Bill Gates finally voiced what has been implicit in much of his approach to development – he sees aid and development critics as highly problematic people who slow down progress (or whatever Bill thinks passes for progress).  Honestly, this is thoroughly unsurprising to anyone who has paid any attention to what Bill has said all along, or indeed anything the Gates Foundation does.  There just isn’t much room for meta-criticism at the foundation or its work – sure, they evaluate their programs, but there isn’t much evaluation/consideration of whether or not the guiding principals behind those programs make much sense.  There is an assumption that Gates’ goals are somehow self-evident, and therefore critics are just problems to be solved.

Let’s just start with this part of what Gates said. To me, his comments represent a profound misunderstanding of the place of aid and development criticism – his comments represent critics as annoyances to be brushed away, implying that criticism is an end unto itself. I do not know a single aid/development critic for whom criticism is the end. Critical thinking, and any resultant criticism, is a means to the end of changing the world. Simply put, without critical thinkers to constantly evaluate, challenge, and push the thinking of those in the world of development policy and implementation, where would we be? Take gender, for example. Today, nobody questions the need to consider the gender of the beneficiary when we think about policies or programs, but in the late 1960s those who first raised this issue were critics, often viewed as “annoyances” who slowed down the process of designing and implementing projects with their silly concerns about the needs of women. Gates does his foundation, and the entire enterprise/discipline of development a disservice in this rather sad misrepresentation of the aid critic.

Had Gates simply said what he did about aid critics in the abstract, I think it would have passed without much comment. But he didn’t. Instead, he singled out Dambisa Moyo as an archetype of aid criticism. As a result, he gave a platform to someone who clearly loves the attention. I fear he also somehow made her the archetype for the aid critic, validating a writer whose “critical” arguments are rife with errors and problems (I detailed these in an extended review of her book here, here, here, here, and here). In short, Gates was rather clever here: he picked the contemporary aid critic with the greatest conceptual shortcomings and held her up as the problem, as if the rest of the critical thinkers shared her thinking, shallow arguments, and factual problems. Further, he (apparently rightly, given the reaction of twitter and the blogosphere) seems to have assumed that such critics should and would rally to her support.

Well, not me.

I am without question a critical thinker when it comes to development and aid. I have a hell of a paper trail to prove it. But I do not see myself as a colleague or contemporary of Dambisa Moyo. I’d prefer to be a colleague of Bill Easterly, Arturo Escobar, James Ferguson, James Scott, and Timothy Mitchell (all more senior than me), and I see myself as a colleague of Katharine McKinnon, Kat O’Reilly, Mara Goldman, and Farhana Sultana (all friends or colleagues of my generation).  All of these scholars have conducted extensive scholarly work on the problems of development, and backed up their work with evidence. I don’t think any of these scholars is perfect, and some have produced pieces of work that I see as deeply flawed, but all hold their work to a much higher standard than that I saw in Dead Aid.

The fact is that Gates was right: Moyo doesn’t know much about aid and what it is doing – Dead Aid made this rather clear (seriously, read my review of the book). On her webpage, she argues that she “dedicated many years to economic study up to the Ph.D. level, to analyze and understand the inherent weaknesses of aid, and why aid policies have consistently failed to deliver on economic growth and poverty alleviation.” First, a Ph.D. is no guarantee of knowing anything – and I say that as someone who holds two Ph.D.s! I have seen absolutely no scholarly output from Moyo’s Ph.D. work that supports any sense that she developed a rigorous understanding of aid at all. Indeed, her very phrasing – she sought to analyze and understand the inherent weakness of aid – suggests that her work is not analytical, but political. And after two years in D.C., one thing I have learned is that the political has very little to do with facts or evidence. In that regard, I can safely say that Dead Aid is a political book.

Second, being born and raised in a poor country does not mean that one understands the experiences of everyone in that country. Zambia is a culturally, economically, and environmentally diverse country, home to many different experiences.  Just as I cannot make any claim to understand the experiences of all Americans just because I was born here, majored in American Studies, and have lived in five states and a federal colony (D.C.), Moyo’s implicit claim that being born in Zambia allows her to speak for all those living in countries that receive aid, let alone all Zambians, is absurd.

Finally, she argues that she has served as a consultant at the World Bank, implicitly suggesting this gives her great purchase on development thought. It does not. As I have argued elsewhere, working as a consultant for a donor is not the same thing as working as an employee of a donor. I too have been a consultant at the World Bank. Technically, I am currently a consultant for USAID. These are very different roles from those I occupied while employed at USAID. Consultants are not privy to the internal conversations and machinations of development donors, and have at best partial understandings of what drives decisions about development policy and implementation.  Moyo has no practical experience at all with the realities of development donors, a fact that comes through in Dead Aid.

