Check out the brilliant RSAnimate series of youtubes. When David Harvey gave a thought-provoking RSA lecture on a marxist approach to the economic crisis, he got 16,000 hits; since they got a brilliant animator to illustrate it, it’s gone to 390,000 and counting. It’s like a realtime version of the old Writers and Readers Beginners Guide series – I’ve still got Freud for Beginners on my shelves somewhere. The animators are amazing and available for hire. [h/t Richard King]

‘Leadership lessons from dancing guy’ : pure genius [h/t Richard Casson]

Hope in a changing climate – uplifting stuff from China, c/o the BBC [h/t Alex Evans]

and if you like that, get the rest of the programme here

Finally, more poor communities adapting to climate change, c/o Oxfam America [h/t John Magrath]

Other Oxfam America climate videos (on El Salvador, Ethiopia, Vietnam and the US Gulf Coast) here

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Sabina Alkire responds to the previous posts by Martin Ravallion and me on her new ‘Multidimensional Poverty Index’. She is director of the Oxford Poverty and Human Development Initiative (OPHI).

“As Martin Ravallion points out, we agree that poverty is multidimensional. The question is whether our efforts to incorporate multiple dimensions into the very definition of who is poor and the measurement of poverty “contributes to better thinking about poverty, and to better policies for fighting poverty.” Let me explain what I think the MPI adds.

The MPI measure has meaning in itself and can also be broken down immediately into its component parts.  Every time you see an MPI figure – for a person, an ethnic group, a state, or a country – you know that it also contains what could be thought of as a drop-down menu in two layers. The first layer shows incidence and intensity. The second breaks the MPI down by indicator and shows what poverty is made of.

If we know someone is income poor, we do not know if they are also illiterate or malnourished. If we know someone is multidimensionally poor, we can unpack the MPI to see how they are poor. That is one added value of our methodology.  That is why we call it a high resolution lens: you can zoom in and see more.

This feature could add value to the MDG indicators too. These show us the percentage of people who are malnourished, and the rate of child mortality and many other things, but not how the deprivations overlap. If 30% of people are malnourished and 30% of children are out of school, it would be useful to know if these deprivations affect the same families or different ones. With the MDG indicators we cannot see that; with the MPI, we can. Of course not for all MDG indicators, but it’s a start.

For example, the Somali have the highest multidimensional poverty of all ethnic groups in Kenya followed by the Masai. Looking at the MPI drop-down menu, we see that 96% of Masai are poor and 88% of the Somali. But poverty among the Somali is more intense: on average they are deprived in 67% of dimensions; the Masai in 62%. Zooming in further we note that the Somali are more deprived in education and child mortality, whereas malnutrition and standard of living indicators are worse among the Masai. So the MPI opens out into a wider field of information. 

The other thing the MPI does is clean data of anomalies and focus on poor people. While indicators drawn from different surveys are tremendously useful for many purposes, they do not identify who is multidimensionally poor, so every MPI poor person experiences multiple deprivations. Consider a self-made millionaire who didn’t go to school. A MDG indicator includes this millionaire in the percentage of people who are uneducated. The MPI does not – if she’s not deprived in anything else, she’s not considered poor. In times of tighter fiscal resources we focus on people who are deprived in several things at the same time.

So, the MPI – and the general methodology it uses that James Foster and I developed – adds value because of how it evaluates poverty. The method first determines the dimensions in which a person is deprived, and then ‘adds up’ that person’s deprivations using weights that reflect the relative importance of each deprivation. A person who is sufficiently ‘multiply deprived’ is considered poor. We measure multidimensional poverty as the incidence (or the percentage of the population that is poor) times the intensity (or the average percentage of deprivations poor people experience). Unlike the HDI, this construction does not add up achievement levels, which requires strong assumptions concerning the variables in question as Martin noted. Instead, we add up deprivations, which does not.

OK, now to the issue of weights. Income poverty aggregates within a country using actual or imputed prices (these are critical for fixing the income poverty standard across countries and time). Setting prices is not unproblematic in practice, particularly in Colombia where I am writing from. Indeed the Presidential address to the 2010 American Economic Association raised concerns such as the prices attributed to housing (Deaton 2010). Chen and Ravallion 2008 carefully review the robustness of their results to different pricing approaches. 

