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OPHIOxford Poverty & Human Development InitiativeDepartment of International DevelopmentQueen Elizabeth House, University of Oxford www.ophi.org.uk
Multidimensional poverty for monitoring development
progress
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Outline–Multidimensional Poverty
Measurement–AF Methodology in breve– Illustration of metrics: MPI
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Multidimensional Poverty Measurement
Why the surge in interest?
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Motivation
“Human lives are battered and diminished in all kinds of different ways.”
Amartya Sen
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Relevant Data
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• “We are almost blind when the metrics on which action is based are ill-designed or when they are not well understood. For many purposes, we need better metrics.”
Political Demand
Stiglitz Sen Fitoussi: Commission on the Measurement of Economic Performance and
Social Progress
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Policy Demand:Target the poorest: “Achieving the MDGs
will require increased attention to those most vulnerable.” MDG Report 2010
Address interconnections efficiently: “Acceleration in one goal often speeds up progress in others” Roadmap towards the Implementation of the MDGs 2010
Show changes directly & quickly: Monitoring & incentives
Plan and Evaluate PolicyTo identify & use the most effective kind and sequencing of policies.
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AF Methodology:Since 2000, a surge in new
methodologies to measure multidimensional poverty.
AF method is based on the FGT, counting and basic needs traditions, & can use ordinal data.
It can also be decomposed into policy relevant and intuitive subindices.
The technology is flexible: you choose the dimensions, indicators, weights, & cutoffs.
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Achievement Matrix
z = ( 13 12 3 1 ) Cutoffs
Dimensions
Persons
131120
01105.12
0572.15
14141.13
Y
AF in breve: Achievement Matrix
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AF in breve: Deprivation Matrix
0=non-deprived1=deprived ‘count’
g0
0 0 0 0
0 1 0 1
1 1 1 1
0 1 0 0
0
2
4
1
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AF in breve: Dual Cut-off Identification
z = deprivation cutoffk = poverty cutoff
Deprivation Matrix Censored Deprivation Matrix, k=2
g0
0 0 0 0
0 1 0 1
1 1 1 1
0 1 0 0
0
2
4
1
g0(k)
0 0 0 0
0 1 0 1
1 1 1 1
0 0 0 0
0
2
4
0
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AF in breve: Aggregation
Deprivation Matrix Censored Deprivation Matrix, k=2
g0
0 0 0 0
0 1 0 1
1 1 1 1
0 1 0 0
0
2
4
1
g0(k)
0 0 0 0
0 1 0 1
1 1 1 1
0 0 0 0
0
2
4
0
M0 is the mean of the matrixThis matrix also generates H and ACensored Headcounts for each dimensionPercent Contributions for each dimensionAnd all of these for subgroups
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– An international measure of acute poverty covering 104 developing countries in UNDP’s 2010 HDR.
– Complements income poverty measures by showing direct deprivations and their joint distribution
– A high resolution lens, using AF methodology
– Constrained by data availability
– Aims to encourage the development of better national and regional measures of multidimensional poverty
Multidimensional Poverty Index
(MPI) acute poverty in developing countries
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1. Data for the MPI: Surveys
Demographic & Health Surveys (DHS - 48) Multiple Indicator Cluster Surveys (MICS - 35) World Health Survey (WHS – 19)
Additionally we used 2 special surveys covering Mexico and urban Argentina.
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2. Dimensions Indicators & Weights of MPI
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2. Data constraints
The MPI is deeply affected by the lack of comparable data. key indicators are not collected (stock, quality) • data for some dimensions are missing• missing values lead to sample size
reduction/biases • respondent(s) vary; individual level data is
sparse• surveys updated every 3-5 years, and in
different years • data exclude certain populations (elders,
institutionalized)• income/consumption surveys lack MPI
health indicators.
These can be addressed at a national level for national measures.
“Improving data gathering and its quality in all countries should be a central focus ...”
Bourguignon et al. 2008 page 6
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3. Methodology: Alkire and Foster - Identification
A person is multidimensionally poor if they are deprived in 33% of the dimensions.
33%
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3. Methodology Alkire and Foster: Aggregation
• We construct the MPI using the AF method:
• H is the percentage of people who are 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.
