Tag: poverty

On livelihoods and global environmental change

In a comment on my earlier post critiquing the recent ENSO and conflict piece that appeared in nature Nature , Joe pointed out that my argument that the authors of the piece did not understand livelihoods was not necessarily clear to the reader.  I think this is completely fair – I am buried in livelihoods . . . it is a concept at the core of what I have researched for the past 14 years, and therefore what may seem obvious to me is not so obvious to everyone else.
First, to clarify: I think the top-line issue I was shorthanding in my response to Solomon was the causal framework: it is totally unclear to me how they think environmental change is translated into conflict.  It is possible that they had no explicit notion of how this connection is made, but I think that would create an enormous set of problems for the study as it would make it impossible to know what variables to control for in the study (to some extent, I think this is a problem with the study anyway).  However, the study, and Solomon’s response, led me to believe that they did have a very basic framing of this connection, where weather impacts livelihoods which impacts behavior.  In this apparent framing, it seems to me that they treated livelihoods as a straightforward set of activities – and the impact of weather on those activities could be easily and generally understood, and the human outcomes of those impacts could also be easily and generally understood.  If this is true, it is a serious misunderstanding of livelihoods.
There is a lot of stuff I could say about livelihoods – my current intellectual project involves rethinking how we understand livelihoods, because I think current analytical frameworks cannot really engage with actual livelihoods decision-making on the ground.  As a result, a lot of our understandings of what people do, and why they do it, are wide of the mark, and the interventions we design to improve/augment/replace existing means of making a living in particular places are often misguided and prone to “surprise” outcomes.
First, a quick definition of livelihoods as they are treated in the contemporary literature: “the capabilities, assets (stores, resources, claims and access) and activities required for a means of living” (Chambers and Conway, 1992:7).  As Brent McCusker and I have argued:

this definition of livelihoods moves past income toward a more holistic consideration of the manner in which a person obtains a living. In practice, this definition has resulted in a number of approaches to livelihoods that focus closely on access to various types of assets drawn upon by individuals to make a living. These approaches tend to categorize these assets as one of five types of capital: natural, physical, human, financial and social. Land comes under natural capital, “the natural resource base (land, water, trees) that yields products utilized by human populations for their survival,” though an improved field might come under the heading of physical capital, which generally includes “assets brought into existence by economic production processes.”

My problem with the livelihoods approach that dominates the literature, and subtly undergirds the Nature piece I was critiquing, is not the broad definition of livelihoods.  Instead, the problem lies in the subtle assumption of this approach that, in its focus on the requirements for a means of living, concentrates on material circumstances and outcomes as a metric for the success and viability of particular livelihoods.  As I have demonstrated repeatedly (for example here, and in my book Delivering Development), livelihoods are double-edged: they are aimed at both meeting certain material requirements of life and maintaining the privileges of the powerful.  Above certain very, very low thresholds, the social goals of livelihoods actually trump the material goals.  Therefore, if we want to understand livelihoods decisions and outcomes, we must understand the social context at least as well as we do the material conditions in a particular place.  Using generalized assumptions about human motivations to explain responses to livelihoods shifts will smooth over really significant differences in decision-making, and therefore obscure any possible causal connection between things like environmental change and the incidence of conflict – material maximization/deprivation is only part of the story of human motivations, and a relatively small part at that.
How does this all relate to the Nature piece and my criticism? While the authors never specified the means by which this would happen in the piece, only offering general speculation in their response to my criticism, I found Solomon’s response to my blog post really telling:

The study is trying to understand whether choosing to engage in conflict is a “livelihood decision” that individuals in modern societies select more often when El Nino events occur. Our findings tells us that for some reason, people’s willingness to engage in organized violence changes when the global climate changes. One hypothesis is that perhaps “predation” (i.e. the forceful extraction of property from others) is a form of “adaptation” to climate changes.

