{"id":791801,"name":"Historical numeracy estimates","unit":"%","createdAt":"2023-08-15T15:19:58.000Z","updatedAt":"2026-05-11T20:36:59.000Z","coverage":"","timespan":"1500-1970","datasetId":6185,"shortUnit":"%","columnOrder":0,"shortName":"numeracy","catalogPath":"grapher/education/2023-08-09/clio_infra_education/clio_infra_education#numeracy","descriptionShort":"Age heaping is observed in age data collection, where people tend to report their ages in rounded figures, often ending in 0 or 5, rather than their exact age. For example, individiauals might state their age as 30, 35, or 40 and avoid being precise with figures like 31, 36, or 39. This is believed to happen more often in populations that lack basic numeracy skills.\n\nAge heaping can therefore be used to estimate historic data on basic numeracy across different regions and timeframes.\n\nTo estimate age heaping (also known as Whipple index) the authors of this dataset divide the number of people who report their ages to end in 0 or 5 by the expected real age distribution if ages were evenly distributed, meaning ages ending in 0 or 5 would appear about one-fifth of the time. They then perform a simple transformation of the Whipple index that can be interpreted as the share of individuals that correctly report their age.","type":"float","dataChecksum":"10378235941284008332","metadataChecksum":"5147923133186011455","datasetName":"Historical numeracy and years of schooling estimates (Clio Infra)","datasetVersion":"2023-08-09","nonRedistributable":false,"display":{"unit":"%","shortUnit":"%","numDecimalPlaces":1},"schemaVersion":2,"presentation":{},"dimensions":{"years":{"values":[{"id":1900},{"id":1910},{"id":1920},{"id":1930},{"id":1940},{"id":1950},{"id":1890},{"id":1680},{"id":1690},{"id":1700},{"id":1710},{"id":1720},{"id":1730},{"id":1740},{"id":1750},{"id":1760},{"id":1770},{"id":1780},{"id":1790},{"id":1810},{"id":1820},{"id":1830},{"id":1840},{"id":1850},{"id":1860},{"id":1870},{"id":1880},{"id":1960},{"id":1620},{"id":1800},{"id":1970},{"id":1670},{"id":1500},{"id":1520},{"id":1590},{"id":1570},{"id":1650}]},"entities":{"values":[{"id":15,"name":"Afghanistan","code":"AFG"},{"id":17,"name":"Algeria","code":"DZA"},{"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":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":167,"name":"Democratic Republic of Congo","code":"COD"},{"id":166,"name":"Costa Rica","code":"CRI"},{"id":143,"name":"Cote d'Ivoire","code":"CIV"},{"id":165,"name":"Croatia","code":"HRV"},{"id":164,"name":"Cuba","code":"CUB"},{"id":162,"name":"Czechia","code":"CZE"},{"id":161,"name":"Denmark","code":"DNK"},{"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":156,"name":"Estonia","code":"EST"},{"id":158,"name":"Ethiopia","code":"ETH"},{"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":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":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":138,"name":"Hungary","code":"HUN"},{"id":207,"name":"Iceland","code":"ISL"},{"id":137,"name":"India","code":"IND"},{"id":136,"name":"Indonesia","code":"IDN"},{"id":135,"name":"Iran","code":"IRN"},{"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":131,"name":"Kazakhstan","code":"KAZ"},{"id":129,"name":"Kenya","code":"KEN"},{"id":208,"name":"Kuwait","code":"KWT"},{"id":126,"name":"Kyrgyzstan","code":"KGZ"},{"id":122,"name":"Latvia","code":"LVA"},{"id":121,"name":"Liberia","code":"LBR"},{"id":120,"name":"Libya","code":"LBY"},{"id":119,"name":"Lithuania","code":"LTU"},{"id":210,"name":"Luxembourg","code":"LUX"},{"id":66,"name":"North 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Zealand","code":"NZL"},{"id":105,"name":"Nicaragua","code":"NIC"},{"id":104,"name":"Niger","code":"NER"},{"id":103,"name":"Nigeria","code":"NGA"},{"id":102,"name":"Norway","code":"NOR"},{"id":101,"name":"Pakistan","code":"PAK"},{"id":100,"name":"Panama","code":"PAN"},{"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":88,"name":"Serbia","code":"SRB"},{"id":86,"name":"Singapore","code":"SGP"},{"id":85,"name":"Slovakia","code":"SVK"},{"id":83,"name":"Slovenia","code":"SVN"},{"id":81,"name":"South Africa","code":"ZAF"},{"id":127,"name":"South Korea","code":"KOR"},{"id":9,"name":"Spain","code":"ESP"},{"id":141,"name":"Sri Lanka","code":"LKA"},{"id":229,"name":"Saint Lucia","code":"LCA"},{"id":234,"name":"Suriname","code":"SUR"},{"id":78,"name":"Eswatini","code":"SWZ"},{"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":151,"name":"Gambia","code":"GMB"},{"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":63,"name":"Uruguay","code":"URY"},{"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"}]}},"origins":[{"id":14843,"title":"Numeracy (Total)","description":"Age heaping refers to the tendency for age data collected in surveys or censuses to be disproportionately reported at certain \"attractive\" or \"round\" numbers. This phenomenon often manifests itself in ages being reported at numbers that end in 0 or 5.\n\nFor example, when people are asked their age, they might be more likely to report it as 30, 35, 40, etc., rather than the exact age of 31, 36, or 39.\n\nThe authors of this dataset use this \"age heaping\" to measure cognitive ability in quantitative reasoning, or \"numeracy\" and construct a database of age heaping-based estimates of basic numeracy with broad geographic and temporal coverage.","producer":"Baten et al.","citationFull":"Baten, Joerg, University of Tuebingen; with the help of several coauthors, without implying any responsibility to them for potential mistakes (Valeria Prayon, Dorothee Crayen, Dácil Juif, Ralph Hippe, Christina Mumme and many others) (2013). Numeracy (Total) via Clio Infra.","attributionShort":"Clio Infra","urlMain":"https://clio-infra.eu/Indicators/NumeracyTotal.html","urlDownload":"https://clio-infra.eu/data/Numeracy(Total)_Compact.xlsx","dateAccessed":"2023-08-09","datePublished":"2013-12-26","license":{"url":"https://clio-infra.eu/index.html#about"}}]}