explorers: inequality-lis

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                    "name": "Country",
                    "slug": "country",
                    "type": "EntityName",
                    "tolerance": "5",
                    "sourceLink": "https://www.lisdatacenter.org/our-data/lis-database/",
                    "sourceName": "Luxembourg Income Study (2024)",
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                    "sourceLink": "https://www.lisdatacenter.org/our-data/lis-database/",
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                    "sourceLink": "https://www.lisdatacenter.org/our-data/lis-database/",
                    "sourceName": "Luxembourg Income Study (2024)",
                    "description": "The Gini coefficient measures inequality on a scale from 0 to 1. Higher values indicate higher inequality.\\n\\nIncome is \u2018post-tax\u2019 \u2014 measured after taxes have been paid and most government benefits have been received.\\n\\nIncome has been equivalized \u2013 adjusted to account for the fact that people in the same household can share costs like rent and heating.\\n\\nNOTES ON HOW WE PROCESSED THIS INDICATOR\\n\\nWe 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.\\n\\nWe obtain after tax income by using the disposable household income variable (`dhi`).\\n\\nWe 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.\\n\\nWe 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.\\n\\nWe 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.\\n\\nWe 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.\\n\\nWe obtain Gini coefficients by using [Stata\u2019s ineqdec0 function](https://ideas.repec.org/c/boc/bocode/s366007.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). We also calculate mean and median values from this function..",
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                    "sourceLink": "https://www.lisdatacenter.org/our-data/lis-database/",
                    "sourceName": "Luxembourg Income Study (2024)",
                    "description": "The share of income received by the richest 10% of the population.\\n\\nIncome is \u2018post-tax\u2019 \u2014 measured after taxes have been paid and most government benefits have been received.\\n\\nIncome has been equivalized \u2013 adjusted to account for the fact that people in the same household can share costs like rent and heating.\\n\\nNOTES ON HOW WE PROCESSED THIS INDICATOR\\n\\nWe 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.\\n\\nWe obtain after tax income by using the disposable household income variable (`dhi`).\\n\\nWe 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.\\n\\nWe 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.\\n\\nWe 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.\\n\\nWe 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.\\n\\nIncome shares and thresholds by decile are obtained by using [Stata\u2019s sumdist function](https://ideas.repec.org/c/boc/bocode/s366005.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt) and the number of quantile groups as 10. We estimate threshold ratios, share ratios and averages by decile in Python after processing in the LISSY platform.",
                    "dataPublishedBy": "Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; June 2024). Luxembourg: LIS.",
                    "colorScaleScheme": "OrRd",
                    "colorScaleNumericBins": "20;25;30;35;40;45",
                    "colorScaleEqualSizeBins": "true",
                    "colorScaleNumericMinValue": "100.0"
                },
                {
                    "name": "Income share of the poorest 50% (after tax)",
                    "slug": "share_bottom50_dhi_eq",
                    "type": "Numeric",
                    "unit": "%",
                    "shortUnit": "%",
                    "tolerance": "5",
                    "sourceLink": "https://www.lisdatacenter.org/our-data/lis-database/",
                    "sourceName": "Luxembourg Income Study (2024)",
                    "description": "The share of income received by the poorest 50% of the population.\\n\\nIncome is \u2018post-tax\u2019 \u2014 measured after taxes have been paid and most government benefits have been received.\\n\\nIncome has been equivalized \u2013 adjusted to account for the fact that people in the same household can share costs like rent and heating.\\n\\nNOTES ON HOW WE PROCESSED THIS INDICATOR\\n\\nWe 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.\\n\\nWe obtain after tax income by using the disposable household income variable (`dhi`).\\n\\nWe 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.\\n\\nWe 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.\\n\\nWe 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.\\n\\nWe 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.\\n\\nIncome shares and thresholds by decile are obtained by using [Stata\u2019s sumdist function](https://ideas.repec.org/c/boc/bocode/s366005.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt) and the number of quantile groups as 10. We estimate threshold ratios, share ratios and averages by decile in Python after processing in the LISSY platform.",
                    "dataPublishedBy": "Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; June 2024). Luxembourg: LIS.",
                    "colorScaleScheme": "Blues",
                    "colorScaleNumericBins": "10;15;20;25;30;35",
                    "colorScaleEqualSizeBins": "true",
