Beyond MDG Dashboards: Consideration of Joint Distribution in Measuring Poverty Evidence and...
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Transcript of Beyond MDG Dashboards: Consideration of Joint Distribution in Measuring Poverty Evidence and...
Beyond MDG Dashboards: Consideration of Joint
Distribution in Measuring Poverty
Evidence and Measures of Progress in International Development
RSS 2013 International Conference, Newcastle UK
Suman SethSeptember 5, 2013
Outline
• Why is there a need to consider joint distribution and a multidimensional framework for measuring poverty
• The Multidimensional Poverty Index: A Proposal– Methodology– Illustrations
• MPI 2.0 and the post 2015 discussion
What we have: Technical• Increasing data• Improving methodologies
What we need: Policy• Make growth to be inclusive through active
policies • Go beyond income poverty (it is important but
insufficient) • Go beyond dazzlingly complex dashboards of
indicators• Understanding the joint distribution across
deprivations
Path ahead: Ethical and Political• Political critique of current metrics;
exploration • Measures in 2010 HDR sparked interest and
debate• Post-2015 requires re-thinking Data and
Measures
Why New Emphasis on Poverty Measurement?
Economic Growth is Not Always InclusiveIndicators Year India Banglade
sh Nepal
Gross National Income per Capita (in International $)
1990 860 550 510 2011 3620 1940 1260Growth (p.a.) 6.8% 5.9% 4.2%
Under-5 Mortality1990 114.2 138.8 134.62011 61.3 46.0 48.0Change -52.9 -92.8 -86.6
DPT Immunization Rate1990 70 69 432010 72 95 82Change 2 26 39
Adult Pop. with no Education
1990 51.6 55.5 65.82010 32.7 31.9 37.2Change -18.9 -23.6 -28.6
Access to Improved Sanitation (rural pop)
1990 7 34 72010 23 55 27Change 16 21 20
Source: Alkire and Seth (2013). The table is inspired by Drèze and Sen (2011), with minor additions.
Eradicating Income Poverty is not Sufficient (Global Monitoring Report
Progress Status, 2013)
Reduction in income poverty does not reduce other MDG
deprivations automatically. Source: World Bank Data
0
16
32
48
64
80
96
112
128
144
Extreme Poverty Improved Water Primary Completion
Undernourishment Sanitation Infant Mortality
Num
ber o
f Cou
ntries
Target Met Sufficient Progress Insufficient Progress
Moderately Off Target Seriously Off Target Insufficient Data
MDG Dashboards Fail to Reflect Joint Distribution of Deprivations
MDG1
MDG2
MDG3
MDG4
1 0 0 0
0 1 0 0
0 0 1 0
0 0 0 1
An example with four persons (deprived=1, non-deprived=0)
MDG1
MDG2
MDG3
MDG4
0 0 0 0
0 0 0 0
0 0 0 0
1 1 1 1Case 1 Case 2
In both cases, 25% deprived in each MDG indicator
BUT, in Case 2, one person is severely deprived
Motivation for a Multidimensional Approach
• “MDGs did not focus enough on reaching the very poorest” - High-Level Panel on the Post-2015 Development Agenda (2013)– Should be able to distinguish poorest from the less
poor. How?– Deprived in many dimensions simultaneously?
• “Acceleration in one goal often speeds up progress in others; to meet MDGs strategically we need to see them together” - What Will It Take to Achieve the Millennium Development Goals? (2010)– Emphasis on joint distribution and synergies
• “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 (2009)– One summary index is more powerful in drawing
policy attention
Value-added of a Multidimensional Approach
What can a meaningful multidimensional measure do?
• Provide an overview of multiple indicators at-a-glance
• Show progress quickly and directly (Monitoring/Evaluation)
• Inform planning and policy design• Target poor people and communities• Reflect people’s own understandings
(Flexible)• High Resolution
– zoom in for details by regions, groups, or dimensions
The Multidimensional
Poverty Index
Alkire Foster Methodology
1. Select dimensions, indicators and weights (Flexible)
2. Set deprivation cutoffs for each indicator (Flexible)
3. Apply to indicators for each person from same survey
4. Set a poverty cutoff to identify who is poor (Flexible)
5. Calculate Adjusted Headcount Ratio (M0) – for ordinal data (such as MDG indicators),
– Reflects incidence, intensity
Sabina Alkire and James Foster, J. of Public Economics 2011
Multidimensional Poverty Index (MPI)
An adaptation of Alkire and Foster (2011) which can deal with the binary or categorical data and was introduced by Alkire and Santos (2010) and UNDP (2010)
A person is identified as poor using a counting approach in two steps1) A person is identified as deprived or not in each dimension using a set of deprivation cutoff2) Based on the deprivation profile, a person is identified as poor or not
Terms: deprived and poor are not synonymous
How is MPI Computed?
