Transcript Reducing inequalities and poverty: Insights from Multidimensional Measurement Sabina Alkire 16 October 2012, 4th OECD Forum, New Delhi.

Reducing inequalities and poverty:
Insights from Multidimensional Measurement
Sabina Alkire
16 October 2012, 4th OECD Forum, New Delhi
Motivation
Measurement: usually income or consumption data.
Trends: reflect trends in nutrition, services, education?
No: direct and lagged relationships are more complex
Hence additional indicators required to study change.
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Why Multidimensional Measures?
Unidimensional measures such as MDGs are essential:
consumption poverty, primary school attendance, malnutrition,
immunization, housing, drinking water, etc.
Value-added of multidimensional measures
1) joint distribution of deprivations (what one person experiences)
a) focus on poorest of the poor
b) address interconnected deprivations efficiently
2) signal trade-offs explicitly: open to scrutiny
3) provide an overview plus an associated consistent dashboard
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Why not?
Won’t an ‘overview’ index lose vital detail and information?
Aren’t weights contentious and problematic?
How to contextualise the measure?
4
Why not?
Won’t an ‘overview’ index lose vital detail and information?
AF methodology: can be broken down by dimension, group.
Aren’t weights contentious and problematic?
How to contextualise the measure?
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Why not?
Won’t an ‘overview’ index lose vital detail and information?
AF methodology: can be broken down by dimension, group.
Aren’t weights contentious and problematic?
Weights are set anyway: budgets, policies, human resources.
Sen: the need to set weights is no embarrassment
Measures should be made robust to a range of plausible weights
How to contextualise the measure?
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Why not?
Won’t an ‘overview’ index lose vital detail and information?
AF methodology: can be broken down by dimension, group.
Aren’t weights contentious and problematic?
Weights are set anyway: budgets, policies, human resources.
Sen: the need to set weights is no embarrassment
Measures should be made robust to a range of plausible weights
How to contextualise the measure?
The dimensions, cutoffs and weights can be tailor-made.
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Multidimensional Poverty Index (MPI)
The MPI implements an Alkire and Foster (2011) M0 measure that
can use ordinal data. It was introduced by Alkire and Santos
(2010) and UNDP (2010) for 100+ countries
A person is identified as poor in two steps:
1)
A person is identified as deprived or not in 10 indicators
2)
A person is identified as poor if
their deprivation score >33%
How is MPI Computed?
The MPI uses the Adjusted Headcount Ratio M0:
Formula: MPI = H × A
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.
.
Useful Properties
Subgroup Consistency and Decomposability
Enables the measure to be broken down by regions or social
groups.
Dimensional Breakdown
Means that the measure can be immediately broken down into
its component indicators. - Essential for policy
Dimensional Monotonicity
Gives incentives
a) to reduce the headcount and
b) the intensity of poverty among
the poor.
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Changes in the Global MPI
from 2011 MPI Update
Alkire, Roche, Seth 2011
Multidimensional Poverty Index (MPI)
Changes over time in MPI for 10 countries
• MPI fell for all 10 countries
• Survey intervals: 3 to 6 years.
How and How much?
Ghana, Nigeria, and Ethiopia
Let us Take a Step Back in Time
Ethiopia
2000
Nigeria
2003
Ghana
2003
Ethiopia: 2000-2005 (Reduced A more than H)
Ethiopia
2000
Nigeria
Nigeria
2003
2008
Ghana
2003
Ghana
2008
Ethiopia
2005
Nigeria 2003-2008 (Reduced H more than A)
Ethiopia
2000
Nigeria
Nigeria
2003
2008
Ghana
2003
Ghana
2008
Ethiopia
2005
Ghana 2003-2008 (Reduced A and H Uniformly)
Ethiopia
2000
Nigeria
Nigeria
2003
2008
Ghana
2003
Ghana
2008
Ethiopia
2005
Pathways to Poverty Reduction
Assets
Annualized Absolute Change
in the Percentage Who is Poor and Deprived in...
0
Cooking Fuel
-1
Flooring
Safe Drinking
Water
Improved
Sanitation
Electricity
-2
-3
Nutrition
-4
Child Mortality
-5
School
Attendance
Years of
Schooling
-6
Ghana
Nigeria
Ethiopia
Performance of Sub-national
Regions
Ethiopia’s Regional Changes Over Time
Harari
Addis Ababa
Nigeria’s Regional Changes Over Time
North Central
South South
Looking Inside the Regions of Nigeria…
Annualized Absolute Change in the
Percentage Who is Poor and Deprived in...
3.0
2.0
Assets
1.0
Cooking Fuel
0.0
Flooring
-1.0
Safe Drinking
Water
Improved
Sanitation
Electricity
-2.0
-3.0
-4.0
Nutrition
-5.0
Child Mortality
-6.0
-7.0
-8.0
North
Central
North
East
North
West
South
East
South
South
South
West
Years of
Schooling
School
Attendance
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
Nigeria: Indicator Standard Errors
An Indian Example
Almost MPI 1999-2006
Alkire and Seth In Progress
India: Almost-MPI over time
We use two rounds of National Family Health Surveys for trend
analysis
NFHS-2 conducted in 1998-99
NFHS-3 conducted in 2005-06
Less information is available in the NFHS-2 dataset; so we have
generated two strictly comparable measures, with small changes
in mortality, nutrition, and housing.
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How did MPI decrease for India?
1999
2006
Change
MPI-I
0.299
0.250
-0.049*
Headcount
56.5%
48.3%
-8.2%*
Intensity
52.9%
51.7%
-1.2%
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Absolute Change in CH Ratio
How did MPI decrease for India?
