The global Multidimensional Poverty Index (MPI) and the SDGs. Gisela Robles, 9 Nov 2016, MPPN meeting, Acapulco.

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1 The global Multidimensional Poverty Index (MPI) and the SDGs Gisela Robles, 9 Nov 2016, MPPN meeting, Acapulco.

2 High Resolution lenses Alkire & Foster (AF) method Formula: MPI = M 0 = H A 1) Incidence or the headcount ratio (H ), the percentage of people who are poor. 2) Intensity of people s deprivation (A), the average share of dimensions (proportion of weighted deprivations) people suffer at the same time.

3 Dimensions, Indicators & Their Weights Ten Indicators Health 1/3 Nutrition Child Mortality 1/6 1/6 Three Dimensions of Poverty Education 1/3 Years of Schooling School Attendance 1/6 1/6 Living Standard 1/3 Cooking Fuel Improved Sanitation Safe DrinkingWater Electricity Flooring Asset Ownership 1/18 1/18 1/18 1/18 1/18 1/18

4 Population Coverage by Income Category 5.3 billion people living in all six geographical regions MPI 2016 covers 93% of the combined population in Middle and Low Income Countries Income Categories Population in MPI countries (million) Total Pop in regions % Pop covered High income: OECD 0 1,063 0% High income: non-oecd % Upper middle income 2,046 2, % Lower middle income 2,657 2, % Low income % Total 5,297 7, %

5 Global MPI findings

6 Where MPI poor people live: National Income Category Total population by income category Low income 10% Lower middle income 50% High income: nonoecd 1% Upper middle income 39% MPI poor people by income category High income: nonoecd 0% Upper middle income 7% Low income 25% Most poor people (75%) live in middle-income countries (MICS) Lower middle income 68% 2012 Population Data

7 South Sudan Niger Ethiopia Chad Burkina Faso Somalia Sierra Leone Burundi Mali Central African Republic Guinea-Bissau Guinea Congo, Democratic Republic of the Liberia Uganda Mozambique Timor-Leste Madagascar Afghanistan Tanzania, United Republic of Benin Gambia Cote d'ivoire Sudan Senegal Zambia Malawi Rwanda India Nigeria Mauritania Togo Haiti Cameroon Yemen Pakistan Namibia Bangladesh Kenya Congo, Republic of Comoros Lesotho Sao Tome and Principe Lao People's Democratic Republic Ghana Cambodia Vanuatu Zimbabwe Djibouti Nepal Bhutan Bolivia, Plurinational State of Swaziland Gabon Nicaragua Honduras Indonesia Morocco Tajikistan Iraq South Africa Philippines Peru Mongolia Guyana Viet Nam Turkmenistan Suriname Trinidad and Tobago Colombia Azerbaijan Brazil China Maldives Dominican Republic Belize Syrian Arab Republic Egypt Ecuador Argentina Mexico Uzbekistan Jamaica Jordan Thailand Libya Albania Ukraine Tunisia Palestine, State of Saint Lucia Barbados Georgia Moldova, Republic of Macedonia, The former Yugoslav Republic of Bosnia and Herzegovina Kyrgyzstan Armenia Montenegro Serbia Kazakhstan Belarus Comparing the Headcount Ratios of MPI Poor and $1.90/day Poor 100% Destitute MPI Poor people $1.90 a day within 3 years of MPI survey 90% 80% 70% 60% 50% 40% 30% 20% 10% 0%

8 Annualized Changes in MPI for Africa Rwanda Ghana The Republic of the Mauritania Liberia The Republic of the Tanzania Uganda Burundi Nigeria Congo, Democratic Kenya Gambia Sao Tome and Principe Mozambique Zambia Mali /13 Cameroon Namibia Cote d'ivoire /12 Malawi Niger Central African Republic Madagascar MPI (H)

9 $1.90/day MPI

10 Global MPI: Headline + Disaggregated detail Governance 10 Leave No One Behind

11 The Global MPI is used to inform the SDGs

12 End poverty in all its forms everywhere by Target 1.2: By 2030, reduce at least by half the proportion of men, women and children of all ages living in poverty in all its dimensions according to national definitions Bhutan, Chile, Colombia, Costa Rica, Ecuador, El Salvador, Honduras, Mexico, Pakistan, Peru, Philippines, Seychelles, and Viet Nam are reporting the incidence (H) of multidimensional poverty associated with their global and/or national MPI for SDG indicator 1.2.2

A robust way to measure SDG indicator 1.2.

13 A robust way to measure SDG indicator The global MPI allows policy makers to: Leave No One Behind : it tracks poverty for different groups: sub-national regions, rural areas, children, ethnic and caste groups. Integrated, coordinated policy: it gives incentives to coordinate policy, to break down silos for policy impact. Universal relevance: indicators reflect contextual values and different meanings of poverty.

14 OPHI-MPI Team 2016 OPHI Research Team: Sabina Alkire (Director), James Foster (Research Fellow), John Hammock (Co-Founder and Research Associate), Adriana Conconi (coordination MPI 2013/14), José Manuel Roche (coordination MPI 2011, 2013), Maria Emma Santos (coordination MPI 2010), Gisela Robles (coordination MPI 2015/16) Bouba Housseini, Christoph Jindra, Yangyang Shen, Ana Vaz, Diego Zavaleta. Data analysts and MPI calculation: Christoph Jindra, Lu Gram, Usha Kanagaratnam, Christian Oldiges, Monica Pinilla Roncancio and Yangyang Shen. Special contributions: Bouba Housseini (new Ground Reality Check field material), Alejandro Olayo-Mendez (Research Assistance) and Conway Reinders, Mashrur Khan (country briefings edition) Communication Team: Paddy Coulter (Director of Communications), Heidi Fletcher (Web Manager), Felipe Roa and Joanne Tomkinson (Research Communications Assistants), Matthew Robson (Design Assistant). Administrative Support: Rachel Pearson (OPHI Project Coordinator) and Emma-Jane Grieg OPHI estimate the MPI, the UNDP Human Development Reports also publish it and we are grateful to our colleagues in HDRO for their support.

Programme planning levels for regular resources in 2017 (10 January 2017)

Programme planning levels for regular resources in 2017 (10 January 2017) planning for regular resources in (10 January ) In accordance with Executive Board decision 2008/15, following is the country breakdown of regular resources planning for. The planning have been computed