Entries tagged with “economics”.
Did you find what you wanted?
Wed 21 Nov 2012
Update: 11/22: So, after seeing Tom Murphy’s Storify of the twitter exchange, it is now clear that Sachs was on fire – the man was engaged in several conversations at once along the lines below…and he seems to have been responding to all of them pretty coherently, and in real time. I admit to being impressed (No, seriously, click on the Storify link there and just scroll. It is boggling). So recognize that what you see below is what I saw in my feed (his other conversations were with people I don’t follow, so I didn’t realize they were ongoing). Still, glad to get geography’s foot back in the door…
So, quite by surprise, I found myself on the end of an extended twitter exchange with Jeff Sachs. I’ve hassled him via twitter before, and never had a response. So, I was a bit taken aback to see my feed light up about 30 seconds after I tweeted with @JeffDSachs at the front end! To give Sachs credit, he stayed quite engaged and did seem to be taking on some of my points. Granted, 140 characters is hardly enough to really convey the issues at hand, but I did the best I could to represent contemporary human geography. Y’all be the judge – this is the feed, slightly rejiggered to clarify that at times Sachs and I were crossing each other’s messages – he was clearly responding to a previous message sometimes when he tweeted back after one of my tweets. Also, Samuel Danthine was also on the conversation, and I kept him in the timeline as it seems he and I were coming from the same place:
Sat 20 Oct 2012
I just witnessed a fascinating twitter exchange that beautifully summarizes the divide I am trying to bridge in my work and career. Ricardo Fuentes-Nieva, the head of research at Oxfam GB, after seeing a post on GDP tweeted by Tim Harford (note: not written by Harford), tweeted the following:
To which Harford tweeted back:
This odd standoff between two intelligent, interesting thinkers is easily explained. Bluntly, Harford’s point is academic, and from that perspective mostly true. Contemporary academic thinking on development has more or less moved beyond this question. However, to say that it “never has been” an important question ignores the history of development, where there is little question that in the 50s and 60s there was significant conflation of GDP and well-being.
But at the same time, Harford’s response is deeply naive, at least in the context of development policy and implementation. The academic literature has little to do with the policy and practice of development (sadly). After two years working for a donor, I can assure Tim and anyone else reading this that Ricardo’s point remains deeply relevant. There are plenty of people who are implicitly or explicitly basing policy decisions and program designs on precisely the assumption that GDP growth improves well-being. To dismiss this point is to miss the entire point of why we spend our time thinking about these issues – we can have all the arguments we want amongst ourselves, and turn up our noses at arguments that are clearly passé in our world…but if we ignore the reality of these arguments in the policy and practice world, our thinking and arguing will be of little consequence.
I suppose it is worth noting, in full disclosure, that I found the post Harford tweeted to be a remarkably facile justification for continuing to focus on GDP growth. But it is Saturday morning, and I would rather play with my kids than beat that horse…
Sun 18 Sep 2011
Marc Bellemare at Duke has been using Delivering Development in his development seminar this semester. On Friday, he was kind enough to blog a bit about one of the things he found interesting in the book: the finding that women were more productive than men on a per-hectare basis. As Marc notes, this runs contrary to most assumptions in the agricultural/development economics literature, especially some rather famous work by Chris Udry:
Whereas one would expect men and women to be equally productive on their respective plots within the household, Udry finds that in Burkina Faso, men are more productive than women at the margin when controlling for a host of confounding factors.
This is an important finding, as it speaks to our understanding of inefficiency in household production . . . which, as you might imagine given Udry’s findings, is often assumed to be a problem of men farming too little and women farming a bit too much land. So Marc was a bit taken aback to read that in coastal Ghana the situation is actually reversed – women are more productive than men per unit area of land, and therefore to achieve optimal distributions of agricultural resources (read:land) in these households we would actually have to shift land out of men’s production into women’s production.
I knew that this finding ran contrary to Udry and some other folks, but I did not think it was that big a deal: Udry worked in the Sahel, which is quite a different environment and agroecology than coastal Ghana. Further, he worked with folks of a totally different ethnicity engaged with different markets. In short, I chalked his findings up to the convergence of any number of factors that had played out somewhat differently in my research context. I certainly don’t see my findings as generalizable much beyond Akan-speaking peoples living in rural parts of Ghana . . .