Formula: MPI = M0 = H × A
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3. Methodology: MPI g0(k) matrixAdjusted Headcount Ratio = M0 = HA = .442
k=3.333 (have MPI for all k values)
Indicators c(k) c(k)/d
H = headcount = ¾ = 75% A = average deprivation share among poor = .59
= 59% HA = MPI = 0.442
0
0 0 0 0 0 0 0 0 0 0
1.67 1.67 1.67 1.67 .55 0 0 0 0 .55
0 1.67 0 1.67 .55 0 .55 .55 .55 0
0 0 0 1.67 .55 .55 .55 0 .55 .
)
5
(
5
g k
0
7.76
5.53
4.42
0
.776
.553
.442
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Example: Tabitha
OPHI has done ground reality
checks in Kenya, Madagascar, Indonesia,
Bhutan, and India.
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What’s new?Intensity:
The MPI and its related indices reflects each
household’s deprivation profile.
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Stéphanie’s Intensity
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Adil’s Intensity
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Jiyem’s Intensity
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Others
The MPI helps showWho they are (Headcount) & how they are poor (Intensity)
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4. 2010 Results:
These results are for 104 developing countries, selected because they have DHS, MICS or WHS data since 2000. Special surveys were used for Mexico and urban Argentina.
They cover 78.5% of the world population (2007).
In 2011’s HDR this will be increased to 109 countries, and updated data are available for over 20 countries.
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The MPI headcounts fall between $1.25 and $2.00/day,
but are quite different.
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Arab States298.3
6%
Central and Eastern Europe and the
Commonwealth of Independent States
(CIS)4008%
East Asia and the Pacific1867.7
35%
Latin America and Caribbean
490.89%
South Asia1543.9
29%
Sub-Saharan Africa712.313%
Regional Distribution of the World's Total Population 2007 (millions)
Most poor people in the world by MPI live in
South Asia, followed by Sub-Saharan Africa.
Poor People
Total Population
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Intensity tends to be highest with high Incidence
Nepal
30%
35%
40%
45%
50%
55%
60%
65%
70%
75%
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Ave
rage
Bre
adth
of P
over
ty (A
)
Percentage of People Considered Poor (H)
MPI = A x H Poorest Countries,Highest MPI
India
Pakistan
BangladeshIndonesia
China
Nigeria
Low incoHigh Income
Upper-MiddleIncome
Lower-Middle Income
Low Income
Niger
Ethiopia
DR CongoBrazil
Jordan
Vietnam
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Decompose by region & ethnicity
• In Kerala India 16% of the population is MPI poor; in Bihar it is 81%.
India MPIKerala
Bihar
Keral
a
Punja
b
Tamil
Nadu
Mah
aras
htra
Gujar
at
Andhr
a Pra
desh
INDIA
Wes
t Ben
gal
Rajas
than
Chhat
tisga
rh
Jhar
khan
d0
0.1
0.2
0.3
0.4
0.5
0.6
MPI for Indian States/regions
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Madhya Pradesh, India
DR Congo
Population 2007
69.97M 62.50M
MPI 0.39 0.39
MPI Headcount
69.5% 73.2%
Avg Intensity
56% 53.7%
Comparisons: Headcount plus
Intensity & Composition
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Composition of Poverty: key for policy (equal MPIs)
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DRC: Larger Std of Living Deprivations
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Madhya Pradesh: Larger Malnutrition
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IndiaMPI = 0.296 A = 53.5%
Cameroon0.29954.7%
Kenya0.30250%
Intensity – who is the poorest of all?
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Pathways to Poverty Reduction
-60%
-50%
-40%
-30%
-20%
-10%
0%
10%
Bangladesh Ethiopia Ghana
Percent Variation in H (Δ%H) Percent Variation in A (Δ%A)
Interaction term (Δ%H* Δ%A)
Ghana and Bangladesh reduced H relatively more than A, Ethiopia the other
way round.
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Bangladesh improved child enrolment, Ethiopia nutrition and water, Ghana
many at the same time.
