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Methodological Innovations in
Multidimensional Poverty
Measurement
Oxford Poverty & Human Development Initiative (OPHI)
University of Oxford
Rabat, 4 June 2014
Why such interest? Ethics “Human lives are battered and diminished in all
kinds of different ways.” Amartya Sen
Overview “While assessing quality-of-life requires a plurality of
indicators, there are strong demands to develop a single
summary measure.” Stiglitz Sen Fitoussi Commission Report
Effectiveness “Acceleration in one goal often speeds up
progress in others;” to meet MDGs strategically we need to
see them together. UNDP 2010 50-country study
Management Track progress towards national plan; M&E.
Feasibility Surveys; measure deprivations directly; computations
3
Atkinson, A. B., E. Marlier, F. Monatigne, and A. Reinstadler (2010)
‘Income poverty and income inequality’, in Income and Living Conditions in
Europe, Atkinson and Marlier (eds), Eurostat.
Income poverty Material Deprivation
Joblessness
All 3
deprivations
Empirical Motivation: A Better Picture of Poverty
EU2020-Europe’s Multidimensional measure
Recent Developments in Multidimensional Poverty
Measurement
Introduction: The Global Multidimentional
Poverty Index (MPI)
Robustness tests of multidimensional poverty
rankings
Leaving no one behind: Inequality among the
Poor and Destitution
Changes over time of multidimensional poverty.
GLOBAL MPI 2013
METHODOLOGY
What is the MPI? • The global MPI is an index of acute multidimensional poverty for
over 100 developing countries.
• It has been reported and updated in UNDP’s Human Development
Report since 2010. OPHI compute estimates.
• National MPIs use different specifications and priorities.
• The MPI methodology was developed by Alkire and Foster
(Journal of Public Economics 2011)
• Robustness tests for the global MPI 2010 are in Alkire and Santos
(World Development 2014)
• Systematic presentation: Multidimensional Poverty: Measurement &Analysis
(Oxford University Press, 2015)
• A new global MPI-2015+ may be used alongside $1.25/day
1. Data: Surveys Note: in 2014 we have updated MPI for 30 countries and 2.5B people.
Demographic & Health Surveys (DHS - 51)
Multiple Indicator Cluster Surveys (MICS - 30)
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 2002-2011. Not all have precisely the same
indicators.
2. MPI Dimensions, Weights & Indicators
Note: there are no PPPs for multidimensional
poverty as deprivations are measured directly.
3. Identification: Who is poor?
People are multidimensionally poor if they are
deprived in 33% of the dimensions.
33%
Endah’s
deprivations: 73%
4. What is the MPI?
• The MPI is one implementation of the first measure of
the Alkire & Foster family, M0.
• The MPI is the product of two components:
1) Incidence ~ the percentage of people who are
disadvantaged, or the headcount ratio H.
2) Intensity of people’s deprivation ~ the average share of
dimensions in which disadvantaged people are deprived A.
MPI = H × A
The MPI: High Resolution
The MPI can be broken down in different ways:
1. By Headcount – to show how many are poor
2. By Dimension – to show how people are poor
3. By Intensity – to show who has greatest intensity
4. By Sub-group – to show how groups vary (in
headcount, intensity, and composition)
In fact, it is the MPI Plus a dashboard (a set)
of consistent subindices that unpack the
AF analysis and supply powerful analysis.
GLOBAL MPI 2013
SOME RESULTS
104 Developing Countries:
~ 29 Low Income Countries, (681M), 86%
~ 67 Middle Income Countries, (4634), 93%: ~ 41 Lower Middle Income (2433M) 98%
~ 26 Upper Middle Income (2201M) 89%
~ 8 High Income Countries (43M), of which: ~ 5 OECD (29M)
~ 3 non-OECD (13M)
Total Population: 5.4 Billion people Which is 78% of the world’s population
(population figures from 2010; data from 2002-2011).
