poverty Insights from Multidimensional Measurement Sabina Alkire 16 October 2012 4 th OECD Forum New Delhi Motivation Measurement usually income or consumption data Trends reflect trends in nutrition services education ID: 661746
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Slide1
Reducing
inequalities and
poverty:
Insights from Multidimensional Measurement
Sabina Alkire
16 October 2012, 4
th
OECD Forum, New DelhiSlide2
Motivation
Measurement: usually income or consumption data.
Trends: reflect trends in nutrition, services, education? No: direct and lagged relationships are more complexHence additional indicators required to study change.
2Slide3
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 measures1)
joint distribution
of deprivations (what one person experiences)
a) focus on poorest of the poor b) address interconnected deprivations efficiently2) signal trade-offs explicitly: open to scrutiny3) provide an overview plus an associated consistent dashboard
3Slide4
Why not?
Won’t an ‘overview’ index lose vital
detail and information?Aren’t weights contentious and problematic?
How to
contextualise
the measure?4Slide5
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?5Slide6
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?
6Slide7
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.
7Slide8
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+ countriesA 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% Slide9
How is MPI Computed?
The
MPI
uses the Adjusted Headcount Ratio M
0
: 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.
.
Formula: MPI
=
H
×
ASlide10
Useful Properties
10
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 policyDimensional Monotonicity Gives incentives a) to reduce the headcount and b) the intensity of poverty among
the poor.
Slide11
Changes in the Global MPI
from 2011 MPI Update
Alkire, Roche, Seth 2011Slide12
Changes over time in MPI for 10 countries
MPI
fell for all 10 countries
Survey intervals: 3
to 6
years.
Multidimensional Poverty Index (MPI)Slide13
How and How much?
Ghana, Nigeria, and EthiopiaSlide14
Let us Take a Step
B
ack in Time
Ghana
2003
Nigeria
2003
Ethiopia
2000Slide15
Ethiopia
: 2000-2005 (Reduced A more than H)
Ghana
2008
Nigeria
2008
Ethiopia
2005
Ghana
2003
Nigeria
2003
Ethiopia
2000Slide16
Nigeria
2003-2008 (Reduced H more than A)
Ghana
2008
Nigeria
2008
Ethiopia
2005
Ghana
2003
Nigeria
2003
Ethiopia
2000Slide17
Ghana
2003-2008 (Reduced A and H Uniformly)
Ghana
2008
Nigeria
2008
Ethiopia
2005
Ghana
2003
Nigeria
2003
Ethiopia
2000Slide18
Pathways
to
Poverty
ReductionSlide19
Performance of Sub-national RegionsSlide20
Ethiopia’s
Regional Changes Over Time
Addis Ababa
HarariSlide21
Nigeria’s
Regional Changes Over Time
South
South
North CentralSlide22
Looking Inside the Regions of Nigeria… Slide23
Nigeria: Indicator Standard ErrorsSlide24
An Indian Example
A
lmost MPI 1999-2006
Alkire and Seth
In ProgressSlide25
India: Almost-MPI over time
25
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
.Slide26
How did MPI decrease for India?
26
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%Slide27
How did MPI decrease for India?
27Slide28
Absolute Reduction in Acute Poverty Across Large States
28
We combined Bihar and Jharkhand, Madhya Pradesh and
Chhattishgarh
, and Uttar Pradesh and
Uttarakhand
Significant reduction in all states except Bihar, MP and Haryana. Slide29
Change in MPI by caste
29
M
0
-99
M
0
-06
Change
H-99
H-06
Change
A-99
A-06
Change
Scheduled Tribe
0.454
0.411
-0.043
79.7%
73.2%
-6.5%
56.9%
56.1%
-0.8%
Scheduled Caste
0.378
0.308
-0.070
68.7%
58.3%
-10.4%
55.0%
52.8%
-2.2%
OBCs
0.298
0.258
-0.040
57.4%
50.8%
-6.5%
52.0%
50.7%
-1.2%
None
Above
0.228
0.163
-0.065
45.0%
32.7%
-12.3%
50.7%
49.8%
-0.9%
Disparity Increases
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’. Slide30
Change in MPI by Caste
30
M
0
-99
M
0
-06
Change
H-99
H-06
Change
A-99
A-06
Change
Scheduled Tribe
0.454
0.411
-0.043
79.7%
73.2%
-6.5%
56.9%
56.1%
-0.8%
Scheduled Caste
0.378
0.308
-0.070
68.7%
58.3%
-10.4%
55.0%
52.8%
-2.2%
OBCs
0.298
0.258
-0.040
57.4%
50.8%
-6.5%
52.0%
50.7%
-1.2%
None
Above
0.228
0.163
-0.065
45.0%
32.7%
-12.3%
50.7%
49.8%
-0.9%
Change in Censored Headcount Ratio
Least change in Mortality and Nutrition among STSlide31
Deprivation Score
Ultra Poor: Changing Both Deprivation and Poverty Cutoffs
50%
Deprived
33%
No Deprivations
MPI POOR
MPI
z
Cutoffs
Ultra
z
Cutoffs
Not Severe
k cutoffs
Severely
Poor
Ultra PoorSlide32
Inequality Among the Poor
India 1999-2006 Alkire and Seth
32
Year
M
0
H (MPI)
High Intensity
High
Depth
Intense
& Deep
1999
0.299
56.5%
30.6%
37.9%
15.8%
% of MPI poor
54.2%
67.1%
28.0%
2006
0.250
48.3%
24.7%
31.7%
12.5%
% of MPI poor
51.1%
65.6%
25.9%
Change
in MPI
-.049
-8.2%
-5.9%
-6.2%
-3.3%Slide33
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.
S
ubgroup 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. 33Slide34
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 poor2. 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 Slide35
Ultra-poverty Deprivation
Cutoffs
Subset of MPI poor that are most deprived in each dimension
35
Indicator
Acute Deprivation Cut-off
‘Ultra’
Cutoff
Nutrition
Any adult or child in the household with nutritional information is
undernourished
(2SD below z score or 18.5 kg/m
2
BMI
)
3SD or 17 BMI
Child
mortality
Any child has died in the household
Years of
schooling
No household member has completed
five years
of schooling
No Schooling
School
attendance
Any school-aged child
is
not attending school up to
class 8
Electricity
The household has no electricity
Sanitation
The household´s sanitation facility is
not improved
or it is shared with other households
Anything except bush/field
Drinking water
The household does not have access to safe drinking water
or safe water is more than 30 minutes walk round trip
Unprotected well and 45 Minutes
House
The
house
is
kachha
,
or semi-
pucca
and owns <1 acre
or <
0.5 irrigated
kaccha
& no land
Cooking
fuel
The household cooks with dung, wood or charcoal.
Wood, grass,
Crops, dung
Assets
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