{"id":1354863,"name":"Share of population living in multidimensional poverty (National) - Current margin estimate","unit":"%","createdAt":"2026-11-05T15:15:38.000Z","updatedAt":"2026-11-05T15:15:38.000Z","coverage":"","timespan":"2014-2024","datasetId":8247,"shortUnit":"%","columnOrder":0,"shortName":"headcount_ratio__area_national__flavor_current_margin_estimate","catalogPath":"grapher/ophi/2026-11-04/multidimensional_poverty_index/multidimensional_poverty_index#headcount_ratio__area_national__flavor_current_margin_estimate","dimensions":{"years":{"values":[{"id":2022},{"id":2017},{"id":2018},{"id":2023},{"id":2019},{"id":2015},{"id":2021},{"id":2016},{"id":2014},{"id":2024},{"id":2020}]},"entities":{"values":[{"id":15,"name":"Afghanistan","code":"AFG"},{"id":16,"name":"Albania","code":"ALB"},{"id":17,"name":"Algeria","code":"DZA"},{"id":19,"name":"Angola","code":"AGO"},{"id":21,"name":"Argentina","code":"ARG"},{"id":22,"name":"Armenia","code":"ARM"},{"id":25,"name":"Azerbaijan","code":"AZE"},{"id":28,"name":"Bangladesh","code":"BGD"},{"id":31,"name":"Belize","code":"BLZ"},{"id":32,"name":"Benin","code":"BEN"},{"id":33,"name":"Bhutan","code":"BTN"},{"id":34,"name":"Bolivia","code":"BOL"},{"id":36,"name":"Botswana","code":"BWA"},{"id":37,"name":"Brazil","code":"BRA"},{"id":40,"name":"Burkina Faso","code":"BFA"},{"id":41,"name":"Burundi","code":"BDI"},{"id":42,"name":"Cambodia","code":"KHM"},{"id":43,"name":"Cameroon","code":"CMR"},{"id":174,"name":"Central African Republic","code":"CAF"},{"id":173,"name":"Chad","code":"TCD"},{"id":171,"name":"China","code":"CHN"},{"id":170,"name":"Colombia","code":"COL"},{"id":169,"name":"Comoros","code":"COM"},{"id":168,"name":"Congo","code":"COG"},{"id":167,"name":"Democratic Republic of Congo","code":"COD"},{"id":166,"name":"Costa Rica","code":"CRI"},{"id":143,"name":"Cote d'Ivoire","code":"CIV"},{"id":164,"name":"Cuba","code":"CUB"},{"id":160,"name":"Dominican Republic","code":"DOM"},{"id":201,"name":"Ecuador","code":"ECU"},{"id":65,"name":"Egypt","code":"EGY"},{"id":259,"name":"El Salvador","code":"SLV"},{"id":78,"name":"Eswatini","code":"SWZ"},{"id":158,"name":"Ethiopia","code":"ETH"},{"id":202,"name":"Fiji","code":"FJI"},{"id":153,"name":"Gabon","code":"GAB"},{"id":151,"name":"Gambia","code":"GMB"},{"id":152,"name":"Georgia","code":"GEO"},{"id":150,"name":"Ghana","code":"GHA"},{"id":148,"name":"Guatemala","code":"GTM"},{"id":147,"name":"Guinea","code":"GIN"},{"id":94,"name":"Guinea-Bissau","code":"GNB"},{"id":146,"name":"Guyana","code":"GUY"},{"id":145,"name":"Haiti","code":"HTI"},{"id":139,"name":"Honduras","code":"HND"},{"id":137,"name":"India","code":"IND"},{"id":136,"name":"Indonesia","code":"IDN"},{"id":134,"name":"Iraq","code":"IRQ"},{"id":132,"name":"Jamaica","code":"JAM"},{"id":130,"name":"Jordan","code":"JOR"},{"id":131,"name":"Kazakhstan","code":"KAZ"},{"id":129,"name":"Kenya","code":"KEN"},{"id":204,"name":"Kiribati","code":"KIR"},{"id":126,"name":"Kyrgyzstan","code":"KGZ"},{"id":125,"name":"Laos","code":"LAO"},{"id":123,"name":"Lesotho","code":"LSO"},{"id":121,"name":"Liberia","code":"LBR"},{"id":120,"name":"Libya","code":"LBY"},{"id":118,"name":"Madagascar","code":"MDG"},{"id":117,"name":"Malawi","code":"MWI"},{"id":211,"name":"Maldives","code":"MDV"},{"id":115,"name":"Mali","code":"MLI"},{"id":114,"name":"Mauritania","code":"MRT"},{"id":113,"name":"Mexico","code":"MEX"},{"id":112,"name":"Mongolia","code":"MNG"},{"id":215,"name":"Montenegro","code":"MNE"},{"id":110,"name":"Morocco","code":"MAR"},{"id":109,"name":"Mozambique","code":"MOZ"},{"id":142,"name":"Myanmar","code":"MMR"},{"id":218,"name":"Nauru","code":"NRU"},{"id":107,"name":"Nepal","code":"NPL"},{"id":104,"name":"Niger","code":"NER"},{"id":103,"name":"Nigeria","code":"NGA"},{"id":66,"name":"North