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1、GLOBAL MULTIDIMENSIONAL POVERTY INDEX 2019 ILLUMINATING INEQUALITIES OPHI Oxford Poverty the size of the bubble reflects the number of multidimensionally poor people. The figure is based on 1,119 subnational regions in 83 countries plus national averages for 18 countries. Data are from surveys condu
2、cted between 2007 and 2018. Source: Alkire, Kanagaratnam and Suppa (2019) based on Human Development Report Office and Oxford Poverty and Human Development Initiative calculations. Illuminating Inequalities | 3 Across the 101 countries covered by the global MPI, 23.1percent of people are multidimens
3、ionally poor, but the incidence of multidimensional poverty varies across developing regions from 1.1percent in Europe and Central Asia to 57.5percent in Sub-Saharan Africa 94million multidimensionally poor people live in upper-middle-income countries, where the subnational incidence of multidi- men
4、sional poverty ranges from 0percent to 69.9percent. 792 million multidimensionally poor live in lower-middle-income countries, where the subnational incidence of multi- dimensional poverty ranges from 0percent to 86.7percent. 440million multidimensionally poor people live in low-income countries, wh
5、ere the sub- national incidence of multidimensional pov- erty ranges from 0.2percent to 99.4percent. This shows that the challenge of reducing multi dimensional poverty is not confined to low-income countries. Inequality between and within countries The global MPI highlights inequalities at the glob
6、al, regional, national, subnational and even household level. Each layer of analysis yields a new understanding of inequality and provides a far richer picture than the $1.90 a day poverty rate. Two examples illustrate how subnational disaggregations shine a light on inequality. Where multidimension
7、ally poor people live The global MPI indicates that 1.3billion peo- ple live in multidimensional poverty. But where are they? Increasing levels of disaggregation can help locate them: Poorest two developing regions: Ranking developing regions by average MPI value re- veals that Sub-Saharan Africa an
8、d South Asia are the poorest (figure 3). Poorest 49 countries: Ranking countries by MPI value reveals that the poorest 49 coun- tries are home to as many multidimensionally poor people as Sub-Saharan Africa and South Asia. These 49 countries are spread across all developing regions except Europe and
9、 Central Asia. Poorest 675 subnational regions: Ranking subnational regions by MPI value reveals that the poorest 675 subnational regions, located in 65 countries in all developing regions except Europe and Central Asia, are home to as many poor people as Sub-Saharan Africa and South Asia combined.5
10、 Without disaggregation, the striking inequality within countries is easily missed. Disaggregation matters Across the 101 countries covered by the global MPI, 23.1percent of people are multi- dimensionally poor, but the incidence of multidimensional poverty varies across devel- oping regionsfrom 1.1
11、percent in Europe and Central Asia to 57.5percent in Sub-Saharan Africa. In Sub-Saharan Africa the incidence varies across countriesfrom 6.3percent in South Africa to 91.9percent in South Sudan (see figure 3). And within countries the inci- dence varies across subnational regions. For instance, the
12、incidence of multidimensional poverty in Uganda is 55.1percentsimilar to the Sub-Saharan Africa average. But within Uganda the incidence ranges from 6.0percent in Kampala to 96.3percent in Karamoja meaning that some regions of the country have an incidence similar to that of South Africa, while othe
13、rs have an incidence similar to that of South Sudan. Poverty is everywhere Action against poverty is needed in all devel- oping regions. While Sub-Saharan Africa and South Asia are home to the largest proportions of multidimensionally poor people (84.5per- cent of all multidimensionally poor people
14、live in the two regions), countries in other parts of the world also have a high incidence of multi- dimensional poverty: Sudan (52.3percent), Yemen (47.7percent), Timor-Leste (45.8per- cent) and Haiti (41.3percent). Stark inequalities across countries in the same developing region In Sub-Saharan Af
15、rica the incidence of multidimensional poverty is 91.9percent in South Sudan and 90.5percent in Niger but 14.9 percent in Gabon and 6.3 percent in South Africa. In South Asia it is 55.9percent in Afghanistan but 0.8percent in the Maldives. In the Arab States it is 52.3percent in Sudan and 4 | GLOBAL
16、 MULTIDIMENSIONAL POVERTY INDEX 2019 47.7percent in Yemen but less than 1.0percent in Jordan. In Latin America it is 41.3percent in Haiti but 0.6percent in Trinidad and Tobago. In East Asia and the Pacific it is 45.8percent in Timor-Leste but 3.9percent in China and 0.8percent in Thailand. In Europe
17、 and Central Asia it is 7.4percent in Tajikistan but 0.2per- cent in Armenia. What intensity adds The MPI is the product of the incidence and the intensity of multidimensional poverty, and both are important aspects. Any reduction in intensity reduces MPI (even if incidence remains unchanged) and re