As Martin observed, instead of using prices, the MPI sets weights as value judgements. Amartya Sen among others sees this feature as a strength not an embarrassment: “There is indeed great merit… in having public discussions on the kind of weights that may be used” (1997a).

In extensive writings, Sen presents several pertinent observations in favor of setting weights: first, prices may not exist for some aspects of poverty (morbidity, mortality, illiteracy) but giving zero weight to these does not seem right either. Second, setting precise weights may not be necessary: comparisons may be robust to a range of weights. Third, the weights trigger public debates which may be constructive as policy makers weight tradeoffs in practice anyway.

The key, Sen suggests, is to make the weights explicit: “It is not so much a question of holding a referendum on the values to be used, but the need to make sure that the weights – or ranges of weights – used remain open to criticism and chastisement, and nevertheless enjoy reasonable public acceptance” (1997b; see also Decancq and Lugo 2008).

Given this situation, Maria Emma Santos and I proceeded in a very practical way in the MPI. First, the weights are not buried; they are totally transparent (1/3 per dimension, and each indicator within a dimension equally weighted). If people disagree with these weights, they can propose improvements and also recalculate with different weights to check robustness. Second, the weights give some non-zero value to each dimension, which is a starting point. Third, the MPI fixes weights between countries to enable cross-national comparisons; alongside this we strongly encourage countries to develop national measures having richer dimensions, and indicators and weights that reflect their context as Mexico did and Colombia is doing. Fourth, we weight the three dimensions equally, this was corroborated by expert opinion (Chowdhury and Squire 2006), helps make it easy to understand (Atkinson et al. 2002) and at least for the HDI is quite robust (Foster McGillivray and Seth 2009). We do need to create robustness tests for MPI weights, and new methodologies of analysis to guide policy, and OPHI plan to work with other researchers on these. But the key thing is that the present MPI weights are transparent, and critical scrutiny of them is welcomed.

Finally, both previous blogs mentioned data contraints. Duncan criticised the MPI for including only three dimensions, “partly because it still relies on existing data sets.” Well, actually data constraints are the only reason only these three dimensions appear. We and the HDRO wish to include others without losing focus: indeed OPHI’s other research theme highlights the need to gather internationally comparable data on ‘Missing Dimensions’ of poverty – violence, informal work, disempowerment, and isolation/humiliation — given the importance these have in poor people’s lives. Our methodology is flexible enough to accommodate additional dimensions as they become available and we are eager to do so.

Finally, as Martin observed, our data must come from the same survey or from matched surveys. Yet multi-topic surveys have expanded rapidly, especially since the MDGs. The MPI is not perfect, but it uses these surveys to explore joint distribution – the deprivations that batter poor people’s lives at the same time. Such a multidimensional poverty measure complements existing tools. So though no measure is enough, we hope this work will enable others fight poverty and empower poor people more effectively.”

Phew. Thanks to Martin and Sabina for raising the intellectual tone with these top notch contributions. Something altogether more superficial tomorrow, promise.

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Martin Ravallion is Director of the World Bank’s research department, the Development Research Group. These are the views of the author, and need not reflect those of the World Bank.

“Everyone agrees that poverty is not just about low consumption of market commodities by a household.  There are also important non-market goods, such as access to public services, and there are issues of distribution within the household. It is agreed that consumption or income poverty measures need to be supplemented by other measures to get a complete picture.

But does that mean we should add up the multiple dimensions of poverty into a single composite index? Or should we instead measure consumption poverty with the best data available, while also looking for the best data on other dimensions of poverty as appropriate to the country context?

The Oxford Poverty and Human Development Initiative (OPHI) has recently launched a Multidimensional Poverty Index (MPI), and calculated it for over 100 countries.   The MPI is a composite of indicators selected for consistency with the UNDP’s famous Human Development Index (HDI). The HDI uses aggregate country-level data, while the MPI uses household-level data, which is then aggregated to country level. The index has ten components; two represent health (malnutrition, and child mortality), two are educational achievements (years of schooling and school enrolment), and six aim to capture “living standards” (including both access to services and proxies for household wealth).  The three broad categories–health, education, and living standards–are weighted equally (one-third each) to form the composite index.   