The MPI uses the Adjusted Headcount Ratio:
H: The percent of people identified as poor, it shows the incidence of multidimensional poverty
A: The average proportion of deprivations people suffer at the same time; it shows the intensity of people’s poverty
Alkire, Roche, Santos, and Seth (2013)
.
Formula: MPI = H × A
One implementation of the Global MPI (104 countries): Dimensions, Weights &
Indicators
3 Dimensions
10 Indicators
Years of Schooling
(1/ 6)
School Attendance
(1/ 6)
Education (1/ 3)
Child Mortality
(1/ 6)
Nutrition
(1/ 6)
Health (1/ 3) Standard of Living (1/ 3)
Coo
king
Fue
l
Sani
tation
Wat
er
Ele
ctrici
ty
Flo
or
Ass
et O
wne
rshi
p
(1/ 18 Each)
Identify Who is PoorA person is multidimensionally poor
if she is deprived in 1/3 of the weighted indicators.
(censor the deprivations of the non-poor)
33.3%
39%
Properties Useful for Policy
15
The MPI
• Can be broken down into incidence (H) and the intensity (A)
• Is decomposable across population subgroups– Overall poverty is population-share weighted average of
subgroup poverty
• Overall poverty can be broken down by dimensions to understand their contribution
What Kind of Policy
Analysis Can be Done?
Country A:
Country B:
Policy Relevance: Incidence vs. Intensity
50.00
55.00
60.00
65.00
70.00
75.00
50.00
51.00
52.00
53.00
54.00
55.00
56.00
57.00
58.00
59.00
60.00
0.30
0.31
0.32
0.33
0.34
0.35
0.36
0.37
0.38
0.39
0.40
0.41
0.42
Before
MultidimensionalHeadcount
(H)
Intensity of Deprivations
(A)
Multidimensional Poverty Index(MPI = H * A)
50.00
55.00
60.00
65.00
70.00
75.00
50.00
51.00
52.00
53.00
54.00
55.00
56.00
57.00
58.00
59.00
60.00
0.30
0.31
0.32
0.33
0.34
0.35
0.36
0.37
0.38
0.39
0.40
0.41
0.42
Before
MultidimensionalHeadcount
(H)
Intensityof Deprivations
(A)
Multidimensional Poverty Index(MPI = H * A)
50.00
55.00
60.00
65.00
70.00
75.00
50.00
51.00
52.00
53.00
54.00
55.00
56.00
57.00
58.00
59.00
60.00
0.30
0.31
0.32
0.33
0.34
0.35
0.36
0.37
0.38
0.39
0.40
0.41
0.42
After
Before
MultidimensionalHeadcount
(H)
Intensity of Deprivations
(A)
Multidimensional Poverty Index(MPI = H * A)
50.00
55.00
60.00
65.00
70.00
75.00
50.00
51.00
52.00
53.00
54.00
55.00
56.00
57.00
58.00
59.00
60.00
0.30
0.31
0.32
0.33
0.34
0.35
0.36
0.37
0.38
0.39
0.40
0.41
0.42
After
Before
MultidimensionalHeadcount
(H)
Intensity of Deprivations
(A)
Multidimensional Poverty Index(MPI = H * A)
Policy oriented to the poorest of the poorPoverty reduction policy (without inequaliy focus)
Source: Roche (2013)Country B reduced the intensity of
deprivation among the poor more. The final index reflects this.
