{"id":1355221,"name":"Share of firms with a female top manager","unit":"% of firms","createdAt":"2026-11-06T10:21:39.000Z","updatedAt":"2026-11-06T10:21:39.000Z","coverage":"","timespan":"2007-2025","datasetId":8249,"shortUnit":"%","columnOrder":0,"shortName":"ic_frm_femm_zs","catalogPath":"grapher/wb/2026-11-06/gender_statistics/gender_statistics#ic_frm_femm_zs","descriptionShort":"“Top manager” refers to the highest-ranking manager or chief executive officer of the organization. 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**General comments:** Statistical concept(s): The sampling methodology of the World Bank’s Enterprise Survey generates sample sizes appropriate to achieve two main objectives: first, to benchmark the business environment of individual economies across the world and across groups of firms within each economy; second, to conduct firm performance analyses focusing on how the business environment affect firm-level outcomes such as productivity, job creation, investment, and growth. \n\nTo achieve both objectives the sampling methodology: (a) generates a sample representative of the non-agricultural, non-extractive formal private economy,  (b) generates large enough sample sizes for selected industries and other groups of firms to conduct statistically robust analyses with a minimum 7.5% precision for 90% confidence intervals of:  (i) Estimates of population proportions (percentages); and (ii) estimates of the mean of log of sales.\n\nThe Universe of Inference of the Enterprise Surveys includes the following list, following ISIC, revision 3.1: all manufacturing sectors (group D), construction (group F), services (groups G and H), transport, storage, and communications (group I), and subsector 72 (from Group K). Following ISIC revision 4 the Universe includes: sections C, F, G, H, I, and divisions 61 and 62 of section J.2 Additionally, the Universe of Inference includes all establishments with five or more employees, fully or partially privately owned: one hundred percent government-owned firms, cooperatives, and firms with less than five employees are excluded. \n\nEnterprise Surveys are stratified by sector of activity, firm size, and geographical location. Stratification by firm size divides the population of firms into 3 strata: small firms (5-19 employees), medium-size firms (20-99 employees), and large firms (100 or more employees); in very large economies a fourth size stratum is added, the top 1% of firms by size. Geographical stratification is defined to reflect the distribution of the non-agricultural economic activity of each country, which in most cases implies covering the main urban centers of the country. Around the world most of the non-agricultural, non-mining economic activity, the ES Universe, is clustered around the main centers of population. \n\nStratification by sector of activity depends on the size of the economy as measured by the Gross National Income (GNI). Very small economies (below $20 billion GNI of 2016) are stratified into 2 groups: manufacturing and services with 75 interviews allocated to each group. For small economies, GNI between $20 billion and $30 billion, the universe is stratified into manufacturing, retail, and the rest services. Medium-size economies, GNI between $30 and 100 billion, single-out the 2 most important manufacturing industries and the remaining manufacturing industries are grouped together into a residual stratum, “rest of manufacturing”; retail and “rest of services” provide the final two strata. For large and very large economies, further manufacturing and services subsectors are single-out for stratification preserving the residual categories to complete exhaust the universe. Also, given the size of these economies the minimum sample size per level of stratification of 120 can be augmented to account for potential non-response to financial variables that are key for the computation of productivity.","type":"float","patchConfigIdETL":"01a1b060-d9ad-7218-a704-0ec99c4dac26","datasetName":"World Bank Gender Statistics","updatePeriodDays":365,"datasetVersion":"2026-11-06","nonRedistributable":false,"display":{"unit":"% of firms","shortUnit":"%","tolerance":5,"roundingMode":"significantFigures","numSignificantFigures":2},"schemaVersion":2,"presentation":{"attribution":"World Bank","topicTagsLinks":["Women's Rights","Global Education"]},"dimensions":{"years":{"values":[{"id":2008},{"id":2014},{"id":2025},{"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":23,"name":"Australia","code":"AUS"},{"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":38,"name":"Brunei","code":"BRN"},{"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":169,"name":"Comoros","code":"COM"},{"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":"WB_EAP"},{"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":"WB_ECA"},{"id":115117,"name":"European Union (27)","code":"OWID_EU27"},{"id":202,"name":"Fiji","code":"FJI"},{"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":94,"name":"Guinea-Bissau","code":"GNB"},{"id":146,"name":"Guyana","code":"GUY"},{"id":457,"name":"High-income countries","code":"OWID_HIC"},{"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":14,"name":"Japan","code":"JPN"},{"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":379,"name":"Kosovo","code":"OWID_KOS"},{"id":208,"name":"Kuwait","code":"KWT"},{"id":126,"name":"Kyrgyzstan","code":"KGZ"},{"id":125,"name":"Laos","code":"LAO"},{"id":349170,"name":"Latin America and Caribbean (WB)","code":"WB_LAC"},{"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":"OWID_LIC"},{"id":460,"name":"Lower-middle-income countries","code":"OWID_LMC"},{"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":211,"name":"Maldives","code":"MDV"},{"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":"WB_NA"},{"id":66,"name":"North Macedonia","code":"MKD"},{"id":102,"name":"Norway","code":"NOR"},{"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":226,"name":"Qatar","code":"QAT"},{"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":232,"name":"Sao Tome and Principe","code":"STP"},{"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":82,"name":"Somalia","code":"SOM"},{"id":81,"name":"South Africa","code":"ZAF"},{"id":277956,"name":"South Asia (WB)","code":"WB_SA"},{"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":"WB_SSA"},{"id":79,"name":"Sudan","code":"SDN"},{"id":234,"name":"Suriname","code":"SUR"},{"id":10,"name":"Sweden","code":"SWE"},{"id":7,"name":"Switzerland","code":"CHE"},{"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":"OWID_UMC"},{"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":28355,"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, 2026. 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":"2026-11-06","datePublished":"2026-10-14","license":{"url":"https://datacatalog.worldbank.org/public-licenses#cc-by","name":"CC BY 4.0"}}]}