Multidimensional Poverty Index (MPI) Disparity and

Dynamics

Sabina Alkire, José Manuel Roche,

and Suman SethRome, 22 May 2012

OPHI – MPI TeamOPHI 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 -

1. What is new?2. MPI in Middle Income

Countries3. Disparities4. MPI over Time5. Conclusions

What is the MPI?• The MPI 2011 is an internationally

comparable index of poverty for 109 developing countries.

• It was launched in 2010 in the Human Development Report, and updated in 2011

• The MPI methodology can be adapted for national poverty measures – using indicators and cutoffs for each policy context.

MPI

METHODOLOGY

1. Data: Surveys

Demographic & 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 Weights & Indicators

Deprivation score of indicators, cutoffs, & weights built for

each person

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?• The MPI uses the Alkire Foster method:

• H is the percent of people who are identified as poor, it shows the incidence of multidimensional poverty.

• A is 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 is appropriate for ordinal data, and satisfies properties like subgroup consistency, dimensional monotonicity, poverty & deprivation focus. MPI is like the poverty gap measure – but looks at breadth instead – what batters a person at the same time.

Formula: MPI = M0 = H × A

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.

1212

1313

1414

15

1616

1717

1818

1919

20

PhubaDeprived in 67% of

dimensions.It doesn’t tell the full

storyBut it gives some idea.

MPI – 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 populationOf 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

MPI poor people by region

Total Population in 109 MPI countries

The MPI Headcount Ratios and the $1.25/day Poverty

0%

10%

20%

30%

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50%

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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.

Intensity is highest in the poorest countries.

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 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 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

DISPARITIES

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

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

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

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

Ghana, Nigeria, and Ethiopia

Ethiopia’s Regional Disparities

Ethiopia

Ethiopia’s Regional Disparities

Addis Ababa

Somali

Afar

HarariDire

Dawa

Nigeria’s Regional Disparities

Nigeria

Nigeria’s Regional Disparities

South West

North East

Nigeria

Ghana’s Regional Disparities

Ghana

Ghana’s Regional Disparities

Greater Accra

NorthernGhana

National MPI(109 Countries)

Sub-national disparities in MPI(Highest disaggregation available)

43

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

THE COMPOSITION

OF MULTIDIMENSION

AL POVERTY

45

Nutrition (CH)

Child Mortality (CH)

Safe Drinking Water (CH)

School Attendance (CH)

Similar MPI, but Different Composition

Asset OwnershipCooking FuelFlooringSafe Drinking WaterImproved SanitationElectricity

NutritionChild Mortality

School AttendanceYears of Schooling

47

Different MPI, Similar Dimensional Composition

Asset OwnershipCooking FuelFlooringSafe Drinking WaterImproved SanitationElectricity

NutritionChild Mortality

School AttendanceYears of Schooling

Different Poverty Profiles (Contributions)

48

0.2

.4.6

.81

% C

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Reg

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l MP

I

Schooling Attendance Child Mortality Nutrition Electric.

Sanitation Water Flooring Cooking Fuel Assets

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

were used

IndiaMPI = 0.283 A = 52.7%

Lao0.26756.5%

Kenya0.22948.0%

Lao and India have more severe poor. Lao has the most

deprived in over 70%

Let’s break intensity:Whose Deprivation Score

exceeds 50? 70%? (Look at the poorest of the poor)

CHANGES OVER TIME

Changes over time in MPI

• MPI fell for all countries

• All have 10 indicators• Survey intervals: 3 to 6

years.

Mul

tidim

ensi

onal

Pov

erty

In

dex

(MPI

)

Ghana, Nigeria, and Ethiopia

Let us Take a Step Back in Time

Ghana2003

Nigeria2003

Ethiopia2000

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

Ghana2008

Nigeria2008

Ethiopia2005

Ghana2003

Nigeria2003

Ethiopia2000

Nigeria 2003-2008 (Reduced H more than A)

Ghana2008

Nigeria2008

Ethiopia2005

Ghana2003

Nigeria2003

Ethiopia2000

Ghana 2003-2008 (Reduced A and H Uniformly)

Ghana2008

Nigeria2008

Ethiopia2005

Ghana2003

Nigeria2003

Ethiopia2000

How did poverty decrease?

Nigeria: Indicator Standard Errors

Performance of Sub-national Regions

Ethiopia’s Regional Changes Over Time

Addis Ababa

Harari

Nigeria’s Regional Changes Over Time

South South

North Central

Inside the Regions of Nigeria

Robustness 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%• For 90% of the possible pairs of

countries we can say that one country is unambiguously poorer than another regardless of whether we require a poor person to be deprived in 20, 30 or 40% of the weighted indicators.

MPI is robust to varying k= 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

• Recall: MPI varies from 0 to 0.642 and the headcount varies from 0 to 93%.

• Re-weight each dimension:– 33% 50% 25% 25%– 33% 25% 50% 25%– 33% 25% 25% 50%

• How does this affect:– MPI, H, A– Ranking of countries

Robustness to WeightsMPI Weights 1 MPI Weights 2 MPI Weights 3Equal weights:

33% each (Selected Measure)

50% Education 25% Health 25% LS

50% Health25% Education 25% LS

Pearson 0.992Spearman 0.979Kendall (Taub) 0.893Pearson 0.995 0.984Spearman 0.987 0.954Kendall (Taub) 0.918 0.829Pearson 0.987 0.965 0.975Spearman 0.985 0.973 0.968Kendall (Taub) 0.904 0.863 0.854

Number of countries: 109

MPI Weights 2

50% Education 25% Health 25% LS

MPI Weights 3

50% Health 25% Education 25% LS

MPI Weights 4

50% LS 25% Education 25% Health

Robustness to WeightsSummary:• Correlations: 0.97 and above• Rank Concordance: 0.90 and above• 85% of all possible pairwise comparisons are robust

Some additional ‘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

Complements $1.25/day poverty measuresGives 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 poorestAdaptable for National Poverty MeasuresResearch and Policy: agenda is ongoing

Uses of an MPI

National MeasuresMexicoColombia, et al.

Well-being MeasuresBhutan’s Gross National

Happiness IndexAdaptations

Women’s Empowerment in Agriculture Index

Some other applications

Living

Standard

Ecological Diversity

and Resilience

Communit

y Vitality

Good

Go

vern

anc

e

Cultural

Diversity

and Resilience

Time - Use

Health

Educ

atio

n

Psychological

well-being

Thank you

www.ophi.org.uk