One can debate the precise indicators chosen for the MPI by the Oxford team (who are clearly aware of the many data concerns). For example, the MPI’s six “living standard” indicators are likely to be correlated with consumption or income, but they are unlikely to be very responsive to economic fluctuations.  The MPI would probably not capture well the impacts on poor people of economic downturns (such as the Global Financial Crisis) or rapid upswings in macro-economic performance.

The precise indicators used in the MPI were not in fact chosen because they are the best available data on each dimension of poverty. Rather they were chosen because the methodology used by the MPI requires that the analyst has all the indicators for exactly the same sampled household. So they must all come from one survey. There is much better data available on virtually all of the components of the MPI, but these better data can’t be used in the MPI since they are only available from different surveys. This aspect of their methodology greatly constrains the exercise. If one chooses not to form the composite at household level but to look instead at the separate dimensions of poverty one is free to choose the best available data on each dimension of poverty.

There is a deeper concern about the MPI, which holds even if the best data all came from just one survey. The index is essentially adding up “apples and oranges” without knowing their relative price. When one measures aggregate consumption from household-survey data for the purpose of measuring poverty, as in the World Bank’s “$1 a day” measures, one relies on economic theory, which says that (under certain conditions) market prices provide the correct weights for aggregation. We have no such theory for an index like the MPI. A decision has to be taken, and no consensus exists on how the multiple dimensions should be weighted to form the composite index. 

On closer scrutiny, the embedded trade-offs (stemming from the weights chosen by the analyst) can be questioned, and may be unacceptable to many people.  In the context of the HDI, I pointed out 15 years ago that by aggregating GDP per capita with life expectancy the HDI implicitly put a value on an extra year of life, and I showed that this value rises from a very low level in poor countries to a remarkably high level in rich ones (4-5 times GDP per capita).   If it was made clearer to users, I expect that they would question this trade-off embedded in the HDI.

The MPI index faces the same problem. How can one contend (as the MPI does implicitly) that the death of a child is equivalent to having a dirt floor, cooking with wood, and not having a radio, TV, telephone, bike or car?  Or that attaining these material conditions is equivalent to an extra year of schooling (such that someone has at least 5 years) or to not having any malnourished family member?  These are highly questionable value judgments. Sometimes such judgments are needed in policy making at country level, but we would not want to have them buried in some aggregate index.  Rather, they should be brought out explicitly in the specific country and policy context, which will determine what trade off is considered appropriate; any given dimension of poverty will have higher priority in some countries and for some policy problems than others. 

Poverty is indeed multidimensional.  But it is not obvious how a composite multidimensional poverty index such as the MPI contributes to better thinking about poverty, or better policies for fighting poverty.  Being multidimensional about poverty is not about adding up fundamentally different things in arbitrary ways. Rather it is about explicitly recognizing that there are important aspects of welfare that cannot be captured in a single index.”

Sabina Alkire of OPHI (and the creator of the MPI) responds tomorrow. For Duncan’s introductory post on the MPI see here.

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The Oxford Poverty and Human Development Initiative (OPHI) has been working for years to try and develop such metrics, and they recently launched the ‘Multidimensional Poverty Index’ (MPI), which will feature in this year’s UNDP Human Development Report, celebrating its 20th anniversary. I’ll briefly summarize it here, before unleashing an exchange of guest blogs between the World Bank and OPHI.

The MPI brings together 10 indicators of health (child mortality and nutrition), education (years of schooling and child enrolment) and standard of living (access to electricity, drinking water, sanitation, flooring, cooking fuel and basic assets like a radio or bicycle). It’s thus a logical extension of its predecessor, UNDP’s pioneering Human Development Index, launched in the first Human Development Report back in 1990, which combined life expectancy, education (literacy + enrolment rates) and GDP per capita.

What were the results when they crunched the numbers? Here’s the blurb from the launch press release:

“OPHI researchers analysed data from 104 countries with a combined population of 5.2 billion (78 per cent of the world total). About 1.7 billion people in the countries covered – a third of their entire population – live in multidimensional poverty, according to the MPI. This exceeds the 1.3 billion people, in those same countries, estimated to live on $1.25 a day or less, the more commonly accepted measure of ‘extreme’ poverty.

