Where does power lie in a fragile state like Eastern Congo? What does it mean for aid organizations?

June 16, 2014

Links I liked

June 16, 2014

Are we measuring the right things? The latest multidimensional poverty index is launched today – what do you think?

June 16, 2014
empty image
empty image

I’m definitely not a stats geek, but every now and then, I get caught up in some of the nerdy excitement generated by measuring the state of the world. measuring povertyTake today’s launch (in London, but webstreamed) of a new ‘Global Multidimensional Poverty Index 2014’ for example – it’s fascinating.

This is the fourth MPI (the first came out in 2010), and is again produced by the Oxford Poverty and Human Development Initiative (OPHI), led by Sabina Alkire, a definite uber-geek on all things poverty related. The MPI brings together 10 indicators, with equal weighting for education, health and living standards (see table). If you tick a third or more of the boxes, you are counted as poor.

MDPI

Here’s the basics for MPI 2014:

  • It covers 108 countries, with 78% of the world’s population
  • As well as multi-dimensional poverty, it adds a new, more extreme category of ‘destitution’ for 49 countries (eg two or more children have died in your household, rather than one, see second table)
  • It analyses changes over time since the last index for 34 countries, covering 2.5 billion people (a third of humanity)

 

Key findings?

  • A total of 1.6 billion people are living in multidimensional poverty; more than 30% of the people living in the 108 countries analysed (compare that with a global figure of 1.2 billion in income poverty)
  • Of these 1.6 billion people, 52% live in South Asia, and 29% in Sub-Saharan Africa. Most MPI poor people – 71% – live in Middle Income Countries (I won’t try and compare this with regional income breakdowns, as the MPI doesn’t cover all countries yet)
  • The country with the highest percentage of MPI poor people is still Niger; 2012 data from Niger shows 89.3% of its population are multi-dimensionally poor
  • Of the 1.6 billion identified as MPI poor, 85% live in rural areas; significantly higher than income poverty estimates of 70-75%
  • Of 34 countries for which we have time-series data, 30 – covering 98% of the MPI poor people across all 34 – had statistically significant reductions in multidimensional poverty
  • The countries that reduced MPI and destitution most in absolute terms were mostly Low Income Countries and Least Developed Countries
  • Nepal made the fastest progress, showing a fall in the percentage of the population who were MPI poor from 65% to 44% in a five-year period (2006-2011). Other star performers include Rwanda, Ghana, Bangladesh, Cambodia, Tanzania and Bolivia
  • Nearly all countries that reduced MPI poverty also reduced inequality among the poor
  • Over 638 million people are destitute across the 49 countries analysed so far – half of all MPI poor people
  • India is home to 343.5 million destitute people – 28.5% of its population is destitute.
  • In Niger, 68.8% of the population is destitute – the highest share of any country

 

MDPI destitution

What does the MPI add to our understanding of poverty?

  • It more closely matches the actual lives of the poor. As the World Bank’s great Voices of the Poor study showed fully 15 years ago, poverty is a state of being – characterized by shame, humiliation, anxiety and worry, much more than it is about ‘do I have more/less than $1.25 a day’. The MPI is only a first step away from the reductionism of income measures (we don’t have comparable data on shame and fear yet), but it’s a start.
  • It measures the intensity of poverty – being poor and sick is very different from being poor and healthy. As a result it provides incentives to policymakers to try to help people become ‘less poor’, and recognition when they succeed in doing so; not just plaudits for those people lifted from one side to the other of a poverty line.
  • It compares deprivations directly (have any children died in your household?), so no need to mess around with Purchasing Power Parity calculations. That’s both more tangible, and a relief when periodic adjustments in PPP creates such doubt and confusion over income poverty comparisons (by one calculation, global income poverty fell by half between a Tuesday and a Wednesday last month!).
  • It allows you to go into some fascinating fine grain analysis, eg Benin and Kenya both had significant poverty reductions, but when you disaggregate by ethnic group, in Benin poverty reduction was virtually zero among the poorest ethnic group (the Peulh), whereas in Kenya, poverty among Somalis fell faster than for the better off ethnic groups.
  • The rural/urban finding is interesting – lots of discussion elsewhere about whether income poverty can be meaningfully compared between urban and rural settings, for example because you need money for lots of things in urban settings that come free in rural (so urban poverty is higher, for a given level of income ). But the MPI finds the opposite – in terms of multi-dimensional poverty, the benefits of urban outweigh the costs, so the proportion of the MD poor is higher in rural areas than for income poverty. That should get the urbanistas going. [update: yep, got that right]
  • Each indicator actually pulls its own weight – for example 10 countries’ poverty was tugged down by significant changes in all indicators. Not one is a laggard that never moves.

 

Which all seems really important, but as I said, I’m not a stats nerd, and I’d be interested in your views on the value (or otherwise) of the index.

11 comments

  1. It will be interesting to see how this is adapted once the post-2015 goals require universal reporting, on all countries. In the UK, for example, as elsewhere, poverty is “characterised by shame, humiliation, anxiety and worry”, as you state, but the current MPI indicators wouldn’t capture UK poverty. Which makes me wonder if they really do capture poverty in the 108 countries covered…

  2. Power is one of the main aspects of multidimensional poverty. It is not in the index. Those multidimensional indexes in general give a picture where the total is way less than the sum of its parts.

