variables: 791801

This table contains all the variables of all the datasets we have at Our World In Data. Note that this includes only the information about the variables like name and default display settings, not the data itself.

Data license: CC-BY

This data as json

id name unit description createdAt updatedAt code coverage timespan datasetId sourceId shortUnit display columnOrder originalMetadata grapherConfigAdmin shortName catalogPath dimensions schemaVersion processingLevel processingLog titlePublic titleVariant attributionShort attribution descriptionShort descriptionFromProducer descriptionKey descriptionProcessing licenses license grapherConfigETL type sort dataChecksum metadataChecksum
791801 Historical numeracy estimates % 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. Age heaping can therefore be used to estimate histroic data on basic numeracy across different regions and timeframes. To 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. 2023-08-15 15:19:58 2024-07-08 17:23:59     1500-1970 6185 28319 % 
{
    "unit": "%",
    "shortUnit": "%",
    "numDecimalPlaces": 1
}
 0     numeracy grapher/education/2023-08-09/clio_infra_education/clio_infra_education#numeracy   1                 
[]
         float 
[]
 e8b95097eaa1193063ba212da9620962 f5eabac9edf7c20fcfcb5d0dae8978cf

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