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
Tag: Ghana
Does being a middle-income country mean ANYTHING anymore?
Andy Sumner and Charles Kenny (disclosure – Andy and Charles are friends of mine, and I need to write up my review of Charles’ book Getting Better . . . in a nutshell, you should buy it) have a post on the Guardian’s Poverty Matters Blog addressing the two most recent challenges to the idea of the “poverty trap”: Ghana and Zambia’s recent elevations to middle-income status (per capita GNIs of between $1,006 and $3,975) by the World Bank.
Quick background for those less versed in development terminology: GNI (Gross National Income) is the value of all goods and services produced in a country, as well as all overseas investments and remittances (money sent home from abroad). Per capita GNI divides this huge number by the population to get a sense of the per-person income of the country (there is a loose assumption that the value of goods and services will be paid in the form of wages). So, loosely speaking, a per capita GNI of $1006 is roughly equivalent to $2.75/day. Obviously $2.75 buys a lot more in rural Africa than it does basically anywhere inside the US, but this is still a pretty low bar at which to start “Middle Income.”
I do not want to engage an argument about where Middle Income should start in this post – Andy and Charles take this up near the end of their post, and nicely lay out the issues. The important point that they are making, though, is that the idea that there are a lot of countries out there mired in situations that make an escape from food insecurity, material deprivation, absence of basic healthcare, and lack of opportunity (situations often called “poverty traps”) is being challenged by the ever-expanding pool of countries that seem to be increasing economic productivity rapidly and significantly. The whole point of a “poverty trap”, as popularized by Paul Collier’s book on The Bottom Billion and Jeffrey Sach’s various writings, is that it cannot be escaped without substantial outside aid interventions (a la Sachs) or may not be escapable at all. Well, Ghana certainly has received a lot of aid, but its massive growth is not the product of a new “big push”, a massive infusion of aid across sectors to get the country up into this new income category. Turns out the poorest people in the world might not need us to come riding to their rescue, at least not in the manner that Sachs envisions in his Millennium Villages Project.
That said, I’ve told Andy that I am deeply concerned about fragility – that is, I am thrilled to see things changing in places like Ghana, but how robust are those changes? At least in Ghana, a lot of the shift has been driven by the service sector, as opposed to recent oil finds (though these will undoubtedly swell the GNI figure in years to come) – this suggests a broader base to change in Ghana than, say, Equatorial Guinea . . . where GNI growth is all about oil, which is controlled by the country’s . . . problematic . . . leader (just read the Wikipedia post). But even in Ghana, things like climate change could present significant future challenges. The loss of the minor rainy season, for example, could have huge impacts on staple crop production and food security in the country, which in turn could hurt the workforce, exacerbate class/ethnic/rural-urban tensions, and generally hurt social cohesion in what is today a rather robust democracy. Yes, things have gotten better in Ghana . . . but this is no time to assume, a la Rostow, that a largely irreversible takeoff to economic growth has occurred. Aid and development are important and still needed in an increasingly middle-income world, but a different aid and development that supports existing indigenous efforts and consolidates development gains.
3.36 Billion Africans in 2100?
Schuyler Null has a post up on The New Security Beat on the 2010 revision of the United Nations (UNDESA) World Population Prospects, noting that this new revision suggests that by 2100 roughly 1 in every 3 people in the world will live in sub-Saharan Africa – a total of 3.36 billion people. It is far too early to pick apart these projections, especially as the underlying assumptions used to guide their construction are not yet available to the public. Null is quite right to note:
the UN’s numbers are based on projections that can and do change. The range of uncertainty for the sub-Saharan African region, in particular, is quite large. The medium-variant projection for the region’s total population in 2100 is 3.36 billion people, but the high variant projection is 4.85 billion and the low variant is 2.25 billion.