It is possible that Solomon’s reference to conflict as a livelihoods decision was simply echoing the terms of my criticism.  However, both the article and his response seems to reflect an implicit framing of the environment-to-conflict connection as somehow passing through livelihoods in a straightforward manner.  Because the authors never actually unpack how the environment impacts livelihoods, and in turn how those impacts are translated into human impacts, they become guilty of the same issue that plagues nearly everyone using the livelihoods framework these days: they implicitly embrace an over-generalized framing of livelihoods decisions that relies too heavily on a relatively minor driver of decision-making (material conditions), and completely ignores the dominant factors that shape the character of particular activities and therefore result in particular outcomes for the well-being of those living under that strategy.  I am sure that predation does occur.  I am also absolutely certain that this is not a general response – it does not happen very often (plenty of empirical studies show other behaviors).  It is not interesting to know that it occurs – we already know that.  What is interesting and important is why it occurs.  Going for “story time” explanations of complex behavior does not contribute to our understanding of human behavior, or the impact of climate change on human well-being.
I am working on a reframing of livelihoods that elevates the social component to its proper place in livelihoods decision-making (in review at the Journal of Development Studies).  The thinking behind this reframing is intensely theoretical and really, really academic (for a taste of what I mean, see this piece I wrote with Brent).  My goal in the forthcoming piece is to take this really esoteric theory and turn it into an approach that can be understood and employed widely.  With any luck it will be accepted and published relatively soon . . . I will put up a pre-print as soon as I am able.  But even with this reframing, we are going to have to work really hard at understanding when large-scale studies such as the one I have been critiquing are appropriate for furthering our understanding of things we really need to know, when they merely illustrate what we already know, and when they present really problematic findings with a misleading level of certainty.



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Book Review: Getting Better by Charles Kenny

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.


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Measuring poverty to address climate change

Otaviano Canuto, the World Bank’s Vice President for Poverty Reduction, had an interesting post on HuffPo yesterday in which he argues that we cannot understand the true cost of climate change until we can better measure poverty – “as long as we are unable to measure the poverty impact of climate change, we run the risk of either overestimating or underestimating the resources that will be needed to face it.”  I agree – we do not have a particularly good handle on the economic costs of climate change right now, just loose estimates that I fear are premised on misunderstandings of life in the Global South (I have an extended discussion of this problem in the second half of my book).
However, I find the phrasing of this concern a bit tortured – we need to better understand the impact of climate change on poverty so we can figure out how much it will cost us to solve the problem . . . but which problem?  Climate change or poverty?  Actually, I think this tortured syntax leads us to a more productive place than a focus on either problem – just as I am pretty sure we can’t address poverty for most living in the Global South unless we do something about climate change (which I think is what Canuto was after), I don’t think you can address climate change without addressing poverty.  As I argue in my book:

Along globalization’s shoreline the effects of climate change are felt much more immediately and more directly than in advanced economies. More and more, as both climate change and economic change impact their capacity to raise the food and money they need to get through each day, residents of this shoreline find themselves forced into trade-offs they would rather not make.

For example, most of the farmers in Dominase and Ponkrum agree that deforestation lowers the agricultural productivity of their farms, due to both the loss of local precipitation that accompanies deforestation and the loss of shade that enables the growth of sensitive crops, such as cocoa. At the same time, the sound of chainsaws can still be heard around these villages every once in a while, as a head of lineage allows someone from town to cut down one of the few remaining trees in the area for a one-time payment of a few hundred dollars. These heads of family know that in allowing the cutting of trees they are mortgaging the future fertility of this land, but they see little other choice when crops do not come in as expected or jobs are hard to find.

From a global perspective, this example may not seem that dire. After all, when one tree falls, the impact on the global carbon cycle is minuscule. However, if similar stresses and decisions result in the cutting of thousands of trees each day, the impact can be significant. All along the shoreline, people are forced into this sort of trade-off every day, and in their decision- making the long-term conservation of needed natural resources usually falls by the wayside.

Simply put, we have no means of measuring or even estimating the aggregate effect of many, many small livelihoods choices and the land use impacts of those choices, yet in aggregate these will have impacts on regional and global biophysical processes.  When we fail to address poverty, and force the global poor into untenable decisions about resource use and conservation, we create conditions that will give us more climate change.  If we don’t do a better job of measuring poverty and the relationship of the livelihoods and land use decision-making of the poor (something I have addressed here), we are going to be caught by surprise by some of the biophysical changes that persistent poverty might trigger.
 

