explorers: inequality-comparison

{
    "blocks": [
        {
            "args": [],
            "type": "graphers",
            "block": [
                {
                    "tab": "chart",
                    "note": "LIS data refers to disposable household income per capita, WID data to net national income after tax per adult, and, depending on the country and year, PIP data refers to income measured after taxes and benefits, or to consumption, per capita.",
                    "title": "Gini coefficient (after tax)",
                    "ySlugs": "gini p0p100_gini_posttax_nat gini_dhi_pc",
                    "subtitle": "The Gini coefficient measures inequality on a scale from 0 to 1. Higher values indicate higher inequality. The definition of income varies across the data sources.",
                    "yAxisMin": "0",
                    "hasMapTab": "false",
                    "tableSlug": "poverty_inequality",
                    "defaultView": "true",
                    "mapTargetTime": "0",
                    "Indicator Dropdown": "Gini coefficient",
                    "selectedFacetStrategy": "entity",
                    "Income measure Dropdown": "After tax"
                },
                {
                    "tab": "chart",
                    "note": "LIS data refers to disposable household income per capita, WID data to net national income after tax per adult, and, depending on the country and year, PIP data refers to income measured after taxes and benefits, or to consumption, per capita.",
                    "title": "Income share of the richest 10% (after tax)",
                    "ySlugs": "decile10_share p90p100_share_posttax_nat share_p100_dhi_pc",
                    "subtitle": "The share of income received by the richest 10% of the population. The definition of income varies across the data sources.",
                    "yAxisMin": "0",
                    "hasMapTab": "false",
                    "tableSlug": "poverty_inequality",
                    "mapTargetTime": "0",
                    "Indicator Dropdown": "Share of the richest 10%",
                    "selectedFacetStrategy": "entity",
                    "Income measure Dropdown": "After tax"
                },
                {
                    "tab": "chart",
                    "note": "LIS data refers to disposable household income per capita, WID data to net national income after tax per adult, and, depending on the country and year, PIP data refers to income measured after taxes and benefits, or to consumption, per capita.",
                    "title": "Income share of the poorest 50% (after tax)",
                    "ySlugs": "bottom50_share p0p50_share_posttax_nat share_bottom50_dhi_pc",
                    "subtitle": "The share of income received by the poorest 50% of the population. The definition of income varies across the data sources.",
                    "yAxisMin": "0",
                    "hasMapTab": "false",
                    "tableSlug": "poverty_inequality",
                    "mapTargetTime": "0",
                    "Indicator Dropdown": "Share of the poorest 50%",
                    "selectedFacetStrategy": "entity",
                    "Income measure Dropdown": "After tax"
                },
                {
                    "tab": "chart",
                    "note": "LIS data refers to disposable household income per capita, WID data to net national income after tax per adult, and, depending on the country and year, PIP data refers to income measured after taxes and benefits, or to consumption, per capita.",
                    "title": "Palma ratio (after tax)",
                    "ySlugs": "palma_ratio palma_ratio_posttax_nat palma_ratio_dhi_pc",
                    "subtitle": "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. The definition of income varies across the data sources.",
                    "yAxisMin": "0",
                    "hasMapTab": "false",
                    "tableSlug": "poverty_inequality",
                    "mapTargetTime": "0",
                    "Indicator Dropdown": "Palma ratio",
                    "selectedFacetStrategy": "entity",
                    "Income measure Dropdown": "After tax"
                },
                {
                    "tab": "chart",
                    "note": "LIS data refers to disposable household income per capita, WID data to net national income after tax per adult, and, depending on the country and year, PIP data refers to income measured after taxes and benefits, or to consumption, per capita.",
                    "title": "Share of people in relative poverty (after tax)",
                    "ySlugs": "headcount_ratio_50_median headcount_ratio_50_median_dhi_pc",
                    "subtitle": "The share of population with income below 50% of the median. Relative poverty reflects the extent of inequality within the bottom of the distribution. The definition of income varies across the data sources.",
                    "yAxisMin": "0",
                    "hasMapTab": "false",
                    "tableSlug": "poverty_inequality",
                    "mapTargetTime": "0",
                    "Indicator Dropdown": "Share in relative poverty",
                    "selectedFacetStrategy": "entity",