A few preliminary thoughts, though. I pulled up the data for a country I know reasonably well – Ghana. Under this new revised projection, Ghana’s population is expected to reach more than 67 million by 2100. Peak population growth is supposed to take place between 2035 and 2040, with steady declines in population growth after that. With life expectancies projected to rise to 79 years by 2100, certainly a lot more Ghanaians will be around for a lot longer than they are today (current life expectancy is just shy of 60 years). That said, these numbers trouble me. First, I don’t quite see how Ghana will be able to sustain a population of this size at any point in the future – the number is just too massive. Second, it seems to me that the life expectancy estimates and the population size estimates contradict one another – as Charles Kenny quite ably demonstrates in Getting Better, as life expectancies rise and more children reach adulthood, the general trend is to lower total fertility. The only way Ghana’s projection can be made to work is to assume massive demographic momentum that I am not sure will play out in the face of expected declines in infant mortality and the increased cost burden for prospective parents supporting older family members for much longer than they do today. In other words, this seems to me to be a rather dire overestimation of where Ghana is going to be in the future.
Now, this is just a quick cut at what appear to be the assumptions for one country, but I worry that this potential overestimation has a certain political utility. The Malthusian specter, however inaccurate it may be, remains a great motivator for aid and development spending. Further, presuming massive demographic momentum requires we assume that adequate reproductive health options are not in place in places like Ghana. Given that the monitoring of reproductive health, presumably to better direct development interventions, seems to be a large focus of UNDESA’s and other UN organizations’ mandate, they might have a bit of a built-in bias against a lower population number because such a number would presume significant progress on the reproductive health front, thus challenging the need for this particular service. In a wider sense, it seems to revive fears of a population bomb, albeit in this case limited to Africa. While I have no doubt that demography will be an important challenge to address in the future, I think the current numbers, even the low estimates, seem overstated.
Besides, any projection of any social process 90 years into the future probably has gigantic error bars that could encompass anything from negative growth to massive overgrowth . . . the problem here is that policy makers often fail to grasp this uncertainty, see the 100-year projection, freak out entirely and reorient the next 5 years worth of aid programming to address a problem that may not exist.
On explanation in development research
I was at a talk today where folks from Michigan State were presenting research and policy recommendations to guide the Feed the Future initiative. I greatly appreciate this sort of presentation – it is good to get real research in the building, and to see USAID staff that have so little time turn out in large numbers to engage. Once again, folks, its not that people in the agencies aren’t interested or don’t care, its a question of time and access.
In the course of one of the presentations, however, I saw a moment of “explanation” for observed behavior that nicely captures a larger issue that has been eating at me as the randomized control trials for development (RCT4D) movement gains speed . . . there isn’t a lot of explanation there. There is really interesting data, rigorously collected, but explanation is another thing entirely.
In the course of the presentation, the presenter put up a slide that showed a wide dispersion of prices around the average price received by farmers for their maize crops around a single market area (near where I happen to do work in Malawi). Nothing too shocking there, as this happens in Malawi, and indeed in many places. However, from a policy and programming perspective, it’s important to know that the average price is NOT the same thing as what a given household is taking home. But then the presenter explained this dispersion by noting (in passing) that some farmers were more price-savvy than others.
1) there is no evidence at all to support this claim, either in his data or in the data I have from an independent research project nearby
2) this offhand explanation has serious policy ramifications.
This explanation is a gross oversimplification of what is actually going on here – in Mulanje (near the Luchenza market area analyzed in the presentation), price information is very well communicated in villages. Thus, while some farmers might indeed be more savvy than others, the prices they are able to get are communicated throughout the village, thus distributing that information. So the dispersion of prices is the product of other factors. Certainly desperation selling is probably part of the issue (another offhand explanation offered later in the presentation). However, what we really need, if we want a rigorous understanding of the causes of this dispersion and how to address it, is a serious effort to grasp the social component of agriculture in this area – how gender roles, for example, shape household power dynamics, farm roles, and the prices people will sell at (this is a social consideration that exceeds explanation via markets), or how social networks connect particular farmers to particular purchasers in a manner that facilitates or inhibits price maximization at market. These considerations are both causal of the phenomena that the presenter described, and the points of leverage on which policy might act to actually change outcomes. If farmers aren’t “price savvy”, this suggests the need for a very different sort of intervention than what would be needed to address gendered patterns of agricultural strategy tied to long-standing gender roles and expectations.