                    "colorScaleNumericMinValue": "100.0"
                },
                {
                    "name": "Palma ratio (after tax)",
                    "slug": "palma_ratio_dhi_eq",
                    "type": "Numeric",
                    "tolerance": "5",
                    "sourceLink": "https://www.lisdatacenter.org/our-data/lis-database/",
                    "sourceName": "Luxembourg Income Study (2024)",
                    "description": "The Palma ratio is a measure of inequality that divides the share received by the richest 10% by the share of the poorest 40%. Higher values indicate higher inequality.\\n\\nIncome is \u2018post-tax\u2019 \u2014 measured after taxes have been paid and most government benefits have been received.\\n\\nIncome has been equivalized \u2013 adjusted to account for the fact that people in the same household can share costs like rent and heating.\\n\\nNOTES ON HOW WE PROCESSED THIS INDICATOR\\n\\nWe 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.\\n\\nWe obtain after tax income by using the disposable household income variable (`dhi`).\\n\\nWe 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.\\n\\nWe 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.\\n\\nWe 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.\\n\\nWe 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.\\n\\nIncome shares and thresholds by decile are obtained by using [Stata\u2019s sumdist function](https://ideas.repec.org/c/boc/bocode/s366005.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt) and the number of quantile groups as 10. We estimate threshold ratios, share ratios and averages by decile in Python after processing in the LISSY platform.",
                    "dataPublishedBy": "Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; June 2024). Luxembourg: LIS.",
                    "colorScaleScheme": "YlOrBr",
                    "colorScaleNumericBins": "0.5;1;1.5;2;2.5;3;3.5;4",
                    "colorScaleEqualSizeBins": "true",
                    "colorScaleNumericMinValue": "0.0"
                },
                {
                    "name": "Share in relative poverty (after tax)",
                    "slug": "headcount_ratio_50_median_dhi_eq",
                    "type": "Numeric",
                    "unit": "%",
                    "shortUnit": "%",
                    "tolerance": "5",
                    "sourceLink": "https://www.lisdatacenter.org/our-data/lis-database/",
                    "sourceName": "Luxembourg Income Study (2024)",
                    "description": "The share of the population with income below 50% of the median.\\n\\nThis 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.\\n\\nIncome is \u2018post-tax\u2019 \u2014 measured after taxes have been paid and most government benefits have been received.\\n\\nIncome has been equivalized \u2013 adjusted to account for the fact that people in the same household can share costs like rent and heating.\\n\\nNOTES ON HOW WE PROCESSED THIS INDICATOR\\n\\nWe 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.\\n\\nWe obtain after tax income by using the disposable household income variable (`dhi`).\\n\\nWe 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.\\n\\nWe 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.\\n\\nWe 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.\\n\\nWe 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.\\n\\nWe obtain poverty indicators by using [Stata\u2019s 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.",
                    "dataPublishedBy": "Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; June 2024). Luxembourg: LIS.",
                    "colorScaleScheme": "YlOrBr",
                    "colorScaleNumericBins": "5;10;15;20;25;30",
                    "colorScaleEqualSizeBins": "true",
                    "colorScaleNumericMinValue": "0.0"
                },
                {
                    "name": "Gini coefficient (after tax)",
                    "slug": "gini_dhi_pc",
                    "type": "Numeric",
                    "tolerance": "5",
                    "sourceLink": "https://www.lisdatacenter.org/our-data/lis-database/",
                    "sourceName": "Luxembourg Income Study (2024)",
                    "description": "The Gini coefficient measures inequality on a scale from 0 to 1. Higher values indicate higher inequality.\\n\\nIncome is \u2018post-tax\u2019 \u2014 measured after taxes have been paid and most government benefits have been received.\\n\\nIncome is per capita, which means that household income is divided by the total number of household members.\\n\\nNOTES ON HOW WE PROCESSED THIS INDICATOR\\n\\nWe 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.\\n\\nWe obtain after tax income by using the disposable household income variable (`dhi`).\\n\\nWe 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.\\n\\nWe 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.\\n\\nWe 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.\\n\\nWe 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.\\n\\nWe obtain Gini coefficients by using [Stata\u2019s ineqdec0 function](https://ideas.repec.org/c/boc/bocode/s366007.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). We also calculate mean and median values from this function..",
                    "dataPublishedBy": "Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; June 2024). Luxembourg: LIS.",
                    "colorScaleScheme": "Oranges",