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A world with less poverty . . . maybe

Brookings has come out with a report suggesting a dramatic decrease in the number of people living in poverty (using the $1.25/day mark as a measure of poverty) since 2004.  The report suggests that where 1.3 billion people met this description in 2004, today less than 900 million are dealing with similar circumstances.  In short, we are on target to achieve the first Millennium Development Goal (MDG) of cutting the global rate of poverty to half of the 1990 rate – indeed, the report suggests that:

the MDG1a target has already been met—approximately three years ago. Furthermore, by 2015, we will not only have halved the global poverty rate, as per MDG1a, but will have halved it again. (p.4)

This is remarkable news.  Brookings notes that the rate of poverty reduction varies dramatically by region, with East and South Asia cutting rates by about 50% between 2005 and 2010, while sub-Saharan Africa’s rate fell just under 8% in that same period.  Further, just two countries can account for the majority of this drop: India and China.  So there are still big challenges out there to be addressed, but things are looking up.
Or are they?
A glance at the methodology employed by this study leads me to think that the error bars on this study are rather huge.  Indeed, the authors are fully aware of the data and analytic challenges related to any effort to estimate poverty levels.  As the authors note, in development

we find it remarkably difficult to measure whether it is happening, and if so how fast. This is especially the case when it comes to producing global poverty data, as the challenges of national poverty data collection are multiplied several times over and then further compounded by the tricky—and unsatisfactory—business of converting national results into internationally comparable terms.

In short, the authors know that the project on which they have embarked is likely to generate estimates with significant potential errors – “error bars” as it were, around their data points, in which reality might actually exist.  Oddly, the report makes no effort to present these error bars.  Instead, it makes rather bold claims about reductions in the level of poverty largely without caveat.  I am not convinced these claims are warranted.
First, there are major data issues here.  Their 2005-2010 measures are predicated on recent household survey data.  Here is the problem with household survey data in sub-Saharan Africa: a lot of it is junk.  I’ve tried to deal with such data in Ghana, a country that has a relatively robust infrastructure for this sort of work, and found their survey data to be a mess.  I suspect that in some regions (Latin America, parts of Asia) the data is actually quite good, on the whole.  But in a lot of places (most of SSA and Southeast Asia) the data is likely very problematic.  And even where the data infrastructure is pretty good, the survey methodologies are often found wanting.  I was part of a team that tried to get a handle on livelihoods near a forest reserve area in Southern Malawi – to do so, we sampled 300 households across four villages (75 households/village) quarterly for a year, to capture things like seasonality in our dataset.  2400 structured interviews had to be undertaken to do this, and those interviews were supplemented by semi-structured interviews with subsamples of the group to explore issues like household power and gender relations to give context to that larger dataset.  This was enormously labor-intensive . . . and necessary to really understand what was going on in those villages.  Most household surveys are not done in this manner, and thus are subject to seasonal bias, or the presentation of data as comparable across the country when, in fact, it has very locally-specific meanings rooted in local social context. I do not expect that all national household surveys will be as rigorous or labor-intensive as ours was, but one should acknowledge the limitations of the data.  No discussion of this in the paper, but that can put a pretty wide margin of error on your findings.
I won’t even wade into the issues with population data that they gloss over in this study – I spend a good bit of time in chapter 9 of Delivering Development talking about census issues and the problems of compounding data error in estimations of economic growth.  Let’s just say that there are significant uncertainties around census data that compound any other errors in the data – again, growing error bars.
Second, there is a moment in the analysis that I found stunning – their projections to 2015 predicated on a surprising assumption – that distribution of wealth will stay the same.  Well, given that economic growth is, by and large, predicated on unevenness within regions, countries and between countries, there is basically no chance that the distribution of wealth will remain the same in any place that is growing.  Generally speaking, the GINI coefficient goes up as growth goes up . . . and a lot of places they are talking about are meant to experience fairly robust rates of growth now and in the near future.  More error.
What does this mean?  Well, to me it means that the data they presented like this:

Really has a wide margin of error, even for past observed data (but compounded going forward) that should look be presented like this (with margins of error in red, and not to scale.  I did not calculate them, as this is just illustrative):

OK, so perhaps there should have been some error bars in there.  So what?  Well, this is a policy brief, with policy recommendations that might actually be followed by someone . . . and this brief is arguing that we are on top of the whole poverty reduction thing, which is sure to become an argument for looking for ways to trim development budgets.
Even if the budgetary ax does not fall because of this brief, there is a risk of reprioritization that may not yet be appropriate.  In the recommendation

aid donors must adapt to the evolving poverty landscape and update their policies and programming to reflect current needs and priorities


the brief implicitly argues that agencies should be reevaluating their programming based on the findings in the brief – toward a focus on Africa and fragile states, and away (apparently) from much of Asia and those parts of Latin America, the Caribbean, and the Pacific where we currently work.  However, this is a recommendation based on much thinner evidence than it seems.
The worst part is that I think this presentation of the data undermines one of their excellent policy points:

One final policy recommendation revealed by this analysis is the need to improve the quantity, quality and timeliness of poverty data, at both the national and the global level. For both developing country governments and aid agencies working to fight poverty, it is impossible to efficiently allocate resources toward this goal using poverty data that is incomplete, unreliable or out of date.

