{"id": 997692, "name": "Share of population deprived in the indicator Nutrition (National) - Current margin estimate", "unit": "%", "createdAt": "2024-11-08T16:53:13.000Z", "updatedAt": "2024-11-28T16:20:25.000Z", "coverage": "", "timespan": "2011-2023", "datasetId": 6792, "shortUnit": "%", "columnOrder": 0, "shortName": "uncensored_headcount_ratio__indicator_nutrition__area_national__flavor_current_margin_estimate", "catalogPath": "grapher/ophi/2024-10-28/multidimensional_poverty_index/multidimensional_poverty_index#uncensored_headcount_ratio__indicator_nutrition__area_national__flavor_current_margin_estimate", "dimensions": {"years": {"values": [{"id": 2022}, {"id": 2017}, {"id": 2018}, {"id": 2015}, {"id": 2019}, {"id": 2012}, {"id": 2021}, {"id": 2016}, {"id": 2011}, {"id": 2014}, {"id": 2013}, {"id": 2023}]}, "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": 28, "name": "Bangladesh", "code": "BGD"}, {"id": 29, "name": "Barbados", "code": "BRB"}, {"id": 31, "name": "Belize", "code": "BLZ"}, {"id": 32, "name": "Benin", "code": "BEN"}, {"id": 34, "name": "Bolivia", "code": "BOL"}, {"id": 35, "name": "Bosnia and Herzegovina", "code": "BIH"}, {"id": 36, "name": "Botswana", "code": "BWA"}, {"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": 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": 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": 111, "name": "Moldova", "code": "MDA"}, {"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": 108, "name": "Namibia", "code": "NAM"}, {"id": 107, "name": "Nepal", "code": "NPL"}, {"id": 105, "name": "Nicaragua", "code": "NIC"}, {"id": 104, "name": "Niger", "code": "NER"}, {"id": 66, "name": "North Macedonia", "code": "MKD"}, {"id": 101, "name": "Pakistan", "code": "PAK"}, {"id": 140, "name": "Palestine", "code": "PSE"}, {"id": 98, "name": "Paraguay", "code": "PRY"}, {"id": 97, "name": "Peru", "code": "PER"}, {"id": 91, "name": "Rwanda", "code": "RWA"}, {"id": 229, "name": "Saint Lucia", "code": "LCA"}, {"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": 71, "name": "Tunisia", "code": "TUN"}, {"id": 69, "name": "Turkmenistan", "code": "TKM"}, {"id": 237, "name": "Tuvalu", "code": "TUV"}, {"id": 68, "name": "Uganda", "code": "UGA"}, {"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 deprived in the indicator _nutrition_.", "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 developed by Alkire and Foster (2011). The global MPI was developed in 2010 by Alkire and Santos (2014, 2010) in collaboration with the UNDP\u2019s Human Development Report Office (HDRO). Since its inception, the global MPI has used information from 10 indicators, which are grouped into three equally weighted dimensions: health, education, and living standards. These dimensions are the same as those used in the UNDP\u2019s Human Development Index.\n\nIn 2018, the first major revision of the global MPI was undertaken, considering improvements in survey microdata and better align to the 2030 development agenda insofar as possible (Alkire and Jahan, 2018; OPHI, 2018). The revision consisted of adjustments in the definition of five out of the ten indicators, namely child mortality, nutrition, years of schooling, housing and assets. Alkire, Kanagaratnam, Nogales and Suppa (2022) provide a comprehensive analysis of the consequences of the 2018 revision. 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). The revision of assets indicator is detailed in Vollmer and Alkire (2022).\n\nThe global MPI begins by establishing a deprivation profile for each person, showing which of the 10 indicators they are deprived in. Each person is identified as deprived or non-deprived in each indicator based on a deprivation cutoff. In the case of health and education, each household member may be identified as deprived or not deprived according to available information 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 \u2013 which was required by the data \u2013 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\u2019s 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 normalised indicator weight structure of the global MPI means that the living standard 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 or 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 the person\u2019s deprivation score is equal to or higher than the poverty cutoff of 33.33 percent. 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 percentage of weighted deprivations experienced by the poor. We then compute the adjusted poverty headcount ratio (M0) or MPI by combining H and A in a multiplicative form (MPI = H x A).