                    "colorScaleNumericBins": "0.25;0.3;0.35;0.4;0.45;0.5",
                    "colorScaleEqualSizeBins": "true",
                    "colorScaleNumericMinValue": "1.0"
                },
                {
                    "name": "Income share of the richest 10% (after tax)",
                    "slug": "share_p100_dhi_pc",
                    "type": "Numeric",
                    "unit": "%",
                    "shortUnit": "%",
                    "tolerance": "5",
                    "sourceLink": "https://www.lisdatacenter.org/our-data/lis-database/",
                    "sourceName": "Luxembourg Income Study (2024)",
                    "description": "The share of income received by the richest 10% of the population.\\n\\nIncome is \u2018post-tax\u2019 \u2014 measured after taxes have been paid and most government benefits have been received.\\n\\nIncome is per capita, which means that household income is divided by the total number of household members.\\n\\nNOTES ON HOW WE PROCESSED THIS INDICATOR\\n\\nWe 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.\\n\\nWe obtain after tax income by using the disposable household income variable (`dhi`).\\n\\nWe 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.\\n\\nWe 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.\\n\\nWe 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.\\n\\nWe 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.\\n\\nIncome shares and thresholds by decile are obtained by using [Stata\u2019s sumdist function](https://ideas.repec.org/c/boc/bocode/s366005.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt) and the number of quantile groups as 10. We estimate threshold ratios, share ratios and averages by decile in Python after processing in the LISSY platform.",
                    "dataPublishedBy": "Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; June 2024). Luxembourg: LIS.",
                    "colorScaleScheme": "OrRd",
                    "colorScaleNumericBins": "20;25;30;35;40;45",
                    "colorScaleEqualSizeBins": "true",
                    "colorScaleNumericMinValue": "100.0"
                },
                {
                    "name": "Income share of the poorest 50% (after tax)",
                    "slug": "share_bottom50_dhi_pc",
                    "type": "Numeric",
                    "unit": "%",
                    "shortUnit": "%",
                    "tolerance": "5",
                    "sourceLink": "https://www.lisdatacenter.org/our-data/lis-database/",
                    "sourceName": "Luxembourg Income Study (2024)",
                    "description": "The share of income received by the poorest 50% of the population.\\n\\nIncome is \u2018post-tax\u2019 \u2014 measured after taxes have been paid and most government benefits have been received.\\n\\nIncome is per capita, which means that household income is divided by the total number of household members.\\n\\nNOTES ON HOW WE PROCESSED THIS INDICATOR\\n\\nWe 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.\\n\\nWe obtain after tax income by using the disposable household income variable (`dhi`).\\n\\nWe 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.\\n\\nWe 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.\\n\\nWe 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.\\n\\nWe 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.\\n\\nIncome shares and thresholds by decile are obtained by using [Stata\u2019s sumdist function](https://ideas.repec.org/c/boc/bocode/s366005.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt) and the number of quantile groups as 10. We estimate threshold ratios, share ratios and averages by decile in Python after processing in the LISSY platform.",
                    "dataPublishedBy": "Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; June 2024). Luxembourg: LIS.",
                    "colorScaleScheme": "Blues",
                    "colorScaleNumericBins": "10;15;20;25;30;35",
                    "colorScaleEqualSizeBins": "true",
                    "colorScaleNumericMinValue": "100.0"
                },
                {
                    "name": "Palma ratio (after tax)",
                    "slug": "palma_ratio_dhi_pc",
                    "type": "Numeric",
                    "tolerance": "5",
                    "sourceLink": "https://www.lisdatacenter.org/our-data/lis-database/",
                    "sourceName": "Luxembourg Income Study (2024)",
                    "description": "The Palma ratio is a measure of inequality that divides the share received by the richest 10% by the share of the poorest 40%. Higher values indicate higher inequality.\\n\\nIncome is \u2018post-tax\u2019 \u2014 measured after taxes have been paid and most government benefits have been received.\\n\\nIncome is per capita, which means that household income is divided by the total number of household members.\\n\\nNOTES ON HOW WE PROCESSED THIS INDICATOR\\n\\nWe 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.\\n\\nWe obtain after tax income by using the disposable household income variable (`dhi`).\\n\\nWe 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.\\n\\nWe 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.\\n\\nWe 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.\\n\\nWe 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.\\n\\nIncome shares and thresholds by decile are obtained by using [Stata\u2019s sumdist function](https://ideas.repec.org/c/boc/bocode/s366005.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt) and the number of quantile groups as 10. We estimate threshold ratios, share ratios and averages by decile in Python after processing in the LISSY platform.",