So let’s divorce the two things that Bill Gates did in his comments. He completely misrepresented aid critics in two ways: first, in failing to recognize the contributions of aid criticism to the improvement of aid and development programs, and second in lumping aid critics into the same basket as Dambisa Moyo.  This lumping is pretty egregious, and the overall characterization represents a significant flaw in Gates’ thinking about development that is likely to come back to bite his foundation in the ass in the near future – without criticism of the overall ideas behind the foundation, it’s programs will wither and die.  We can separate this first problem from Gates critique of Dambisa Moyo, which aside from characterizing her as doing evil (which is just going too far, really), pretty much got the assessment of her thinking right.

In short, let’s push back against Bill’s thinking on development criticism, but not valorize Moyo’s crap arguments in the process.

This work also included a week-long team effort to build an approach to monitoring and evaluation for this project that might also yield broader recommendations for M&E of climate services projects in other contexts.  The conversations ranged from fascinating to frustrating, but in the process I learned an enormous amount and, I think, gained some clarity on my own thinking about project design, monitoring, and evaluation. For the purposes of this blog, I want to elaborate on one of my long-standing issues in development – the use of panel surveys, or even broad baseline surveys, to design policies and programs.

At best, people seem to assume that the big survey instrument helps us to identify the interesting things that should be explained through detailed work. At worst, people use these instruments to identify issues to be addressed, without any context through which to interpret the patterns in the data. Neither case is actually all that good. Generally, I often find the data from these surveys to be disaggregated/aggregated in inappropriate manners, aimed at the wrong issues, and rife with assumptions about the meaning of the patterns in the data that have little to do with what is going on in the real world (see, for example, my article on gendered crops, which was inspired by a total misreading of Ghanaian panel survey data in the literature). This should be of little surprise: the vast bulk of these tools are designed in the abstract – without any prior reference to what is happening on the ground.

What I am arguing here is simple: panel surveys, and indeed any sort of baseline survey, are not an objective, inductive data-gathering process. They are informed by assumptions we all carry with us about causes and effects, and the motivations for human behavior. As I have said time and again (and demonstrated in my book Delivering Development), in the world of development these assumptions are more often than not incorrect. As a result, we are designing broad survey instruments that ask the wrong questions of the wrong people. The data from these instruments is then interpreted through often-inappropriate lenses. The outcome is serious misunderstandings and misrepresentations of life on globalization’s shoreline. These misunderstandings, however, carry the hallmarks of (social) scientific rigor even as they produce spectacular misrepresentations of the decisions, events, and processes we must understand if we are to understand, let alone address, the challenges facing the global poor.  And we wonder why so many projects and policies produce “surprise” results contrary to expectations and design? These are only surprising because the assumptions that informed them were spectacularly wrong.

This problem is easily addressed, and we are in the process of demonstrating how to do it in Kaffrine. There are baseline surveys of Kaffrine, as well as ongoing surveys of agricultural production by the Senegalese agricultural staff in the region. But none of these is actually tied to any sort of behavioral model for livelihoods or agricultural decision-making. As a result, we can’t rigorously interpret any patterns we might find in the data.  So what we are doing in Kaffrine (following the approach I used in my previous work in Ghana) is spending a few weeks establishing a basic understanding of the decision-making of the target population for this particular intervention. We will then refine this understanding by the end of August through a full application of the LAG approach, which we will use to build a coherent, complex understanding of livelihoods decision-making that will define potential pathways of project impact. This, in turn, will shape the design of this program in future communities as it scales out, make sense of the patterns in the existing baseline data and the various agricultural services surveys taking places in the region, and enable us to build simple monitoring tools to check on/measure these pathways of impact as the project moves forward. In short, by putting in two months of serious fieldwork up front, we will design a rigorous project based on evidence for behavioral and livelihoods outcomes. While this will not rule out surprise outcomes (African farmers are some pretty innovative people who always seem to find a new way to use information or tools), I believe that five years from now any surprises will be minor ones within the framework of the project, as opposed to shocks that result in project failure.

Incidentally, the agricultural staff in Kaffrine agrees with my reading of the value of their surveys, and is very excited to see what we can add to the interpretation of their data. They are interested enough to provide in-town housing for my graduate student, Tshibangu Kalala, who will be running the LAG approach in Kaffrine until mid-July. Ideally, he’ll break it at its weak points, and by late July or early August we’ll have something implementable, and by the end of September we should have a working understanding of farmer decision-making that will help us make sense of existing data while informing the design of project scale up.