                    "Income measure Dropdown": "After tax"
                },
                {
                    "tab": "chart",
                    "note": "LIS data refers to market household income per capita, and WID data to net national income before tax per adult, but after the payment of public and private pensions.",
                    "title": "Gini coefficient (before tax)",
                    "ySlugs": "p0p100_gini_pretax gini_mi_pc",
                    "subtitle": "The Gini coefficient measures inequality on a scale from 0 to 1. Higher values indicate higher inequality. The definition of income varies across the data sources.",
                    "yAxisMin": "0",
                    "hasMapTab": "false",
                    "tableSlug": "poverty_inequality",
                    "mapTargetTime": "0",
                    "Indicator Dropdown": "Gini coefficient",
                    "selectedFacetStrategy": "entity",
                    "Income measure Dropdown": "Before tax"
                },
                {
                    "tab": "chart",
                    "note": "LIS data refers to market household income per capita, and WID data to net national income before tax per adult, but after the payment of public and private pensions.",
                    "title": "Income share of the richest 10% (before tax)",
                    "ySlugs": "p90p100_share_pretax share_p100_mi_pc",
                    "subtitle": "The share of income received by the richest 10% of the population. The definition of income varies across the data sources.",
                    "yAxisMin": "0",
                    "hasMapTab": "false",
                    "tableSlug": "poverty_inequality",
                    "mapTargetTime": "0",
                    "Indicator Dropdown": "Share of the richest 10%",
                    "selectedFacetStrategy": "entity",
                    "Income measure Dropdown": "Before tax"
                },
                {
                    "tab": "chart",
                    "note": "LIS data refers to market household income per capita, and WID data to net national income before tax per adult, but after the payment of public and private pensions.",
                    "title": "Income share of the poorest 50% (before tax)",
                    "ySlugs": "p0p50_share_pretax share_bottom50_mi_pc",
                    "subtitle": "The share of income received by the poorest 50% of the population. The definition of income varies across the data sources.",
                    "yAxisMin": "0",
                    "hasMapTab": "false",
                    "tableSlug": "poverty_inequality",
                    "mapTargetTime": "0",
                    "Indicator Dropdown": "Share of the poorest 50%",
                    "selectedFacetStrategy": "entity",
                    "Income measure Dropdown": "Before tax"
                },
                {
                    "tab": "chart",
                    "note": "LIS data refers to market household income per capita, and WID data to net national income before tax per adult, but after the payment of public and private pensions.",
                    "title": "Palma ratio (before tax)",
                    "ySlugs": "palma_ratio_pretax palma_ratio_mi_pc",
                    "subtitle": "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. The definition of income varies across the data sources.",
                    "yAxisMin": "0",
                    "hasMapTab": "false",
                    "tableSlug": "poverty_inequality",
                    "mapTargetTime": "0",
                    "Indicator Dropdown": "Palma ratio",
                    "selectedFacetStrategy": "entity",
                    "Income measure Dropdown": "Before tax"
                }
            ]
        },
        {
            "args": [
                "https://catalog.ourworldindata.org/explorers/poverty_inequality/latest/poverty_inequality/poverty_inequality.csv",
                "poverty_inequality"
            ],
            "type": "table",
            "block": null
        },
        {
            "args": [
                "poverty_inequality"
            ],
            "type": "columns",
            "block": [
                {
                    "name": "Country",
                    "slug": "country",
                    "type": "EntityName",
                    "tolerance": "5",
                    "sourceLink": "https://pip.worldbank.org",
                    "sourceName": "World Bank Poverty and Inequality Platform (2024)",
                    "dataPublishedBy": "World Bank (2024). Poverty and Inequality Platform (version 20240326_2017 and 20240326_2011) [Data set]. World Bank Group. https://pip.worldbank.org/. Accessed March 27, 2024.",
                    "colorScaleEqualSizeBins": "true",
                    "colorScaleNumericMinValue": "0"
                },
                {
                    "name": "Year",
                    "slug": "year",
                    "type": "Year",
                    "tolerance": "5",
                    "sourceLink": "https://pip.worldbank.org",
                    "sourceName": "World Bank Poverty and Inequality Platform (2024)",
