If we ever have a ‘Green Economy’ would we know that we live in one?

A move to a green economy requires changes in the way we make things, move, allocate resources, produce energy and consume stuff. It requires changes to our planning of cities, trade policy and budget allocations. It requires governments to do things differently and promote policies that encourage citizens, businesses and civil society to behave differently and have different aspirations. And that, these days, also entails some sort of measurements to tell whether or not the new policies are having the desired effects and creating the sort of change that a green economy requires.

There is currently a great deal of attention paid to the measures by which we know about our societies and global change. This is partly because the 169 targets set for the 17 Sustainable Development Goals (SDGs) all have an agreed set of (230) indicators for which governments now have to collect data to monitor their progress towards achieving those goals. It is partly because there is renewed criticism and interest in the accuracy and reliability of many of the measures currently used to assess the state of the world (not least GDP).

SDG Projections: Massive scale projections and  peoples’ voices to celebrate UN70 and visually depict the 17 Global Goals
Projections on Sustainable Development Goals and 70th Anniversary of the United Nations Photo: United Nations Photo under Creative Commons License. Downloaded from Flickr – https://www.flickr.com/photos/un_photo/22825341484/

 

In this context, my contribution to the greenmentality project takes on two tasks:

First, I will examine the construction, robustness and meanings of indicators used to evaluate performance of those aspects of the SDGs which are relevant to assessing progress towards a ‘Green Economy’. I will do this by exploring the construction of national level statistics. This will entail interviews with the government officials who work on these statistics.

Second, I will explore what changes have been taking place in different parts of rural Tanzania. We know that there have been dramatic changes in the country in the last 20-30 years. It is less clear to what extent the changes, which have happened would be compatible with moves towards a ‘green economy’ and to what extent the proposed indicators might be able to capture them.

The SDG indicators and the green economy are not identical but they are related. In the first place some proponents of a green economy hold that their vision is part of the ‘pathways towards the sustainable development goals’ (according to UNEP). In the second there are organisations promoting green growth, which assemble readily available indicators as part of their endeavours in irrigation activity to plot progress towards a green economy. Finally, some of the indicators are plainly relevant to the condition and state of any green economy. These include measures of CO2 production, use of fresh water, management of forests, and presence of forest cover and so on. This means that, as countries try to strengthen their statistical apparatus and surveillance capabilities, so they will be producing official ways of knowing about measures which will be taken as indicative of the strength (weakness) and presence (absence) of the green economy.

We know, however, that these measures can be flawed. Sometimes the indicators chosen are inappropriate (expanding protected areas for example can be inimical to local development). Extent of forest cover is not good for measuring the health of grassland systems. Alternatively, the problem can lie in the systems of state surveillance and monitoring, which are used to determine the condition of society and environment. The data used are sometimes inaccurate, flawed or misleading. It is an interesting and important exercise to consider what those weaknesses are, what misunderstandings they lead to and how well-known these problems are.

The purpose of the first part of this project therefore is to examine, through a study of the construction of SDG indicators, what view of the world is perpetrated by these statistics, and what errors can arise from them. This entails working with the people who produce and compile these statistics and with those who work in quality control and evaluation and those who use them. It entails exploring internal contradictions and complementaries within the data themselves. Outcomes may be that we shed doubt upon the value of these numbers, or, alternatively, that we need to move on from past criticisms about the invalidity of these statistics and recognize their fundamental reliability in core areas. This part of the project could entail some interesting collaborations with government officers who have been tasked with constructing strong and reliable statistical measures.

If the first part of the project is about accuracy, the second is about sensitivity. This element of the research project will build on current work in Tanzania, which is identifying places, which have seen transformations in their local economy (which may or may not have led to changes in local prosperity). This phase of the project explores in more detail the nature of and drivers of these changes and asks to what extent they are captured, and could be captured by current indicators of movement towards a green economy.

This will entail village level fieldwork interviewing key people whose lives and livelihoods have transformed or are meant to have been transformed by green economic measures. It will entail examining the reasons for and agents of change (networks, media, NGOs, government officers, companies selling new seeds etc.). It will entail exploring the local consequences of these transformations (inclusive growth, class formation etc.). It will entail considering on the basis of these data how ‘green’ these transformations have been. This part of the project will build on research projects, which are now completing and which have explored transformations in irrigation activity and in livelihood change and asset ownership in Tanzania.

Finally, having gleaned some understanding of the greenness of changes involved in place, which we know have been subject to forces of change, we can ask how sensitive current indicators of a green economy are to the changes we have observed. This part of the project could entail some interesting engagements with villagers’ own conceptions of the changes and transformations of their lives and what the consequences have been for them.

This blog first appeared on the Greenmentality site here.

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Measuring and Measurement: A new volume of Environment and Society

The most recent issue of Environment & Society explores different practical, theoretical and conceptual problems associated with measuring and measurement across diverse environmental issues. In this blog we have reproduced the introduction to that collection, which is available on open access here. The arguments here should whet your appetite for more, and given some idea as to why this issue is so important.