All of that said, Marc points out that with regard to my findings:
Of course, this would need to be subjected to the proper empirical specification and to a battery of statistical tests . . .
Well, that is an interesting question. So, a bit of transparency on my data (it is pretty transparent in my refereed pubs, but the book didn’t wade into all of that):
- The data was gathered during the main rainy season, typically as the harvest was just starting to come in. This required folks to make some degree of projection about the productivity of their fields at least a month into the future, and often several months into the future
- The income figures for each crop, and therefore for total agricultural productivity, were self-reported. I was not able to cross-check these reported figures by counting the actual amount of crop coming off each farm.
- I also gathered information on expenses, and when I totaled up expenses and subtracted them from reported income, every household in the village was running in the red. I know that is not true, having lived there for some 18 months of my life.
- There is no doubt in my mind that production figures were underestimated, and expenses overestimated, in my data – this fits into patterns of income reporting among the Akan that are seen elsewhere in the literature.
- Therefore, you cannot trust the reported figures as accurate absolute measures of farm productivity.
- The data was replicated across three field seasons. The first two field seasons, I conducted all data collection with my research assistant. However, in the final year of data collection, I lead a team of four interviewers from the University of Cape Coast, who worked with local guides to identify farms and farmers to interview – in the last year, we interviewed every willing farmer in the village (nearly 100% of the population).
- It turns out that my snowball sample of households in the first two years of data collection actually covered the entire universe of households operating under non-exceptional household circumstances (i.e. they are not samples, they are reports on the activities of the population).
- In other words, you don’t have to ask about my sampling – there was no sampling. I just described the activities of the entire relevant population in all three years.
- This removes a lot of concerns people have about the size of my samples – some household strategies only had 7 or 8 households working with them in a given year, which makes statistical work a little tricky Well, turns out there is no real need for stats, as this is everyone!
- The only exception to this: female-headed households. I grossly underinterviewed them in years 1 and 2 (inadvertently), and the women I did interview do not appear to be representative of all female-headed households. I therefore can only make very limited claims about trends in these households.
- Even with completely new interviewers who had no preconceived notions about the data, the income findings came in roughly the same as when I gathered the data. That’s replicability, folks! Well, at least as far as qualitative social science gets in a dynamic situation.
- Though the data was gathered at only one point in the season, at that point farmers were already seeing how the first wave of the harvest was doing and could make reasonable projections about the rest of the harvest.
I’m probably forgetting other problems and answers . . . Marc will remind me, I’m sure! In any case, though, Marc asks a really interesting question at the end of his post:
Assuming the finding holds, it would be interesting to compare the two countries given that Burkina Faso and Ghana share a border. Is the change in gender differences due to different institutions? Different crops?
The short answer, for now, has to be a really unsatisfying “I don’t know.” Delivering Development lays out in relatively simple terms a really complex argument I have building for some time about livelihoods, that they are motivated by and optimized with reference to a lot more than material outcomes. The book builds a fairly simple explanation for how men balanced the need to remain in charge of their households with the need to feed and shelter those households . . . but I have elaborated on this in a piece in review at the Development and Change. I will send them an email and figure out where this is in review – they have been struggling mightily with reviewers (last I heard, they had gone through 13!?!) and put up a preprint as soon as I am able. This is relevant here because I would need a lot more information about the Burkina setting to work through my new livelihoods framework before I could answer Marc’s question.
Tue 6 Sep 2011
Posted by Ed under development, environment, policy, research
OK, a last thought on the development initiatives and markets thread: let’s leave the predictive markets thing aside for the moment, and get to what I think is a more serious question for development initiatives – do we use all the information we might to evaluate the likely impact of our programs? I think a lot of folks misread the intent of my initial post – I was NOT suggesting we bet on mortality rates and other direct measures of project effectiveness. That is something I could see as an academic exercise, but is way too morbid for my tastes, even in that setting.
But everyone who lunged in that direction seemed to miss the point that any major development initiative will, if it succeeds, have radiating impacts through different markets. That is, a successful food security initiative will change harvest sizes of different crops, thereby influencing commodities markets. A successful public health intervention might increase the size of the workforce, or its efficiency. And so on. My simple thought was that any fund investor worth his/her salt should be examining these initiatives and their expected outcomes to decide 1) if the initiative worked, what markets might be affected, how and when and 2) do they think the initiative will actually work.
