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Are We Measuring the Right Things? The Latest Multidimensional Poverty Index is Launched Today – What do You Think?

Duncan Green's picture

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. Take 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.



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

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.
  • 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.

This post first appeared on From Poverty to Power

Photograph by Curt Carnemark via World Bank Photo Collection, available here
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