The imperatives of measurement seem particularly prominent in today’s social environmental concerns. This is partly because of the problems different societies and international bodies have set themselves to solve. Whether these be the monitoring required to meet climate change targets or prevent biodiversity loss, the formulation of indicators to track progress to meet the targets set for the Sustainable Development Goals, concern about the return of high levels of inequality or crises in the validity of political polling, how we measure and monitor the world is increasingly on the agenda.

The prevalence of measurement is also partly due to the new possibilities and ways of knowing that are now being revealed through “big data” that track social media records, mobile phone use, and new abundances of machine-read satellite data. These data are themselves complicated constructs; they combine proxies, signals, indicators, and diverse interpretations and inferences. But this does not make them any less “factual”—after all, that is what fact has almost always constituted. The result is that many parts of the world look different now because we have new tools to see them with.

Measuring, monitoring, and counting have come to be close to the heart of research and academia. In Britain, academics’ research is evaluated periodically in national comparative procedures (now called the Research Excellence Framework), and their teaching is about to be also (the Teaching Excellence Framework). Metrics are increasingly likely to dominate these scorings (Wilsden et al. 2016). And independently of any official surveillance, many academics have a fairly good idea as to our citation scores and h-index. In some disciplines these will even be printed on the back of business cards. The fact that these measures can become such driving goals is a good measure (indicator if you prefer) of the social power of indicators.

Getting measurement right is important because it can become a means of spotting patterns and of holding powers to account. “Without measurement and standards, organizational agents operate under the tyranny of cronyism” (Power 2004: 774). But the means of seeing that measurement affords can also become ways of not seeing, of invisibilizing, of failing to recognize people, places and ways of knowing. As Paige West and colleagues (2006: 254) observed over a decade ago, with reference to a then newly comprehensive database of protected areas, new machines for measurement can become “a way of seeing the world with blindspots and blurred vision not easily perceived by its operators, but these blindspots become darker and fuzzier as the machine becomes better.” Michael Power (ibid) argues that when measurement becomes a means of comparing performance in organizations, “new secret organisational worlds” form that create and exploit invisibility in order to appear better in performance metrics.

More than that, we must understand measurement not just as a way of seeing but also as a creative and organizing project that co-produces the very world it aspires to describe. Theodore Porter (1994) is quite clear on this. Understanding measurement (this discipline is called metrology) is not just about how measurement describes society and nature. We have to understand how it reconfigures relations between and with them, and how it imposes and erases meaning. Understanding this is what metrology is all about. As Mark Cooper (2015: 1789) puts it: “[i]t invites us to question the social, political, and scientific conditions under which agreements about measurement and commensuration do or do not occur, and the consequences or effects of particular metrological systems.” Careful historical approaches are required to reconstruct how measurement came to be and what effects it has had (cf. Jacob 2001).

Measurement was integral to colonial and imperial projects. The quest to govern and the quest to measure and classify were intimately related (Freidberg 2007). Histories of the failures and dramas of colonial rule are replete with failed and violent attempts to create new categories of people or nature and ways for these newly identified categories to behave. Similarly, new forms of auditing and rule by standards are likened to new forms of neo-colonial endeavor.

But the transformations of measurement can also be experienced in subtler ways, and closer to home. Porter gives a useful example of the case of the US Corps of Engineers, which was instructed to ensure that the benefits of flood control schemes exceeded the costs. This sounds innocuous enough, except that the means by which costs and benefits were to be calculated was not explicated. Determining the costs and benefits of different proposed schemes then became the stuff of political battles between corporate interests and different branches of the state. More revealing still, when attempts to reconcile competing visions began, quantifying actual costs and benefits proved to be “startlingly elusive” (Porter 1994: 395). Even the agreed format for doing so that eventually came out was still added to and interpreted in idiosyncratic ways. Measurement could not contain the difference it was meant to arbitrate between.

The case illustrates a broader point. Measurement is not about describing the world. It requires building an apparatus that makes that description possible. As Porter (1994: 404) puts it:

‘To quantify a quality is not merely to solve an intellectual problem. It is to create what Latour calls a center of calculation, surrounded by a network of allies . . . The quantification of qualities is as much an administrative accomplishment as an intellectual one. And no matter what the skeptics may say, many social qualities have already been successfully quantified, in a variety of ways. Those who seek to do it differently, or to spread the net of quantified qualities still wider, need to consider not only epistemological questions but also moral and political ones. There is strength in numbers, and anyone who proposes to wield them more effectively must ask not only about their validity but also about how the world might be changed by adopting new forms of quantification.’

This way of thinking is clearly vital if we are to understand how markets govern, as Cooper (2015) has argued.