                    "dataPublishedBy": "Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; June 2024). Luxembourg: LIS.",
                    "colorScaleScheme": "YlOrBr",
                    "colorScaleNumericBins": "0.5;1;1.5;2;2.5;3;3.5;4",
                    "colorScaleEqualSizeBins": "true",
                    "colorScaleNumericMinValue": "0.0"
                },
                {
                    "name": "Share in relative poverty (after tax)",
                    "slug": "headcount_ratio_50_median_dhi_pc",
                    "type": "Numeric",
                    "unit": "%",
                    "shortUnit": "%",
                    "tolerance": "5",
                    "sourceLink": "https://www.lisdatacenter.org/our-data/lis-database/",
                    "sourceName": "Luxembourg Income Study (2024)",
                    "description": "The share of the population with income below 50% of the median.\\n\\nThis 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.\\n\\nIncome is \u2018post-tax\u2019 \u2014 measured after taxes have been paid and most government benefits have been received.\\n\\nIncome is per capita, which means that household income is divided by the total number of household members.\\n\\nNOTES ON HOW WE PROCESSED THIS INDICATOR\\n\\nWe 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.\\n\\nWe obtain after tax income by using the disposable household income variable (`dhi`).\\n\\nWe 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.\\n\\nWe 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.\\n\\nWe 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.\\n\\nWe 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.\\n\\nWe obtain poverty indicators by using [Stata\u2019s 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.",
                    "dataPublishedBy": "Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; June 2024). Luxembourg: LIS.",
                    "colorScaleScheme": "YlOrBr",
                    "colorScaleNumericBins": "5;10;15;20;25;30",
                    "colorScaleEqualSizeBins": "true",
                    "colorScaleNumericMinValue": "0.0"
                },
                {
                    "name": "Gini coefficient (before tax)",
                    "slug": "gini_mi_eq",
                    "type": "Numeric",
                    "tolerance": "5",
                    "sourceLink": "https://www.lisdatacenter.org/our-data/lis-database/",
                    "sourceName": "Luxembourg Income Study (2024)",
                    "description": "The Gini coefficient measures inequality on a scale from 0 to 1. Higher values indicate higher inequality.\\n\\nIncome is \u2018pre-tax\u2019 \u2014 measured before taxes have been paid and most government benefits have been received.\\n\\nIncome has been equivalized \u2013 adjusted to account for the fact that people in the same household can share costs like rent and heating.\\n\\nNOTES ON HOW WE PROCESSED THIS INDICATOR\\n\\nWe 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.\\n\\nWe obtain after tax income by using the disposable household income variable (`dhi`).\\n\\nWe 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.\\n\\nWe 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.\\n\\nWe 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.\\n\\nWe 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.\\n\\nWe obtain Gini coefficients by using [Stata\u2019s ineqdec0 function](https://ideas.repec.org/c/boc/bocode/s366007.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). We also calculate mean and median values from this function..",
                    "dataPublishedBy": "Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; June 2024). Luxembourg: LIS.",
                    "colorScaleScheme": "Oranges",
                    "colorScaleNumericBins": "0.4;0.45;0.5;0.55;0.6",
                    "colorScaleEqualSizeBins": "true",
                    "colorScaleNumericMinValue": "1.0"
                },
                {
                    "name": "Income share of the richest 10% (before tax)",
                    "slug": "share_p100_mi_eq",
                    "type": "Numeric",
                    "unit": "%",
                    "shortUnit": "%",
                    "tolerance": "5",
                    "sourceLink": "https://www.lisdatacenter.org/our-data/lis-database/",
                    "sourceName": "Luxembourg Income Study (2024)",
                    "description": "The share of income received by the richest 10% of the population.\\n\\nIncome is \u2018pre-tax\u2019 \u2014 measured before taxes have been paid and most government benefits have been received.\\n\\nIncome has been equivalized \u2013 adjusted to account for the fact that people in the same household can share costs like rent and heating.\\n\\nNOTES ON HOW WE PROCESSED THIS INDICATOR\\n\\nWe 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.\\n\\nWe obtain after tax income by using the disposable household income variable (`dhi`).\\n\\nWe 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.\\n\\nWe 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.\\n\\nWe 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.\\n\\nWe 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.\\n\\nIncome shares and thresholds by decile are obtained by using [Stata\u2019s sumdist function](https://ideas.repec.org/c/boc/bocode/s366005.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt) and the number of quantile groups as 10. We estimate threshold ratios, share ratios and averages by decile in Python after processing in the LISSY platform.",