0.0%
-2.0%
-4.0%
-6.0%
-8.0%
-10.0%
-12.0%
Indicator (Statistical Significance) [Initial CH Ratio]
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Significant
reduction in all
states except
Bihar, MP and
Haryana.
-0.110
-0.090
Bihar () [0.443]
Punjab (*) [0.114]
Haryana () [0.187]
Madhya Pradesh () [0.358]
Rajasthan (*) [0.34]
Uttar Pradesh (*) [0.344]
Himachal Pradesh (*) [0.149]
Eastern States (*) [0.319]
Jammu & Kashmir (*) [0.214]
West Bengal (*) [0.336]
Gujarat (*) [0.246]
Karnataka (*) [0.253]
Orissa (*) [0.38]
Maharashtra (*) [0.23]
Tamil Nadu (*) [0.194]
Kerala (*) [0.14]
Andhra Pradesh (*) [0.296]
-0.070
-0.050
-0.030
States (Significance) [MPI-I in 1999]
Absolute Reduction in Acute Poverty
Across Large States
-0.010
Absolute Change (99-06) in MPI-I
We combined Bihar and Jharkhand, Madhya Pradesh and
Chhattishgarh, and Uttar Pradesh and Uttarakhand
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Change in MPI by caste
M0-99 M0-06
Scheduled Tribe 0.454 0.411
Scheduled Caste 0.378 0.308
OBCs
0.298 0.258
None Above
0.228 0.163
Disparity
Increases
Change
-0.043
-0.070
-0.040
-0.065
H-99
79.7%
68.7%
57.4%
45.0%
H-06 Change A-99 A-06 Change
73.2% -6.5% 56.9% 56.1% -0.8%
58.3% -10.4% 55.0% 52.8% -2.2%
50.8% -6.5% 52.0% 50.7% -1.2%
32.7% -12.3% 50.7% 49.8% -0.9%
MPI Poverty decreased least among the poorest. The STs
(8.5% population share) are the poorest, but the change is
lowest for them and for OBCs, who have a higher pop share.
STs saw almost no reduction of mortality or undernutrition.
MPI Poverty decreased most for SC and ‘None’.
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Change in MPI by Caste
M0-99 M0-06
Scheduled Tribe 0.454 0.411
Scheduled Caste 0.378 0.308
OBCs
0.298 0.258
None Above
0.228 0.163
Change in
Censored
Headcount
Ratio
Change
-0.043
-0.070
-0.040
-0.065
H-99
79.7%
68.7%
57.4%
45.0%
H-06 Change A-99 A-06 Change
73.2% -6.5% 56.9% 56.1% -0.8%
58.3% -10.4% 55.0% 52.8% -2.2%
50.8% -6.5% 52.0% 50.7% -1.2%
32.7% -12.3% 50.7% 49.8% -0.9%
2%
-1%
-4%
-7%
-10%
-13%
Least change in Mortality
and Nutrition among ST
-16%
Scheduled Tribe
Scheduled Caste
Other Backward Castes
None Above
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Ultra Poor: Changing Both Deprivation
and Poverty Cutoffs
No
Deprivations
MPI z Cutoffs
MPI POOR
Ultra z Cutoffs
Severely
Poor
Deprived
Ultra Poor
k cutoffs
50%
33%
Deprivation Score
Inequality Among the Poor
India 1999-2006
M0
Year
0.299
1999
% of MPI poor
0.250
2006
% of MPI poor
Change in
MPI
-.049
H (MPI)
56.5%
48.3%
-8.2%
Alkire and Seth
High
Intensity
30.6%
High
Depth
37.9%
Intense &
Deep
15.8%
54.2%
67.1%
28.0%
24.7%
31.7%
12.5%
51.1%
65.6%
25.9%
-5.9%
-6.2%
-3.3%
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Multidimensional Poverty Reduction in India,
1999-2006
• Multidimensional poverty declined across India, with an 8%
fall in the percentage of poor.
• But disparity among the poor may have increased
• Progress has been slowest for STs, for hh with uneducated
head of household, for Bihar MP and Rajasthan, and for
Muslims.
• Subgroup decomposable indicators of inequality among the
poor may be constructed, and their precise trends tracked.
• We are unable to update these results: new data are
unavailable for India since 2005/6.
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Why MPI post-2015, & National MPIs?
1. Birds-eye view – trends can be unpacked
a. by region, ethnicity, rural/urban, etc
b. by indicator, to show composition
c. by ‘intensity,’ to show inequality among poor
2. New Insights:
a. focuses on the multiply deprived
b. shows joint distribution of deprivation.
3. Incentives to reduce headcount and intensity.
4. Flexible: you choose indicators/cutoffs/values
5. Robust to wide range of weights and cutoffs
Ultra-poverty Deprivation Cutoffs
Subset of MPI poor that are most deprived in each dimension
Indicator
Nutrition
Child mortality
Years of schooling
School attendance
Electricity
Sanitation
Drinking water
House
Cooking fuel
Assets
Acute Deprivation Cut-off
Any adult or child in the household with nutritional information is
undernourished (2SD below z score or 18.5 kg/m 2 BMI)
Any child has died in the household
No household member has completed five years of schooling
‘Ultra’ Cutoff
3SD or 17 BMI
No Schooling
Any school-aged child is not attending school up to class 8
The household has no electricity
Anything except
bush/field
The household does not have access to safe drinking water or safe water is Unprotected well
more than 30 minutes walk round trip
and 45 Minutes
The house is kachha, or semi-pucca and owns <1 acre or < 0.5 irrigated
kaccha & no land
Wood, grass,
The household cooks with dung, wood or charcoal.
Crops, dung
The household´s sanitation facility is not improved or it is shared with
other households
The household does not own more than one of: radio, TV, telephone, bike,
motorbike or refrigerator, and does not own a car or truck
even one
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