But measurement is not just conjured up through marshaling administrative support; it changes the world because, once established, it also entails altering practices to fit with, or respond to, the measurement. Porter gives the example of the US Forest Service, which had been instructed to cut no more trees than were being renewed through regrowth. Regrowth, however, can be boosted if fertilizers and new varieties of fast-growing tree are introduced—which means that more trees and larger trees can still be harvested (Porter 1994: 401). Indeed, the whole point of incentives offered by diverse forms of neoliberal conservation and environmental policy in the form of payment for environmental services is precisely to change the world by changing the behavior of (rational, profit maximizing) individuals. And some of the individuals can even become rational profit maximizers in the process.

The travesties and distortions required to see the world like a market or state can suggest that managing by counting is inherently flawed. But Power (Power 2004) argues that things are more complicated than that. The social processes that constitute measurement, and how people respond to measurement, create cycles of crisis and reform. He distinguishes between first- and second-order measurement, with the former establishing the classifications that make counting possible and the second combining counts into indices and composite indicators, which can forget the social origins and circumstances that produce them. This can result in multitudes of inappropriate numbers and strange uses of them. And this helps to drive the “cycles of reform” that characterizes measurement. According to Power (Power 2004: 778), the social and political responses to different sorts of (flawed numbers) are not about trust or distrust:

‘Dreams of measurement for control purposes are articulated; these are shown to be defective and/or leading to adverse unintended consequences; new measures and refinements are proposed. Any so-called trust in numbers is tempered by the general cultural acceptance of numbers in all aspects of modern society. Equally, specific episodes of distrust and critique lead to the reconstitution and revision of performance metrics, rather than their abandonment.’

Understanding measurement, therefore, is required to understand the societies demanding that measurement, and produced by it. That was the imperative behind this collection. The call for this volume posed a number of challenges. It asked authors to take on a variety of questions, including: How do we approach, measure, quantify, and qualify socio-environmental issues and phenomena? How does what we measure or the way we measure it affect what we know and how we act? How do particular types of, or approaches to, measurement become embedded in epistemic communities and with what consequences? What new things can we learn with new forms and techniques of measurement?

The response was rich, and the eight articles published here capture some of the diversity of interests and approaches. You can read more about what the authors argued and their different approaches in the rest of this open access introduction (start on page 3). In summary there are a number of abiding themes in this collection that are anticipated, and elucidated, in the metrological literature. The first is that measurement is an administrative achievement. The construction of indicators for market governance, community resilience, proxies of performance, heat, fish stocks, and much more requires complicated governing apparatuses and networks of state, private, and civil society interests. A second is that this has unexpected consequences –which perhaps itself should hardly be unexpected at all. The final point is that counting life, whether in societies or environments, is clearly problematic, if not also violent. It does not solve problems but creates a host of new ones.

But it does not follow from the last point that not counting becomes the solution to the problem. It may be a worse fate still not to appear on any register. The issue is not whether to appear, but on whose list, how, for what purpose, and in what circumstances. We cannot avoid measurement. As social beings, we count, calibrate, classify, and measure. How we see ourselves and others, and how we cohere, depends on such processes. It is difficult to imagine societies that do not do any of that. And it is precisely this inevitability and ubiquity of measurement that makes it so necessary to contest it more vigorously.

References

Cooper, Mark. H. 2015. “Measure for Measure? Commensuration, Commodification, and Metrology in Emissions Markets and Beyond.” Environment and Planning A 47: 1787–1804.

Freidberg, Susanne. 2007. “Supermarkets and Imperial Knowledge.” Cultural Geographies 14 (3): 321–342.

Jacob, Margaret C. 2001. “Factoring Mary Poovey’s A History of the Modern Fact.” History and Theory 40 (2): 280–289.

Porter, Theodore M. 1994. “Making Things Quantitative.” Science in Context 7 (3): 389–407.

Power, Michael. 2004. “Counting, Control and Calculation: Reflections on Measuring and Management.” Human Relations 57 (6): 765–783.

West, Paige, James Igoe, and Dan Brockington. 2006. “Parks and Peoples: The Social Impact of Protected Areas.” Annual Review of Anthropology 35: 251–277.

Wilsdon, James. 2015. The Metric Tide. The Independent Review of the Role of Metrics in Research Assessment and Management. London, Sage.

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Building constructive engagements between social science and conservation

One of the hardest things to do as academic researchers is to conduct genuine interdisciplinary research that reaches across and engages with practitioners who are trying to put into practice things that you write about. This is especially true when studying conservation from a critical perspective.

There is a rich literature now on the social science of conservation, but we have, for different reasons not always found it particularly satisfactory. On the one hand, Peter Bille Larsen, working in both conservation and social anthropology, has long sought to bridge the gap between conservation anthropology from within with the growing body of critical scholarship on NGOs from the outside. On his part I, as a career social scientist, could spend a fair bit of time only writing about the social science of conservationists, for other social scientists, who would likely engage thoroughly and rewardingly with the whole enterprise. But the rather voluminous body of work resulting is unlikely to have much purchase on the conservation movement about which he writes. Social scientists can create echo chambers as well as anyone. But, ultimately being in such an echo chamber, is not particularly satisfactory.