Macedonia","code":"MKD"},{"id":101,"name":"Pakistan","code":"PAK"},{"id":140,"name":"Palestine","code":"PSE"},{"id":99,"name":"Papua New Guinea","code":"PNG"},{"id":98,"name":"Paraguay","code":"PRY"},{"id":97,"name":"Peru","code":"PER"},{"id":96,"name":"Philippines","code":"PHL"},{"id":91,"name":"Rwanda","code":"RWA"},{"id":239,"name":"Samoa","code":"WSM"},{"id":232,"name":"Sao Tome and Principe","code":"STP"},{"id":89,"name":"Senegal","code":"SEN"},{"id":88,"name":"Serbia","code":"SRB"},{"id":233,"name":"Seychelles","code":"SYC"},{"id":87,"name":"Sierra Leone","code":"SLE"},{"id":81,"name":"South Africa","code":"ZAF"},{"id":141,"name":"Sri Lanka","code":"LKA"},{"id":79,"name":"Sudan","code":"SDN"},{"id":234,"name":"Suriname","code":"SUR"},{"id":76,"name":"Tajikistan","code":"TJK"},{"id":64,"name":"Tanzania","code":"TZA"},{"id":75,"name":"Thailand","code":"THA"},{"id":225,"name":"East Timor","code":"TLS"},{"id":74,"name":"Togo","code":"TGO"},{"id":235,"name":"Tonga","code":"TON"},{"id":73,"name":"Trinidad and Tobago","code":"TTO"},{"id":71,"name":"Tunisia","code":"TUN"},{"id":69,"name":"Turkmenistan","code":"TKM"},{"id":237,"name":"Tuvalu","code":"TUV"},{"id":68,"name":"Uganda","code":"UGA"},{"id":62,"name":"Uzbekistan","code":"UZB"},{"id":221,"name":"Vanuatu","code":"VUT"},{"id":84,"name":"Vietnam","code":"VNM"},{"id":61,"name":"Yemen","code":"YEM"},{"id":60,"name":"Zambia","code":"ZMB"},{"id":80,"name":"Zimbabwe","code":"ZWE"}]}},"descriptionShort":"Multidimensional poverty is defined as being deprived in a range of health, education and living standards indicators. This is the share of the population that is multidimensionally poor.","descriptionFromProducer":"The global MPI is a measure of acute poverty covering over 100 countries in the developing regions of the world. This measure is based on the dual cutoff counting approach to poverty measurement developed by Alkire and Foster (2011). The global MPI was developed in 2010 by Alkire and Santos (2010; 2014) in collaboration with the United Nations Development Programme’s (UNDP) Human Development Report Office (HDRO). Since its inception, the global MPI has used information from 10 indicators, grouped into three equally weighted dimensions: health, education and living standards. These dimensions are the same as those used in the UNDP’s Human Development Index.\n\nThe first major revision of the global MPI was undertaken in 2018, considering improvements in survey microdata and to better align with the 2030 Agenda for Sustainable Development insofar as possible (Alkire and Jahan, 2018; OPHI, 2018; Alkire and Kanagaratnam, 2021). The revision adjusted the definitions of five of the ten indicators: child mortality, nutrition, years of schooling, housing and assets. The normative and empirical decisions that underlie the revision of the global MPI, and adjustments related to the child mortality, nutrition, years of schooling and housing indicators, are discussed in Alkire and Kanagaratnam (2021). Alkire et al. (2022) provide a comprehensive analysis of the consequences of the 2018 revision. The revision of the assets indicator is detailed in Vollmer and Alkire (2022).\n\nThe global MPI begins by establishing a deprivation profile for each person, showing in which of the 10 indicators they are deprived. Each person is identified as deprived or non-deprived in each indicator based on its respective deprivation cutoff. In the case of health and education, each household member may be identified as deprived or not deprived according to the information available for other household members. For example, if any household member for whom data exist is undernourished, each person in that household is considered deprived in nutrition. Taking this approach – which was required by the data – does not reveal intrahousehold disparities, but is intuitive and assumes shared