\n\nBoth the incidence and the intensity of these deprivations are highly relevant pieces of information for poverty measurement. The incidence of poverty is intuitive and understandable by anyone. People always want to know how many poor people there are in a society as a proportion of the whole population. Media tend to pick up on the incidence of poverty easily. Yet, the proportion of poor people as the 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 close to the one-third threshold, that is, if they are deprived in 20 to 33.32 percent of weighted indicators. The tables also apply a higher poverty cutoff to identify those in severe poverty, meaning those deprived in 50 percent or more of the dimensions.\n\nThe AF methodology has a property that makes the global MPI even more useful\u2014dimensional breakdown. This property makes it possible to consistently compute the percentage of the population who are multidimensionally poor and simultaneously deprived in each indicator. This is known as the censored headcount ratio of an indicator. The weighted sum of censored headcount ratios of all MPI indicators is equal to the MPI value.\n\nThe censored headcount ratio shows the extent of deprivations among the poor 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, because 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.", "type": "float", "dataChecksum": "9941454295901499817", "metadataChecksum": "-2617534815517684282", "datasetName": "Global Multidimensional Poverty Index (MPI)", "updatePeriodDays": 365, "datasetVersion": "2024-10-28", "nonRedistributable": false, "display": {"name": "Share of population deprived in the indicator Nutrition", "unit": "%", "shortUnit": "%", "tolerance": 12, "numDecimalPlaces": 1}, "schemaVersion": 2, "processingLevel": "minor", "presentation": {"titlePublic": "Share of population deprived in the indicator Nutrition", "titleVariant": "Most recent year\n", "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 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).", "A person in a household is deprived in the indicator _nutrition_ if any person under 70 years of age for whom there is nutritional information is undernourished. This indicator is part of the _health_ dimension.\n", "This indicator is a current margin estimate (CME), meaning that it relies on the most recent survey data available for each country.\n"], "origins": [{"id": 1949, "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 \u2018MPI poor\u2019. The extent \u2013 or intensity \u2013 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 \u2013 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\u2019s flagship Human Development Report in 2010. It has been published annually by OPHI and in the HDRs ever since.", "producer": "Alkire, Kanagaratnam and Suppa", "citationFull": "- Alkire, S., Kanagaratnam, U., and Suppa, N. (2024). The Global Multidimensional Poverty Index (MPI) 2024. Country Results and Methodological Note. OPHI MPI Methodological Note 58, Oxford Poverty and Human Development Initiative, University of Oxford.\n- Alkire, S., Kanagaratnam, U., and Suppa, N. (2024). The Global Multidimensional Poverty Index (MPI) 2024. Disaggregation Results and Methodological Note. OPHI MPI Methodological Note 59, Oxford Poverty and Human Development Initiative, University of Oxford.", "attribution": "Alkire, Kanagaratnam and Suppa (2024) - The Global Multidimensional Poverty Index (MPI) 2024", "versionProducer": "2024", "urlMain": "https://ophi.org.uk/global-mpi", "urlDownload": "https://cloud-ophi.qeh.ox.ac.uk/index.php/s/eRLL5jGKPLTygYT/download?path=%2F&files=GMPI2024_puf.csv", "dateAccessed": "2024-10-28", "datePublished": "2024-10-17", "license": {"url": "https://ophi.org.uk/global-mpi-frequently-asked-questions", "name": "CC BY 4.0"}}, {"id": 1950, "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 \u2018MPI poor\u2019. The extent \u2013 or intensity \u2013 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 \u2013 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\u2019s flagship Human Development Report in 2010. It has been published annually by OPHI and in the HDRs ever since.", "producer": "Alkire, Kanagaratnam and Suppa", "citationFull": "- Alkire, S., Kanagaratnam, U., and Suppa, N. (2024). A methodological note on the global Multidimensional Poverty Index (MPI) 2024 changes over time results for 86 countries. OPHI MPI Methodological Note 60, Oxford Poverty and Human Development Initiative, University of Oxford.\n- Alkire, S., Kanagaratnam, U., and Suppa, N. (2024). The Global Multidimensional Poverty Index (MPI) 2024. Disaggregation Results and Methodological Note. OPHI MPI Methodological Note 59, Oxford Poverty and Human Development Initiative, University of Oxford.", "attribution": "Alkire, Kanagaratnam and Suppa (2024) - The Global Multidimensional Poverty Index (MPI) 2024", "versionProducer": "2024", "urlMain": "https://ophi.org.uk/global-mpi", "urlDownload": "https://cloud-ophi.qeh.ox.ac.uk/index.php/s/eRLL5jGKPLTygYT/download?path=%2F&files=GMPI_HOT_2024_puf.csv", "dateAccessed": "2024-10-28", "datePublished": "2024-10-17", "license": {"url": "https://ophi.org.uk/global-mpi-frequently-asked-questions", "name": "CC BY 4.0"}}]}