In order to address these satisfactions we have recently published an edited collection The Anthropology of Conservation NGOs: rethinking the boundaries which brings together several years of thinking and writing about conservation practices. The book argues broadly for an anthropology of conservation NGOs, which moves beyond stereotypes of NGOs forms and action, on the one hand, and brings together multiple voices, on the other hand. It calls for more empirical study of conservation organizations as boundary organizations constantly expanding, or being challenged around, the boundaries of action through multiple forms of engagement with the state and market forces.

Structured around a series of chapters, written by scholars from multiple disciplinary perspectives, the book combines new articles with previously published papers. But, crucially, this collection is different from others in that it also includes a series of short essays in which different observers, academics and practitioners, reflect on these contributions. Commentators were given free reign to do so with no editorial control. It was, on other words, an attempt to create and nurture the dialogue and exchange of ideas that we were missing.

However, a book that is not read or discussed will hardly provoke much dialogue. It needs to be opened, challenged and disagreed with. The invitation from Bhaskar Vira, one of the contributors, to present the book in Cambridge was therefore welcome. We were privileged to launch this collection in the recently-completed David Attenborough Building which houses 10 different conservation NGOs, including the University of Cambridge Conservation Research Institute (which Bhaskar directs). It is a hive of conservation activity and thinking about conservation, and indeed an important space for dialogue. It would be difficult to find a better place to start the conversation we sought.

It was still rather daunting. When your own words about conservation NGOs which seemed perfectly right at the time of writing are read back to you in front of an audience of conservation NGO professionals, it necessarily prompts new questions and reflexive interrogation. Echo chambers suddenly seemed a good idea.

The three discussants who presented their thoughts about the book produced an effective mix of critique and engagement required for the engagement we sought. They paid a number of compliments to the collection, but we will not detain you with them (you can hear them on the podcast of the launch). In addition, they suggested a number of ways that this conversation could become more engaging.

Bill Adams (Cambridge University) took the lead by observing that conservation is a social phenomenon and so required the skills of social science and disciplines like anthropology to understand its decision making, beliefs and social consequences. He also observed that to be done really well this work required empathy – which he found rather lacking in this collection. His telling analogy was that Richard Dawkins (a British TV presenter famous for his atheism) could probably produce a wonderful documentary about the Catholic Church, but, while it would likely be incisive, it would be unlikely to help us understand why Catholics were Catholic. There is an intimacy to good social science which is not yet well demonstrated.

Jo Elliot (Flora and Fauna International) also welcomed the contribution of this book and its search for a constructive middle-ground. She argued that conservation NGOs have been doing effective social change for some time (in CAMPFIRE programmes, land titling exercises, and natural resource management schemes), which deserved more attention alongside revisiting the changing funding conditions of NGOs. Critiques need to recognise more of the achievements of existing work and engage much more with the specific kinds of social science questions prompted.

David Gibbons (RSPB), took issue with the language and conclusions of chapters emphasizing the emergence of neoliberalism in conservation. He could not recognise himself as the neoliberal operator that the parts of the book describes. Descriptions did not seem to capture the tasks with which he was faced on a daily basis. While at the same time he wanted to celebrate the funding successes of a growing conservation movement, its engagement with the private sector and use of market instruments to pursue conservation goals. He was missing the top ten ideas to make things better and called for more solutions from the social sciences.

These instructions to be more empathetic, better observers and to get better empirical data are all well taken. The call for more social science involvement in building conservation solutions is equally acknowledged. They imply, and this is particularly welcome, that the contribution of this collection is a step in the right direction because we need to do more of this work.

At the same time we recognise that this will be hard to do. Many are trying to, and have been for years. Bill Adams has been producing empathetic, well grounded, closely observed and engaged critiques since the start of his career. These are too numerous to list but selected books include Trade-offs in Conservation, Future Nature, Against Extinction and Transtion to Sustainability and there are many papers too. Likewise anthropologists like Peter Brosius and Paige West. The conversation in this sense may feel a bit like repetition for some of the protagonists, yet it is also clear that both conservation discourses and realities continue to prompt new questions not merely of a problem-solving nature, but equally so about the social, political and ecological effects of conservation NGO action. We can only really start grasping these complex realities in detail by working together, even if it may generate the occasional friction and disagreement. This is part of the condition of working across disciplines and professional boundaries.

We continually have to get used to each other, and repeat the experience of doing so until we are. On the 14th of December the debate continues in Geneva through the Geneva Environmental Network. Stay tuned.

This blog first appeared on the SIID site here.

A panel discussion of the book at the WWF and IUCN headquarters in Gland, Switzerland, is available here.