18、flects progress to- wards moving people out of poverty. The poor- est countries exhibit not just higher incidence of multidimensional poverty, but also higher intensity, with each poor person deprived in more indicators. Some countries have similar incidences but very different intensities. The inci
19、dence of multidimensional poverty in Pakistan and Myanmar is 38.3percent, but the intensity is considerably higher in Pakistan (51.7percent) than in Myanmar (45.9per- cent). Another stark contrast is Nigeria, with incidence of 51.4 percent and intensity of 56.6percent, and Malawi, with incidence of
20、52.6percent, and intensity of 46.2percent. FIGURE 3 Going beyond averages shows great subnational disparities in Uganda Developing regions 23.1% Sub-Saharan Africa 57.5% Uganda, 2016 55.1% NutritionChild mortalityYears of schoolingSchool attendanceCooking fuelSanitationDrinking waterElectricityHousi
21、ngAssets South Sudan, 2010 91.9% Uganda South Africa, 2016 6.3% Kampala 6.0% Karamoja 96.3% Sub-Saharan Africa Europe and Central Asia 1.1% Contribution of deprivation in each indicator to overall multidimensional poverty Percent values represent incidence of multidimensional poverty South Asia Arab
22、 States Latin America and the Caribbean East Asia and the Pacifi c Source: Alkire, Kanagaratnam and Suppa (2019) based on Human Development Report Office and Oxford Poverty and Human Development Initiative calculations. Illuminating Inequalities | 5 Children bear the greatest burden Disaggregating t
23、he global MPI by age reveals inequality across age groups. Children under age18 bear the greatest burden of multidimen- sional poverty. This section spotlights the 2bil- lion children1.1billion of whom are under age10living in the 101 countries covered by the global MPI. Half of multidimensionally p
24、oor people are children, and a third are children under age10 Of the 1.3billion people who are multidimen- sionally poor, 663million are childrenand 428million of them (32.3percent) are under age10. One adult in six is multidimensionally poorcompared with one child in three While 17.5percent of adul
25、ts in the countries covered by the MPI are multidimensionally poor, the incidence of multidimensional pover- ty among children is 33.8percent. Over 85percent of multidimensionally poor children live in South Asia and Sub-Saharan Africa Most of the 663million multidimensionally poor children live in
26、South Asia and Sub- Saharan Africa, split roughly equally between both regions.6 Some 63.5 percent of children in Sub- Saharan Africa are multidimensionally poorthe highest incidence among all de- veloping regions. In Burkina Faso, Chad, Ethiopia, Niger and South Sudan 90percent or more of children
27、under age10 are multidimensionally poor. Children are more likely than adults to be multidimensionally poor and deprived in all indicators A higher proportion of children than of adults are multidimensionally poor and deprived in every one of the MPI indicators, and the youngest children bear the gr
28、eatest burden (fig- ure 4). This is a clarion call for action. FIGURE 4 A higher proportion of children than of adults are multidimensionally poor, and the youngest children bear the greatest burden 0 10 20 30 40 NutritionChild mortality Years of schooling School attendance Cooking fuel SanitationDr
29、inking water ElectricityHousingAssets Share of individuals who are multidimensionally poor and deprived (percent) Children ages 09Children ages 10 17Adults ages 18 and older Age groups Note: Data are from surveys conducted between 2007 and 2018. Source: Alkire, Kanagaratnam and Suppa (2019) based on
30、 Human Development Report Office and Oxford Poverty and Human Development Initiative calculations. 6 | GLOBAL MULTIDIMENSIONAL POVERTY INDEX 2019 Inside the home: a spotlight on children in South Asia There are many lenses through which to view the experience of children in poverty.7 The global MPI
31、identifies each childs deprivation by gender and age and places it in the context of the deprivation of other children in the household and of the household as a whole. This section synthesizes a new United Nations Childrens Fundsupported study of individual child-level data for three of the global
32、MPI indicators in South Asia: nutrition, school attendance and years of schooling (figure 5). Nutrition In South Asia 70 million children under age542.8percentare stunted or under- weight.8 Intrahousehold disparities in depriva- tion in nutrition among children under age5 in the region are stark. So
33、me 22.7percent of chil- dren under age5 live in a household in which at least one child is malnourished and at least one child is not. In Pakistan over a third of children under age5 experience intrahousehold inequal- ity in deprivation in nutrition. Out-of-school children Across South Asia 36.7mill
34、ion children do not attend school through grade 8. Some 32.3mil- lion (88.0percent) out-of-school children live in multidimensionally poor households. In terms of gender disparities, 9.0percent of boys in South Asia are out of school and live in a multidimensionally poor household, compared with 10.
