The Multidimensional Poverty Index:
Achievements, Conceptual, and Empirical
Issues
Caroline Dotter
Stephan Klasen
Universität Göttingen
Milorad Kovacevic
HDRO
HDRO Workshop
March 4, 2013
1
The MPI
• Measuring acute multidimensional poverty;
• Based on dual cut-off approach (1/3);
• Dimensions: Health (mortality and nutrition),
Education (years and enrolement), Standard of
living (house, water, sanitation, electricity, cook
fuel, assets);
• MPI = Headcount * Intensity;
• Data used: DHS, MICS, WHS
• Calculated for some 110 countries (increasingly
available for more than 1 period);
2
In praise of an MPI-type Indicator
• Direct multidimensional complement/competitor to $ a
day indicator;
– Similar breadth and coverage
– Could possibly calculate and monitor global poverty;
• Also based on capability approach (as is the HDI);
• Actionable and policy-relevant at the national (and subnational level); advantage largely unexploited by UNDP;
• Consistent with reasonable set of poverty measurement
axioms (in contrast to HPI);
• Based on high quality and comparable data, with
potential to measure poverty over time;
3
Conceptual Issues
• Dual cut-off navigates between union and intersection
approach
– But leads to formal and interpretational problems: deprivations
entirely ignored below the cut-off seems problematic;
– Union approach conceptually to be preferred?
• Neglect of inequality in the spread of dimensions across
the population, which is also problematic;
– Proposal by Rippin: In the poverty identification step, use square
of weighted deprivation share as poverety indicator (and add
those up in aggregation step);
– Other proposals in the literature;
• Use of intensity in the MPI:
– cannot compare with $ a day headcount
– little variation in intensity (heavily driven by second cut-off);
– use headcount as headline indicator with intensity-inequality
sensitive measure as complementary indicator?
4
Empirical Issues
• WHS limiting and problematic (and now superfluous?);
suggestion to just use MICS and DHS;
• Standard of living:
– Unclear interpretation of electricity access (unequal use!),
cooking fuel (depends on cooking situation), and sanitation
(needs differ across rural/urban, regions);
– Quite large influence on overall MPI;
– 3 indicators would suffice (and capture others as well): floor,
assets, and drinking water;
• Enrolments:
– One child not enrolled, household deprived;
– Problem of late enrolments;
– Adjust time window to allow for late enrolments (e.g. allow for 2
years late enrolment);
5
Share of population deprived in enrolment
Whole population
Original enrolment window 25.32
Shorter enrolment window 17.42
Population with school-aged children
(original category)
38.87
26.71
6
Empirical Issues
• Mortality:
– Only consider recent child deaths (MICS: only consider deaths of
women who gave births in last 10 years?);
• Nutrition:
– BMI of adults and childhood undernutrition cut-offs not directly
comparable;
– BMI and underweight subject to bias due to nutrition transition;
– Focus on children beyond 6 months?
– Proposal: Just focus on childhood undernutrition and stunting;
• Education:
– Cut-off (one person with 5 years enough for non-deprivation) and
implies perfect economies of scale (asymmetry);
– Proposal: deprived if less than 50% of adults have 5 years+
7
Empirical Issues
• Asymmetric cut-offs in health, enrolment, nutrition,
education:
– Has systematic influence on impact of household size on MPI;
– Not clear that asymmetries are justified;
– Define cut-offs with respect to hh size (e.g. 20% of children are
undernourished);
• Ineligible population:
– No children (in school-going age or with nutritional
measurement);
– Presumed non-deprived in MPI (serious problem and bias!);
– Makes severe poverty near-impossible for hh without eligible
population;
– A serious problem of differential importance across countries;
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Relative importance of households without eligible population
base
Nutrition (health)
Mortality (health)
all
9.1%
17.84%
Armenia
14.81%
23.58%
India
8.57%
17.13%
Ethiopia
11.07%
21.23%
Old hh (above35)
28.44%
32.48%
Enrollment (education)
36.97%
51.25%
37.90%
24.38%
38.24%
• All solutions problematic:
•Non-deprivation assumption;
•Dropping observations;
•Using other indicator from same dimension;
•Proposal: Hybrid approach: Use indicator from same dimension if
one indicator is missing, and adjust overall MPI cut-off if both are
missing (can be easily implemented);
•Advantage: Keeps all observations in, uses information to
maximum extent; likely to generate least bias;
•Disadvantage: Decompositoion no longer possible;
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Implementing the Proposals
• A reduced and (more robust) MPI?