 

 

 

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It’s time to take ‘the case for colonialism’ as seriously as it deserves

One of the more intriguing debates conservationists have to deal with is whether they represent some sort of colonising influence. Critics of conservation will contend that some of the main ideas in conservation were imposed by colonising powers, or extended through neo-imperial influence. Defenders insist that much conservation is home grown; it’s foolish to treat it as an invasive exotic given that it is so thoroughly blended with local institutions and independent governmen

Recently, however, the whole colonialism debate took a surprising twist when an article unashamedly advocating colonialism as a good thing was published. And, what is worse it was published in a journal (Third World Quarterly) that has long been home to left-leaning scholars. And, worse still, the ‘scholarship’ behind this piece was appalling.

There has been a vigorous response to this paper. You could almost hear the spluttering of cappuccino hitting the computer screens of indignant readers. The editorial board of the journal has resigned.The author himself asked at one point that the article be retracted. There have been a host of intelligent reasoned responses to this paper: try here on clickbait, here for an intelligent commentary on the TWQ itself, and here for a summary of the whole sorry affair. There may be some positive outcomes, in that the board’s resignation may see new, juster publishing initiatives emerge. There has also been some disgraceful hounding of scholars who spoke out against the article by alt-right trolls.

I don’t think further discussion on TWQ’s pages, despite the invitation to do so, provides a suitable vent for the issues this article raises. The basic problem is that the scholarship that underpins the article is so poor that it does not deserve that sort of critical engagement. We need to see how ridiculous it is. To that end, I think we should approach this problem slightly differently and ask: If ‘colonisation’ is the answer, what is the question? To kick things off I have developed one response to that challenge, and am proud to launch the new Need for Colonisation (N4C) Index. This takes the form of a new miniature survey and scorecard that I believe can produce rapidly available, policy relevant findings.

The N4C Index is based on a curious but fundamental truth that is buried deep within the argument of Gilley’s article: just because a state is a state, why should that mean that it gets to govern itself? Is self-determination an inalienable right, or a privilege that is earned? We know that states are recent inventions in the course of human history. If they are communities at all, then they have to be imagined as such, conjured up by media, representation and re-written histories. Their borders are arbitrary, participation in the election of their rulers often derisory, and their governments subject to corporate whim and multi-lateral restructuring policies. No decent left-leaning intellectual in her heart of hearts deserves her cardigan if she is also a blind-to-all-faults nationalist. We may have to recognise that in some cases things are so bad that colonialism could be the answer to the problems that face our different countries.

But we need also to recognise that in today’s academic circles this cannot be a mere theoretical argument. We need applicable findings that can make a difference and change the world. Based, therefore, on an extensive review of colonial invasions, wars and imperial conquest, I have derived a colonisation score-card whereby you can determine how desperately your country needs to be colonised and by what sort of power. It works very simply—you answer the question, score your answer and add up the total. The sum will reveal how much colonisation you need.

Scores will be submitted to a major international conference— to be held in Berlin— that will demarcate the new global political geography and who will wield power over whom. I invite any legitimate government and potential colonising forces (as well as governments deserving colonisation) to participate. I also suggest that trends towards scores of zero (indicating perfect legitimate self-government) be monitored as part of counting progress towards the Sustainable Development Goals.

The questions are:

1. Has your country’s leadership been overtaken by a narcissistic buffoon with a penchant for media gaffes and a silly hairstyle?

Yes–60 points;
Nearly–50 points;
No–10 points;
There is no way that you can call that hairstyle silly–1000 points

2. Does the leadership have a penchant for military spending and nuclear weapons?

Yes–100 points
No–5 points
No, because they do not spend nearly enough money on nuclear weapons, who could?–500 points

3. Does your country have a great history of colonial rule and military conquest?

Yes–2000 points
No–10 points
No, we have never sought an Empire–100 points

4. Are your country’s international borders fenced?

No–5 points
Not any more because someone dug a tunnel underneath the one secure border we had and anyway anyone can get in from Scotland–1000 points
Not yet and I am personally overseeing the construction of the one nearest me to the south–1000 points (if you are from the US) or 5 points (if you are from Scotland or Canada)

5. Do you already have the ideal leader?

No, there is no such thing–5 points
Yes – 50 points
I think to be ideal then we would be looking for some sort of cross between Rodrigo Duterte, Kim Jong-un, Robert Mugabe, Saparmurat Niyazov, Donald Trump and Jacob Zuma–50,000 points

6. Are you still answering these questions?

No–0 points
Yes–500 points

Scores:

Over 10-good, only minor colonisation required.

You could probably still do with a short visit from a small expeditionary force given that you were concerned enough to answer these questions in the first place.

Between 100-1000–serious; urgent colonisation required.

Please invite your nearest neighbour to come and sign some quick treaties ceding territory and trading rights to them. Make sure they are signed by unrepresentative leaders, preferably in languages you don’t understand.

Over 1000-Even more serious.

Your country may or may not be alright but you are a basket case.  Are you from Nambia?