35、7percent of girls (figure 6). Country patterns vary considerably. In Afghanistan 24.8 percent of boys ages 715 are multi- dimensionally poor and out of school, com- pared with 44.0percent of girls. In Bangladesh FIGURE 5 Child-level data in the global Multidimensional Poverty Index Children ages 04
36、Children ages 1017 Children ages 614 Health Education Standard of living Nutrition Child mortality Three dimensions of poverty Years of schooling School attendance Cooking fuel Sanitation Drinking water Electricity Housing Assets Child-level data Source: Oxford Poverty and Human Development Initiati
37、ve 2018. Illuminating Inequalities | 7 Children are bringing about change in South Asia. Of 436 million people who live in a household in which no adult has completed six years of schooling, 135 million live with a child age 1017 who has completed six years of schooling the gender pattern is reverse
38、d: 12.1percent of boys are multidimensionally poor and out of school, compared with 7.2percent of girls. Do all children in the same household fare the same? No. In South Asia one child in nine is multidimensionally poor and lives in a house- hold where some school-age children attend school but oth
39、ers do not. Pioneer children: a story of hope Education deprivations continue to affect South Asia. A shocking 436million South Asians one in fourlive in a household in which no adult has completed six years of schooling. But children are bringing about change. Of those 436million people, 135million
40、just under a thirdlive with a child age1017 who has completed six years of schooling. As the only people in their households to have completed six years of schooling, these “pioneer children” are breaking new ground. While they might seem to be a rare phenome- non, 37.5million children ages1017 in S
41、outh Asiaor one in eightare pioneer children. And more than half of those children are girls. However, completing six years of schooling is no panacea. Schools may be ramshackle, and teachers may not teach, so six years of schooling may convey little. Nor does schooling snuff out poverty at once. So
42、me 28.4percent of pioneer children live in a multidimensionally poor household, which means they experience other deprivations that may affect their capacity to learn. And inequalities continue to plague even those households. For instance, 31.5percent of pioneer children in Afghanistan live with at
43、 least one other child age1017 who has not completed six years of schooling and has already left school. Yet, despite the adversity in their lives, these 37.5million children can bring change. FIGURE 6 In South Asia the percentage of school-age children who are multidimensionally poor and out of sch
44、ool varies by country School-age children who are multidimensionally poor and out of school (percent) 0 10 20 30 40 50 Afghanistan 2015 Bangladesh 2014 Bhutan 2010 India 2015/16 Maldives 2017 Pakistan 2017/18 South Asia BoysGirls Note: Out-of-school children are school-age children who do not attend
45、 school through grade 8. Source: Alkire, Ul Haq and Alim 2019. 8 | GLOBAL MULTIDIMENSIONAL POVERTY INDEX 2019 An analysis of 10 countries with a combined population of about 2 billion people illustrates different patterns of reduction in MPI value over time Leaving no one behind The global MPI shows
46、 the incidence of multi- dimensional poverty each year.9 Disaggregating trends by age or locationwhich requires strictly harmonized datasetsindicates whether people are being left behind. This section uses 10 countries from a larger OPHI study to illustrate different patterns of reduc- tion in MPI v
47、alue over time.10 Their com- bined population is about 2billion people, they cover every developing region and they span three income categories: upper middle (Peru), lower middle (Bangladesh, Cambodia, India, Nigeria, Pakistan, Viet Nam) and low (Democratic Republic of the Congo, Ethiopia, Haiti).
48、The big picture Overall, the 10 countries made progress to- wards SDG 1. Eight countries saw a statistically significant reduction in their MPI value and a combined drop in the number of multidi- mensionally poor people from 1.1billion to 782million. This improvement occurred de- spite the rapid pop
49、ulation growth in African countries that unfortunately led to an increase in the number of multidimensionally poor people in Democratic Republic of the Congo, Ethiopia and Nigeria. The fastest absolute reductions in MPI val- ue were in India, Cambodia and Bangladesh, followed by Ethiopia and Haiti. Peru joined Cambodia in experiencing the largest reduc- tion relative to its starting MPIT (7.1percent a year). Signs of progress Examples of pro-poor reduction, where the poorest regions improved the fastest, included Mondol Kiri and Rattanak Kiri in