–
–
–
–
–
3 standard of living indicators;
Nutrition: stunting (>6mts)
Mortality: only recent deaths;
Enrolment: allow for late enrolment;
Cut-offs more uniform (>20% affected in nutrition, enrolment,
mortality, <50% with 5 years+ education);
– Hybrid approach for ineligible population;
• Implement approach using DHS for Armenia, Ethiopia,
and India;
• Changes incidence (mainly due to education cut-off), but
also correlates of poverty (e.g. hh size);
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Table 2: Multidimensional Poverty across sub-groups and countries
H
A
All
54.85%
55.28%
Urban
20.82%
48.47%
Rural
69.44%
56.15%
small hh
44.31%
49.31%
medium hh
50.46%
54.73%
large hh
66.03%
57.63%
female-headed hh
54.68%
56.51%
above 35
51.59%
55.78%
below 35
55.81%
55.14%
Armenia
0.57%
38.24%
Ethiopia
90.48%
64.59%
India
52.76%
53.17%
Improved multidimensional poverty estimation
H
all
urban
rural
small hh
medium hh
large hh
female-headed hh
above 35
below 35
Armenia
Ethiopia
India
MPI
0.303206
0.100921
0.389922
0.218485
0.276131
0.380515
0.308982
0.287795
0.307708
0.002194
0.584382
0.28055
A
60.28%
27.22%
73.24%
53.53%
57.58%
64.78%
59.98%
57.44%
60.02%
2.96%
92.25%
57.82%
MPI
61.46%
55.57%
62.89%
59.77%
61.70%
62.89%
61.41%
60.77%
62.20%
46.89%
69.25%
60.37%
0.370522
0.151271
0.460657
0.319907
0.355257
0.407391
0.368327
0.349068
0.373302
0.013863
0.638847
0.349068
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Conclusion
• MPI has been a good start to develop internationally
comparable multidimensional poverty indicator;
• But there are open issues and problems, and
refinements at conceptual and empirical level warranted
• Conceptual level: Union approach, incorporating
inequality, headcount the headline indicator?
• Empirical level: Changes to indicators, cut-offs, data sets
used, and assumptions about ineligible population;
• Most issues can be readily addressed and are worth
addressing.
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Original
(current) MPI
Headline index
Complementary
indicators of
poverty
Cut-off
approach
Dimension cutoff
Dimension
weights
Within
dimension
weights
MPI
Headcount,
Intensity
Dual
Absolute
New proposal
Implications
Headcount of MP
Better comparability
with income poverty
Intensity, Inequality
Intensity of MP; but
Which approach to
inequality of
deprivation ?
Dual → MP
Union approach →
Measure of
deprivation, inequality
in deprivation
Consider ‘relative’
cut-offs
Equal (1/3)
Equal (1/3)
Equal
Equal
Possible differentiation
of deprivation and
multidimensional
poverty. More analytic
power.
Hard to implement and
also arbitrary?
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Living
standard
Education
Health
Original
(current) MPI
Drinking water,
sanitation,
electricity,
cooking fuel,
floor,
assets
Any schoolEnrollment aged child is not
(ages 6-14) attending school
in grades 1 to 8
Years of
schooling is a
Years of
public good
schooling
(no one has 5 or
(age 15 and
more years of
above)
primary
education)
• BMI for adults
Nutrition
• Weight-for-age
for children
Death of
children any
Mortality
age, no
reference period
New proposal
Drinking water,
floor,
assets
Implications
Reduces the
importance of living
standard;
Reduces the
headcount
Shorter the enrollment
window by 2 years (8 to Reduces the
14); size adjustment (1 headcount
in 5)
Some economies of
scale but not full;
Size adjustment (1 in 2
adults)
Increases the
headcount
• Exclude BMI for adults No health indicator
• Height-for-age
for adults; reduces
for children
the headcount
Death of children below
age 5 in the past 5
Reference period ?
14
years;
Original
(current) MPI
No eligible
population
Severe
poverty
Enrollment,
HH is nonHealth
deprived
New proposal
Hybrid approach:
1.Double the weight
on adult education
2.BMI of adults
3.Lower cut-off: 2/9
Implications
Large number of hh
(20%); messy
calculation
At least 50% of
Deprived in
eligible population in
more than 1/2 of HH is deprived in
Reduced headcount
weighted
enrollment and health;
indicators
no assets;
Cut-off 1/3
15