I think Torjer is dead right, and I think I can extend his argument a bit here. By failing to acknowledge the production of knowledge and information as itself an innovation, we basically allow ourselves to write off the global poor as lacking innovation. This enables our usual narratives of development – of a helpless global poor waiting for someone to come save them from their routinized ways. This is enhanced by climate change, as this narrative, run to its logical end, suggests that the global poor have pretty much nothing to contribute in their own efforts to adapt, and therefore require massive interventions from the “innovative north”.

This is a major problem for development, especially as major donor start embracing the idea of innovation.  While at USAID, I looked up at the wrong time in a meeting and was tasked with identifying the Agency definition of innovation.  My friend and colleague Mike Hanowski kindly threw himself under the bus and volunteered to help me. What followed was a fairly hilarious afternoon where Mike and I called various people in the Agency to obtain this definition. Every person we called passed us to another person, until we were passed back to the first person we had called.  Really.  So, no formal definition of innovation (maybe this has changed, but I doubt much of the Agency would know if even if it had).

Now, I am a fan of the Development Innovation Ventures (DIV) folks at USAID (a group that was started after the aforementioned story). They do promote interesting, relatively edgy ideas within the Agency. But look at what DIV does – every project amounts to the use of a technology to address a “big challenge” for development.  In and of itself, this is fine…but in this focus, DIV (inadvertently) reinforces the trope of the helpless global poor, waiting for the “innovative north” (or ideally an “innovative southerner”, presented as an outlier who can lead the helpless poor in his/her country or community to a brighter future). Even as we find interesting solutions to development challenges, we are reinforcing the idea that such solutions are the Global North’s to give to those in the Global South. As long as this is the case, we will continue to miss the interesting opportunities to address these and other challenges that exist in the minds and practices of the global poor.

This is a graph of the annual rainfall at one rain gauge in Ghana, near where I conducted the research that made up a big part of my book Delivering Development.  The downward trend in rainfall is clear (and representative of the trend in this part of coastal West Africa that, according to my colleagues at IRI, continues to this day and is confirmed by satellite measurements).  There are complex things happening inside these annual figures, including shifting timing of rainfall, but for the purposes of discussion here, it serves to make a point.  While there is indeed a downward trend that continues to this day, there have already been several years where the total precipitation was much lower than the current average precipitation, or the likely annual precipitation for the next few decades.

This means is that the farmers in Dominase and Ponkrum, like so many around the world, have already seen the future – that is, they have already lived through at least one, if not several, seasons like those we expect to become the norm some decades in the future.  These farmers survived those seasons, and learned from them, adjusting their expectations and strategies to account for the possibility of recurrence. These adjustments are likely over- or under-compensating for the likelihood of recurrence right now, as livelihoods strategies in these villages are largely reactive, reflecting last season’s events more than the average season. Further, the year-to-year hedging of farms against climate variability can be a costly practice – the likely “insurance premium” of lost production in a good year (due to planting in less-than-ideal, but precipitation-hedged situations like the tops and bottoms of hills – see my discussion in Chapter 4 of Delivering Development) probably eats up somewhere between 10% and 20% of total potential production.  So these management strategies are not ideal. But they do reflect local capacities to adjust and account for extreme conditions, the extreme rainfall or drought events out along the tails of historical distributions whose unpredictable recurrences characterize a changing climate regime.  Many of these farmers have little or no access to inputs, limited to no access to seasonal forecasts, and live in states without safety nets, yet they have repeatedly survived very difficult seasons.  Clearly, their capacity for survival in an uncertain environment and economy is worthy of our respect.

This is not a phenomenon specific to Ghana.  For example, the farmers in southern Mali with whom I (and many others) have been working to deliver better and more relevant climate services, such as seasonal and short-term forecasts.  Most, if not all, of these farmers are using local indicators, such as the flowering of a particular tree or the emergence of a particular insect, as indicators that help them time various activities in their agricultural cycle. Many trust their local indicators more than the forecasts, perhaps with some reason – their indicators actually seem to work and the forecasts are not yet as accurate as anyone would like.  But these indicators work under current climate regimes, and these regimes are changing. At some point, the tree will start to flower at a different, perhaps less appropriate time, or simply cease to flower. The insect will emerge at a different time, or perhaps be driven away by the emergence of new predators that can now move into the area. If the climate continues to change, local indicators will eventually fail.