                    "dataPublishedBy": "World Bank (2024). Poverty and Inequality Platform (version 20240326_2017 and 20240326_2011) [Data set]. World Bank Group. https://pip.worldbank.org/. Accessed March 27, 2024.",
                    "colorScaleEqualSizeBins": "true",
                    "colorScaleNumericMinValue": "0"
                },
                {
                    "name": "Gini coefficient (PIP data)",
                    "slug": "gini",
                    "type": "Numeric",
                    "tolerance": "5",
                    "sourceLink": "https://pip.worldbank.org",
                    "sourceName": "World Bank Poverty and Inequality Platform (2024)",
                    "description": "The Gini coefficient measures inequality on a scale from 0 to 1. Higher values indicate higher inequality.\\n\\nDepending on the country and year, the data relates to income measured after taxes and benefits, or to consumption, per capita. 'Per capita' means that the incomes of each household are attributed equally to each member of the household (including children).\\n\\nNon-market sources of income, including food grown by subsistence farmers for their own consumption, are taken into account.\\n\\nNOTES ON HOW WE PROCESSED THIS INDICATOR\\n\\nFor most countries in the PIP dataset, estimates relate to _either_ disposable income or consumption, for all available years. A number of countries, however, have a mix of income and consumption data points, with both data types sometimes available for particular years.\\n\\nIn most of our charts, we present the data with some data points dropped in order to present single series for each country. This allows us to make readable visualizations that combine multiple countries and metrics. In choosing which data points to drop, we try to strike a balance between maintaining comparability over time and showing as long a time series as possible. As such, the exact approach varies somewhat across countries.\\n\\nIf you would like to see the original data with _all_ available income and consumption data points shown separately, you can do so in our [Inequality - World Bank Data Explorer](https://ourworldindata.org/explorers/inequality-wb?country=ROU~CHN~BLR~PER&Indicator=Gini+coefficient&Household+survey+data+type=Show+data+from+both+income+and+consumption+surveys&Show+breaks+between+less+comparable+surveys=true). You can also download this data in our [complete dataset](https://github.com/owid/poverty-data#a-global-dataset-of-poverty-and-inequality-measures-prepared-by-our-world-in-data-from-the-world-banks-poverty-and-inequality-platform-pip-database) of the World Bank PIP data.",
                    "dataPublishedBy": "World Bank (2024). Poverty and Inequality Platform (version 20240326_2017 and 20240326_2011) [Data set]. World Bank Group. https://pip.worldbank.org/. Accessed March 27, 2024.",
                    "colorScaleScheme": "Oranges",
                    "colorScaleNumericBins": "0.25;0.3;0.35;0.4;0.45;0.5;0.55;0.6",
                    "colorScaleEqualSizeBins": "true",
                    "colorScaleNumericMinValue": "0"
                },
                {
                    "name": "Income or consumption share of the richest 10% (PIP data)",
                    "slug": "decile10_share",
                    "type": "Numeric",
                    "unit": "%",
                    "shortUnit": "%",
                    "tolerance": "5",
                    "sourceLink": "https://pip.worldbank.org",
                    "sourceName": "World Bank Poverty and Inequality Platform (2024)",
                    "description": "The share of after tax income or consumption received by the richest 10% of the population.\\n\\nDepending on the country and year, the data relates to income measured after taxes and benefits, or to consumption, per capita. 'Per capita' means that the incomes of each household are attributed equally to each member of the household (including children).\\n\\nNon-market sources of income, including food grown by subsistence farmers for their own consumption, are taken into account.\\n\\nNOTES ON HOW WE PROCESSED THIS INDICATOR\\n\\nFor most countries in the PIP dataset, estimates relate to _either_ disposable income or consumption, for all available years. A number of countries, however, have a mix of income and consumption data points, with both data types sometimes available for particular years.\\n\\nIn most of our charts, we present the data with some data points dropped in order to present single series for each country. This allows us to make readable visualizations that combine multiple countries and metrics. In choosing which data points to drop, we try to strike a balance between maintaining comparability over time and showing as long a time series as possible. As such, the exact approach varies somewhat across countries.\\n\\nIf you would like to see the original data with _all_ available income and consumption data points shown separately, you can do so in our [Inequality - World Bank Data Explorer](https://ourworldindata.org/explorers/inequality-wb?country=ROU~CHN~BLR~PER&Indicator=Gini+coefficient&Household+survey+data+type=Show+data+from+both+income+and+consumption+surveys&Show+breaks+between+less+comparable+surveys=true). You can also download this data in our [complete dataset](https://github.com/owid/poverty-data#a-global-dataset-of-poverty-and-inequality-measures-prepared-by-our-world-in-data-from-the-world-banks-poverty-and-inequality-platform-pip-database) of the World Bank PIP data.",