This is a microcosm of what I am seeing in the RCT4D world right now – really rigorous data collection, followed by really thin interpretations of the data. It is not enough to just point out interesting patterns, and then start throwing explanations out there – we must turn from rigorous quantitative identification of significant patterns of behavior to the qualitative exploration of the causes of those patterns and their endurance over time. I’ve been wrestling with these issues in Ghana for more than a decade now, an effort that has most recently led me to a complete reconceptualization of livelihoods (shifting from understanding livelihoods as a means of addressing material conditions to a means of governing behaviors through particular ways of addressing material conditions – the article is in review at Development and Change). However, the empirical tests of this approach (with admittedly tiny-n size samples in Ghana, and very preliminary looks at the Malawi data) suggest that I have a better explanatory resolution for explained behaviors than possible through existing livelihoods approaches (which would end up dismissing a lot of choices as illogical or the products of incomplete information) – and therefore I have a better foundation for policy recommendations than available without this careful consideration of the social.
See, for example, this article I wrote on how we approach gender in development (also a good overview of the current state of gender and development, if I do say so myself). I empirically demonstrate that a serious consideration of how gender is constructed in particular places has large material outcomes on whose experiences we can understand, and therefore the sorts of interventions we might program to address particular challenges. We need more rigorous wrestling with “the social” if we are going to learn anything meaningful from our data. Period.
In summary, explanation is hard. Harder, in many ways, than rigorous data collection. Until we start spending at least as much effort on the explanation side as we do on the collection side, we will not really change much of anything in development.
When you've won the Peace Corps, you've won the war
Absolute best personal tweet I saw today, from @goldenmeancap:
@edwardrcarr Reading Delivering Dev. Memories flooding back of 1 team member’s @PeaceCorps service in Swedru, GH C/R. He can’t put it down!
No matter which Swedru he means (there are two in Ghana’s Central Region, Agona Swedru – pretty big – and Swedru – pretty small), I actually think I know the place he’s referring to. Not well, of course, but I have passed through Agona Swedru one time, and passed by Swedru I don’t know how many times . . . pretty cool.
But a larger point – when you can get a Peace Corps volunteer to start having (largely positive) flashbacks to their fieldwork, you know you’ve done something right . . . at least in the first half of the book, which takes the reader down to the village and into the lives of the residents. This tweet review made my day.
Necessary adjustments – but quant and qual still meet
The other day, I posted about the convergence between my own qualitative findings on the food security outcomes of food price instability and those of Marc Bellemare, Chris Barrett and David Just: that, at least in various parts of Africa, such instability was most likely to impact the middle and upper income cohorts more than the lower income cohorts of a given population. However, I jumped too quickly in assuming that their dataset included rural and urban households – as Marc pointed out on his blog, they used a panel of rural household surveys. So my initial argument about convergence does not hold up, as they did not consider the urban context in their work.
This is not to say that I am backing away from my assessment of the vulnerabilities of urban populations to this sort of challenge – I stand by it, having seen it, if only anecdotally, in towns and cities in Ghana over the past 13 years. Urban populations are generally much more dependent on markets for their food supply than those living in rural areas (though this is not always true), and therefore price instability does create significant livelihoods uncertainty that is very difficult to manage, especially for the urban poor. I therefore stand by my argument that we need to be keeping a close eye on the relative impact of price volatility on urban and rural populations, as the impacts of such volatility is likely to have very different impacts on these groups.
But recognizing that Bellemare et al’s work only addresses rural outcomes is not a problem for my argument about what I am loosely calling temporary deglobalization as a strategy for managing price instability (and price increases) – indeed, I think it strengthens the argument because it means that their dataset is now commensurate with mine, which was also rural. As I argued in an extended comment on Marc’s blog:
The rural farmers most hit by price instability are those most integrated with global markets – the ones least able to deglobalize, as it were, when things get uncertain . . . Meanwhile, the bottom 60% is not as engaged with markets in which price volatility matters, and therefore can back away from them in terms of how they use their crops. In my work in Ghana, I found very few true cash crops (in the area I was working). Instead, some crops were treated like “cash crops” in years where price conditions and farm outputs of staple crops were favorable, and as staple crops when either prices were not favorable (including periods of volatility) or outputs of other staples used for subsistence were not adequate to meet household food needs. (Note: in many cases, the treatment of a crop as “cash” or “staple/subsistence” was highly gendered as well). The real difference between the rich and poor (relative terms in the Ghana sample) is the overall livelihoods strategy – one strategy (seen among the wealthier) is much, much more engaged in production for local markets, while the other (seen among the poorer) hedged market production with significant subsistence production (again, highly gendered). In years of volatility (or really in the face of most shocks), the market-oriented livelihoods were simply less resilient than the more diversified livelihoods strategies of the poorer households.