This blog first appeared in Current Conservation here.

 

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A brief guide to Conservation NGOs

There is no getting away from it. NGOs make the conservation world go round. They do a lot of the best science, have some of the best fundraising ideas, and inspire new cadres of conservationists to join up. They have also done a good job at keeping social scientists in gainful employment, either in academia writing critiques, or, more recently in the organisations themselves responding to their academic colleagues.

There are two problems with all this attention however. First of all, and this is an old bug bear of mine, the literature seems to separate conservation NGOs and ‘development’ NGOs even though there is no real difference between them – and even though analyses of them draw almost identical conclusions. The second is the crudity of the categories at work. We seem to have just space for ‘BINGOs’ (Big International Non-Governmental Organisations) and all the rest, when in fact the scene is much more complicated than that. So, in a bid to get the taxonomy going, here is a list of NGO species that Katherine Scholfield and I observed in a large-scale survey of conservation NGOs operating in Africa.

 

As you can see the important thing with these classifications is to get the acronym right. In fact we have also identified a number of suitable acronyms for other categories, but are hesitant to name the NGOs they describe, or have been unable to find any to fit them. These include OH NGO! (the set of particularly silly NGOs); BROWN NGOs (the NGOs who will say anything to get approval); sOGNNGOs (just because it’s an unpronounceable palindrome); PINGOs the cold hearted callous NGOs; LINGO (non-Anglophone NGOs); SORRY I DON’T SPEAK THE LINGO (Anglophone NGOs); SONGs (Francophone and musical); WRONGs (Francophone, but misguided); TANGOs (pairs of NGOs moving in harmony, very rare); NGORONGORO (multiple use NGOs in northern Tanzania), and their neighbours over the border: NGONGs (a collection of Anglophone and Francophone brass pecussion NGOs focussing on conservation south of Nairobi).

More suggestions welcome.

This blog first appeared in Current Conservation here.

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New Development Data – the Limits to Monitoring and Need for Counterfactuals

I argued in the previous blog that new directions in development data and demand for those data are opening up new possibilities for empirical data collection, and for feeding it into policy. I argued too that these new developments will not make it necessarily any easier to produce data-informed policy. There are all sorts of obstacles at work.

But we must now consider a slightly different order of problem. These derive not from any failings of data per se – because the facts in this case are plainly known and indisputable. Rather they derive from thinking that monitored, incremental change will be sufficient to bring transformation. This can be a dangerous lie.

In some instances, instead of looking to data to provide evidence for policy we need to recognise that the most powerful elements of the worldviews we are dealing with can be the myths and narratives that underpin them. Part of the condition of evidence free policy is that it exists in a realm where data may not have any purchase. The empirics depend upon the myths and narratives that lie behind them. Or to put this differently, some development goals are simply fantasies that cannot be combatted in the realm of fact or data.

For example consider the goal achieving full and productive employment. The stated Millenium Development Target 1B was achieving full and productive employment and decent work for all, including women and young people. This was plainly and indisputably not achieved by 2015. And yet the very same target has been set for 2030. This again will not happen. Only very particular forms of capitalism have ever got close to full employment, and that entailed not counting women’s work well. Taken at face value this target suggests a radical re-ordering and restructuring of society and a transformation of our norms and values. It is not the sort of thing which can be done through better use of data or more fact.

This kind of transformation is not achieved by charting progress over time. Rather, as Thomas Pogge has argued, examining

‘[W]hether an institutional order is harming people . . .  depends . . . on a counterfactual comparison with its feasible institutional alternatives’ (page 25).

Pogge had in mind slavery – you don’t chart progress from slavery by showing how slaves homes or health are improving. Ultimately the ills of slavery can only be addressed through emancipation. You have to imagine the alternative – the counterfactual comparison, of a world with slavery compared to one without – and then realise it.

But he goes on to point out that counterfactual alternatives already exist for many development injustices, and that where they exist then we cannot feel content with limited progressive change. This is a significant challenge for anyone keen on monitoring progress towards the sustainable development goals. As Pogge says

‘Most citizens of the affluent countries take comfort in the asserted decline of global poverty . . . They should instead take intense discomfort in the fact that a feasible alternative global order could have avoided most life-threatening poverty and its associated evils.’ (ibid)

What then are the important myths and their counterfactuals that we might want to recognise and take on in international development affairs? There are many, but, let me just touch on a couple. Perhaps the most problematic is the enduring myths of the primacy of GDP and economic growth, the alternatives to which are oddly both radical and obvious.

GDP is widely recognised to be the wrong way of measuring progress. GDP measures production, whether that be in pursuit of good things, or cleaning up the problems. If you produce stuff without causing pollution or health problems your GDP will be so high, but if you pollute and you additionally then have to spend money cleaning up the river, or curing the diseases that that pollution causes, then your GDP will get higher still. A sick or violent society in which millions of people are prescribed expensive anti-depressants or which imprisons large proportions of its population in expensive jails, and spends a huge amount on security and policing will, despite all that misery, enjoy a higher GDP than societies who are not so productive. As Lorenzo Fioramonti has shown growing GDP does not progress other indicators of well-being. And yet GDP remains the main indicator of progress and prosperity.

