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1、Unstacking global poverty:Data for high impact actionMULTIDIMENSIONAL POVERTY INDEX 2023GLOBALOPHIOxford Poverty&Human Development InitiativeFind out moreThis report describes the 2023 update of the global Multidimensional Poverty Index(MPI),whose data are open source available to anyone interested
2、in multidimensional poverty.To further explore the data,read the technical and methodological notes and learn about ongoing research,visit http:/hdr.undp.org and http:/ophi.org.uk.Recent global MPI reports have shared research on a variety of pertinent issues:Deprivation bundles,showing interlinkage
3、s across deprivations(Global MPI Report 2022).Which countries are on track to halve poverty by 2030(Global MPI Report 2020).How much multidimensional poverty increased globally due to the COVID-19 pandemic(Global MPI Report 2020 and Global MPI Report 2022).Gendered and intrahousehold analyses of fem
4、ale schooling(Global MPI Report 2021).Global MPI disaggregated by ethnicity(Global MPI Report 2021).Global MPI disaggregated by gender of household head(Global MPI Report 2021 and OPHI Table 7).How the global MPI is related to Sustainable Development Goal indicators(Global MPI Report 2020).Inequalit
5、ies among poor people(Global MPI Report 2019).Copyright 2023 by the United Nations Development Programme and Oxford Poverty and Human Development InitiativeThe team that created this report included Sabina Alkire,Ines Belchior,Marjan Blumberg,Cecilia Caldern,Pedro Conceio,Maya Evans,Alexandra Fortac
6、z,Moumita Ghorai,Seockhwan Bryce Hwang,Admir Jahic,Usha Kanagaratnam,Tasneem Mirza,Som Kumar Shrestha,Marium Soomro,Nicolai Suppa and Heriberto Tapia.Additionally,many thanks go to Agustin Casarini,Fanni Kovesdi and Lhachi Seldon for ensuring the quality of the report and to Pascal Mensah for resear
7、ch assistance.Peer reviewers included Alissar Chaker,Arturo Martinez(Jr.),Jonathan Moyer,Mizuho Okimoto-Kaewtathip and Max Roser.The team would like to thank the wider OPHI team for their feedback as well as the editors and layout artists at Communications Development Incorporatedled by Bruce Ross-L
8、arson,with Joe Caponio,Christopher Trott and Elaine Wilson.Unstacking global poverty:Data for high-impact actionGLOBAL MULTIDIMENSIONAL POVERTY INDEX 2023OPHIOxford Poverty&Human Development Initiative ContentsUnstacking global poverty:Data for high-impact action 1What is the global Multidimensional
9、 Poverty Index?4Where do poor people live?6Where is poverty most intense?7Which groups are the poorest?9What do deprivation indicators tell us about povertyfrom the regional to the subnational level?11What deprivations do poor people experience?12How do monetary and multidimensional poverty compare?
10、13How has poverty changed?13How to use the global Multidimensional Poverty Index for impact 16Notes 17References 18Statistical tables 19STATISTICAL TABLES1 Multidimensional Poverty Index:developing countries 202 Multidimensional Poverty Index:changes over time based on harmonized estimates 23BOXES1
11、Urgently needed:Multidimensional poverty data 32 Data used to compute the global Multidimensional Poverty Index 43 Deepas story and what the global Multidimensional Poverty Index measures 54 What about people vulnerable to poverty?85 Poverty reduction in Cambodia from 2014 to 2021/2022 156 Reducing
12、global Multidimensional Poverty Index values is possibleat speed and to scale 16FIGURES1 Structure of the global Multidimensional Poverty Index 42 Nearly half of poor people live in Sub-Saharan Africa,and over a third live in South Asia 63 Poverty disproportionately affects low-income countries 74 T
13、he higher the incidence of poverty,the higher the intensity of poverty that poor people tend to experience 75 More than two-fifths of poor people experience severe poverty 86 Sub-Saharan Africa is home to the poorest of the poor 97 Poorer subnational regions tend to have higher intensity and inciden
14、ce of poverty 108 Across world regions most poor people live in rural areas 119 Multidimensional Poverty Index values and indicator composition vary widely across world regions,countries and subnational regions 1210 What deprivations do poor people experience by region?1311 The incidence of multidim
15、ensional and monetary poverty shows how human lives are battered in multiple ways 14TABLEA Countries that halved their global Multidimensional Poverty Index value 14iiGLOBAL MULTIDIMENSIONAL POVERTY INDEX/2023 Unstacking global poverty:Data for high-impact actionIn 2015 the 2030 Agenda for Sustainab
16、le Develop-ment and Sustainable Development Goal(SDG)1 set out to overcome the greatest global challenge:ending poverty in all its forms.At the midpoint to 2030,peo-ples lives continue to be battered in multiple ways simultaneously.Globally,an array of challenges im-pedes poverty reductionwidespread
17、 inequality,po-litical instability and conflict,a climate emergency,COVID-19 pandemic recovery,and cost of living and other crises.There are both commonalities and spe-cifics that cloud the way for each country.Measures of multidimensional poverty attempt to offer clear priorities for addressing pov
18、erty,going be-yond monetary deprivations.The annual global Mul-tidimensional Poverty Index(MPI),jointly published by the Human Development Report Office(HDRO)of the United Nations Development Programme and the Oxford Poverty and Human Development Initiative(OPHI)at the University of Oxford since 201
19、0,measures interlinked deprivations in health,education and standard of living that directly affect a persons life and wellbeing.The global MPI is the only counting-based index that measures overlap-ping deprivations for more than 100 countries and 1,200 subnational regions and offers a key perspec-
20、tive on SDG 1,while encompassing indicators related to other SDGs.The global MPI can be pictured as a stack of blocks,each of which represents a depriva-tion of a poor person.The goal is to eliminate depri-vations so the height of the stack declines.This report presents a compact update on the state
21、 of multidimensional poverty(henceforth referred to as“poverty”)in the world.It compiles data from 110 developing countries covering 6.1 billion people,accounting for 92 percent of the population in devel-oping countries.It tells an important and persistent story about how prevalent poverty is in th
22、e world and provides insights into the lives of poor people,their deprivations and how intense their poverty isto in-form and accelerate efforts to end poverty in all its forms.As still only a few countries have data from after the COVID-19 pandemic,the report urgent-ly calls for updated multidimens
23、ional poverty data(box 1).And while providing a sobering annual stock take of global poverty,the report also highlights ex-amples of success in every region.Among the 1.1 billion poor people.Who are the poorest?The higher the incidence of poverty,the higher the intensity of poverty that poor people
24、experience.485 million poor people live in severe poverty across 110 countries,experiencing 50100%of weighted deprivations.99 million poor people experience deprivations in all three dimensions(70100%of weighted deprivations).10 million of the 12 million poor people with the highest deprivation scor
25、es(90100%)live in Sub-Saharan Africa.Which groups are the poorest?Subnational regions are being left behind in two ways:where poverty is widespread,poverty is also most intense.Half of the 1.1 billion poor people(566 million)are children under 18 years of age.84%of all poor people live in rural area
26、s.Rural areas are poorer than urban areas in every world region.UNSTACKING GLOBAL POVERTY:DATA FOR HIGH-IMPACT ACTION1MULTIDIMENSIONAL POVERTY IN 2023WHERE DO POOR PEOPLE LIVE?1.1 billion out of 6.1 billionAcross 110 countries,people are poor.That is,just over 18%are estimated to live in acute multi
27、dimensional poverty.534 millionout of 1.1 billion poor peoplehalf of all poor peoplelive in Sub-Saharan Africa.389 million people.Over a third of all poor peoplelive in South AsiathatsMiddle-income countries730 millionnearly two-thirdsof all poor peoplelive in.Low-income countries387 million.host ov
28、er one-third of all poor peopleVALUE well within 15 years.25 countries halved their global POVERTY REDUCTION IS POSSIBLE.2GLOBAL MULTIDIMENSIONAL POVERTY INDEX/2023What deprivations do poor people experience?824991 million out of the 1.1 billion poor people do not have adequate sanitation,housing or
29、 cook-ing fuel.600 million poor people live with a person who is undernourished in their household.Gaps in years of schooling is a cross-regional issue:In all regions except Europe and Central Asia,around half of poor people do not have a single member of their household who has completed six years
30、of schooling.How do monetary and multidimensional poverty compare?In 42 of 61 countries more people live in multidi-mensional poverty,based on the global MPI,than in extreme monetary poverty,based on the World Banks$2.15 a day measure.How has poverty changed?72 of 81 countries,covering well over 5 b
31、illion peo-ple,experienced a significant absolute reduction in MPI value during at least one period.But nearly all data are from before the COVID-19 pandemic.25 countries halved their global MPI value well within 15 years,showing that progress at scale is attainable.In 42 countriesover half of those
32、 coveredchil-dren are being left behind.In 15 countries the rate of poverty reduction was outpaced by population growth:The number of poor people increased despite poverty rates declining.Cambodia halved its MPI in 7.5 years(20142021/2022),including COVID-19 pandemic years,despite increases in depri
33、vations in school attendance.Box 1 Urgently needed:Multidimensional poverty dataTimely and disaggregated poverty data are essential for effective policymaking and achieving the goals of the 2030 Agenda for Sustainable Development.Although this report makes best use of existing data,full data from af
34、ter the COVID-19 pandemic are unavailable for nearly all 110 countries covered by the global Multidimensional Poverty Index(MPI).Unfortunately,the“Data Revolution”seems to be leaving multidimensional poverty data behind.Yet gathering data on multidimensional poverty is faster than many realize.The g
35、lobal MPI is constructed based on 43 survey questionsor at most 5 percent of the number of questions in Demographic and Health Surveys and Multiple Indicator Cluster Surveys(which currently include at least 859 questions each).1In the Report of the Commission on Global Poverty,Sir Tony Atkinson echo
36、ed thenWorld Bank President Jim Yong Kims observation that“Collecting good data is one of the most powerful tools to end extreme poverty”and affirmed the pledge“to do something that makes common sense and is long overdue:to conduct surveys in all countries that will assess whether peoples lives are
37、improving.”2 The commission recommended“a major investment in statistical sources”for poverty.As Atkinson explained,“The aimisnot only to increase resources but also to signal the need for higher priority to be given to poverty statistics.”3We reaffirm the urgent postpandemic call for concerted inve
38、stment in the data required to measure acute and moderate multidimensional poverty across all developing regions.Notes1.The 2019 Nepal Multiple Indicator Cluster Survey(MICS)has 859 questions,the 2019 Chad MICS has 875,the 20192021 Mauritania Demographic and Health Survey(DHS)has 933 and the 2019202
39、1 India DHS has 1,124.The 5 percent figure is based on 43/859.2.World Bank 2017,p.190.3.World Bank 2017,p.191.UNSTACKING GLOBAL POVERTY:DATA FOR HIGH-IMPACT ACTION3What is the global Multidimensional Poverty Index?The global MPI is a key international resource that measures acute multidimensional po
40、verty across more than 100 developing countries(box 2).First launched in 2010 by HDRO and OPHI,the global MPI advances SDG 1ending poverty in all its forms everywhereand measures interconnected deprivations across indi-cators related to SDGs 1,2,3,4,6,7 and 11.The global MPI begins by constructing a
41、 depriva-tion profile for each household and person in it that tracks deprivations in 10 indicators spanning health,education and standard of living(figure 1).For ex-ample,a household and all people living in it are de-prived if any child is stunted or any child or adult for whom data are available
42、is underweight;if any child died in the past five years;if any school-aged child is not attending school up to the age at which he or she would complete class 8 or no household member has completed six years of schooling;or if the house-hold lacks access to electricity,an improved source of drinking
43、 water within a 30 minute walk round trip,1 an improved sanitation facility that is not shared,2 nonsol-id cooking fuel,durable housing materials,and basic assets such as a radio,animal cart,phone,television,computer,refrigerator,bicycle or motorcycle.All indi-cators are equally weighted within each
44、 dimension,so Box 2 Data used to compute the global Multidimensional Poverty IndexThe 2023 global Multidimensional Poverty Index(MPI)uses the most recent comparable data available for 110 coun-tries22 low-income countries,85 middle-income countries and 3 high-income countries(see table 1 at the end
45、of the report).These countries are home to about 92 percent of the population in developing regions.1 The global MPI shows who they are,where they live and what deprivations hold them back from achieving the wellbeing they deserve.Global MPI values,incidence and intensity of poverty,and component in
46、dicators are disaggregated for 1,281 subnational regions as well as by age group,rural-urban area and gender of the household head.The estimates are based on Multiple Indicator Cluster Surveys for 54 countries,Demographic and Health Surveys for 43 countries and national surveys for 13 countries.The
47、year of the surveys ranges from 2011 to 2021/2022.For 87 countries,home to 85.4 percent of poor people,data were fielded in 2016 or laterafter the Sustainable Devel-opment Goals were adopted.Of these,41 countries,home to 49.0 percent of poor people,have data fielded in 2019 or laterbut in only 7 cou
48、ntries were all data collected in 2021 or 2022.This edition provides updated estimates for Cambodia(2021/2022),Madagascar(2021),Mexico(2021),Mozambique(2019/2020),Nigeria(2021)and Peru(2021)and introduces estimates for Fiji(2021)and Uzbekistan(2021/2022).Trends in global MPI values are available for
49、 81 countries using data from 2000 to 2021/2022(see table 2 at the end of the report).Of these 81 countries,42 have data for two points in time,35 have data for three points in time and 4 have data for four points in time.Harmonized trends are also available by subnational region,age group and rural
50、-urban area.Disaggregated trends help in monitoring the central,transformative promise of the 2030 Agenda for Sustainable Development:to leave no one behind.Although this report makes best use of existing data,full data from after the COVID-19 pandemic are not available for nearly all 110 countries;
51、hence the report urgently calls for updated data.Note1.All population figures refer to 2021(in continuation of past reports,which update the population figures by one year from the previous edition)and are drawn from UNDESA(2022).Figure 1 Structure of the global Multidimensional Poverty IndexNutriti
52、onChild mortalityYears of schoolingSchool attendanceCooking fuelSanitationDrinking waterElectricityHousingAssetsHealthEducationLiving standardsDimensionsGlobal Multidimensional Poverty IndexIndicatorsSource:HDRO and OPHI.4GLOBAL MULTIDIMENSIONAL POVERTY INDEX/2023the health and education indicators
53、are weighted 1/6 each,and the standard of living indicators are weight-ed 1/18 each.A persons deprivation score is the sum of the weighted deprivations she or he experiences.The global MPI identifies people as multidimensionally poor if their deprivation score is 1/3 or higher(box 3).MPI values are
54、the product of the incidence(H,or the proportion of people who live in multidimension-al poverty)and intensity of poverty(A,or the average deprivation score among multidimensionally poor people).Put simply,MPI=H A.The MPI ranges from 0 to 1,and higher values imply higher poverty.Global MPI values de
55、cline when fewer people are poor or when poor people have fewer deprivations.The precise definition of each indicator is available online,together with any country-specific adjust-ments and the computer code used to calculate the global MPI value for each country.3By identifying who is poor,the natu
56、re of their poverty(their deprivation profile)and how poor they are(their deprivation score),the global MPI complements the international$2.15 a day pover-ty rate,bringing into view interlinked nonmonetary deprivations.4Box 3 Deepas story and what the global Multidimensional Poverty Index measuresDe
57、epa lives in a small island community in the hill tracts of Rangamati,Bangladeshnestled in tropical forests,waterfalls and rich biodiversity.She belongs to the Chak-ma tribe,the countrys largest ethnic group.She is among the 100,000 indigenous people who lost their land and homes during the construc
58、tion of the Kaptai Dam in 1960.She remembers walking empty-handed out of her home as a child,losing everything she and her family owned.Deepa lives with her husband,her daughter and her six-year-old granddaughter,who has a speech disability.Her home is made of basic materialsthe floors and walls are
59、 made from mud.The front part of the home is a small shop where the family sells basic toiletries and food,from which they earn about$1 a day.Besides a few shops on the island,Deepa and the rest of the islanders obtain all their personal supplies and food by boat,as no bridge connects to the island.