Or, as Marc himself noted in his response to my post on his blog:
[The wealthier] households tend to be hurt by price volatility because they are producers and therefore net sellers of most of (if not all) the seven commodities retained for analysis (i.e., coffee, maize, beans, wheat, teff, barley, sorghum).
So this means that the “temporary deglobalization” argument is not merely a rural-versus-urban argument, but one that can separate households in the same rural community. This, I think, strengthens one of the arguments I was making in my original post:
- Demanding that rural producers orient themselves toward greater and greater integration with global markets in the absence of robust fallback measures (such as established, transparent microinsurance and microsavings initiatives) will likely extend the impact of future price instability further into the poorest populations.
Where Quant and Qual meet: On speculation, price instability and food insecurity
UPDATE: Marc Bellemare pointed out some issues with this post, which I have addressed here. These issues, though, strengthen the argument about strategic deglobalization . . .
§§§§§§
There have been an interesting series of blog posts going around about the issue of price speculation in food markets, and the impact of that speculation on food security and people’s welfare. Going back through some of these exchanges, it seems to me that a number of folks are arguing past one another.
The most recent discussion was spurred by a post on the Guardian’s Global Development blog by John Vidal that took on the issue of speculation in food markets. In the post, Vidal argues that food speculation is a key driver of price instability on global food markets, which results in serious impacts for the poorest people in the world – a sort of famine profiteering, as it were.
The weakness of this post, as I see it, are twofold. First, it doesn’t take the issue of price arbitrage seriously – that is, how speculation is supposed to function. Aid Thoughts, via one of the comments on Vidal’s post, takes Vidal to task for this. As Aid Thoughts/the commenter point out, the idea behind speculation is to pull future price impacts of shortage into the present, stimulating responses to future shortages before they occur. Thus, a blanket condemnation of speculation makes very little sense from the perspective of one who wants to see food security enhanced around the world – without speculation, there will be no market signal for future shortage, creating a world that addresses shortages in a reactive instead of proactive manner. This is a completely fair critique of Vidal, I think.
However, neither Vidal nor those responding to him actually address the evidence for significant market manipulation, and the intentional generation of instability for the purposes of profiteering. This evidence first emerged in a somewhat anecdotal manner in Fredrick Kaufman’s “The Food Bubble: How Wall Street starved millions and got away with it.” In this article, Kaufman uses a fairly limited number of informants to lay out a case for the intentional manipulation of wheat markets in 2008. It is an interesting read, though I argued in an earlier post that it suffers from trying to be a parable for the pervasive presence of complex investment vehicles in the modern world. And in the end, its findings can hardly be called robust.
Though Kaufman’s argument might, by itself, be less than robust, it received a serious empirical boost from the International Food Policy Research Institute (IFPRI) in the fall of 2010. In a discussion paper that remains underreported and under-considered in food security circles (trust me, it is difficult to get anyone to even talk about speculation in program settings), Bryce Cooke and Miguel Robles demonstrate quantitatively that the dramatic price rises for food in 2008 is best explained by various proxies for speculation and activity on futures markets. Now, we can argue about how large an impact that activity had on actual prices, but it seems to me that Cooke and Robles, when taken in concert with the Kaufman piece, have demonstrated that the speculation we see in the markets right now is not merely a normal market response to potential future shortage – indeed, the Food and Agricultural Organization (FAO) of the United Nations has been arguing for months that there are no likely supply issues that should be triggering the price increases we see. In other words, while it is foolish to simply blame price arbitrage for food insecurity, it is equally blind to assume that all of those practicing such arbitrage are doing so in the manner prescribed in the textbooks. Someone will always try to game the system, and in tightly connected markets, a few efforts to game a market can have radiating impacts that draw in honest arbitrage efforts. There is need for regulatory oversight. But regulation will not solve all our food problems.