I humbly suggest that instead of reengineering entire agroecological systems and their associated economies in the here and now (a fairly high risk enterprise), we should be building upon the capacities that already exist.  For example, we can plan for the eventual failure of local indicators – we can study the indicators to understand under what conditions their behaviors will change, identify likely timeframes in which such changes are likely to occur, and create of new tools and sources of information that will be there for farmers when their current sources of information no longer work. We should be designing these tools and that information with the farmers, answering the questions they have (as opposed to the questions we want to ask).  We should be building on local capacity, not succumbing to crisis narratives that suggest that these farmers have little capacity, either to manage their current environment or to change with the environment.

Farmers in the Global South have already fed the future. Perhaps they did not do it all that well, and all they managed was to stave off catastrophe. But given the absence of safety nets in most places in the Global South (see Theme 3, points 2 and 3), and the limited access so many farmers have to inputs and irrigation, avoiding catastrophe is an accomplishment that warrants study and serious consideration. We should build on that capacity, not blow it up.

The key principals and points:

1)   A future under climate change is not a great unknown for farmers in the Global South. Most farmers have already managed several seasons as difficult, or more difficult, than what we project to be normal in the next few decades. Presuming these farmers are facing a catastrophe they cannot see coming fails to grasp the ways in which these past seasons inform contemporary planning.

2)   Farmers have already developed strategies for addressing extreme seasons (i.e. drought or excessive precipitation). We should start with an understanding of what they already do, and why, before moving in with our interventions, lest we inadvertently undo otherwise functional safety nets.

3)   Existing indigenous strategies for managing climate variability are not perfect. They tend to overestimate or underestimate actual risks of particular weather and climate states, tend to magnify the importance of the previous season (as opposed to historical averages, or current trends) when planning for the next season, and tend to be very costly in terms of lost potential agricultural productivity.

4)   Current indigenous tools for making agricultural decisions, such as local indicators, are likely more robust than any climate product we can deliver right now. Just because this information comes in the form of a plant or animal behavior does not make it any less valid.

5)   Current indigenous tools for making agricultural decisions will likely start to fail as climate regimes change. This fact presents an opportunity for development organizations to start working with farmers to identify useful information and ways of providing it such that this information is available when local indicators fail.

*Given the propagation of uncertainty in models of the global climate, global water availability, global land cover, the global economy, and global population (all of which, incidentally, impact one another), I don’t pay much attention to model results beyond about 2040, with 2030 being the really outer threshold of information that might usefully inform planning or our understanding of biophysical process.  On the 100-year scale, we may as well be throwing darts at a wall as running models. I have no idea why we bother.

For example, simplifying one’s farm to focus on only a few key crops for which there is “comparative advantage”, and then using the proceeds to buy food, clothing, shelter and other necessities, works great when the market for those crops is strong. But what happens when the food you need to buy becomes more dear than the crops you are growing, for example through food price spikes or a shift in markets that leave one’s farm worth only a fraction of what is needed to feed and clothe one’s family? In the world’s poorest countries, where most food security and agricultural development work takes place, there is little capacity to provide safety nets to vulnerable citizens that might address such outcomes.

This is not a call for the provision of these safety nets (microinsurance is very interesting, but a long way from implementation).  While useful and, in some contexts, critical, they are, in the end, band-aids for a larger conceptual problem – the framing of market engagement as a panacea for the problems of agricultural development and food security.  Often, such programs also presume a lack of existing safety nets at the community or household level – a sort of “we can’t make things worse” mentality that marks much development thought. However, farmers in these countries have long operated without a state-level safety net. They hedge against all kinds of uncertainties, from the weather to markets.  For example, one form of hedging I have seen in my own work is an emphasis on growing a mix of crops that can be sold or eaten, depending on market and weather conditions.  If, in coastal Ghana, you are growing maize and cassava as your principal crops, you can sell both in years where the market is good, and you can eat both in years where the market turns on you. I have referred to opting out of markets as temporary deglobalization, where people opt in and out of markets as they gauge their risks and opportunities.

Forcing farmers away from this model, toward one that focuses on enhancing the economic efficiency of agricultural production by reducing the focus of a country and its farmers to a few crops that are their “comparative advantage”, and which they should sell to purchase the rest of their dietary needs, removes the option of turning away from markets and eating the crops in conditions of years where the markets are not favorable.  This is even more true when some of that newly reduced crop mix only takes value from sale on global markets (i.e. cocoa) and/or which cannot be eaten (i.e. cotton). In short, such restructuring in the name of economic efficiency makes people dependent on the political structures of the state that govern the markets in which they participate.  Most of our work takes place in the Global South, where the state rarely has the capacity to step in and help in times of crisis.  It is pretty easy to do the math here: done wrong, food security programs principally framed around ideas of economic efficiency can enhance state capacity to extract value from farmers without a comparable improvement in the delivery of services or safety nets.  This is an acceptable outcome if you are trying to compel people to submit to the state and the markets the state regulates, which is one way to boost measurable GDP and state revenue. However, it is really bad if you are actually trying to improve people’s food security.