60、Deepa and her family members are nutritionally deprived.The houses in the hundred-person community lack access to piped water and toilets but do have basic electricity for lighting.For water Deepa must walk uphill to reach a newly constructed school,where she can fill her bucket from a tap.This jour
61、ney is becoming onerous as she is reaching age 70 and suffers from arthritis.Deepa also spends considerable time gathering solid fuel for cooking.Deepa does not own any basic assets such as a mobile phone.Her granddaughter attends a special school for her disability.Deepa is hopeful that someday she
62、 will be able to complete her secondary education and maybe even go to university.According to the global Multidimensional Poverty Index,Deepa is poor.Her deprivation score is 44.4 percent(1/6+5 1/18=8/18)(see figure).Her deprivation score would need to be less than 33.3 percent to be nonpoor.How th
63、e global Multidimensional Poverty Index measures Deepas deprivationsNutritionChild mortality Years of schoolingSchool attendanceHealthEducationLiving standardsCooking fuelSanitationDrinking waterElectricityAssetsHousingNote:Indicators in white refer to a nondeprivation.UNSTACKING GLOBAL POVERTY:DATA
64、 FOR HIGH-IMPACT ACTION5Where do poor people live?Across 110 countries,1.1 billion of 6.1 billion peo-ple are poor.Understanding where poor people live is crucial for policymaking.Roughly five out of six poor people live in Sub-Saharan Africa or South Asia:534 million(47.8 percent)in Sub-Saharan Afr
65、ica and 389 million(34.9 percent)in South Asia(figure 2).Some 65 percent of the remaining poor people live in just five countries:China(2014),Indonesia(2017),Myanmar(2015/16),Sudan(2014)and Yemen(2013).More recent data for these countries would allow their global MPI value to be updated to reflect c
66、urrent conditions.Across countries the incidence of poverty rang-es from less than 1 percent in 21 countries5 to over 50 percent in 22 countries,6 19 of which are in Sub-Sa-haran Africa,including the poorest four:Burundi(75.1 percent in 2016/2017),Central African Repub-lic(80.4 percent in 2018/2019)
67、,Chad(84.2 percent in 2019)and Niger(91 percent in 2012).There is also extensive variation across regions.Every region has at least one country with incidence below 1 percent.The countries with the highest incidence in their re-gion are Afghanistan(55.9 percent in 2015/2016),Haiti(41.3 percent in 20
68、16/2017),Niger(91 per-cent in 2012),Papua New Guinea(56.6 percent in 2016/2018),Sudan(52.3 percent in 2014)and Tajik-istan(7.4 percent in 2017).These countries urgently require updated data.Poverty disproportionately affects low-income countries.They are home to only 10 percent of the population cov
69、ered by the global MPI but 34.7 per-cent(387 million)of poor people(figure 3).Some 65.3 percent of poor people(730 million)live in middle-income countries,where the incidence of poverty ranges from 0.1 percent in Serbia(in 2019)to 66.8 percent in Benin(in 2017/2018)at the na-tional level and from 0.
70、0 percent in Jaweng,Bot-swana(in 2015/2016),to 89.5 percent in Alibori,Benin(in 2017/2018)at the subnational level.The fact that most poor people live in countries that have shifted to middle-income status(as measured by gross national income per capita),highlights the importance of looking at both
71、national and disaggregated data.Figure 2 Nearly half of poor people live in Sub-Saharan Africa,and over a third live in South AsiaEurope and Central Asia 0.2%2 million4.7%53 million9.5%106 millionLatin America and the Caribbean3.0%33 million34.9%389 million47.8%534 millionArab StatesEast Asia and th
72、e PacificSouth AsiaSub-Saharan AfricaShare of total world population by regionShare and number of poor people by region9.7%5.7%2.4%17.6%30.9%33.8%Source:Table 1 at the end of the report.6GLOBAL MULTIDIMENSIONAL POVERTY INDEX/2023Where is poverty most intense?The global MPI uses intensity to further
73、probe the lived reality of multidimensional poverty.Plotting the intensity and incidence of poverty of 110 devel-oping countries reveals a troubling upward trend:the higher the incidence,the higher the intensity that poor people tend to experience(figure 4).The poorest countries by global MPI value
74、tend to have both the highest incidence and the highest intensity.For example,in both Central African Republic(2018/2019)and Chad(2019),more than 80 percent Figure 3 Poverty disproportionately affects low-income countries8.1%90 million34.7%387 million57.3%639 millionLower middle incomeLowincomeShare
75、 of total world population by income categoryShare and number of poor people by income category10%36.2%53.7%Upper middleincomeSource:Table 1 at the end of the report.Figure 4 The higher the incidence of poverty,the higher the intensity of poverty that poor people tend to experience70605040330 20 40
76、60 80 100 Intensity(percent)Incidence(percent)China,2014India,2019/2021Bangladesh,2019Nigeria,2021Papua New Guinea,2016/2018Ethiopia,2019Mozambique,2019/2020Chad,2019Niger,2012Pakistan,2017/2018Central African Republic,2018/2019 Arab States Latin America and the Caribbean East Asia and the Pacific S
77、outh Asia Europe and Central Asia Sub-Saharan AfricaNote:The minimum value for intensity of poverty is 33.3 percent because a person is identified as poor if she or he experiences 33.3 percent or more of all weighted deprivations.The size of each bubble shows the number of poor people in each countr
78、y.Source:Table 1 at the end the report.UNSTACKING GLOBAL POVERTY:DATA FOR HIGH-IMPACT ACTION7of the population are poor and experience more in-tense poverty:57.3 percent and 61.4 percent,respec-tively.Some countries buck this trend.For instance,Papua New Guinea has low intensity(46.5 percent)for its
79、 incidence(56.6 percent)compared with other countries.Of the 1.1 billion poor people,438 million(39.2 per-cent)have a low deprivation score of 33.339.9 per-cent and are thus close to the poverty cutoff(figure 5 and box 4).But 485 million people(43.4 percent)experience severe poverty,with a deprivati
80、on score of 50100 percent.This calls for urgent attention to the poorest of the poor and their overlapping deprivations.In Sub-Saharan Africa the intensity of poverty is particularly serious.The region is home not only to the highest number of poor people but also to the poorest of the poor.Across t
81、he 110 countries cov-ered by the global MPI,99 million poor people have a deprivation score of 70100 percent,meaning that they experience deprivations in all three dimensions and in over two-thirds of weighted indicators.Some 12 million people10 million of them in Sub-Saharan Africahave a deprivatio
82、n score of 90100 percent(figure 6).Box 4 What about people vulnerable to poverty?In countries with low acute multidimensional poverty,it is useful to look at the proportion of people close to the poverty line to assess the populations exposure to future shocks and disruptions(see table 1 at the end
83、of the re-port).The global MPI covers 22 Small Island Developing States(SIDS).1 In many of them,acute poverty is low:14 of them have an incidence of less than 5 percent,2 and only 3 have an incidence of 535 percent.Vulnerabilitythe share of people who are not poor but have deprivations in 2033.3 per
84、cent of all weighted indicatorscan be much higher.For example,in Fiji 1.5 percent of people are poor,but 7.4 percent are vulnerable.In 10 SIDS,1384 percent of people are either poor or vulnerable.3 For example,in Kiribati 19.8 percent of people are poor,but 30 percent of people are vulnerable,so 50
85、percent of people are experiencing poverty or vulnerability.Notes1.Barbados(2012),Belize(2015/2016),Comoros(2012),Cuba(2019),Dominican Republic(2019),Fiji(2021),Guinea-Bissau(2018/2019),Guyana(2019/2020),Haiti(2016/2017),Jamaica(2018),Kiribati(2018/2019),Maldives(2016/2017),Papua New Guinea(2016/201
86、8),Saint Lucia(2012),Samoa(2019/2020),Sao Tome and Principe(2019),Seychelles(2019),Suriname(2018),Timor-Leste(2016),Tonga(2019),Trinidad and Tobago(2011)and Tuvalu(2019/2020).2.Barbados(2012),Belize(2015/2016),Cuba(2019),Dominican Republic(2019),Fiji(2021),Guyana(2019/2020),Jamaica(2018),Maldives(20
87、16/2017),Saint Lucia(2012),Seychelles(2019),Suriname(2018),Tonga(2019),Trinidad and Tobago(2011)and Tuvalu(2019/2020).3.This refers to countries where the sum of the incidence of poverty and the inci-dence of vulnerability rounds up to 1384 percent:Belize(2015/2016),Comoros(2012),Guinea-Bissau(2018/
88、2019),Haiti(2016/2017),Kiribati(2018/2019),Papua New Guinea(2016/2018),Samoa(2019/2020),Sao Tome and Principe(2019),Timor-Leste(2016)and Tuvalu(2019/2020).Figure 5 More than two-fifths of poor people experience severe poverty1.1%12 million39.2%438 million19.7%220 million2.9%33 million4.9%55 million1
89、4.9%166 million17.4%194 million 90100%8089.9%7079.9%6069.9%5059.9%4049.9%33.339.9%Highest deprivation scores(poorest)Lowest deprivation scores(less poor)Note:A persons deprivation score is the sum of the weighted deprivations she or he experiences.The minimum value for deprivation scores is 33.3 per
90、cent because a person is identified as poor if she or he experiences 33.3 percent or more of all weighted deprivations.Source:Authors calculations based on Alkire,Kanagaratnam and Suppa(2023a).8GLOBAL MULTIDIMENSIONAL POVERTY INDEX/2023Which groups are the poorest?Disaggregating poverty data by subn
91、ational region,age group and rural-urban area illuminates striking inequalities within countries and reveals what groups are being left behind.7Subnational regionsPlotting incidence and intensity of poverty for 1,281 subnational regions reveals considerable disparity,even within world regions(figure
92、 7).For example,the poorest country in the Arab States has an incidence of just over 52 percent,but 20 subnational regions have a higher incidence,up to 83.8 percent.Disaggregating by subnational region also reaf-firms the troubling trend that in the places with the highest incidence of poverty,each
93、 poor person on av-erage experiences a higher share of overlapping dep-rivations.But regional patterns vary:the Arab States have a steeper curve than East Asia and the Pacific and Latin America and the Caribbean,while Sub-Sa-haran Africa,with the highest intensity,also has greater dispersion across
94、subnational regions with in-cidence above 80 percent.ChildrenOver half(566 million)of the 1.1 billion poor people are children under age 18.Some 54.1 percent of poor children live in Sub-Saharan Africa,making pover-ty reduction for these 306 million children a vital focus for the region.South Asia i
95、s home to 177 mil-lion poor children,or 31 percent of poor children.Across 110 countries 27.7 percent of children are poor,compared with 13.4 percent of adults.This sit-uation calls for unflagging engagement in reducing child poverty.Rural areasAlmost 84 percent of poor people live in rural areas,an
96、d rural poverty dominates in every world re-gion(figure 8).Rural-urban disparities are glaring in South Asia,where nearly 340 million(87.5 per-cent)poor people live in rural areas,compared with 49 million(12.5 percent)in urban areas.While urban poverty is serious and household surveys may need to do
97、 better at capturing it,most poor people live in rural areas.Figure 6 Sub-Saharan Africa is home to the poorest of the poor 33.339.9%4049.9%5059.9%6069.9%7079.9%8089.9%90100%Sub-Saharan Africa South AsiaArab StatesLatin America and the CaribbeanEast Asia and the PacificEurope and Central Asia 26.6%1
98、42 million17.1%91 million21.4%114 million21.8%116 million8.5%33 million16.0%8 million8%3 million10.8%11 million10.7%184 thousand 83.7%1.43 million46.8%50 million56.4%19 million38.2%20 million52.9%206 million13.2%51 million19.9%78 million19.7%10 million16.3%9 million16.6%6 million35%37 million16.7%6
99、million0 10 20 30 40 50 60 70 80 90 100534 million389 million53 million33 million106 million1.71 million6.3%34 million 5.3%6 million Highest deprivation scores(poorest)Lowest deprivation scores(less poor)Note:A persons deprivation score is the sum of the weighted deprivations she or he experiences.T
100、he minimum value for deprivation scores is 33.3 percent because a person is identified as poor if she or he experiences 33.3 percent or more of all weighted deprivations.Source:Authors calculations based on Alkire,Kanagaratnam and Suppa(2023a).UNSTACKING GLOBAL POVERTY:DATA FOR HIGH-IMPACT ACTION9Fi
101、gure 7 Poorer subnational regions tend to have higher intensity and incidence of povertyNorth Dafur(Sudan)East Dafur(Sudan)Central Dafur(Sudan)Hela(Papua New Guinea)Southern Highlands (Papua New Guinea)0 20 40 60 80 33Intensity(percent)Incidence(percent)Arab States0 20 40 60 80 1007060504
102、033Intensity(percent)Incidence(percent)East Asia and the PacificGrande Anse(Haiti)Centre(Haiti)Matanzas(Cuba)Khatlon(Tajikistan)Southeast(North Macedonia)0 20 40 60 80 33Intensity(percent)Incidence(percent)Europe&Central Asia0 20 40 60 80 33Intensity(percent)Incidence(percent)L
103、atin America and the CaribbeanNooristan (Afghanistan)Chandigarh(India)Urozgan (Afghanistan)Kandahar (Afghanistan)Punjab(Pakistan)Sindh(Pakistan)Kano(Nigeria)Kwilu(DemocraticRepublic ofthe Congo)Kasa (DemocraticRepublic ofthe Congo)Karamoja (Uganda)Kanem(Chad)Nampula(Mozambique)Sofala(Mozambique)0 20
104、 40 60 80 33Intensity(percent)Incidence(percent)South Asia0 20 40 60 80 33Intensity(percent)Incidence(percent)Sub-Saharan AfricaRakhine(Myanmar)Savannakhet(Lao PeoplesDemocraticRepublic)Western Region (China)Bni Mellal-Khnifra(Morocco)Khartoum(Sudan)Al-Jawf(Yemen)Rest-Ouest(Hai
105、ti)Sipaliwini(Suriname)Apurimac(Peru)Zacapa(Guatemala)Note:The minimum value for the intensity of poverty is 33.3 percent because the global MPI identifies people as multidimensionally poor if their deprivation score is 1/3 or higher.The size of each bubble shows the number of poor people in each su
106、bnational region.Source:Alkire,Kanagaratnam and Suppa 2023b.10GLOBAL MULTIDIMENSIONAL POVERTY INDEX/2023What do deprivation indicators tell us about povertyfrom the regional to the subnational level?The global MPI tells a story about poverty and dispar-ities at the regional,national and subnational