But this all leaves one last question unanswered: what is the impact of price instability, whether caused by actual likely future shortages or by efforts to game markets for short-term profits, on the welfare of the poor? Vidal, Kaufman and many others assume that the impacts are severe. Well, maybe. You see, where matters (again – yep, I’m a geographer). In a very interesting paper, Marc Bellemare (along with Chris Barrett and David Just) demonstrates that, at least in Ethiopia:
contrary to conventional wisdom, the welfare gains from eliminating price volatility would be concentrated in the upper 40 percent of the income distribution, making food price stabilization a distributionally regressive policy in this context.
This finding may be a shock to those working in aid at first glance, but this finding is actually intuitive. In fact, in my book (out tomorrow!) I lay out a qualitative picture of livelihoods in rural Ghana that aligns perfectly with this finding. In Bellemare et al, I would bet my house that the upper 40% of the population is that segment of the population living in urban areas and/or wealthy enough to be purchasing large amounts of processed food. Why does this matter? This is the segment of the population that typically has the most limited options when food prices begin to get unstable. On the other hand, the bottom 60% of the population, especially those in this cohort living in rural areas (it is unclear from the study how much of an overlap between poor and rural there is in the sample, but I am betting it is pretty high), has a much more limited engagement with global food markets. As a result, when food prices begin to spike, they have the ability to effect a temporary partial, or even complete, disengagement from the global market. In other words, much as I saw in Ghana, this study seems to suggest that temporary deglobalization is a coping strategy that at least some people in Ethiopia use to guard against the vagaries of markets. Ironically, those best positioned to effect such a strategy are the poorest, and therefore they are better able to manage the impact of price instability on food markets.
In short, I would argue that Marc’s (and his co-authors’) work is a quantitative empirical demonstration of one of my core arguments in Delivering Development:
2. At globalization’s shoreline the experience of “development” is often negative. The integration of local economies, politics, and society into global networks is not the unmitigated boon to human well- being presented by many authors. Those living along the shores of globalization deal with significant challenges in their lives, such as degrading environments, social inequality that limits opportunity for significant portions of society, and inadequate medical care. The integration of these places into a global economy does not necessarily solve these problems. In the best cases such integration provides new sources of income that might be used to address some of these challenges. In nearly all cases, however, such integration also brings new challenges and uncertainties that come at a cost to people’s incomes and well- being. (pp.14-15)
I’m not suggesting Marc endorses this claim – hell, for all I know he’ll start throwing things when he sees it. But there is an interesting convergence happening here. I’m glad I met Marc at a tweet-up in DC a few weeks ago. We’re going to have to talk some more . . . I see the beginning of a beautiful friendship.
In summary, while efforts to game global food markets do exist, and have very serious impacts on at least some people, they do not crush everyone in the Global South. Instead, this instability will be most felt by those in urban areas – in the form of a disaffected middle and upper class, and a large cohort of the urban poor who, lacking alternative food sources, might be pushed over the brink by price increases. The policy implications are clear:
- We need to be watching the impact of price increases on urban food insecurity more than rural insecurity
- Demanding that rural producers orient themselves toward greater and greater integration with global markets in the absence of robust fallback measures (such as established, transparent microinsurance and microsavings initiatives) will likely extend the impact of future price instability further into the poorest populations.
- We need to better understand the scope of artificially-generated instability and uncertainty in global food markets, and establish means of identifying and regulating this activity without closing price arbitrage down entirely.
Don't tell us the food price index is rising! Tell us why . . .
The rising price of food has been a subject of many news stories over the past few months, with the intensity of attention ratcheting up recently upon news that the FAO’s food price index has just surpassed its 2008 peak. Stories about this issue – well, at least the good stories – point out the highly variable way in which this increase in the price of food has played out in different places. One good example of this sort of reportage is from Saturday’s Washington Post.