The MPI also captures distinct and broader aspects of poverty. For example, in Ethiopia 90 per cent of people are ‘MPI poor’ compared to the 39 per cent who are classified as living in ‘extreme poverty’ under income terms alone. Conversely, 89 per cent of Tanzanians are extreme income-poor, compared to 65 per cent who are MPI poor. The MPI captures deprivations directly – in health and educational outcomes and key services, such as water, sanitation and electricity. In some countries these resources are provided free or at low cost; in others they are out of reach even for many working people with an income.

Half of the world’s poor as measured by the MPI live in South Asia (51 per cent or 844 million people) and one quarter in Africa (28 per cent or 458 million). Niger has the greatest intensity and incidence of poverty in any country, with 93 per cent of the population classified as poor in MPI terms.

Even in countries with strong economic growth in recent years, the MPI analysis reveals the persistence of acute poverty. India is a major case in point. There are more MPI poor people in eight Indian states alone (421 million in Bihar, Chhattisgarh, Jharkhand, Madhya Pradesh, Orissa, Rajasthan, Uttar Pradesh, and West Bengal) than in the 26 poorest African countries combined (410 million). The MPI also reveals great variations within countries: Nairobi has the same level of MPI poverty as the Dominican Republic, whereas Kenya’s rural northeast is poorer in MPI terms than Niger.”

My views on all this (largely stolen from my colleague Claire Hutchings)? It’s a step forward on the previous Human Development Index, but only a limited one. There are still many facets of poverty that it doesn’t touch on, such as conflict, personal security, domestic and social violence, issues of power/ empowerment, or intra-household dynamics. This is partly because it still relies on existing data sets, focusing on how to use differently the data we are already collecting, rather than proposing/ starting from a fresh conceptual framework on critical dimensions of poverty. That makes the proposal more practical, but less radical.

The comparison of extreme income poverty scores vs multidimensional poverty scores is interesting (see chart – the bar is the MPI score, the line is the income poverty score) – it would be great to see further research into possible explanations for the divergences, such as the role of social services and social protection- both formal and informal, and the potential implications for policy development.

Another advantage for policy development and assessment is that this index responds more rapidly than income to different policy interventions. A child feeding programme or scrapping user fees will have an immediate impact, whereas it may take years for government policies to filter through into income stats.

Great that it’s all open source – as with all measures there is scope to choose the mix of indicators to back up your particular argument, but at least making this data open source allows other people to challenge your analysis.

Finally, while it does allow for comparisons of groups within countries it is still a very aggregate picture, designed primarily to enable comparisons between countries.

See here  for a nice interactive map, and check out the coverage in the FT and Guardian

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Sceptics v Science: Rebuttals of all the usual denialists’ arguments on a single page – very handy [h/t John Magrath]

27 of Europe’s biggest and most important companies demand tougher targets on emissions

Richard Gowan links US rap culture and emerging gangs in South Sudan

Spoof 1: Stieg Larsson novels – why do we love them when they’re so terribly written? While pondering this, enjoy this great New Yorker spoof [h/t Chris Blattman]

Spoof 2: France to pay Haiti $22 billion in reparations for forcing the former French colony to pay an equivalent sum in exchange for its independence in the nineteenth century? Sadly, not, but still enjoyable and very convincing [h/t Kate Raworth]

Claire Melamed discusses a woman killer we often overlook – smoke inhalation

The Economist reports that Brazil is fast becoming one of the world’s biggest aid providers. But like Chinese or Korean assistance, its “diplomacy of generosity” has a very different look to the North American or European version.

On the 25th anniversary of the Ethiopian famine, Reuters celebrates the country’s unique beauty and history.

This year’s Ashden Award winners for sustainable energy cover the usual crop of inspiring examples, including the overall winner – D Light Design solar lanterns in India.  Unlike the lanterns, the video is distinctly old school, but worth a look

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Development people claim to be interested in ideas, yet an alarming number of my colleagues roll their eyes when I sing Google Reader’s praises and wail ‘I haven’t got time to read’. Pathetic. The point is that GR (or its equivalents) saves you time, and allows you to skim your gurus’ (or competitors’) output as it appears. It makes you better at your job, not least by giving you bullshitter’s rights in the first meeting of the day – ‘well the New York Times had a piece on this today, and it said…..’.

