Detailed MPI data is available to download from the tables below. Tables 1.1 – 5.3 were updated in Winter 2014/2015 and are appendices to the Methodological Note – Winter 2014/2015. They include:
Table 6.1-6.6 was updated in June 2014 and covers changes to multidimensional poverty over time for 34 countries and their sub-national regions where possible. It is an appendix to the Methodological Note – Winter 2014/2015 andMultidimensional Poverty Dynamics: Methodology and Results for 34 countries.
The tables are divided into sheets to help in navigating through the data. The chart below provides detailed information on what is included in each data table and sheet. You can download the tables by clicking on the icons in the right-hand column.
| Table | Contents | Download |
| Tables 1.1-2.3 | Main MPI results, headcount ratio by dimensions, contribution of deprivations and other measures of poverty and wellbeing at the national level (110 countries) |  |
| Tables 3.1-4.3 | Multidimensional poverty, headcount ratio by dimension and contribution of deprivations in rural and urban areas (108 countries) |  |
| Tables 5.1-5.3 | Multidimensional poverty, headcount ratio by dimension and contribution of deprivations at the sub-national level (803 regions of 71 countries) | |
| Tables 6.1-6.6 | Changes to MPI poverty over time, including annualised changes in headcount ratio and intensity, changes in each indicator at the national level and changes in destitution where available (34 countries) | |
Citations: Please cite MPI data from tables 1.1 – 5.3 as: Alkire, S., Conconi, A., Robles, G. and Seth, S. (2015). “Multidimensional Poverty Index, Winter 2014/2015: Brief Methodological Note and Results.” OPHI Briefing 27, University of Oxford, January.
Please cite data from tables 6.1-6.6 as: Alkire, S., J. M. Roche and A. Vaz (2014): “Multidimensional Poverty Dynamics: Methodology and Results for 34 countries”, Oxford Poverty and Human Development Initiative, Oxford University. ophi.qeh.ox.ac.uk
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Afghanistan (AFG)
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Guatemala (GTM) | Nigeria (NGA) |
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Albania (ALB)
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Guinea (GIN)
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Pakistan (PAK)
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Algeria (DZA)
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Guinea-Bissau (GNB)
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Palestine, State of (PSE)
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Angola (AGO)
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Guyana (GUY)
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Paraguay (PRY)
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Armenia (ARM)
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Haiti (HTI)
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Peru (PER)
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Azerbaijan (AZE)
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Honduras (HND)
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Philippines (PHL)
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Bangladesh (BGD)
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India (IND)
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Rwanda (RWA)
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Barbados (BRB)
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Indonesia (IDN)
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Saint Lucia (LCA)
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Benin (BEN)
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Iraq (IRQ)
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Sao Tome & Principe (STP)
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Belize (BLZ)
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Jamaica (JAM)
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Senegal (SEN)
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Bhutan (BTN)
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Jordan (JOR)
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Serbia (SRB)
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Bolivia (BOL)
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Kazakhstan (KAZ)
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Sierra Leone (SLE)
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Bosnia & Herzegovina (BIH)
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Kenya (KEN)
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Somalia (SOM)
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Brazil (BRA)
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Kyrgyzstan (KGZ)
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South Africa (ZAF)
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Burkina Faso (BFA)
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Lao PDR (LAO)
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South Sudan (SSD)
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Burundi (BDI)
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Liberia (LBR)
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Sudan (SDN)
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Cambodia (KHM)
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Libya (LBY)
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Suriname (SUR)
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Cameroon (CMR)
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Lesotho (LSO)
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Swaziland (SWZ)
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Central African Rep. (CAF)
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Macedonia (MKD)
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Syrian Arab Republic (SYR)
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Chad (TCD)
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Madagascar (MDG)
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Tajikistan (TJK)
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China (CHN)
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Malawi (MWI)
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Tanzania (TZA)
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Colombia (COL)
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Maldives (MDV)
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Thailand (THA)