Policy Relevance: Incidence vs. Intensity
Bangladesh 2004MPI=0.365
Bangladesh 2007MPI=0.289
Nepal 2006MPI=0.350
Nepal 2011MPI=0.217
48%
49%
50%
51%
52%
53%
54%
55%
40% 45% 50% 55% 60% 65% 70% 75% 80%
Inte
nsity
(A)
Incidence (H)
Very similar annual reduction in MPI
Alkire and Roche (2013)
India (1999-2006): Uneven Reduction in MPI across Population Subgroups
19-0.110 -0.090 -0.070 -0.050 -0.030 -0.010
Urban (*) [0.116]
Rural (*) [0.368]
General (*) [0.229]
OBC (*) [0.301]
SC (*) [0.378]
ST (*) [0.458]
Sikh (*) [0.115]
Christian () [0.196]
Hindu (*) [0.306]
Muslim () [0.32]
Absolute Change (99-06) in MPI-I
Stat
es (Si
gnifi
canc
e) [M
PI-
I in
199
9]
Religion
Caste
Slower progress for Scheduled Tribes (ST)
and Muslims
Alkire and Seth (2013)
Reduction in MPI across Indian States
20We combined Bihar and Jharkhand,
Madhya Pradesh and Chhattishgarh, and Uttar Pradesh and Uttarakhand
Stronger reductions
in Southern
states
Slower reductions in initially
poorer states
Comparison with Change in Income Poverty Headcount Ratio
(p.a.)
21
-3.50%-3.00%-2.50%-2.00%-1.50%-1.00%-0.50%0.00%0.50%
Change in MD Poverty (k = 1/3) Change in PCE Poverty
Dimensional Breakdown Nationally?
22
-12.0%
-10.0%
-8.0%
-6.0%
-4.0%
-2.0%
0.0%
Abs
olut
e Cha
nge
in C
H R
atio
Indicator (Statistical Significance) [1999 CH Ratio]
Dimensional Breakdown in Six States?
23
Distribution of Intensities among the Poor
Madagascar (2009)MPI = 0.357
H = 67%
Rwanda (2010)MPI = 0.350
H = 69%
The MPI 2.0 and the Post-2015 discussion
0%
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30%
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90%
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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 vs. $1.25-a-day
Height of the bar: MPI Headcount RatioHeight at ‘•’ : $1.25-a-day Headcount Ratio
Measuring the Post-2015 MDGs
What we found from Global MPI - $1.25/poverty and MPI do not move
together- MPI reduction is often faster than
$1.25/day poverty- Political incentives from MPI are more
direct
-5
-4
-3
-2
-1
0
1
2
3
Ann
ualiz
ed abs
olut
e va
riat
ion
MPI Incidenece $1.25 incidence
Measuring the Post-2015 MDGs
28
Create an MPI 2.0 in post 2015 MDGs (Alkire and Sumner 2013)
- To complement $1.25/day poverty- To reflect interconnections between
deprivations- To track ‘key’ goals using data from same
survey- To celebrate success
Note: MPI is not a Composite Index like the HDI or the HPI
Multidimensional Poverty Index - MPI
• Shows joint distribution of deprivations (overlaps)
• Changes over time: informative by region, social group, indicator (inequality)
• National MPIs: tailored to context, priorities
• MPI 2.0: comparable across countries• National MPI and Global MPI 2.0 can be
reported like national income poverty and $1.25/day
• Data needs: feasible – use 39 of 625 questions in DHS
Published: in annual Human Development Report of UNDP
Method: Alkire and Foster 2011 J Public Economics Examples: see www.ophi.org.uk
The Global Multidimensional Poverty Peer Network (Global
MPPN)
Angola, Bhutan, Brazil, Chile, China, Colombia, ECLAC, Ecuador, El Salvador, Dominican Republic, Germany, India, Iraq, Malaysia, Mexico, Morocco, Mozambique, Nigeria, OECD, the Organization of Caribbean States, OPHI, Peru, Philippines, SADC, and Vietnam
Joined by: President Juan Manuel Santos of Colombia
Nobel Laureate Amartya Sen
Launched: June 6, 2013
The Global Multidimensional Poverty Peer Network (Global
MPPN)• On 24 September, 2013: event in the United
Nations N Lawn Conf room 7• Attendees: Ministers from Philippines, Nigeria,
Mexico, Colombia, El Salvador, the Secretary of State of Germany, President of Colombia, Head of DAC at OECD, and others
• Subject: Speak on an MPI 2.0– The Network has decided to advocate a MPI 2.0 as part
of the post-2015 process as a measure of income poverty is not enough, and nor is a dashboard.