This variability, however, tends to be illustrated instead of interrogated, with explanations remaining remarkably shallow (see my earlier complaints about how explanations related to “local specificity” and “cultural difference” tend to obscure important processes and blame the victims of larger processes). However, a quick examination of the information we have about food prices and their impacts points to the fact that global food prices are not all that useful for understanding the variable food outcomes we see in the Global South. First, we have to understand that the increase everyone is talking about is in an index of food prices – that is, the price data drawn from a number of different foods. Though the index is going up, this does not mean that the prices of all foods are rising equally. As the WaPo and others have noted (and is quite clear in the FAO presentation of the data), when you disaggregate the crops and their prices, the biggest increases globally are in sugar, cooking oils and some fats (there are, of course, local surges in price for particular crops, but those are often independent of the larger global markets). While cereal prices are increasing, they are not rising as quickly as these other foods, and they remain below 2008 levels. So who is hit by these prices has a lot to do with who consumes sugar, or products heavily constituted by sugar and oils. Oils are widely distributed in diets, but sugar is not – the poorest tend to have the least access outside the Global North (ironically, this is reversed in the Global North, as noted by Fast Food Nation and Morgan Spurlock’s Super Size Me). Meanwhile, staple crop prices are not rising anywhere near as rapidly. So the principal drivers of the rising price index are not a huge portion of the diets of those in Global South . . . with one key exception: urban populations. More on that in a second.
Second, who is hit by these prices has to do with the degree to which producers and consumers are linked to global markets. Many rural producers are consumers of their own produce, or the produce of their neighbors. As a result, they are somewhat insulated from shifts in commodity prices. I’ve seen this at work in Ghana firsthand – it is a disaster for incomes in these areas, but not for food security. Instead, people just eat the crops they might otherwise have sold at market. Of course, this comes with other costs, such as in terms of the purchases of needed household goods, and sometimes in terms of children’s education (in places where school fees are still charged). But in terms of food security, not so much. FEWS-NET has offered this same interpretation of the impact of rising food prices on the countries in which it operates, arguing that this increase in this index is not as worrying as what we saw in 2008. This is one of those instances where integration with global markets, long seen as a goal of development programs and a clear pathway to prosperity, can also produce significant new challenges for the global poor . . . or at least that segment of the rural poor whose livelihoods and production are highly integrated with global markets.
So, where people are dependent on global commodities that are internationally sourced for their food or incomes, shifting global food prices are more likely to result in direct shocks to their food security. While there are certainly rural populations that fit this description, once again it is the urban poor who are most generally and directly exposed to this challenge. With little food production of their own, they are dependent on purchased food that has passed through one or more middlemen from the source of production. By definition, their food supply is more commodified, and more connected to global markets, than most of their rural counterparts.
Therefore, there isn’t a whole lot of point to looking at global price indexes to understand the relationship between these prices and food insecurity. Instead, we have to look at who is affected by these prices, and how – the connections are complex and often involve tracing what appear to be unrelated factors as they radiate out from these price changes. This is the only way to appropriately design interventions to address these issues . . .
Don’t tell us that the food price index is rising – tell us why it is rising . . . then we can do something about it.
Oh, Ghana!
For any of you who might have spent time in Ghana, you’ve likely heard that shout: “Oh, Ghana!” It is a good-natured expression of frustration with the everyday annoyances that make life what it is in Ghana. Power cuts out in the middle of a World Cup match? “Oh, Ghana!” Traffic completely stops in Cape Coast because the local herd of cattle have gotten into the road? “Oh, Ghana!” Anyway, you get it.
Well, today’s “Oh, Ghana!” moment comes courtesy of Ghanaian President John Atta Mills, who has taken a particularly depressing stance on the turmoil in Ghana’s neighbor, Cote d’Ivoire:
“Ghana is not taking sides,” he said, pointing out that “We have about one million Ghanaians living in Ivory Coast who could be victims of any military intervention.”
Super, the head of state of the most legitimate democracy in West Africa, and arguably all of sub-Saharan Africa, has decided not to cash in any of that legitimacy to help resolve a fairly clear electoral situation right next door. Of course, this ignores the fact that there are many millions more Ghanaians living along the border with Cote d’Ivoire that could be affected if things go badly, or that cross-border flows of Ivorians trying to escape conflict could pour into Ghana, which lacks the capacity to adequately address their needs. Further, Mills’ response to the crisis is . . . prayer. Prayer is fine, but it is no substitute for working in this world for a solution. No, Mills’ stance is a depressing bit of hedging one’s bets.
The good news, I suppose, is that there is nothing inherently Ghanaian about this attitude toward the situation in Cote d’Ivoire. Nana Akufo-Addo, the New Patriot Party’s (NPP) presidential candidate in 2008 (and likely in 2012), issued a statement earlier this week that more or less addressed the absurdity of Mills’ position.