                    "dataPublishedBy": "World Bank (2024). Poverty and Inequality Platform (version 20240326_2017 and 20240326_2011) [Data set]. World Bank Group. https://pip.worldbank.org/. Accessed March 27, 2024.",
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                    "colorScaleEqualSizeBins": "true",
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                },
                {
                    "name": "Income or consumption share of the poorest 50% (PIP data)",
                    "slug": "bottom50_share",
                    "type": "Numeric",
                    "unit": "%",
                    "shortUnit": "%",
                    "tolerance": "5",
                    "sourceLink": "https://pip.worldbank.org",
                    "sourceName": "World Bank Poverty and Inequality Platform (2024)",
                    "description": "The share of after tax income or consumption received by the poorest 50% of the population.\\n\\nDepending on the country and year, the data relates to income measured after taxes and benefits, or to consumption, per capita. 'Per capita' means that the incomes of each household are attributed equally to each member of the household (including children).\\n\\nNon-market sources of income, including food grown by subsistence farmers for their own consumption, are taken into account.\\n\\nNOTES ON HOW WE PROCESSED THIS INDICATOR\\n\\nFor most countries in the PIP dataset, estimates relate to _either_ disposable income or consumption, for all available years. A number of countries, however, have a mix of income and consumption data points, with both data types sometimes available for particular years.\\n\\nIn most of our charts, we present the data with some data points dropped in order to present single series for each country. This allows us to make readable visualizations that combine multiple countries and metrics. In choosing which data points to drop, we try to strike a balance between maintaining comparability over time and showing as long a time series as possible. As such, the exact approach varies somewhat across countries.\\n\\nIf you would like to see the original data with _all_ available income and consumption data points shown separately, you can do so in our [Inequality - World Bank Data Explorer](https://ourworldindata.org/explorers/inequality-wb?country=ROU~CHN~BLR~PER&Indicator=Gini+coefficient&Household+survey+data+type=Show+data+from+both+income+and+consumption+surveys&Show+breaks+between+less+comparable+surveys=true). You can also download this data in our [complete dataset](https://github.com/owid/poverty-data#a-global-dataset-of-poverty-and-inequality-measures-prepared-by-our-world-in-data-from-the-world-banks-poverty-and-inequality-platform-pip-database) of the World Bank PIP data.",
                    "dataPublishedBy": "World Bank (2024). Poverty and Inequality Platform (version 20240326_2017 and 20240326_2011) [Data set]. World Bank Group. https://pip.worldbank.org/. Accessed March 27, 2024.",
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                    "colorScaleEqualSizeBins": "true",
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                },
                {
                    "name": "Palma ratio (PIP data)",
                    "slug": "palma_ratio",
                    "type": "Numeric",
                    "tolerance": "5",
                    "sourceLink": "https://pip.worldbank.org",
                    "sourceName": "World Bank Poverty and Inequality Platform (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\\nDepending on the country and year, the data relates to income measured after taxes and benefits, or to consumption, per capita. 'Per capita' means that the incomes of each household are attributed equally to each member of the household (including children).\\n\\nNon-market sources of income, including food grown by subsistence farmers for their own consumption, are taken into account.\\n\\nNOTES ON HOW WE PROCESSED THIS INDICATOR\\n\\nFor most countries in the PIP dataset, estimates relate to _either_ disposable income or consumption, for all available years. A number of countries, however, have a mix of income and consumption data points, with both data types sometimes available for particular years.\\n\\nIn most of our charts, we present the data with some data points dropped in order to present single series for each country. This allows us to make readable visualizations that combine multiple countries and metrics. In choosing which data points to drop, we try to strike a balance between maintaining comparability over time and showing as long a time series as possible. As such, the exact approach varies somewhat across countries.\\n\\nIf you would like to see the original data with _all_ available income and consumption data points shown separately, you can do so in our [Inequality - World Bank Data Explorer](https://ourworldindata.org/explorers/inequality-wb?country=ROU~CHN~BLR~PER&Indicator=Gini+coefficient&Household+survey+data+type=Show+data+from+both+income+and+consumption+surveys&Show+breaks+between+less+comparable+surveys=true). You can also download this data in our [complete dataset](https://github.com/owid/poverty-data#a-global-dataset-of-poverty-and-inequality-measures-prepared-by-our-world-in-data-from-the-world-banks-poverty-and-inequality-platform-pip-database) of the World Bank PIP data.",
