variables: 904774

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
904774 40% of the median - Share of population in poverty (Pretax) (Estimated) %   2024-05-28 15:35:29 2024-07-25 23:07:55     1820-2022 6538   % 
{
    "name": "40% of the median - Share of population in poverty (Pretax) (Estimated)",
    "unit": "%",
    "shortUnit": "%",
    "tolerance": 5,
    "numDecimalPlaces": 0
}
 0     headcount_ratio_40_median_pretax grapher/wid/2024-05-24/world_inequality_database/world_inequality_database#headcount_ratio_40_median_pretax   2 major   40% of the median - Share of population in poverty (Pretax) (Estimated)       Share of the population living below the poverty line of 40% of the median   
[
    "Income is \u2018pre-tax\u2019 \u2014 measured before taxes have been paid and most government benefits have been received. It is, however, measured after the operation of pension schemes, both private and public.",
    "The data is estimated from a combination of household surveys, tax records and national accounts data. This combination can provide a more accurate picture of the incomes of the richest, which tend to be captured poorly in household survey data alone.",
    "These underlying data sources are not always available. For some countries, observations are extrapolated from data relating to other years, or are sometimes modeled based on data observed in other countries. For more information on this methodology, see this related [technical note](https://wid.world/document/countries-with-regional-income-imputations-on-wid-world-world-inequality-lab-technical-note-2021-15/)."
]
 We extract estimations of Gini, mean, percentile thresholds, averages, and shares via the [`wid` Stata command](https://github.com/thomasblanchet/wid-stata-tool). We calculate threshold and share ratios by dividing different thresholds and shares, respectively. This data has been estimated by calculating the 40% of the median, and then checking that value against the closest threshold in the percentile distribution. The headcount ratio is then the percentile, the share of the population below that threshold. Interpolations and extrapolations are excluded by using the option `exclude` in the Stata command.     
{
    "$schema": "https://files.ourworldindata.org/schemas/grapher-schema.003.json",
    "originUrl": "https://ourworldindata.org/economic-inequality"
}
 float 
[]
 ce0c151ff9bfd3ad5dc8dc87da797101 163292286fe5d7a81f0922ae1efd7dd1

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