107、levels.In Sub-Saharan Africa poverty affects an average of 49.5 percent of the population,but incidence and MPI values vary widely across countries,from 0.9 percent to 91 percent and from 0.003 to 0.601,and across sub-national regions within those countries.For example,in Senegal(2019),where 50.8 pe
108、rcent of people are poor and the MPI value is 0.263,the incidence in subnation-al regions ranges from 18.3 percent to 85.7 percent,and MPI values range from 0.084 to 0.502(figure 9).How people are poor also varies across coun-tries and subnational regions.For example,in Sub-Saharan Africa the depriv
109、ations in living stand-ards together total around 50 percent,highlighting how tackling those deprivations is critical to over-coming poverty there(see left panel of figure 9).But the deprivations also vary at the country level in Sub-Saharan Africa(see middle panel of figure 9)and at the subnational
110、 level within those countries.Compare two subnational regions of Senegal(2019).Kdougou,in the southeast,and Fatick,on the coast,have similar global MPI values(see the right panel of figure 9).Yet deprivation in school attendance contrib-utes more to poverty in Fatick,while deprivations in housing an
111、d electricity are stronger contributors to pov-erty in Kdougouso pathways to poverty reduction dif-fer.In short,achieving the greatest impact on poverty requires looking below the surface to understand which indicators merit most action in a particular area.Figure 8 Across world regions most poor pe
112、ople live in rural areas0 100 200 300 400 500 600Number of poor(millions)Europe and Central AsiaLatin America and the CaribbeanArab StatesEast Asia and the Pacific South AsiaSub-Saharan Africa Rural UrbanSource:Alkire,Kanagaratnam and Suppa 2023b.UNSTACKING GLOBAL POVERTY:DATA FOR HIGH-IMPACT ACTION
113、11What deprivations do poor people experience?To end poverty in all its forms,the interlinked depriva-tions that poor people experience need to be addressed to reduce the intensity of poverty and thereby empow-er poor people to exit poverty.Recall that people liv-ing in multidimensional poverty ordi
114、narily experience multiple deprivations simultaneously.Breaking the global MPI down by indicator reveals which overlap-ping deprivations are the most widespread(figure 10):Across 110 countries 824991 million of the 1.1 bil-lion poor people lack adequate sanitation,housing or cooking fuel.More than h
115、alf of poor people are de-prived in nutrition,electricity or years of schooling.The number of poor people deprived in nutrition is similar in South Asia and Sub-Saharan Africa(around 245 million).Almost 80 percent of poor people who lack access to electricity444 millionlive in Sub-Saharan Africa and
116、 are being left behind in an increasingly digital world.In all regions except Europe and Central Asia,around half of poor people live in a household where no member has completed six years of schooling,making this a vexing cross-regional issue.Figure 9 Multidimensional Poverty Index values and indic
117、ator composition vary widely across world regions,countries and subnational regionsNigerChadCentral African RepublicBurundiMadagascarMaliGuineaMozambiqueBeninEthiopiaGuineaBissauDemocratic Republic of the CongoMauritaniaSierra LeoneUnited Republic of TanzaniaAngolaUgandaSenegalLiberiaCte dIvoireCame
118、roonZambiaMalawiRwandaGambiaNamibiaComorosTogoNigeriaKenyaCongoGhanaZimbabweLesothoKingdom of EswatiniBotswanaGabonSao Tome and PrincipeSouth AfricaSeychellesSubnational regions in Senegal(2019)MPI ranges from 0.084 to 0.502KaffrineKoldaSdhiouTambacoundaDiourbelMatamKaolackLougaKdougouFatickSaintLou
119、isThisZiguinchorDakarWorld regions MPI ranges from 0.004 to 0.262Countries in Sub-Saharan Africa MPI ranges from 0.003 to 0.601Sub-Saharan AfricaSouth AsiaArab StatesLatin America and the CaribbeanEast Asia and the PacificEurope and Central AsiaAllMPIMPI 0.0 0.1 0.2 0.3 0.4 0.5 MPI 0.0 0.1 0.2 0.3 0
120、.4 0.5 0.6 0.0 0.1 0.2 0.3 Nutrition Child mortality Years of schooling School attendance Cooking fuel Sanitation Drinking water Electricity Housing AssetsNote:The bars are divided into segments that show the absolute contribution of each indicator to the Multidimensional Poverty Index(MPI)value.Sou
121、rce:Table 1 at the end the report and Alkire,Kanagaratnam,and Suppa(2023b).12GLOBAL MULTIDIMENSIONAL POVERTY INDEX/2023How do monetary and multidimensional poverty compare?Multidimensional metrics complement monetary pov-erty metrics by measuring nonmonetary deprivations.Multidimensional poverty usi
122、ng the global MPI is often more widespread than extreme monetary pov-erty.In 42 of the 61 countries with data,8 the inci-dence of multidimensional poverty is higher than the incidence of extreme monetary poverty,measured by the World Bank at$2.15 a day(figure 11).9In Chad,Guinea and Mali the inciden
123、ce of multi-dimensional poverty is 50 percentage points higher than that of monetary poverty,but in Malawi the in-cidence of monetary poverty is 20 percentage points higher than that of multidimensional poverty.It is clear that human lives are battered in multiple ways and that patterns vary.How has
124、 poverty changed?The global MPI includes harmonized trends for 81 countries,covering more than 5 billion people,and 124 country periods,disaggregated by subnational region,age group and rural-urban area(see table 2 at the end of the report).The findings at a glance are encouraging,showing that pover
125、ty reduction is possible,even though most progress occurred before the COVID-19 pandemic:72 of the 81 countries with trend data had a signifi-cant absolute reduction in global MPI value during at least one period.10 And 24 of these countries had a significant reduction across two periods.11 25 count
126、ries had a significant reduction in poor peoples deprivations in every indicator.12 25 countries halved their global MPI value well within 15 years,showing that progress towards SDG target 1.2 at scale is attainable(table A).13?At least one country in every world region halved its global MPI value,i
127、ncluding small countries such as Sao Tome and Principe(2008/20092014)and large ones such as China(20102014),India(2005/20062015/2016)and Indonesia(20122017).?Countries with different incidences of poverty also halved their global MPI value.While 17 countries that did so had an incidence under 25 per
128、cent in the first period,14 India and Congo both had a starting incidence above 50 percent.?Countries took 412 years to halve their global MPI value,suggesting that achieving SDG target 1.2 is feasible.?Do these trends continue after the COVID-19 pandemic?Data for 2021 or 2022 are available only for
129、 Cambodia(box 5),Madagascar,Mexico,Figure 10 What deprivations do poor people experience by region?Arab States Latin America and the Caribbean East Asia and the Pacific South Asia1,0008006004002000Number of people living in multidimensional poverty(millions)99725725Child mortal
130、itySchool attendanceDrinking WaterAssetsYears of schoolingElectricityNutritionSanitationHousingCooking fuel Europe and Central Asia Sub-Saharan AfricaIndicators that multidimensionally poor people are deprived inSource:Table 1 at the end the report.UNSTACKING GLOBAL POVERTY:DATA FOR HIGH-IMPACT ACTI
131、ON13Figure 11 The incidence of multidimensional and monetary poverty shows how human lives are battered in multiple ways The height of the bar represents the incidence of multidimensional poverty The height of the bar represents the incidence of severe multidimensional poverty The dot represents the
132、 incidence of monetary poverty($2.15 in purchasing power parity terms a day)0Percentage of the population who are poor by each measureSerbiaArmeniaUkraineTurkmenistanGeorgiaNorth MacedoniaKyrgyzstanArgentinaJordanKazakhstanPalestine,State ofCosta RicaThailandTrinidad and TobagoMaldivesCub
133、aAlbaniaTunisiaSeychellesTongaRepublic of MoldovaMontenegroAlgeriaFijiUzbekistanGuyanaSaint LuciaLibyaViet NamEcuadorTuvaluBosnia and HerzegovinaBarbadosDominican RepublicJamaicaSri LankaSurinameIndonesiaChinaBrazilMexicoBelizeParaguayColombiaEgyptPhilippinesSamoaSouth AfricaPeruMoroccoMongoliaTajik
134、istanEl SalvadorIraqBoliviaSao Tome and PrincipeHondurasIndiaGabonCambodiaBotswanaNepalNicaraguaKiribatieSwatiniLesothoBangladeshLao Peoples Democratic RepublicZimbabweGhanaCongoGuatemalaKenyaNigeriaMyanmarTogoComorosNamibiaGambiaPakistanHaitiTimor-LesteRwandaMalawiZambiaCameroonCte dIvoireYemenLibe
135、riaSenegalPapua New GuineaAfghanistanSudanUgandaAngolaUnited Republic of TanzaniaSierra LeoneMauritaniaDemocratic Republic of the CongoGuinea-BissauEthiopiaBeninMozambiqueGuineaMaliMadagascarBurundiCentral African RepublicChadNigerNote:Includes 110 countries for which multidimensional poverty data a
136、re available between 2011 and 2022,93 of which also had data on monetary poverty for the same period.For both measures the most recent data available were used(see table 1 at the end of the report).Source:Table 1 at the end the report.Table A Countries that halved their global Multidimensional Pover
137、ty Index value19 countries halved their global Multidimensional Poverty Index(MPI)value during one periodAlbania(2008/20092017/2018)Guyana(20142019/2020)North Macedonia(2005/20062011)Plurinational State of Bolivia(20082016)Honduras(2011/20122019)Sao Tome and Principe(2008/20092014)Cambodia(20142021/
138、2022)India(2005/20062015/2016)Serbia(20142019)China(20102014)Indonesia(20122017)Turkmenistan(20062015/2016)Congo(20052014/2015)Kyrgyzstan(2005/20062014 and again in 20142018)aViet Nam(2013/20142020/2021Dominican Republic(20072014)Morocco(20112017/2018)Gabon(20002012)Nicaragua(20012011/2012)6 countri
139、es halved their global MPI value across two or more periodsLesotho(20092014,20142019)Nepal(20112016,20162019)Suriname(20062010,20102018)Mongolia(20102013,20132018)Peru(20122018,20192021)Thailand(20122015/2016,2015/20162019)a.Kyrgyzstan halved its global MPI value twice(once during each period indica
140、ted).Note:Halving the global MPI value means that the ratio of the global MPI value in the latter period to the global MPI value in the initial period rounds to 0.5 or lower.Souce:Table 2 at the end the report and Alkire,Kanagaratnam,and Suppa(2023c).14GLOBAL MULTIDIMENSIONAL POVERTY INDEX/2023Niger
141、ia and Peru,but only Cambodia,Nigeria and Peru showed significant reductions.Once again,we call urgently for poverty data that per-mit updates to these global trends.?10 Sub-Saharan African countries had an ab-solute rate of reduction during one period that was similar to or faster than that of the
142、fastest 4 countries that halved their global MPI value.15 But these Sub-Saharan African countries did not halve their MPI value due to much higher initial levels.In addition to India(box 6),where 415 million people moved out of poverty during 2005/20062019/2021,large numbers of people also exited po
143、verty in China(69 million during 20102014),Bangladesh(19 million during 20152019),Indonesia(8 million during 20122017),Pakistan(7 million during 2012/20132017/2018)and Nigeria(5 million during 20182021).In 42 countriesover half of those coveredei-ther there was no significant reduction in poverty am
144、ong children,or the global MPI value fell more slowly among children than among adults during at least one period.16 While 25 of the countries are in Sub-Saharan Africa,17 are in other regions.In 14 countries in Sub-Saharan Africa and 1 country in the Arab States,population growth outpaced poverty r
145、eduction.17 Despite a significant decrease in incidence of poverty,the number of poor people increased during at least one period.Box 5 Poverty reduction in Cambodia from 2014 to 2021/2022Cambodias global Multidimensional Poverty Index(MPI)value plummeted from 0.168 in 2014 to 0.070 in 2021/2022,and
146、 incidence of poverty fell from 36.7 percent to 16.6 percent.So,one in five Cambodians moved out of poverty in just 7.5 years.The number of poor people was halved from 5.6 million to 2.8 million.Children experienced the fastest progress:incidence of poverty among children declined from 42.7 percent
147、in 2014 to 20.5 percent in 2021/2022.Of the 25 subnational regions,17 had significant reductions in global MPI value and incidence of poverty.The poor-est two subnational regionsKratie,and Preah Vihear and Stung Trengsignificantly reduced their global MPI value and incidence the fastest,and the five
148、 next-fastest reductions1 were among the six next-poorest regions.Incidence fell from 64.3 percent to 34.6 percent in Preah Vihear and Stung Treng.However,despite the significant decrease in poverty in rural areas,there was no significant reduction in global MPI value or incidence or intensity in ur
149、ban areas.The period saw strong rural to urban migration.2Deprivations in 9 of the 10 indicators fell significantlyby more than 21 percentage points for electricity,sanitation and cooking fuel,by 11.6 percentage points for nutrition and by 11 percentage points for years of schooling.The only indicat
150、or for which deprivation did not significantly decline was school attendance.It is unclear whether this was related to the COVID-19 pandemic.From 2014 to 2021/2022 the percentage of the urban population who were poor and deprived in school attendance rose significantly,from 3.8 percent to 6.7 percen
151、t,which might reflect school closures during the pandemic.In the same period roughly 12 percent of the rural population were poor and deprived in school attendance.Considering all deprivations,including those of nonpoor people,reveals a disturbing rise in out-of-school children.In 2014,12.7 percent
152、of people lived with a child who was not attending school,and nearly all those people were poor.But by 2021/2022 the share had mushroomed to 23 percent.Yet this increase is not evident among rural or urban poor people,which suggests that interventions directed at poor children might have taken hold.