positive (or negative) effects of achieving (or not achieving) certain outcomes. Next, looking across indicators, each person’s deprivation score is constructed by adding up the weights of the indicators in which they are deprived. The indicators use a nested weight structure: equal weights across dimensions and an equal weight for each indicator within a dimension. The indicator weight structure of the global MPI means that the living standards indicators receive lower weight than health and education-related indicators because, from a policy perspective, each of the three dimensions is of roughly equal normative importance.\n\nIn the global MPI, a person is identified as multidimensionally poor (MPI poor) if they are deprived in at least one-third of the weighted MPI indicators. In other words, a person is MPI poor if their deprivation score is equal to or higher than the poverty cutoff of 33.33%. After the poverty identification step, we aggregate across individuals to obtain the incidence of poverty, or headcount ratio (H), which represents the percentage of poor people in the population. We then compute the intensity of poverty (A), representing the average deprivation score, or the average percentage of weighted deprivations, experienced by poor people. We compute the adjusted poverty headcount ratio (M0) or the MPI by multiplying together H and A (MPI = H x A).\n\nBoth the incidence and the intensity of multidimensional poverty are highly relevant pieces of information for poverty measurement. The incidence of poverty is intuitive and understandable by anyone. People want to know how many poor people there are in a society as a proportion of the whole population, and the media tend to pick up on the incidence of poverty. Yet, the proportion of poor people as a headline figure is not enough (Alkire, Oldiges and Kanagaratnam, 2021).\n\nA headcount ratio is also estimated using two other poverty cutoffs. The global MPI identifies individuals as vulnerable to poverty if they are not poor but are close to the poverty threshold; that is, if they are deprived in 20% to 33.32% of weighted indicators. The method also applies a higher poverty cutoff to identify individuals in severe poverty, meaning those deprived in 50% or more of the dimensions.\n\nThe Alkire-Foster method has a property that makes the global MPI even more useful – dimensional breakdown. This property makes it possible to consistently compute the percentage of the population who are multidimensionally poor and simultaneously deprived in an indicator. This is known as the censored headcount ratio of an indicator. While above we described MPI to be the product of H x A, the MPI can equivalently be computed as the weighted sum of the censored headcount ratios of all MPI indicators.\n\nThe censored headcount ratio shows the extent of deprivations among poor people but does not reflect the weights or relative values of the indicators. Two indicators may have the same censored headcount ratios but different contributions to overall poverty, as the contribution depends both on the censored headcount ratio and on the weight assigned to each indicator. As such, a complementary analysis to the censored headcount ratio is the percentage contribution of each indicator to overall multidimensional poverty.","descriptionProcessing":"Some surveys run over two or more calendar years, such as 2023–2024. We show each of these surveys in the first year of that period.","type":"float","patchConfigIdETL":"01a1ac47-a4c9-70a7-8523-7c7418c6e98f","datasetName":"Global Multidimensional Poverty Index (MPI)","updatePeriodDays":365,"datasetVersion":"2026-11-04","nonRedistributable":false,"display":{"name":"Share of population living in multidimensional