The key points and principals here:

1)   Are you addressing food insecurity or strengthening the state’s capacity to raise revenue and measure economic activity? These are not the same thing – generally, they are at odds with one another, as making agricultural practice easier to see and measure only serves to improve the capacity to extract revenues from farmers, without any guarantee of improved services proceeding from those revenues.

2)   Economic efficiency is a desirable characteristic of agricultural livelihoods, but in the absence of safety nets cannot be the organizing principal of food security interventions. All else being equal, it is better when farmers use their scarce resources as efficiently as possible. However, the measurement of efficiency must take place within an assessment of the various risks currently managed through “inefficiencies” – as many such inefficiencies are in fact parts of robust, community- and household-level safety nets.

3)   Food security programming should be able to identify the difference between an inefficiency and a critical part of a community- or household-level safety net.  Regardless of the consequences for economic efficiency, programs and projects should not destabilize these until such time as new, reliable safety nets exist to take their place.

4)   Opting out is OK. Farmers should be allowed to structure their farms such that they can opt out of markets if things turn bad, even if this limits their total incomes in “good”/optimal years. This should not be assessed in terms of the average outcome, when best and worst cases are averaged.  Your best case is some more money. Your worst case is severe deprivation and death. These are not equal. Averting the latter is more important than achieving the former.

• locally-accessible food production
• local livelihoods options that might provide sufficient, reliable income or sources of food
• local social relations (which mobilize and create social divisions by gender, class, age, etc.) which shape access to both livelihoods opportunities and available food within communities and even households
• structures of governance and markets in which that production takes place
• global markets for food and other commodities that can impinge on local pricing.

Changes in the natural environment play into this mix in that they generally impinge upon locally-accessible production and on global markets. The experience of the Famine Early Warning System Network (FEWS-NET) provides evidence to this effect.  FEWS-NET builds its forecasts through a consideration of all of these factors, and as it has gained resolution on things like local livelihoods activities and market pricing and functions, its predictive resolution has increased.

Despite decades of literature and body of experience to the contrary, it seems that the policy world, and indeed much development implementation, continues to view issues of hunger as the relatively straightforward outcome of production shortfalls that can be addressed through equally straightforward technical fixes ranging from changed farming techniques to new agricultural technologies such as GMOs.  This view is frustrating, given its persistence in the face of roughly five decades of project failures and ephemeral results that evaporated at the end of “successful” projects. More nuanced work has started to think about issues of production in concert with the distribution function of markets.  However, the bulk of policy and implementation along these lines couples the simplistic “technical fix” mentality of earlier work on food security with a sort of naïve market triumphalism that tends to focus on the possible benefits of market engagement with little mention or reasonable understanding of likely problematic outcomes.  Put another way, most of this thinking can be reduced to:

increased agricultural production = increased economic productivity = increased food security and decreased poverty

The problem with this equation is that the connection between agricultural productivity and economic growth is pretty variable/shaky in most places, and the connection between economic growth and any specific development outcome is shaky/nonexistent pretty much everywhere unless there has been careful work done to make sure that new income is mobilized in a specific manner that addresses the challenge at hand.  Most of the time, the food security via economic growth crowd has not done this last bit of legwork. In short, the mantra of “better technology and more markets” as currently manifest in policy circles is unlikely to advance the cause of food security and address global hunger any more effectively than prior interventions based on a version of the same mantra.

These issues present us with several key points about the problem we are trying to solve that should shape a general approach to food insecurity:

1)   Because food insecurity is the outcome of the complex interplay of many factors, sectoral approaches are doomed to failure.  At best, they will address a necessary but insufficient cause of the particular food insecurity issue at hand.  However, in leaving other key causes unaddressed, these partial solutions nearly always succumb to problems in the unaddressed causes.

2)   Production-led solutions will rarely, if ever, address enough significant causes of food insecurity to succeed.  Simply put, while production is a necessary part of understanding food insecurity, it is insufficient for explaining the causes of particular food insecurity situations, or identifying appropriate solutions for those situations.

3)   Increased production is not guaranteed to lead to economic growth. The crops at hand, who consumes them, the infrastructure for their transport, and national/global market conditions all shape this particular outcome, which can shift from season to season.