At the country level, there has already been a significant uptake in the use of household surveys and an improvement in their quality. Yet in remarkably few countries is there a standardized, recurrent—and therefore consistent—approach to household survey data collection and analysis.  A renewed, long-term commitment to build capacity in domestic statistical agencies would be a valuable use of aid agencies’ resources.

I agree completely, and have argued for this need, but by presenting the data as so clear and robust, they have essentially undermined this argument.  Any policy maker looking at this will wonder why s/he should give more funding to something that already works . . .
Folks, policy makers will never learn to deal with uncertainty until they are faced with it . . . if we keep copping out and “firming up” mushy results into single bold trendlines, they will expect certain outcomes from uncertain data indefinitely.

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Availability isn't validity . . .

So, to clarify one one my points from my previous post, let me use an example to show why building an index of development (or an index of anything, really) on data based on its availability can lead to tremendous problems – and result in a situation where the index is actually so misleading as to be worse than having no index at all.
A few years ago, Nate Kettle, Andrew Hoskins and I wrote a piece examining poverty-environment indicators (link here, or check out chapter 9 of Delivering Development when it comes out in January) where we pointed out that the data used by one study to evaluate the relationship between poverty and the environment in Nigeria did not bear much relationship to the meaningful patterns of environment and livelihood in Nigeria.  For example, one indicator of this relationship was ‘percentage of irrigated area in the total agricultural area’, an index whose interpretation rested on the assumption that a greater percentage of irrigated area will maximize the environment’s agricultural potential and lead to greater income and opportunity for those living in the area.  While this seems like a reasonable interpretation, we argued that there were other, equally plausible interpretations:
“While this may be a relatively safe assumption in places where the irrigated area is a very large percentage of total agricultural area, it may not be as applicable in places where the irrigated area is relatively small and where the benefits of irrigation are not likely to reach the entire population. Indeed, in such settings those with access to irrigation might not only experience greater opportunities in an average year, but also have incomes that are much more resistant to environmental shocks that might drive other farmers to adopt severe measures to preserve their livelihoods, such as selling off household stocks or land to those whose incomes are secured by irrigation. In such situations, a small but rising percentage of area under irrigation is as likely to reflect a consolidation of wealth (and therefore declining incomes and opportunities for many) in a particular area as it does greater income and opportunity for the whole population.” (p.90)
The report we were critiquing made no effort to control for these alternative interpretations, at least in part because it had gathered data at the national scale for Nigeria.  The problem here is that Nigeria contains seven broad agroecological zones (and really many more subzones) in which different crops and combinations of crops will be favored – averaging this across the country just homogenizes important differences in particular places into a general, but meaningless indicator.  When we combined this environmental variability with broad patterns of land tenure (people’s access to land), we found that the country really had to be divided up into at least 13 different zones – in each zone, the interpretation of this poverty-environment indicator was likely to be consistent, but there was no guarantee that it would be consistent from zone to zone.  In some zones, a rising area under irrigation would reflect a positive shift in poverty and environmental quality, while in others it might reflect declining human well-being.
To add to this complexity, we then mapped these zones against the smallest administrative units (states) of Nigeria at which meaningful data on poverty and the environment are most likely to be available.  What resulted was this:

A map contrasting the 13 agroecological zones in which poverty-environment indicators might be consistently interpreted and the boundaries of the smallest administrative units (states) in Nigeria that might have meaningful poverty and environmental data

As you can see, there are several states with multiple zones inside their borders – which means a single indicator cannot be assumed to have the same interpretion across the state (let alone the entire country).  So, while there might be data on poverty and environmental quality available at the state level such that we can identify indicators and build indexes with it, the likelihood is that the interpretation of that data will be, in many cases, incorrect, leading to problematic policies (like promoting irrigation in areas where it leads to land consolidation and the marginalization of the poor) – in other words, making things much worse than if there was no index or indicator at all.
Just because the data is available doesn’t mean that it is useful, or that it should be used.

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