Multidimensional Poverty Index (MPI) Disparity and Dynamics
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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.

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Multidimensional poverty index mpi disparity and dynamics sabina alkire jos manuel roche

Multidimensional Poverty Index (MPI) Disparity and Dynamics

Sabina Alkire,

José Manuel Roche,

and Suman Seth

Rome, 22 May 2012


Ophi mpi team

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

Disparities

MPI over Time

Conclusions


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.


Multidimensional poverty index mpi disparity and dynamics sabina alkire jos manuel roche

MPI

METHODOLOGY


Multidimensional poverty index mpi disparity and dynamics sabina alkire jos manuel roche

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


Multidimensional poverty index mpi disparity and dynamics sabina alkire jos manuel roche

Deprivation score of indicators, cutoffs, & weights built for each person


3 identification who is poor

3. Identification: Who is poor?

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

(censor the deprivations of the non-poor)

33%


How do you calculate the mpi

How do you calculate the MPI?

  • 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?

Intensity.

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.


Multidimensional poverty index mpi disparity and dynamics sabina alkire jos manuel roche

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Multidimensional poverty index mpi disparity and dynamics sabina alkire jos manuel roche

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Multidimensional poverty index mpi disparity and dynamics sabina alkire jos manuel roche

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Multidimensional poverty index mpi disparity and dynamics sabina alkire jos manuel roche

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Multidimensional poverty index mpi disparity and dynamics sabina alkire jos manuel roche

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Multidimensional poverty index mpi disparity and dynamics sabina alkire jos manuel roche

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Multidimensional poverty index mpi disparity and dynamics sabina alkire jos manuel roche

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Phuba

Phuba

Deprived in 67% of dimensions.

It doesn’t tell the full story

But it gives some idea.


Multidimensional poverty index mpi disparity and dynamics sabina alkire jos manuel roche

MPI – Global Results


Global results

Global Results:

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).


Multidimensional poverty index mpi disparity and dynamics sabina alkire jos manuel roche

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

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.


Multidimensional poverty index mpi disparity and dynamics sabina alkire jos manuel roche

Intensity is highest in the poorest countries.


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).


Multidimensional poverty index mpi disparity and dynamics sabina alkire jos manuel roche

Most 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)


Multidimensional poverty index mpi disparity and dynamics sabina alkire jos manuel roche

A person is ‘severely’ poor if he or she is deprived in 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


Multidimensional poverty index mpi disparity and dynamics sabina alkire jos manuel roche

DISPARITIES


Multidimensional poverty index mpi disparity and dynamics sabina alkire jos manuel roche

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


Multidimensional poverty index mpi disparity and dynamics sabina alkire jos manuel roche

H in High- and Upper Middle-income countries 1-40%


Multidimensional poverty index mpi disparity and dynamics sabina alkire jos manuel roche

H in Middle- and High-income Countries 1-77%


Multidimensional poverty index mpi disparity and dynamics sabina alkire jos manuel roche

H in Low-income Countries ranges from 5-92%


Multidimensional poverty index mpi disparity and dynamics sabina alkire jos manuel roche

Ghana, Nigeria, and Ethiopia


Multidimensional poverty index mpi disparity and dynamics sabina alkire jos manuel roche

Ethiopia’s Regional Disparities

Ethiopia


Multidimensional poverty index mpi disparity and dynamics sabina alkire jos manuel roche

Ethiopia’s Regional Disparities

Afar

Somali

Dire Dawa

Harari

Addis Ababa


Multidimensional poverty index mpi disparity and dynamics sabina alkire jos manuel roche

Nigeria’s Regional Disparities

Nigeria


Multidimensional poverty index mpi disparity and dynamics sabina alkire jos manuel roche

Nigeria’s Regional Disparities

North East

Nigeria

South West


Multidimensional poverty index mpi disparity and dynamics sabina alkire jos manuel roche

Ghana’s Regional Disparities

Ghana


Multidimensional poverty index mpi disparity and dynamics sabina alkire jos manuel roche

Ghana’s Regional Disparities

Northern

Ghana

Greater Accra


National mpi 109 countries

National MPI(109 Countries)


Sub national disparities in mpi highest disaggregation available

Sub-national disparities in MPI(Highest disaggregation available)


Multidimensional poverty index mpi disparity and dynamics sabina alkire jos manuel roche

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

519mill.

