Transcript UNDP WASHINGTON ROUNDTABLE “Rethinking Human …

Multidimensional Poverty Index
Milorad Kovacevic
Human Development Report Office
Multidimensional Poverty Index
• The dimensions of poverty go far beyond inadequate
income—to
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poor health and nutrition,
low education and skills,
inadequate livelihoods,
bad housing conditions,
social exclusion and lack of participation
Workshop on HD Approach and Measurement for the GCC States, Doha, 9-11 May, 2011
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Multidimensional Poverty Index
• HDR 2010, in collaboration with Oxford University’s Poverty
and Human Development Initiative, introduced a new
Multidimensional Poverty Index (MPI)
– only for 104 developing countries (due to lack of comparable data)
– The 104 countries include 92% of the population in 98 developing
countries
– in 2011 – at most 120
• The MPI is an index of acute multidimensional poverty and is
meant to complement monetary based measures
Workshop on HD Approach and Measurement for the GCC States, Doha, 9-11 May, 2011
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Dimensions of the Multidimensional Poverty Index
• MPI identifies overlapping deprivations at the household level
• Composed of 10 indicators corresponding to the same 3 dimensions
as the HDI: Health, Education and Living Standards
- Each dimension is equally weighted
- Each indicator has equal weight within its dimension
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Dimensions of the Multidimensional Poverty Index
• The MPI shows the average number of poor people and
the average number of deprivations with which poor
households contend
• A household is multidimensionally poor if it is deprived in
at least 30% of the weighted indicators (2 to 6 indicators)
• The MPI reveals a different pattern of poverty than
income poverty
– it illuminates a different set of deprivations
Workshop on HD Approach and Measurement for the GCC States, Doha, 9-11 May, 2011
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Data Sources
Data: Household Surveys
‐ Demographic & Health Surveys (DHS – 48 countries)
‐ Multiple Indicator Cluster Surveys (MICS – 35 countries)
‐ World Health Survey (WHS – 19 countries)
‐ Additionally used 2 special surveys covering Mexico and
urban Argentina
‐ WHS 2003 for United Arab Emirates
‐MPI is deeply affected by lack of comparable data
Workshop on HD Approach and Measurement for the GCC States, Doha, 9-11 May, 2011
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Methodology
• MPI corresponds to the first measure of the Alkire & Foster
(2007) family of multidimensional poverty measures, called M0
• It is constructed using the AF method:
H is the percentage of people who are poor - shows the
incidence of multidimensional poverty: (H=q/n)
A is the average proportion of weighted deprivations people
suffer at the same time - shows the intensity of
𝒒
multidimensional poverty: 𝑨 = [ 𝟏 𝑪𝒊 /𝟏𝟎]/𝒒
Workshop on HD Approach and Measurement for the GCC States, Doha, 9-11 May, 2011
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Methodology
• Two step procedure applied to identify who is
multidimensionally poor, uses dual cutoff method:
1. Identify all individuals deprived in any dimension
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Within dimension cutoff
2. Identify who is multidimensionally poor
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Cross dimensional cutoff
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Deprived in at least 30% of the weighted indicators
Workshop on HD Approach and Measurement for the GCC States, Doha, 9-11 May, 2011
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Measurement Indicators and Cutoffs
Health (each indicator weighted equally at 1/6 )
- Child Mortality: If any child has died in the family
- Malnutrition: If any interviewed adult in the family
has low Body Mass Index; if any child is more than 2
standard deviations below the reference normal weight
for age, (WHO standards)
[WHS has male data but no child data; MICS has child
data but no adult data]
Workshop on HD Approach and Measurement for the GCC States, Doha, 9-11 May, 2011
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Measurement Indicators and Cutoffs
Education (each indicator weighted equally at 1/6 )
- Years of Schooling: if no person in the household
has completed 5 years of schooling
- Child Enrolment: if any school-aged child is out of
school, where school-aged is an 8 year period from
the national starting age
Workshop on HD Approach and
Measurement for the GCC States, Doha, 911 May, 2011
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Measurement Indicators and Cutoffs
Standard of Living (each indicator weighted equally at 1/18)
- Electricity (no electricity is poor)
- Drinking water (MDG definitions)
- Sanitation (MDG definitions + not shared)
- Flooring (dirt/sand/dung are poor)
- Cooking Fuel (wood/charcoal/dung are poor)
- Assets (poor if do not own a car/truck and do not
own more than one of these: radio, tv, telephone,
bike, motorbike, or refrigerator)
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Measurement for the GCC States, Doha, 911 May, 2011
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Illustration
• Ali’s household is deprived in nutrition and child enrolment. Is
Ali’s household multidimensionally poor?
10(1/6)+10(1/6) = 3.34 (> 3) Yes
• Maira’s household is deprived in electricity, water, sanitation,
and has a dirt floor. Is Ali’s household multidimensionally
poor?
10(1/18)+10(1/18) + 10(1/18)+10(1/18) = 2.20 (<3) No
• Tom’s household is deprived in years schooling, sanitation,
assets, and cooking fuel. Is Tom’s household
multidimensionally poor?