-70%
-60%
-50%
-40%
-30%
-20%
-10%
0%
Bangladesh Ethiopia Ghana
Per
cent
Var
iatio
n in
eac
h de
privat
ion
of th
e po
or
Assets
Cooking Fuel
Floor
Water
Sanitation
Electricity
Nutrition
Mortality
Child Enrolment
Schooling
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0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
Point estimate
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0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
Point estimate
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0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
Point estimate
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Time series show: Reduction in HeadcountReduction in IntensityChanges in each indicator’s censored headcountsChanges in percent contributions of each indicator
(Composition of poverty)
Time series can be used to:Understand how poverty evolves …
across time, regions, and dimensions. Evaluate policy (if natural experiments found)Observe shocks (positive or negative)Observe patterns (interconnections)
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Media Coverage of the 2010 MPI
The Report was covered in over 60 countries, e.g. in:
• TIME Magazine• The New York Times• The Wall Street Journal• BBC• The Economist• The Guardian • The Financial Times• The Huffington Post• Foreign Policy • The Hindu• Christian Science Monitor• The Globe and Mail• The Times of India
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Applications and Experiments Bhutan – Gross National Happiness index
released 2008 México – National multidimensional poverty
index 2009 Colombia: integrated into the National Plan in
2011. Chile: presentations and course (2 weeks);
under construction Iraq, Venezuela, Malaysia: presentations; trial
measures Bhutan: course taught (3 days) and trial
measures constructed Thailand: course taught (3 days) Egypt: course will be taught (6 days) El Salvador: course will be taught; trial
measures constructed EU: trial measures constructed using EU-SILC
data
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www.coneval.gob.mx
Poverty Measurement Methodology
December, 2009
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Degree of social cohesion
Territorial
What are the main features of the new methodology?
Social RightsDeprivations
Population
Wellb
ein
g
Incom
e
Current income per capita 50%
Social : 50%
• Education
•Health
•Social Security
•Housing
• Basic services
•Nutrition
03 2 1456
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Social RightsDeprivations
Poverty Identification
EWL
With deprivations
EXTREME Multidimensional
Poverty
03
Moderate MultidimensionalPoverty
Vulnerable people by social
deprivations
Vulnerable people
by income
5 24 16
Ideal Situatio
n
MWL
$1,921.7 U$1,202.8 R $874.6 U
$613.8 R
Without
Deprivations
MULTIDIMENSIONALLY POOR
Economic wellbeing line
Minimum wellbeing line
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MODERATE POVERTY 33.7%
36.0 million 2.3 Deprivation
Social RightsDeprivations
Wellb
ein
gIn
com
e
Vulnerable people by
income
Vulnerable by social
deprivations
Total Population 2008
18.3%19.5 million
33.0%35.2 million2.0 Deprivation average
03 2 1456
EXTREME POVERTY
averageaverage
10.5% 11.2 million 3.9 Deprivation
4.5%4.8 million
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MODERATE POVERTY 36.5 %
2.5 millions 3.1 Deprivation
Social RightsDeprivations
Wellb
ein
gIn
com
e
Vulnerable people by
income
Vulnerable people by
social deprivation
s
Indigenous population 2008
1.2%.1 millions
20.0 %1.4 millions2.8 Deprivation average
03 2 1456
EXTREME POVERTY
averageaverage
39.2 % 2.7 millions 4.2 Deprivation
3.1%0.21 millions
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European studies call for more panel research on multidimensional
poverty dynamics.
Source: Whelan Layte Maitre 2004 Understanding the Mismatch between Income Poverty & Deprivation
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With Panel data we can identify different types of
poor people
1. Chronic poor – across many time periods
2. Churning going in and out of poverty
3. Falling into poverty4. Moving out of poverty.
With data from two periods we generate
transition matrices showing the probability of entry and exit for H and A. Apablaza & Yalonetzky
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Panel data enables new analyses about chronicity and
poverty transitions: 1. How do the four groups differ – either
demographically or in the structure of their poverty?
2. Poverty traps? What is the composition of poverty for the chronic poor? Are any dimensions always deprived?
3. Does the composition of poverty for chronic poor change? Does chronic poverty decrease over time?
4. Policy sequences: what chains do they catalyze? Which sequence of policies has highest impact?
5. How does poverty evolve across different ages? For different racial groups and household types?