                    "dataPublishedBy": "Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; June 2024). Luxembourg: LIS.",
                    "colorScaleScheme": "OrRd",
                    "colorScaleNumericBins": "20;25;30;35;40;45",
                    "colorScaleEqualSizeBins": "true",
                    "colorScaleNumericMinValue": "100.0"
                },
                {
                    "name": "Income share of the poorest 50% (before tax)",
                    "slug": "share_bottom50_mi_eq",
                    "type": "Numeric",
                    "unit": "%",
                    "shortUnit": "%",
                    "tolerance": "5",
                    "sourceLink": "https://www.lisdatacenter.org/our-data/lis-database/",
                    "sourceName": "Luxembourg Income Study (2024)",
                    "description": "The share of income received by the poorest 50% of the population.\\n\\nIncome is \u2018pre-tax\u2019 \u2014 measured before taxes have been paid and most government benefits have been received.\\n\\nIncome has been equivalized \u2013 adjusted to account for the fact that people in the same household can share costs like rent and heating.\\n\\nNOTES ON HOW WE PROCESSED THIS INDICATOR\\n\\nWe 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.\\n\\nWe obtain after tax income by using the disposable household income variable (`dhi`).\\n\\nWe 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.\\n\\nWe 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.\\n\\nWe 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.\\n\\nWe 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.\\n\\nIncome shares and thresholds by decile are obtained by using [Stata\u2019s sumdist function](https://ideas.repec.org/c/boc/bocode/s366005.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt) and the number of quantile groups as 10. We estimate threshold ratios, share ratios and averages by decile in Python after processing in the LISSY platform.",
                    "dataPublishedBy": "Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; June 2024). Luxembourg: LIS.",
                    "colorScaleScheme": "Blues",
                    "colorScaleNumericBins": "10;15;20;25",
                    "colorScaleEqualSizeBins": "true",
                    "colorScaleNumericMinValue": "100.0"
                },
                {
                    "name": "Palma ratio (before tax)",
                    "slug": "palma_ratio_mi_eq",
                    "type": "Numeric",
                    "tolerance": "5",
                    "sourceLink": "https://www.lisdatacenter.org/our-data/lis-database/",
                    "sourceName": "Luxembourg Income Study (2024)",
                    "description": "The Palma ratio is a measure of inequality that divides the share received by the richest 10% by the share of the poorest 40%. Higher values indicate higher inequality.\\n\\nIncome is \u2018pre-tax\u2019 \u2014 measured before taxes have been paid and most government benefits have been received.\\n\\nIncome has been equivalized \u2013 adjusted to account for the fact that people in the same household can share costs like rent and heating.\\n\\nNOTES ON HOW WE PROCESSED THIS INDICATOR\\n\\nWe 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.\\n\\nWe obtain after tax income by using the disposable household income variable (`dhi`).\\n\\nWe 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.\\n\\nWe 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.\\n\\nWe 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.\\n\\nWe 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.\\n\\nIncome shares and thresholds by decile are obtained by using [Stata\u2019s sumdist function](https://ideas.repec.org/c/boc/bocode/s366005.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt) and the number of quantile groups as 10. We estimate threshold ratios, share ratios and averages by decile in Python after processing in the LISSY platform.",
                    "dataPublishedBy": "Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; June 2024). Luxembourg: LIS.",
                    "colorScaleScheme": "YlOrBr",
                    "colorScaleNumericBins": "1;2;3;4;5;6",
                    "colorScaleEqualSizeBins": "true",
                    "colorScaleNumericMinValue": "0.0"
                },
                {
                    "name": "Share in relative poverty (before tax)",
                    "slug": "headcount_ratio_50_median_mi_eq",
                    "type": "Numeric",
                    "unit": "%",
                    "shortUnit": "%",
                    "tolerance": "5",
                    "sourceLink": "https://www.lisdatacenter.org/our-data/lis-database/",
                    "sourceName": "Luxembourg Income Study (2024)",