Half of the world’s MPI
people live in South
Asia, and 29% in Sub-
Saharan Africa
MPI poor people
by region
Total Population in 104 MPI countries
Europe and Central Asia
7,5% Arab States 4,2%
Latin America and Caribbean
9,5%
East Asia and Pacific
34,6%
South Asia 29,8%
Sub-Saharan Africa 14,3%
Europe and Central Asia
0.7%
Arab States 2.12%
Latin America & Caribbean
2.2% East Asia &
Pacific 14.9%
South Asia 51.3%
Sub-Saharan Africa 28.90%
High Income
0,1%
Upper Middle Income 12,3%
Lower Middle Income 60,1%
Low Income 27,5%
Most poor people live in middle-income countries. 72% of MPI poor people live in Middle Income Countries
Total Population by
Income Category MPI Poor Population
High Income 0,8%
Upper Middle Income 41,1%
Lower Middle Income 45,4%
Low Income 12,7%
2010 Population
Data
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Comparing the Headcount Ratios of MPI Poor and $1.25/day Poor
Intensity 69.4% & More Intensity 50-69.4% Intensity 44.4-50% Intensity 33.3-44.4% $1.25 a day
MPI varies greatly within income categories
High Income
MPI varies: High and Upper Middle Income
Brazil
China
Namibia
Peru
Turkey
Czech Republic
Hungary
30%
35%
40%
45%
50%
55%
60%
65%
70%
75%
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Ave
rage
Inte
nsi
ty o
f P
ove
rty
(A)
Percentage of People Considered Poor (H)
Poorest Countries, Highest MPI
The size of the bubbles is
a proportional representation
of the total number of
MPI poor in each country
High Income
Upper-Middle
Income
Lower-Middle
Income
Low Income
MPI varies: including Lower Middle Income
Brazil
China
Namibia
Turkey
Bhutan
Cote d'Ivoire
Egypt
Ghana
Honduras
India
Indonesia
Nigeria
Pakistan
Philippines
Senegal
Zambia
Czech Republic
Hungary
30%
35%
40%
45%
50%
55%
60%
65%
70%
75%
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Ave
rage
Inte
nsi
ty o
f P
ove
rty
(A)
Percentage of People Considered Poor (H)
Poorest Countries, Highest MPI
The size of the bubbles is
a proportional representation
of the total number of
MPI poor in each country
High Income
Upper-Middle
Income
Lower-Middle
Income
Low Income
MPI varies: including Low Income
Brazil
China
Namibia
Turkey
Bhutan
Cote d'Ivoire
Egypt
Ghana
Honduras
India
Indonesia
Nigeria
Pakistan
Philippines
Senegal
Niger
Ethiopia
Burundi
Burkina Faso
Liberia
Guinea
Mozambique
Sierra Leone
DR Congo
Benin
Uganda
Madagascar
Tanzania
Bangladesh Kenya
Cambodia
Nepal
Zimbabwe
Tajikistan
Kyrgyzstan
Czech Republic
Hungary
30%
35%
40%
45%
50%
55%
60%
65%
70%
75%
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Ave
rage
Inte
nsi
ty o
f P
ove
rty
(A)
Percentage of People Considered Poor (H)
Poorest Countries, Highest MPI
The size of the bubbles is
a proportional representation
of the total number of
MPI poor in each country
High Income
Upper-Middle Income
Lower-Middle Income
Low Income
Robustness tests
Robustness tests
• Household Composition
• Changes in indicators
• Changes in deprivation cutoffs
• Changes in Poverty cutoff
• Changes in weights
Alkire and Santos 2014
Robustness of Poverty cutoff
k= 20% to 40% • 93% to 96% of pairwise comparisons are robust overall.
• 90% or more pairwise comparisons are robust in categories
of SA, SSA, AS, EAP, DHS, MICS, 10 indicators, and low
and lower-middle income countries.
• For China, 95 to 97% of the significant pairwise comparisons
obtained with the baseline MPI hold when we vary the
poverty cutoff from 33.33% to either 20% or 40%.
