or programs from this, and I would argue that you can’t really assess the risk of food insecurity from the map or the underlying index either.  FEWS-NET’s maps are what actionable information looks like . . .

I appreciate the point Carrington is trying to make on his blog – food security is a really important issue.  But if we are to address the challenges of hunger and conflict, we need to build our understanding of the connection between them from meaningful data . . . and probably work from the outstanding material already available via FEWS-NET and others.

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.

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

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.

A number of folks have contacted me asking for a post that discusses how we might address the rapidly worsening famine in the Horn of Africa. In short, folks want to know what is being done, and what they can do, both in terms of the immediate famine and to prevent this from happening again.

First, in addressing the acute situation right now: please understand that aid agencies are moving as fast as they possibly can where they possibly can. There are a lot of challenges in southern Somalia, and these political-logistical hurdles matter greatly because the only remedy for the immediate situation is massive relief efforts to address the acute food insecurity in the area. There are complex logistics behind where those supplies might come from. That said, agencies are already moving to preposition aid materials as best they can.

If you want to help with the immediate relief effort, send money. Yes, money. Don’t send clothes, shoes, or any other stuff. It’s hard and expensive to deliver, and usually the donation of material goods just screws up local economies, making recovery from the crisis much harder and prolonged. Look into the groups, such as the Red Cross and the World Food Program, that are on the ground delivering aid. Examine their philosophies and programs, and donate to those you can agree with. There is a world of advice on donating to aid organizations out there on the blogs and twitter, so do a little research before donating. Oh, and please, please stay the hell out of the Horn of Africa, as you’ll just get in the way of highly trained, experienced people who are working under enough strain. I will make an exception for those with experience in emergency relief work – feel free to work through your networks to see if you are needed. If you don’t have a network to work through, you shouldn’t be going. It’s really that simple.

The question of how we will prevent the next famine is an open one. In my personal opinion (which, incidentally, counts for exactly nothing right now), addressing the causes of this famine, and the continuing sources of insecurity in this region, are going to require a rather different approach to development than that we have taken to this point. In my book (Delivering Development – hence the title of the post) I argue that part of the reason that development programs don’t end up solving the challenges that lead to things like famine is because we fundamentally misunderstand how development and globalization work. We are going to have to step back and move beyond technical fixes to particular challenges, and start to think about development as a catalyst for change. This means thinking broadly about what changes we want to see in the region, and how our resources might be used to initiate processes that bring those changes about. As I keep telling my students, there is no such thing as a purely technical, apolitical development intervention. Even putting a borehole in a village invokes local politics – who gathered the water before? Who gathers it now? Who can access the borehole, and who cannot? If the borehole has resulted in the creation of free time for whoever is responsible for water collection, what do they do with that free time? The answers to these questions and dozens of others will vary from place to place, but they shape the outcome of that borehole.

At the same time, such a process requires redefining the “we” in the sentence “thinking broadly about what changes we want to see in the region . . .,” because it really doesn’t matter what people, living in the United States or anywhere else outside the Horn of Africa, want to see in the region. It’s not their region. Instead, this “we” is going to have to emerge from a real partnership between those who live in the Horn of Africa, their governments, and the aid agencies with the resources to make particular programs and projects happen. For example, we are going to have to use our considerable science and technology capacity to really explore the potential of mobile communications as a source of rapidly-updated, geolocatable information about conditions on the ground to which people are responding with their livelihoods strategies. However, this technology and data will only be useful if it is interpreted into programs in concert with the sources of that data: people who are already managing tremendous challenges with few resources. Information about rainfall is just a data point, until we place it into social context – whose crops are most impacted by the absence/overabundance of water? Whose boreholes will dry up first? Whose cattle will be the first to die off? You can see how even changes in rainfall are nothing more than catalysts for local social process, as the answers to these latter questions will vary dramatically, but in the context of trying to understand how things will play out, they are far, far more important than simple biophysical measures of the environment (or quantitative analyses of the economy, for that matter).