Constructing counterfactual measures of well-being and using them as the key indicators by which we will guide our collective economic lives is proving difficult. Nevertheless there are ever increasingly auspicious authorities supporting this shift. It is found in the work of Nobel Prize Winners such as Amartya Sen and Joseph Stiglitz. Tim Jackson’s ESRC funded research centre is precisely based on a vision of prosperity which may be possible without economic growth. Geographers JK Gibson-Graham insist that so much of what we actually do with our time, creativity and resources is not captured by capitalist enterprise. More generally, the degrowth movement, which argues for reduced GDP, is gathering strength with a variety of models of what degrowth might constitute and how it might come into being.

So while we seek better development data, I am also looking for the counterfactuals, that will allow us to imagine different ways of doing things, and of ordering our lives, and that make us feel discomfort when they present valid alternatives to present misfortune. Only with such counterfactuals can we find the home, the nurturing environment, for the new development data that I look forward to contributing.

This blog first appeared on the SIID site here.

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New Development Data – Excitements and Limitations

See the full lecture on which this blog is drawn here.

There is much excitement, for good reason, about development data. This comes from a combination of the imperatives for more and better data from the Sustainable Development Goals (the SDGs), from new conceptions of what should be counted, and from new ways and technologies for counting and measuring social life. In this blog, drawn from my inaugural lecture, I explain why each of these elements are indeed exciting. I then outline why we must be modest in our expectations of how those data might inform policy. There have simply been far too many cases where the data are irrelevant to policy. In the second blog I will take the argument a step further to consider the limits even of policies which are profoundly informed by data.

The Sustainable Development Goals were adopted by the United Nations in September 2015. There are 17 goals, covering all the traditional interests (poverty, health, gender equality and education) as well as a whole new set including energy provision, infrastructure, inequality and many more. These 17 goals have, collectively, realized 169 targets that they aim to achieve by 2030, and these targets have produced 230 indicators which will be used to determine whether or not they have been successful.

The SDGs are auspicious for anyone interested in development data because they set up a highly specified vision of what a prosperous world should look like. If these goals are to be successful on their own terms then this will hinge upon using the right sort of data in the right way. This means that getting better data, and understanding the limitations facing these data, becomes all the more important.

It seems that we are now awash with new ways of collecting data that could be useful for the SDGs. Some of these derive from using remote sensing, new algorithms and digital data. The spatial precision of satellite data has improved, as has their temporal frequency, and machine-reading of these data can be used to understand changing land use and household investments. The spread of mobile phones, and the growth of social and economic activity on social media, provide new proxies of human development, and new arenas of eminently measurable activity.

But it is not just the means of measurement which is exciting here – it is what we chose to measure and why. The SDGs are more broad-minded than the MDGs they have superseded. The MDGs were concerned to reduce poverty, and used poverty lines to determine who was poor. The SDGs want to eradicate all forms of poverty, and will be using a broader set of measures of what constitutes wealth and poverty as criteria for success. I find this particularly exciting because if we take a broader notion of what wealth and poverty mean we can end up seeing societies in different parts of the world in new ways.

I can illustrate this with a research project that we are completing in Tanzania, on which I am working with Christine Noe and Moses Mnzava from the University of Dar es Salaam. The project examines rural livelihood change and prosperity in Tanzania, a country which has long been poor, but which has seen dramatic increases in economic growth in the last 30 years or so. The trouble is that there were numerous signs that, according to poverty line data, this wealth was not being shared, particularly in the rural areas where most poor people can be found. Using different measures of poverty, which hinge on controlling and using assets, we are finding families have become wealthier. They have been able to invest the returns from farming activity into better homes, agricultural equipment and education.

Assets matter partly because they feature most prominently in the local definitions of wealth. But they are also centrally important to examine in any study of long term poverty dynamics, because the rural poor invest in assets. Consider this statement from a participant in a focus group in south-east Tanzania which I heard earlier this year.

We get money seasonally. [We] have earned three millions shillings, or two million shillings. Some people when they get money after the harvest they buy a TV, or solar panels, or all manner of things but now if he’s struck by some problem and needs 50,000 shillings he’ll have to wait 5 months.  In July if you ask someone for 500,000 shillings they will give it to you, but go to them in November and ask to borrow 200,000 to deal with a problem and they will tell you I have nothing, I have bought a TV, I’ve bought a plot, I’ve bought bricks.’

Assets matter to poor rural families because they provide a focus of investment that is particularly valuable in the absence of good banking services, and without regular, frequent sources of income.

