UNDP and Oxford University’s Multidimensional Poverty Index to measure poverty

When the United Nations Development Programme (UNDP) releases its annual Human Development Report for 2010, there will this time be a new measure of poverty in countries from the trade income based measure of poverty.

The UNDP and Oxford University have launched a new index to measure poverty levels which they said give a “multidimensional” picture of people living in hardship, and could help target development resources more effectively.

The two institutions said in a joint news release that the new measure, the Multidimensional Poverty Index, or MPI, was developed and applied by the Oxford Poverty and Human Development Initiative (OPHI) with UNDP support.

The MPI will be featured in the forthcoming 20th anniversary edition of the UNDP Human Development Report, and replaces the Human Poverty Index, which had been included in these reports since 1997.

“The MPI provides a fuller measure of poverty than the traditional dollar-a-day formulas. It is a valuable addition to the family of instruments we use to examine broader aspects of well-being, including UNDP’s Human Development Index and other measures of inequality across the population and between genders,” says Jeni Klugman, Director of the UNDP Human Development Report Office and the principal author of this year’s report.

This year’s Human Development Report will be published in late October, but research findings from the MPI were made available today at a policy forum in London and on line on the websites of OPHI (http://www.ophi.org.uk) and the UNDP Human Development Report (http://hdr.undp.org/en/).

The MPI assesses a range of critical factors or “deprivations” at the household level: from education to health outcomes to assets and services. “Taken together, these factors provide a fuller portrait of acute poverty than simple income measures,” OPHI and UNDP said in the statement.

The measure reveals the nature and extent of poverty at different levels: from household up to regional, national and international levels. The multidimensional approach to assessing poverty has been adapted for national use in Mexico, and is now being considered by Chile and Colombia.

“The MPI is like a high resolution lens which reveals a vivid spectrum of challenges facing the poorest households,” said OPHI Director Sabina Alkire, who created the MPI with James Foster of George Washington University.

The UNDP Human Development Report Office says they are joining forces with OPHI to promote international discussions on the practical applicability of this multidimensional approach to measuring poverty.

New Multidimensional Poverty Measure Index map

According to the statement, half of the world’s poor as measured by the MPI live in South Asia (51 per cent or 844 million people) and one quarter in Africa (28 per cent or 458 million).

The recently released 2010 UN Millennium Development Goals Report stressed that the MDGs – the eight poverty eradication and social development targets which countries have committed to try to achieve by 2015 – will be fully realized only by addressing the needs of those most disadvantaged by geography, age, gender or ethnicity, OPHI researchers point out.

“Our measure identifies the most vulnerable households and groups and enables us to understand exactly which deprivations afflict their lives,” said Ms. Alkire. “The new measure can help governments and development agencies wishing to target aid more effectively to those specific communities,” she added.
The MPI captures distinct and broader aspects of poverty. For example, in Ethiopia 90 per cent of people are “MPI poor” compared to the 39 per cent who are classified as living in “extreme poverty” under income terms alone.



Conversely, 89 per cent of Tanzanians are extreme income-poor, compared to 65 per cent who are MPI poor. The MPI captures deprivations directly – in health and educational outcomes and key services, such as water, sanitation and electricity. In some countries these resources are provided free or at low cost; in others they are out of reach even for many working people with an income.

Niger has the greatest intensity and incidence of poverty in any country, with 93 per cent of the population classified as poor in MPI terms.

Even in countries with strong economic growth in recent years, the MPI analysis reveals the persistence of acute poverty. India is a major case in point. There are more MPI poor people in eight Indian states alone (421 million in Bihar, Chhattisgarh, Jharkhand, Madhya Pradesh, Orissa, Rajasthan, Uttar Pradesh, and West Bengal) than in the 26 poorest African countries combined (410 million).

The MPI also reveals great variations within countries: Nairobi has the same level of MPI poverty as the Dominican Republic, whereas Kenya’s rural northeast is poorer in MPI terms than Niger.

See details and visualization of the MPI at work

By Ultimate Media

Country Multidimensional Poverty Index (MPI) (null) (No year)
Slovenia 0
Slovakia 0
Czech Republic 0.0000515
Belarus 0.0000804
Latvia 0.0014039
United Arab Emirates 0.0020124
Kazakhstan 0.0021675
Palestinian Territories 0.0026515
Georgia 0.0028182
Hungary 0.0029484
Bosnia and Herzegovina 0.0030181
Serbia 0.0033291
Albania 0.0036619
Russian Federation 0.004914
Uruguay 0.0058453
Thailand 0.0063474
Montenegro 0.0063739
Croatia 0.0066471
Ukraine 0.0078279
Macedonia 0.0078303
Armenia 0.0082274
Rep. of Moldova 0.0082328
Uzbekistan 0.0084111
Ecuador 0.0091946
Jordan 0.0095726
Tunisia 0.010469
Argentina 0.0112819
South Africa 0.0143359
Mexico 0.0154625
Kyrgyzstan 0.0188584
Trinidad and Tobago 0.0197402
Sri Lanka 0.0206195
Azerbaijan 0.0207166
Syrian Arab Republic 0.0207464
Belize 0.0236837
Egypt 0.0258936
Estonia 0.0263797
Turkey 0.0389074
Brazil 0.0391825
Colombia 0.0406378
Suriname 0.0438603
Dominican Republic 0.04783
Guyana 0.0546115
China 0.0559935
Iraq 0.0587957
Paraguay 0.0642984
Mongolia 0.0646115
Philippines 0.0672428
Tajikistan 0.0684319
Vietnam 0.0750767
Peru 0.0853588
Myanmar (Burma) 0.0880144
Indonesia 0.0953236
Guatemala 0.1270255
Djibouti 0.138538
Morocco 0.1391553
Ghana 0.1396917
Honduras 0.1595332
Gabon 0.1608929
Zimbabwe 0.1738981
Bolivia 0.1751358
Swaziland 0.1827849
Namibia 0.1869626
Nicaragua 0.2112119
Lesotho 0.220099
São Tomé and Principe 0.236401
Cambodia 0.2633314
Lao People’s Dem. Rep. 0.2669367
Pakistan 0.2753857
Yemen 0.2832491
Togo 0.2844177
Bangladesh 0.2913768
India 0.2962426
Cameroon 0.2985434
Kenya 0.3020832
Haiti 0.3055187
Côte d’Ivoire 0.3201939
Gambia 0.3236082
Zambia 0.3253048
Chad 0.3441871
Nepal 0.3499077
Mauritania 0.3520175
Tanzania 0.3673323
Nigeria 0.3676438
Senegal 0.3841565
Malawi 0.3843631
Congo 0.3931986
Comoros 0.4084577
Benin 0.412274
Madagascar 0.4127711
Rwanda 0.4425673
Angola 0.4520091
Mozambique 0.4807159
Liberia 0.4839164
Sierra Leone 0.4891497
Guinea 0.5046654
Cent African Rep 0.51229
Somalia 0.5137413
Burundi 0.529762
Burkina Faso 0.5358329
Mali 0.5639204
Ethiopia 0.5823998
Niger 0.6424667

Leave a Reply

Your email address will not be published.

This site uses Akismet to reduce spam. Learn how your comment data is processed.