473 mill.

0.366

26 poorest regions of South Asia


Multidimensional poverty index mpi disparity and dynamics sabina alkire jos manuel roche

THE COMPOSITION OF MULTIDIMENSIONAL POVERTY


Nutrition ch

Child Mortality (CH)

Nutrition (CH)

School Attendance (CH)

Safe Drinking Water (CH)


Similar mpi but different composition

Similar MPI, but Different Composition


Different mpi similar dimensional composition

Different MPI, Similar Dimensional Composition


Different poverty profiles contributions

Different Poverty Profiles (Contributions)

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


Multidimensional poverty index mpi disparity and dynamics sabina alkire jos manuel roche

Let’s break intensity:

Whose Deprivation Score exceeds 50? 70%?

(Look at the poorest of the poor)

India

MPI = 0.283

A = 52.7%

Lao

0.267

56.5%

Kenya

0.229

48.0%

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


Changes over time

CHANGES OVER TIME


Multidimensional poverty index mpi disparity and dynamics sabina alkire jos manuel roche

Changes over time in MPI

  • MPI fell for all countries

  • All have 10 indicators

  • Survey intervals: 3 to 6 years.

Multidimensional Poverty Index (MPI)


Multidimensional poverty index mpi disparity and dynamics sabina alkire jos manuel roche

Ghana, Nigeria, and Ethiopia


Multidimensional poverty index mpi disparity and dynamics sabina alkire jos manuel roche

Let us Take a Step Back in Time

Ethiopia

2000

Nigeria

2003

Ghana

2003


Multidimensional poverty index mpi disparity and dynamics sabina alkire jos manuel roche

Ethiopia: 2000-2005 (Reduced A more than H)

Ethiopia

2000

Ethiopia

2005

Nigeria

2003

Nigeria

2008

Ghana

2003

Ghana

2008


Multidimensional poverty index mpi disparity and dynamics sabina alkire jos manuel roche

Nigeria 2003-2008 (Reduced H more than A)

Ethiopia

2000

Ethiopia

2005

Nigeria

2003

Nigeria

2008

Ghana

2003

Ghana

2008


Multidimensional poverty index mpi disparity and dynamics sabina alkire jos manuel roche

Ghana 2003-2008 (Reduced A and H Uniformly)

Ethiopia

2000

Ethiopia

2005

Nigeria

2003

Nigeria

2008

Ghana

2003

Ghana

2008


Multidimensional poverty index mpi disparity and dynamics sabina alkire jos manuel roche

How did poverty decrease?


Multidimensional poverty index mpi disparity and dynamics sabina alkire jos manuel roche

Nigeria: Indicator Standard Errors


Performance of sub national regions

Performance of Sub-national Regions


Multidimensional poverty index mpi disparity and dynamics sabina alkire jos manuel roche

Ethiopia’s Regional Changes Over Time

Harari

Addis Ababa


Multidimensional poverty index mpi disparity and dynamics sabina alkire jos manuel roche

Nigeria’s Regional Changes Over Time

North Central

South South


Multidimensional poverty index mpi disparity and dynamics sabina alkire jos manuel roche

Inside the Regions of Nigeria


Robustness checks

Robustness checks


Some basic checks

Some basic checks:

  • 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 isrobusttovaryingk= 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 isrobusttovaryingk= 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

RobustnesstoAlternativeWeights

  • 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

RobustnesstoWeights


Robustness to weights1

RobustnesstoWeights

  • Summary:

  • Correlations: 0.97 and above

  • Rank Concordance: 0.90 and above

  • 85% of all possible pairwise comparisons are robust


Some additional tools

Someadditional ‘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


Multidimensional poverty index mpi disparity and dynamics sabina alkire jos manuel roche

Uses of an MPI

  • 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


Multidimensional poverty index mpi disparity and dynamics sabina alkire jos manuel roche

Some other applications

Living

Standard

Education

  • 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

Psychological

well-being

Health

Community Vitality

Time - Use

Cultural Diversity and Resilience

Good Governance


Thank you www ophi org uk

Thank youwww.ophi.org.uk


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