                    "description": "The share of the population with income below 50% of the median.\\n\\nThis 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.\\n\\nIncome is \u2018pre-tax\u2019 \u2014 measured before taxes have been paid and most government benefits have been received.\\n\\nIncome has been equivalized \u2013 adjusted to account for the fact that people in the same household can share costs like rent and heating.\\n\\nNOTES ON HOW WE PROCESSED THIS INDICATOR\\n\\nWe 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.\\n\\nWe obtain after tax income by using the disposable household income variable (`dhi`).\\n\\nWe 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.\\n\\nWe 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.\\n\\nWe 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.\\n\\nWe 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.\\n\\nWe obtain poverty indicators by using [Stata\u2019s 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.",
                    "dataPublishedBy": "Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; June 2024). Luxembourg: LIS.",
                    "colorScaleScheme": "YlOrBr",
                    "colorScaleNumericBins": "15;20;25;30;35",
                    "colorScaleEqualSizeBins": "true",
                    "colorScaleNumericMinValue": "100.0"
                },
                {
                    "name": "Gini coefficient (before tax)",
                    "slug": "gini_mi_pc",
                    "type": "Numeric",
                    "tolerance": "5",
                    "sourceLink": "https://www.lisdatacenter.org/our-data/lis-database/",
                    "sourceName": "Luxembourg Income Study (2024)",
                    "description": "The Gini coefficient measures inequality on a scale from 0 to 1. Higher values indicate higher inequality.\\n\\nIncome is \u2018pre-tax\u2019 \u2014 measured before taxes have been paid and most government benefits have been received.\\n\\nIncome is per capita, which means that household income is divided by the total number of household members.\\n\\nNOTES ON HOW WE PROCESSED THIS INDICATOR\\n\\nWe 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.\\n\\nWe obtain after tax income by using the disposable household income variable (`dhi`).\\n\\nWe 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.\\n\\nWe 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.\\n\\nWe 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.\\n\\nWe 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.\\n\\nWe obtain Gini coefficients by using [Stata\u2019s ineqdec0 function](https://ideas.repec.org/c/boc/bocode/s366007.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). We also calculate mean and median values from this function..",
                    "dataPublishedBy": "Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; June 2024). Luxembourg: LIS.",
                    "colorScaleScheme": "Oranges",
                    "colorScaleNumericBins": "0.4;0.45;0.5;0.55;0.6",
                    "colorScaleEqualSizeBins": "true",
                    "colorScaleNumericMinValue": "1.0"
                },
                {
                    "name": "Income share of the richest 10% (before tax)",
                    "slug": "share_p100_mi_pc",
                    "type": "Numeric",
                    "unit": "%",
                    "shortUnit": "%",
                    "tolerance": "5",
                    "sourceLink": "https://www.lisdatacenter.org/our-data/lis-database/",
                    "sourceName": "Luxembourg Income Study (2024)",
                    "description": "The share of income received by the richest 10% of the population.\\n\\nIncome is \u2018pre-tax\u2019 \u2014 measured before taxes have been paid and most government benefits have been received.\\n\\nIncome is per capita, which means that household income is divided by the total number of household members.\\n\\nNOTES ON HOW WE PROCESSED THIS INDICATOR\\n\\nWe 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.\\n\\nWe obtain after tax income by using the disposable household income variable (`dhi`).\\n\\nWe 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.\\n\\nWe 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.\\n\\nWe 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.\\n\\nWe 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.\\n\\nIncome shares and thresholds by decile are obtained by using [Stata\u2019s sumdist function](https://ideas.repec.org/c/boc/bocode/s366005.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt) and the number of quantile groups as 10. We estimate threshold ratios, share ratios and averages by decile in Python after processing in the LISSY platform.",
                    "dataPublishedBy": "Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; June 2024). Luxembourg: LIS.",
                    "colorScaleScheme": "OrRd",
                    "colorScaleNumericBins": "20;25;30;35;40;45",
                    "colorScaleEqualSizeBins": "true",
                    "colorScaleNumericMinValue": "100.0"
                },
                {
                    "name": "Income share of the poorest 50% (before tax)",
                    "slug": "share_bottom50_mi_pc",
                    "type": "Numeric",
                    "unit": "%",
                    "shortUnit": "%",
                    "tolerance": "5",
                    "sourceLink": "https://www.lisdatacenter.org/our-data/lis-database/",
                    "sourceName": "Luxembourg Income Study (2024)",