In other words, I think that any effort to really address the next famine before it happens is going to be long and extraordinarily involved – and is going to require the help of agencies, implementing partners, academics, affected governments, and the people on the ground living through these challenges. It sounds utopian . . . but it is not. It is necessary. To end up doing the Horn of Africa famine dance again in a few years for lack of ambition, or because of an unwillingness to take a hard look at how we think about development and how it does not work, is an outcome I cannot accept. We will be judged by history for how we respond (if you have doubts, feel free to read Davis’ Late Victorian Holocausts and look at how the British come off).

After reading a lot of news and blog posts on the situation in the Horn of Africa, I feel the need to make something clear: the drought in the Horn of Africa is not the cause of the famine we are seeing take shape in southern Somalia.  We are being pounded by a narrative of this famine that more or less points to the failure of seasonal rains as its cause . . . which I see as a horrible abdication of responsibility for the human causes of this tragedy.

First, I recommend that anyone interested in this situation – or indeed in food security and famine more generally, to read Mike Davis’ book Late Victorian Holocausts.  It is a very readable account of massive famines in the Victorian era that lays out the necessary intersection of weather, markets and politics to create tragedy – and also makes clear the point that rainfall alone is poorly correlated to famine.  For those who want a deeper dive, have a look at the lit review (pages 15-18) of my article “Postmodern Conceptualizations, Modernist Applications: Rethinking the Role of Society in Food Security” to get a sense of where we are in contemporary thinking on food security.  The long and short of it is that food insecurity is rarely about absolute supplies of food – mostly it is about access and entitlements to existing food supplies.  The HoA situation does actually invoke outright scarcity, but that scarcity can be traced not just to weather – it is also about access to local and regional markets (weak at best) and politics/the state (Somalia lacks a sovereign state, and the patchy, ad hoc governance provided by al Shabaab does little to ensure either access or entitlement to food and livelihoods for the population).

For those who doubt this, look at the FEWS NET maps I put in previous posts (here and here).  Famine stops at the Somali border.  I assure you this is not a political manipulation of the data – it is the data we have.  Basically, the people without a functional state and collapsing markets are being hit much harder than their counterparts in Ethiopia and Kenya, even though everyone is affected by the same bad rains, and the livelihoods of those in Somalia are not all that different than those across the borders in Ethiopia and Kenya.  Rainfall is not the controlling variable for this differential outcome, because rainfall is not really variable across these borders where Ethiopia, Kenya and Somalia meet.

This is not to say that rainfall doesn’t matter – it certainly does.  But it is not the most important thing.  However, when we focus on rainfall variability exclusively, we end up in discussions and arguments that detract from understanding what went wrong here, and what we might do going forward.  Yes, the drought reflects a climate extreme . . . but this extreme is not that stunningly anomalous in this part of the world – we are getting similar (but not quite as bad) results quite often these days.  Indeed, these results seem to be coming more frequently, and appear to be tied to a shift in the climate of the region – and while it is a bit soon to say this definitively, this climate shift is very likely is a product of anthropogenic climate change.  So, one could indirectly argue that the climate change (mostly driven by big emitters in the Global North) is having a terrible impact on the poorest and weakest in the Global South.  It will take a while to make this a firm argument, though.

On the other hand, it is clear that politics and markets have failed the people of Somalia – and the rainfall just pushed a very bad situation over the precipice into crisis.  Thus, this is a human crisis first and foremost, whatever you think of anthropogenic climate change.  Politics and markets are human inventions, and the decisions that drive them are also human.  We can’t blame this famine on the weather – we need to be looking at everything from local and national politics that shape access and entitlements to food to global food markets that have driven the price of needed staples up across the world, thus curtailing access for the poorest.  The bad news: Humans caused this.  The good news: If we caused it, we can prevent the next one.