poverty","unit":"%","shortUnit":"%","tolerance":10,"numDecimalPlaces":1},"schemaVersion":2,"processingLevel":"minor","presentation":{"titlePublic":"Share of population living in multidimensional poverty","titleVariant":"Most recent year","attributionShort":"OPHI","topicTagsLinks":["Poverty"],"faqs":[{"gdocId":"1gGburArxglFdHXeTLotFW4TOOLoeRq5XW6UfAdKtaAw","fragmentId":"mpi-definition"},{"gdocId":"1gGburArxglFdHXeTLotFW4TOOLoeRq5XW6UfAdKtaAw","fragmentId":"mpi-sources"},{"gdocId":"1gGburArxglFdHXeTLotFW4TOOLoeRq5XW6UfAdKtaAw","fragmentId":"mpi-indicators-unavailable"},{"gdocId":"1gGburArxglFdHXeTLotFW4TOOLoeRq5XW6UfAdKtaAw","fragmentId":"mpi-comparability"},{"gdocId":"1gGburArxglFdHXeTLotFW4TOOLoeRq5XW6UfAdKtaAw","fragmentId":"mpi-other-sources"}]},"descriptionKey":"- Being in multidimensional poverty means that a person lives in a household deprived in a third or more of ten weighted indicators, grouped into three dimensions of well-being: **health** (using two indicators: nutrition, child mortality), **education** (using two indicators: years of schooling, school attendance), and **living standards** (using six indicators: cooking fuel, sanitation, drinking water, electricity, housing, assets).\n- Each household is assessed against specific thresholds for these indicators. For example, a household is considered deprived in the _electricity_ indicator if it does not have access to it. [This article](https://ourworldindata.org/multidimensional-poverty-index) discusses specific thresholds in more detail.\n- Each indicator contributes to one of the three dimensions of well-being. Health and education indicators are weighted more (1/6 each) than living standards indicators (1/18 each) so that all three dimensions contribute equally to the overall measure.\n- This indicator is a current margin estimate (CME), meaning that it relies on the most recent survey data available for each country. Look for the [harmonized over time (HOT) estimate](https://ourworldindata.org/grapher/share-of-population-multidimensionally-poor-hot) to see trends over time.","origins":[{"id":28344,"titleSnapshot":"Global Multidimensional Poverty Index (MPI) – Current margin estimates (CME)","title":"Global Multidimensional Poverty Index (MPI)","descriptionSnapshot":"This dataset contains current margin estimates (CME), based on the most recent survey data.","description":"The global Multidimensional Poverty Index (MPI) is an international measure of acute multidimensional poverty covering over 100 developing countries. It complements traditional monetary poverty measures by capturing the acute deprivations in health, education, and living standards that a person faces simultaneously.\n\nThe MPI assesses poverty at the individual level. If a person is deprived in a third or more of ten (weighted) indicators, the global MPI identifies them as ‘MPI poor’. The extent – or intensity – of their poverty is also measured through the percentage of deprivations they are experiencing.\n\nThe global MPI shows who is poor and how they are poor and can be used to create a comprehensive picture of people living in poverty. It permits comparisons both across countries and world regions, and within countries by ethnic group, urban/rural area, subnational region, and age group, as well as other key household and community characteristics. For each group and for countries as a whole, the composition of MPI by each of the ten indicators shows how people are poor.\n\nThis makes the MPI and its linked information platform invaluable as an analytical tool to identify the most vulnerable people – the poorest among the poor, revealing poverty patterns within countries and over time, enabling policy makers to target resources and design policies more effectively.