If there is no movement around these initiatives, it seems to me that these two factors might be important – at the first step in this decision-making, investors might decide that in the event of a successful intervention, the markets affected might not be accessible or profitable, or the timeframe of any movement in the market might be so long as to make immediate response unnecessary. Thus, we would see no market response to the announcement of an intervention. At that point, it doesn’t matter if the intervention will work or not – that assessment never comes into the picture.
However, in at least some cases, I have to think that there are initiatives out there (in a world of rising food prices, I am a bit fixated on food security at the moment) that would affect significant markets, and not only at a national scale (where markets might be illiquid or otherwise inaccessible). Take the case of cocoa and Cote d’Ivoire this past winter: the civil conflict in CIV cut off a significant amount of global supply, and futures markets got skittish over the further constriction of trade, driving cocoa prices upward. This is a niche crop, heavily produced by only a few countries, but the price movement could have meant big dollars for a fund that correctly anticipated this trend. Surely there are (or will be) food security initiatives that could similarly affect the overall supplies of and access to particular (perhaps niche) crops for entire regions, or even shift global availability/perception enough to shift commodities prices in much larger, more transparent markets in the short term. Don’t fixate on national markets for these initiatives – what about really big development movers that could affect global supplies of grain in an era where all the slack has been taken out of various global grain markets? You can’t tell me that everyone at these trading desks is simply ignoring the food security world . . . surely they are at least assessing through step 1) above. So if there is no market response to these initiatives, either the timeframe of movement is too distant to warrant interest, or the traders simply don’t think these initiatives will succeed enough to significantly influence the markets in which they trade. Perhaps the price of oil and its impact on transport is much, much more important than increasing harvest size when it comes to shaping food commodities prices . . . in which case, it would probably be good for those designing food security initiatives to know this at the outset and address it in project design (for example by thinking about transportation issues as integral to the initiative).
Of course, there is option 3): traders have no idea what sorts of initiatives are out there, and are operating in ignorance of these potential large drivers. This is entirely possible, but a bit hard to believe . . .
Tue 6 Sep 2011
Lots of comments pouring in via twitter regarding my earlier post on development initiatives and markets. First, I found it interesting that readers went in two directions – they either took the post to be about prediction markets alone, or they caught the reference to hedge funds and realized that I was talking about “betting” in a much more general sense: that is, in the sense of hedge fund investment, which is really a set of (ideally) well-researched, carefully-hedged bets on the direction of particular stocks, commodities and sometimes whole segments of the market.
For now, let’s take up the issue of predictive markets. I love Bill Easterly’s response tweet, asking what development initiative I (or anyone else) would bet my own money on. I think prediction markets are interesting tools. They are hardly perfect, as like other markets they are subject to bubbles and manipulation, but there is some evidence to suggest that they do yield interesting information under the right conditions. It would be interesting to set up parallel prediction markets, and populate one with development professionals at agencies and NGOs, one with development academics, and one that blends the two, and then have them start to buy and sell the likelihood of success (as defined by the initiative, both in terms of outcomes and timeframe) for any number of development initiatives. While I doubt these parallel markets would move in lockstep, I wonder if they would come to radically different assessments of these initiatives. And we could examine how well they worked as predictive devices. I’m pretty sure most academics would have started shorting the Millennium Village Project at its inception (academic paper here) . . . so what things would the development blogosphere/twittersphere short today? What would you go long on (that is, what would you hold in the expectation it would meet expectations and rise in value)? Have at it in the comments . . .
I’ll address the wider meaning of “betting” that I was also aiming at later . . .
Mon 5 Sep 2011
Welcome to a new feature of Open the Echo Chamber, a quick post on something that interests me. Yes, I am capable of writing less than 1000 words in a post, but most of the time I take on subjects that need a lot of attention. Going forward, I am going to try to intersperse some “quick thoughts” on the blog for those who lack the 15 minutes and headspace to deal with my longer fare . . .
I’ve been doing a lot of reading about hedge funds lately, and it recently hit me: does anyone in the markets bet for or against development initiatives? It seems to me that you could – after all, a big initiative from either a multilateral or large bilateral donor will often come with quite a bit of money attached (at least initially), a lot of publicity, and some clearly stated goals that are almost always tied to economic growth or diversification. So, do investors look at these initiatives and bet for or against them? I’m not saying they bet directly on an initiative, but on its outcome: for example, do funds look at large food security initiatives in a particular country and bet on the prices of the crops involved in that initiative?