                    "description": "The share of income received by the poorest 50% of the population.\\n\\nIncome is \u2018pre-tax\u2019 \u2014 measured before taxes have been paid and most government benefits have been received.\\n\\nIncome is per capita, which means that household income is divided by the total number of household members.\\n\\nNOTES ON HOW WE PROCESSED THIS INDICATOR\\n\\nWe 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.\\n\\nWe obtain after tax income by using the disposable household income variable (`dhi`).\\n\\nWe 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.\\n\\nWe 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.\\n\\nWe 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.\\n\\nWe 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.\\n\\nIncome shares and thresholds by decile are obtained by using [Stata\u2019s sumdist function](https://ideas.repec.org/c/boc/bocode/s366005.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt) and the number of quantile groups as 10. We estimate threshold ratios, share ratios and averages by decile in Python after processing in the LISSY platform.",
                    "dataPublishedBy": "Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; June 2024). Luxembourg: LIS.",
                    "colorScaleScheme": "Blues",
                    "colorScaleNumericBins": "10;15;20;25",
                    "colorScaleEqualSizeBins": "true",
                    "colorScaleNumericMinValue": "100.0"
                },
                {
                    "name": "Palma ratio (before tax)",
                    "slug": "palma_ratio_mi_pc",
                    "type": "Numeric",
                    "tolerance": "5",
                    "sourceLink": "https://www.lisdatacenter.org/our-data/lis-database/",
                    "sourceName": "Luxembourg Income Study (2024)",
                    "description": "The Palma ratio is a measure of inequality that divides the share received by the richest 10% by the share of the poorest 40%. Higher values indicate higher inequality.\\n\\nIncome is \u2018pre-tax\u2019 \u2014 measured before taxes have been paid and most government benefits have been received.\\n\\nIncome is per capita, which means that household income is divided by the total number of household members.\\n\\nNOTES ON HOW WE PROCESSED THIS INDICATOR\\n\\nWe 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.\\n\\nWe obtain after tax income by using the disposable household income variable (`dhi`).\\n\\nWe 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.\\n\\nWe 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.\\n\\nWe 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.\\n\\nWe 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.\\n\\nIncome shares and thresholds by decile are obtained by using [Stata\u2019s sumdist function](https://ideas.repec.org/c/boc/bocode/s366005.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt) and the number of quantile groups as 10. We estimate threshold ratios, share ratios and averages by decile in Python after processing in the LISSY platform.",
                    "dataPublishedBy": "Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; June 2024). Luxembourg: LIS.",
                    "colorScaleScheme": "YlOrBr",
                    "colorScaleNumericBins": "1;2;3;4;5;6",
                    "colorScaleEqualSizeBins": "true",
                    "colorScaleNumericMinValue": "0.0"
                },
                {
                    "name": "Share in relative poverty (before tax)",
                    "slug": "headcount_ratio_50_median_mi_pc",
                    "type": "Numeric",
                    "unit": "%",
                    "shortUnit": "%",
                    "tolerance": "5",
                    "sourceLink": "https://www.lisdatacenter.org/our-data/lis-database/",
                    "sourceName": "Luxembourg Income Study (2024)",
                    "description": "The share of the population with income below 50% of the median.\\n\\nThis 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.\\n\\nIncome is \u2018pre-tax\u2019 \u2014 measured before taxes have been paid and most government benefits have been received.\\n\\nIncome is per capita, which means that household income is divided by the total number of household members.\\n\\nNOTES ON HOW WE PROCESSED THIS INDICATOR\\n\\nWe 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.\\n\\nWe obtain after tax income by using the disposable household income variable (`dhi`).\\n\\nWe 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.\\n\\nWe 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.\\n\\nWe 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.\\n\\nWe 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.\\n\\nWe obtain poverty indicators by using [Stata\u2019s 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.",
                    "dataPublishedBy": "Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; June 2024). Luxembourg: LIS.",
                    "colorScaleScheme": "YlOrBr",
                    "colorScaleNumericBins": "15;20;25;30;35",
                    "colorScaleEqualSizeBins": "true",
                    "colorScaleNumericMinValue": "100.0"
                }
            ]
        }
    ],
    "_version": 1,
    "selection": [
        "Chile",
        "Brazil",
        "South Africa",
        "United States",
        "France",
        "China"
    ],
    "wpBlockId": "57755",
    "entityType": "country or region",
    "googleSheet": "https://docs.google.com/spreadsheets/d/1UFdwB1iBpP2tEP6GtxCHvW1GGhjsFflh42FWR80rYIg",
    "explorerTitle": "Inequality - Luxembourg Income Study",
    "explorerSubtitle": "Explore Luxembourg Income Study data on inequality.",
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}