variables: 943152

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
943152 40% of the median - Average shortfall ($) (Market income, equivalized) international-$ in 2017 prices   2024-06-25 14:16:25 2024-07-25 22:55:45     1968-2022 6582   $ 
{
    "name": "40% of the median - Average shortfall ($) (Market income, equivalized)",
    "unit": "international-$ in 2017 prices",
    "shortUnit": "$",
    "tolerance": 5,
    "numDecimalPlaces": 0
}
 0     avg_shortfall_40_median_mi_eq grapher/lis/2024-06-13/luxembourg_income_study/luxembourg_income_study#avg_shortfall_40_median_mi_eq   2 major           This is the amount of money that would be theoretically needed to lift the incomes of all people in poverty up to the poverty line of 40% of the median, averaged across the population in poverty.   
[
    "This is a measure of _relative_ poverty \u2013 it captures the share of people whose income is low by the standards typical in their own country.",
    "Income is \u2018pre-tax\u2019 \u2014 measured before taxes have been paid and most government benefits have been received.",
    "Income has been equivalized \u2013 adjusted to account for the fact that people in the same household can share costs like rent and heating."
]
 We create the Luxembourg Income Study data from standardized household survey microdata available in their [LISSY platform](https://www.lisdatacenter.org/data-access/lissy/). The estimations follow the methodology available in LIS, Key Figures and DART platform. We obtain after tax income by using the disposable household income variable (`dhi`). We estimate before tax income by calculating the sum of income from labor and capital (variable `hifactor`), cash transfers and in-kind goods and services from privates (`hiprivate`) and private pensions (`hi33`). We do this only for surveys where tax and contributions are fully captured, collected or imputed. We obtain after tax income (cash) by using the disposable household cash income variable (`dhci`). We convert income data from local currency into international-$ by dividing by the [LIS PPP factor](https://www.lisdatacenter.org/resources/ppp-deflators/), available as an additional database in the LISSY platform. We top and bottom-code incomes by replacing negative values with zeros and setting boundaries for extreme values of log income: at the top Q3 plus 3 times the interquartile range (Q3-Q1), and at the bottom Q1 minus 3 times the interquartile range. We equivalize incomes by dividing each household observation by the square root of the number of household members (nhhmem). Per capita estimates are calculated by dividing incomes by the number of household members. We obtain poverty indicators by using [Stata’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty.       float 
[]
 1a0332368394e107de185275f70c4669 5a108146c0b99555eb998388d83b9ce2

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