Here is why I care: if nobody is betting on them, it pretty much signals that these initiatives are largely irrelevant. Either they are not large enough to move any market in the short or long term, or they are not aimed at anything likely to induce a transformation of economy and society through some set of cascading impacts in the long term. If this is the case, it seems to me we ought to back out of those initiatives right away. This is not to say that we should not be addressing the needs of the most vulnerable people in the world, but to suggest that an absence of interest in these initiatives might mean that our efforts to address these needs are not likely to come to much.
On the other hand, if we see significant betting on the outcomes of initiatives, it seems to me we might start to look at the direction of this betting (short or long) to get a sense of how things are likely to play out, and start looking for problems/leveraging opportunities as soon as possible.
Just a quick thought . . .
Wed 17 Aug 2011
Posted by Ed under Adaptation, Climate Change, Delivering Development, development, Development Institutions, environment, Food Security, globalization, Livelihoods, policy, research, sustainable development
Yesterday, I took the relief community to task for not spending more time seriously thinking about global environmental change. To be clear, this is not because that community pays no attention, or is unaware of the trend toward increasing climate variability and extreme weather events in many parts of the world that seems to be driving ever-greater needs for intervention. That part of the deal is pretty well covered by the humanitarian world, though some folks are a bit late to the party (and it would be good to see a bit more open, informal discussion of this – most of what I have seen is in very formal reports and presentations). I am more concerned that the humanitarian community gives little or no thought to the environmental implications of its interventions – in the immediate rush to save lives, we are implementing projects and conducting activities that have a long-term impact on the environment at scales ranging from the community to the globe. We are not, however, measuring these impacts in really meaningful ways, and therefore run the risk of creating future problems through our current interventions. This is not a desirable outcome for anyone.
But what of the development community, those of us thinking not in terms of immediate, acute needs as much as we are concerned with durable transformations in quality of life that will only be achieved on a generational timescale? You’d think that this community (of which I count myself a part) would be able to grasp the impact of climate change on people’s long-term well-being, as both global environmental changes (such as climate change and ecosystem collapse) and development gains unfold over multidecadal timescales. Yet the integration of global environmental change into development programs and research remains preliminary and tentative – and there is great resistance to such integration from many people in this community.
Sometimes people genuinely don’t get it – they either don’t think that things like climate change are real problems, or fail to grasp how it impacts their programs. These are the folks who would lose at the “six degrees of Kevin Bacon” game – I’ve said it before, and I will say it again: global environmental change is development’s Kevin Bacon: I can link environmental change to any development challenge in three steps or less. Sometimes the impacts are really indirect, which can make this hard to see. For example, take education: in some places, climate change will alter growing seasons such that farm productivity will be reduced, forcing families to use more labor to get adequate food and income, which might lead parents to pull their kids from school to get that labor. Yep, at least some education programs are impacted by climate change, an aspect of global environmental change.
Other times, though, I think that the resistance comes from a very legitimate place: many working in this field are totally overtaxed as it is. They know that various aspects of global environmental change are problems in the contexts in which they work, but lack the human and financial resources to accomplish most of their existing tasks. Suddenly they hear that they will have to take something like climate change into account as they do their work, which means new responsibilities that will entail new training, but often come without new personnel or money. It is therefore understandable when these folks, faced with these circumstances, greet the demand for the integration of global environmental change considerations into their programs with massive resistance.
I think the first problem contributes to the second – it is difficult to prioritize people and funding for a challenge that is poorly understood in the development community, and whose impacts on the project or initiative at hand might be difficult to see. But we must do this – various forms of global environmental change are altering the future world at which we are aiming with our development programs and projects. While an intervention appropriate to a community’s current needs may result in improvements to human well-being in the short term, the changes brought on by that intervention may be maladaptive in ten or twenty years and end up costing the community much more than it gained initially.
Global environmental change requires us to think about development like a fade route in football (American), or the through ball in soccer (the other football). In both cases, the key is to put the ball where the target (the receiver of the pass) is going to be, not where they are now. Those who can do this have great success. Those that cannot have short careers. Development needs to start working on its timing routes, and thinking about where our target communities are going to be ten and twenty years from now as we design our programs and projects.