                    "dataPublishedBy": "World Bank (2024). Poverty and Inequality Platform (version 20240326_2017 and 20240326_2011) [Data set]. World Bank Group. https://pip.worldbank.org/. Accessed March 27, 2024.",
                    "colorScaleScheme": "YlOrBr",
                    "colorScaleNumericBins": "0.5;1;1.5;2;2.5;3;3.5;4;4.5;5;5.5",
                    "colorScaleEqualSizeBins": "true",
                    "colorScaleNumericMinValue": "0"
                },
                {
                    "name": "Share in relative poverty (PIP data)",
                    "slug": "headcount_ratio_50_median",
                    "type": "Numeric",
                    "unit": "%",
                    "shortUnit": "%",
                    "tolerance": "5",
                    "sourceLink": "https://pip.worldbank.org",
                    "sourceName": "World Bank Poverty and Inequality Platform (2024)",
                    "description": "The share of population with after tax income or consumption 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\\nDepending on the country and year, the data relates to income measured after taxes and benefits, or to consumption, per capita. 'Per capita' means that the incomes of each household are attributed equally to each member of the household (including children).\\n\\nNon-market sources of income, including food grown by subsistence farmers for their own consumption, are taken into account.\\n\\nNOTES ON HOW WE PROCESSED THIS INDICATOR\\n\\nMeasures of relative poverty are not directly available in the World Bank PIP data. To calculate this metric we take the median income or consumption for the country and year, calculate a relative poverty line \u2013 in this case 50% of the median \u2013 and then run a specific query on the PIP API to return the share of population below that line.\\n\\nFor most countries in the PIP dataset, estimates relate to _either_ disposable income or consumption, for all available years. A number of countries, however, have a mix of income and consumption data points, with both data types sometimes available for particular years.\\n\\nIn most of our charts, we present the data with some data points dropped in order to present single series for each country. This allows us to make readable visualizations that combine multiple countries and metrics. In choosing which data points to drop, we try to strike a balance between maintaining comparability over time and showing as long a time series as possible. As such, the exact approach varies somewhat across countries.\\n\\nIf you would like to see the original data with _all_ available income and consumption data points shown separately, you can do so in our [Inequality - World Bank Data Explorer](https://ourworldindata.org/explorers/inequality-wb?country=ROU~CHN~BLR~PER&Indicator=Gini+coefficient&Household+survey+data+type=Show+data+from+both+income+and+consumption+surveys&Show+breaks+between+less+comparable+surveys=true). You can also download this data in our [complete dataset](https://github.com/owid/poverty-data#a-global-dataset-of-poverty-and-inequality-measures-prepared-by-our-world-in-data-from-the-world-banks-poverty-and-inequality-platform-pip-database) of the World Bank PIP data.",
                    "dataPublishedBy": "World Bank (2024). Poverty and Inequality Platform (version 20240326_2017 and 20240326_2011) [Data set]. World Bank Group. https://pip.worldbank.org/. Accessed March 27, 2024.",
                    "colorScaleScheme": "YlOrBr",
                    "colorScaleNumericBins": "5;10;15;20;25;30",
                    "colorScaleEqualSizeBins": "true",
                    "colorScaleNumericMinValue": "0"
                },
                {
                    "name": "Gini coefficient (WID data)",
                    "slug": "p0p100_gini_posttax_nat",
                    "type": "Numeric",
                    "tolerance": "5",
                    "sourceLink": "https://wid.world",
                    "sourceName": "World Inequality Database (WID.world) (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\\nThe 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.\\n\\nThese 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/).\\n\\nIn the case of national post-tax income, when the data sources are not available, distributions are constructed by using the more widely available pre-tax distributions, combined with tax revenue and government expenditure aggregates. This method is described in more detail in this [technical note](https://wid.world/document/preliminary-estimates-of-global-posttax-income-distributions-world-inequality-lab-technical-note-2023-02/).",
                    "dataPublishedBy": "World Inequality Database (WID), https://wid.world",
                    "colorScaleScheme": "Oranges",
                    "colorScaleNumericBins": "0.3;0.35;0.4;0.45;0.5;0.55;0.6;0.65;0.7",
                    "colorScaleEqualSizeBins": "true",