153、Sustainable Development Goal target 1.2 calls on countries to halve poverty based on national definitions.Cambo-dias stellar performance during 20142021/2022 shows that such progress is feasible.Notes1.Siem Reap,Pursat,Kampong Thom,Otdar Meanchey and Kampong Chhnang.2.The share of the population liv
154、ing in rural areas declined from 83.8 percent in 2014 to 61.5 percent in 2021/2022,and the share living in urban areas rose from 16.2 percent to 38.5 percent.UNSTACKING GLOBAL POVERTY:DATA FOR HIGH-IMPACT ACTION15How to use the global Multidimensional Poverty Index for impactHow can the global MPI a
155、nd its associated informa-tionincidence and intensity of poverty,and com-ponent indicatorsinform tangible and pragmatic actions to achieve SDG 1 by 2030?The global MPI provides the crucial birds-eye view to detect acute poverty across developing countries.Incidence of poverty reveals where people li
156、ve and how widespread acute poverty is within regional,na-tional and subnational borders and among population groups.Intensity of poverty provides invaluable infor-mation on the depths of poor peoples poverty,shining light on the poorest of poor people.The global MPI is disaggregated to illuminate p
157、ockets of poverty and who is left behind.Finally,breakdown by component indicator shows what deprivations poor people experi-ence,which can guide the choice of poverty reduction interventions to achieve the greatest impact.The global MPI can be pictured as a stack of blocks,where each deprivation of
158、 each poor person is indicat-ed by a block whose colour signifies the indicator and whose thickness signifies the indicators weight.When all the blocks are stacked on top of each otherre-flecting all the weighted deprivations of all poor peo-plethe height of the stack is the global MPI value.So,remo
159、ving a block from the stackthat is,eliminating a poor persons deprivationreduces the global MPI value.The colour and thickness of the blocks help in identifying action pathways to reduce poverty.This report has shown many ways that the MPI da-tabase,covering 6.1 billion people and 1.1 billion poor p
160、eople,can be used to better understand multidimen-sional poverty,disparities and indicator composition.It has highlighted stories of success in reducing MPI equitably,so that the poorest groups are not left be-hind but progress the fastest.Indeed,it found that 25 diverse countries halved their globa
161、l MPI value well within 15 years,showing that progress is possible,at speed and to scale.Policy design is contextual and must engage local as well as international institutions.Multidimen-sional poverty also exacerbates or is exacerbated by other contextual challenges such as conflict,environ-mental
162、 threats,governance challenges and economic uncertainties.Yet the hope is that the global MPI data will be used by many actorsacross institutions,world regions,disciplines and sectorsto design high-impact,cost-efficient and evidence-based policies for pover-ty reduction.Special focus is needed on th
163、e poorest places and groups,many of which are in Sub-Saharan Africa.By using these data on MPI values,the propor-tion of poor people,the intensity of their poverty,the number of poor people and indicator composition,many actors can concentrate on the multiple depri-vations that batter poor peoples l
164、ivesand reduce acute multidimensional poverty.Box 6 Reducing global Multidimensional Poverty Index values is possibleat speed and to scaleIndia 415 million poor people moved out of poverty from 2005/2006 to 2019/2021.Incidence fell from 55.1 percent to 16.4 percent.Deprivation in all indicators decl
165、ined.The poorest states and groups,including children and people in disadvantaged caste groups,had the fastest absolute progress.1Sierra Leone During 20132017 Sierra Leone had the fastest reduction in global MPI value of any country in any period.Incidence fell from 74.0 percent to 58.3 percent.Depr
166、ivation in all indicators declined.Children had the second fastest reduction in global MPI value of any country.This occurred during the Ebola pandemic.Note1.UNDP and OPHI 2022.16GLOBAL MULTIDIMENSIONAL POVERTY INDEX/2023Notes1 Based on the definition for basic drinking water at https:/washdata.org/
167、monitoring/drinking-water.2 Based on the definition for basic sanitation at https:/washdata.org/monitoring/sanitation.3 Codes to compute the MPI are available at https:/hdr.undp.org/mpi-statistical-programmes.In addition to tables 1 and 2 of this report,disag-gregated estimates by subnational region
168、,age group,rural-urban area and gender of house-hold head;alternative poverty cutoffs;sample sizes;standard errors;and indicator details pro-duced by OPHI are available at https:/ophi.org.uk/multidimensional-poverty-index/data-tables-do-files/.See details in Alkire,Kanagaratnam and Suppa(2023a).4 Wo
169、rld Bank 20225 Albania(2017/2018),Argentina(2019/2020),Armenia(2015/2016),Costa Rica(2018),Cuba(2019),Georgia(2018),Jordan(2017/2018),Ka-zakhstan(2015),Kyrgyzstan(2018),Maldives(2016/2017),Republic of Moldova(2012),North Macedonia(2018/2019),State of Palestine(2019/2020),Serbia(2019),Seychelles(2019
170、),Thailand(2019),Tonga(2019),Trinidad and To-bago(2011),Tunisia(2018),Turkmenistan(2019)and Ukraine(2012).6 Afghanistan(2015/2016),Angola(2015/2016),Benin(2017/2018),Burundi(2016/2017),Cen-tral African Republic(2018/2019),Chad(2019),Democratic Republic of the Congo(2017/2018),Ethiopia(2019),Guinea(2
171、018),Guinea-Bissau(2018/2019),Liberia(2019/2020),Madagascar(2021),Mali(2018),Mauritania(2019/2021),Mo-zambique(2019/2020),Niger(2012),Papua New Guinea(2016/2018),Senegal(2019),Sier-ra Leone(2019),Sudan(2014),United Republic of Tanzania(2015/2016)and Uganda(2016).7 Previous global MPI reports have dr
172、awn atten-tion to gender and ethnic disparities(UNDP and OPHI 2021)and inequalities across sub-national regions,age groups and rural-urban areas(UNDP and OPHI 2019),among other inequalities.8 Of 110 countries with data on multidimensional poverty between 2011 and 2022,61 also have data on extreme mo
173、netary poverty within three years of the survey used for computing the inci-dence of multidimensional poverty(see table 1 at the end of the report).9 Moreover,the lower bound of the incidence of multidimensional poverty is greater than the point estimate for incidence of monetary poverty in 42 of th
174、e 61 countries.If only point estimates are compared,the incidence of multidimensional poverty is higher in 44 of the 61 countries.10 Nine countries had no significant change dur-ing any period:Armenia(20102015/2016),Benin(20142017/2018),Burkina Faso(20062010),Cameroon(20112014,20142018),Guinea-Bissa
175、u(20142018/2019),Jordan(20122017/2018),Montenegro(20132018),State of Palestine(20102014,20142019/2020)and Ukraine(20072012).11 Plurinational State of Bolivia(20032008,20082016),Cambodia(20102014,20142021/2022),Democratic Republic of the Congo(20072013/2014,2013/20142017/2018),Dominican Republic(2007
176、2014,20142019),Ethiopia(20112016,20162019),Gambia(2005/20062013,20132018),Honduras(2005/20062011/2012,2011/20122019),India(2005/20062015/2016,2015/20162019/2021),Kyrgyzstan(2005/20062014,20142018),Lesotho(20092014,20142018),Liberia(20072013,20132019/2020),Mali(20062015,20152018),Mexico(20122016,2016
177、2020),Mongolia(20102013,20132018),Nepal(20112016,20162019),North Macedonia(2005/20062011,20112018/2019),Peru(20122018,20192021),Rwanda(20102014/2015,2014/20152019/2020),Sao Tome and Principe(2008/20092014,20142019),Si-erra Leone(20132017,20172019),Suriname(20062010,20102018),Thailand(20122015/2016,2
178、015/20162019),Zambia(20072013/2014,2013/20142018)and Zimbabwe(2010/20112015,2015209).12 Bangladesh(20142019),Plurinational State of Bolivia(20032008,20082016),Ecua-dor(2013/20142018),Kingdom of Eswati-ni(20102014),Ethiopia(20112016),Gabon(20002012),Guinea(20122016),Hondu-ras(2005/20062011/2012,2011/
179、20122019),India(2005/20062015/2016,2015/20162019/2021),Indonesia(20122017),Iraq(20112018),Kenya(2008/20092014),Lao Peoples Democratic Republic(2011/20122017),Lesotho(20142018),Malawi(20102015/2016),Moroc-co(20112017/2018),Mozambique(20032011),Nicaragua(20012011/2012),Niger(20062012),Sao Tome and Pri
180、ncipe(2008/20092014),Sierra Leone(20132017),Timor-Leste(2009/20102016),Togo(2013/20142017),Vi-et Nam(2013/20142020/2021)and Zambia(20072013/2014).13 Periods differ in length.Halving the global MPI value means that the ratio of the global MPI value in the latter period to the global MPI val-ue in the
181、 initial period rounds to 0.5 or lower.14 Albania(2.06 percent in 2008/2009),Plurina-tional State of Bolivia(20.62 percent in 2008),China(from 9.47 percent in 2010),Dominican Republic(from 7.27 percent in 2007),Guy-ana(from 3.30 percent in 2014),Honduras(from 22.83 percent in 2011/2012),Indonesia(fr
182、om 6.87 percent in 2012),Kyrgyzstan(from 9.39 percent in 2005/2006),Mongolia(from 19.59 percent in 2010),Morocco(17.26 percent in 2011),North Macedonia(7.63 percent in 2005/2006),Peru(12.66 percent in 2012),Ser-bia(0.1 percent in 2019),Suriname(12.74 per-cent in 2006),Thailand(1.39 percent in 2012),
183、Turkmenistan(3.25 percent in 2006)and Viet Nam(4.93 percent in 2013/2014).15 The 10 Sub-Saharan African countries are Cte dIvoire(2011/20122016),Gambia(20132018),Guinea(20122016),Ethiopia(20162019),Liberia(20072013),Malawi(20102015/2016),Mali(20152018),Mozam-bique(20032011),Sierra Leone(20132017)and
184、 Togo(2013/20142017),16 In 31 countries there was no significant re-duction in child poverty during at least one period:Armenia(20102015/2016),Benin(20142017/2018),Burkina Faso(20062010),Cam-eroon(20112014,20142018),Central African Republic(20102018/2019),Chad(2014/20152019),Colombia(20102015/2016),
185、Gambia(20182019/2020),Ghana(20112014),Guinea(20162018),Guinea-Bissau(20142018/2019),Guyana(20092014),Jordan(20122017/2018),Madagascar(20182021),Malawi(2015/20162019/2020),Mauritania(20152019/2021),Mexico(20162020,20202021),Republic of Moldova(20052012),Montenegro(20132018),Nigeria(20132016/2017,2016