So, how do we start putting our projects through on goal? One place to start would be by addressing two big barriers: the persistence of treating global environmental change as a development sector like any other, and the failure of economics to properly cost the impacts of these changes.
First, global environmental change is not a sector. It is not something you can cover in a section of your project plan or report, as it impacts virtually all development sectors. Climate change alters the range and number of vectors for diseases like malaria. Overfishing to meet the demands of consumers in the Global North can crush the food security of poor coastal populations in the Global South. Deforestation can intensify climate change, lead to soil degradation that compromises food security, and even distort economic policy (you can log tropical hardwoods really quickly and temporarily boost GNP in a sort of “timber bubble”, but eventually you run out of trees and those 200-500 year regrowth times means that the bubble will pop and a GNP downturn is the inevitable outcome of such a policy). If global environmental change is development’s Kevin Bacon, it is pretty much omnipresent in our programs and projects – we need to be accounting for it now. That, in turn, requires us to start thinking much longer term – we cannot design projects with three to five year horizons – that is really the relief-to-recovery horizon (see part 1 for my discussion of global environmental change in that context). Global environmental change means thinking about our goals on a much longer timescale, and at a much more general (and perhaps ambitious) scale. The uncertainty bars on the outcomes of our work get really, really huge on these timescales . . . which to me is another argument for treating development as a catalyst aimed at triggering changes in society by facilitating the efforts of those with innovative, locally-appropriate ideas, as opposed to imposing and managing change in an effort to achieve a narrow set of measurable goals at all costs. My book lays out the institutional challenges to such a transformation, such as rethinking participation in development, which we will have to address if this is ever to work.
Second, development economics needs to catch up to everyone else on the environment. There are environmental economists, but not that many – and there are virtually no development economists that are trained in environmental economics. As a result, most economic efforts to address environmental change in the context of development are based on very limited understandings of the environmental issues at hand – and this, in turn, creates a situation where much work in development economics either ignores or, in its problematic framings of the issue, misrepresents the importance of this challenge to the development project writ large. Until development economists are rewarded for really working on the environment, in all its messiness and uncertainty (and that may be a long way off, given how marginal environmental economists are to the discipline), I seriously doubt we are going to see enough good economic work linking development and the environment to serve as a foundation for a new kind of thinking about development that results in durable, meaningful outcomes for the global poor. In the meantime, it seems to me that there is a huge space for geographers, anthropologists, sociologists, political scientists, new cultural historians, and others to step up and engage this issue in rich, meaningful ways that both drive how we do work now and slowly force new conversations on both economics and the practice of development.
I do admit, though, that my expanding circle of economics colleagues (many of which I connected to via this blog and twitter) have given me entrée into a community of talented people that give me hope – they are interested and remarkably capable, and I hope they continue to engage me and my projects as they go forward . . . I think there is a mutual benefit there.
Let me be clear: the continuing disconnect between development studies and environmental studies is closing, and there are many, many opportunities to continue building connections between these worlds. This blog is but one tiny effort in a sea of efforts, which gives me hope – with lots of people at work on this issue, someone is bound to succeed.
In part three, I will take up why global environmental change means that we have to rethink the RCT4D work currently undertaken in development – specifically, why we need much, much better efforts at explanation if this body of work is to give us meaningful, long-term results.
Tue 9 Aug 2011
Mike Hulme has an article in the July issue of Nature Climate Change titled “Meet the humanities,”[paywalled] in which he argues that “An introduction needs to be made between the rich cultural knowledge of social studies and the natural sciences.” Overall, I like this article – Hulme understands the social science side of things, not least through his own research and his work as editor of Global Environmental Change, one of the most influential journals on the human dimensions of global change*. Critically, he lays out how, even under current efforts to include a wider range of disciplines in major climate assessments, the conversation has been dominated for so long by the biophysical sciences and economics that it is difficult for other voices to break in:
policy discussions have become “improving climate predictions” and “creating new economic policy instruments”; not “learning from the myths of indigenous cultures” or “re-thinking the value of consumption.”