And yes, I realize that if you’re reading this, you’re probably already connected, but why not make it your mission to go and persuade some colleagues (especially technophobic bosses) to do likewise?

For sage advice on the practicalities, and a great development blog list, read Owen Barder. Meanwhile, here are some highlights from my morning trawl.

What does the World Cup tell Ranil Dissanayake about the nuances of identity in Africa?

Alanna Shaikh explores the limitations and dangers of the aid industry’s obsession with assessing impact and shows how crowdsourcing can work in practice, as she raises the funds for a violence-reducing Ushahidi project in Kyrgyzstan in 8 hours using Twitter 

Bill Easterly asks ‘Was the poverty of Africa determined in 1000 BC?’ and concludes that history, while not destiny, is a pretty good predictor of future performance

“The only way in which we can get rid of “growth mania” is by getting rid of capitalism. It is not possible to have capitalism without growth.” Alejandro Nadal thinks the de-growthers have got it all wrong (or are being very disingenuous) 

Laura Freschi discusses the impact of right to information initiatives in India and Africa

Is the best way to tackle climate change bottom up (technological innovation) or top down (global agreements)? Matthew Lockwood reviews the arguments  and mines some data to ask ‘Do equality and security help the politics of climate?’ 

And finally, ‘What did the Chinese ever do for us?’ Texas in Africa takes some photos to demonstrate – the same section of road before and after China arrived in North Kivu in the DRC. Impact assessment anyone?

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Why Doesn’t Microfinance Work? The Destructive Rise of Local Neoliberalism, by Milford Bateman (see here for reviews of his previous work) is a passionate polemic that takes on a development shibboleth – sometimes it feels as though doubting microfinance is as heretical as criticising Nelson Mandela. But Bateman does so, arguing that microfinance doesn’t actually work, relies largely on hype, and is uncritically welcomed because it fits with an anti-state, pro-market mindset of the Washington Consensus (shades of de Soto and the debate on property rights). He thinks that microfinance has squeezed out more beneficial and effective approaches, such as local-level industrial policy. One minor criticism – he doesn’t seem to distinguish between microcredit and other, genuinely useful activities such as microsavings – it’s the microcredit bit that has been massively oversold.

Update: For another survey of the evidence, which finds a more mixed balance of success and failure (apparently independent, though funded by the Grameen Foundation), see Taking Another Look, by Kathleen Odell, summarized on his blog by CGD’s David Roodman.

Global Warring: How Environmental, Economic and Political Crises will Redraw the World Map, by Cleo Paskal of Chatham House (and author of the Tonga renewables article I posted on a while ago) links up debates on security, development and environment, exploring the intersection between geopolitics and climate change: will it accelerate the decline of the West and the move to a multipolar world? Will it alter global trade routes and the geopolitics that they shape? How will shifting rainfall patterns and rising sea levels change Asia and the Pacific?

What Works for the Poorest? Poverty Reduction Programmes for the World’s Extreme Poor, by David Lawson, David Hulme, Imran Matin and Karen Moore, showcases some of the excellent research by the Chronic Poverty Research Centre (see here for more on its work). It focuses on the 400 million or so ‘chronic poor’ – the people who are likely to be stuck below the $1.25 global poverty line for decades or lifetimes. Trickle-down economic growth often doesn’t work for groups such as the impoverished elderly, disabled people, or excluded ethnic or religious groups. The book seeks practical solutions elsewhere, with case studies of the nitty-gritty of targeting (in Bangladesh and Kenya), cash transfers and other social protection systems (Chile, Viet Nam), women’s empowerment in Gujarat, the inevitable chapter on India’s National Rural Employment Guarantee Scheme, decent work (South Africa) and health equity funds (Cambodia). Finally it tackles finance with chapters on microfinance and domestic resource mobilization.