4)   Economic growth does not solve things magically. Even if you can generate economic growth through increased agricultural production, this does not mean you will be addressing food insecurity. Programs must think carefully about where the proceeds from this new economic growth will go in the economy and society at hand, and if/how those pathways will result in greater opportunity for the food insecurity.

5)   Embrace the fact complexity takes different forms in different places. In some places, markets will be a major cause of insecurity. In other places, environmental degradation might play this role. In still other places, failed governance will be the biggest issue driving food outcomes.  In nearly all cases, though, all three of these factors will be present, and accompanied by others.  Further, the form this insecurity takes will be highly variable within countries, provinces, districts, communities and even households, depending on the roles people play and the places in which they play them.  There is no good template in which to fit a particular case of food insecurity, just a lot of causal factors that require extensive teasing out if one hopes to explain food outcomes and therefore address the problem.

Nor, in fact, are we likely to be looking at a global food shortage any time soon.  There is no doubt that climate change will present challenges to our food system.  The combination of changing temperatures and precipitation regimes will challenge existing crops in many parts of the world, and benefit the crops in other parts of the world.  Further, the global markets for food have created substantially tighter interconnections between places than ever before, and there is less excess marketable supply than ever before.  Note that there is less excess marketable supply – this is the amount of food we produce that actually reaches market, not the total amount of food grown and raised each year.  As I will discuss later (point 4: The Future is Already Being Fed), these trends are not as terrifying as some might paint them.  The simple point here is that these trends are manageable if we can get over the idea of food security as a question of production.

The idea of scarcity is perhaps the biggest challenge we face in addressing the world’s food needs.  As long as food security policy and programs remain focused on solving scarcity, food security will remain focused on technical fixes for hunger: greater technology, greater inputs, greater efficiency.  This narrative of scarcity has trumped any reasonable effort to measure actual levels of production in the world today, the return on greater technological inputs versus solving the causes of waste in existing systems, and even served as a useful foil through which to obscure the deepening unsustainability of the very agricultural systems that are often treated as a model, those here in the United States and Europe.

Simply put, it is cheaper and easier to enhance agricultural extension to improve local food storage techniques, build and maintain good roads, and improve electrical grids and other parts of the cold chain that preserves produce from farm to market than it is to completely reengineer an agricultural ecology.  It makes far more sense to make basic infrastructural investments than it does to tether ever more farmers to inputs that require finite fossil fuel and mineral resources.  It makes more sense to better train farmers in storing what they already produce in a manner that preserves more of the harvest than it does to invest billions in the modification of crops, especially when the bulk of genetic modification in agriculture these days is defensive – that is, guarding against future yield loss, not enhancing yields in the present.

This is not to say that there is no place for agricultural research or technology in achieving food security.  There are places in the world where the state cannot provide services, or maintain the basic order necessary for functional markets, that would enable the movement of food are reasonable prices, and where the local environmental conditions are such that new and innovative technologies will be required to make them productive.  Here, new agricultural technologies might have a place.  But these places are few and far between, and so we should put the push for ever-more agricultural technology into its place as but one of many possible solutions for food insecurity.  When a problem has many causes, it requires many solutions.  But this requires understanding that the problem has many causes.

This points to several key points/principals:

1)   When confronted with an instance of food insecurity, program/project/policy folks must suspend all assumptions about food supply until they can be validated by empirical evidence.

2)   Any initial arguments that define the causes of a given situation as scarcity should be assessed in terms of understanding why this has come to be the explanation.  Since scarcity is rarely the actual cause of food insecurity, explanations that hinge on scarcity alone are deeply suspect and should be critically evaluated before they are used to shape responses. For example, are there local misperceptions of markets at play, or are there those with vested interests in particular solutions trying to drive the response?

3)   Any assessment of the food security of a population should account not only for the amount of food they can access and are entitled to, but also the total food produced both by that population and within that population’s market-shed.  This allows for a greater understanding of the causes of food insecurity, such as waste caused by insufficient infrastructural quality or inappropriate on-farm practices, or the failure of the state to provide the necessary structures for functional markets.  There is little point to bringing new genetically-modified crops to populations whose real problem is not production, but an inability to get their existing harvest to market.

So, over the next few blog posts I am going to try something that many academics and critics would never risk: I am going to put some ideas down about how we perhaps should be building food security programs right now.  You all can have at these.  I can make some changes and edits.  We can argue some more.  And somewhere in there, maybe something that is both workable and more likely to actually work will emerge.