Hulme is not arguing that we are wrong to be trying to improve climate predictions or develop new economic policy instruments – instead, he is subtly asking if these are the right tools for the job of addressing climate change and its impacts. My entire research agenda is one of unearthing a greater understanding of why people do what they do to make a living, how they decide what to do when their circumstances change, and what the outcomes of those decisions are for their long-term well being. Like Hulme, I am persistently surprised at the relative dearth of work on this subject – especially because the longer I work on issues of adaptation and livelihoods, the more impressed I am with the capacity of communities to adjust to new circumstances, and the less impressed I am with anyone’s ability to predictably (and productively) intervene in these adjustments.
This point gets me to my motivation for this post. Hulme could not cover everything in his short commentary, but I felt it important to identify where a qualitative social science perspective can make an immediate impact on how we think about adaptation (which really is about how we think about development, I think). I remain amazed that so many working in development fail to grasp that there is no such things as a completely apolitical, purely technical intervention. For example, in development we all too often assume that a well is just a well – that it is a technical intervention that delivers water to people. However, a well is highly political – it reshapes some people’s lives, alters labor regimes, could empower women (or be used as an excuse to extract more of their labor on farms, etc.) – all of this is contextual, and has everything to do with social relations and social power. So, we can introduce the technology of a well . . . but the idea and meaning of a well cannot be introduced in the same manner – these are produced locally, through local lenses. It is this basic failure of understanding that lies at the heart of so many failed development projects that passed technical review and various compliance reviews: they were envisioned as neutral and technical, and were probably very well designed in those arenas. However, these project designers gave little concern to the contextual, local social processes that would shape the use and outcomes of the intervention, and the result was lots of “surprise” outcomes.
When we start to approach these issues from a qualitative social scientific standpoint, or even a humanities standpoint (Hulme conflates these in his piece, I have no idea why. They are not the same), the inherent politics of development become inescapable. This was the point behind my article “The place of stories in development: creating spaces for participation through narrative analysis.” In that article, I introduce the story I used to open Delivering Development to illustrate how our lived experience of development often plays out in ways best understood as narratives, “efforts to present information as a sequence of connected events with some sort of structural coherence, transforming ‘the real into an object of desire through a formal coherence and a moral order that the real.” These narratives emerge in the stories we are told and that we overhear in the course of our fieldwork, but rarely make it into our articles or reports (though they do show up on a few fantastic aid blogs, like Shotgun Shack and Tales from the Hood). They become local color to personal stories, not sources of information that reveal the politics of our development efforts (though read the two aforementioned blogs for serious counterpoints).
In my article, I demonstrated how using the concept of narrative, drawn from the humanities, has allowed me to identify moments in which I am placed into a plot, a story of development and experience not of my making:
In this narrative [“the white man is so clever,” a phrase I heard a lot during fieldwork], I was cast as the expert, one who had knowledge and resources that could improve their lives if only I would share it with them. [The community] cast themselves in the role of recipients of this knowledge, but not participants in its formation. This narrative has been noted time and again in development studies (and post-colonial studies), and in the era of participation we are all trained to subvert it when we see it emerge in the work of development agencies, governments, and NGOs. However, we are less trained to look for its construction by those living in the Global South. In short, we are not trained to look for the ways in which others emplot us.
The idea of narrative is useful not only for identifying when weird neocolonial moments crop up, but also for destabilizing those narratives – what I call co-authoring. For example, when I returned to the site of my dissertation fieldwork a few years later, I found that my new position as a (very junior) professor created a new set of problems:
This new identity greatly hindered my first efforts at fieldwork after taking this job, as several farmers openly expected me to tell them what to plant and how to plant it. I was able to decentre this narrative when, after one farmer suggested that I should be telling him what to plant instead of asking him about his practices, I asked him ‘Do I look like a farmer?’ He paused, admitted that I did not, and then started laughing. This intervention did not completely deconstruct his narrative of white/developed and black/developing, or my emplotment in that narrative. I was still an expert, just not about farming. This created a space for him to speak freely to me about agriculture in the community, while still maintaining a belief in me as the expert.