\n\nThe global MPI was developed by OPHI with the UN Development Programme (UNDP) for inclusion in UNDP’s flagship Human Development Report in 2010. It has been published annually by OPHI and in the HDRs ever since.","producer":"Alkire et al.","citationFull":"- Alkire, S., Mishra, R. and Suppa, N. (2026). The Global Multidimensional Poverty Index (MPI) 2026: Country Results and Methodological Note. OPHI MPI Methodological Note 64, Oxford Poverty and Human Development Initiative (OPHI), University of Oxford.\n- Alkire, S., Mishra, R. and Suppa, N. (2026). The Global Multidimensional Poverty Index (MPI) 2026: Disaggregation Results and Methodological Note. OPHI MPI Methodological Note 65, Oxford Poverty and Human Development Initiative (OPHI), University of Oxford.","attribution":"Alkire et al. (2026) – The Global Multidimensional Poverty Index (MPI) 2026","versionProducer":"2026","urlMain":"https://ophi.org.uk/global-mpi","dateAccessed":"2026-11-04","datePublished":"2026-10-12","license":{"url":"https://ophi.org.uk/global-mpi-frequently-asked-questions","name":"CC BY 4.0"}},{"id":28345,"titleSnapshot":"Global Multidimensional Poverty Index (MPI) – Harmonized over time (HOT)","title":"Global Multidimensional Poverty Index (MPI)","descriptionSnapshot":"This dataset contains harmonized over time (HOT) estimates. This harmonization seeks to make two or more MPI estimations comparable by aligning the indicator definitions in each survey.","description":"The global Multidimensional Poverty Index (MPI) is an international measure of acute multidimensional poverty covering over 100 developing countries. It complements traditional monetary poverty measures by capturing the acute deprivations in health, education, and living standards that a person faces simultaneously.\n\nThe MPI assesses poverty at the individual level. If a person is deprived in a third or more of ten (weighted) indicators, the global MPI identifies them as ‘MPI poor’. The extent – or intensity – of their poverty is also measured through the percentage of deprivations they are experiencing.\n\nThe global MPI shows who is poor and how they are poor and can be used to create a comprehensive picture of people living in poverty. It permits comparisons both across countries and world regions, and within countries by ethnic group, urban/rural area, subnational region, and age group, as well as other key household and community characteristics. For each group and for countries as a whole, the composition of MPI by each of the ten indicators shows how people are poor.\n\nThis makes the MPI and its linked information platform invaluable as an analytical tool to identify the most vulnerable people – the poorest among the poor, revealing poverty patterns within countries and over time, enabling policy makers to target resources and design policies more effectively.\n\nThe global MPI was developed by OPHI with the UN Development Programme (UNDP) for inclusion in UNDP’s flagship Human Development Report in 2010. It has been published annually by OPHI and in the HDRs ever since.","producer":"Alkire et al.","citationFull":"- Alkire, S., Mishra, R. and Suppa, N. (2026). The Global Multidimensional Poverty Index (MPI) 2026: Changes over Time Results for 89 Countries. OPHI MPI Methodological Note 66, Oxford Poverty and Human Development Initiative (OPHI), University of Oxford.\n- Alkire, S., Mishra, R. and Suppa, N. (2026). The Global Multidimensional Poverty Index (MPI) 2026: Disaggregation Results and Methodological Note. OPHI MPI Methodological Note 65, Oxford Poverty and Human Development Initiative (OPHI), University of Oxford.","attribution":"Alkire et al. (2026) – The Global Multidimensional Poverty Index (MPI) 2026","versionProducer":"2026","urlMain":"https://ophi.org.uk/global-mpi","dateAccessed":"2026-11-04","datePublished":"2026-10-12","license":{"url":"https://ophi.org.uk/global-mpi-frequently-asked-questions","name":"CC BY 4.0"}}]}