{"id": 1104866, "name": "Share of firms with a female top manager", "unit": "% of firms", "createdAt": "2025-09-13T20:17:53.000Z", "updatedAt": "2025-09-16T20:22:35.000Z", "coverage": "", "timespan": "2007-2024", "datasetId": 7209, "shortUnit": "%", "columnOrder": 0, "shortName": "ic_frm_femm_zs", "catalogPath": "grapher/wb/2025-09-08/gender_statistics/gender_statistics#ic_frm_femm_zs", "descriptionShort": "Top manager refers to the highest ranking manager or CEO of the establishment. This may be the owner if she works as the manager of the firm.", "descriptionFromProducer": "**Definition:** Percentage of firms with females as the top manager.\n\n**Aggregation method:** Unweighted average\n\n**Limitations and exceptions:** The sampling methodology for Enterprise Surveys is stratified random sampling. In a simple random sample, all members of the population have the same probability of being selected and no weighting of the observations is necessary. In a stratified random sample, all population units are grouped within homogeneous groups and simple random samples are selected within each group. This method allows computing estimates for each of the strata with a specified level of precision while population estimates can also be estimated by properly weighting individual observations. The sampling weights take care of the varying probabilities of selection across different strata. Under certain conditions, estimates' precision under stratified random sampling will be higher than under simple random sampling (lower standard errors may result from the estimation procedure).\n\nThe strata for Enterprise Surveys are firm size, business sector, and geographic region within a country. Firm size levels are 5-19 (small), 20-99 (medium), and 100+ employees (large-sized firms). Since in most economies, the majority of firms are small and medium-sized, Enterprise Surveys oversample large firms since larger firms tend to be engines of job creation. Sector breakdown is usually manufacturing, retail, and other services. For larger economies, specific manufacturing sub-sectors are selected as additional strata on the basis of employment, value-added, and total number of establishments figures. Geographic regions within a country are selected based on which cities/regions collectively contain the majority of economic activity.\n\nIdeally the survey sample frame is derived from the universe of eligible firms obtained from the country\u2019s statistical office. Sometimes the master list of firms is obtained from other government agencies such as tax or business licensing authorities. In some cases, the list of firms is obtained from business associations or marketing databases. In a few cases, the sample frame is created via block enumeration, where the World Bank \u201cmanually\u201d constructs a list of eligible firms after 1) partitioning a country\u2019s cities of major economic activity into clusters and blocks, 2) randomly selecting a subset of blocks which will then be enumerated. In surveys conducted since 2005-06, survey documentation which explains the source of the sample frame and any special circumstances encountered during survey fieldwork are included with the collected datasets.\n\nObtaining panel data, i.e. interviews with the same firms across multiple years, is a priority in current Enterprise Surveys. When conducting a new Enterprise Survey in a country where data was previously collected, maximal effort is expended to re-interview as many firms (from the prior survey) as possible. For these panel firms, sampling weights can be adjusted to take into account the resulting altered probabilities of inclusion in the sample frame.\n\n**Notes from original source:** All surveys were administered using the Enterprise Surveys methodology as outlined in the Methodology page which can be found from www.enterprisesurveys.org.