Certainly, this is not a perfect outcome. But this is a lot better than the relationship that would have developed without an awareness of this emerging narrative, and my efforts to co-author that narrative. Long and short, the humanities have a lot to offer both studies of climate change impacts and development – if we can bring ourselves to start taking things like stories seriously as sources of data. As Hulme notes, this is not going to be an easy thing to do – there is a lot of inertia in both development and climate change studies. But changes are coming, and I for one plan to leverage them to improve our understandings of what is happening in the world as a result of our development efforts, climate change, global markets, and any number of other factors that impact life along globalization’s shoreline – and to help co-author different, and hopefully better, outcomes than what has come before.
*full disclosure: I’ve published in Global Environmental Change, and Hulme was one of the editors in charge of my article.
Sun 31 Jul 2011
Posted by Ed under development, policy, research
Charles Kenny’s* book Getting Better has received quite a bit of attention in recent months, at least in part because Bill Gates decided to review it in the Wall Street Journal (up until that point, I thought I had a chance of outranking Charles on Amazon, but Gates’ positive review buried that hope). The reviews that I have seen (for example here, here and here) cast the book as a counterweight to the literature of failure that surrounds development, and indeed Getting Better is just that. It’s hard to write an optimistic book about a project as difficult as development without coming off as glib, especially when it is all too easy to write another treatise that critiques development in a less than constructive way. It’s a challenge akin to that facing the popular musician – it’s really, really hard to convey joy in a way that moves the listener (I’m convinced this ability is the basis of Bjork’s career), but fairly easy to go hide in the basement for a few weeks, pick up a nice pallor, tune everything a step down, put on a t-shirt one size too small and whine about the girlfriend/boyfriend that left you.
Much of the critical literature on development raises important challenges to development practice and thought, but does so in a manner that makes addressing those challenges very difficult (if not intentionally impossible). For example, deep (and important) criticisms of development anchored in poststructural understandings of discourse, meaning and power (for example, Escobar’s Encountering Development and Ferguson’s The Anti-Politics Machine) emerged in the early and mid-1990s, but their critical power was not tied in any way to a next step . . . which eventually undermined the critical project. It also served to isolate academic development studies from the world of development practice in many ways, as even those working in development who were open to these criticisms could find no way forward from them. Tearing something down is a lot easier than building something new from the rubble.
While Getting Better does not reconstruct development, its realistically grounded optimism provides what I see as a potential foundation for a productive rethinking of efforts to help the global poor. Kenny chooses to begin from a realistic grounding, where Chapters 2 and 3 of the book present us with the bad news (global incomes are diverging) and the worse news (nobody is really sure how to raise growth rates). But, Kenny answers these challenges in three chapters that illustrate ways in which things have been improving over the past several decades, from sticking a fork in the often-overused idea of poverty traps to the recognition that quality of life measures appear to be converging globally. This is more than a counterweight to the literature of failure – this book is a counterweight to the literature of development that all-too-blindly worships growth as its engine. In this book, Kenny clearly argues that growth-centric approaches to development don’t seem to be having the intended results, and growth itself is extraordinarily difficult to stimulate . . . and despite these facts, things are improving in many, many places around the world. This opens the door to question the directionality of causality in the development and growth relationship: is growth the cause of development, or its effect?
Here, I am pushing Kenny’s argument beyond its overtly stated purpose in the book. Kenny doesn’t overtly take on a core issue at the heart of development-as-growth: can we really guarantee 3% growth per year for everyone forever? But at the same time, he illustrates that development is occurring in contexts where there is little or no growth, suggesting that we can delink the goal of development from the impossibility of endless growth. If ever there were a reason to be an optimist about the potential for development, this delinking is it.
I feel a great kinship with this book, in its realistic optimism. I also like the lurking sense of development as a catalyst for change, as opposed to a tool or process by which we obtain predictable results from known interventions. I did find Getting Better’s explanations for social change to rest a bit too heavily on a simplistic diffusion of ideas, a rather exogenous explanation of change that was largely abandoned by anthropology and geography back in the structure-functionalism of the 1940s and 50s. The book does not really dig into “the social” in general. For example, Kenny’s discussion of randomized control trials for development (RCT4D), like the RCT4D literature itself, is preoccupied with “what works” without really diving into an exploration of why the things that worked played out so well. To be fair to Kenny, his discussion was not focused on explanation, but on illustrating that some things that we do in development do indeed make things better in some measurable way. I also know that he understands that “what works” is context specific . . . as indeed is the very definition of “works.” However, why these things work and how people define success is critical to understanding if they are just anecdotes of success in a sea of failure, or replicable findings that can help us to better address the needs of the global poor. In short, without an exploration of social process, it is not clear from these examples and this discussion that things are really getting better.
