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Multidimensional Poverty Index (MPI) Disparity and Dynamics

Sabina Alkire,

José Manuel Roche,

and Suman Seth

Rome, 22 May 2012

Ophi mpi team

OPHI Research Team: Sabina Alkire (Director), James Foster (Research Fellow), John Hammock (Co-Founder and Research Associate), José Manuel Roche (coordination MPI 2011), Maria Emma Santos (coordination MPI 2010), Suman Seth, Paola Ballon, Gaston Yalonetzky, Diego Zavaleta.

Data analysts and MPI calculation since 2011: Mauricio Apablaza, Adriana Conconi, Ivan Gonzalez DeAlba, Gisela Robles Aguilar, Juan Pablo Ocampo Sheen, Sebastian Silva Leander, Christian Oldiges, Nicole Rippin, and Ana Vaz.

Special contributions:Mauricio Apablaza (analysis of family planning), Yadira Diaz (preparation of the maps), Maja Jakobsen (research assistance and preparation of graphs), Nicole Rippin (methodological inputs) Christian Oldiges (research assistance for regional decomposition), Gisela Robles Aguilar (tables, data compilation and preparation of the maps), John Hammock, Sabina Alkire and James Jewell (new Ground Reality Check field material), Maria Emma Santos (methodological inputs and adjustments of MPI methodology), Gaston Yalonetzky (design and programming for standard error calculation).

Communication Team:Paddy Coulter (Director of Communications), Joanne Tomkinson (Research Communications Officer), Heidi Fletcher (Web Manager), Moizza B Sarwar (Research Communications Assistant), and Sarah Valenti (Research Communications Consultant) and Cameron Thibos (Design Assistant).

Administrative Support: Tery van Taack (OPHI Project coordinator), Laura O'Mahony (OPHI Project Assistant)

OPHI prepare the MPI for publication in the UNDP Human Development Report and we are grateful to our colleagues in HDRO for their support.

Multidimensional poverty index mpi acute poverty in developing countries
Multidimensional Poverty Index (MPI) - acute poverty in developing countries -

What is new?

MPI in Middle Income Countries


MPI over Time


What is the mpi
What is the MPI?

  • The MPI 2011 isaninternationally comparable index of poverty for 109 developingcountries.

  • Itwaslaunched in 2010 in theHuman Development Report, and updated in 2011

  • The MPI methodology can be adapted for nationalpovertymeasures – usingindicators and cutoffs for eachpolicycontext.



1. Data: SurveysDemographic & Health Surveys (DHS - 54) Multiple Indicator Cluster Surveys (MICS - 32)World Health Survey (WHS – 17)Additionally we used 6 special surveys covering urban Argentina (ENNyS), Brazil (PNDS), Mexico (ENSANUT), Morocco (ENNVM), Occupied Palestinian Territory (PAPFAM), and South Africa (NIDS)Constraints: Data are 2000-2010. Not all have precisely the same indicators.

2 mpi dimensions w eights indicators
2. MPI Dimensions Weights & Indicators

3 identification who is poor
3. Identification: Who is poor? for each person

A person is multidimensionally poor if they are deprived in 33% of the dimensions.

(censor the deprivations of the non-poor)


How do you calculate the mpi
How do you calculate the MPI? for each person

  • The MPI uses the Alkire Foster method:

  • His the percentof people who are identified as poor, it shows the incidence of multidimensional poverty.

  • Ais the average proportion of weighted deprivations people suffer at the same time. It shows the intensity of people’s poverty – the joint distribution of their deprivations.

    The MPI isappropriate for ordinal data, and satisfiespropertieslikesubgroupconsistency, dimensional monotonicity, poverty & deprivationfocus. MPI islikethepoverty gap measure – but looks at breadthinstead – whatbatters a person at thesame time.

Formula: MPI = M0 = H × A

What is new
What is new? for each person


The MPI starts with each person, and constructs a deprivation profile for each person.

Some people are identified as poor based on their joint deprivations. The others are identified as non-poor.

  • Most multidimensional poverty measures look at deprivations one by one, not at the household level.

  • Counting measures do look at coupled deprivations but only provide a headcount, giving no incentive to target those who are deprived in most things at the same time or to reduce intensity.