This graph shows the growth in assets in Mtowisa in south west Tanzania where I once lived for a year in 1999-2000. It shows investment in houses, metal roofing, oxen and so one.

 

You can see the phenomenal growth in housing in that village in just ten years in this google earth images. This shows the village in 2003.

And this in 2013, and with all the newly built houses with metal roofs in that 10 year period circled in black.

But, and here is the catch, in every single one of the 20 plus village where we have worked on the livelihood change and prosperity project, the precise nature of the change, its drivers, its timing, the gendered distribution of benefits within households, the role of elites, the role of emigration or immigration, the crops and agricultural innovation involved – in every single case there is something different going on. The story here is that there is no single story.

And this is part of the research agenda that new conceptions of poverty and prosperity can unleash.  We can challenge problems of inequality by examining the different understandings of what wealth is and how it is distributed and by understanding the diversity of stories that need to be told to combat central notions and of what constitutes wealth and progress.

A second reason to be excited about development data emerges from an increasing conglomeration of questions about the accuracy and basic validity of data used in development. The work of scholars like Morten Jerven who has combined a quantitative analysis of the flaws and inconsistencies of GDP data, with an ethnography of the construction of those data, to argue that basic notions such as GDP have been mismeasured for years. Governments have not been able to measure their economies, particularly where so much activity is informal and very difficult to observe and count. Famous examples of these failings include the massive increase in wealth in Ghana that occurred overnight when its GDP baseline was recalibrated.

I am particularly pleased to be taking part in projects which extend this sort of critique. We are doing so specifically with respect to agricultural data, which are notoriously inaccurate because so many small-holders conduct their affairs informally and are generally rather suspicious and not necessarily truthful when answering surveys.

In the SAFI project lead by Phil Woodhouse and diverse partners internationally we have been able to show that, conceptually, ideas of what irrigation is are too narrow in these countries. Irrigation appears to be thought of as something requiring large concrete intakes, engineered channels and carefully planned division points. It is not deemed to refer to local practices of rice cultivation which involve small temporary dams in low lying areas, and water harvesting on raised ground. That, as Phil puts it, is just moving water around fields. It is not real irrigation.

But these official definitions are unsatisfactory, they do not seem to explain or describe farmer behaviour. Surveys of farmers in Tanzania suggest that many are growing rice, but that only 5% of them are irrigating it. This is unlikely. We predict that irrigation activity, defined as farmers’ deliberate management of temporary flooding in their fields is much more extensive. Preliminary results from analysis of Sentinel II radar data suggest that this is precisely the case – we can find evidence of irrigation that is one order of magnitude higher than agricultural census data predicts.

This work excites me because it combines all that is new in development data, for it re-conceptualises what we need to examine, challenges existing data, and proposes different data sources. With fresh thinking, interdisciplinary collaboration, and fancy kit like radar and good algorithms, we can shed new light on old problems.

But before we get too entranced by this brave new world of more relevant, accurate and believable facts we must remind ourselves of the limited role that data can play in tackling development problems. I have found repeatedly in my research that facts about things as diverse as basic aspects of environmental change in East Africa, to the role and influence of celebrity advocacy in the UK, seem largely irrelevant to the public debate, and policy discourse, about those topics. Data-informed policy is rare; often policy can be remarkably data free.

As we learn in any basic research methods class, new data and methods present us with a set of insights and then another range of problems that come with them. So, for example, there is a great deal of excitement and interest in the potential of new digital data derived from mobile phone use, and its ability to reveal new facts about social change. But there should be a deep concern for the inequalities and blind spots of these new data, that derive, for example from the unequal gendered access to and use of mobile phones. Fighting inequality requires continual vigilance lest new data renew marginalisation.

And, if we ever get good data, then a different set of problems emerges. Consider, for example, what happens when the data clearly demonstrate that international development goals are not achieved. For example, several of the millennium development goals failed to reach their targets. Reductions in infant mortality of 67% was demanded by 2015; but reductions of only 53% achieved. Reductions in maternal mortality of 75% were demanded by 2015 and only 45% achieved.

We must place these failures in context. David Hulme and Armando Barrientos have argued these standards have made a difference, they have raised interest in poverty alleviation strategies by national governments. In the absence of these goals achievements could have been even lower. But these are not, as one commentator claims, a set of promises that the world makes to itself. For these are promises which can be broken, they are disengaged from political processes. There is no mechanism for holding to account leaders who do not stick to these grand plans. Failure in these contexts is not costly, failure is free.

So, revolutions in development data are absorbing, exciting and reveal all sorts of new aspects about social life and the environment. We need to embrace them as much as possible. But we cannot expect that these new data will lead somehow, to evidence-informed policy. The barriers to that are multiple and enduring. They will be as impervious to new development data as they have been to old.

There is, however, a more profound objection that we have always to remember surrounding development data, and this is best visible not in the weaknesses of development data, but when they are strong – and that is the subject of the next blog.

This blog first appeared on the SIID site here.

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