\n\n**General comments:** Relevance to gender indicator: Women are vastly underrepresented in decision making positions at the top level in the private sector and this indicator monitors progress that has been made.", "type": "float", "dataChecksum": "11160821519300774764", "metadataChecksum": "1500350205972224696", "datasetName": "World Bank Gender Statistics", "updatePeriodDays": 365, "datasetVersion": "2025-09-08", "nonRedistributable": false, "display": {"unit": "% of firms", "shortUnit": "%", "tolerance": 5, "numDecimalPlaces": 1}, "schemaVersion": 2, "presentation": {"attribution": "World Bank", "topicTagsLinks": ["Women's Rights", "Global Education"]}, "descriptionKey": ["This measures the percentage of firms where the top manager is a woman.", "The top manager is usually the CEO or highest-ranking executive, but may also be the owner if she manages the firm.", "Female leadership is important for gender equality in business and decision-making."], "dimensions": {"years": {"values": [{"id": 2008}, {"id": 2014}, {"id": 2013}, {"id": 2019}, {"id": 2010}, {"id": 2024}, {"id": 2017}, {"id": 2009}, {"id": 2020}, {"id": 2021}, {"id": 2007}, {"id": 2022}, {"id": 2023}, {"id": 2018}, {"id": 2016}, {"id": 2015}, {"id": 2011}, {"id": 2012}]}, "entities": {"values": [{"id": 15, "name": "Afghanistan", "code": "AFG"}, {"id": 16, "name": "Albania", "code": "ALB"}, {"id": 19, "name": "Angola", "code": "AGO"}, {"id": 20, "name": "Antigua and Barbuda", "code": "ATG"}, {"id": 21, "name": "Argentina", "code": "ARG"}, {"id": 22, "name": "Armenia", "code": "ARM"}, {"id": 24, "name": "Austria", "code": "AUT"}, {"id": 25, "name": "Azerbaijan", "code": "AZE"}, {"id": 26, "name": "Bahamas", "code": "BHS"}, {"id": 27, "name": "Bahrain", "code": "BHR"}, {"id": 28, "name": "Bangladesh", "code": "BGD"}, {"id": 29, "name": "Barbados", "code": "BRB"}, {"id": 30, "name": "Belarus", "code": "BLR"}, {"id": 4, "name": "Belgium", "code": "BEL"}, {"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": 35, "name": "Bosnia and Herzegovina", "code": "BIH"}, {"id": 36, "name": "Botswana", "code": "BWA"}, {"id": 37, "name": "Brazil", "code": "BRA"}, {"id": 39, "name": "Bulgaria", "code": "BGR"}, {"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": 44, "name": "Canada", "code": "CAN"}, {"id": 45, "name": "Cape Verde", "code": "CPV"}, {"id": 174, "name": "Central African Republic", "code": "CAF"}, {"id": 173, "name": "Chad", "code": "TCD"}, {"id": 172, "name": "Chile", "code": "CHL"}, {"id": 171, "name": "China", "code": "CHN"}, {"id": 170, "name": "Colombia", "code": "COL"}, {"id": 168, "name": "Congo", "code": "COG"}, {"id": 166, "name": "Costa Rica", "code": "CRI"}, {"id": 143, "name": "Cote d'Ivoire", "code": "CIV"}, {"id": 165, "name": "Croatia", "code": "HRV"}, {"id": 163, "name": "Cyprus", "code": "CYP"}, {"id": 162, "name": "Czechia", "code": "CZE"}, {"id": 167, "name": "Democratic Republic of Congo", "code": "COD"}, {"id": 161, "name": "Denmark", "code": "DNK"}, {"id": 154, "name": "Djibouti", "code": "DJI"}, {"id": 200, "name": "Dominica", "code": "DMA"}, {"id": 160, "name": "Dominican Republic", "code": "DOM"}, {"id": 349172, "name": "East Asia and Pacific (WB)", "code": null}, {"id": 225, "name": "East Timor", "code": "TLS"}, {"id": 201, "name": "Ecuador", "code": "ECU"}, {"id": 65, "name": "Egypt", "code": "EGY"}, {"id": 259, "name": "El Salvador", "code": "SLV"}, {"id": 159, "name": "Equatorial Guinea", "code": "GNQ"}, {"id": 157, "name": "Eritrea", "code": "ERI"}, {"id": 156, "name": "Estonia", "code": "EST"}, {"id": 78, "name": "Eswatini", "code": "SWZ"}, {"id": 158, "name": "Ethiopia", "code": "ETH"}, {"id": 349171, "name": "Europe and Central Asia (WB)", "code": null}, {"id": 115117, "name": "European Union (27)", "code": null}, {"id": 155, "name": "Finland", "code": "FIN"}, {"id": 3, "name": "France", "code": "FRA"}, {"id": 153, "name": "Gabon", "code": "GAB"}, {"id": 151, "name": "Gambia", "code": "GMB"}, {"id": 152, "name": "Georgia", "code": "GEO"}, {"id": 6, "name": "Germany", "code": "DEU"}, {"id": 150, "name": "Ghana", "code": "GHA"}, {"id": 149, "name": "Greece", "code": "GRC"}, {"id": 206, "name": "Grenada", "code": "GRD"}, {"id": 148, "name": "Guatemala", "code": "GTM"}, {"id": 147, "name": "Guinea", "code": "GIN"}, {"id": 146, "name": "Guyana", "code": "GUY"}, {"id": 457, "name": "High-income countries", "code": null}, {"id": 139, "name": "Honduras", "code": "HND"}, {"id": 144, "name": "Hong Kong", "code": "HKG"}, {"id": 138, "name": "Hungary", "code": "HUN"}, {"id": 207, "name": "Iceland", "code": "ISL"}, {"id": 137, "name": "India", "code": "IND"}, {"id": 136, "name": "Indonesia", "code": "IDN"}, {"id": 134, "name": "Iraq", "code": "IRQ"}, {"id": 2, "name": "Ireland", "code": "IRL"}, {"id": 133, "name": "Israel", "code": "ISR"}, {"id": 8, "name": "Italy", "code": "ITA"}, {"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": 