12 for each person

13 for each person

14 for each person

16 for each person

17 for each person

18 for each person

19 for each person

Phuba for each person

Deprived in 67% of dimensions.

It doesn’t tell the full story

But it gives some idea.

MPI – Global Results for each person

Global results
Global Results: for each person

These results are for 109 developing countries, selected because they have DHS, MICS or WHS data since 2000. Special surveys were used for Argentina, Brazil, Mexico, Morocco, Occupied Palestinian Territory, and South Africa

They cover 5.3 billion people - 78.6% of the world’s population

Of these 5.3 billion people, 31% of people are poor.

That is 1.65 billion people.

(2008 population figures taken from Population Prospects 2011; 2010 Revision).

Half of the world’s MPI poor people live in South Asia, and 29% in Sub-Saharan Africa

Total Population in 109 MPI countries

MPI poor people by region

The mpi headcount ratios and the 1 25 day poverty
The MPI Headcount Ratios and the $1.25/day Poverty and 29% in Sub-Saharan Africa

103 of our 109 Countries have income; only 71 have income poverty data within 3 years of MPI. Income data ranges from 1992-2008; MPI from 2000-2010.

Good coverage of low and middle income countries over 90
Good coverage of Low and Middle Income Countries – over 90%.

~ 31 Low Income Countries,(700.9M), 92%

~ 70 Middle Income Countries, (1189.2M), 94%:

~ 42 Lower Middle Income (2378.9M) 97%

~ 28 Upper Middle Income (2178.9M) 90%

~ 8 High Income Countries (41.2M), of which:

~ 5 OECD (29.2M)

~ 2 non-OECD (12M)

Total Population: 5.3 Billion people

(population figures from 2008; data from 2000-2010).

Most 90%.poor people live in middle-income countries. More than twice as many poor people live in middle-income countries (1,189M) compared to low-income countries (459M).

Total Population by Income Category (2008)

MPI Poor Population (2008)

A person is ‘severely’ poor if he or she is deprived in 90%.half of the dimensions – not just 33%.

There are more than twice as many ‘severely poor’ people in MICS as in LICS.

And 50% of the world’s 869M severely poor also live in South Asia

But there is variety… 90%.H in High-income countries 1-7%

Ethiopia’s 90%. Regional Disparities


Ethiopia’s 90%. Regional Disparities



Dire Dawa


Addis Ababa

Nigeria’s 90%. Regional Disparities


Nigeria’s 90%. Regional Disparities

North East


South West

Ghana’s 90%. Regional Disparities


Ghana’s 90%. Regional Disparities



Greater Accra

National mpi 109 countries
National MPI 90%.(109 Countries)

Sub national disparities in mpi highest disaggregation available
Sub-national disparities in MPI 90%.(Highest disaggregation available)

A sub-regional MPI within South Asia is as large and as high as Sub-Saharan Africa’s


473 mill.


26 poorest regions of South Asia

Nutrition ch

Child Mortality (CH) as Sub-Saharan Africa’s

Nutrition (CH)

School Attendance (CH)

Safe Drinking Water (CH)

Similar mpi but different composition
Similar MPI, but as Sub-Saharan Africa’sDifferent Composition

Different mpi similar dimensional composition
Different MPI, Similar as Sub-Saharan Africa’sDimensional Composition

Different poverty profiles contributions
Different Poverty Profiles (Contributions) as Sub-Saharan Africa’s

Data for 49 countries and 497 sub-national regions with 10 indicators were used

Let’s break intensity: as Sub-Saharan Africa’s

Whose Deprivation Score exceeds 50? 70%?

(Look at the poorest of the poor)


MPI = 0.283

A = 52.7%







Lao and India have more severe poor. Lao has the most deprived in over 70%

Changes over time

CHANGES OVER TIME as Sub-Saharan Africa’s

Changes over time in MPI as Sub-Saharan Africa’s

  • MPI fell for all countries

  • All have 10 indicators

  • Survey intervals: 3 to 6 years.

