Acute Multidimensional Poverty: A New Index for Developing ... - OPHI

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Acute Multidimensional Poverty: A New Index for Developing CountriesAlkire & SantosFigure 20: MPI estimates of Kenyan states compared with aggregate MPI in other countries0.00MexicoBrazil0.100.20ChinaDominicanRepublicIndonesiaGhanaBoliviaNairobiCentralCentralUrbanCentralRuralMPI Value0.300.40IndiaKenyaTanzaniaEasternWesternCoastRift ValleyNyanza0.50Mozambique0.60MaliNorthEasternUrban0.70NigerNorthEasternNorthEasternRural0.80Figure 21: Composition of poverty in two Indian states100%90%11.7%4.6%9.0%80%70%60%50%40%30%20%10%0%18.2%12.8%23.3%9.4%7.4%10.4%5.2%Punjab11.5%31.7%11.9%6.5%12.4%8.5%Himachal PradeshSchoolingSchool AttendanceMortalityNutritionElectricitySanitationWaterFloorCooking FuelAssetswww.ophi.org.uk July 2010 50
Acute Multidimensional Poverty: A New Index for Developing CountriesAlkire & SantosAnother category that can be tremendously important for policy relates to ethnicity, religionsaffiliation, and caste. For example, Mexico’s national multidimensional poverty measure, launched in2009, highlighted the problem of indigenous poverty because the multidimensional poverty rates ofindigenous peoples were much higher. For example, in Kenya, the MPI headcount ranged from 29percent for the Embu to 96 percent for the Turkana and Masai. In Bolivia, poverty among mestizoswas 27 percent, but 1.6 times that among the Quechua. In India, the decomposition was performedfor caste groupings. The Scheduled Tribes have the highest MPI (0.482), almost the same asMozambique, and a headcount of 81 percent. The Scheduled Castes have a headcount of 66 percentand their MPI is a bit better than Nigeria. Fifty-eight percent of other Backward Castes are MPIpoor. About one in three of the remaining Indian households are multidimensionally poor, and theirMPI is just below that of Honduras.4.5 Clustered Deprivations 66Another key question for policy is whether it is possible to identify certain ‘types’ ofmultidimensional poverty, which would suggest distinctive policy pathways. Our results here arepreliminary and suggest that this will be a fruitful area to explore. For example, consider in Figure 22Ghana and Mali – two countries with very different MPI values. In Ghana, 30 percent people areMPI poor where as in Mali it is 87 percent. Yet what is interesting is the pattern of theirdeprivations. The spider diagrams below have one spoke for each of the ten indicators. 67 What isevident is that in both countries, deprivations in cooking fuel, sanitation, and electricity are thehighest, and health deprivations are relatively low.A very different situation is present in comparing the Gambia and Zambia, which have equal MPIvalues, but a different configuration of deprivations, with deprivations in floor, water, and sanitationbeing much higher in Zambia, whereas schooling and education are more problematic in Gambia.Figure 22: MPI Composition patternsCookingFuelAssetsSchooling1.00.80.60.40.20.0SchoolAttendanceMortalityCookingFuelAssetsSchooling1.00.80.60.40.20.0SchoolAttendanceMortalityFloorNutritionFloorNutritionDrinkingWaterSanitationElectricityDrinkingWaterSanitationElectricityGhana (MPI=0.140)Mali (MPI=0.564)Gambia (MPI=0.324)Zambia (MPI=0.325)Note: the deprivations graphed are the censored headcounts, that is, the proportion of population that is poor anddeprived in that particular indicator.66 We are grateful to Jose Manuel Roche for very helpful insights for this section and for performing the cluster analysis.67 Ideally there should be 3 main spokes for each dimension at 120 degrees, and the asset indicators should bedistributed so that the spokes also reflect our weighting.www.ophi.org.uk July 2010 51
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Acute Multidimensional Poverty: A New Index for Developing ... - OPHI

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