“Much as most of us Ghanaians believe in the efficacy of prayer, prayer cannot be a replacement of or substitute for an active policy of Ghanaian diplomacy and engagement. It is said that heaven helps those who help themselves.”
Amen. Now go, Ghana. Do something now.
Measurement matters . . .
Todd Moss at the Center for Global Development has a post about Ghana and the Millennium Challenge Corporation (MCC). Overall, he makes some good points about the purpose of MCC compacts, and whether or not it makes sense to re-up with Ghana in 2012 for a second compact. While Moss makes a number of good points in his post (including the fact that Ghana has a lot of capital incoming from oil, and a ready market for its debt, both of which seem to negate the need for continued grants), I was brought up short by one stunning statement:
Ghana is (suddenly) just barely “low income”. A recent rebasing of its GDP found the country was 63% richer than everyone thought. Ghana might still technically qualify for the MCC but the rationale for another huge compact drops pretty significantly.
Now, to be fair to Moss, he has an excellent post here on the implications of such rebasing. Importantly, the second lesson he takes away from this sudden revaluation of Ghana’s economy is:
Boy, we really don’t know anything. Over the past thirty years Ghana has been one of the most scrutinized, measured, studied, picked-over economies in Africa. (yes, I too did my PhD on Ghana…) Yet, we were all taking as gospel a number that was off by a tremendous margin. If we are nearly two-thirds wrong on Ghana’s GDP, what hope can we possibly have in stats for Chad? Everyone knows that data is dubious, but this seems to add a whole new level of doubt.
His fourth point is closely related:
I’m still confused… but it probably doesn’t matter. The Reuters article quotes the government statistician as estimating GDP per capita at $1318 instead of $753. This doesn’t add up to the total GDP figures also given since this implies a 75% increase. If the $1318 is correct, then that either implies that the government thinks there are only 19.4 million people instead of the normal estimates of about 24 million. Or, if the total GDP number of $25.6 billion is right, then per capita GDP is really $1067 per capita. (I think I’m already violating my lesson from #2.)
I have a chapter in my book dedicated to understanding why our measurements of the economy and environment in the Global South are mostly crap, and even when the data is firm it often does not capture the dynamics we think it does. I then spend a few chapters suggesting what to do about it (including respatializing data/data collection so that it can be organized into spatial units that have social, economic, and ecological meaning, and using basic crowdsourcing techniques to both collect data and ground truth of existing statistics). Even better, this is rooted in a discussion of Ghana’s economy. I give Moss credit for being willing to point out the confusing numbers, and acknowledge that they confuse him. They should.
But Moss gets it totally wrong here:
Ghana has long aspired to be a middle-income country by 2020, and this now seems like it will happen many years early. Accra certainly feels like a middle-income city.
This statement explains how he can label Ghana “barely low-income”, even after he has called the very statistics that make such a claim possible into question: he’s focused on Accra. Accra has very little to do with how the bulk of the Ghanaian population lives – and most of that population is very, very poor. Ghana is not barely low income – it is still quite low income, with some pockets of extreme wealth starting to distort the national statistics. It doesn’t matter how Accra feels – that city is home to at best 10% of the population. Kumasi is home to between 5-8% more. Generously including Tamale and Takoradi in the middle-income city categories (this is very generous) nets you probably 25% of the population – nobody else is living in a middle income country. Like Moss, I did my dissertation work in Ghana. I still work there. The difference is that I did my work in rural villages, and still do. $1 a day beyond subsistence is a common income in the rural areas of the Central Region, even now – and the Central Region has a lot more infrastructure than most of the Northern, Upper East and Upper West Regions. This population remains poorly educated – failed by poor rural schools. They cannot support a transformation of the Ghanaian economy. Most of Ghana is still a very low income country, not ready for any sort of sustained economic growth. The country has seen enormous success in recent years – I am stunned by what I have seen in the past 13 years – but the fruits of that success are not distributed evenly. While the cities have boomed, the villages are nearly unchanged. This is Ghana’s new challenge – to spread this new wealth out and foster a diverse, resilient economy.
This is not to say that an MCC compact is the right tool to foster this, or that Ghana is the best place to be putting MCC money. However, declaring “success” too soon creates its own set of risks – let’s use some nuance when considering how a country is doing, so we can identify the real challenges to overcome and successes to build on moving forward.