Multidimensional Poverty Index (MPI)

Ghana, Nigeria, and Ethiopia as Sub-Saharan Africa’s

Let us Take a Step as Sub-Saharan Africa’sBack in Time







Ethiopia as Sub-Saharan Africa’s: 2000-2005 (Reduced A more than H)













Nigeria as Sub-Saharan Africa’s 2003-2008 (Reduced H more than A)













Ghana as Sub-Saharan Africa’s 2003-2008 (Reduced A and H Uniformly)













How did poverty decrease? as Sub-Saharan Africa’s

Nigeria: Indicator Standard Errors as Sub-Saharan Africa’s

Performance of sub national regions

Performance of Sub-national Regions as Sub-Saharan Africa’s

Ethiopia’s as Sub-Saharan Africa’s Regional Changes Over Time


Addis Ababa

Nigeria’s as Sub-Saharan Africa’s Regional Changes Over Time

North Central

South South

Inside the Regions of Nigeria as Sub-Saharan Africa’s

Robustness checks

Robustness checks as Sub-Saharan Africa’s

Some basic checks
Some basic checks: as Sub-Saharan Africa’s

  • Quality Checks – triangulating our results with other data sources

  • Robustness of measure to different deprivation cutoffs In the 2010 round we implemented a total of 18 measures, having different indicators and cutoffs.

  • Robustness to changes in poverty cutoff

  • Robustness to changes in weights

  • Identification of the poor: does it identify the same households as poor as a) income poor; and b) bottom quintile by the DHS wealth index?

Mpi is robust to varying k 20 to 40
MPI as Sub-Saharan Africa’sisrobusttovaryingk= 20% to 40%

  • For 90% of thepossiblepairs of countrieswe can saythatone country isunambiguouslypoorerthananotherregardless of whetherwerequire a poorpersontobedeprived in 20, 30 or 40% of theweightedindicators.

Mpi is robust to varying k 20 to 401
MPI as Sub-Saharan Africa’sisrobusttovaryingk= 20% to 40%

By region:

  • Sub-Saharan Africa: 97% of pairwise comparisons are robust (38 countries)

  • South Asia: 100% (7 countries)

  • Latin America and Caribean: 92% (18 countries)

  • Arab States: 94.5% (11 countries)

  • East Asia and Pacific: 87% (11 countries)

  • Central Europe and CIS: 68% (24 countries)

Robustness to alternative weights
Robustness as Sub-Saharan Africa’stoAlternativeWeights

  • Recall: MPI variesfrom 0 to 0.642 and theheadcountvariesfrom 0 to 93%.

  • Re-weighteachdimension:

    • 33% 50% 25% 25%

    • 33% 25% 50% 25%

    • 33% 25% 25% 50%

  • Howdoesthisaffect:

    • MPI, H, A

    • Ranking of countries

Robustness to weights
Robustness as Sub-Saharan Africa’stoWeights

Robustness to weights1
Robustness as Sub-Saharan Africa’stoWeights

  • Summary:

  • Correlations: 0.97 and above

  • Rank Concordance: 0.90 and above

  • 85% of all possible pairwise comparisons are robust

Some additional tools
Some as Sub-Saharan Africa’sadditional ‘tools’

  • Analytical Standard Errors & confidence intervals Bootstrapping

  • Time series and Panel data analysis

  • HH composition test

  • Davidson & Duclos dominance to k cutoff test

  • Basic test statistics

Conclusions next steps

Conclusions & Next Steps as Sub-Saharan Africa’s

Uses of an MPI as Sub-Saharan Africa’s

  • Complements $1.25/day poverty measures

  • Gives a ‘high resolution’ lens on poor people’s lives

    • An overview and a ‘dashboard’

  • Changes over time – can change relatively quickly

  • Provides incentives to reduce intensity and incidence.

  • Can be used to identify the poorest

  • Adaptable for National Poverty Measures

  • Research and Policy: agenda is ongoing

Some other applications as Sub-Saharan Africa’s




  • National Measures

    • Mexico

    • Colombia, et al.

  • Well-being Measures

    • Bhutan’s Gross NationalHappiness Index

  • Adaptations

    • Women’s Empowerment in Agriculture Index

Ecological Diversity and Resilience




Community Vitality

Time - Use

Cultural Diversity and Resilience

Good Governance

Thank you www ophi org uk

Thank you as Sub-Saharan Africa’