Stephan Klasen

January 15, 2018 | Author: Anonymous | Category: Science, Health Science, Pediatrics
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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?

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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);

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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+

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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 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,
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