                    "colorScaleNumericMinValue": "0"
                },
                {
                    "name": "Income share of the richest 10% (WID data)",
                    "slug": "p90p100_share_posttax_nat",
                    "type": "Numeric",
                    "unit": "%",
                    "shortUnit": "%",
                    "tolerance": "5",
                    "sourceLink": "https://wid.world",
                    "sourceName": "World Inequality Database (WID.world) (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\\nThe 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.\\n\\nThese 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/).\\n\\nIn the case of national post-tax income, when the data sources are not available, distributions are constructed by using the more widely available pre-tax distributions, combined with tax revenue and government expenditure aggregates. This method is described in more detail in this [technical note](https://wid.world/document/preliminary-estimates-of-global-posttax-income-distributions-world-inequality-lab-technical-note-2023-02/).",
                    "dataPublishedBy": "World Inequality Database (WID), https://wid.world",
                    "colorScaleScheme": "OrRd",
                    "colorScaleNumericBins": "20;25;30;35;40;45;50;55;60",
                    "colorScaleEqualSizeBins": "true",
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                },
                {
                    "name": "Income share of the poorest 50% (WID data)",
                    "slug": "p0p50_share_posttax_nat",
                    "type": "Numeric",
                    "unit": "%",
                    "shortUnit": "%",
                    "tolerance": "5",
                    "sourceLink": "https://wid.world",
                    "sourceName": "World Inequality Database (WID.world) (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\\nThe 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.\\n\\nThese 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/).\\n\\nIn the case of national post-tax income, when the data sources are not available, distributions are constructed by using the more widely available pre-tax distributions, combined with tax revenue and government expenditure aggregates. This method is described in more detail in this [technical note](https://wid.world/document/preliminary-estimates-of-global-posttax-income-distributions-world-inequality-lab-technical-note-2023-02/).",
                    "dataPublishedBy": "World Inequality Database (WID), https://wid.world",
                    "colorScaleScheme": "Blues",
                    "colorScaleNumericBins": "10;15;20;25;30;35",
                    "colorScaleEqualSizeBins": "true",
                    "colorScaleNumericMinValue": "0"
                },
                {
                    "name": "Palma ratio (WID data)",
                    "slug": "palma_ratio_posttax_nat",
                    "type": "Numeric",
                    "tolerance": "5",
                    "sourceLink": "https://wid.world",
                    "sourceName": "World Inequality Database (WID.world) (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\\nThe 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.\\n\\nThese 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/).\\n\\nIn the case of national post-tax income, when the data sources are not available, distributions are constructed by using the more widely available pre-tax distributions, combined with tax revenue and government expenditure aggregates. This method is described in more detail in this [technical note](https://wid.world/document/preliminary-estimates-of-global-posttax-income-distributions-world-inequality-lab-technical-note-2023-02/).",
                    "dataPublishedBy": "World Inequality Database (WID), https://wid.world",
                    "colorScaleScheme": "YlOrBr",
                    "colorScaleNumericBins": "1;2;3;4;5;6;7;8",
                    "colorScaleEqualSizeBins": "true",
                    "colorScaleNumericMinValue": "0"
                },
                {
                    "name": "Gini coefficient (WID data)",
                    "slug": "p0p100_gini_pretax",
                    "type": "Numeric",
                    "tolerance": "5",
                    "sourceLink": "https://wid.world",
                    "sourceName": "World Inequality Database (WID.world) (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. It is, however, measured after the operation of pension schemes, both private and public.\\n\\nThe 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.\\n\\nThese 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/).",
                    "dataPublishedBy": "World Inequality Database (WID), https://wid.world",
                    "colorScaleScheme": "Oranges",
                    "colorScaleNumericBins": "0.4;0.45;0.5;0.55;0.6;0.65;0.7;0.75",
                    "colorScaleEqualSizeBins": "true",
                    "colorScaleNumericMinValue": "0"
                },
                {
                    "name": "Income share of the richest 10% (WID data)",
                    "slug": "p90p100_share_pretax",
                    "type": "Numeric",
                    "unit": "%",
