Seeing poverty up close

13 July, 2015

Poverty measures reported at the national level provide only a sketch of the reality poor people face. Multidimensional poverty estimations released on 22 June 2015 by the Oxford Poverty and Human Development Initiative (OPHI), University of Oxford, fly low to show what poverty is like in different regions of countries. They also reveal how people are poor – the multiple disadvantages that affect their lives.

A fuller picture of poverty across over 100 developing countries can be used in many ways – to assign resources where they will have impact, to analyse the nexus of poverty and environmental hazards, to design policies that are tailored to the specific shape poverty takes in a region, to coordinate integrated programmes across sectors and levels of government, to monitor and evaluate progress, and to share information with the private sector and civil society actors.

The Global Multidimensional Poverty Index (MPI), which complements income poverty measures, has been estimated by OPHI, and published in UNDP Human Development Reports since 2010, making this year’s launch the five year anniversary. The index reflects the simultaneous deprivations poor people experience in ten different aspects of life, including nutrition, child mortality, schooling, water, sanitation, assets, and cooking fuel. If people are deprived in at least one-third of ten weighted indicators, they are identified as multidimensionally poor. The MPI is the product of incidence – the percentage of people who are poor – and intensity – how many (weighted) deprivations poor people have.

What is startling is how much clearer the picture of non-monetary poverty has gotten in five short years. When the Global MPI was first released in 2010, subnational decompositions were provided for only 3 countries. Since this time, OPHI has released MPI estimations for well over 1,350 regions within countries. This year’s analysis, for example, publishes MPI values and associated in-depth statistics for 894 regions in 76 countries.

To get a sense of the progress, consider Sub-Saharan Africa, the region with the highest MPI, in which 61% of people are in acute multidimensional poverty, and experience deprivations in half of the Global MPI’s weighted indicators. Every single MPI for Sub-Saharan African (SSA) countries included in the 2015 analysis has been updated since 2010. For example, the 2014 MPI provided new estimations for 13 SSA countries in addition to the just-released updates for 17 countries – so estimations have been updated for 30 countries in 2 years. In all but one of the 39 SSA countries covered, the 2015 Global MPI uses surveys whose fieldwork ended 2009-14 – so the estimations are relatively up to date, thanks to new DHS and MICS surveys released into the public domain.

In fully 37 of these 39 countries the MPI can be disaggregated, so in June 2015 MPI figures have been published for 391 regions of SSA countries alone, the poorest of which is Salamat in Chad. Not only that, but you can open up the MPI for each subnational region to see how poor people fare in each of ten poverty indicators – information which can be used by many actors to fight poverty deftly and powerfully in all its dimensions.

What difference does it make if we can fly low over poverty and see it up close? Rather a lot, in fact. That’s why the Sustainable Development Goals (SDGs) are calling for disaggregated data. Having a map can shape action: if you can see what regions and groups are the poorest, and whether they are reducing poverty fastest or not, you can make mid-course adjustments in projects and policies to make sure the poorest are not overlooked. It is not possible at present for these injustices to be readily seen from a national sketch.

OPHI’s analysis of the disaggregated Global MPI data reveals stark differences in poverty across subnational regions. In 53 of the 884 regions covered by the Global MPI, more than 90% of people are MPI poor, but in another 150, the proportion it’s less than 5%.

As just one example of regional differences, think of South Sudan and Sudan, which only a few years ago were a single country, and which today have MPI estimates for the first time. According to the Global MPI, in Sudan, an Arab State, 58% of people are MPI poor. This compares to over 90% in South Sudan, so partition already distinguished them. Delving further, within Sudan, one-third of people in urban areas are poor, whereas in rural areas the proportion is over two-thirds. Within Sudan’s 15 regions, poverty ranges from 17% in the Northern area to 86% in West Dafur. Zooming in on the poorest two regions, West and South Dafur, we see that the deprivation patterns vary, with malnutrition, children out of school, and asset deprivations more prevalent in West Dafur, but child mortality, few years of adult schooling, and lack of sanitation rates higher in South Dafur.

In the June 2015 updates of the Global MPI, the fly-low subnational data on multidimensional poverty have been updated for 82% of the population covered in East Asia and the Pacific (including China), 72% in the Arab States, 43% in Sub-Saharan Africa, plus 5 countries in each of Latin America, Europe and Central Asia.

A pivotal ingredient for improving poverty measurement is good data. Five years ago, OPHI’s MPI estimations used data from 2000-2008. In 2015, the MPI of 85 countries use data from surveys that were fielded 2009-2014. In half of these – 43 countries – the surveys ended 2012-2014. There were also initial problems with missing indicators – in 2010 only 60% of countries had MPIs with all 10 indicators – now that’s gone up to 83%, with gains in data quality and robustness as well.

With the end of the Millennium Development Goals (MDGs) on the horizon and the SDGs gathering speed, it is important to find measures like MPI that are information-rich (to energise action) and feasible (in terms of data and ease of communication). Indeed a ‘Data Revolution’ is quietly well underway. And these data – designed for the MDGs – are being used swiftly to create in-depth maps of poverty’s interlocking deprivations. For example, the DHS datasets of Egypt and the Gambia were just released in May 2015, yet they were included in the June 2015 Global MPI updates.

So this year’s Global MPI updates are significant and well worth a look. The OPHI website includes maps and data visualisation tools you can tailor to your own curiosity. Overall the Global MPI covers 101 developing countries which are home to 5.2 billion people, or 75% of the world’s population.  It covers 96% of the people living in Sub-Saharan Africa, 93% in East Asia and the Pacific, and 85% in Latin America. Data are from 2005-2015, mainly collected by USAID’s Demographic and Health Survey (DHS) and UNICEF’s Multiple Indicators Cluster Survey (MICS), as well as national surveys.

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About the author(s)
Sabina Alkire
Director, OPHI, and Associate Professor