The World Bank Unveiled: Inside the Revolutionary Struggle for Transparency, by David Ian Shaman is a bit long (568 pages) and shrill for my taste, but at least it is written by an insider – Shaman was involved in trying to improve Bank transparency through an internet-based broadcasting station putting out unedited internal discussions and debates to the public. As can be imagined, that hit a lot of internal opposition, leading Shaman to conclude ‘I believe there are two World Banks. One recognizes mistakes and limitations; the other rejects its own fallibility, promotes its superiority and shelters itself within the confines of its authority.’ Sounds about right.

And finally, something altogether different and exciting: SMS Uprising: Mobile Activism in Africa, by Sokari Ekine (editor, and one of Nigeria’s top bloggers), is a brief edited set of case studies of how activists are using mobile phone technologies to change Africa. It’s a ‘try these ideas in your campaign’ manual, with examples from Zimbabwe, South Africa, Uganda, Kenya and the DRC. Well worth a trawl.

Apologies for cursory reviews, and if readers know of more serious treatments, please add links in the comments section. If any of the publishers feel short-changed and want their copies back, they only have to ask.

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The overall story is one of depopulation – the island is dotted with abandoned villages and ‘lazy beds’, where farmers previously struggled to raise potatoes and cereals in the harsh Hebridean climate. Now they buy them at Tescos. The population of the island is 18,000 and both ageing and falling, as the youngsters, especially the best and brightest, head off via university on the mainland, just as my father-in-law did some 60 years ago. Many end up emigrating further, to New Zealand, Australia, Canada etc.

But within this overall story of decline, there are countervailing tides. First new people are arriving – ‘incomers’ make up about a quarter of the 40 or so families in my father-in-law’s village, bringing in capital and entrepreneurial skills. Most are from England or elsewhere in Europe, but the island’s capital Stornoway also boasts a small Bangladeshi community.

As technology and society changes, the island’s ‘comparative advantage’ is shifting – it has a LOT of wind and sea, so renewables are developing, (albeit with big struggles over planning permission) and the very emptiness is bringing in tourism, while the Hebridean light is a magnet for artists – there are galleries everywhere (see here for an outstanding example).

As for farming, most remaining crofters are largely ‘hobby crofters’, relying on non farm income. Common Agricultural Policy subsidies are the only reason why many crofts with just 50 sheep can get through the year – I met one accountant-turned farmer who complements his income by helping his neighbours fill in their subsidy applications. But he is also training a new generation of crofters, some of whom commute to the North Sea oil rigs but no longer have to emigrate due to improved air links to the mainland. They farm during their weeks off.

Some of the more entrepreneurial crofters are developing niche markets. My wife’s cousin is building a herd of highlander cattle (the shaggy ones with the big horns – see pic) and cannot meet the demand for their premium organic beef, not least via the website and webcam (yes, you can watch his cows on the internet, if that’s your thing, and he gets regular emails from their fanclub).
 
One of the main existential battlegrounds is culture. This is a Gaelic-speaking community (my father in law only learned English when he went to school, where he was beaten for speaking Gaelic) and while young people prefer English, a boom in Gaelic schooling and media is trying to reverse the trend, with mixed results (one of Stornoway’s second generation Bangladeshis reportedly became president of Glasgow University’s Gaelic society). Religion, particularly the evangelical ‘Free Church of Scotland’, plays a vital role in holding communities together in a harsh environment, but that cohesion is being undermined by the very incomers who are boosting the economy.

Migration, culture, non farm income, the role of faiths – you could be talking about peasant communities anywhere. So what does spending a week in Lewis add to my understanding? Firstly, leaving home and heading for the cities is always a wrench, but provided it is a positive choice, not ‘distress migration’, it brings opportunities and new horizons. Those returning for the wedding treasure their cultural roots, but showed little urge to come back home to the rain and cold.

Secondly, migration is becoming less traumatic. People can return home for holidays (or weddings), talk on the phone, skype or email, or watch the cows on the webcam. They no longer have to leave their old world behind them as Lewis disappears over the horizon.

Thirdly, peasant communities are never static. Technology, culture, ideas and people flow in and out at an ever-accelerating rate. Any city-dwelling ‘peasant romantic’ seeking a timeless, unchanging, pre-modern ‘other’ in smallholder communities is likely to be disappointed. Good thing too.

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