The major points of how I think we ought to be addressing world hunger look like this:

1)   Get over production: it’s rarely about production, and focusing on it draws us away from the real causes of hunger

2)   Embrace complexity: sectoral responses are doomed to fail. Please stop programming sectoral responses, and start thinking integration

3)   Create exit points: a critical problem in agricultural development is the all-too-rapid march to market integration, without appropriate attention being paid to the new risks such integration creates. In most places where agricultural development takes place, market integration predicated on the simplification of existing agricultural activities to even fewer crops is a recipe for disaster that removes the safety nets that the rural poor have already created.

4)   The future is already being fed: while we live in a world of economic and environmental change, these changes are not linear.  We’ve already seen extremes of both that represent conditions beyond what we expect to see as the “new normal” in the future. Why not figure out what people did to address those extreme events, and build off of that?

I will elaborate each of these points in its own blog post over coming days.  The goal will be to make each point clear and actionable. The other goal is to present a real alternative to what I firmly believe are misguided initiatives dominating the contemporary food security conversation. We’ll see if I can pull it off.

My problem with this laser focus on the hard sciences and on medicine is that it pretends that people’s quality of life simply depends on physical phenomena—how fast computers are or how much their knee hurts and so on.   That’s simply not true.  Much of people’s happiness—indeed, including whether they have access to computers or can endure a physical malady—depends on social phenomena.

Even more compelling is Mark Slouka’s 2009 article in Harpers, which offers one of the clearest defenses of the humanities I have ever read: simply put, without the humanities it is very difficult to be a functional citizen in a democracy (but in their absence it is very easy to produce a docile population of workers).

Let me take Slouka’s argument past what really read like something of an either/or tradeoff between the humanities and what he called “mathandscience” and toward a point of complementarity here: simply put, science is a way of seeing the world that enables particular understandings of that world. Science has facilitated spectacular changes in the way we live, from household technologies to medical advances.  But science is but one way of seeing the world, one that does not tell us what we should do, or what else we should do.  Those questions are the province of ethics, justice, and empathy. Science is poorly equipped to address any of these.

This is why science and technology require the social sciences and humanities. They help us separate what is possible in the world from what should be done in the world. Remember, history is littered with examples of highly rational, scientific projects that killed huge numbers of people in the name of a greater good or a logical goal (anyone remember the Soviet collectivization of agriculture under Stalin? How about the far less brutal, but still problematic ujamaa collectivization in Tanzania?). Without the arts, humanities, and social sciences, we are left with a tool (science) and no guidance about how to use it.  Further, the growing field of science and technology studies shows that the capacities of particular technologies, in and of themselves, tell us little about who will adopt them and why. Trevor Birkenholtz’s work in India, for example, demonstrates that farmers continue to use tubewells, even though they know that this practice contributes to groundwater depletion, because the use of tubewells is closely bound up in one’s identity as a good and prosperous farmer.  Without such insights, how can we work with farmers in this region to identify locally-appropriate alternative water-supply technologies?

Cantor, and those like him, live in an odd world where technologies and commodities are social goods unto themselves with universal and obvious value. Existing social scientific work already demonstrates this to be untrue. Defunding such work will not make his beliefs more true, it will just make it harder to make the world a better place with the scientific tools we have and will develop in the future.

Then, the other day in seminar (I was covering for a colleague in our department’s “Contemporary Approaches to Geography” graduate seminar, discussing the long history of environmental determinism within and beyond the discipline), I found myself in a similar discussion related to explanation…and I think I figured out what has been going on.  The remote sensing and GIS students in the course, all of whom are extraordinarily well-trained in quantitative methods, got to thinking about how to determine if, in fact, the environment was “causing” a particular behavior*. In the course of this discussion, I realized that what they meant by “cause” was simple (I will now oversimplify): when you can rule out/control for the influence of all other possible factors, you can say that factor X caused event Y to happen.  Indeed, this does establish a causal link.  So, I finally get what everyone was saying when they said that, via well-constructed regressions, etc., one can establish causality.

So it turns out I was wrong…sort of. You see, I wasn’t really worried about causality…I was worried about explanation. My point was that the information you would get from a quantitative exercise designed to establish causal relationships isn’t enough to support rigorous project and program design. Just because you know that the construction of a borehole in a village caused girl-child school attendance to increase in that village doesn’t mean you know HOW the borehole caused this change in school attendance to happen.  If you cannot rigorously explain this relationship, you don’t understand the mechanism by which the borehole caused the change in attendance, and therefore you don’t really understand the relationship. In the “more pure” biophysical sciences**, this isn’t that much of a problem because there are known rules that particles, molecules, compounds, and energy obey, and therefore under controlled conditions one can often infer from the set of possible actors and actions defined by these rules what the causal mechanism is.