10(1/6)+ 10(1/18)+10(1/18) + 10(1/18)= 3.33 (>3) Yes
Workshop on HD Approach and
Measurement for the GCC States, Doha, 911 May, 2011
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Missing Dimensions
Missing dimensions include:
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Work
Empowerment
Safety from Violence (crime, conflict)
Political Freedom
Relationships (social capital, inclusion, dignity)
(Cultural/Spiritual/Subjective Well-being)
Data are not available to incorporate any of these into the MPI for 100+ countries
Workshop on HD Approach and
Measurement for the GCC States, Doha, 911 May, 2011
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Patterns of Multidimensional Poverty
• 32% the population in 104 developing countries, about
1.75 billion people, are MPI poor
– The 104 countries includes 92% of the population in 98
developing countries
• Regional rates vary from 3% in Europe and Central Asia to
65% in sub-Saharan Africa
• South Asia is home to the largest number of MPI poor,
followed by sub-Saharan Africa
Workshop on HD Approach and
Measurement for the GCC States, Doha, 911 May, 2011
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Patterns of Multidimensional Poverty
Half the world’s MPI poor live in South Asia, but the intensity of
MPI poor is highest in sub-Saharan Africa
Eight Indian states are
home to 421 million
MPI poor people more than the 410
million poor living in
the 26 poorest African
countries combined
Workshop on HD Approach and
Measurement for the GCC States, Doha, 911 May, 2011
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Patterns of Multidimensional Poverty
Countries with higher multidimensional poverty headcounts tend to
have more deprivations
Workshop on HD Approach and Measurement for the GCC States, Doha, 9-11 May, 2011
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Patterns of Multidimensional Poverty
South Asia has highest incidence of multidimensional poverty in
the world-ranging from 38.7% in Sri Lanka to 54.1% in Nepal
Sub-Saharan Africa has
significant variation–
ranging from 3% in South
Africa to 93% in Niger
East Asia and the Pacific
has relatively low rates of
multidimensional
poverty– but over half of
Cambodians are MPI poor
MPI poor population
(millions)
1000
800
600
400
200
0
East Asia and the
Pacific
Sub-Saharan
Africa
Workshop on HD Approach and
Measurement for the GCC States, Doha, 911 May, 2011
South Asia
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Patterns of Multidimensional Poverty
Europe and Central Asia’s incidence of Multidimensional poverty is
lowest of the developing country regions – close to zero in several
countries, while Tajikistan is the highest with 17%
MPI poor population
(millions)
60
50
40
30
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10
0
Central and
Eastern Europe
and the CIS
Arab States
Latin America
and Caribbean
Arab states MPI values are
generally below 7%, but
as high as 52% in Yemen
and 81% in Somalia
Latin America and
Caribbean MPI values
range from 2% (Uruguay)
to 57% (Haiti)
Workshop on HD Approach and
Measurement for the GCC States, Doha, 911 May, 2011
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Patterns of Multidimensional Poverty
United Arab Emirates (WHS 2003)
MPI
H
0.002
A
@ Risk At least one severe deprivation
(1)
Education Health
Living
Standard
0.6% 35.3% 2.0%
0.6%
5.4%
0.0%
(1) Suffering in 20% of weighted indicators
Workshop on HD Approach and
Measurement for the GCC States, Doha, 911 May, 2011
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Patterns of Multidimensional Poverty
The MPI highlights significant variations:
Within-countries
– Nairobi is similar to
Dominican Republic, rural
northeast is worse than
Niger
Among ethnicities,
religions and castes
– MPI headcount in Kenya
ranged from 29% for the
Embu to 96% for the
Turkana and Masai
Workshop on HD Approach and
Measurement for the GCC States, Doha, 911 May, 2011
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Changes Over Time
MPI at two points in time in Bangladesh, Ethiopia and Ghana
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0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
Bangladesh
Ethiopia
A - 2008
A - 2003
H - 2008
H - 2003
MPI - 2008
MPI - 2003
A - 2005
A - 2000
H- 2005
H - 2000
MPI - 2005
MPI - 2000
A - 2007
A - 2004
H - 2007
H - 2004
MPI - 2007
MPI - 2004
0
Ghana
Workshop on HD Approach and Measurement for the GCC States, Doha, 9-11 May, 2011
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Relevance to Country Level Work
• Can be adapted using indicators and weights that
make sense for the region or the country
• Can be adopted for national poverty eradication
programs
• It can be used to study changes over time
Workshop on HD Approach and
Measurement for the GCC States, Doha, 911 May, 2011
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Policy Applications
• Allocate resources effectively
• Target those with the greatest intensity of poverty
• Identify interconnections among deprivations
• Helps in addressing MDGs strategically
• Design policy
• Show which deprivations are most common in different
groups so that policies can be tailored to particular needs
• Show impacts
• Reflects results of policy interventions quickly
Workshop on HD Approach and
Measurement for the GCC States, Doha, 911 May, 2011
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Limitations of the Multidimensional Poverty Index
• Drawbacks mainly due to data constraints
• Indicators include inputs, outputs and one stock indicator
because flow data unavailable in some instances
• Health data relatively weak
• Judgments necessary where data is missing
• Intrahousehold inequality is not captured
• Does not measure inequality amongst the poor
• Cross-country comparability limited
Workshop on HD Approach and
Measurement for the GCC States, Doha, 911 May, 2011
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Criticism of the Multidimensional Poverty Index
Martin Ravallion’s recent criticism:
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Arbitrariness of components and weights.
Income based measures aggregate consumption across a large number of goods.
Limited information about trade-offs.
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Uncertainty about robustness of resulting rankings.
Responses
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Giving income weight of one is no less arbitrary.
Prices reflect scarcities and current distribution.
Indices can and should enable reasoned public debate about the implicit weights
and trade-offs.
• Contention of HDR approach is that since education and health are public
goods, current prices underestimate their social value.
MPI background research exhaustively evaluated robustness
Workshop on HD Approach and
Measurement for the GCC States, Doha, 911 May, 2011
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