• These results suggest that across poverty cutoff from 20-
40%, rankings are quite stable and robust, particularly for
poorer countries and regions
Alkire and Santos 2014
Robustness across weights
Weighting each dimension 50% and the other two
at 25%, and comparing the rankings with 33%
weights, we obtain:
• High Rank Correlation (Spearman): 0.95 and above
• High Rank Correlation (Kendall): 0.83 and above
• 85% of all possible pair-wise comparisons are robust
Alkire and Santos 2014
Robustness to weights (Kendall)
Table X: Correlation coefficients between MPI using alternative weighting structures
Equal Weights
33% each
50% Education
25% Health
25% LS
50% Health
25% Education
25% LS
50% Education
25% Health
25% LS
0.889
50% Health
25% Education
25% LS
0.925
0.835
50% LS
25% Health
25% Education
0.901
0.852
0.863
Note: LS: Living Standard. In all cases 104 countries were considered. The Spearman rank
correlation coefficients are 0.95 and higher.
Alkire and Santos 2014
LEAVING NO ONE
BEHIND:
Inequality among the Poor
Inequality Among the Poor.
We’ve done inequality measures for each of the MPI2014 countries and
for each of the 780 subnational region for which we have data, to
show disparities across countries and regions.
Empirical results will be published with MPI on 16 June 2014.
The policy goal is to end poverty, not inequality among the poor.
Yet inequality measures help to visualize horizontal inequalities, and
capture the variance in deprivation scores.
Seth and Alkire 2014
27
LEAVING NO ONE
BEHIND:
Ethnic Groups
MPI over time by groups
This year we release a study of how MPI has changed over
time for 34 countries, covering 2.5 Billion people.
We analyse over 330 subnational regions of these countries,
to see where the poorest are being left behind and where
the policies are most strongly pro-poor.
We also study changes over time by ethnic groups.
Alkire, Roche and Vaz 2014.
29
In this country, the poorest ethnic group
saw no change in MPI over time.
They are being left behind.
30
In this country, the poorest ethnic group
reduced MPI the fastest.
They are catching up.
31
LEAVING NO ONE
BEHIND:
Destitution
Destitution: A subset of the poor
This year we release a study of destitutes – people who are
MPI poor but are extremely deprived – experiencing
severe malnutrition, losing 2 children, open defecation, no
one has more than 1 year of school, kids out of primary
school.
We analyse over 49 countries with this measure. We find that
a sad and high percentage of MPI poor are also destitute –
yet that countries vary a lot in eliminating destitution.
Alkire Conconi and Seth 2014
33
Deprivation cutoffs: Destitute
34
Indicator Deprivation Cutoff
Schooling No one completed at least one year of schooling (>=1)
Attendance At least one child not attending school up to the age at which they should
finish class 6
Nutrition Severe Undernourishment of any adult (BMI<17kg/m2) or any child
(-3 standard deviations from median)
Mortality 2 or more children died in the household
Electricity The household has no electricity (No change)
Sanitation There is no facility/bush, or other (open defecation)
Water The household does not have access to safe drinking water, or safe water is
more than a 45-minute walk (round trip)
Floor The household has a dirt, sand, or dung floor (No change)
Cooking fuel The household cooks with dung or wood
(coal/lignite/charcoal are now non-deprived)
Assets The household has no assets (radio, mobile phone, etc) and no car
What % of MPI poor are destitute?
35
0%
15%
30%
45%
60%
75%
90%
0.000 0.130 0.260 0.390 0.520 0.650
Sh
are
of
Dest
itu
e t
o M
PI
Po
or
(HD
/H
)
MPI Countries with similar MPI have
different % of destitutes.
Where MPI is high, a higher
share of poor are destitute.
Upper and Lower circles
have similar MPI values, but
a larger share of MPI poor
are destitute in Upper. Can
we learn from Lower?
In Sum…
The MPI is like a high resolution lens…
The MPI is like a high resolution lens…
You can zoom in
The MPI is like a high resolution lens…
You can zoom in
and see more
