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

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Acute Multidimensional Poverty: A New Index for Developing CountriesAlkire & SantosPercentage of People Under $1.25/dayFigure 12. Scatter plot of MPI vs. $1.25/day headcount100%Tanzania80%MozambiqueSwazilandNigeriaNiger60%DR CongoBangladeshMaliUzbekistanEthiopia40%PhilippinesSenegalChina20%IndonesiaIndiaBrazilPakistan0%‐20% 0% 20% 40% 60% 80% 100%Morocco‐20%Percentage of People MPI Poor4.2.2 MPI and GDP per capita vary widely except for higher income countriesFigure 13 plots GDP per capita in 2008 (PPP, current international $) against MPI headcount. Wehave traced an ad hoc line at a low GDP per capita level (about $1700) and we can see anextraordinary range of MPI levels. This shows that some low GDP countries are able to address theMPI indicators to a considerable extent. Among higher GDP per capita countries MPI is clearlylower in general. However, there are several noteworthy exceptions. For example, Peru, Gabon, andNamibia, classified as high income countries by the World Bank 58 , have relatively high MPIheadcounts relative to their GDP per capita. This is also the case of Angola, a lower-middle incomecountry.58 The World Bank classification is actually done using the Gross National Income per capita, calculated with the Atlasmethod, with the benchmarks being less than $975 per capita for low-income countries, between $975 and $3,855 forlower-middle income ones, between $3,856 and $11,905 for upper-middle income countries and $11,906 or higher forhigh-income countries.www.ophi.org.uk July 2010 40
Acute Multidimensional Poverty: A New Index for Developing CountriesAlkire & SantosFigure 13. Scatter plot of GDP per capita vs. MPI headcountGDP per capita 2008 PPP (current international $)30000250002000015000100005000CroatiaTrinidad and TobagoEstoniaTurkeyBrazilEcuadorArmeniaPeruMoroccoGabonNamibiaAngolaMoldovaIndiaGhanaCote d' IvoireComoros Ethiopia0Niger0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0Multidimensional Poverty Headcount4.2.4 At the household level, MPI and Income diverge among poorer countries. In mostcountries we do not have both income poverty data and MPI for the same households. However theWHS does include a basic consumption module for the households. Thus for the countries forwhich we used the WHS, we are able to explore a key question: to what extent are the samehouseholds identified as poor using two different measures, and to what extent do the differentmeasures identify completely different households as poor? 59 This is an important question becauseincome poverty measures are often used for targeting purposes.The exercise consists of identifying whether each household in the sample is income poor and MPIpoor or not, and then combining all households into four possible groups as in Figure 14 below: (A)Not Income Poor and Not MPI poor; (B) Not Income Poor but MPI poor; (C) Income Poor butNot MPI poor, and (D) Income Poor and MPI poor.Figure 14: Crosstab of income and MPI povertyMPINon-Poor PoorNot Poor A BIncome Poor C DIf Income and MPI were perfectly correlated, then the headcounts would coincide and allhouseholds would either be poor (cell D) or non-poor (cell A). Cells B and C represent Type II(exclusion) and Type I (inclusion) errors correspondingly in the sense that if the income indicatorwas used as a proxy variable to target the multidimensionally poor, B and C indicate the magnitudeof the mismatch between the two identification criterions, either because some multidimensionallypoor people would be ignored or because some multidimensionally non-poor people would beconsidered.We performed this analysis with 18 of the total 19 WHS countries for which this was possible. Weused the US$1.25/day poverty line adjusted by the Purchasing Power Parity Conversion Factor in59 Note that these comparisons are accurate if and only if the consumption data are also accurate.www.ophi.org.uk July 2010 41
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