But when we study people it is never that simple.  The very act of observing people’s behaviors causes shifts in that behavior, making observation at best a partial account of events. Interview data are limited by the willingness of the interviewee to talk, and the appropriateness of the questions being asked – many times I’ve had to return to an interviewee to ask a question that became evident later, and said “why didn’t you tell me this before?”  (to which they answer, quite rightly, with something to the effect of “you didn’t ask”).  The causes of observed human behavior are staggeringly complex when we get down to the real scales at which decisions are made – the community, household/family, and individual. Decisions may vary by time of the year, or time of day, and by the combination of gender, age, ethnicity, religion, and any other social markers that the group/individual chooses to mobilize at that time.  In short, just because we see borehole construction cause increases in girl-child school attendance over and over in several places, or even the same place, doesn’t mean that the explanatory mechanism between the borehole and attendance is the same at all times.

Understanding that X caused Y is lovely, but in development it is only a small fraction of the battle.  Without understanding how access to a new borehole resulted in increased girl-child school attendance, we cannot scale up borehole construction in the context of education programming and expect to see the same results.  Further, if we do such a scale-up, and don’t get the same results, we won’t have any idea why.  So there is causality (X caused Y to happen) and there are causal mechanisms (X caused Y to happen via Z – where Z is likely a complex, locally/temporally specific alignment of factors).

Unfortunately, when I look at much quantitative development research, especially in development economics, I see a lot of causality, but very little work on causal mechanisms that get us to explanation.  There is a lot of story time, “that pivot from the quantitative finding to the speculative explanation.”  In short, we might be programming development and aid dollars based upon evidence, but much of the time that evidence only gets us part of the way to what we really need to know to really inform program and project design.

This problem is avoidable –it does not represent the limits of our ability to understand the world. There is one obvious way to get at those mechanisms – serious, qualitative fieldwork.  We need to be building research and policy teams where ethnographers and other qualitative social scientists learn to respect the methods and findings of their quantitative brethren such that they can target qualitative methods at illuminating the mechanisms driving robust causal relationships. At the same time, the quantitative researchers on these teams will have to accept that they have only partially explained what we need to know when they have established causality through their methods, and that qualitative research can carry their findings into the realm of implementation.

The bad news for everyone…for this to happen, you are going to have to pick your heads up out of your (sub)disciplinary foxholes and start reading across disciplines in your area of interest.  Everyone talks a good game about this, but when you read what keeps getting published, it is clear that cross-reading is not happening.  Seriously, the number of times I have seen people in one field touting their “new discoveries” about human behavior that are already common conversation in other disciplines is embarrassing…or at least it should be to the authors. But right now there is no shame in this sort of thing, because most folks (including peer reviewers) don’t read outside their disciplines, and therefore have no idea how absurd these claims of discovery really are. As a result, development studies gives away its natural interdisciplinary advantage and returns to the problematic structure of academic knowledge and incentives, which not only enable, but indeed promote narrowly disciplinary reading and writing.

Development donors, I need a favor. I need you to put a little research money on the table to learn about whatever it is you want to learn about. But when you do, I want you to demand it be published in a multidisciplinary development-focused journal.  In fact, please start doing this for all of your research-related money. People will still pursue your money, as the shrinking pool of research dollars is driving academia into your arms. Administrators like grant and contract money, and so many academics are now being rewarded for bringing in grants and contracts from non-traditional sources (this is your carrot). Because you hold the carrot, you can draw people in and then use “the stick” inherent in the terms of the grant/contract to demand cross-disciplinary publishing that might start to leverage change in academia. You all hold the purse, so you can call the tune…

*Spoiler alert: you can’t.  Well, you probably can if 1) you pin the behavior you want to explain down to something extraordinarily narrow, 2) can limit the environmental effect in question to a single independent biophysical process (good luck with that), and 3) limit your effort to a few people in a single place. But at that point, the whole reason for understanding the environmental determinant of that behavior starts to go out the window, as it would clearly not be generalizable beyond the study. Trust me, geography has been beating its head against this particular wall for a century or more, and we’ve buried the idea.  Learn from our mistakes.

**by “more pure” I am thinking about those branches of physics, chemistry, and biology in which lab conditions can control for many factors. As soon as you get into field sciences, or starting asking bigger questions, complexity sets in and things like causality get muddied in the manner I discuss below…just ask an ecologist.