The 2016 Multidimensional Poverty Index was launched yesterday. What does it say?

This is at the geeky, number-crunching end of my spectrum, but I think it’s worth a look (and anyway, they asked MDI Tenindicatorsnicely). 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:

  • There are 50% more MPI poor people in the countries analysed than there are income poor people using the $1.90/day poverty line.
  • Almost one third of MPI poor people live in Sub-Saharan Africa (32.%); 53% in South Asia, and 9% in East Asia.
  • As with income poverty, three quarters of MPI poor people live in Middle Income Countries.

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.
  • African MPI 2016Among 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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9 Responses to “The 2016 Multidimensional Poverty Index was launched yesterday. What does it say?”
    • To compute the MPI it is necessary to have access to household level survey data that has information on at least one indicator of the education and health dimensions and three indicators of the living standards dimension. OPHI has been able to get access to such data for 118 countries, of which 102 have collected this data between 2005 and 2015. OPHI is not aware of any national or internationally comparable survey for developing countries in the East Pacific (such as Papua New Guinea), although we are constantly looking for new data to improve our understanding of multidimensional poverty

  1. Ruth Kelly

    I’ve heard a lot of people claim recently that wages are ‘too high’ in East and West Africa – maybe this is a piece of the puzzle to show that they don’t go as far, even when adjusted for PPP. Interesting finding. This could be a prompt to rethink PPP?

  2. Alice Evans

    We need an inter-disciplinary, multi-country team to research what explains rapid poverty reduction in those 19 subnational regions (‘positive deviants’) and to reflect on what we might learn from that/ how it could be amplified. VERY AWESOME. ODI has done Progress Reports but as I recall those all focus at the national level.

  3. Sam

    Probably it is time for a new “language war”. Since the work of Amartya Sen, there is a new definition of poverty.

    It is quite simple: if you cannot manage to feed your children and as a result they are stunted, and can never develop their potential, you are extreme poor. Whatever your nominal income.

    So this index measures total poverty, while the world bank, etc. are only measuring monetised poverty, which is probably one of the least important symptoms of poverty.

    So, based on the most common definitions of poverty, this is the “poverty index” while the other measures are just subsets of data you can use to add up to know how many poor there are.

  4. Irene Guijt

    Absolutely critical to get below national aggregates so this is important. We know that all indices are inevitably simplistic and realistic as you have to deal with the data you have but they can work really well to reveal assumptions being made with broad-brush aggregate stats.

    Another great body of work – to help Ricardo! – t took place in LAC through RIMISP (funded by IDRC). IT was a five year action research programme (with impressive policy changes as well) to understand territorial dynamics. It led to many publications (also in Spanish, Ricardo…) and a special issue (or two I think) of World Development. One of them from late 2015. “Growth, Poverty and Inequality in Sub-National Development: Learning from Latin America’s Territories”.

    The researchers scanned 1000s of municipalities on several indicators and then did a multi-year deep dive in a set of around 10 or so ‘positive deviants’ to try to understand what was explaining convergence of positive indicators (and if the picture was as positive as the relative statistics implied).

    The abstract for one of the articles includes the last sentence (I put it in UPPER CASE) that highlights relevance for development:

    This paper summarizes a study of changes in per-capita income, monetary poverty, and income distribution in 9,045 subnational administrative units of nine Latin American countries between the mid-1990s and mid-2000s. The results largely support spatial convergence of mean household incomes, although the estimates indicate it has been slow. Territorial inequality is found to be persistent and reduces the pro-poor effect of local income growth. Although national-context specific, the estimates also indicate that territorial development dynamics are influenced by the structural features of the territories. IN VIEW OF THE EVIDENCE, TERRITORIAL DEVELOPMENT POLICIES IN LATIN AMERICA SEEM WELL WARRANTED.

    Focusing all efforts on ‘national policy’ or ‘macroeconomic policies’ is deeply flawed, they say. A key implication as the article outlines well in the opening paragraphs is that it makes us question the assumptions underpinning macroeconomic policy. For example: “To improve a country’s development, it was argued, it was enough to create conditions in which the comparative advantages of countries and their regions could be freely expressed.”

    Drilling down below national level statistics (of which MPI is one illustration) has significant critical implications for development policy.