    I do agree most of the indicators are very relevant for multidimensional poverty. Looking at them in a desegregated way, indicator by indicator, split by sex, age, rural-urban, improves their usefulness. So lumping them together in one index is the contrary of what we should do to make the data useful and telling.

    I feel that the drive towards less telling indicators, lumped together is not useful for advocating for development, nor for planning action. Only for diplomatic discussion.

    PS. remind me why we abandoned the telling equity indicator 20 % riches / 20 % poor for the Gini coëfficient?

    1. Thanks Sam, but we’re still a long way from anything like an agreed way to measure power. As for inequality, the proponents of replacing Gini with Palma index (ratio of top 10% to bottom 40%) aren’t giving up

  3. This is another measure of poverty that makes no consideration for how urban contexts differ from rural contexts. Measuring housing conditions by whether there is a dirt floor is hardly appropriate when you have multi storey housing (even many shacks have two or three stories). This index is using the UN (WHO/UNICEF) stats on provision for water and sanitation that have long been shown to be so inappropriate for urban contexts. In urban areas, especially in informal settlements, high prices have to be paid for water from kiosks or vendors, often for keeping children at school…. this is not to gloss over the very inadequate access to services in rural areas but what looks like much better service provision in urban areas often is not because large sections of the urban population cannot get access to these, even if these are close. Water problems are often not distance to water source but the fact that the standpipe is shared with hundreds of others and so queues rather than distance are the issue. Or the issue is the cost. At least for urban areas, what is needed is detailed data on deprivation in each location to inform local action and engagement with those facing deprivation – not another ‘international comparison’ that is based on already collected data

    1. Dear David,

      You are absolutely right. The MPI adds value because it is exactly comparable across urban an rural measures, so it’s interesting to see these results. We shared it for that reason. Yet because it is comparable, it does not reflect urban (or rural, or cultural, or national) deprivations as accurately as it could. For this scale of countries, the datasets we used do not all have the right variables on housing and waste disposal and time-to-use-services and urban environments to make a great urban MPI. Such a measure could (and probably should) be constructed, though, for a smaller set of countries in the next phase. We just wanted to start the conversation, and so began with the simplest comparison, which is absolutely the same.

      The good news is that countries are developing national – and in some cases city-specific – measures of multidimensional poverty. In these, there is a lot more space to create indicators that are tailored to the policy context and to the definitions of poverty. The Multidimensional Poverty Peer Network (http://www.ophi.org.uk/policy/policynetwork/) gathers together the evolving experiences. For example Ho Chi Minh city in Vietnam is exploring this option.

  4. Of course more data and further research are needed… and hopefully this is done at local levels, but I think this is interesting and a great improvement on the simple $1.25 measure.

    I like the simplicity of the criteria, I live in the UK and struggle to imagine what living on $1.25 a day means (I suspect our politicians do too) these factors are easier to both measure and comprehend. I don’t see how they could be split by age or sex when the data are collected at the household level.

  5. Nothing is perfect but this seems a very useful set of data to me. I found the most fascinating slides the ones showing the difference between measures of income poverty ($1.25) and multi-dimensional poverty. Dramatic improvements in Ghana, Bolivia and India only show up on the MPI data, and would not be evident if we only follow trends in $1.25 poverty rates. Given the limitations of income poverty measures, exacerbated by upheaval caused by the new data from the International Comparison Project which will lead to revisions in Purchasing Power Parity and income poverty thresholds, it’s useful to have more tools in the toolbox. I also wonder if this can be used to refine the Progress out of Poverty Index which seems to be growing in usage (eg amongst impact investors that seek to target the poor).

    Indeed, I see this as one of three significant data events in the last month: the MPI index, the new ICP data affecting purchasing power parity income poverty, and the excellent IFC Global Consumption Database which gives market expenditure data by income segment, sector and country. My own blog, admitting these three have made my head spin, is here http://businessinnovationfacility.org/group/inclusive-business-impacts-network/forum/topics/my-head-is-spinning-but-if-you-measure-social-impact-it-needs-to

  6. MPI is good digestive for any layman to understand as complex a phenomenon as poverty. However, it must include indicators of gender, freedom and state engagement without elongating the list. These are important measures especially in context of the Third World where poverty is not individualistic and more about ethnic hierarchies, social inequalities and networks. Unsubscribing these indicators to poverty would not only make these aspects invisible but reckon alleviating solutions unworkable. MPI does not capture the paradox of poverty prevalent in South Asia where one may be socially rich and income poor and vice versa.

  7. The challenge of institutions in developing worlds where these data is most critical, they lack the expertise and capacity to use it effectively in human development programs. There is need to bridge this gap.

  8. Just revisiting after Brenda her comment.

    Big data is in a large degree the basis for Big Development. The World Bank or others’ long term planning and expertise driven policy.

    I wonder what this gives with a participatory feedback based problem driven iterative approach. I often wonder that the geek and wonk data love fest is not just another sideshow, the Tyranny of Experts revisited.

    As James Scott says: the poor are statistics, the rich never are, the rich are individual stories.

    I don’t know really, and of course data are important, but doubts begin to grow with every study we fund that costs more than water and sanitation for an average city.

  9. Poverty is a multidimensional phenomena. The weighing of the different factors has not been explained.Multidimensional poverty can also affect revenue, income, employment

Leave a comment