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Comoros (COM)
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Mali (MLI)
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Timor-Leste (TLS)
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Congo (COG)
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Mauritania (MRT)
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Togo (TGO)
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Congo DR (COD)
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Mexico (MEX)
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Trinidad and Tobago (TTO)
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Cote d’Ivoire (CIV)
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Moldova (MDA)
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Tunisia (TUN)
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Djibouti (DJI)
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Mongolia (MNG)
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Turkmenistan (TKM)
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Dominican Republic (DOM)
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Montenegro (MNE)
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Uganda (UGA)
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Ecuador (ECU)
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Morocco (MAR)
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Ukraine (UKR)
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Egypt (EGY)
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Mozambique (MOZ)
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Uzbekistan (UZB)
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El Salvador (SLV)
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Myanmar (MMR)
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Vanuatu (VUT)
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Ethiopia (ETH)
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Namibia (NAM)
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Viet Nam (VNM)
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Gabon (GAB)
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Nepal (NPL)
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Yemen (YEM)
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Gambia (GMB)
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Nicaragua (NIC)
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Zambia (ZMB)
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Ghana (GHA)
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Niger (NER)
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Zimbabwe (ZWE)
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Many poverty measures identify a household as poor or non-poor based on the achievements of all its members. Using the household as the unit of identification has the benefit of enabling a poverty measure to draw on information about persons of different ages and genders, and in different life situations. However, it also loses individual information because this is summarized at the level of the household. For example, the underlying microdata contain additional information on individual children. As a consequence, gendered and intrahousehold inequalities, for instance, are not evident even when data for them exist. This paper proposes methods to augment a household multidimensional poverty index (MPI) by applying individual-level analyses to the same dataset, and analysing these alongside the matrix of deprivations underlying an MPI. In particular we scrutinise (i) what proportion of deprived children live in multidimensionally poor households; (ii) what proportion of deprived children are girls or boys; and (iii) what proportion of deprived children live in households in which other children are not deprived in that same indicator. We also observe (iv) what other deprivations deprived and poor children experience in addition to the focal deprivation. Finally, we study what proportion of people live in households where children of different ages experience two different child deprivations concurrently. More complex analyses can also be undertaken that combine information on the deprivation status of more than one eligible member, and we illustrate this to identify pioneer children, who completed six years of schooling although adults in their household have not. Overall, this study provides a prototype methodology that can be mainstreamed into subsequent national and global MPI analyses in order to shine a light on child poverty multidimensionally. We illustrate the methodology with analyses of the global MPI for seven countries in South Asia.
Citation: Alkire, S., Ul Haq, R. and Alim, A. (2019). ‘The state of multidimensional child poverty in South Asia: a contextual and gendered view’, OPHI Working Paper 127, University of Oxford.
This joint handbook by United Nations Development Programme (UNDP) and the Oxford Poverty and Human Development Initiative (OPHI), University of Oxford, provides practical guidance for policymakers, statisticians, practitioners and others seeking or engaged in developing a national Multidimensional Poverty Index (MPI). It is also intended as a resource for academics, journalists and others interested in broadening their country’s poverty metrics, in line with the 2030 Agenda and Sustainable Development Goals (SDGs). With the use of the national MPI, UNDP and OPHI hope to strengthen national capacities of policymakers, technical experts and other stakeholders to:
Download the Handbook here.
| Tables 1.1-1.7 | Main MPI results, headcount ratio by dimensions, contribution of deprivations and other measures of poverty and wellbeing at the national level (105 countries). Download |
| Tables 2.1-2.12 | Multidimensional poverty, headcount ratio by dimension and contribution by different poverty cut-offs (105 countries). Download |
| Tables 3.1-3.6 | Multidimensional poverty, headcount ratio by dimension and contribution of deprivations by age groups. Download |
| Table 4.1-4.6 | Multidimensional poverty, headcount ratio by dimension and contribution of deprivations in rural and urban areas (105 countries). Download |
| Table 5.1-5.6
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Multidimensional poverty, headcount ratio by dimension and contribution of deprivations at the sub-national level. Download
5a. India disaggregation by district. Download |
| Table 6a | 6a. India: Changes over time. Download |