379, "name": "Kosovo", "code": "OWID_KOS"}, {"id": 126, "name": "Kyrgyzstan", "code": "KGZ"}, {"id": 125, "name": "Laos", "code": "LAO"}, {"id": 349170, "name": "Latin America and Caribbean (WB)", "code": null}, {"id": 122, "name": "Latvia", "code": "LVA"}, {"id": 124, "name": "Lebanon", "code": "LBN"}, {"id": 123, "name": "Lesotho", "code": "LSO"}, {"id": 121, "name": "Liberia", "code": "LBR"}, {"id": 119, "name": "Lithuania", "code": "LTU"}, {"id": 461, "name": "Low-income countries", "code": null}, {"id": 460, "name": "Lower-middle-income countries", "code": null}, {"id": 210, "name": "Luxembourg", "code": "LUX"}, {"id": 118, "name": "Madagascar", "code": "MDG"}, {"id": 117, "name": "Malawi", "code": "MWI"}, {"id": 116, "name": "Malaysia", "code": "MYS"}, {"id": 115, "name": "Mali", "code": "MLI"}, {"id": 212, "name": "Malta", "code": "MLT"}, {"id": 114, "name": "Mauritania", "code": "MRT"}, {"id": 213, "name": "Mauritius", "code": "MUS"}, {"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": 5, "name": "Netherlands", "code": "NLD"}, {"id": 106, "name": "New Zealand", "code": "NZL"}, {"id": 105, "name": "Nicaragua", "code": "NIC"}, {"id": 104, "name": "Niger", "code": "NER"}, {"id": 103, "name": "Nigeria", "code": "NGA"}, {"id": 278896, "name": "North America (WB)", "code": null}, {"id": 66, "name": "North Macedonia", "code": "MKD"}, {"id": 101, "name": "Pakistan", "code": "PAK"}, {"id": 140, "name": "Palestine", "code": "PSE"}, {"id": 100, "name": "Panama", "code": "PAN"}, {"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": 11, "name": "Poland", "code": "POL"}, {"id": 95, "name": "Portugal", "code": "PRT"}, {"id": 92, "name": "Romania", "code": "ROU"}, {"id": 12, "name": "Russia", "code": "RUS"}, {"id": 91, "name": "Rwanda", "code": "RWA"}, {"id": 227, "name": "Saint Kitts and Nevis", "code": "KNA"}, {"id": 229, "name": "Saint Lucia", "code": "LCA"}, {"id": 230, "name": "Saint Vincent and the Grenadines", "code": "VCT"}, {"id": 239, "name": "Samoa", "code": "WSM"}, {"id": 90, "name": "Saudi Arabia", "code": "SAU"}, {"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": 86, "name": "Singapore", "code": "SGP"}, {"id": 85, "name": "Slovakia", "code": "SVK"}, {"id": 83, "name": "Slovenia", "code": "SVN"}, {"id": 195, "name": "Solomon Islands", "code": "SLB"}, {"id": 81, "name": "South Africa", "code": "ZAF"}, {"id": 277956, "name": "South Asia (WB)", "code": null}, {"id": 127, "name": "South Korea", "code": "KOR"}, {"id": 258, "name": "South Sudan", "code": "SSD"}, {"id": 9, "name": "Spain", "code": "ESP"}, {"id": 141, "name": "Sri Lanka", "code": "LKA"}, {"id": 277950, "name": "Sub-Saharan Africa (WB)", "code": null}, {"id": 79, "name": "Sudan", "code": "SDN"}, {"id": 234, "name": "Suriname", "code": "SUR"}, {"id": 10, "name": "Sweden", "code": "SWE"}, {"id": 77, "name": "Syria", "code": "SYR"}, {"id": 76, "name": "Tajikistan", "code": "TJK"}, {"id": 64, "name": "Tanzania", "code": "TZA"}, {"id": 75, "name": "Thailand", "code": "THA"}, {"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": 70, "name": "Turkey", "code": "TUR"}, {"id": 69, "name": "Turkmenistan", "code": "TKM"}, {"id": 68, "name": "Uganda", "code": "UGA"}, {"id": 67, "name": "Ukraine", "code": "UKR"}, {"id": 1, "name": "United Kingdom", "code": "GBR"}, {"id": 13, "name": "United States", "code": "USA"}, {"id": 459, "name": "Upper-middle-income countries", "code": null}, {"id": 63, "name": "Uruguay", "code": "URY"}, {"id": 62, "name": "Uzbekistan", "code": "UZB"}, {"id": 221, "name": "Vanuatu", "code": "VUT"}, {"id": 238, "name": "Venezuela", "code": "VEN"}, {"id": 84, "name": "Vietnam", "code": "VNM"}, {"id": 355, "name": "World", "code": "OWID_WRL"}, {"id": 61, "name": "Yemen", "code": "YEM"}, {"id": 60, "name": "Zambia", "code": "ZMB"}, {"id": 80, "name": "Zimbabwe", "code": "ZWE"}]}}, "origins": [{"id": 7157, "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 Gender Statistics", "citationFull": "World Bank Gender Statistics, World Bank, 2025. Licence: CC BY 4.0.", "attributionShort": "World Bank", "urlMain": "https://genderdata.worldbank.org/en/home", "urlDownload": "https://databank.worldbank.org/data/download/Gender_Stats_CSV.zip", "dateAccessed": "2025-09-08", "datePublished": "2025", "license": {"url": "https://datacatalog.worldbank.org/public-licenses#cc-by", "name": "CC BY 4.0"}}]}