{"id": 930810, "name": "Decision maker about a woman's own health care: mainly wife\u00a0 (% of women age 15-49)", "unit": "%", "createdAt": "2024-06-11T17:33:36.000Z", "updatedAt": "2025-08-21T10:14:29.000Z", "coverage": "", "timespan": "1999-2022", "datasetId": 6554, "shortUnit": "%", "columnOrder": 0, "shortName": "sg_dmk_hlth_wf_zs", "catalogPath": "grapher/wb/2024-06-10/gender_statistics/gender_statistics#sg_dmk_hlth_wf_zs", "descriptionFromProducer": "**Long definition from World Bank:** Decision maker about women own health care: mainly wife  is Percentage of currently married women aged 15-49 for whom the decision maker for their own health care is mainly the respondent\n\n**Source from World Bank:** Demographic and Health Surveys (DHS)\n\n**Statistical concept and methodology from World Bank:** Decision maker about Own health care: mainly wife  is the number of currently married women aged 15-49 for whom the decision maker for their own health care is mainly the respondent, expressed as percentage of currently married women aged 15-49 who have been interviewed .\n\n**License type from World Bank:** CC BY-4.0\n\n**Development relevance from World Bank:** Women\u2018s participation in decisions being made in their own households, that is households in which they usually live with their spouse and/or children with or without others, is widely accepted as a universal indicator of women\u2018s empowerment. The ability of women to make decisions that affect their personal circumstances is an essential element of their empowerment and serves as an important contributor to their overall development.\n\n**World Bank variable id:** SG.DMK.HLTH.WF.ZS", "type": "float", "dataChecksum": "7895150397780080246", "metadataChecksum": "-8414908525890845916", "datasetName": "World Bank Gender Statistics", "updatePeriodDays": 365, "datasetVersion": "2024-06-10", "nonRedistributable": false, "display": {"unit": "%", "shortUnit": "%", "numDecimalPlaces": 1}, "schemaVersion": 2, "presentation": {"topicTagsLinks": ["Global Education"]}, "dimensions": {"years": {"values": [{"id": 2015}, {"id": 2009}, {"id": 2018}, {"id": 2016}, {"id": 2000}, {"id": 2005}, {"id": 2010}, {"id": 2006}, {"id": 2004}, {"id": 2007}, {"id": 2011}, {"id": 2014}, {"id": 2001}, {"id": 2012}, {"id": 2003}, {"id": 2008}, {"id": 2017}, {"id": 2022}, {"id": 2013}, {"id": 2002}, {"id": 2021}, {"id": 2020}, {"id": 2019}, {"id": 1999}]}, "entities": {"values": [{"id": 15, "name": "Afghanistan", "code": "AFG"}, {"id": 16, "name": "Albania", "code": "ALB"}, {"id": 19, "name": "Angola", "code": "AGO"}, {"id": 22, "name": "Armenia", "code": "ARM"}, {"id": 25, "name": "Azerbaijan", "code": "AZE"}, {"id": 28, "name": "Bangladesh", "code": "BGD"}, {"id": 32, "name": "Benin", "code": "BEN"}, {"id": 34, "name": "Bolivia", "code": "BOL"}, {"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": 173, "name": "Chad", "code": "TCD"}, {"id": 170, "name": "Colombia", "code": "COL"}, {"id": 169, "name": "Comoros", "code": "COM"}, {"id": 167, "name": "Democratic Republic of Congo", "code": "COD"}, {"id": 168, "name": "Congo", "code": "COG"}, {"id": 143, "name": "Cote d'Ivoire", "code": "CIV"}, {"id": 160, "name": "Dominican Republic", "code": "DOM"}, {"id": 65, "name": "Egypt", "code": "EGY"}, {"id": 157, "name": "Eritrea", "code": "ERI"}, {"id": 78, "name": "Eswatini", "code": "SWZ"}, {"id": 158, "name": "Ethiopia", "code": "ETH"}, {"id": 153, "name": "Gabon", "code": "GAB"}, {"id": 151, "name": "Gambia", "code": "GMB"}, {"id": 150, "name": "Ghana", "code": "GHA"}, {"id": 148, "name": "Guatemala", "code": "GTM"}, {"id": 147, "name": "Guinea", "code": "GIN"}, {"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": 130, "name": "Jordan", "code": "JOR"}, {"id": 129, "name": "Kenya", "code": "KEN"}, {"id": 126, "name": "Kyrgyzstan", "code": "KGZ"}, {"id": 123, "name": "Lesotho", "code": "LSO"}, {"id": 121, "name": "Liberia", "code": "LBR"}, {"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": 111, "name": "Moldova", "code": "MDA"}, {"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": 103, "name": "Nigeria", "code": "NGA"}, {"id": 101, "name": "Pakistan", "code": "PAK"}, {"id": 99, "name": "Papua New Guinea", "code": "PNG"}, {"id": 97, "name": "Peru", "code": "PER"}, {"id": 96, "name": "Philippines", "code": "PHL"}, {"id": 91, "name": "Rwanda", "code": "RWA"}, {"id": 232, "name": "Sao Tome and Principe", "code": "STP"}, {"id": 89, "name": "Senegal", "code": "SEN"}, {"id": 87, "name": "Sierra Leone", "code": "SLE"}, {"id": 81, "name": "South Africa", "code": "ZAF"}, {"id": 76, "name": "Tajikistan", "code": "TJK"}, {"id": 64, "name": "Tanzania", "code": "TZA"}, {"id": 225, "name": "East Timor", "code": "TLS"}, {"id": 74, "name": "Togo", "code": "TGO"}, {"id": 69, "name": "Turkmenistan", "code": "TKM"}, {"id": 68, "name": "Uganda", "code": "UGA"}, {"id": 67, "name": "Ukraine", "code": "UKR"}, {"id": 61, "name": "Yemen", "code": "YEM"}, {"id": 60, "name": "Zambia", "code": "ZMB"}, {"id": 80, "name": "Zimbabwe", "code": "ZWE"}]}}, "origins": [{"id": 910, "title": "World Bank Gender Statistics", "description": "The World Bank Gender Statistics dataset provides a comprehensive range of gender-related indicators grouped by various topics. These indicators are categorized under different themes such as education, employment and time use, entrepreneurship, environment, health, leadership, norms and decision-making, technology, violence, and contextual information. Each category contains numerous specific indicators, covering a wide range of issues such as literacy rates, employment by sector, legal rights, health statistics, and more. This dataset offers detailed information and insights into various aspects of gender disparity and equality across different regions and countries.", "producer": "World Bank", "citationFull": "World Bank Gender Statistics, World Bank, 2024. Licence: CC BY 4.0.", "attributionShort": "World Bank", "urlMain": "https://databank.worldbank.org/source/gender-statistics#", "dateAccessed": "2024-06-10", "datePublished": "2024-04-15", "license": {"url": "https://datacatalog.worldbank.org/public-licenses#cc-by", "name": "CC BY 4.0"}}]}