186、/20172018),North Macedonia(20112018/2019),Pakistan(2012/20132017/2018),State of Pales-tine(20102014,20142019/2020),Peru(20182019),Senegal(20172019),Serbia(20102014,20142019),Suriname(20062010),Thai-land(20122015/2016,2015/20162019),Togo(20102013/2014),Turkmenistan(2015/20162019)and Ukraine(2007-2012
187、).In 16 countries(including some of the 30 in which there was no significant reduction in child poverty during at least one period)the MPI value fell more slow-ly among children than among adults during at least one period:Central Africa(20002010,20102018/2019),Democratic Republic of the Congo(2013/
188、20142017/2018),Cte dIvoire(2011/20122016),Dominican Republic(20142019),Ethiopia(20112016),Gabon(20002012),Gambia(2005/20062013),Guinea(20122016),Madagascar(2008/20092018),Malawi(20102015/2016),Mali(20152018),Mozambique(20032011),Niger(20062012),Rwanda(2014/20152019/2020),Sierra Leone(20132017)and Un
189、ited Republic of Tanzania(20102015/2016).17 Burundi(20102016/2017),Central African Re-public(2000-10),Democratic Republic of the Con-go(2013/20142017/2018),Ethiopia(20112016),Gambia(2005/06-13),Madagascar(2008/20092018),Malawi(2015/20162019/2020),Mali(20062015),Mauritania(2011-15),Mozambique(2003201
190、1),Niger(20062012),Senegal(20052017),Sudan(20102014),United Repub-lic of Tanzania(20102014)and Zambia(20072013/2014,2013/20142018).UNSTACKING GLOBAL POVERTY:DATA FOR HIGH-IMPACT ACTION17ReferencesAlkire,S.,Kanagaratnam,U.,and Suppa,N.2023a.“The Global Multidimensional Poverty Index(MPI)2023 Country
191、Results and Methodological Note.”OPHI MPI Methodological Note 55,Oxford Poverty and Human Development Initiative,University of Oxford,UK.Alkire,S.,Kanagaratnam,U.,and Suppa,N.2023b.“The Global Multidimensional Poverty Index(MPI)2023 Disaggregation Results and Methodological Note.”OPHI MPI Methodolog
192、ical Note 56,Oxford Poverty and Human Development Initiative,University of Oxford,UK.Alkire,S.,Kanagaratnam,U.,and Suppa,N.(2023c).“A Methodological Note on the Global Multidimension-al Poverty Index(MPI)2023 Changes over Time Results for 84 Countries.”OPHI MPI Methodological Note 57,Oxford Poverty
193、and Human Development Initiative,University of Oxford,UK.2018 University of OxfordUNDP(United Nations Development Programme)and OPHI(Oxford Poverty and Human Development Initiative).2019.Global Multidimensional Poverty Index 2019:Illuminating Inequalities.New York and Oxford,UK.UNDP(United Nations D
194、evelopment Programme)and OPHI(Oxford Poverty and Human Development Initiative).2021.Global Multidimensional Poverty Index 2021:Unmasking Disparities by Ethnicity,Caste and Gender.New York and Oxford,UK.UNDP(United Nations Development Programme)and OPHI(Oxford Poverty and Human Development Initiative
195、).2022.Global Multidimensional Poverty In-dex 2022:Unpacking Deprivation Bundles to Reduce Multidimensional Poverty.New York and Oxford,UK.World Bank.2017.Monitoring Global Poverty:Report of the Commission on Global Poverty.Washington,DC.World Bank.2022.World Development Indicators database.Washingt
196、on,DC.http:/data.worldbank.org.Accessed 7 May 2023.18GLOBAL MULTIDIMENSIONAL POVERTY INDEX/2023Statistical tablesCountrySDG 1.2SDG 1.2SDG 1.1Multidimensional Poverty IndexaPopulation in multidimensional povertyaPopulation vulnerable to multidimensional povertyaContribution of deprivation in dimensio
197、n to overall multidimensional povertyaPopulation living below monetary poverty line(%)Intensity of deprivationInequality among the poorPopulation in severe multidimensional povertyHealthEducationStandard of livingNational poverty linePPP$2.15 a dayHeadcountYear and surveyb(thousands)20112022Value(%)
198、In survey year2021(%)Value(%)(%)(%)(%)(%)20112021c20112021cEstimates based on surveys for 20172022Albania2017/2018 D0.0030.7202039.1.d0.15.028.355.116.721.80.0Algeria2018/2019 M0.0051.459061039.20.0070.23.631.249.319.55.50.5Argentina2019/2020 Me0.001 f0.4 f195 f196 f34.0 f.d0.0 f1.6 f69.7 f21.4 f8.9
199、 f42.01.0Bangladesh2019 M0.10424.640,78441,73042.20.0106.518.217.337.645.124.313.5Benin2017/2018 D0.36866.87,9768,68255.00.02540.914.720.836.342.938.519.9Burundi2016/2017 D0.409 g75.1 g8,378 g9,426 g54.4 g0.022 g46.1 g15.8 g23.8 g27.2 g49.0 g64.965.1Cambodia2021/2022 D0.07016.62,7912,76142.30.0094.1
200、20.521.548.030.517.7.Cameroon2018 D0.23243.610,93111,85653.20.02624.617.625.227.647.137.525.7Central African Republic2018/2019 M0.46180.44,1894,38857.40.02555.812.920.227.852.0.Chad2019 M0.51784.213,57514,46161.40.02464.610.719.136.644.342.330.9Congo(Democratic Republic of the)2017/2018 M0.33164.556
201、,18761,86951.30.02036.817.423.119.957.063.969.7Costa Rica2018 M0.002 f,h0.5 f,h27 f,h28 f,h37.1 f,h.d0.0 f,h2.4 f,h40.5 f,h41.0 f,h18.5 f,h30.01.2Cuba2019 M0.003 f0.7 f80 f80 f38.1 f.d0.1 f2.7 f10.1 f39.8 f50.1 f.Dominican Republic2019 M0.0092.324725238.80.0060.24.814.646.239.221.00.9Ecuador2018 N0.
202、0082.135637238.00.0040.15.933.927.338.833.03.6Ethiopia2019 D0.36768.778,44382,67953.30.02241.918.414.031.554.523.527.0Fiji2021 M0.0061.5141438.1.d0.27.438.017.444.624.11.3Gambia2019/2020 D0.19841.71,0741,10147.50.01617.328.032.733.034.348.617.2Georgia2018 M0.001 f0.3 f13 f13 f36.6 f.d0.0 f2.1 f47.1
203、f23.8 f29.1 f21.35.5Ghana2017/2018 M0.11124.67,6068,08945.10.0148.420.123.630.545.923.425.2Guinea2018 D0.37366.28,3138,96056.40.02543.516.421.438.440.343.713.8Guinea-Bissau2018/2019 M0.34164.41,2691,32752.90.02135.920.019.135.045.847.721.7Guyana2019/2020 M0.007 i1.8 i15 i15 i39.3 i0.007 i0.2 i6.5 i3
204、0.4 i22.4 i47.2 i.Haiti2016/2017 D0.20041.34,4834,72448.40.01918.521.818.524.657.058.529.2Honduras2019 M0.05112.01,1931,23142.70.0113.014.818.839.242.048.012.7India2019/2021 D0.06916.4230,739230,73942.00.0104.218.732.228.239.721.910.0Indonesia2017 D0.014 j3.6 j9,572 j9,907 j38.7 j0.006 j0.4 j4.7 j34
205、.7 j26.8 j38.5 j9.83.5Iraq2018 M0.0338.63,5053,75937.90.0051.35.233.160.96.018.90.1Jamaica2018 N0.011 k2.8 k78 k78 k38.9 k0.005 k0.2 k5.0 k52.2 k20.9 k26.9 k19.9.Jordan2017/2018 D0.0020.4454835.4.d0.00.737.553.59.015.7.Kiribati2018/2019 M0.08019.8252640.50.0063.530.230.312.157.621.91.7Kyrgyzstan2018
206、 M0.0010.4242636.3.d0.05.264.617.917.525.31.3Lao Peoples Democratic Republic 2017 M0.10823.11,6151,71347.00.0169.621.221.539.738.818.37.1Lesotho2018 M0.084 h19.6 h431 h447 h43.0 h0.009 h5.0 h28.6 h21.9 h18.1 h60.0 h49.732.4Liberia2019/2020 D0.25952.32,6622,71749.60.01824.923.319.728.651.750.927.6Mad
207、agascar2021 D0.38668.419,78419,78456.40.02645.815.417.831.650.670.780.7Malawi2019/2020 M0.23149.99,6669,92246.30.01217.527.518.625.555.950.770.1Maldives2016/2017 D0.0030.84434.4.d0.04.880.715.14.25.40.0Mali2018 D0.37668.313,62214,96855.00.02244.715.319.641.239.344.614.8Mauritania2019/2021 D0.32758.4
208、2,6972,69756.00.02438.012.317.742.439.931.86.5Mexico2021 N0.016 l,m4.1 l,m5,156 l,m5,156 l,m40.5 l,m0.007 l,m0.8 l,m3.5 l,m64.1 l,m13.6 l,m22.3 l,m43.93.1Mongolia2018 M0.028 n7.3 n230 n243 n38.8 n0.004 n0.8 n15.5 n21.1 n26.8 n52.1 n27.80.7Montenegro2018 M0.0051.28839.6.d0.12.958.522.319.222.62.8Moro
209、cco2017/2018 P0.027 o6.4 o2,285 o2,358 o42.0 o0.012 o1.4 o10.9 o24.4 o46.8 o28.8 o4.81.4Mozambique2019/2020 N0.372 k,p61.9 k,p19,310 k,p19,866 k,p60.0 k,p0.037 k,p43.0 k,p13.9 k,p27.3 k,p26.3 k,p46.4 k,p46.164.6Nepal2019 M0.07417.55,0475,25842.50.0104.917.823.233.943.0.Nigeria2021 M0.175 j,q33.0 j,q
210、70,516 j,q70,516 j,q52.9 j,q0.027 j,q18.1 j,q16.6 j,q19.5 j,q35.5 j,q45.0 j,q40.130.9North Macedonia2018/2019 M0.0010.48838.2.d0.12.229.652.617.821.82.7Pakistan2017/2018 D0.19838.384,22888,70151.70.02321.512.927.641.331.121.94.9Palestine,State of2019/2020 M0.0020.6282935.0.d0.01.362.931.06.129.20.5P
211、apua New Guinea2016/2018 D0.263 j56.6 j5,283 j5,634 j46.5 j0.016 j25.8 j25.3 j4.6 j30.1 j65.3 j.Peru2021 N0.0266.62,2362,23638.90.0060.910.414.033.652.430.12.9Philippines2017 D0.024 j5.8 j6,187 j6,600 j41.8 j0.010 j1.3 j7.3 j20.3 j31.0 j48.7 j16.73.0Rwanda2019/2020 D0.23148.86,4186,57247.30.01419.72
212、2.719.026.654.438.252.0Samoa2019/2020 M0.0256.3141439.10.0030.512.936.931.231.920.31.2Sao Tome and Principe2019 M0.04811.7252640.90.0072.117.018.736.644.666.715.6Senegal2019 D0.26350.88,1348,57951.70.01927.718.220.748.430.946.79.3Serbia2019 M0.000 f,r0.1 f,r8 f,r8 f,r38.1 f,r.d0.0 f,r2.1 f,r30.9 f,r
213、40.1 f,r29.0 f,r21.71.6TABLE 1Multidimensional Poverty Index:developing countries20GLOBAL MULTIDIMENSIONAL POVERTY INDEX/2023CountrySDG 1.2SDG 1.2SDG 1.1Multidimensional Poverty IndexaPopulation in multidimensional povertyaPopulation vulnerable to multidimensional povertyaContribution of deprivation
214、 in dimension to overall multidimensional povertyaPopulation living below monetary poverty line(%)Intensity of deprivationInequality among the poorPopulation in severe multidimensional povertyHealthEducationStandard of livingNational poverty linePPP$2.15 a dayHeadcountYear and surveyb(thousands)2011
215、2022Value(%)In survey year2021(%)Value(%)(%)(%)(%)(%)20112021c20112021cSeychelles2019 N0.003 h,s0.9 h,s1 h,s1 h,s34.2 h,s.d0.0 h,s0.4 h,s66.8 h,s32.1 h,s1.1 h,s25.30.5Sierra Leone2019 D0.29359.24,7654,98749.50.01928.021.323.024.153.056.826.1Suriname2018 M0.0112.9171739.40.0070.44.020.443.835.8.Tajik
216、istan2017 D0.0297.466472639.00.0040.720.147.826.525.826.36.1Thailand2019 M0.002 f0.6 f412 f414 f36.7 f0.003 f0.0 f6.1 f38.3 f45.1 f16.7 f6.80.0Togo2017 M0.18037.62,9543,25247.80.01615.223.820.928.150.945.528.1Tonga2019 M0.0030.91138.1.d0.06.438.240.721.1.1.8Tunisia2018 M0.0030.8949736.5.d0.12.424.46
217、1.614.015.20.1Turkmenistan2019 M0.001 h0.2 h15 h16 h34.0 h.d0.0 h0.3 h82.4 h15.5 h2.1 h.Tuvalu2019/2020 M0.0082.10038.20.0020.012.236.543.620.0.Uzbekistan2021/2022 M0.006 j,t1.7 j,t599 j,t589 j,t35.3 j,t0.001 j,t0.0 j,t0.2 j,t94.5 j,t0.0 j,t5.5 j,t14.1.Viet Nam2020/2021 M0.008 j1.9 j1,871 j1,871 j40
218、.3 j0.010 j0.4 j3.5 j22.9 j40.7 j36.4 j6.70.7Zambia2018 D0.23247.98,5449,32948.40.01521.023.921.525.053.554.461.4Zimbabwe2019 M0.11025.83,9614,12642.60.0096.826.323.617.359.238.339.8Estimates based on surveys for 20112016Afghanistan2015/2016 D0.272 j55.9 j19,365 j22,420 j48.6 j0.020 j24.9 j18.1 j10.