                    "shortUnit": "%",
                    "tolerance": "5",
                    "sourceLink": "https://wid.world",
                    "sourceName": "World Inequality Database (WID.world) (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. It is, however, measured after the operation of pension schemes, both private and public.\\n\\nThe 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.\\n\\nThese 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/).",
                    "dataPublishedBy": "World Inequality Database (WID), https://wid.world",
                    "colorScaleScheme": "OrRd",
                    "colorScaleNumericBins": "30;35;40;45;50;55;60;65",
                    "colorScaleEqualSizeBins": "true",
                    "colorScaleNumericMinValue": "0"
                },
                {
                    "name": "Income share of the poorest 50% (WID data)",
                    "slug": "p0p50_share_pretax",
                    "type": "Numeric",
                    "unit": "%",
                    "shortUnit": "%",
                    "tolerance": "5",
                    "sourceLink": "https://wid.world",
                    "sourceName": "World Inequality Database (WID.world) (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. It is, however, measured after the operation of pension schemes, both private and public.\\n\\nThe 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.\\n\\nThese 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/).",
                    "dataPublishedBy": "World Inequality Database (WID), https://wid.world",
                    "colorScaleScheme": "Blues",
                    "colorScaleNumericBins": "10;15;20;25;30",
                    "colorScaleEqualSizeBins": "true",
                    "colorScaleNumericMinValue": "0"
                },
                {
                    "name": "Palma ratio (WID data)",
                    "slug": "palma_ratio_pretax",
                    "type": "Numeric",
                    "tolerance": "5",
                    "sourceLink": "https://wid.world",
                    "sourceName": "World Inequality Database (WID.world) (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. It is, however, measured after the operation of pension schemes, both private and public.\\n\\nThe 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.\\n\\nThese 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/).",
                    "dataPublishedBy": "World Inequality Database (WID), https://wid.world",
                    "colorScaleScheme": "YlOrBr",
                    "colorScaleNumericBins": "2;4;6;8;10;12;14;16;18",
                    "colorScaleEqualSizeBins": "true",
                    "colorScaleNumericMinValue": "0"
                },
                {
                    "name": "Gini coefficient (LIS data)",
                    "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": "0"
                },
                {
                    "name": "Income share of the richest 10% (LIS data)",
                    "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": "0"
                },
                {
                    "name": "Income share of the poorest 50% (LIS data)",
                    "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": "0"
                },
                {
                    "name": "Palma ratio (LIS data)",
                    "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"
                },
                {
                    "name": "Share in relative poverty (LIS data)",
                    "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"
                },
                {
                    "name": "Gini coefficient (LIS data)",
                    "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": "0"
                },
                {
                    "name": "Income share of the richest 10% (LIS data)",
                    "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": "0"
                },
                {
                    "name": "Income share of the poorest 50% (LIS data)",
                    "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": "0"
                },
                {
                    "name": "Palma ratio (LIS data)",
                    "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"
                },
                {
                    "name": "Share in relative poverty (LIS data)",
                    "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": "0"
                }
            ]
        }
    ],
    "_version": 1,
    "selection": [
        "Chile",
        "Brazil",
        "South Africa",
        "United States",
        "France",
        "China"
    ],
    "wpBlockId": "57742",
    "entityType": "country or region",
    "googleSheet": null,
    "explorerTitle": "Inequality - World Bank, WID, and LIS",
    "explorerSubtitle": "Compare World Bank, WID, and LIS data on inequality.",
    "pickerColumnSlugs": [
        "gini decile10_share palma_ratio headcount_ratio_50_median p0p100_gini_posttax_nat p90p100_share_posttax_nat palma_ratio_posttax_nat gini_dhi_pc share_p100_dhi_pc palma_ratio_dhi_pc headcount_ratio_50_median_dhi_pc"
    ]
}