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

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Acute Multidimensional Poverty: A New Index for Developing CountriesAlkire & Santos4.1.8 Often deprivations in living standard contribute the most to multidimensional poverty.Figure 10 shows the dimensional contribution to MPI for each country. The contribution of eachdimension is calculated as the sum of the contribution of each indicator. 55 Deprivation in livingstandards (the green portion) often contributes more than deprivation in either of the other twodimensions although this varies. 56 In most countries, the second biggest contribution comes fromeducational deprivations.Figure 10: Contribution by dimension to MPIArmeniaHungaryMontenegroEcuadorCroatiaIraqUzbekistanAlbaniaKyrgyzstanMacedoniaThailandLatviaCote d'IvoireSouth AfricaPhilippinesGuatemalaMoroccoTurkeySurinameTajikistanSenegalMexicoMaliDominican RepublicBelizeGuineaMoldovaNigerCambodiaArgentinaMozambiqueMadagascarParaguayTogoBoliviaEthiopiaSao Tome and …Sierra LeoneCameroonGabonTanzaniaMalawiNamibiaZambiaRwandaSri LankaLesothoPeru0% 20% 40% 60% 80% 100%Contribution of education Contribution of health Contribution of living standards55 Each indicator´s contribution is the proportion of people who are poor and deprived in that particular indicator (thecensored headcount) multiplied by the indicator´s weight and divided by the total number of indicators times the overallMPI. For example, as can be seen in Table 2.A of the Appendix, 55.6 percent of people are poor and live in a householdwhere no one has completed five years of education. This indicator´s weight is 10/6 and Mozambique´s MPI is 0.48.Then, the contribution of deprivation in years of education in Mozambique is 55.6*(10/6)/(10*0.48)=19.3 percent.Following the same procedure, because the censored headcount in child school attendance is 40.3, deprivation in thisindicator contributes 14 percent to overall multidimensional poverty. Therefore, both indicators together, whichconstitute the education dimension, contribute 33 percent to overall poverty.56 Specifically, this is the case in 55 out of the 104 countries, whereas in 22 countries deprivation in education is thebiggest contributor and in 25 countries health deprivations contribute the most to overall poverty. Recall our note abovewhich said that the higher deprivation headcounts in living standards indicators create a higher implicit weight on thisdimension.www.ophi.org.uk July 2010 38
Acute Multidimensional Poverty: A New Index for Developing CountriesAlkire & Santos4.2 Income poverty, wealth poverty, and the MPIThe most widely used measure of poverty at present is income poverty, either measured accordingto a national poverty line or by an international standard. The MPI comparisons with incomepoverty are illuminating. The preliminary analysis suggests that the MPI is capturing a slightlyoverlapping but largely distinct aspect of poverty.4.2.1 MPI and Income Poverty are relatedFigure 11 presents different correlation coefficients between three income headcounts (using the$1.25/day, $2/day and national poverty lines) and deprivations in each of the three dimensions ofthe MPI, as well as with the MPI itself. 57 In the first place, we can see that the headcounts with thetwo international poverty lines are highly correlated with the MPI, but correlations are much lowerwith the headcounts using the national poverty lines. Secondly, as expected, income poverty is mosthighly correlated with deprivation in the living standard dimension. This correlation is followed byhealth deprivation and then by education deprivation (this is the case with the $1.25 and $2/dayheadcounts, the opposite is true for the correlation with the national poverty headcount). However,as we explore below and in the following section, behind these relatively high correlations there is awide range of examples of mismatches between the two poverty criterion.Figure 11: Correlations of income poverty headcounts with MPI and dimensional headcountsPearson Spearman Kendall Tau-a Kendall Tau-bH Education 0.73 0.78 0.57 0.58$1.25/dayHeadcountH Health 0.78 0.82 0.61 0.62H Living Standard 0.88 0.90 0.72 0.74MPI 0.85 0.88 0.67 0.70$2/dayHeadcountNationalPovertyHeadcountH Education 0.77 0.79 0.59 0.60H Health 0.82 0.83 0.63 0.64H Living Standard 0.90 0.90 0.73 0.74MPI 0.86 0.88 0.70 0.71H Education 0.53 0.54 0.37 0.37H Health 0.58 0.57 0.41 0.41H Living Standard 0.58 0.61 0.43 0.44MPI 0.55 0.57 0.42 0.42Figure 12 below plots the headcounts of those who are income poor against those who are MPIpoor. The size of each bubble is given by the number of MPI poor people in that country. Thegreen line plots the 45º line, while the black one plots the linear equation that best fits the scatterplot. The fact that the black line runs below the green one makes it clear that in most countries morepersons are MPI poor than income poor (as expected from the global headcounts). Obviously, thereare exceptions to this, as well as cases in which the MPI estimate is overwhelmingly higher than theincome headcount and we have named some of these outliers on both sides.57 These headcounts are those reported in Table 4 of the Appendix.www.ophi.org.uk July 2010 39
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