219、0 j45.0 j45.0 j54.5.Angola2015/2016 D0.28251.114,89917,63355.30.02432.515.521.232.146.832.331.1Armenia2015/2016 D0.001 g0.2 g5 g5 g36.2 g.d0.0 g2.8 g33.1 g36.8 g30.1 g26.50.5Barbados2012 M0.009 k2.5 k7 k7 k34.2 k.d0.0 k0.5 k96.0 k0.7 k3.3 k.Belize2015/2016 M0.0174.3161739.80.0070.68.439.520.939.6.Bo
220、livia(Plurinational State of)2016 N0.0389.11,0201,09441.70.0081.912.118.731.549.836.42.0Bosnia and Herzegovina2011/2012 M0.008 k2.2 k80 k72 k37.9 k0.002 k0.1 k4.1 k79.7 k7.2 k13.1 k16.90.1Botswana2015/2016 N0.073 u17.2 u405 u446 u42.2 u0.008 u3.5 u19.7 u30.3 u16.5 u53.2 u.15.4Brazil2015 Nv0.016 f,j,
221、v3.8 f,j,v7,883 f,j,v8,234 f,j,v42.5 f,j,v0.008 f,j,v0.9 f,j,v6.2 f,j,v49.8 f,j,v22.9 f,j,v27.3 f,j,v.5.8China2014 Nw0.016 x,y3.9 x,y53,815 x,y55,396 x,y41.4 x,y0.005 x,y0.3 x,y17.4 x,y35.2 x,y39.2 x,y25.6 x,y0.00.1Colombia2015/2016 D0.020 j4.8 j2,308 j2,497 j40.6 j0.009 j0.8 j6.2 j12.0 j39.5 j48.5
222、j39.36.6Comoros2012 D0.18137.325530648.50.02016.122.320.831.647.642.418.6Congo2014/2015 M0.11224.31,2291,41646.00.0139.421.323.420.256.440.935.4Cte dIvoire2016 M0.23646.111,15512,65951.20.01924.517.619.640.440.039.511.4Egypt2014 D0.020 g,h5.2 g,h5,008 g,h5,724 g,h37.6 g,h0.004 g,h0.6 g,h6.1 g,h40.0
223、g,h53.1 g,h6.9 g,h32.51.5El Salvador2014 M0.0327.948849641.30.0091.79.915.543.441.126.23.6Eswatini(Kingdom of)2014 M0.08119.221622942.30.0094.420.929.317.952.858.936.1Gabon2012 D0.070 g15.6 g287 g365 g44.7 g0.013 g5.1 g18.4 g32.7 g21.4 g46.0 g33.42.5Guatemala2014/2015 D0.13428.94,6215,08646.20.01311
224、.221.126.335.038.759.39.5Kazakhstan2015 M0.002 f,g0.5 f,g81 f,g87 f,g35.6 f,g.d0.0 f,g1.8 f,g90.4 f,g3.1 f,g6.4 f,g5.20.0Kenya2014 D0.171 g37.5 g17,176 g19,865 g45.6 g0.014 g12.4 g35.8 g23.5 g15.0 g61.5 g36.129.4Libya2014 P0.0072.012213537.10.0030.111.439.048.612.4.Moldova(Republic of)2012 M0.0040.9
225、332937.4.d0.13.79.242.448.424.50.0Myanmar2015/2016 D0.17638.319,88320,61345.90.01513.821.918.532.349.224.82.0Namibia2013 D0.185 g40.9 g901 g1,034 g45.2 g0.013 g13.1 g19.2 g31.6 g13.9 g54.4 g17.415.6Nicaragua2011/2012 D0.074 g16.5 g993 g1,128 g45.3 g0.013 g5.6 g13.4 g11.5 g36.2 g52.3 g24.93.9Niger201
226、2 D0.601 g91.0 g16,333 g22,973 g66.1 g0.026 g76.3 g4.9 g21.4 g36.7 g41.8 g40.850.6Paraguay2016 M0.0194.528230241.90.0131.07.214.338.946.826.90.7Saint Lucia2012 M0.007 k1.9 k3 k3 k37.5 k.d0.0 k1.6 k69.5 k7.5 k23.0 k25.05.1South Africa2016 D0.0256.33,5303,71639.80.0050.912.239.513.147.455.520.5Sri Lan
227、ka2016 N0.0112.962663638.30.0040.314.332.524.443.04.11.0Sudan2014 M0.27952.319,36323,89253.40.02330.917.721.129.249.8.15.3Tanzania(United Republic of)2015/2016 D0.284 g57.1 g31,046 g36,288 g49.8 g0.016 g27.5 g23.4 g22.5 g22.3 g55.2 g26.444.9Timor-Leste2016 D0.222 g48.3 g591 g637 g45.9 g0.014 g17.4 g
228、26.8 g29.3 g23.1 g47.6 g41.824.4Trinidad and Tobago2011 M0.002 f0.6 f9 f10 f38.0 f.d0.1 f3.7 f45.5 f34.0 f20.5 f.Uganda2016 D0.281 g57.2 g22,152 g26,214 g49.2 g0.017 g25.7 g23.6 g24.0 g21.6 g54.5 g20.342.2Ukraine2012 M0.001 g,j0.2 g,j111 g,j106 g,j34.4 g,j.d0.0 g,j0.4 g,j60.5 g,j28.4 g,j11.2 g,j1.60
229、.0Yemen2013 D0.245 g48.5 g13,078 g15,985 g50.6 g0.021 g24.3 g22.3 g29.0 g30.4 g40.6 g48.619.8Developing countries0.08818.21,051,6111,116,71348.50.0177.914.824.231.644.220.110.5RegionsArab States0.07415.144,11952,63648.90.0196.99.026.134.339.723.44.7East Asia and the Pacific0.0225.1102,302105,84542.4
230、0.0080.914.428.135.836.13.80.8Europe and Central Asia0.0041.21,6711,71337.10.0030.12.566.716.516.812.20.7Latin America and the Caribbean0.0245.631,71233,25843.10.0111.56.533.527.638.937.94.9South Asia0.09120.5380,793389,48844.60.0146.917.927.933.738.322.69.2Sub-Saharan Africa0.26249.5491,015533,7725
231、2.90.02227.918.620.629.649.841.137.4TABLE 1UNSTACKING GLOBAL POVERTY:DATA FOR HIGH-IMPACT ACTION21Notesa Not all indicators were available for all countries,so cau-tion should be used in cross-country comparisons.When an indicator is missing,weights of available indicators are adjusted to total 100
232、percent.See Technical note at https:/hdr.undp.org/system/files/documents/mpi2023tech nicalnotes.pdf and Methodological Note 55 at https:/ophi.org.uk/mpi-methodological-note-55/for details.b D indicates data from Demographic and Health Sur-veys,M indicates data from Multiple Indicator Cluster Surveys
233、,N indicates data from national surveys and P indicates data from Pan Arab Population and Family Health Surveys(see https:/hdr.undp.org/mpi-2023-faqs and Methodological Note 55 at https:/ophi.org.uk/mpi-methodological-note-55/for the list of national surveys).c Data refer to the most recent year ava
234、ilable during the period specified.d Value is not reported because it is based on a small number of multidimensionally poor people.e Urban areas only.f Considers child deaths that occurred at any time be-cause the survey did not collect the date of child deaths.g Revised estimate from the 2020 MPI.h
235、 Missing indicator on cooking fuel.i Revised estimate from the 2022 MPI based on the sur-vey microdata update.j Missing indicator on nutrition.k Missing indicator on child mortality.l Child mortality data were not used because the data were collected from a sample of women ages 1549 that was not rep
236、resentative of the female population in that age group.m Anthropometric data were collected from all children under age 5 and from selected individuals who are age 5 or older.Construction of the nutrition indicator was re-stricted to children under age 5 since the anthropomet-ric sample is represent
237、ative of the under 5 population.n Indicator on sanitation follows the national classification in which pit latrine with slab is considered unimproved.o Following the national report,latrines are considered an improved source for the sanitation indicator.p Some 235 households were present in the indi
238、vidual datafile but not in the asset datafile.It is assumed that these households owned zero relevant assets.q The analytical sample was restricted to the Multiple Indicator Cluster Survey sample,and its sample weight was used,because child mortality information was not collected for the National Im
239、munization Coverage Sur-vey sample.r Because of the high proportion of children excluded from nutrition indicators due to measurements not being taken,estimates based on the 2019 Serbia Multiple Indi-cator Cluster Survey should be interpreted with caution.The unweighted sample size used for the mult
240、idimen-sional poverty calculation is 82.8 percent.s Missing indicator on school attendance.t The analytical sample was restricted to the round 2 sample because standard of living questions were not collected for the round 1 sample.u Captures only deaths of children under age 5 who died in the last f
241、ive years and deaths of children ages 1218 years who died in the last two years.v The methodology was adjusted to account for missing indicator on nutrition and incomplete indicator on child mortality(the survey did not collect the date of child deaths).w Based on the version of data accessed on 7 J
242、une 2016.x Given the information available in the data,child mortality was constructed based on deaths that occurred between surveysthat is,between 2012 and 2014.Child deaths reported by an adult man in the household were taken into account because the date of death was reported.y Missing indicator
243、on housing.DefinitionsMultidimensional Poverty Index:Proportion of the population that is multidimensionally poor adjusted by the intensity of the deprivations.See Technical note https:/hdr.undp.org/system/files/documents/mpi2023technicalnotes.pdf and Methodologi-cal Note 55 at https:/ophi.org.uk/mp
244、i-methodological-note-55/for details on how the Multidimensional Poverty Index is calculated.Multidimensional poverty headcount:Population with a depri-vation score of at least 33.3 percent.It is expressed as a share of the population in the survey year,the number of multidimen-sionally poor people
245、in the survey year and the projected num-ber of multidimensionally poor people in 2021.Intensity of deprivation of multidimensional poverty:Average deprivation score experienced by people in multidimensional poverty.Inequality among the poor:Variance of individual deprivation scores of poor people.I
246、t is calculated by subtracting the depri-vation score of each multidimensionally poor person from the intensity,squaring the differences and dividing the sum of the weighted squares by the number of multidimensionally poor people.Population in severe multidimensional poverty:Percentage of the popula
247、tion in severe multidimensional povertythat is,those with a deprivation score of 50 percent or more.Population vulnerable to multidimensional poverty:Percent-age of the population at risk of suffering multiple deprivationsthat is,those with a deprivation score of 2033.3 percent.Contribution of depri
248、vation in dimension to overall multidi-mensional poverty:Percentage of the Multidimensional Pover-ty Index attributed to deprivations in each dimension.Population living below national poverty line:Percentage of the population living below the national poverty line,which is the poverty line deemed a
249、ppropriate for a country by its au-thorities.National estimates are based on population-weighted subgroup estimates from household surveys.Population living below PPP$2.15 a day:Percentage of the population living below the international poverty line of$2.15(in 2017 purchasing power parity PPP terms
250、)a day.Main data sourcesColumn 1:Refers to the year and the survey whose data were used to calculate the countrys Multidimensional Poverty Index value and its components.Columns 212:HDRO and OPHI calculations based on data on household deprivations in health,education,and standard of liv-ing from va
251、rious surveys listed in column 1 using the methodolo-gy described in Technical note(available at https:/hdr.undp.org/system/files/documents/mpi2023technicalnotes.pdf)and Meth-odological Note 55 at https:/ophi.org.uk/mpi-methodological-note-55/.Columns 4 and 5 also use population data from Unit-ed Na
252、tions Department of Economic and Social Affairs.2022.World Population Prospects:The 2022 Revision.New York.https:/population.un.org/wpp/.Accessed 9 April 2023.Columns 13 and 14:World Bank.2022.World Development In-dicators database.Washington,DC.http:/data.worldbank.org.Accessed 2 May 2023.TABLE 122
253、GLOBAL MULTIDIMENSIONAL POVERTY INDEX/2023CountryMultidimensional Poverty IndexaPopulation in multidimensional povertyPeople who are multidimensionally poor and deprived in each indicatorHeadcountIntensity of deprivationNutritionChild mortalityYears of schoolingSchool attendanceCooking fuelSanitatio
254、nDrinking waterElectricityHousingAssets(thousands)Year and surveybValue(%)In survey year(%)(%)(%)(%)(%)(%)(%)(%)(%)(%)(%)Albania2008/2009 D0.0082.16037.81.30.30.41.01.81.00.80.01.30.3Albania2017/2018 D0.0030.72039.1 c0.50.00.5 c0.40.30.10.20.0 c0.10.0Algeria2012/2013 M0.0082.180038.51.20.41.50.90.20
255、.80.60.30.80.2Algeria2018/2019 M0.0051.459039.2 c0.80.21.00.60.1 c0.6 c0.4 c0.2 c0.40.1 cArmenia2010 D0.0010.41235.90.40.10.00.20.00.20.10.00.00.0Armeniad2015/2016 D0.001 c0.2 c535.9 c0.1 c0.00.0 c0.1 c0.1 c0.2 c0.0 c0.0 c0.0 c0.0 cBangladeshd2014 D0.17537.658,58246.516.42.325.39.535.928.24.123.835.
