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#2 from 2016: The 2016 Multidimensional Poverty Index was launched last week. What does it say?

Duncan Green's picture

Our Top Ten blog posts by readership in 2016. This post was originally published on June 14, 2016. 

This is at the geeky, number-crunching end of my spectrum, but I think it’s worth a look (and anyway, they asked nicely). The 2016 Multi-Dimensional Poverty Index was published yesterday. It now covers 102 countries in total, including 75 per cent of the world’s population, or 5.2 billion people. Of this proportion, 30 per cent of people (1.6 billion) are identified as multidimensionally poor.

The Global MPI has 3 dimensions and 10 indicators (for details see here and the graphic, right). A person is identified as multidimensionally poor (or ‘MPI poor’) if they are deprived in at least one third of the dimensions. The MPI is calculated by multiplying the incidence of poverty (the percentage of people identified as MPI poor) by the average intensity of poverty across the poor. So it reflects both the share of people in poverty and the degree to which they are deprived.

The MPI increasingly digs down below national level, giving separate results for 962 sub-national regions, which range from having 0% to 100% of people poor (see African map, below). It is also disaggregated by rural-urban areas for nearly all countries as well as by age.

Headlines from the MPI 2016:

This year’s MPI focuses on Africa:

  • In the 46 African countries analysed, 544 million people (54% of total population) endure multidimensional poverty, compared to 388 million poor people according to the $1.90/day measures.
  • The differences between the proportion of $1.90 and MPI poor people are greatest in East and West Africa. By the $1.90/day poverty line, 48% in West Africa and 33% in East Africa are poor, whereas by the MPI, 70% of people in East Africa are MPI poor and 59% in West Africa. The MPI thus reveals a hidden face of poverty that may be overlooked if we consider only its income aspects.
  • Among 35 African countries where changes to poverty over time were analysed, 30 of them have reduced poverty significantly. Rwanda was the standout star, but every MPI indicator was significantly reduced in Burkina Faso, Comoros, Gabon and Mozambique as well.
  • Disaggregated MPI results are available for 475 sub-national regions in 41 African countries. The poorest region continues to be Salamat in Chad, followed by Est in Burkina Faso and Hadjer Iamis in Chad. The region with the highest percentage of MPI poor people is Warap, in South Sudan, where 99% of its inhabitants are considered multidimensionally poor. The least poor sub-national regions include Grand Casablanca in Morocco and New Valley in Egypt, with less than 1% of the population living in multidimensional poverty.
  • The MPI registered impressive reductions in some unexpected places. 19 sub-national regions – regional ‘runaway’ successes – have reduced poverty even faster than Rwanda. The fastest MPI reduction was found in Likouala in the Republic of the Congo.
  • The Sahel and Sudanian Savanna Belt contains most of the world’s poorest sub-regions, showing the interaction between poverty and harsh environmental conditions.
  • Poverty looks very different in different parts of the continent. While in East Africa deprivations related to living standards contribute most to poverty, in West Africa child mortality and education are the biggest problems.
  • The deprivations affecting the highest share of MPI poor people in Africa are cooking fuel, electricity and sanitation.
  • The number of poor people went down in only 12 countries. In 18 countries, although the incidence of MPI fell, population growth led to an overall rise in the number of poor people.

See here for my post on the MPI 2014. I’d be interested in your reflections on what MPI adds to the usual $ per day metrics, in terms of our understanding of development.

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This post first appeared on From Poverty to Power.


Submitted by Vivek Menon on

As CEO if indias largesr encironmental NGO WTI i am surpriaed and diss appointed that the WBank dors bot consider Environment as a key indicator and in fact as a crosscutting dimension of poverty. I would apprwciate a response from the Bank on this

VIvek Menon

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