An analogy to illustrate my point – while we have very good data on rainfall over the past several decades in many parts of West Africa that illustrate a clear downward trend in overall precipitation, and some worrying shifts in the rainy seasons (at least in Ghana), we do not yet have a strong handle on the particular climate dynamics that are producing these trends. As a result, we cannot say for certain that the trend of the past few decades will continue into the future – because we do not understand the underlying mechanics, all we can do is say that it seems likely, given the past few decades, that this trend will continue into the future. This problem suggests a need to dig into such areas as atmospheric physics, ocean circulation, and land cover change to try to identify the underlying drivers of these observed changes to better understand the future pathways of this trend. In Getting Better (and indeed in the larger RCT4D literature), we have a lot of trends (things that work), but little by way of underlying causes that might help us to understand why these things worked, whether they will work elsewhere, or if they will work in the same places in the future.
In the end, I think Getting Better is an important counterweight to both the literature of failure and a narrowly framed idea of development-as-growth. My minor grumbles amount to a wish that this counterweight was heavier. It is most certainly worth reading, and it is my hope that its readers will take the book as a hopeful launching point for further explorations of how we might actually achieve an end to global poverty.
*Full disclosure: I know Charles, and have had coffee with him in his office discussing his book and mine. If you think that somehow that has swayed my reading of Getting Better, well, factor that into your interpretation of my review.
Wed 13 Jul 2011
An interesting review of Paul Collier’s The Bottom Billion and Wars, Guns and Votes by Yale Anthropologist Mike McGovern has gotten a little bit of attention recently in development circles, speaking as it does to ongoing debates about the role of statistical analysis, what counts as explanation, and where qualitative research fits into all of this. I will take up McGovern’s good (but incomplete, in my opinion) review in another post. Here, I needed to respond to a blog entry about this review.
On the Descriptive Statistics, Causal Inference and Social Science blog, Andrew Gelman discusses McGovern’s review. While there is a lot going on in this post, one issue caught my attention in particular. In his review, McGovern argues that “Much of the intellectual heavy lifting in these books is in fact done at the level of implication or commonsense guessing,” what Gelman (quoting Fung) calls “story time”, the “pivot from the quantitative finding to the speculative explanation.” However, despite the seemingly dismissive term for this sort of explanation, in his blog post Gelman argues ”story time can’t be avoided.” His point:
On one hand, there are real questions to be answered and real decisions to be made in development economics (and elsewhere), and researchers and policymakers can’t simply sit still and say they can’t do anything because the data aren’t fully persuasive. (Remember the first principle of decision analysis: Not making a decision is itself a decision.)
From the other direction, once you have an interesting quantitative finding,of course you want to understand it, and it makes sense to use all your storytelling skills here. The challenge is to go back and forth between the storytelling and the data. You find some interesting result (perhaps an observational data summary, perhaps an analysis of an experiment or natural experiment), this motivates a story, which in turn suggests some new hypotheses to be studied.
Now, on one hand I take his point – research is iterative, and answering one set of questions (or one set of new data) often raises new questions which can be interrogated. But Gelman seems to presume that explanation only comes from more statistical analysis, without considering what I saw as McGovern’s subtle point: qualitative social scientists look at explanation, and do not revert to story time to do so (good luck getting published if you do). We spend a hell of a lot of time fleshing out the causal processes behind our observations, including establishing rigor and validity for our data and conclusions, before we present stories. This is not to say that our explanations are immediately complete or perfect, nor is it to suggest that our explanations do not raise new questions to pursue. However, there is no excuse for the sort of “story time” analysis that McGovern is pointing out in Collier’s work – indeed, I would suggest that is why the practice is given a clearly derisive title. That is just guessing, vaguely informed by data, often without even thinking through alternative explanations for the patterns at hand (let alone presenting those alternatives).
I agree with Gelman’s point, late in the post – this is not a failing of statistics, really. It is a failure to use them intelligently, or to use appropriate frameworks to interpret statistical findings. It would be nice, however, if we could have a discussion between quant and qual on how to avoid these outcomes before they happen . . . because story time is most certainly avoidable.