256、826.2Bangladesh2019 M0.10124.139,83042.08.71.316.66.522.815.31.44.622.815.9Belize2011 M0.0307.42441.14.62.61.93.54.51.90.82.84.42.5Belizee2015/2016 M0.0204.91840.2 c3.5 c1.7 c0.7 c1.73.2 c2.3 c0.7 c2.6 c3.0 c1.3Benin2014 M0.34663.26,71254.732.011.542.531.062.761.532.454.244.316.3Benind2017/2018 D0.3
257、62 c66.0 c7,88054.9 c33.7 c10.3 c44.2 c35.565.6 c63.8 c36.954.7 c42.5 c17.6 cBolivia(Plurinational State of)2003 D0.16733.93,07049.217.04.215.913.027.133.215.422.332.719.1Bolivia(Plurinational State of)2008 D0.09520.62,03746.210.22.711.63.417.920.18.213.217.011.4Bolivia(Plurinational State of)2016 N
258、0.0389.11,02541.73.70.55.81.47.28.73.13.87.53.8Bosnia and Herzegovinaf2006 M0.0153.916038.93.3.0.80.42.50.60.30.10.70.4Bosnia and Herzegovinaf2011/2012 M0.0082.28037.9 c2.0.0.20.2 c1.50.30.00.1 c0.00.1Burkina Faso2006 M0.60788.712,70468.449.352.062.762.788.388.455.580.381.318.2Burkina Fasod,e2010 D0
259、.574 c86.3 c13,91166.5 c41.649.9 c68.758.9 c85.8 c77.942.083.4 c72.813.8Burundi2010 D0.46482.37,51156.453.38.750.528.082.156.553.781.478.860.8Burundi2016/2017 D0.40975.18,37854.450.6 c7.9 c42.624.074.945.742.873.570.653.3Cambodia2010 D0.22547.16,76847.828.73.126.310.446.642.125.242.629.114.6Cambodia
260、2014 D0.16836.75,58645.820.21.821.510.7 c35.830.320.026.121.76.5Cambodia2021/2022 D0.07016.62,79142.38.60.510.59.8 c12.18.65.04.64.63.7Cameroond2011 D0.25847.69,74254.228.011.324.218.146.936.333.338.840.424.2Cameroon2014 M0.243 c45.4 c10,13253.6 c24.49.7 c23.5 c17.6 c44.7 c40.3 c28.837.0 c39.0 c22.8
261、 cCameroond2018 D0.229 c43.2 c10,84353.1 c25.2 c8.4 c19.3 c19.4 c42.6 c33.326.7 c34.6 c36.8 c22.1 cCentral African Republic2000 M0.57389.63,36764.045.745.544.263.688.969.644.384.878.269.2Central African Republic2010 M0.48181.23,78659.237.340.638.733.181.060.055.277.974.667.3 cCentral African Republi
262、ce2018/2019 M0.51684.34,39461.244.335.946.333.8 c83.971.163.077.9 c78.474.3Chad2010 M0.60190.010,70866.747.244.664.849.389.283.864.687.787.750.6Chade2014/2015 D0.57889.4 c12,63664.746.0 c40.157.752.5 c88.3 c85.3 c61.2 c85.1 c86.0 c45.8Chade2019 M0.562 c87.7 c14,14364.1 c44.8 c32.658.0 c59.985.280.34
263、8.383.9 c83.345.1 cChinae,g,h2010 N0.0419.5127,72143.26.30.85.81.38.54.47.20.3.5.5Chinae,g,h2014 N0.0184.258,31341.6 c3.40.62.21.4 c3.11.02.10.0 c.1.2Colombiai2010 D0.0246.02,66840.4.0.94.81.14.54.23.61.54.51.9Colombiai2015/2016 D0.0204.82,30840.6 c.0.73.90.83.73.53.3 c1.4 c4.0 c1.2Congod2005 D0.258
264、53.81,97448.026.510.310.415.552.652.838.745.742.644.4Congo2014/2015 M0.11424.71,25346.112.63.19.7 c4.024.123.415.220.519.714.1Congo(Democratic Republic of the)d2007 D0.42876.746,25155.843.814.222.041.276.565.462.773.070.858.9Congo(Democratic Republic of the)d2013/2014 D0.37571.9 c54,69252.244.1 c11.
265、7 c18.5 c24.571.7 c60.6 c58.6 c68.9 c67.4 c51.6Congo(Democratic Republic of the)2017/2018 M0.33764.856,43852.1 c38.87.216.4 c26.7 c64.159.9 c50.857.958.648.7 cCte dIvoire2011/2012 D0.31058.912,96052.730.511.237.432.956.854.027.037.730.716.1Cte dIvoire2016 M0.23646.111,15551.220.67.131.725.443.440.22
266、3.0 c29.024.110.0Dominican Republici2007 D0.0307.368341.0.1.65.32.23.73.91.51.76.64.3Dominican Republici2014 M0.0143.737938.6.1.4 c2.30.61.91.90.51.01.61.5Dominican Republici2019 M0.0112.830638.7 c.1.2 c1.60.6 c1.21.40.30.41.5 c1.1Ecuador2013/2014 N0.0194.774340.03.01.51.61.01.82.92.30.72.42.2Ecuado
267、re2018 N0.0113.050438.12.11.20.80.71.11.20.90.51.11.3Egyptj2008 D0.0328.06,69240.15.81.04.45.3.1.60.50.22.81.7Egyptj2014 D0.0184.94,67637.63.50.8 c2.83.1.0.70.3 c0.00.70.2Eswatini(Kingdom of)2010 M0.13029.332244.318.25.48.94.627.518.819.827.015.213.8Eswatini(Kingdom of)2014 M0.08119.221642.311.42.96
268、.02.717.813.112.915.68.89.1Ethiopiad2011 D0.49183.576,63458.934.97.257.239.983.178.570.177.083.174.9Ethiopiad2016 D0.43677.481,52656.330.15.652.233.476.874.758.470.777.063.4Ethiopia2019 D0.36768.878,48553.326.9 c4.038.231.0 c68.364.846.857.367.655.0Gabon2000 D0.14530.939347.015.36.212.86.824.529.221
269、.419.518.924.3Gabon2012 D0.06815.328144.79.53.75.73.19.514.39.87.49.16.6Gambia2005/2006 M0.38768.01,16456.935.340.734.138.267.634.728.760.044.215.6Gambiad,e2013 D0.33961.91,31654.837.5 c34.622.138.9 c61.643.016.651.430.87.5TABLE 2Multidimensional Poverty Index:changes over time based on harmonized e
270、stimatesUNSTACKING GLOBAL POVERTY:DATA FOR HIGH-IMPACT ACTION23CountryMultidimensional Poverty IndexaPopulation in multidimensional povertyPeople who are multidimensionally poor and deprived in each indicatorHeadcountIntensity of deprivationNutritionChild mortalityYears of schoolingSchool attendance
271、Cooking fuelSanitationDrinking waterElectricityHousingAssets(thousands)Year and surveybValue(%)In survey year(%)(%)(%)(%)(%)(%)(%)(%)(%)(%)(%)Gambiae2018 M0.25750.01,22351.529.230.3 c16.628.149.833.715.0 c30.118.43.8Gambiad,e2019/2020 D0.241 c48.2 c1,24150.0 c26.3 c32.0 c12.828.8 c47.8 c31.6 c10.628
272、.6 c12.53.7 cGhana2011 M0.15331.88,34147.914.84.916.98.731.530.419.123.620.913.0Ghanad2014 D0.13028.4 c8,01245.712.6 c3.114.9 c10.2 c28.0 c27.0 c14.415.516.79.9Ghana2017/2018 M0.112 c24.77,62445.2 c12.4 c3.4 c12.5 c8.1 c24.5 c22.812.3 c10.913.78.0Guinead2012 D0.42171.27,68559.134.313.850.547.071.263
273、.041.464.750.929.7Guinea2016 M0.33661.97,38454.329.08.639.738.461.751.035.553.233.522.8Guinead2018 D0.36465.0 c8,15556.031.7 c12.045.939.6 c64.6 c54.8 c36.5 c48.438.824.0 cGuinea-Bissau2014 M0.36366.01,15155.035.312.539.732.265.364.027.560.663.813.2Guinea-Bissau2018/2019 M0.341 c64.4 c1,26952.932.2
274、c6.940.8 c30.7 c64.2 c61.2 c34.045.463.5 c12.8 cGuyanad2009 D0.0235.44141.93.50.71.51.33.12.62.34.63.53.7Guyana2014 M0.014 c3.3 c2541.7 c2.1 c0.6 c0.60.9 c2.1 c1.8 c1.5 c2.7 c2.2 c1.8Guyana2019/2020 M0.0071.81439.31.10.20.5 c0.40.90.70.61.01.41.2 cHaiti2012 D0.23748.44,89448.919.34.832.66.248.043.13
275、6.242.534.533.3Haiti2016/2017 D0.19239.94,33648.1 c15.63.822.86.5 c39.735.128.635.729.031.4 cHondurasd,k2005/2006 D0.18636.72,83950.715.72.018.624.334.125.712.9.32.922.0Hondurasd,k2011/2012 D0.10822.82,00747.29.61.010.613.621.716.27.4.20.98.2Hondurask2019 M0.04910.81,08044.94.90.65.65.510.25.91.9.8.
276、15.4India2005/2006 D0.28355.1645,67651.344.34.524.019.852.950.416.429.044.937.5India2015/2016 D0.12227.7370,50944.021.12.211.65.526.024.45.78.623.59.5India2019/2021 D0.06916.4230,73942.011.81.57.73.913.911.32.72.113.65.6Indonesiai2012 D0.0286.917,19840.3.2.02.92.15.65.14.11.83.03.6Indonesiai2017 D0.
277、0143.69,50938.7.1.51.50.72.42.21.30.81.31.7Iraq2011 M0.05714.44,66539.69.92.66.911.10.91.92.10.75.00.5Iraq2018 M0.0338.63,50537.95.01.45.56.50.21.40.40.11.30.2Jordan2012 D0.0020.53833.80.20.30.20.30.00.00.00.00.00.0Jordan2017/2018 D0.002 c0.4 c4535.30.2 c0.2 c0.2 c0.2 c0.0 c0.00.1 c0.0 c0.1 c0.0 cKa
278、zakhstane2010/2011 M0.0030.915036.20.60.70.00.10.40.00.40.00.50.1Kazakhstane2015 M0.0020.58235.5 c0.5 c0.4 c0.0 c0.0 c0.00.0 c0.10.0 c0.10.0Kenya2008/2009 D0.24752.221,08947.333.55.512.08.551.746.037.650.152.028.9Kenya2014 D0.17137.517,17645.620.63.59.95.436.833.026.935.037.420.0Kyrgyzstan2005/2006
279、M0.0369.449338.04.46.10.01.78.12.04.40.28.04.6Kyrgyzstane2014 M0.0123.419537.2 c2.41.90.2 c0.52.20.12.00.1 c2.80.1Kyrgyzstane2018 M0.0041.16836.9 c1.00.90.0 c0.2 c0.40.1 c0.30.0 c0.10.0 cLao Peoples Democratic Republic2011/2012 M0.21040.22,61952.121.25.430.916.640.231.718.521.826.715.7Lao Peoples De
280、mocratic Republic2017 M0.10823.11,61547.012.01.916.69.122.917.210.46.112.07.1Lesothod,j2009 D0.19542.284746.219.14.015.010.9.38.025.741.334.530.6Lesothod,j2014 D0.12828.359445.012.53.1 c11.65.3.20.417.028.024.520.5Lesothoj2018 M0.08419.643143.09.61.55.53.7.14.811.618.415.915.2Liberia2007 D0.46381.42
281、,95956.941.410.835.956.781.377.134.080.661.664.5Liberia2013 D0.32663.52,81251.332.38.430.523.663.459.531.1 c61.748.638.0Liberia2019/2020 D0.25952.32,66249.624.66.125.618.951.846.822.847.836.635.4 cMadagascard2008/2009 D0.43375.715,99457.133.26.259.026.475.775.356.072.568.956.0Madagascar2018 M0.37968
282、.618,42555.228.15.249.426.8 c68.567.852.6 c54.861.348.8Madagascar2021 D0.364 c65.7 c19,00055.4 c26.05.5 c47.7 c25.3 c65.5 c62.151.3 c56.6 c57.248.3 cMalawid2010 D0.33066.89,82549.530.27.833.215.666.763.040.264.659.839.8Malawid2015/2016 D0.24452.69,15146.325.94.626.37.352.528.930.551.648.334.0Malawi2
283、019/2020 M0.231 c49.99,67446.3 c22.23.627.6 c7.8 c49.732.222.346.844.936.8Malid2006 D0.50183.711,40659.943.019.468.654.083.545.044.877.071.226.1Mali2015 M0.41873.113,24557.143.9 c17.039.356.7 c72.855.533.952.260.95.7Malid2018 D0.36166.413,24454.429.911.745.845.965.950.833.4 c43.248.88.2Mauritania201
284、1 M0.35662.72,20856.830.78.343.141.850.552.739.651.551.122.9Mauritania2015 M0.30756.22,21754.727.85.042.0 c30.347.0 c46.231.348.1 c47.9 c17.1Mauritaniad2019/2021 D0.321 c57.4 c2,64955.9 c27.6 c5.3 c40.1 c42.247.3 c41.8 c30.0 c46.9 c46.2 c20.0Mexicof2012 N0.0266.37,32841.84.1.1.91.13.43.11.40.53.51.9
285、Mexicof2016 N0.0215.26,33039.83.5 c.1.6 c0.72.6 c2.00.70.12.71.2Mexicof2020 N0.0153.94,97539.0 c3.2 c.0.50.5 c1.7 c0.90.20.11.60.8 cMexicof2021 N0.016 c4.1 c5,15640.5 c3.2 c.0.6 c0.7 c1.9 c1.3 c0.5 c0.2 c2.1 c0.6 cMoldova(Republic of)d2005 D0.0061.56036.60.30.10.90.41.20.90.50.10.71.3Moldova(Republi
286、c of)2012 M0.0030.93137.6 c0.2 c0.00.6 c0.2 c0.60.7 c0.5 c0.1 c0.5 c0.5TABLE 224GLOBAL MULTIDIMENSIONAL POVERTY INDEX/2023CountryMultidimensional Poverty IndexaPopulation in multidimensional povertyPeople who are multidimensionally poor and deprived in each indicatorHeadcountIntensity of deprivation
287、NutritionChild mortalityYears of schoolingSchool attendanceCooking fuelSanitationDrinking waterElectricityHousingAssets(thousands)Year and surveybValue(%)In survey year(%)(%)(%)(%)(%)(%)(%)(%)(%)(%)(%)Mongolia2010 M0.08119.653041.46.19.14.51.618.719.512.69.717.43.9Mongoliae,l2013 M0.05613.438141.7 c
288、3.86.24.3 c1.012.913.28.47.511.21.2Mongoliae,l2018 M0.0399.931439.32.94.12.91.69.59.66.40.98.40.8Montenegroe2013 M0.0020.4244.20.10.20.20.20.30.20.00.10.20.1Montenegroe2018 M0.005 c1.2 c839.6 c1.0 c0.8 c0.3 c0.3 c1.1 c0.2 c0.0 c0.0 c0.3 c0.0 cMorocco2011 P0.07817.35,68045.56.36.613.76.85.58.811.45.3