| Table 7 | The table presents an archive of all MPI estimations published since 2010. These are not harmonised for comparisons over time. Table 7 covers 361 estimations for 120 countries in 2018, of which 256 estimates are based on the original MPI and 105 estimates are based on the 2018 revised MPI. Download |
| Data tables 1.1-2.3 | Main MPI results, headcount ratio by dimensions, contribution of deprivations and other measures of poverty and wellbeing at the national level (104 countries). Download |
| Data tables 3.1-4.3 | Multidimensional poverty, headcount ratio by dimension and contribution of deprivations in rural and urban areas (104 countries). Download |
| Data tables 5.1-5.4 | Multidimensional poverty, headcount ratio by dimension and contribution of deprivations at the sub-national level. Download |
| Table 7 | The table presents an archive of all MPI estimations published since 2010. These are not harmonized for comparisons over time. Table 7 covers 256 estimations for 120 countries in 2016. Download |
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Children’s Multidimensional Poverty: disaggregating the global MPI Download |
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Global Multidimensional Poverty Index 2017 Download |
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‘Multidimensional Poverty Index – Summer 2017: |
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Press release: Half the world’s poor are children Download |
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Data tables 1.1-2.3 Main MPI results, headcount ratio by dimensions, contribution of deprivations and other measures of poverty and wellbeing at the national level (103 countries) Download |
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Data tables 3.1-4.3 Multidimensional poverty, headcount ratio by dimension and contribution of deprivations in rural and urban areas (102 countries) Download |
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Data tables 5.1-5.4 Multidimensional poverty, headcount ratio by dimension and contribution of deprivations at the sub-national level (988 regions of 78 countries) Download |
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Table 7 The table presents an archive of all MPI estimations published since 2010. These are not harmonized for comparisons over time. Table 7 covers 256 estimations for 120 countries in 2016. Download |
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Table 8 Multidimensional poverty, headcount ratio by dimensions and contribution of deprivations for different age groups at the national level (103 countries). Download |
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Multidimensional Poverty Index – Winter 2016: Brief methodological note and results.
OPHI MPI Methodological Note 43, 2016. |
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Data tables 1.1-2.3 Main MPI results, headcount ratio by dimensions, contribution of deprivations and other measures of poverty and wellbeing at the national level (103 countries) Download |
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Data tables 3.1-4.3 Multidimensional poverty, headcount ratio by dimension and contribution of deprivations in rural and urban areas (101 countries) Download |
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Data tables 5.1-5.4 Multidimensional poverty, headcount ratio by dimension and contribution of deprivations at the sub-national level (962 regions of 77 countries) Download |
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Table 7 The table presents an archive of all MPI estimations published since 2010. These are not harmonized for comparisons over time (for harmonized estimations see Table 6 from summer 2016 update). Table 7 covers 241 estimations for 119 countries in Winter 2016. Download |
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Multidimensional poverty in Africa (8 pages). OPHI Briefing 40, 2016. Download |
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The Global Multidimensional Poverty Index 2016: In brief (2 pages). OPHI Briefing 41, 2016. Download |
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Multidimensional Poverty in Africa: An overview (8 pages) (in French). OPHI Briefing 43, 2016. Download |
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Multidimensional Poverty Index – Summer 2016: Brief methodological note and results. OPHI Briefing 42, 2016. Download |
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Press release: Latest figures on where 1.6 billion poor people live Download |
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Data tables 1.1-2.3 Main MPI results, headcount ratio by dimensions, contribution of deprivations and other measures of poverty and wellbeing at the national level (102 countries) Download |
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Data tables 3.1-4.3 Multidimensional poverty, headcount ratio by dimension and contribution of deprivations in rural and urban areas (100 countries) Download |
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Data tables 5.1-5.4 Multidimensional poverty, headcount ratio by dimension and contribution of deprivations at the sub-national level (962 regions of 78 countries) Download |
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Data tables 6.1-6.6 Changes to MPI poverty over time, including annualised changes in headcount ratio and intensity, changes in each indicator at the national level and changes in destitution where available (50 countries) Download |
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Table 7 The table presents an archive of all MPI estimations published since 2010. These are not harmonized for comparisons over time (for harmonized estimations see Table 6). Table 7 covers 232 estimations for 118 countries in 2016. Download |
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Poverty Maps. OPHI Briefing 37, 2015. Download (pdf) |
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MPI – Winter 2015/2016: Brief methodological note and results. OPHI Briefing 36, 2015. Download (pdf) |
Alkire and Foster’s 2011 paper “Counting and Multidimensional Poverty Measurement” is posted by the Journal of Public Economics as their most cited article. The paper proposes a new methodology for multidimensional poverty measurement, and uses examples from the US and Indonesia to illustrate.
To read the article, click here.
