The Multidimensional Poverty Index: Achievements, Conceptual, and Empirical Issues

Caroline Dotter Stephan Klasen Universität Göttingen Milorad Kovacevic HDRO HDRO Workshop March 4, 2013

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 sub national 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?

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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

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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; 8

Relative importance of households without eligible population base all Armenia India Ethiopia Old hh (above35) Nutrition (health) 9.1% 14.81% 8.57% 11.07% 28.44% Mortality (health) 17.84% 23.58% 17.13% 21.23% 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; 9

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); 10

Table 2: Multidimensional Poverty across sub-groups and countries All Urban Rural small hh medium hh large hh female-headed hh above 35 below 35 Armenia Ethiopia India H 54.85% 20.82% 69.44% 44.31% 50.46% 66.03% 54.68% 51.59% 55.81% 0.57% 90.48% 52.76% A 55.28% 48.47% 56.15% 49.31% 54.73% 57.63% 56.51% 55.78% 55.14% 38.24% 64.59% 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 60.28% 27.22% 73.24% 53.53% 57.58% 64.78% 59.98% 57.44% 60.02% 2.96% 92.25% 57.82% A 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 61.46% 55.57% 62.89% 59.77% 61.70% 62.89% 61.41% 60.77% 62.20% 46.89% 69.25% 60.37% MPI 0.370522 0.151271 0.460657 0.319907 0.355257 0.407391 0.368327 0.349068 0.373302 0.013863 0.638847 0.349068 11

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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Headline index Complementary indicators of poverty Cut-off approach Dimension cut off Dimension weights Within dimension weights Original (current) MPI New proposal Implications

MPI Headcount, Intensity Dual Absolute Equal (1/3) 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 Possible differentiation of deprivation and multidimensional poverty. More analytic power.

Hard to implement and also arbitrary?

Equal (1/3) Equal Equal 13

Living standard Education Health

Enrollment (ages 6-14) Years of schooling (age 15 and above) Nutrition Mortality

Original (current) MPI

Drinking water, sanitation, electricity, cooking fuel, floor, assets Any school aged child is not attending school in grades 1 to 8 Years of schooling is a public good ( no one has 5 or more years of primary education) • BMI for adults • Weight-for-age for children Death of children any age, no reference period

New proposal

Drinking water, floor, assets Shorter the enrollment window by 2 years (8 to 14); size adjustment (1 in 5) Some economies of scale but not full; Size adjustment (1 in 2 adults) • Exclude BMI for adults • Height-for-age for children Death of children below age 5 in the past 5 years;

Implications

Reduces the importance of living standard; Reduces the headcount Reduces the headcount Increases the headcount No health indicator for adults; reduces the headcount Reference period ?

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Original (current) MPI New proposal Implications No eligible population

Enrollment, Health

Severe poverty

HH is non deprived Hybrid approach: 1.Double the weight on adult education 2.BMI of adults 3.Lower cut-off: 2/9 Large number of hh (20%); messy calculation Deprived in more than 1/2 of weighted indicators At least 50% of eligible population in HH is deprived in enrollment and health; no assets; Cut-off 1/3 Reduced headcount 15