289、6.44.1Moroccoe2017/2018 P0.0337.92,82442.53.73.65.43.11.92.53.71.12.51.3Mozambique2003 D0.51684.316,18361.241.812.865.641.584.084.068.181.568.758.0Mozambique2011 D0.40171.216,91256.336.97.650.229.770.863.254.866.749.642.9Namibia2006/2007 D0.20543.086447.727.24.611.611.840.640.020.039.437.725.3Namibi
290、a2013 D0.15835.177444.923.23.7 c7.47.733.032.318.7 c31.627.514.8Nepald2011 D0.18539.110,67147.420.02.427.68.038.634.19.119.137.621.0Nepald2016 D0.11125.77,16443.213.71.8 c17.94.124.916.33.46.424.311.8Nepal2019 M0.07517.75,10542.4 c9.41.011.73.6 c16.46.62.7 c5.6 c16.410.4 cNicaragua2001 D0.22141.72,1
291、6852.916.32.826.821.140.736.727.926.434.230.6Nicaragua2011/2012 D0.07416.599345.34.50.612.53.716.26.213.611.513.59.1Niger2006 D0.66892.913,34771.964.626.181.865.792.890.267.587.985.264.8Niger2012 D0.59489.916,13566.157.918.874.357.789.384.059.982.580.946.0Nigeriai2013 D0.23042.373,98254.4.13.025.725
292、.141.030.628.632.135.516.3Nigeriai2016/2017 M0.215 c40.8 c78,99052.6.13.2 c23.1 c21.139.7 c31.7 c24.131.5 c32.3 c15.7 cNigeriai2018 D0.208 c38.2 c75,74254.5.14.1 c20.623.2 c37.0 c30.0 c20.827.729.014.3 cNigeriai2021 M0.17533.070,51652.9.10.216.720.631.826.418.224.9 c24.715.6 cNorth Macedoniaf2005/20
293、06 M0.0317.615940.75.8.2.02.04.21.90.70.21.60.7North Macedoniaf2011 M0.0102.55337.71.8.0.50.51.60.8 c0.10.0 c0.8 c0.2North Macedoniaf2018/2019 M0.0051.42937.8 c1.2 c.0.2 c0.1 c0.70.4 c0.0 c0.1 c0.00.1 cPakistan2012/2013 D0.23344.591,32652.332.38.725.727.538.229.49.16.335.917.3Pakistan2017/2018 D0.19
294、838.384,22851.7 c27.05.924.8 c24.3 c31.221.77.9 c7.1 c30.612.2Palestine,State of2010 M0.0041.14435.40.80.50.20.60.10.30.00.30.10.2Palestine,State of2014 M0.003 c0.8 c3535.8 c0.6 c0.5 c0.1 c0.5 c0.1 c0.00.0 c0.00.0 c0.1 cPalestine,State of2019/2020 M0.002 c0.5 c2734.7 c0.5 c0.3 c0.0 c0.3 c0.0 c0.1 c0
295、.0 c0.0 c0.0 c0.0 cPeru2012 D0.05312.73,76641.65.90.55.61.911.511.26.06.012.56.0Peru2018 N0.0297.42,37639.62.40.43.32.2 c6.16.23.12.37.13.2Peru2019 N0.029 c7.4 c2,41639.7 c2.3 c0.4 c2.92.85.8 c6.1 c3.1 c2.1 c6.9 c3.1 cPeru2021 N0.0266.62,23338.9 c1.90.3 c2.52.7 c5.4 c5.8 c2.7 c1.66.3 c2.5Philippines
296、i,m2013 D0.0377.17,10152.0.2.24.4.6.64.42.43.75.14.4Philippinesi,m2017 D0.0285.65,93949.8.1.53.7 c.4.83.11.72.23.83.1Rwanda2010 D0.33866.86,88850.634.86.743.611.566.629.846.665.363.446.8Rwanda2014/2015 D0.28257.56,69749.027.13.336.910.9 c57.429.0 c40.452.454.139.4Rwanda2019/2020 D0.23148.86,41847.32
297、3.03.3 c28.98.048.724.934.836.544.436.9 cSao Tome and Principed2008/2009 D0.18540.77345.417.44.427.812.136.335.116.829.31.328.4Sao Tome and Principe2014 M0.09122.04341.68.51.715.35.315.019.68.915.10.313.0Sao Tome and Principe2019 M0.04911.92641.3 c4.70.87.14.0 c9.411.03.47.00.3 c7.5Senegald2005 D0.3
298、8164.27,05059.330.219.052.147.452.832.434.949.233.837.4Senegal2017 D0.28252.47,93753.828.9 c9.032.444.5 c49.0 c31.8 c17.833.121.010.5Senegal2019 D0.260 c50.3 c8,04851.626.6 c5.832.4 c43.7 c46.5 c28.7 c15.6 c25.615.310.0 cSerbiae2010 M0.0010.21442.60.10.10.10.10.20.10.00.00.10.1Serbiae2014 M0.001 c0.
299、3 c2442.5 c0.1 c0.0 c0.3 c0.1 c0.3 c0.2 c0.0 c0.1 c0.2 c0.1 cSerbiae2019 M0.0000.1838.1 c0.00.1 c0.10.0 c0.1 c0.00.0 c0.00.0 c0.0Sierra Leoned2013 D0.40974.15,15855.239.015.937.432.073.969.745.771.257.745.0Sierra Leone2017 M0.30058.34,47851.525.47.933.019.958.054.534.054.643.337.1Sierra Leoned2019 D
300、0.27255.24,44349.324.0 c9.426.915.155.150.833.9 c51.8 c38.434.1Sudan2010 M0.31757.019,23255.528.87.431.329.350.050.940.748.456.932.5Sudan2014 M0.27952.319,36353.429.8 c5.627.021.943.846.135.842.651.930.3 cSurinamef2006 M0.05912.76646.27.3.7.02.26.07.55.34.35.16.6Surinamef2010 M0.0419.55243.2 c5.6.4.
301、9 c1.5 c4.0 c5.4 c2.62.4 c3.2 c3.3Surinamef2018 M0.0266.74038.64.9 c.1.81.0 c1.22.20.51.01.41.8Tajikistan2012 D0.04912.297040.410.52.80.46.37.91.37.50.510.31.7Tajikistan2017 D0.0297.466139.0 c6.22.1 c0.1 c4.53.40.33.50.1 c5.60.3Tanzania(United Republic of)2010 D0.34267.830,56550.540.97.614.725.367.5
302、64.055.465.961.336.6Tanzania(United Republic of)2015/2016 D0.28557.131,07449.8 c32.55.912.325.7 c56.953.743.455.247.426.5TABLE 2UNSTACKING GLOBAL POVERTY:DATA FOR HIGH-IMPACT ACTION25Notes Suggested citation:Alkire,S.,Kanagaratnam,U.,and Suppa,N.(2023).A methodological note on the global Multidimens
303、ional Poverty Index(MPI)2023 changes over time results for 84 countries.OPHI MPI Methodo-logical Note 57,Oxford Poverty and Human Develop-ment Initiative.2018 University of Oxford This methodological note details the harmonization principles and decisions.More extensive data tables,including disaggr
304、egated information,are available at http:/www.ophi.org.uk.a When an indicator is missing,weights of available indica-tors are adjusted to total 100 percent.See Technical note at https:/hdr.undp.org/system/files/documents/mpi2023 technicalnotes.pdf and Methodological Note 55 at https:/ophi.org.uk/mpi
305、-methodological-note-55/for details.b D indicates data from Demographic and Health Surveys,Mindicates data from Multiple Indicator Cluster Surveys,P indicates data from Pan Arab Population and Family Health Surveys and N indicates data from national surveys.c The difference between harmonized estima
306、tes for this survey year and for the previous survey year is not statis-tically significant at the 95 percent confidence interval.d At least one other survey collected data on child nutrition only;in order to harmonize the data for trends,data on adult nutrition from this survey were omitted from th
307、e calculations.Typically,Demographic and Health Surveys collect data on child and adult nutrition,while Multiple Indi-cator Cluster Surveys collect data on child nutrition only.e Considers child deaths that occurred at any time because the survey at one or all points in time did not collect data on
308、the date of child deaths.f Missing indicator on child mortality.g Based on the version of data accessed on 7 June 2016.h Missing indicator on housing.i Missing indicator on nutrition.j Missing indicator on cooking fuel.k Missing indicator on electricity.l Indicator on sanitation follows the national
309、 classification in which pit latrine with slab is considered unimproved.m Missing indicator on school attendance.DefinitionsMultidimensional Poverty Index:Proportion of the population that is multidimensionally poor adjusted by the intensity of the deprivations.See Technical note at https:/hdr.undp.
310、org/system/files/documents/mpi2023technicalnotes.pdf and Methodological Note 57 at https:/ophi.org.uk/mpi-methodological-note-57/for details on how the Multidimensional Poverty Index is calculated.Multidimensional poverty headcount:Population with a depri-vation score of at least 33.3 percent.It is
311、expressed as a share of the population in the survey year and the number of poor people in the survey year.Intensity of deprivation of multidimensional poverty:Average deprivation score experienced by people in multidimensional poverty.People who are multidimensionally poor and deprived in each indi
312、cator:Percentage of the population that is multidi-mensionally poor and deprived in the given indicator(censored headcount ratio).Main data sourcesColumn 1:Refers to the year and the survey whose data were used to calculate the countrys MPI value and its components.Columns 215:Data and methodology a
313、re described in Alkire,S.,Kanagaratnam,U.,and Suppa,N.(2023c).A methodological note on the global Multidimensional Poverty Index(MPI)2023 changes over time results for 84 countries.OPHI MPI Meth-odological Note 57,Oxford Poverty and Human Development Initiative.2018 University of Oxford.Column 5 als
314、o uses pop-ulation data from United Nations Department of Economic and Social Affairs.2022.World Population Prospects:The 2022 Revision.New York.https:/population.un.org/wpp/.Accessed 9 April 2023.TABLE 2CountryMultidimensional Poverty IndexaPopulation in multidimensional povertyPeople who are multi
315、dimensionally poor and deprived in each indicatorHeadcountIntensity of deprivationNutritionChild mortalityYears of schoolingSchool attendanceCooking fuelSanitationDrinking waterElectricityHousingAssets(thousands)Year and surveybValue(%)In survey year(%)(%)(%)(%)(%)(%)(%)(%)(%)(%)(%)Thailande2012 M0.
316、0051.496136.90.80.51.00.20.80.20.20.10.30.3Thailande2015/2016 M0.0030.859239.0 c0.40.3 c0.60.3 c0.30.2 c0.10.1 c0.2 c0.1Thailande2019 M0.0020.6 c41236.7 c0.3 c0.1 c0.4 c0.2 c0.3 c0.1 c0.0 c0.0 c0.1 c0.1 cTimor-Leste2009/2010 D0.36269.675852.049.75.721.530.169.349.340.854.861.454.4Timor-Leste2016 D0.
317、21546.957445.933.23.615.914.845.631.718.619.240.729.1Togo2010 M0.32158.23,82855.124.429.632.415.358.156.540.152.337.827.4Togod,e2013/2014 D0.301 c55.1 c4,01854.5 c25.1 c29.7 c26.615.7 c54.9 c53.4 c36.6 c46.837.6 c20.6Togoe2017 M0.21343.03,37349.618.317.719.311.342.540.724.733.027.715.5Tunisia2011/20
318、12 M0.0061.415440.00.60.21.10.50.20.70.70.20.10.6Tunisia2018 M0.0030.89436.50.4 c0.10.7 c0.4 c0.0 c0.20.20.00.1 c0.1Turkmenistanj2006 M0.0123.316137.82.12.60.01.3.0.41.10.01.10.8Turkmenistane,j2015/2016 M0.0041.16334.90.91.00.0 c0.2.0.1 c0.00.0 c0.00.0Turkmenistane,j2019 M0.003 c0.9 c5833.6 c0.9 c0.
319、9 c0.00.2 c.0.0 c0.0 c0.0 c0.0 c0.0 cUganda2011 D0.34967.722,55051.542.29.729.315.267.360.351.466.461.931.9Uganda2016 D0.28157.222,15749.235.15.322.613.8 c56.950.441.950.249.726.4Ukrainei2007 D0.0010.416536.4.0.30.10.00.10.10.00.00.10.1Ukrainei2012 M0.001 c0.2 c10734.5.0.2 c0.1 c0.1 c0.1 c0.0 c0.0 c
320、0.0 c0.0 c0.0 cViet Nami2013/2014 M0.0194.94,49539.3.0.93.61.44.54.11.30.43.11.2Viet Nami2020/2021 M0.0081.91,87140.3 c.0.51.30.61.51.30.50.11.20.6Zambiad2007 D0.34365.28,08252.736.69.318.730.764.158.351.463.055.639.8Zambiad2013/2014 D0.26353.38,38849.331.36.413.721.853.045.035.450.644.225.2Zambia20
321、18 D0.23247.98,54448.425.74.212.0 c22.8 c47.637.728.644.540.2 c24.3 cZimbabwed2010/2011 D0.15636.14,70243.318.84.24.48.135.529.623.734.326.825.0Zimbabwed2015 D0.13030.24,27643.0 c16.73.7 c4.1 c5.929.724.521.7 c29.420.916.5Zimbabwe2019 M0.11025.83,96242.6 c12.33.2 c3.5 c7.825.221.419.8 c19.316.415.0 c26GLOBAL MULTIDIMENSIONAL POVERTY INDEX/2023United Nations Development Programme One United Nations PlazaNew York,NY 10017,USA www.undp.orgOxford Poverty&Human Development Initiative(OPHI)3 Mansfield Road Oxford OX1 3TB,UKwww.ophi.org.ukOPHIOxford Poverty&Human Development Initiative